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Cisplatin activates mitochondrial oxphos leading to acute treatment resistance in bladder cancer

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Cisplatin activates mitochondrial oxphos leading to acute treatment resistance in bladder cancer

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Content CISPLATIN ACTIVATES MITOCHONDRIAL OXPHOS LEADING TO ACUTE
TREATMENT RESISTANCE IN BLADDER CANCER
by
Maheen Iqbal
Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CANCER BIOLOGY & GENOMICS)
May 2024
Copyright 2024 Maheen Iqbal

ii
Epigraph
"And whoever saves a life it is as though he had saved the lives of all mankind"
― Holy Quran (5:32)

iii
Dedication
Dedicating to all oppressed people of the world, whose humanity is stripped away from them
based on race, ethnicity, color, or religion.

iv
Acknowledgements
I would like to begin by expressing my heartfelt gratitude to my family for their immense
support over the last 5 years, and over my entire life. Their unwavering support and love have been
the cornerstone of my academic journey. Their encouragement, understanding, and sacrifices have
been instrumental in my pursuit of knowledge and the completion of this thesis. To my parents,
Farkhanda and Iqbal, your boundless belief in my abilities has been my driving force. Your
sacrifices and constant encouragement have shaped me into the person I am today. I am profoundly
grateful for the values you instilled in me, the freedom you gave to my choices and the sacrifices
you made to provide me with educational opportunities. My brother, Sadan, has been my
camaraderie, understanding me, and providing the much-needed occasional distractions to
maintain my balance and joy during the challenging times of this academic endeavor.
Especially, I would like to express my deepest appreciation and love to my husband, Affan,
whose unwavering love, understanding, and support have been my guiding lights throughout this
academic venture. Affan, your encouragement, patience, and belief in my abilities have been my
constant source of strength. Your unwavering support, whether it was providing a quiet space to
work, lending a listening ear during challenging times, or celebrating even the smallest victories,
has made this journey more meaningful and less daunting. And to my dear son, Hasan, thank you
for bearing with your mama’s absence during your very infancy and for all the times when she was
too busy with her work and shirked on spending beautiful growing moments with you. Mama loves
you; your beautiful self has brought immense balance, joy, and perspective to our lives. I am
profoundly grateful for the sacrifices both of you have made to accommodate the demands of my
academic pursuits. Your love has been my motivation, and this thesis is as much a reflection of

v
our shared journey as it is of my individual efforts. Thank you to my entire family for being my
rock, my cheerleaders, and my biggest supporters. I couldn't have reached this milestone without
you by my side. Thank you, from the bottom of my heart.
To Dr. Amir Goldkorn, thank you for each and everything. I am truly fortunate to have had
the privilege of being mentored by someone of your caliber. Thank you for your incredible
mentorship, not just related to my academic journey, but also in instilling valuable lessons that
were instrumental in shaping both my academic and personal growth. Thank you for teaching me
to do science the right way, for being tough on me when needed, challenging me when I over
complied, and especially for providing me with your encouragement, guidance, wisdom, and
sympathetic words whenever things weren’t working out and I would find myself in the desperate
pit of imposter syndrome. As I take a moment to reflect on the incredible journey of pursuing my
Ph.D., I find myself overwhelmed with gratitude for the invaluable role you played as my mentor.
Throughout this challenging yet rewarding journey, your mentorship has been a beacon of
inspiration. Your expertise, dedication, and passion for the subject matter have not only expanded
my intellectual horizons but have also instilled in me a deep appreciation for the pursuit of
knowledge. Your commitment to fostering a nurturing and collaborative academic environment
has created a space where creativity and critical thinking could thrive. I am particularly grateful
for the time and effort you invested in providing constructive feedback on my research, pushing
me to explore new perspectives and challenging me to think beyond the conventional boundaries.
Your mentorship extends beyond the confines of academia, and I am grateful for the mentormentee relationship that has evolved into a meaningful connection. I want to express my deepest
appreciation for your role in shaping my academic and professional trajectory. The knowledge and

vi
skills I have gained under your mentorship will undoubtedly serve as a solid foundation for my
future endeavors.
To the other members of the Goldkorn lab, thank you for your support and
friendship. Especially thank you Tong Xu, as I reach the culmination of my Ph.D. journey, I find
myself reflecting on the incredible mentorship you provided me during my initial days in the lab
and beyond that, for always being a guiding force, a rigid critic, and a compassionate friend
throughout my time in the lab. To all the other members of the lab, your collective dedication,
enthusiasm, and collaborative spirit have made this academic adventure not only productive but
also immensely enjoyable. I want to express my sincere appreciation for the unwavering support
and camaraderie that defined our time together in the lab. Working alongside such a talented and
motivated group has been a privilege, and I am grateful for the countless hours we spent
brainstorming, troubleshooting, and celebrating our achievements, both big and small. Our shared
experiences, whether they be long after-hours experiments or the thrill of discovering something
new, have created memories that will stay with me for a lifetime.
Thank you to my committee members, Dr. Peggy Farnham and Dr. Nicholas Graham, for
their continued support and valuable feedback over the years. I also want to thank Dr. Scott Fraser
and Dr. Pinchas Cohen for their invaluable guidance and critique throughout the last four years.
Thanks to everyone, past and present, on the 6th floor of NRT for your help throughout my journey.
Special thanks to all my friends in Pakistan, for cheering for me always. I love you all so much.

vii
Table of Contents
Epigraph………………………………………………………………………………………. ii
Dedication…………………………………………………………………………………….. iii
Acknowledgements…………………………………………………………………………… iv
List of Tables...………………………………………………………………………………... x
List of Figures...………………………………………………………………………..………
Abbreviations………………………………………………………………………………….
xi
xiv
Abstract……………………………………………………………………………………….. xvii
Chapter 1: Introduction……………………………………………………………………… 1
1.1 Bladder Cancer and Cisplatin Resistance…......…………………………………… 1
1.2 Phenotypic Plasticity and Metabolic Shift….…………..………………..………… 4
1.3 Mitochondrial OxPhos and Drug Resistance……………..……………..………….
1.4 Regulation of Mitochondrial Biogenesis…………………………………………...
1.5 Goals of Dissertation……………..…………………………………………….…..
6
10
13
Chapter 2: Cisplatin activates mitochondrial DNA replication & transcription via
TFAM ……...…………………..………………………………………………... 16
Introduction……………………………………………………………………………. 16
2.1 Cisplatin upregulates mitochondrial DNA replication & transcription…….………
2.2 Pharmacological inhibition of OxPhos synergizes with cisplatin to reduce cell
survival……………………………..………………………………………………
2.3 Cisplatin upregulates TFAM gene expression in BC cells and patient-derived
organoids (PDOs)...……………….…………..…………………………………....
2.4 TFAM depletion abrogates mtDNA levels and OxPhos activity in BC cells and
patient-derived organoids (PDOs).………………………………………...……….
Discussion………………………………………………………………………………
20
22
23
28
34
Chapter 3: TFAM mediated mitochondrial activation results in drug efflux and cell
survival……………………...………………………………..………………….. 36
Introduction…………………………………………………………………………….
3.1 Cisplatin increases ATP production and drug efflux, while TFAM abrogates it….
36
38

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Chapter 4: Cisplatin activates ATM and the hypothesized downstream signaling, and
its inhibition abrogates cisplatin resistance….…………………………………
Introduction.…...………………………….……………………………………………
46
46
4.1 Cisplatin increases phospho-H2AX protein levels...………………..…………...… 52
4.2 Cisplatin increases phospho-ATM and the levels of the hypothesized downstream
signaling cascade, while ATM inhibition decreases them…………………………
4.3 ATM inhibition synergizes with cisplatin to reduce cell survival in BC cells and
PDOs………..………………………………..…………..…………………………
Discussion…………………..……………….……………………..…………………...
Chapter 5: AMPK's role in the proposed ATM-AMPK-PGC1α-TFAM signaling
cascade and its impact on cisplatin sensitivity…………………………………
Introduction.……………………………………………………………………………
5.1 Cisplatin increases phospho-AMPK and the levels of the hypothesized
downstream signaling cascade, while AMPK inhibition decreases them………….
5.2 AMPK inhibition synergizes with cisplatin to reduce cell survival in BC cells
and PDOs…………………………………………………………………………...
5.3 AMPK’s cancer promoting role is recapitulated in TCGA database analysis.…......
Discussion…………..……………….……………………..…………………………...
Chapter 6: PGC1α’s role in the proposed ATM-AMPK-PGC1α-TFAM signaling
cascade and its impact on cisplatin sensitivity…………………………………
Introduction.………………………………………………………...………………….
6.1 Cisplatin increases PGC1α and TFAM levels, while PGC1α inhibition decreases
TFAM levels...…………………………………………………………...…………
6.2 PGC1α inhibition synergizes with cisplatin to reduce cell survival in BC cells
and PDOs…………………………………………………………………………...
6.3 PGC1α’s cancer promoting role is recapitulated in TCGA database analysis ….....
Discussion…………..……………….……………………..…………………………...
53
58
60
61
61
65
72
74
78
80
80
85
90
92
94
Chapter 7: Conclusion……………………………………………………………………….. 96
Chapter 8: Materials and Methods……..……..………………………………..…………… 102
Cell Culture…………..……………..……………………..……………………………102
Human Bladder Cancer Organoid Culture…………………………………………..… 102
Histology…………………..…………..………………………………………………. 104
3.2 TFAM depletion synergizes with cisplatin to reduce cell survival in BC cells and
PDOs………………….…...………………………………..………………………
3.3 TFAM’s cancer promoting role is recapitulated in TCGA database analysis……...
Discussion…………………..…………………….…………………………………….
41
43
44

ix
Treatment with Pharmacological Agents.…………..………………………………..... 104
Trypan Blue Exclusion Survival Assay…………...…………...………………………. 106
Mitochondrial DNA qPCR……..………………….……..……………………………. 107
Quantitative Reverse Transcription-PCR Assays...………..………..…………………. 107
Western Blotting…….…..……..……………………………..………………………... 108
siRNA Transient Transfection……..………….……………………………………….. 108
Measurement of Oxygen Consumption Rate (OCR)………………..…………………. 110
Non-descanned Multiphoton Fluorescence Lifetime Imaging Microscopy (FLIM)…... 110
Multi-Drug Resistance (MDR) Efflux……………………………………………...….. 111
ATP Detection Assay……………...…………………………………………………... 112
FACS Sorting………………………………………………………………………….. 112
Immunofluorescence Microscopy…..…………………...………………….................. 113
Schematic Figures...………………………………..………………………………….. 114
Statistical Analysis……..………………………..………………………………..…… 114
Bibliography…………………………………………………………………………………... 117
Appendices
Appendix A: Establishing a Repository of BC Patient-Derived Organoids…..
Appendix B: References provided for literature review in Fig. 1.3.…………..

152
164

x
List of Tables
Table 8.1 Primer sequences used for qRT-PCR analysis………………………………. 115
Table 8.2 Antibodies used in Western blots……………………………………………. 116
Table 8.3 Antibodies used in Immunofluorescence imaging…………………………... 116
Table A1 Detailed composition of the ‘human bladder organoid media’………………. 161
Table A2 List of the patient-derived BC tissues received from USC and the PDOs
received from NCI PDMR…………………………………………………… 162
Table A3 List of references for the comprehensive literature review in Fig. 1.3……….. 164

xi
List of Figures
1.1 Tumor resistance is driven by a range of mechanisms…………………………………... 4
1.2 Depiction of the OxPhos pathway and its preferential utilization by CSCs……………... 9
1.3 A comprehensive literature review identifying pathways leading to TFAM regulation… 12
1.4 Overview Schema: In bladder cancer, cisplatin induces an adaptive signaling cascade
that activates mitochondrial OxPhos and drives a rapid transition to cisplatin resistance. 15
2.1 Overview of BC phenotypic plasticity model…………………………………………… 18
2.2 Gene expression in SP cells is enriched for OxPhos pathway…………………………... 19
2.3 Effect of cisplatin on mtDNA’s replication & transcription…………………………….. 21
2.4 Effect of phenformin and cisplatin co-treatment on cancer cell survival………………... 23
2.5 Cisplatin upregulates TFAM gene and protein expression……………………………… 25
2.6 Cisplatin upregulates TFAM expression in PDOs………………………………………. 28
2.7 TFAM Knockdown in BC cells and PDOs……………………………………………… 30
2.8 TFAM Knockdown reduces OxPhos activity in BC cells and PDOs……………………. 33
3.1 Elevated ATP production serves as a favorable phenotypic driver of aggressive cancer
tumorigenicity and possible resistance…………………………………………………... 37
3.2 Cisplatin bolsters ATP levels and drug efflux, TFAM depletion abrogates the effect….. 40
3.3 TFAM depletion increases cisplatin sensitivity in BC cells and PDOs…………………. 42
3.4 TFAM gene expression analysis using TCGA………………………………………….. 44
4.1 Cisplatin activation and DNA damage induction……………………………………….. 48

xii
4.2 Canonical responses to DNA damage……………………………………………………..
49
4.3 Hypothesized adaptive chemotherapy resistance pathway, wherein cisplatin-induced
nuclear DNA damage leads to TFAM upregulation……………………………………... 51
4.4 Cisplatin increases phospho-H2AX levels………………………………………………. 53
4.5 p53 phosphorylation was used as a control for ATM inhibitor activity………………… 55
4.6 Cisplatin activates ATM and increases the levels of the hypothesized downstream
signaling cascade, while ATM inhibition decreases them……………………………….
57
4.7 ATM inhibition synergizes with cisplatin in BC cells and PDOs……………………….. 59
5.1 AMPK's upstream kinases and downstream effectors…………………………………… 62
5.2 AMPK inhibitor abrogates AMPK activity, while AMPK activator increases it………... 66
5.3 Evaluation of AMPK’s effect on the proposed downstream signaling mediators………. 68
5.4 AMPK’s siRNA-mediated knockdown and its effect on the proposed downstream
signaling mediators……………………………………………………………………….
70
5.5 Evaluation of AMPK’s effect on the proposed downstream signaling mediators in
PDOs……………………………………………………………………………………...
71
5.6 AMPK inhibition synergizes with cisplatin in BC cells and PDOs……………………... 73
5.7 AMPK gene expression analysis using TCGA-BLCA subset…………………………… 75
5.8 AMPK gene expression analysis using TCGA-PANCAN………………………………. 77
6.1 Various pathways that serve as regulators for PGC1α…………………………………... 81
6.2 Regulation of mitochondrial biogenesis by PGC1α……………………………………... 84
6.3 Investigation of cisplatin’s effect on PGC1α, Nrf1, Nrf2, and PGC1α inhibition………. 86
6.4 Effect of PGC1⍺ inhibition on TFAM levels……………………………………………. 88
6.5 Inhibiting the proposed players of the nuclear-mitochondrial signaling cascade did not
inhibit the hypothesized ‘upstream’ proteins in the cascade……………………………..
89

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6.6 AMPK inhibition synergizes with cisplatin in BC cells and PDOs……………………... 91
6.7 PGC1 gene expression analysis using TCGA………………………………………….. 93
A1 An overview of comparing PDOs to other models and generating PDOs from patient
Tumors…………………………………………………………………………………… 154
A2 Characterization of patient-derived organoids (PDOs)………………………………….. 156
A3 An example of a patient-derived tumor sample being measured and minced in our lab,
prior to digestion…………………………………………………………………………. 159
The details of the figures used from previous publications are provided at the end of the
‘Bibliography’ section.

xiv
Abbreviations
ABC ATP-Binding Cassette
ACC1 Acetyl-CoA carboxylase 1
AICAR 5-Aminoimidazole-4-carboxamide ribonucleoside
Akt Protein Kinase B
AMP Adenosine Monophosphate
AMPK AMP-activated protein Kinase
ATCC American Type Culture Collection
ATF Activating Transcription Factor
ATM Ataxia Telangiectasia–Mutated
ATP Adenosine Triphosphate
ATP5G1 ATP synthase (Complex V)
ATR Ataxia Telangiectasia Related
BC Bladder Cancer
BCG Bacillus Calmette–Guérin
BLCA Bladder Cancer
BME Basement Membrane Extract
BRCA1 Breast Cancer type 1
CAMKK2 Calcium/Calmodulin Dependent Protein Kinase Kinase 2
CBP CREB binding protein
CDI Coefficients of Drug Interaction
cGAS cyclic GMP-AMP synthase
CHK2 Checkpoint Kinase 2
Chr Chromosome
Ck Cytokeratin
CoQ Coenzyme Q
COX5A Cytochrome c Oxidase subunit 5a
CREB cyclic AMP-Responsive Element-Binding protein
CSCs Cancer Stem-like Cells
Cyt c Cytochrome c
DAPI 4',6-diamidino-2-phenylindole
DMEM Dulbecco's Modified Eagle's Medium
DMEM/F-12 Dulbecco's Modified Eagle Medium/Nutrient Mixture F-12
DMSO Dimethyl Sulfoxide
DNA Deoxyribonucleic Acid
DNA-PKs DNA-dependent Protein Kinases
DOI Digital Object Identifier
DSBs Double-Strand Breaks
eIF Eukaryotic Initiation Factors
ERR Estrogen Related Receptor
ETC Electron Transport Chain
FACS Fluorescence-Activated Cell Sorting
FADH Flavin Adenine Dinucleotide
FCCP tri-Fluorocarbonylcyanide Phenylhydrazone
FGF Fibroblast Growth Factor
FLIM Fluorescence Lifetime Imaging Microscopy
FOXO Forkhead box O transcription factor
G6PC Glucose-6-Phosphatase-α

xv
GAPDH Glyceraldehyde-3-Phosphate Dehydrogenase
GBM Glioblastoma Multiforme
GCN5 General Control Non-Repressed Protein 5
GFP Green-Fluorescent Labeling
GSEA Gene Set Enrichment Analysis
GSK Glycogen Synthase Kinase
GTRD Gene Transcription Regulation Database
HDACs Histone Deacetylases
HEPES (4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid
HIF Hypoxia-Inducible Factors
HRI Heme-regulated eIF2α kinase
HSP Heat-Shock Protein
ICLs Interstrand Crosslinks
IDH Isocitrate Dehydrogenase
IF Immunofluorescence Microscopy
IMM Inner Mitochondrial Membrane (IMM
IRB Institutional Review Board
K kilo (1000)
KD Knockdown
KEGG Kyoto Encyclopedia of Genes and Genomes (KEGG)
LKB1 Liver Kinase B1
MAPKs Mitogen-Activated Protein Kinases
MDR Multi-Drug Resistance
MEF2 Myocyte Enhancer Factor 2
MFN Mitofusin
MIBC Muscle-Invasive Bladder Cancer
MOTS-c Mitochondrial Open-reading-frame of the Twelve S rRNA type-c
mRNA messenger RNA
MRN MRE11-RAD50-NBS1
mt-CO2 Mitochondrial Cytochrome c Oxidase subunit 2
mt-ND5 mitochondrial NADH-ubiquinone Oxidoreductase core subunit 5
mtDNA mitochondrial Deoxyribonucleic Acid
mTORC mammalian Target of Rapamycin Complex
NAD Nicotinamide Adenine Dinucleotide
NADH Nicotinamide Adenine Dinucleotide + Hydrogen
NCI-PDMR National Cancer Institute Patient-Derived Models Repository
NF-κB Nuclear factor kappa-light-chain-enhancer of activated B cells
NMIBC Non-Muscle-Invasive Bladder Cancer
Nrf1 Nuclear respiratory factor 1
Nrf2 Nuclear respiratory factor 2
NSP Non-Side Population
OCR Oxygen Consumption Rate
OxPhos Oxidative Phosphorylation
PAN-CAN Pan-Cancer
PBS Phosphate-Buffered Saline
PCK1 Phosphoenolpyruvate Carboxykinase 1
PCNA Proliferating Cell Nuclear Antigen
PDK4 Pyruvate Dehydrogenase Kinase 4
PDOs Patient-Derived Organoids
PDXs Patient-Derived Xenografts
PFK-1 Phosphofructokinase-1

xvi
PGC1 Peroxisome proliferator-activated receptor-γ Coactivator 1-α
PI3K Phosphoinositide 3-kinase
PIKK Phosphatidylinositol 3-kinase-related Kinase
PINK1 PTEN-induced kinase 1
PP2Cα Protein Phosphatase 2C Α
PPAR peroxisome proliferator-activated receptor
PTEN Phosphatase and Tensin Homolog deleted on Chromosome 10
qPCR quantitative Polymerase Chain Reaction
qRT-PCR quantitative Reverse Transcription Polymerase Chain Reaction
RC Radical Cystectomy
RIPA Radioimmunoprecipitation Assay buffer
RNA Seq Ribonucleic Acid- Sequencing
ROS Reactive Oxygen Species
RPMI Roswell Park Memorial Institute media
RRID Research Resource Identifiers
sc-siRNA scrambled-short interfering RNA
SEM Standard Error of the Mean
Ser Serine
SHLP Small Humanin-Like Peptide
siRNA short interfering RNA
SIRT1 Silent Information Regulator 1
SOD2 Superoxide Dismutase 2
sORF short Open Reading Frame
SP Side Population
SSBs Single-Strand Breaks
STAT Signal Transducer and Activator of Transcription
STING Stimulator of Interferon Genes
STR Short Tandem Repeat
TAK1 Transforming growth factor-β-activated kinase 1
TCGA The Cancer Genome Atlas program
TFAM Transcription Factor A, Mitochondrial
TFs Transcription Factors
Thr Threonine
TNF Tumor Necrosis Factor
TOMM20 Translocase of Outer Mitochondrial Membrane 20
TPM Transcripts Per Million
tRNA transfer Ribonucleic Acid
TURBT Transurethral Resection of the Bladder Tumor
xCT cystine/glutamate transporter
γH2AX phosphorylated (gamma) H2A histone family member X

xvii
Abstract
Cisplatin comprises the backbone of combination chemotherapy for locally advanced and
metastatic bladder cancer (BC), but its efficacy is limited by the emergence of resistance. We
previously reported that cisplatin resistance can arise in BC through mutation-independent
phenotypic plasticity, marked by a rapid metabolic shift towards oxidative phosphorylation
(OxPhos), a trend that intensified with exposure to cisplatin chemotherapy. Based on these
findings, we hypothesized that cisplatin could induce a nuclear–mitochondrial signaling pathway
that mediates a rapid transition to cisplatin resistance. To test this hypothesis, we treated both BC
cell lines and patient-derived organoids (PDOs) with cisplatin and analyzed its effects on signaling
from the nucleus to the mitochondria. We found that nuclear DNA damage induced by cisplatin
led to sequential activation of ATM (ataxia telangiectasia–mutated), AMPK (AMP-activated
protein kinase), and PGC1α (peroxisome proliferator-activated receptor-γ coactivator 1 α),
culminating in upregulation of TFAM (transcription factor A, mitochondrial), a master activator
of mitochondrial replication and transcription. TFAM in turn activated mitochondrial OxPhos,
leading to enhanced drug efflux and cisplatin resistance. Our findings are described in this
dissertation as follows: In chapter 2 we demonstrate that cisplatin induces mitochondrial
biogenesis and OxPhos activity through increased TFAM expression, and that inhibiting the
OxPhos pathway synergizes with cisplatin to enhance cancer cell death. In chapter 3, we show that
cisplatin induction of TFAM and in mitochondrial activation in turn increases ATP production,
promotes drug efflux and therefore improves cancer cell survival. Conversely, TFAM depletion
counters these effects by decreasing ATP production and drug efflux, and thus reduces cisplatin
resistance in BC cell lines and PDOs. In chapter 4, based on prior literature and our own

xviii
experiments, we propose a signaling cascade (ATM-AMPK-PGC1α) linking cisplatin-induced
DNA damage with downstream TFAM activation. We show that cisplatin activates ATM kinase,
a master sensor of DNA double-stranded breaks, which in turn phosphorylates AMPK, a central
metabolic regulator, suggesting a potential link between DNA damage and mitochondrial
biogenesis. In Chapter 5, we move along the hypothesized signaling cascade and demonstrate that
AMPK activates PGC1α to mediate mitochondrial activation and therapy resistance in response to
cisplatin. In Chapter 6, we show that inhibiting PGC1α reverses cisplatin resistance and resensitizes BC cell lines and PDOs. In addition, we analyze publicly available cancer data in the
TCGA database that support our in vitro findings with correlations between the expression levels
of these signaling mediators and clinical outcomes. Lastly, we detail the establishment of patientderived bladder cancer organoid lines, which have been utilized to validate the key questions
inquired in this dissertation. While the individual roles of ATM, AMPK, and PGC1α in cell
signaling and cancer have been well-studied, to the best of our knowledge, our research is the first
to provide novel insights into their coordinated contribution to a cisplatin-induced pro-resistance
pathway in BC. This critical adaptive signaling pathway warrants additional investigation as a
potential therapeutic target to counteract chemotherapy resistance and enhance treatment results.

1
Chapter 1: Introduction
1.1 Bladder Cancer and Cisplatin Resistance
Bladder cancer poses a major health problem globally, with ~550,000 patients diagnosed
yearly worldwide, and more than 80,000 new cases and approximately 17,000 deaths expected in
the United States in 2023(1, 2). Therefore, BC stands as a formidable foe in the realm of oncology,
ranking among the most prevalent and challenging malignancies worldwide. Its diverse clinical
presentations, high recurrence rates, and limited treatment options contribute to the complexity of
managing this disease. As we navigate the intricate landscape of BC, there is an urgent need to
deepen our understanding of its molecular underpinnings, paving the way for more effective
diagnostic approaches, prognostic tools, and targeted therapies. Epidemiologically, BC exhibits
striking disparities in incidence across populations, reflecting the intricate interplay of genetic,
environmental, and lifestyle factors(3, 4). Molecularly, BC is characterized by a mosaic of genetic
and epigenetic alterations, giving rise to distinct molecular subtypes with diverse clinical
behaviors(5). In the therapeutic arena, BC management has undergone notable transformations,
with surgical interventions, chemotherapy, immunotherapy, and targeted therapies comprising the
armamentarium(6). As we delve into the intricacies of these treatment modalities, understanding
their individual and synergistic effects, as well as elucidating mechanisms of resistance, becomes
paramount. BC manifests in various forms, with urothelial carcinoma being the most common
histological subtype(7, 8). Upon initial presentation, 75% of patients are diagnosed with nonmuscle-invasive bladder cancer (NMIBC), while 25% exhibit muscle-invasive (MIBC) or
metastatic disease(9). The primary approach to non-muscle-invasive bladder cancer involves
transurethral resection of the bladder tumor (TURBT), followed by induction and maintenance
immunotherapy using intravesical BCG vaccine (Bacillus Calmette–Guérin vaccine, originally

2
developed against tuberculosis) or intravesical chemotherapy(10). Although NMIBCs frequently
experience recurrence (50-70%), progression to invasion is rare (10-15%), and they generally have
a favorable prognosis(11). In contrast, MIBC necessitates multimodal treatment, including radical
cystectomy, bilateral pelvic lymphadenectomy, and neoadjuvant chemotherapy, such as cisplatin,
as the standard of care(12, 13). Advanced cases typically undergo systemic cisplatin-based
chemotherapy, however, one very recently reported combination treatment of enfortumab vedotin
(EV)(Padcev) and pembrolizumab (Pembro)(Keytruda), has risen as a first-line option, particularly
for those failing initial chemotherapy(14, 15). MIBCs carry a less optimistic prognosis, with a fiveyear survival rate below 50%, often progressing to metastasis due to inherent treatment
resistance(16), presenting a significant global health burden with profound implications for
affected individuals. With its multifaceted etiology, diverse molecular landscapes, and varying
clinical trajectories, BC demands a nuanced exploration that extends beyond conventional
paradigms.
Cisplatin, a platinum-based chemotherapeutic agent, has long been a critical therapeutic
approach for advanced and metastatic BC, used first line in the neoadjuvant or adjuvant setting, or
in patients with metastatic disease who can’t receive immunotherapy (i.e. first-line approach of
EV+Pembro), or as second line therapy after progression on EV+Pembro(15, 17, 18). Despite
initial success, the emergence of cisplatin resistance poses a significant obstacle to effective cancer
management, leading to treatment failure and compromised patient outcomes(19). Drug resistance
remains a major obstacle in combating cancer. While intrinsic resistance exists as a results of preexisting factors, acquired resistance, in contrast, develops over time in response to drug
exposure(20). The traditional paradigm for understanding chemotherapy resistance has revolved
around the concept that resistance emerges gradually through existing or acquired mutations,

3
driven by clonal selection(21). This viewpoint attributed resistance to rare subpopulations of
genetically resistant cells within the primary tumor or to DNA alterations induced by genomic
instability or triggered by drug exposure, subsequently expanded through Darwinian selection(21-
25). However, our research, along with that of other investigators, has demonstrated that even in a
genetically uniform cell population, noteworthy heterogeneity in epigenetic and transcriptional
profiles can emerge(26-35). This involves dynamic epigenetic, transcriptional, and metabolic
processes that confer cancer cells with phenotypic plasticity, which enables cells to transition to a
drug-resistant state without necessitating the introduction of novel genetic mutations. This
diversity may arise through phenotypic plasticity in response to environmental cues and may
significantly contribute to the development of drug resistance(27, 31, 36-39). Another
phenomenon, "bistability," allows cells to reversibly switch between two distinct states(35, 38).
Both mechanisms serve as "bet-hedging" strategies, enabling diverse adaptations within a
population to survive in changing environments. This understanding of non-genetic contributors
adds a crucial layer to the resistance puzzle, as rapid cellular adjustments and phenotypic plasticity
play key roles in the resistance(40). The expansion of drug resistant, aggressive subpopulations of
cancer cells, results in tumor's responsiveness to treatment waning over time, leading to the
development of resistance(41, 42).
Resistance is known to arise through a variety of mechanisms such as the increased drug
efflux facilitated by overactive multi-drug resistance (MDR)-related transporters, an increase in
DNA repair capabilities, impaired apoptosis, and the triggering of pathways that promote cell
survival (Fig. 1.1)(43-46). MDR, a key factor in the metastasis and recurrence of tumors,
implicated in more than 90% of the cases where chemotherapy is ineffective in patients with
metastatic cancers(45, 47). Given the intricate nature of the mechanisms that lead to resistance,

4
employing a combination therapy that targets multiple oncogenic pathways may prove effective
in enhancing treatment efficacy.
1.2 Phenotypic Plasticity and Metabolic Shift
Cancer cell plasticity denotes the capacity of cancer cells to transition between various
phenotypes when exposed to external stimuli. This ability of cancer cells to undergo phenotypic
changes is recognized as a means of evading selective drug treatments. Recent work, by our lab
Figure 1.1. Tumor resistance is driven by a range of mechanisms.
The significant processes contributing to tumor resistance encompass: 1) the activation of compensatory
signaling pathways, 2) enhanced capabilities for DNA repair, 3) impaired apoptotic function, 4) drug
efflux by MDR-associated transport proteins, and 5) diminished cellular drug intake. The figure was
taken from Zhang, M. et al. Cancer Biol Med (2017)(45).

5
and others, suggests that non-genetic mechanisms allow phenotypically distinct subpopulations to
arise within genetically identical tumors, increasing their adaptability and tumorigenic
potential(26-32). These phenotypically distinct subpopulations are “bistable” and interconvert
stochastically and reversibly between more sensitive and more resistant states, where the
phenotypic shifts have been shown to resemble cancer stem-like cell (CSC) characteristics and
epithelial-to-mesenchymal transitions, in response to drug-induced stress(33, 48). To study this
phenotypic plasticity, our lab fractionated drug-resistant and drug-sensitive subpopulations of BC
cell lines by Hoechst staining and FACS and found that these subpopulations exhibited marked
differences at the epigenetic, transcriptional, and protein signaling levels(26-31). The results
revealed that characteristics promoting cancer, such as drug resistance and rapid tumor initiation,
emerge not only as isolated occurrences under selective pressures but also as intricately
coordinated transitions happening simultaneously in numerous cells, even in the absence of
deliberately induced drug selection, ectopic gene expression, or fractionation into subpopulations.
Altered cellular metabolism is recognized as a major cancer hallmark, providing the tumor
cells with an adaptive survival advantage in hostile environments(33, 49-52). Metabolic
adaptations including changes in drug metabolism and cellular energetics, can impact cisplatin
responsiveness and provide a survival advantage to cells exposed to cisplatin-induced stress(19).
Our lab’s transcriptional and metabolic profiling revealed a marked increase in OxPhos activity in
the drug-resistant subpopulation compared to the drug-sensitive(29). However, the signaling
mechanisms by which the cells shift phenotypically to increased OxPhos and drug-resistance, are
not well understood yet in literature(53, 54).

6
1.3 Mitochondrial OxPhos and Cancer Stem Cells
In the 1920s, Otto Warburg made a groundbreaking discovery revealing that even
adequately oxygenated cancer cells exhibit heightened glucose consumption and elevated lactate
production, indicating an upregulation of glycolysis(55, 56). The prevailing notion derived from
this observation suggests a universal downregulation of oxidative phosphorylation (OxPhos) in
cancer compared to normal cells. While this holds true for numerous cancers, an expanding body
of evidence, including our own prior work, challenges this assumption(29, 57-61).
Elaborating the basics of cellular respiration, a glucose molecule undergoes a gradual
breakdown into carbon dioxide and water, yielding ATP directly through the reactions that
transform glucose. Living organisms exhibit two types of cellular respiration: aerobic, occurring
in the presence of oxygen, and anaerobic, occurring in its absence. Aerobic respiration involves
the breakdown of glucose into pyruvic acid via glycolysis, followed by further oxidation of
pyruvate in the mitochondria to produce carbon dioxide(62). This process yields a significant
amount of energy and occurs exclusively in cells equipped with mitochondria. Conversely,
anaerobic respiration, also known as fermentation, takes place in the absence of oxygen and can
follow two pathways: lactic acid fermentation and alcohol fermentation. The energy yield in
cellular respiration is determined by the number of ATP molecules synthesized from high-energy
intermediates like NADH (provides 3 ATP molecules) and FADH2 (contributes 2 ATP
molecules). Glycolysis serves as the basis of both aerobic and anaerobic respiration. In anaerobic
conditions, pyruvate, the final product of glycolysis, is converted to lactate through anaerobic
glycolysis, while yielding two ATP molecules per glucose molecule. While, in aerobic conditions,
pyruvate is transported into the mitochondria for oxidation into acetyl CoA, initiating the
tricarboxylic acid (TCA) cycle and OxPhos, potentially yielding 36 ATP molecules. Hence,

7
aerobic respiration yields a higher amount of ATP due to the complete oxidation of glucose, while
anaerobic respiration is less efficient in energy production(63, 64).
Both the nuclear and mitochondrial genomes regulate the OxPhos system, with the
mitochondrial genome (mtDNA) encoding 13 protein subunits of the OxPhos system, while the
nuclear genome encodes a minimum of 70 OxPhos subunits(65-67). The OxPhos system
diminishes oxygen levels while generating ATP through a sequence of protein complexes
collectively referred to as the electron transport chain (ETC), embedded within the mitochondrial
inner membrane, comprising Complexes I–V and two electron carriers, cytochrome c (Cyt c) and
coenzyme Q (CoQ) (Fig. 1.2a)(68). Within the OxPhos process, NADH facilitates the transfer of
electrons to Complex I, followed by the subsequent transport of electrons to CoQ, while FADH2
directly conveys the electrons to CoQ. CoQ then transfers electrons to Complex IV through
Complex III and Cyt c, generating water through oxygen reduction. Complexes I, III, and IV
actively pump H+ ions from the mitochondrial matrix into the intermembrane space, creating a
proton gradient. Ultimately, Complex V utilizes this proton gradient to synthesize ATP(69, 70).
The notion of modified tumor metabolism is widely acknowledged as a hallmark of cancer,
as there exists metabolic diversity within cancer, specifically, within tumor metabolic programs
originating within cells of a single tumor, such as cancer stem-like cells (CSCs) (71-73). Following
treatment with cytotoxic agents, a small remaining population of cancer cells, which may be rich
in CSCs, can lead to the recurrence of cancer(74, 75). CSCs constitute a subset of tumor cells
displaying elevated tumor-initiating abilities, resistance to conventional therapies, radiation, and
exhibit increased potential for metastasis. Additionally, they may contribute to tumor relapse and
are often enriched in minimal residual disease across various malignancies. Furthermore, CSCs
can alter their metabolic substrate utilization, transitioning between glycolysis and mitochondrial

8
OxPhos, enabling adaptations to survive in stressful environments(76-79). Mitochondria are
central to the functioning of CSCs, influencing their metabolism, resistance to drugs, and apoptotic
processes, therefore, therapeutic strategies that target mitochondrial translation and division, the
OxPhos pathway, or crucial related genes, hold promise in treating CSCs(80-84). CSCs have also
been observed to have high mitochondrial membrane potential and increased mitochondrial gene
expression, including the overexpression of PGC1α(85-87) (Fig. 1.2b). PGC1 serves as a
transcriptional coactivator, fostering mitochondrial biogenesis and respiration by enhancing the
expression of nuclear respiratory factor (Nrf) 1 and 2, both intricately linked with the
mitochondrial bioenergetic machinery(88, 89).

9
Figure 1.2. Depiction of the OxPhos pathway and its preferential utilization by CSCs.
a) The OxPhos pathway within the mitochondria generates ATP, the cell's energy currency. This process
involves five protein complexes (I-V) embedded in the inner mitochondrial membrane (IMM).
Complexes I-IV (electron transport chain, ETC) transfer electrons from NADH and FADH2 to oxygen,
generating an electrochemical gradient across the IMM. This gradient, in turn, powers complex V (ATP
synthase), which uses the proton flow to convert ADP to ATP. b) The diagram shows the notable
differences in mitochondrial function between cancer stem-like cells (CSCs) and the bulk of cancer cells
in a tumor, allowing CSCs to predominantly derive their energy through OxPhos, contributing to the
plasticity of CSCs in adapting to the tumor microenvironment. Both figures were taken from Liu Y. et
al. Int J Biol Sci (2023)(68).

10
1.4 Regulation of Mitochondrial Biogenesis
The resilience of drug resistant tumor subpopulations, like cancer stem-like cells (CSCs),
to treatments such as chemotherapy and radiotherapy can be attributed to a range of factors, notably
the critical role played by mitochondria(90). The elevated OxPhos allows for improved viability,
self-renewal capacity, ability to resist drugs, and the potential for tumor relapse, therefore, a deeper
understanding of the molecular dynamics regulating mitochondrial activity in cancer cells is likely
to aid in devising effective therapies to counter resistance to treatments(91).
Mitochondrial biogenesis involves the activation of a select group of coactivators and
nuclear transcription factors (TFs) by various signaling pathways. When a cell's demand for energy
rises, signaling cascades are triggered, prominently involving members such as PGC1α, nuclear
respiratory factors (Nrf1 and Nrf2), and mitochondrial transcription factor A (TFAM)(92-94).
PGC1α acts as a principal regulator of metabolism and mitochondrial operations in cells, working
in conjunction with SIRT1 (Silent Information Regulator 1) and AMPK (AMP-activated protein
kinase) to form a responsive network to metabolic distress(95). TFAM, a core mitochondrial
transcription factor which is a high-mobility-group box domain protein, is essential for preserving
the quantity and structure of mtDNA and nucleoid maintenance, thereby ensuring the effective
transcription of mtDNA-genes, including OxPhos-related genes like cytochrome c oxidase subunit
2 (mt-CO2) and other OxPhos proteins(96, 97). The binding of TFAM to mtDNA is a process
moderated by phosphorylation, and TFAM that is not bound can be broken down by the Lon
protease(98). While the functions of PGC1α and TFAM have been extensively studied in normal
cells, particularly concerning aging, neurodegeneration, diabetes, and obesity, their specific
contributions to the development of tumors are not yet fully understood(99, 100). TFAM, encoded
by the nucleus, exhibits binding to mtDNA in both a sequence-specific and a non-specific manner.

11
The sequence-specific binding to mtDNA promoter regions is indispensable for initiating
mitochondrial transcription, possibly acting as the RNA primer for the initiation of replication(96,
101). Additionally, TFAM engages in sequence-independent binding to mtDNA, serving to
condense the genome. It is plausible that both modes of mtDNA binding collectively contribute to
TFAM's influence on mtDNA copy number(102).
Given TFAM’s pivotal role in regulating mitochondrial biogenesis and OxPhos, and the
concurrent association of mitochondrial OxPhos with stem-like cancer cells and drug resistance in
existing literature, we analyzed the intricate cellular signaling crosstalk influencing TFAM
expression, illustrated in Figure 1.3.

12
Figure 1.3. A comprehensive literature review identifying pathways leading to TFAM
regulation. (References are provided in Table A3 in Appendix B)

13
1.5 Goals of Dissertation
Cisplatin has been a critical platinum-based chemotherapeutic in bladder cancer for more
than four decades(103). Emerging evidence suggests that across different cancer types, the
metabolic phenotype of elevated OxPhos was related to tumor aggressiveness and metastasis(29,
57-61), including our own recent work where we showed that rapid transition to drug resistance
entails nongenetic metabolic plasticity through upregulation of mitochondrial OxPhos, a trend that
intensified with cisplatin exposure(29). This intriguing finding prompted us to unravel the
underlying signaling pathways governing this metabolic shift and propose novel therapeutic
strategies to counter it.
The overarching goal of this dissertation is to define the novel adaptive nuclearmitochondrial signaling cascade triggered by cisplatin, wherein cisplatin-induced nuclear DNA
damage activates a signaling cascade that culminates in upregulation of nuclear TFAM, the master
activator of mitochondrial replication and transcription(92, 96-98, 104), thereby aspiring to offer
new therapeutic avenues to disrupt acute chemo-resistance and improve treatment outcomes in
BC. The first chapter (Chapter 2) examines cisplatin's effect on mitochondrial DNA’s
replication/transcription and OxPhos activity as well as the impact of OxPhos on cancer cell
survival. Furthermore, we begin dissecting TFAM’s role in the cisplatin-induced increase in
mtDNA and OxPhos, highlighting its importance in cisplatin's mechanism that results in drug
resistance. Chapter 3 focuses on the more mechanistic downstream aspects of cisplatin-induced
mitochondrial activation, that is ATP production and drug efflux, while elucidating TFAM’s role
in these outcomes and its effect on survival in BC. In Chapter 4, building upon prior literature and
our own experiments, we propose a signaling cascade linking cisplatin-induced DNA damage to
ATM-AMPK-PGC1α-TFAM activation. We investigate the cisplatin-induced activation of ATM

14
kinase and analyze its effect on the proposed downstream targets in the signaling cascade, together
with its impact on cell survival in presence of cisplatin. Our studies in Chapter 5 examine the
hypothesized signaling cascade further, where we examine the effect of AMPK modulation, both
inhibition and activation, on the proposed downstream targets, in conjunction with the effect of
these modulations on cell survival in presence of cisplatin and in overcoming resistance. Finally,
rounding off our investigation of the hypothesized ATM-AMPK-PGC1α-TFAM signaling
cascade, we focused on PGC1α as a key signaling mediator affecting TFAM, and observed the
effect of its inhibition on TFAM as well as on reversing cisplatin resistance and improving cisplatin
sensitivity in BC. Additionally, we established a patient-derived bladder cancer organoid
repository to validate our key findings. In this manner, this work identifies a novel cisplatininduced nuclear-mitochondrial signaling axis that drives resistance in bladder cancer (Fig. 1.4).
Unraveling this intricate pathway will pave the way for the development of novel therapeutic
strategies to combat resistance and optimize cisplatin-based treatment regimens, ultimately
improving patient survival and outcomes in BC.

15
Figure 1.4. Overview Schema: In bladder cancer, cisplatin induces an adaptive signaling cascade
that activates mitochondrial OxPhos and drives a rapid transition to cisplatin resistance.

16
Chapter 2: Cisplatin activates mitochondrial DNA replication & transcription via TFAM
All the experiments described in the results section of this chapter were conducted entirely by Maheen
Iqbal, besides the IF imaging done with Lisa Swartz in Fig. 2.5d, and collaborative FLIM imaging done
with Jason Junge in Fig 2.8d. All results of this chapter are part of the manuscript under review.
Authors
Maheen Iqbal1
, Tong Xu1
, Lisa Swartz1
, Jason Junge2
, Sanam Ladi Seyedian3
, Yi-Tsung Lu1
, Scott
Fraser2
,Siamak Daneshmand3
, Amir Goldkorn1,4
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Translational Imaging Center, University of Southern California, Los Angeles, CA, 90089, USA
3Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
4Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. conceived and designed the study, conducted all experiments, interpreted the results, and wrote the
manuscript. A.G. and T.X. provided overall critical feedback and guidance. L.S. helped with IF imaging
and J.J. and S.F. helped with FLIM imaging. S.L.S. and S.D. provided the freshly resected patient TURBT
and RC samples. M.I. analyzed and interpreted the experimental data with A.G. M.I. wrote the original
manuscript. M.I. and A.G. reviewed and edited the manuscript. A.G. provided overall study supervision.
Introduction
Cancer cells were once thought to solely rely on aerobic glycolysis (Warburg effect) for
energy, even in the presence of oxygen(55, 56). However, new research reveals a more complex
picture. While some cancer cells still use this method, others, including stem cell-like populations,
surprisingly rely on functional mitochondria and a different energy-generating process called
OxPhos(51, 105-107). This suggests that cancer cells are metabolically flexible and can switch
between different energy sources depending on their needs and the environment. A study in IDH
wild-type GBM, identified that the tumor cells solely rely on OxPhos and are sensitive to drugs
that target this process(108), and similarly, research on the metabolic characteristics of CSCs also
points towards a preference for OxPhos in these aggressive and drug-resistant
subpopulations(109).

17
Building upon prior investigations into cancer phenotypic plasticity and drug resistance,
our laboratory established a robust model utilizing bladder cell lines(26-31). This model employs
flow cytometry (FACS) with Hoechst 33342 dye exclusion to isolate a distinct subpopulation of
drug-resistant cells exhibiting side population (SP) characteristics alongside a non-side population
(NSP) lacking these properties. SP cells express ATP-binding cassette (ABC) transporters,
enabling efficient efflux of the Hoechst dye and their identification as a distinct "side" peak on the
FACS plot. Combining SP analysis with green-fluorescent labeling (GFP), we unveiled a
remarkable 2-way dynamic equilibrium between SP and NSP phenotypes within both cell culture
and tumor xenograft models, independent of any external selection pressures. This intriguing
process unfolded in a cyclical manner. Initially, SP cells transitioned into NSP, depleting their own
population. However, this decline was not permanent. Through a fascinating process of
coordinated, spontaneous conversion, large cohorts of NSP cells spontaneously reverted to the SP
phenotype over a matter of days, as depicted in Figure 2.1. Further characterization revealed that
SP cells displayed significantly higher tumorigenicity in mice compared to their NSP counterparts.
Additionally, SP cells exhibited profound resistance to chemotherapeutic agents such as docetaxel
and cisplatin, alongside elevated expression of pluripotency-associated genes.

18
Utilizing this established model of isolated side population (SP) and non-side population
(NSP) cells and subsequently using RNAseq and gene set enrichment analysis (GSEA)(110), our
lab revealed a distinct pattern: across all metabolic gene sets in the Kyoto Encyclopedia of Genes
and Genomes (KEGG)(111), OxPhos and mitochondrial-encoded gene sets exhibited the most
significant enrichment in SP cells (Fig. 2.2a, b)(29). This trend was further corroborated by
individual gene analysis, as OxPhos genes were predominantly overrepresented among all
differentially expressed genes between SP and NSP (Fig. 2.2c). Notably, within the subset of
SP cell
population
as % of
entire cell
population
10
20
Cell passages (P) in culture over time (days)
P P
0 4 6 2 4 6
2
P
SP
Cancer
Stem-Like
NSP
Non-Cancer
Stem-Like
Day 1-3
Day 3-6
Figure 5. Bladder cancer phenotypic
plasticity model, in which highly
tumorigenic drug resistant, cancer
stem-like side population (SP) cells
directly convert into non-side
population (NSP) cells lacking these
properties, and vice versa.
SP cell
population
as % of
entire cell
population
10
20
Cell passages (P) in culture over time (days)
P P
0 4 6 2 4 6
2
P
SP
Cancer
Stem-Like
NSP
Non-Cancer
Stem-Like
Day 1-3
Day 3-6
Figure 5. Bladder cancer phenotypic
plasticity model, in which highly
tumorigenic drug resistant, cancer
stem-like side population (SP) cells
directly convert into non-side
population (NSP) cells lacking these
properties, and vice versa.
Figure 2.1. Overview of BC phenotypic plasticity model.
Our model reveals a dynamic and rapid phenotypic plasticity between the aggressive, drug-resistant
cancer stem-like cells (SP) and non-tumorigenic cells (NSP) in bladder cancer. Figure was taken from
Xu T. et al. Int J Cancer (2020)(28).

19
differentially expressed genes, every single OxPhos gene exhibited upregulation in SP cells,
solidifying the observed metabolic shift (Fig. 2.2d).
Our group and others (29, 59, 60, 112, 113), have observed functional mitochondria and
increased OxPhos in tumor cells, enabling greater tumorigenicity and drug resistance. Importantly,
in our previous publication, we showed that via real-time metabolic tracking, that individual cancer
cells can undergo rapid metabolic changes within hours, especially when exposed to
Figure 2.2. Gene expression in SP cells is enriched for OxPhos pathway.
a) OxPhos pathway was the most highly enriched in the KEGG metabolic pathways. b) GSEA analysis
demonstrated that the OxPhos gene set was significantly enriched in SP . c) Volcano plot of differential
gene expression between SP and NSP cells. OxPhos genes (red) were predominantly overexpressed in
SP as compared with the entire transcriptome (black). d) Heatmap (Transcripts Per Million, TPM) of
all significantly differentially expressed genes demonstrating OxPhos gene overexpression in SP. The
heatmap was created using the Complex Heatmap package v.2.4.3 (PMID: 27207943) in R 4.0.1. Figure
was taken from Xu T. et al. Sci Rep (2022)(29).

20
chemotherapy(29). Therefore, having observed that cisplatin itself is somehow able to affect the
metabolic fate of the cell by shifting it towards OxPhos, elucidating the drivers of this metabolic
shift offered the tantalizing possibility of re-sensitizing cancer cells to treatment.
Results
2.1 Cisplatin upregulates mitochondrial DNA replication & transcription
Having observed intensified and rapid transitioning of single cells to an OxPhos phenotype
with exposure to cisplatin chemotherapy(29), we sought to investigate the effect of cisplatin on
the mitochondria, to build up the investigations on cisplatin’s relationship with the cell’s metabolic
switch to OxPhos. Cisplatin is known to wield its cytotoxic effects through diverse mechanisms,
including disrupting DNA replication and triggering apoptosis. Recent research suggests an
intriguing additional target: mitochondrial activity(114). A study in leukemia cells suggested that
increased mitochondrial content and mitofusin-mediated mitochondrial fusion might contribute to
the development of cisplatin resistance, and similarly another investigation in NSCLC correlated
rinotecan-resistance with increased OxPhos(115, 116).
Since mtDNA replication is a critical step in the process of mitochondrial biogenesis,
measuring its rate can offer a reliable way to quantify the overall rate of mitochondrial growth and
expansion(117). Also, mammalian mitochondria have their own genome, which includes 13
proteins that make up the components of the OxPhos system(118). In T24 BC cells, treatment with
cisplatin for 24 hrs induced a significant increase both in mtDNA levels and in mitochondrial gene
expression (Fig.2.3a, b), including expression of genes involved in the OxPhos pathway (mt-ND5,
mt-CO2)(119), as well as more recently discovered short open reading frame (sORF) genes
(Humanin, MOTS-c)(120, 121). Similar increases were observed in an established cisplatin-

21
resistant cell line, T24-R2 (Fig.2.3a, b). The same trends for effect on OxPhos genes’ expression
were observed for UMUC-3 and J82 cells, under cisplatin treatment (Fig.2.3c).
Figure 2.3. Effect of cisplatin on mtDNA’s replication & transcription.
a) mt-tRNALeu-DNA levels (qPCR) in cisplatin-treated T24 cells (10μM, 24 hrs) and in cisplatinresistant T24-R2 cells relative to untreated T24 cells, normalized to nuclear X-Chr region; graph displays
mean ± SEM values in n=3 replicates; p values compare the indicated values. b) Mitochondrial gene
expression (qRT-PCR) in cisplatin-treated T24 cells (10μM, 24 hrs) and in cisplatin-resistant T24-R2
cells, relative to untreated T24 cells, normalized to β-Actin; graph displays mean ± SEM values in n=3
replicates; p values compare the treated cells to respective untreated cells. c) Mitochondrial gene
expression (qRT-PCR) in cisplatin-treated UMUC-3 and J82 cells (10μM, 24 hrs), relative to respective
untreated cells, normalized to β-Actin; graph displays mean ± SEM values in n=3 replicates; p values
compare the treated cells to respective untreated cells. Statistical significance determined using Student’s
two-tailed t-test, *p < 0.01; **p < 0.001.

22
2.2 Pharmacological inhibition of OxPhos synergizes with cisplatin to reduce cell survival
To investigate the potential role of mitochondrial activation in mediating cisplatin
resistance, we employed phenformin, a well-established Complex I inhibitor(122), for its ability to
disrupt mitochondrial energy production. A panel of BC cell lines were co-treated with cisplatin
and phenformin, and we observed a significant synergistic reduction in cell survival across all cell
lines (Fig. 2.4a). Notably, the calculated coefficients of drug interaction (CDI) were consistently
below 0.7, confirming the synergistic effect between cisplatin and phenformin. This synergism
suggests that modulating mitochondrial activity may hold promise for overcoming cisplatin
resistance. To further validate our findings, we focused on T24-R2, a cisplatin-resistant cell line.
T24-R2 cells were a gift from Dr. Seok-Soo Byun at Seoul National University Bundang Hospital,
and were established through serial desensitization of T24 cells to resist 6.6μM cisplatin (details
provided in Chapter 7)(123). Consistent with its resistant phenotype, T24-R2 cells exhibited lower
sensitivity to cisplatin compared to its parental counterpart (Fig. 2.4b). However, when combined
with phenformin, a dramatic restoration of cisplatin sensitivity was observed in T24-R2 cells. This
re-sensitization suggests that the pro-survival role of mitochondrial activation becomes
particularly pronounced in resistant cells, highlighting its potential as a therapeutic target to bypass
resistance mechanisms. These findings prompted us to delve deeper into the underlying
mechanisms by which mitochondrial activity’s inhibition synergizes with cisplatin.

23
2.3 Cisplatin upregulates TFAM RNA and protein expression in BC cells and patient-derived
organoids (PDOs)
Having observed the increased mitochondrial biogenesis, evident in elevated mitochondrial
DNA (mtDNA) levels and gene expression, in response to cisplatin, and finding significant
synergism between mitochondrial activity’s inhibition and cisplatin, we intended to understand the
Figure 2.4. Effect of phenformin and cisplatin co-treatment on cancer cell survival.
a) Cell viability after treatment with phenformin (20μM) or cisplatin (10μM) or both, for 24 hrs, in a
panel of BC cell lines. b) Cell viability of cisplatin-resistant T24-R2 cells after treatment with
phenformin (20μM) or cisplatin (10μM) or both, for 24 hrs. Trypan blue exclusion was used to measure
cell counts and viability for each treatment and to calculate the coefficient of drug interaction (CDI) for
combined ‘cisplatin + phenformin’, where CDI<0.7 indicates significant synergy; graph displays
mean ± SEM values in n=3 replicates; p values compare the indicated values. Statistical significance
determined using Student’s two-tailed t-test, *p < 0.001.

24
signaling pathways underpinning this cisplatin-induced adaptive response. By identifying key
regulatory nodes, we can potentially disrupt this resilience and enhance treatment efficacy. As
mentioned already, our laboratory previously established a robust model for studying phenotypic
plasticity and drug resistance in BC, utilizing Hoechst dye exclusion to isolate distinct
subpopulations with varying sensitivities to cisplatin(26-31). Using this model, we leveraged the
Gene Transcription Regulation Database (GTRD)(124) (Fig. 2.5a), and embarked on a data-driven
approach to analyze RNA-seq data from our previously established drug-resistant and drugsensitive subpopulations, and look for potential transcriptional regulators responsible for the
markedly higher OxPhos gene expression in the former. This analysis yielded an important result:
TFAM (Transcription Factor A, Mitochondrial) emerged as the most significantly associated
regulatory element for differentially expressed genes across these phenotypes between these two
subpopulations. This finding resonated with existing knowledge, as TFAM stands as a master
regulator of mtDNA replication and transcription(93, 125, 126). It resides within the nucleus,
dictating the expression of genes essential for mitochondrial biogenesis and function. Therefore,
TFAM presented itself as a prime candidate for driving the observed increase in mtDNA levels
and gene expression in response to cisplatin treatment.
Comparing the TFAM gene expression levels between the drug-resistant (SP) and drugsensitive (NSP) cells, we observed a significantly higher expression in the drug-resistant (SP) T24
cells (Fig. 2.5b). Furthermore, cisplatin treatment for 24 hrs significantly increased TFAM mRNA
levels in a panel of BC cell lines, and TFAM also was ~1.5 fold higher in the cisplatin-resistant
T24-R2 cells (Fig.2.5c). Western blot results also revealed markedly higher TFAM protein levels
in T24 and UM-UC-3 cells, compared to untreated cells (Fig.2.5c). Considering TFAM’s control
over mtDNA replication and transcription, we characterized cisplatin’s effect on TFAM’s

25
colocalization with mitochondria and changes in mitochondrial morphology using IF. Cisplatintreated BC cells, as well as, cisplatin-resistant T24-R2 cells, exhibited expanded mitochondrial
distribution that colocalized with TFAM (Fig.2.5d).

26
Traditional cancer research models, while indispensable, often fall short of capturing the
full complexity and heterogeneity of human tumors. Cell lines, despite their ease of manipulation,
lack the intricate tumor architecture, and animal models, although offering greater physiological
relevance, may not accurately reflect human disease biology due to interspecies differences(127,
128). Patient-derived organoids (PDOs) are miniaturized, three-dimensional cultures derived
directly from patient tumor tissue. Therefore they faithfully recapitulate the unique architecture,
cellular composition, marker expression and genetic landscape of their tissue of origin, offering a
glimpse into the personalized complexities of each patient's disease and investigating the critical
aspects of tumor progression and therapeutic resistance(129). To confirm our findings in more
clinically relevant patient-derived in vitro models, our lab started developing a repository of
patient-derived bladder cancer organoids, spearheaded by me as a part of my doctoral research
plan. Freshly resected transurethral resection of bladder tumor (TURBT) and radical cystectomy
(RC) tissues were collected with informed consent under an IRB-approved protocol, disaggregated
and digested, then seeded in defined medium and passaged until organoids reached optimum size,
confluency, and morphology (Fig 2.6a). Details of the tissue collection protocol, histological
Figure 2.5. Cisplatin upregulates TFAM gene and protein expression.
a) Flow diagram depicting use of GTRD to gain key differential Transcription Factor from our raw
RNAseq data. b) TFAM expression (qRT-PCR) in T24 SP and NSP cells, normalized using β-Actin.
Graph displays mean ± SEM values in n=3 replicates; p values are calculated using Student’s two-tailed
t-test comparing the SP cells to NSP cells. c) TFAM expression (qRT-PCR) in BC cell lines (UM-UC3, RT4, J82, T24) treated with cisplatin (10μM, 24 hrs), relative to untreated respective cells, and in
cisplatin-resistant T24-R2 cells, relative to parental T24 cells, normalized using β-Actin. Graph displays
mean ± SEM values in n=3 replicates; p values are calculated using Student’s two-tailed t-test
comparing the treated cells to respective untreated cell lines. Protein levels of TFAM after treatment
with cisplatin (10μM, 24 hrs), normalized to ⍺-Tubulin loading controls; blot representative of 3
independent experiments. d) Characterization of TFAM localization and mitochondrial morphology in
response to short-term cisplatin treatment in T24 and UM-UC-3 cells and in cisplatin-resistant T24-R2
cells using IF. Representative immunofluorescence images of nuclei (DAPI; blue), TFAM (red) and
mitochondria (TOMM20; green), show expanded mitochondrial distribution compared to untreated
cells. Confocal images were taken at 40x magnification. Scale bar, 15 μm. Graphs display mean ± SEM
values in n=3 replicates. The p values are calculated using Student’s two-tailed t-test comparing the
respective values as indicated in the graph. *p < 0.001.

27
characterization of organoids, and development of PDO repository are provided in ‘Appendix A’.
For the current study, one of the laboratory PDO lines (CTC-1044) was used in parallel with
another established PDO line generously provided by the National Cancer Institute PatientDerived Models Repository (NCI PDMR, (https://pdmr.cancer.gov(130)). To investigate the role
of TFAM in cisplatin response, we examined its protein and mRNA levels in cisplatin-treated
PDOs. PDOs treated with cisplatin for 24 hours exhibited a significant elevation in TFAM
expression (Fig 2.6b). Consistent with this observation, histological staining also revealed a
marked increase in TFAM protein expression upon cisplatin treatment (Fig 2.6c), further
supporting the TFAM’s potential involvement in response to cisplatin.

28
2.4 TFAM depletion abrogates mtDNA levels and OxPhos activity in BC cells and PDOs
Next, we sought to observe the effect of TFAM depletion on mitochondrial activity. We
opted for siRNA-based knockdown (KD), as it provides a temporary loss-of-function, which
mimics the effects of drugs and depicts the effect on phenotypes more physiologically
Figure 2.6. Cisplatin upregulates TFAM expression in PDOs
a) Schema for establishing BC patient-derived organoids, according to our approved IRB protocol. b)
Gene expression (qRT-PCR) of TFAM, after cisplatin treatment (50μM, 24 hrs). Graph displays
mean ± SEM values in n=3 replicates; p values compare cisplatin treatment to untreated PDOs. c)
Immunohistochemical staining of TFAM protein levels with or without cisplatin treatment (50μM, 24
hrs), scale bar: 500um. The colon tissue panel (below) was used as a positive marker for TFAM staining.
Statistical significance determined using Student’s two-tailed t-test, *p< 0.001.

29
relevantly(131). The siRNA mediated TFAM depletion resulted in ~80-85% knockdown of
TFAM’s gene expression in the UM-UC-3 and T24 cell lines and ~75-80% TFAM KD in the
PDOs (Fig. 2.7a), which was confirmed by western blot analysis (Fig. 2.7b). TFAM KD abrogated
the mtDNA increases observed in cisplatin-treated T24 and UM-UC-3 cells, as well as the
cisplatin-resistant T24-R2 cells (Fig.2.7c). In both BC cell lines, as well as T24-R2 cells, TFAM
KD downregulated the mitochondrial OxPhos pathway-related gene expression (Fig.2.7d). The
same affects were mirrored in the CTC-1044 and CK9151 organoids where TFAM KD abrogated
the mtDNA increases observed in cisplatin-treated PDOs (Fig.2.7e) and downregulated the
mitochondrial gene expression (Fig.2.7f).

30

31
To delve deeper into TFAM's role in regulating mitochondrial function and its impact on
cisplatin-induced responses, we focused on oxygen consumption rate (OCR), a marker of OxPhos
activity and ATP generation (Fig. 2.8a). Consistent with our observations of increased mtDNA
content and expression of OxPhos genes, cisplatin treatment alone resulted in a pronounced
elevation in OCR compared to control cells, while TFAM knockdown markedly abrogated the
OCR upsurge induced by cisplatin. Therefore, TFAM KD played a pivotal role in mediating the
cisplatin-induced increase in mitochondrial respiration. TFAM depletion also significantly
decreased the BC cells’ percentage shift towards drug resistance phenotype (SP), with ~ 0.5-fold
reduction in SP% in T24 cells and ~0.3-fold reduction in UM-UC-3 cells (Fig. 2.8b).
To observe the effect of TFAM KD on OxPhos activity in the organoids, we utilized a
different approach that was more feasible for these three-dimensional models. This was done in
Figure 2.7. TFAM Knockdown in BC cells and PDOs.
a) TFAM KD (48 hrs) in T24 and UM-UC-3 cells quantified by qRT-PCR, relative to respective
untreated, scrambled (sc) siRNA transfected cells; normalized to β-Actin. TFAM KD (72 hrs) in
CK9151 and CTC-1044 PDOs quantified by qRT-PCR, relative to respective untreated, sc-siRNA
transfected PDOs; normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p
values calculated using Student’s two-tailed t-test comparing the TFAM siRNA transfected cells to
respective untreated sc-siRNA transfected cell lines. b) Western blots showing TFAM and Alpha
Tubulin protein levels, relative to respective sc-siRNA transfected BC cells lines and PDO lines, n=2.
c) mt-tRNALeu-DNA levels (qPCR) after treatment with cisplatin (10μM, 24 hrs) or TFAM KD (48
hrs) or both, normalized using nuclear X-Chr region; graph displays mean ± SEM values in n=3
replicates; p values are calculated using Student’s two-tailed t-test comparing the treated cells or TFAM
siRNA transfected cells to respective untreated sc-siRNA transfected cell lines. d) Mitochondrial gene
expression (qRT-PCR) in TFAM KD BC cells (48 hrs), relative to respective untreated cells, normalized
to β-Actin; graph displays mean ± SEM values in n=3 replicates; p values compare the treated cells to
respective untreated cells. e) mt-tRNALeu-DNA levels (qPCR) after treatment with cisplatin (50μM, 24
hrs) or TFAM KD (72 hrs) or both, normalized using nuclear X-Chr region; graph displays mean ± SEM
values in n=3 replicates; p values are calculated using Student’s two-tailed t-test comparing the treated
cells or TFAM siRNA transfected PDOs to respective untreated sc-siRNA transfected PDOs. f) Gene
expression (qRT-PCR) of mt-ND5, and mt-CO2 after treatment with TFAM KD (72 hrs) or cisplatin
(50μM, 24 hrs) or both. Graph displays mean ± SEM values in n=3 replicates; p values compare cisplatin
treatment and/or TFAM KD to sc-siRNA treatment. Statistical significance determined using Student’s
two-tailed t-test, *p < 0.01; **p < 0.001.

32
collaboration with the laboratory of Dr. Scott Fraser, who have pioneered imaging methods using
two-photon fluorescence lifetime imaging microscopy (FLIM), a non-invasive, non-interventive
and label-free microscopy method that allows real-time tracking of metabolism in cells(29). FLIM
measure the rate of fluorescence decay as a reflection of cellular energy metabolism. A short
fluorescence decay lifetime correlates with a high ratio of bound/total NADH, which corresponds
to a lower ratio of free NADH/NAD+ and a more OxPhos metabolic state. Fig. 2.8c describes
decay trends and analysis of NADH FLIM data in the lifetime-phasor. The phasor’s graphical
interface is an elegant and powerful tool for interacting with live-cell imaging data and for
extracting meaning out of metabolic imaging. In both PDO lines (Fig. 2.8d), cisplatin treatment
shifted the phasor plot leftward towards a more OxPhos state (Fig. 2.8d, red arrows) whereas
TFAM depletion shifted the phasor plot rightward towards a more glycolytic state, despite
cisplatin’s presence (Fig. 2.8d, blue arrows).

33

34
Discussion
Our findings offer insights into the interplay between cisplatin and mitochondrial
biogenesis and activity in BC. We demonstrate that cisplatin triggers an upregulation of
mitochondrial DNA (mtDNA) replication and transcription (2.1) while pharmacological inhibition
of OxPhos sensitizes the cancer cells to cisplatin (2.2). The upregulation of mitochondrial
transcription factor A (TFAM) gene in response to cisplatin (2.3) provides a strong explanation for
the observed increase in mtDNA replication and transcription. TFAM plays a pivotal role in
promoting mtDNA replication, transcription, and maintenance, acting as the master regulator of
mitochondrial biogenesis. Our observation that TFAM depletion abrogates mtDNA levels and
OxPhos activity both in cell lines and patient-derived organoids (2.4) highlights the critical role of
TFAM in maintaining mitochondrial function in bladder cancer cells. This finding suggests that
Figure 2.8. TFAM Knockdown reduces OxPhos activity in BC cells and PDOs.
a) TFAM KD (48 hrs) in T24 and UM-UC-3 cells quantified by qRT-PCR, relative to respective
untreated, scrambled (sc) siRNA transfected cells; normalized to β-Actin. TFAM KD (72 hrs) in
CK9151 and CTC-1044 PDOs quantified by qRT-PCR, relative to respective untreated, sc-siRNA
transfected PDOs; normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p
values calculated using Student’s two-tailed t-test comparing the TFAM siRNA transfected cells to
respective untreated sc-siRNA transfected cell lines. b) Flow cytometry assay was used to record the
SP percentage composition after cisplatin treatment (24 hrs), with either scrambled siRNA or TFAM
siRNA (48 hrs) in UM-UC-3 and T24 cells. Graph displays mean ± SEM values in n=2 biological
replicates; p values compare the indicated bars. Statistical significance determined using Student’s twotailed t-test, *p < 0.01; **p < 0.001. c) Phasor overview and roadmap: (left) shows lifetime trends within
the FLIM phasor. The semi-circle marks where single-component exponential decays are plotted. In
buffer, 100% unbound NADH has a single-component exponential decay of 0.4 ns and localizes very
specifically to the lower right side of the semi-circle. NADH, in vivo, has a complex FLIM signal that
is made of many components which typically plot within the wedge-shaped region in the phasor (left
and right). The rainbow scale bar (right) is an example of how we maintain a consistent relationship
between Bound/Total NADH ratiometric measurements (fractional distances along cords) and our
displayed colors. Panel was taken from Xu T. et al. Sci Rep (2022)(29). d) Fluorescence Lifetime
Imaging Microscopy (FLIM) measurement of free/bound NADH, corresponding to OxPhos vs.
glycolytic metabolic state. Organoids are associated with their corresponding phasors by color borders,
the rainbow lookup table at the top of each panel applies to all phasors below them, and the dashed line
highlights the relative change in metabolism among conditions (n=2). Scale bars: 50m.

35
while cisplatin initially stimulates mitochondrial biogenesis, the resulting dependence on the
increased OxPhos activity exposes cancer cells to a vulnerability. This vulnerability can be
exploited by inhibiting OxPhos, as evidenced by the synergistic effect of cisplatin and OxPhos
inhibitors on cell survival.
Therefore, our results demonstrate that mitochondrial activation plays a critical role in
promoting cell survival and, consequently, mediating cisplatin resistance in bladder cancer. The
central role of TFAM in coordinating this metabolic shift raises a tantalizing possibility that
targeting TFAM itself may disrupt the shift to OxPhos and ultimately, enhance sensitivity to
cisplatin in bladder cancer cells.

36
Chapter 3: TFAM mediated mitochondrial activation results in drug efflux & cell survival
All the experiments described in the results section of this chapter were conducted entirely by Maheen
Iqbal. All results of this chapter are part of the manuscript under review.
Authors
Maheen Iqbal1
, Tong Xu1
, Sanam Ladi Seyedian2
, Siamak Daneshmand2
, Amir Goldkorn1,3
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
3Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. conceived and designed the study, conducted all experiments, interpreted the results, and wrote the
manuscript. A.G. and T.X. provided overall critical feedback and guidance. S.L.S. and S.D. provided the
freshly resected patient TURBT and RC samples. M.I. analyzed and interpreted the experimental data with
A.G. M.I. wrote the original manuscript. M.I. and A.G. reviewed and edited the manuscript. A.G. provided
overall study supervision.
Introduction
Drug resistance, a challenging adversary to cancer treatment, arises through diverse
mechanisms, often rendering once-effective treatments ineffective. Among these mechanisms, a
growing chorus points towards altered mitochondrial activity as a key conductor of
resistance(132). This intricate relation between altered bioenergetics and drug evasion warrants
detailed exploration, particularly as it offers potential avenues for therapeutic intervention.
Unraveling the precise metabolic rewiring fueling chemoresistance is crucial for designing
effective strategies to overcome this obstacle and advance cancer therapy. Although cancer cell
metabolism holds immense promise as a therapeutic target, the specific metabolic adaptations
underlying chemoresistance remain largely enigmatic. This metabolic shift, driven by mutations,
epigenetic modifications, or environmental cues, allows cancer cells to generate ATP, the cellular
currency of energy, even under harsh conditions imposed by chemotherapy. Recent work has

37
shown that aggressive cancer cells thrive on high ATP levels, which fuel their relentless growth
and expansion(133). Abundant ATP powers a multitude of aggressive traits, such as rapid
proliferation, stem-like properties, detachment from anchoring structures, enhanced migration and
invasion, metastatic spread, robust antioxidant defenses, and the ability to evade drug action(133,
134). In stark contrast, dormant cancer stem cells operate on a low-energy budget, characterized
by significantly lower ATP levels(83).
The elevated ATP production, results in energy surplus that fuels a multitude of resistanceassociated processes, including drug efflux: ATP-powered pumps, such as ABC transporters,
actively eject chemotherapeutic agents from the cell, diminishing their intracellular concentration
and cytotoxic effects (113, 135). This orchestrated expulsion renders the drugs essentially
Figure 3.1. Elevated ATP production serves as a favorable phenotypic driver of aggressive cancer
tumorigenicity and possible resistance. Figure was taken from Fiorillo, M. et al. Front Oncol
(2021)(133).

38
ineffective, shielding the cancer cells from their lethal effect. In the experiments described in our
Chapter 2, we observed that pharmacological inhibition of mitochondrial OxPhos and ATP
production significantly increased the sensitivity of BC cells to cisplatin. Therefore, in the next set
of experiments described in this chapter, we tested whether manipulating a key regulator of
mitochondrial biogenesis and activity, like TFAM, could significantly impact cisplatin’s effect on
OxPhos, ATP production, drug efflux, and ultimately, cell viability.
Results
3.1 Cisplatin increases ATP production and drug efflux, while TFAM abrogates it.
To further elucidate the role of TFAM in cisplatin-induced metabolic reprogramming and
resistance, we delved into its influence on ATP production and drug efflux. Cisplatin treatment
alone resulted in a significant elevation in ATP compared to untreated cells, in UM-UC-3 and T24
cells, as well as in the inherently resistant T24-R2 cells (Fig. 3.2a). Conversely, TFAM depletion
significantly abrogated the cisplatin-induced increase in ATP, bringing levels back to those
observed in untreated control cells (Fig. 3.2a). This observation provides further evidence that
TFAM is necessary for the cisplatin-driven rise in ATP production. Moreover, the effect of TFAM
depletion mirrored that of oligomycin, a potent inhibitor of mitochondrial ATP synthase, further
cementing the central role of TFAM in regulating this process. To explore the downstream
consequences of TFAM's influence on ATP production, we investigated its impact on drug efflux,
a major contributor to cisplatin resistance(136, 137). Using a fluorescent dye as a surrogate for
drug molecules, we assessed its expulsion from the cells under various conditions. Cisplatin
treatment led to increased dye efflux, indicated by a decrease in intracellular fluorescence intensity
(Fig. 3.2b). This finding corroborated our previous observations and confirmed that cisplatin

39
activates drug efflux pumps, potentially as a resistance mechanism. Conversely, TFAM depletion
significantly reduced dye efflux, leading to a higher intracellular fluorescence intensity (Fig. 3.2b).
This observation mirrored the effect of verapamil, a known inhibitor of ATP-Binding Cassette
(ABC) transporters, the molecular engines driving drug efflux. Taken together, cisplatin stimulated
mitochondrial biogenesis and activity via upregulation of TFAM, leading to increased ATP
production, which fuels and promotes drug efflux, while TFAM depletion rendered the cancer
cells more vulnerable to cisplatin's cytotoxic effects.

40

41
3.2 TFAM depletion synergizes with cisplatin to reduce cell survival in BC cells and PDOs.
Next, we investigated TFAM’s influence on cell survival and proliferation, both in cell
lines and patient-derived organoids (PDOs). Consistent with its observed role in promoting drug
efflux (as described in previous sections), TFAM depletion significantly increased BC cell
sensitivity to cisplatin by decreasing the number of live cells, compared to treatment with cisplatin
alone (Fig. 3.3a). This was true in cisplatin-sensitive cell lines as well as the cisplatin-resistant
T24-R2 cells. Moreover, cisplatin treatment of TFAM-depleted cells caused a marked synergistic
reduction in proliferation-related gene expression (Ki-67, PCNA)(138, 139) and as well as in
survival (Fig. 3.3b and c). These findings suggest that targeting TFAM, even in established
resistance models, can restore cisplatin's lethality.
Figure 3.2. Cisplatin bolsters ATP levels and drug efflux, TFAM depletion abrogates the effect.
a) Total cellular ATP concentration as determined by luciferase assay after treatment with cisplatin
(10μM, 24 hrs), TFAM KD (48 hrs), or oligomycin (10nM, 24 hrs), using a standard curve. Graph
displays mean ± SEM values in n=3 replicates; the p values are calculated using Student’s two-tailed ttest comparing the treated cells to respective untreated cells. b) Drug efflux measured using fluorescent
MDR indicator dye after treatment with cisplatin (10μM, 24 hrs), TFAM KD (48 hrs), or verapamil
(50μM, 24 hrs). Graph displays mean ± SEM values in n=3 replicates; p values are calculated using
Student’s two-tailed t-test comparing the treated cells to respective untreated cells. The p values are
calculated using Student’s two-tailed t-test comparing the respective values as indicated in the graph. *p
< 0.05; **p < 0.01; ***p < 0.001.

42

43
3.3 TFAM’s cancer promoting role is recapitulated in TCGA database analysis.
Our observations of TFAM's impact on mitochondrial biogenesis and cisplatin resistance
in bladder cancer cells lines and PDOs led us to also investigate TFAM’s association with clinical
outcomes using the publicly available patient database, The Cancer Genome Atlas (TCGA) (Fig.
3.4)(140). In TCGA, elevated TFAM RNA expression was significantly associated with higher
histological grade disease (n=436, p<0.01). Moreover, bladder tumors exhibited significantly
higher TFAM RNA expression levels compared to normal bladder tissue (n=436, p<0.001). This
observation adds another layer to the story, suggesting that TFAM upregulation may not only
characterize advanced disease but also play a fundamental role in the very process of
tumorigenesis. Further analysis also revealed an association between elevated TFAM RNA
expression and the presence of metastatic events (n=14, p<0.05). Collectively, these observations
suggest that TFAM upregulation plays a role in bladder tumor formation, aggressive features, and
dissemination. Notably, when assessed across all tumor types, TFAM expression was higher in
metastatic and recurrent tumors compared to those confined to their primary sites (n=10906,
p<0.001). These findings from a large-scale dataset further underscore TFAM's potential role in
progression of bladder cancer and other tumor types.
Figure 3.3. TFAM depletion increases cisplatin sensitivity in BC cells and PDOs.
a) Cell viability after treatment with TFAM KD (48 hrs), cisplatin (10μM, 24 hrs), or both, using trypan
blue exclusion; coefficient of drug interaction (CDI)<0.7 indicates significant synergy. Graphs display
mean ± SEM values in n=3 replicates. b) Treatment, alone or in combination, on expression (qRT-PCR)
of proliferation-related genes (Ki-67, PCNA) after treatment with TFAM KD (72 hrs) or cisplatin
(50μM, 24 hrs) or both, normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p
values compare cisplatin treatment and/or TFAM KD to sc-siRNA treatment. c) Cell viability after
treatment with TFAM KD (72 hrs) or cisplatin (10μM, 24 hrs) or both, using trypan blue exclusion,
calculated relative to sc-siRNA treatment. CDI<0.7 indicates significant synergy. Graphs display
mean ± SEM values in n=3 replicates; p values compare the respective values as indicated in the graph.
Statistical significance determined using Student’s two-tailed t-test, *p < 0.05; **p < 0.01; ***p < 0.001.

44
Discussion
Our findings from cellular models, patient-derived organoids, and clinical databases
highlight a significant association between TFAM expression and activity, mitochondrial
Figure 3.4. TFAM gene expression analysis using TCGA.
Analysis of The Cancer Genome Atlas (TCGA) (http://cancergenome.nih.gov/) revealed that elevated
TFAM gene expression was associated with higher grade disease and worse disease outcomes in BC
patients; interestingly this trend was recapitulated across all tumor types, suggesting a broader role for
TFAM in cancer resistance and progression. TCGA PAN-CAN Bladder Cancer mRNA-Seq expression
cohort and TCGA Bladder Cancer mRNA-Seq expression cohort were used for this analysis, respectively,
and the results are based upon data generated by the TCGA Research
Network: http://cancergenome.nih.gov/.

45
regulation, and survival of cancer cells exposed to cisplatin. Specifically, cisplatin triggered a rise
in TFAM expression, leading to mitochondrial biogenesis and enhanced ATP production, which
in turn fueled ATP-powered drug efflux pumps(113). These results resonated with observations in
other cancer models, where TFAM depletion sensitized cells to various chemotherapeutic agents
(141-143).
Therefore, targeting TFAM, alone or in combination with existing therapies, may
constitute a promising strategy to overcome cisplatin resistance in BC and potentially other
malignancies. These strategies could include developing small-molecule inhibitors targeting
TFAM for in vivo studies and ultimately for clinical translation. However, further research is
necessary to understand the potential risks and side effects of TFAM manipulation and to identify
the most effective and safe drug combinations for clinical efficacy and accuracy. Considering the
multifaceted roles of mitochondria in non-cancerous cells, including their involvement in energy
production, immune responses, programmed cell death, and the metabolism of critical molecules
like calcium, iron-sulfur clusters, one-carbon units, nucleotides, amino acids, and lipids, it is
crucial to be mindful of the potential adverse and unintended consequences of targeting
TFAM(144). Inhibiting TFAM could result in harmful effects stemming from mitochondrial
malfunctions and increased oxidative stress in healthy cells, as well asthe varying effects of TFAM
in different cancers due to the unique metabolic adaptations to diverse tumor environments.
Therefore, it is important to map the dynamic context-dependent role of mitochondrial OxPhos in
cancer and to devise methods for the precise targeting of TFAM in cancer cells, such as
nanoformulations specific to mitochondria of cancerous cells (third/organelle-level drug
targeting), in patient-tailored clinical approaches(145-148).

46
Chapter 4: Cisplatin activates ATM, and ATM inhibition abrogates cisplatin resistance
All the experiments described in the results section of this chapter were conducted entirely by Maheen
Iqbal, besides the IF imaging done with Lisa Swartz in Fig. 4.4a. All results of this chapter are part of the
manuscript under review.
Authors
Maheen Iqbal1
, Tong Xu1
, Lisa Swartz1
, Sanam Ladi Seyedian2
, Siamak Daneshmand2
, Amir
Goldkorn1,3
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
3Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. conceived and designed the study, conducted all experiments, interpreted the results, and wrote the
manuscript. A.G. and T.X. provided overall critical feedback and guidance. L.S. helped with IF imaging.
S.L.S. and S.D. provided the freshly resected patient TURBT and RC samples. M.I. analyzed and
interpreted the experimental data with A.G. M.I. wrote the original manuscript. M.I. and A.G. reviewed
and edited the manuscript. A.G. provided overall study supervision.
Introduction
Cisplatin is one of the most potent anticancer agents, particularly effective against various
solid tumors. Its mechanism of action primarily involves the induction of cytotoxicity in cancer
cells through DNA damage and the inhibition of DNA synthesis(103). Cisplatin exerts its
anticancer effects by forming platinum-DNA adducts, leading to significant structural DNA
damage. During aquation, cisplatin transforms into a highly reactive state, enabling it to bond with
various biomolecules inside the cell. It specifically forms covalent bonds with DNA bases, creating
DNA adducts. Cisplatin predominantly targets the N7-sites of purine bases and when it reacts with
two purines, either on the same strand (leading to intrastrand adducts) or on opposite strands
(resulting in interstrand crosslinks (ICLs)), it links them covalently (Fig. 4.1). These cisplatininduced DNA adducts hinder transcription and DNA replication, initiating a complex cellular

47
signal transduction process aimed at removing these lesions(14, 103). Intrastrand adducts caused
by cisplatin are large abnormalities that disrupt the DNA replication process by halting the action
of replicative DNA polymerases. This extended interruption of the replication forks may lead to
the creation of DNA double-strand breaks (DSBs). This results in cell cycle arrest, allowing time
for DNA repair mechanisms to act. However, if repair is inadequate or the damage is too severe,
cells may undergo apoptosis(149).
Despite its effectiveness, resistance to cisplatin, both inherent and acquired during
treatment, is common and poses a significant challenge in cisplatin-based cancer therapy(150).
Cells employ various strategies to prevent cisplatin from reaching and harming DNA, such as
reducing drug uptake, enhancing drug expulsion, and activating drug detoxification processes.
Nonetheless, once cisplatin binds with DNA, cells must either repair or tolerate the lesions to
withstand the treatment. Failure to do so leads to substantial cell death, including apoptosis, due
to cisplatin-induced DNA damage(151). The effectiveness of cisplatin is closely linked to its
ability to induce these lesions and the cellular response mechanisms that follow, which are
complex processes involving various signaling pathways. The fate of a cell following cisplatin
exposure depends on its capacity to detect and repair the DNA damage or to undergo programmed
cell death if the damage is irreparable(114).

48
The continuous cycle of DNA strand cleavage and repair can give rise to DSBs and initiate
the DNA damage response involving key factors like p53, ataxia telangiectasia mutated (ATM),
and ataxia telangiectasia related (ATR), which may lead to apoptosis(152). ATM kinase,
considered the master sensor of DSBs, responds to DNA DSBs by undergoing autophosphorylation and induces G1 arrest through the phosphorylation of p53(153, 154). On the other
hand, ATR oversees the cellular response to a broad range of DNA damage, encompassing
disruptions that affect DNA replication, such as single-strand breaks (SSBs) and stalled replication
forks. ATM’s downstream effector proteins include checkpoint kinase 2 (CHK2), breast cancer
type 1 susceptibility protein (BRCA1), p53 and others proteins, and this phosphorylation cascade
Figure 4.1. Cisplatin activation and DNA damage induction.
The activation of cisplatin involves the substitution of one or both its chloride ions with water
molecules. Once activated, cisplatin can establish covalent bonds with DNA, primarily resulting in
intrastrand DNA adducts and interstrand crosslinks. Figure was taken from
Rocha, C. R. R. et al, Clinics (2018)(149).
. et al. Front Oncol (2021)(59).

49
results in the activation of cell cycle checkpoints, DNA repair via homologous recombination
(HR), and apoptosis (Fig. 4.2)(155-159). The phosphorylation of H2AX by ATM, forming
γH2AX, is a key marker of DSBs and DNA damage response(160).
ATM is composed of five specific regions, arranged from the N-terminus to the C-terminus
as follows: the HEAT repeat domain, the FRAP-ATM-TRRAP (FAT) domain, the kinase domain
(KD), the PIKK-regulatory domain (PRD), and the FAT-C-terminal (FATC) domain. The HEAT
repeats are known to attach directly to the C-terminus of NBS1, while the FAT domain helps
Figure 4.2. Canonical responses to DNA damage.
This schema shows an overview of the involvement of ATM and ATR in DNA damage induced
signaling. ATM is recruited to DSB site and leads to a p53-dependent transcriptional program via
CHK2, leading to cell cycle arrest or apoptosis. ATR is recruited to SSBs and stalled replication forks,
where it phosphorylates and activates CHK1 that can also activate p53. ATM repairs DNA damage via
HR, which requires DNA-end resection that results in tracts of single-stranded DNA (ssDNA); again,
recruiting ATR to these ssDNA tracts as well. Therefore, in response to DNA damage, the progression
of the cell cycle is primarily arrested due to the activity of ATM and ATR.

50
stabilize the C-terminus of ATM by interacting with its kinase domain. The KD domain is
responsible for initiating kinase activity, with the PRD and FATC domains playing roles in its
regulation. The structural details of ATM have been elucidated in various studies using cryo-EM,
revealing that ATM naturally forms a homodimer when inactive(161, 162). A canonical pathway
believed to lead to ATM activation involves stimulation by the Mre11-Rad50-Nbs1 (MRN)
complex and other factors causing autophosphorylation of ATM (Serine1981), which results in
active ATM monomers capable of phosphorylating downstream targets(163, 164). The MRN
complex plays a crucial role at the beginning stages of repairing DNA DSBs, due to its nuclease
activity and capability to bind to DNA. The Mre11 protein is central to these activities and works
closely with Rad50 ATPase. Nbs1 is believed to help position the MRN complex correctly at the
DSB sites, likely through its interactions with the H2AX histone, which undergoes rapid
phosphorylation when DSBs occur(165). The process by which the MRN complex activates the
ATM protein, an essential step in the DSB repair mechanism, is not fully understood, but it is
believed that Nbs1 and ATM interaction results in its recruitment at DSB sites and this involves a
conformational change that, besides relieving the partial blockage of the substrate-binding site,
realigns the catalytic residues(164, 166).
Beyond its nuclear functions, recent studies have suggested a role for ATM in
mitochondrial responses to DNA damage, however, the detailed mechanism of this signaling
remain obscure. It has been observed that the activation of ATM is associated with its movement
out of the nucleus as part of the DNA repair process initiated by DSBs(167). Hinz et al, along with
other groups, has shown that DNA damage-activated ATM is exported from nucleus to the cytosol
and plasma membrane(168-171). One of the reported substrates of ATM, apart from the factors
involved in DNA repair, includes the master regulator of mitochondrial biogenesis, AMPK(172-

51
180). Studies have demonstrated that ATM can phosphorylate the α subunit of AMPK
(Thr172)(172, 177, 181, 182). Accordingly, we hypothesized that cisplatin-induced ATM
activation may in turn activate AMPK, functioning as the link between DNA damage and
metabolic reprogramming and drug resistance.
Specifically, we hypothesized an ATM-AMPK-PGC1α-TFAM signaling axis, wherein
cisplatin-induced nuclear DNA damage is transduced to downstream mitochondrial biogenesis and
upregulated OxPhos (Fig. 4.3). AMPK has a well-established role in direct and indirect
phosphorylation of PGC1α(183-185), activating its transcriptional co-activator function. PGC1α
drives the expression of numerous genes involved in mitochondrial biogenesis, mostly notably
TFAM(99, 186-189), leading to an expansion of the mitochondrial network and a consequent rise
in oxidative phosphorylation (OxPhos) activity. As mentioned in Chapters 2 and 3, TFAM
promotes mtDNA replication and transcription, thereby increasing mitochondrial activity and
further boosting OxPhos capacity and the ability of drug efflux. Further details of AMPK’s and
PGC1α’s activation and cellular roles are provided in the chapters to follow, (Chapters 5 and 6).
In the next set of experiments (described in this chapter), we focused specifically on the first step
– from cisplatin DNA damage to ATM activation as a link to downstream sequelae.
Figure 4.3. Hypothesized adaptive chemotherapy resistance pathway, wherein cisplatin-induced
nuclear DNA damage leads to TFAM upregulation.
Schema shows our hypothesized ATM-AMPK-PGC1α-TFAM signaling cascade, where TFAM levels
were increased by PGC1α, a transcriptional regulator of TFAM and of mitochondrial biogenesis and
PGC1α is itself upregulated by the activity of AMPK, which in turn is activated by ATM, a canonical
initiator of the DNA damage response.

52
Results
4.1 Cisplatin treatment results in increase of phospho-H2AX protein levels.
In 1998, Bonner and colleagues introduced a sensitive method for detecting DSBs, making
detection of γH2AX foci a characteristic feature of DSBs(190). This involves the phosphorylation
of histone H2AX (Ser139) and these foci are downstream effectors in the DNA damage response
pathway and have been shown to have a direct correlation with the number of DSBs(190-192).
After exposure to ionizing radiation and chemotherapeutic agents like etoposide and cisplatin, that
directly cause DSBs, clusters of phosphorylated H2AX, known as γH2AX foci, are observed(193).
All three primary PIKK proteins – ATM, ATR, and DNA-dependent protein kinase– can mediate
the phosphorylation of H2AX. The phosphorylation of H2AX by ATM, particularly in association
with the induction of DNA double-strand breaks (DSBs), has been extensively documented(164,
194).
Based on the well-established role of γH2AX, as a DNA damage and DSB marker, we
analyzed cisplatin-induced DNA damage by treating T24 and UM-UC-3 BC cells with cisplatin
and measuring γH2AX levels via confocal immunofluorescence imaging (Fig. 4.4a). There was a
significant increase in γH2AX staining even after 16hrs of cisplatin treatment, compared to
untreated cells. Western blot analysis revealed a corresponding increase in γH2AX protein levels,
in BC cells and PDOs (Fig. 4.4b) confirming that cisplatin in fact induces DNA damage as
expected in our models.

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4.2 Cisplatin increases phospho-ATM and the levels of its hypothesized downstream
signaling mediators, whereas ATM inhibition decreases them.
To investigate the role of ATM in initiating and relaying activation through its
hypothesized downstream mediators we inhibited ATM activity using a specific ATM kinase
inhibitor, KU-55933, which is an ATP-competitive inhibitor of ATM(195). KU-55933 has been
developed with high selectivity for ATM, exhibiting a selectivity at least 100 times greater than
Figure 4.4. Cisplatin increases phospho-H2AX levels.
a) Representative immunofluorescence images of nuclei (DAPI; blue), γH2AX (magenta) and
mitochondria (TOMM20; green) in cisplatin-treated (10μM, 16 hrs) and untreated T24 and UMUC-3
cells. Confocal images taken at 40x magnification. Scale bar =50 μm. b) Western blots showing the
effect of cisplatin treatment (10μM, 24 hrs), on γH2AX protein levels (serine-139), compared to
untreated cells, in BC cell lines (T24 and UM-UC-3) and BC PDOs (CK9151 and CTC-1044);
normalized to α-tubulin loading control, n=2.

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that of closely homology-related kinases, including ATR(195-197). For enzymes like protein
kinases, using small molecules for inhibition offers the advantage of preserving their role as
scaffolds for protein-protein interactions, which might be lost with RNAi treatment, while
allowing for a distinction between their enzymatic functions and structural roles. Furthermore,
small molecule methods can be readily adapted across various cell lines, and in more complex
models like organoids, where RNAi strategies are not optimally accomplishable.
To test KU-55933’s activity, phospho-p53 (Ser15) was used as a positive control, as
ATM’s role in directly phosphorylating and stabilizing p53 in response to DNA double-strand
breaks is widely recognized(157, 198-200) (Fig. 4.5a). The western blot results showed a distinct
increase in phospho-p53 levels with cisplatin treatment, which were significantly reduced with
KU-55933’s co-treatment in BC cells (Fig. 4.5b).

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Next, we treated T24 and UM-UC-3 BC cells with cisplatin (24 hrs) and measured the
effect on phosphorylation of ATM and the protein levels of the proposed downstream mediators
in the signaling cascade. As predicted, cisplatin treatment significantly increased the levels of
phosphorylated ATM (p-ATM), phosphorylated AMPK (p-AMPK), PGC1α, and TFAM in both
cell lines (Fig. 4.6a). Notably, this trend was also observed in the inherently cisplatin-resistant
Figure 4.5. p53 phosphorylation was used as a control for ATM inhibitor activity.
a) Overview of the DNA damage recruiting and activating sensors, including ATM, which in turn,
activates p53 as a well-known substrate. Figure was taken from
Vadivel Gnanasundram, S. et al, Genes (Basel) (2021)(157). b) Western blots showing the effect of
cisplatin treatment (10μM, 24 hrs), with or without ATM inhibition (KU-55933; 10μM, 24 hrs), on
protein levels of Total p53 and Phospho-p53 (serine-15), used as a positive control for KU-55933’s
activity; normalized to GAPDH loading control, n=3.

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T24-R2 cells (Fig. 4.6a). Additionally, the levels of γH2AX, a marker of DNA damage, were
elevated upon cisplatin treatment (Fig. 4.6a), as previously shown in Fig 4.4 as well.
As predicted, KU-55933 treatment not only significantly reduced the levels of p-ATM, but
also the levels of its hypothesized downstream mediators, p-AMPK, PGC1α, and TFAM, even in
the presence of cisplatin (Fig. 4.6a). Furthermore, KU-55933 also decreased γH2AX levels,
confirming its suppression of ATM activity (Fig. 4.6a). Mirroring the results from the cell lines,
treatment of CTC-1044 and CK9151 PDOs with cisplatin significantly increased the levels of pATM, p-AMPK, PGC1α, and TFAM, while combination of cisplatin and KU-55933 led to
significant decreases in p-ATM, p-AMPK, PGC1α, and TFAM (Fig. 4.6b). These findings suggest
that ATM acts as the upstream orchestrator of the cascade, mediating the signaling from cisplatin
induced-DNA damage and triggering the subsequent activation of AMPK, PGC1α, and ultimately
TFAM in BC cells.

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4.3 ATM inhibition synergizes with cisplatin to reduce cell survival in BC cells and PDOs.
Given our findings from Section 4.2, where we observed that ATM inhibition ultimately
resulted in reduced TFAM levels (shown earlier to regulate cell survival), we investigated ATM’s
effect on cell survival and proliferation in cell lines and PDOs. We observed significant synergism
between cisplatin treatment and ATM inhibition via KU-55933, resulting in decreased BC cell
survival, (Fig. 4.6a, CDI 0.61 for UM-UC-3; CDI 0.59 for T24). Notably, this synergy extended
to the inherently cisplatin-resistant T24-R2 cells, re-sensitizing them to the cytotoxic effects of the
drug (Fig. 4.6a, CDI 0.65). TFAM-depleted cells caused a marked synergistic reduction in
proliferation and survival (Fig. 3d and 3e). KU-55933 and cisplatin’s synergism was also
recapitulated in PDOs, as we recorded significant coefficient of drug interaction (CDI) values (CDI
< 0.7), for both PDO lines (Fig. 4.6b, CDI 0.62 for CK9151; CDI 0.57 for CTC-1044).
Figure 4.6. Cisplatin activates ATM and increases the levels of the hypothesized downstream
signaling cascade, while ATM inhibition decreases them.
a) Protein levels of candidate signaling mediators after treatment with cisplatin (10μM, 24 hrs), ATM
inhibition (KU-55933; 10μM, 24 hrs), or both, with accompanying fold change based on densitometry
graphs generated using Image J; normalized to GAPDH or ⍺-Tubulin loading controls; blot
representative of 3 independent experiments. b) Protein levels of candidate signaling mediators after
treatment with cisplatin (50μM, 24 hrs), or ATM inhibition (KU-55933; 20μM, 24 hrs), or both, with
accompanying fold change based on densitometry graphs generated using Image J; normalized to
GAPDH and ⍺-Tubulin loading controls; blot representative of 2 independent experiments.

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Figure 4.7. ATM inhibition synergizes with cisplatin in BC cells and PDOs.
a) Cell viability after ATM inhibition (KU-55933; 10μM, 24 hrs) or cisplatin (10μM, 24 hrs) or both,
calculated relative to untreated respective cells, recorded by trypan blue exclusion assay; coefficient of
drug interaction (CDI)<0.7 indicates significant synergy. b) Cell viability after treatment with ATM
inhibition (KU-55933; 20μM, 24 hrs), alone or in combination with cisplatin (50μM, 24 hrs), calculated
relative to untreated respective PDOs, recorded by trypan blue exclusion assay; CDI<0.7 indicates
significant synergism and CDI>1.0 indicates significant antagonism. Graphs display mean ± SEM
values in n=3 replicates, p values compare the respective values indicated in the graphs. Statistical
significance determined using Student’s two-tailed t-test, *p < 0.01; **p < 0.001.

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Discussion
Cisplatin's therapeutic efficacy stems from its ability to cause DNA damage, primarily in
the form of intrastrand and interstrand crosslinks. While successful repair ensures cell survival,
irreparable damage triggers apoptosis, eliminating potentially dangerous cells. We hypothesized
that a subset of cisplatin-treated cancer cells could respond to cisplatin-induced DNA damage in
part by upregulating TFAM, leading to a metabolic shift towards OxPhos and resulting in enhanced
drug efflux and resistance. In this chapter, we tested the first step in this proposed signaling cascade
from cisplatin-induced DNA damage to the activation of ATM-AMPK-PGC1α-TFAM. ATM’s
phosphorylation of AMPK, a central metabolic regulator, suggests a potential link between DNA
damage and mitochondrial biogenesis. It is also worth noting that increased mitochondrial activity
and OxPhos results in greater generation of reactive oxygen species (ROS)(201), which in turn is
known to activate cytosolic ATM(179, 182, 202). Thereby, this possibly results in a feedback loop,
suggesting a self-amplifying cycle where higher levels of activated ATM results in elevated TFAM
levels and mitochondrial OxPhos, which in turn causes increase in activated ATM levels. Further
adding to these interactions, ROS is also known to activate AMPK via LKB1(179, 203). Similarly,
recent papers have indicated a self-reinforcing cycle signifying that AMPK activation leads to the
phosphorylation of FOXO3a, a forkhead box O transcription factor, at multiple sites, and activated
FOXO3a, in turn activates ATM through phosphorylation (Ser1981)(204). Therefore, a deeper
understanding of the complex interplay between DNA damage, cellular stress responses, and
metabolic reprogramming offers a promising avenue for future therapeutic strategies to overcome
cisplatin resistance and improve clinical outcomes for cancer patients.

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Chapter 5: AMPK's role in the proposed ATM-AMPK-PGC1α -TFAM signaling cascade
and its impact on cisplatin sensitivity
All the experiments described in this chapter were conducted entirely by Maheen Iqbal. All results of this
chapter are part of the manuscript under review.
Authors
Maheen Iqbal1
, Tong Xu1
, Sanam Ladi Seyedian2
, Siamak Daneshmand2
, Amir Goldkorn1,3
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
3Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. conceived and designed the study, conducted all experiments, interpreted the results, and wrote the
manuscript. A.G. and T.X. provided overall critical feedback and guidance. S.L.S. and S.D. provided the
freshly resected patient TURBT and RC samples. M.I. analyzed and interpreted the experimental data with
A.G. M.I. wrote the original manuscript. M.I. and A.G. reviewed and edited the manuscript. A.G. provided
overall study supervision.
Introduction
AMP-activated protein kinase (AMPK) is a highly conserved serine/threonine kinase, that
plays a master regulator role in cellular energy homoeostasis coordinating an intricate interplay
between energy metabolism, cellular stress responses, and ultimately cell survival(205). AMPK is
composed of three subunits: a catalytic α subunit and two regulatory subunits, β and γ, and its
activation, triggered by a diverse array of metabolic and environmental cues, impacts cellular
fate(206). At its core, AMPK stands as a sentinel of cellular energy status, functioning as an
"intracellular fuel gauge," it directly senses AMP/ATP ratios, serving as a sensitive marker of
energy depletion(205, 207). A rise in the AMP/ATP ratio, signifying energy deficit, triggers
AMPK activation through phosphorylation by upstream kinases, including ATM, LKB1 (Liver
Kinase B1), TAK1 (Transforming growth factor-β (TGF-β)-activated kinase 1) and CAMKK2
(Calcium/Calmodulin Dependent Protein Kinase Kinase 2)(181, 182, 208-210) (Fig. 5.1a). This

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activated AMPK assumes the role of a metabolic conductor, wielding its influence through
phosphorylation of key metabolic enzymes and transcription factors to restore cellular energy
homeostasis (Fig. 5.1b).
Figure 5.1. AMPK's upstream kinases and downstream effectors.
a) The figure shows an overview of the upstream regulators of AMPK. Figure was taken from
Lim, C. T. et al, J Mol Endocrinol (2010)(209). b) Flow diagram shows the potential pathophysiological
roles of AMPK in neuronal cells. Figure was taken from
Ju, T. C. et al, Cell Mol Life Sci (2012)(210).

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Under conditions like hypoxia or genotoxic stress, AMPK activation promotes autophagy,
a self-degradative process that recycles cellular components and generates energy substrates(211).
Additionally, AMPK plays a crucial role in promoting DNA repair and maintaining genomic
stability through various mechanisms, including activation of p53 and modulation of DNA repair
pathways(212). In the glycolytic pathway, AMPK phosphorylates and inactivates key enzymes
like phosphofructokinase-1 (PFK-1), thereby curtailing glucose breakdown and conserving
precious fuel reserves(213). Conversely, it promotes fatty acid oxidation by phosphorylating
acetyl-CoA carboxylase (ACC), leading to increased fatty acid utilization for ATP
production(214). While AMPK activation generally promotes cell survival under stress, its
influence on cell proliferation remains an intriguing and complex puzzle. AMPK activation serves
to shield normal tissues from the harmful effects of cisplatin, but its influence on cancer varies
depending on the context, and AMPK does not have a consistent, universal role. Some studies
suggest that activating AMPK enhances the apoptosis induced by cisplatin in cancer cells.
Conversely, there are reported instances where AMPK activation has been observed to protect
cancer cells from cisplatin's cytotoxic effects(215, 216). This context-dependent duality
underscores the need for a nuanced understanding of AMPK's role in cancer biology. The tumorsuppressive function arises from AMPK's ability to modulate key cell cycle regulators like mTOR
(mammalian target of rapamycin) and p53, ultimately leading to cell cycle arrest or
senescence(217, 218). However, in other scenarios, AMPK activation might paradoxically
promote tumor growth and metastasis by enhancing metabolic adaptations that fuel cancer cell
proliferation(219-222). The metabolic reprogramming, orchestrated by AMPK, ensures efficient
energy utilization and promotes cellular adaptation during energy stress.

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Various studies have shown that under normal conditions, AMPK shuttles between the
nucleus and cytoplasm to regulate its target in both compartments, and in conditions of intense
stress, the movement of AMPK to the nucleus could be controlled by blocking its export from the
nucleus, however, the detailed mechanism of AMPK trafficking is not well-deciphered yet (185,
223). The nuclear confinement could be helpful to AMPK in directly initiating transcription at the
DNA level, which includes elevating the expression of peroxisome proliferator-activated receptor
α (PPARα) and PPAR-gamma coactivator-1alpha (PGC1α)(93, 185, 212). Recent discoveries have
strengthened the connection between mitochondrial regulation and AMPK, by demonstrating that
AMPK can directly engage with and phosphorylate PGC1α, enhancing its transcriptional activity,
although the specific mechanisms for this remain unclear. PGC1α phosphorylation has been shown
to be essential for the PGC1α-dependent induction of the PGC1α promoter(224-226) and
upregulation of downstream genes like TFAM. Besides phosphorylation, AMPK has also been
shown to transcriptionally regulate PGC1α expression, as it can activate several TFs, including the
FOXO and CREB, which in turn stimulate the expression of PGC1α(227, 228). AMPK can
indirectly regulate PGC1α by regulating NAD which activates SIRT1 which deacetylates and
activates PGC1α(229, 230). Therefore, AMPK plays a critical role in regulating PGC1α by directly
phosphorylating and stabilizing it, activating transcription factors that stimulate its expression, and
inhibiting the deacetylase SIRT1 to increase its acetylation and activity. Therefore, AMPK
stimulated mitochondrial biogenesis can ultimately enhancing the cell's capacity for OxPhos and
ATP generation in face of duress(183-185, 188).
Our hypothesis postulates that a subset of cisplatin-treated cancer cells gains advantage by
exploiting the cisplatin-induced DNA damage to ultimately upregulate TFAM, thus metabolically
shifting the cells towards OxPhos and the benefit of enhanced drug efflux and adaptive resistance.

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As mentioned before (detailed in Chapter 4), ATM is a well-known activator of AMPK through
the phosphorylation of AMPK’s catalytic α subunit at Thr172, either directly, or via activation of
LKB1 (171, 174, 177, 180-182, 208-210, 212, 231, 232). AMPK is known for its direct and indirect
role in the phosphorylation of PGC1α, thereby unleashing its powerful capacity to assist in gene
transcription(183-185). PGC1α, in turn, stimulates multiple genes that are crucial for
mitochondrial biogenesis, notably TFAM(99, 186-189), resulting in the growth of the
mitochondrial network and a subsequent increase in the activity of oxidative phosphorylation
(OxPhos). We hypothesized that this intricate interplay between ATM, AMPK, PGC1α, and
OxPhos orchestrates a powerful resistance strategy against cisplatin.
Results
5.1 Cisplatin treatment activated AMPK and its hypothesized downstream signaling
mediators, while AMPK inhibition decreased downstream signaling.
Building upon our observations linking ATM activation to the upregulation of TFAM and
cisplatin resistance (detailed in Chapter 4), we sought to dissect the functional significance of
AMPK, a downstream target of ATM, in this process. Therefore, moving down the hypothesized
nuclear-mitochondrial signaling cascade model, we analyzed the role of AMPK, in cisplatininduced phenotypic shift to resistance. To accomplish AMPK inhibition, we used Compound C
(dorsomorphin dihydrochloride), a well-known ATP-competitive inhibitor that binds to the highly
conserved active site of AMPK and inhibits its activity(233-236). In parallel, to further validate
the effect of AMPK, on the proposed downstream signaling cascade, we employed AICAR (5-
aminoimidazole-4-carboxamide ribonucleotide), an AMP-mimic that binds to the γ subunit of
AMPK and allosterically activates the enzyme(237-239). Firstly, we verified the effect of the

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AMPK inhibitor and activator, respectively, on the phosphorylation of a well-established substrate
of AMPK, acetyl-CoA carboxylase 1 (ACC1) at Ser79. AMPK’s phosphorylation of ACC1 is
known to inhibit the conversion of acetyl-CoA to malonyl-CoA, inhibiting fatty acid synthesis,
and promoting fatty acid oxidation(240-243).
We verified that cisplatin alone increased the phosphorylation of ACC1 (p-ACC1),
possibly by increasing activated p-AMPK (refer to Fig. 4.6), whereas Compound C reduced the
level of p-ACC1, despite the presence of cisplatin (Fig. 5.2a). Conversely, AICAR increased the
level of p-ACC1 from its basal levels in untreated cells, and this effect was attenuated by cotreatment with Compound C (Fig. 5.2b).

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As anticipated, Compound C effectively suppressed p-AMPK levels, indicating its
inhibitory activity, as well as significantly abrogated the cisplatin-induced increases in p-AMPK,
PGC1α, and TFAM protein levels in both sensitive (T24 and UM-UC-3) and resistant (T24-R2)
BC cell lines (Fig. 5.3a). This finding suggests that AMPK acts as a signaling intermediary,
transducing the cisplatin-induced DNA damage signaling from ATM to downstream effectors like
PGC1α, and ultimately leading to TFAM upregulation. As expected, AICAR treatment mimicked
cisplatin's effects, inducing increases in p-AMPK, PGC1α, and TFAM levels, which were
effectively abrogated by co-treatment with Compound C, further substantiating the role of AMPK
in this signaling cascade (Fig. 5.3b).
Figure 5.2. AMPK inhibitor abrogates AMPK activity, while AMPK activator increases it.
a) Western blots showing the effect of cisplatin treatment (10μM, 24 hrs), with or without AMPK
inhibition (Compound C; 5μM, 24 hrs), on protein levels of Total ACC1 and Phospho-ACC1 (serine79), used as a positive control for Compound C’s activity against AMPK; normalized to -Tubulin
loading control, n=2. b) Western blots showing the effect of AICAR treatment (0.1mM, 24 hrs), with
or without Compound C (5μM, 24 hrs), on protein levels of Total ACC1 and Phospho-ACC1;
normalized to -Tubulin loading control, n=2.

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As Compound C’s selectivity for AMPK has been under debate(244), we adopted a
complementary approach using an siRNA-mediated knockdown of AMPKα1 subunit (Fig. 5.4a).
Figure 5.3. Evaluation of AMPK’s effect on the proposed downstream signaling mediators.
a) Protein levels of candidate signaling mediators after treatment with cisplatin (10μM, 24 hrs), AMPK
inhibition (Compound C; 5μM, 24 hrs) or both, with accompanying fold change based on densitometry
graphs generated using Image J; normalized to GAPDH and ⍺-Tubulin loading controls; blot
representative of 3 independent experiments. b) Protein levels of candidate signaling mediators after
treatment with AICAR (0.1mM, 24 hrs), Compound C (5μM, 24 hrs), or both, with accompanying fold
change based on densitometry graphs generated using Image J; normalized to GAPDH and ⍺-Tubulin
loading controls; blot representative of 3 independent experiments.

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Mammalian AMPK has two isoforms of α subunit, α1 and α2, with the α1 subunit more widely
expressed than the α2 subunit. Our AMPKα1 KD did not affect AMPKα2 levels, tested as a control
for siRNA specificity (Fig. 5.4b).
Cells transfected with AMPKα1 siRNA exhibited a significant decrease in the expression
levels of AMPKα1, and concomitant decreases in PGC1α and TFAM (Fig. 5.4a), supporting a
functional interaction between AMPK and its hypothesized downstream effectors. Furthermore,
AMPKα1 knockdown significantly blunted the cisplatin-induced upregulation of p-AMPK,
PGC1α, and TFAM protein levels compared to scrambled control siRNA (Fig. 5.4c). This
observation links AMPK activity to the observed increase in the proposed downstream proteins
upon cisplatin treatment, substantiating its role in modulating the resistance pathway. Taken
together, the consistent results obtained with both Compound C and siRNA-mediated AMPKα1
knockdown, provide evidence supporting the involvement of AMPK as a key mediator of
cisplatin-induced mitochondrial biogenesis and OxPhos upregulation. These findings reinforce the
importance of the ATM-AMPK-PGC1α-TFAM signaling axis in promoting cisplatin resistance.

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Figure 5.4. AMPK’s siRNA-mediated knockdown and its effect on the proposed downstream
signaling mediators.
a) Expression (qRT-PCR) of AMPKα1, PGC1α and TFAM, after AMPKα1 KD (24 hrs), relative to scsiRNA treatment; normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p values
compare AMPKα1-siRNA to sc-siRNA treatment. b) Gene expression (qRT-PCR) of AMPKα2, to
assess the effect AMPKα1 KD (24 hrs) relative to sc-siRNA treatment; normalized to β-Actin. Graph
displays mean ± SEM values in n=3 replicates; p values comparing AMPKα1-siRNA to sc-siRNA
treatment. c) Protein levels of candidate signaling mediators after cisplatin treatment (10μM, 24 hrs), or
AMPKα1 KD, or both, with accompanying fold change based on densitometry graphs generated using
Image J; normalized to GAPDH and ⍺-Tubulin loading controls; blot representative of 3 independent
experiments. Statistical significance determined using Student’s two-tailed t-test, *p < 0.05; **p < 0.01;
***p < 0.001.

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Building upon our findings in the BC cell lines, we again sought to bridge the gap between
in vitro models and clinical relevance, strengthening the validity of the proposed pathway in
cisplatin resistance. Mirroring our observations in BC cell lines, treatment of both CTC-1044 and
CK9151 PDOs with Compound C, significantly abrogated the cisplatin-induced increases in pAMPK, PGC1α, and TFAM protein levels in both PDOs (Fig 5.5). Furthermore, as expected,
AICAR treatment induced significant increases in p-AMPK, PGC1α, and TFAM levels in both
PDO lines, and co-treatment with Compound C effectively abrogated these AICAR-induced
increases, confirming the specific role of AMPK in mediating these downstream effects (Fig 5.5).
These results extend the validity of the ATM-AMPK-PGC1α-TFAM signaling cascade to patientderived models of BC, and suggest that AMPK acts as a critical intermediary, transducing the
cisplatin-induced DNA damage signal from ATM to downstream effectors like PGC1α, ultimately
leading to TFAM upregulation, as seen in BC cell lines.

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5.2 AMPK inhibition synergizes with cisplatin to reduce cell survival in BC cells and PDOs
We assessed the functional consequences of AMPK modulation on cisplatin sensitivity by
observing the effect on the number of live cells. Compound C exhibited significant synergy with
cisplatin, resulting in dramatically reduced survival of the BC cells and re-sensitization of the
cisplatin-resistant T24-R2 cells (Fig. 5.6a). The combination index (CDI) values, reflecting the
degree of synergy, revealed strong synergistic interactions across all cell lines (CDI < 0.7) (Fig.
5.6a, CDI 0.56 for UM-UC-3; CDI 0.54 for T24; CDI 0.62 for T24-R2). Conversely, AMPK
activation with AICAR significantly antagonized cisplatin's cytotoxicity, fostering BC cell
survival and bolstering resistance in T24-R2 cells (Fig.5.6a, CDI 1.30 for UM-UC-3; CDI 1.15 for
T24; CDI 1.13 for T24-R2).
Similar to Compound C, AMPKα1 knockdown exhibited significant synergy with
cisplatin, leading to reduced survival of all BC cell lines and re-sensitization of T24-R2 cells (Fig.
5.6b, CDI 0.51 for UM-UC-3; CDI 0.65 for T24; CDI 0.67 for T24-R2). Compound C and
cisplatin’s synergism was also recapitulated in PDOs, as we recorded significant coefficient of
drug interaction (CDI) values (CDI < 0.7), for both PDO lines (Fig. 5.6c, CDI 0.60 for CK9151;
CDI 0.66 for CTC-1044). Whereas AMPK activation (via AICAR) antagonized the effects of
cisplatin and increased the survival of PDOs (Fig.5.6c, CDI 1.35 for CK9151; CDI 1.58 for CTC1044).
Figure 5.5. Evaluation of AMPK’s effect on the proposed downstream signaling mediators in
PDOs.
Protein levels of candidate signaling mediators after treatment with AICAR (0.5mM, 24 hrs) or
Compound C (15μM, 24 hrs), alone or in combination with cisplatin (50μM, 24 hrs), with accompanying
fold change based on densitometry graphs generated using Image J; normalized to GAPDH and ⍺-
Tubulin loading controls, blot representative of 2 independent experiments.

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The results presented in this section provide compelling evidence for the critical role of
AMPK in the signaling cascade involved in cisplatin-induced DNA damage to TFAM
upregulation. By employing both pharmacological and genetic approaches, we demonstrated that
AMPK acts as a key mediator downstream of ATM, ultimately leading to TFAM upregulation.
Furthermore, targeting AMPK, either through specific inhibitor or siRNA knockdown, exhibits
significant synergy with cisplatin, effectively reducing survival and re-sensitizing resistant cells.
These findings pave the way for further exploration of AMPK’s role in cisplatin resistance for
bladder cancer.
5.3 AMPK’s cancer promoting role is recapitulated in TCGA database analysis.
Based on the insights from cells lines and PDOs, we also investigated AMPK’s association
with clinical outcomes using the publicly available patient database, The Cancer Genome Atlas
(TCGA).
Analysis of TCGA-BLCA data revealed a significant correlation of elevated AMPKα2
expression with higher histological grade disease (n=433, p<0.05) and increased metastasis
(n=309, p<0.05) (Fig. 5.7). There was also a trend towards reduced patient survival with elevated
Figure 5.6. AMPK inhibition synergizes with cisplatin in BC cells and PDOs.
a) Cell viability after treatment with AMPK inhibition (Compound C; 5μM, 24 hrs) or AMPK activation
(AICAR; 0.1mM, 24 hrs), alone or in combination with cisplatin (10μM, 24 hrs), calculated relative to
untreated respective cells, recorded by trypan blue exclusion assay; coefficient of drug interaction
(CDI)<0.7 indicates significant synergism and (CDI)>1.0 indicates significant antagonism; n=3. b) Cell
viability after treatment with AMPKα1 KD (24 hrs), or cisplatin (10μM, 24 hrs), or both, calculated
relative to sc-siRNA transfection in respective cells, recorded by trypan blue exclusion assay; CDI<0.7
indicates significant synergism; n=3. c) Cell viability after treatment with AMPK activation (AICAR;
0.5mM, 24 hrs) or AMPK inhibition (Compound C; 15μM, 24 hrs), alone or in combination with
cisplatin (50μM, 24 hrs), calculated relative to untreated respective PDOs, recorded by trypan blue
exclusion assay; CDI<0.7 indicates significant synergism and CDI>1.0 indicates significant
antagonism; n=3. Graph displays mean ± SEM values and p values compare the respective values
indicated in the graphs. Statistical significance determined using Student’s two-tailed t-test, *p < 0.05;
**p < 0.01; ***p < 0.001.

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AMPKα2 expression (n=436), although the p value wasn’t significant, given the small sample
number available in the BCLA subset (Fig. 5.7). AMPKα2 data is shown here instead of the more
dominant expressed isoform AMPKα1, as analysis for AMPKα1 did not show statistical
significance, possibly because of smaller sample number of BC patients in the TCGA dataset (data
not shown).

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Further expanding the scope of our investigation from the BLCA subset to AMPK gene
expression across diverse tumor types within the TCGA database, we saw a significant association
between increased AMPKα1 expression and lower survival rates (n= 5391 , p<0.01). AMPKα1
analysis for tumor grade and metastatic correlations with gene expression in TCGA-PANCAN did
not show statistical significance (data not shown), which could be because although AMPKα1 is
the predominant and ubiquitous AMPK isoform, it is however expressed at low levels, while
AMPKα2 has high expression especially in skeletal, cardiac muscle and liver tissues(245, 246).
TCGA-PANCAN analysis revealed significantly higher AMPK2 (PRKAA2) expression in
patients with higher grade tumors (n=6941, p<0.05). Comparing AMPKα2 expression in different
BC stages, we observed significantly higher levels in metastatic, recurrent, and progressive tumors
compared to locoregional tumors (n=10906, p<0.001). Therefore, elevated expression AMPK
correlated significantly with poorer clinical outcomes, including reduced patient survival, higher
disease grade, increased metastasis, and advanced tumor stage. These findings from a large-scale
dataset further underscore AMPK’s potential role in cancer progression and resistance.
Figure 5.7. AMPK gene expression analysis using TCGA-BLCA subset.
TCGA analysis (using PANCAN-BLCA subset) used to calculate the Kaplan-Meier curve revealed a
trend towards elevated AMPK2 (PRKAA2) expression and reduced patient survival. TCGA analysis
(using PANCAN-BLCA subset) also revealed a significant correlation of elevated AMPK2 expression
with higher histological grade disease and increased metastasis. TCGA PANCAN-BLCA mRNA-Seq
expression cohorts were used for this analysis, and the results are based upon data generated by the
TCGA Research Network: http://cancergenome.nih.gov/.

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Figure 5.8. AMPK gene expression analysis using TCGA-PANCAN.
TCGA-PANCAN analysis used to calculate the Kaplan-Meier curve in patients across 5391 patients
indicated a significant (p<0.01) correlation of increased AMPK1 (PRKAA1) gene expression with
reduced patient survival incidence. TCGA- PANCAN analysis also revealed significantly higher
AMPK2 (PRKAA2) expression in higher histological grade disease. TCGA-PANCAN analysis
comparing AMPK2 (PRKAA2) gene expression in locoregional urothelial tumor to metastatic,
recurrent, and progressive disease tumors also revealed significantly elevated AMPK2 expression in
the latter. TCGA PANCAN mRNA-Seq expression cohorts were used for this analysis, and the results
are based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.

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Discussion
This section sheds light on the role of AMP-activated protein kinase (AMPK) as a key
mediator in the proposed ATM-AMPK-PGC1-TFAM signaling cascade, transducing the
cisplatin-induced DNA damage to TFAM upregulation, and ultimately contributing to resistance.
AMPK stands at the helm of cellular energy homeostasis(243). While promoting cell
survival under stress in normal tissues, its influence on cancer is context-dependent, ranging from
inducing apoptosis to paradoxically aiding tumor growth, therefore there is far from a single
unifying role of AMPK signaling in cancer progression(247). Understanding this context-specific
duality is crucial for harnessing AMPK's potential in cancer therapy. While this study highlights
AMPK's potential as a resistance driver, a deeper understanding of its context-dependent roles in
various cancer types and stages is crucial for possible therapeutic implications. Andugulapati et
al., showed the role of AMPK in promoting stem cell characteristics causing resistance to
doxorubicin, notably, showing an increase in AMPK activity and a rise in markers of stemness in
tumor samples from breast cancer patients following chemotherapy(248), whereas Rae C. and
Mairs R. J., reported that AMPK activation sensitized prostate cancer cells to radiotherapy(249),
examples of the dichotomous role of AMPK in cancer, as an oncogene or a tumor suppressor,
depending on the context. Therefore, AMPK’s versatile role as a molecular effector that transduces
oxidative and metabolic stimuli into phosphorylation signals that widely regulate cell responses,
makes it critical to explore its interplay with other signaling pathways for optimizing therapeutic
strategies and avoiding unintended consequences and potential off-target effects(95, 207).
Unveiling these interactions could identify additional therapeutic targets or synergistic
combinations.

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This study proposes a signaling cascade triggered by cisplatin-induced DNA damage.
ATM, the master regulator of the DNA damage response, activates AMPK, leading to the
phosphorylation of PGC1α, a potent transcriptional co-activator, which upregulates TFAM, the
master regulator of mitochondrial biogenesis. The resulting expansion of the mitochondrial
network and enhanced OxPhos activity provide a metabolic advantage for cisplatin-treated cancer
cells, potentially fueling resistance mechanisms like drug efflux. By integrating findings from in
vitro cell lines, patient-derived organoids, and large-scale cancer datasets, we postulate AMPK’s
critical role in transducing this signaling cascade.

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Chapter 6: PGC1α’s role in the proposed ATM-AMPK-PGC1α-TFAM signaling cascade
and its impact on cisplatin sensitivity
All the experiments described in this chapter were conducted entirely by Maheen Iqbal. All results of this
chapter are part of the manuscript under review.
Authors
Maheen Iqbal1
, Tong Xu1
, Sanam Ladi Seyedian2
, Siamak Daneshmand2
, Amir Goldkorn1,3
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
3Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. conceived and designed the study, conducted all experiments, interpreted the results, and wrote the
manuscript. A.G. and T.X. provided overall critical feedback and guidance. S.L.S. and S.D. provided the
freshly resected patient TURBT and RC samples. M.I. analyzed and interpreted the experimental data with
A.G. M.I. wrote the original manuscript. M.I. and A.G. reviewed and edited the manuscript. A.G. provided
overall study supervision.
Introduction
Within cells, the regulation of metabolic equilibrium involves a complex system, primarily
controlled by transcriptional mechanisms(250, 251). These pathways encompass numerous
transcription factors directly interacting with DNA to initiate significant changes in gene
expression. Additionally, transcriptional coregulators play a crucial role in fine-tuning the
transcriptional response. These coregulators are proposed to function as metabolic sensors,
translating alterations in metabolism into changes in gene expression. The pivotal role of
coregulators in metabolic control is exemplified by PGC1α (peroxisome proliferator-activated
receptor γ coactivator 1α)(252, 253).
Mitochondrial biogenesis necessitates the synthesis of new mitochondrial proteins,
membranes, and mitochondrial DNA (mtDNA). The coordination of these activities primarily
relies on transcriptional programs located within the cell nucleus(254). Notably, PGC1α functions

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as a central regulator of mitochondrial mass and itself undergoes regulation by various enzymes,
transcription factors, and intracellular signaling pathways that monitor cellular energy levels and
metabolic conditions(189). Among these regulators are SIRT1 (Silent Information Regulator 1),
AMPK (AMP-activated protein kinase), CREB (cyclic AMP-responsive element-binding protein),
mTOR, FOXOs, and Akt. These molecules either directly or indirectly interact with PGC1α,
influencing or being influenced by it(89, 225, 255, 256). An overview of some of these interactions
is given in Fig. 6.1.
Figure 6.1. Various pathways that serve as regulators for PGC1α.
The figure shows an overview of the key regulators of PGC1, including AMPK, that links our proposed
ATM-AMPK-PGC1-TFAM signaling cascade. Figure was taken from
Swerdlow, R. H. et al, Curr Pharm Des (2011)(255).

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As described in detail in Chapter 5, the intricate adjustments in metabolism regulated by
AMPK are crucial for optimal energy use and help cells adapt their metabolic state in stresses.
AMPK exerts its influence by phosphorylating and activating various transcription factors and
coactivators, including PGC1α, CREB, and MEF2 (Myocyte Enhancer Factor 2), along with
histone deacetylases (HDACs), and metabolic enzymes(189, 257). Previous studies have
established that activated AMPK results in an upsurge of PGC1α expression across various cell
types, including cultured muscle cells, umbilical vein endothelial cells, adipose tissue, and skeletal
muscle(224, 258, 259). More recent work has further clarified this relationship, showing that
AMPK can directly interact with and phosphorylate PGC1α, essential for the PGC1α-dependent
induction of the PGC1α promoter, causing an increase in PGC1α expression(224-226, 260, 261).
In addition to this, AMPK phosphorylation of PGC1α is a prerequisite for its deacetylation by
SIRT1, facilitating PGC1α's entry into the nucleus where it acts as a transcriptional coactivator,
thereby initiating a range of biological responses, including the cell's ability for OxPhos and ATP
production (Fig. 6.2a)(183-185, 188, 225, 262, 263).
Transcriptional coactivators, such as PGC1α, enhance transcription by directly interacting
with transcription factors and amplifying their function. PGC1α, a human protein, is composed of
798 amino acids and weighs approximately 91 kDa. This protein is organized into distinct
functional areas including an activation domain, an inactivation domain, as well as regions
enriched with short serine/arginine residues, known as the RS domain, in addition to an RNA
recognition motif (RRM)(189). Unlike other transcriptional coactivators, PGC1α lacks a DNAbinding domain and does not exhibit the intrinsic histone acetyltransferase activity. Instead,
PGC1α functions predominantly as a transcriptional regulator, providing a docking platform for

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other proteins that are endowed with histone acetyltransferase activity, facilitating transcription
indirectly. The binding of PGC1α to certain transcription factors, induces conformational changes
that enhances the transcription complex's affinity for additional coactivators(233).
The cooperative role of PGC1α, Nrf1 and Nrf2 in TFAM synthesis is well-established,
where PGC1α acts as a co-transcriptional factor, for the transcription factors Nrf1 and Nrf2. These
factors then elevate the levels of TFAM, the mitochondrial transcriptional regulator encoded by
the nuclear DNA, which is responsible for the transcription and duplication of mtDNA (Fig.
6.2b)(93, 99, 186, 264). TFAM is then imported into mitochondria to stabilize mtDNA and
enhance the synthesis of subunits of electron transport chain encoded by mtDNA, leading to
transcription and replication of mtDNA (detailed in Chapter 2 and 3)(94, 265, 266). This results
in the growth of the mitochondrial network and a subsequent increase in cellular OxPhos,
completing the downstream portion of our proposed ATM-AMPK-PGC1α-TFAM signaling
cascade, activated by cisplatin-induced DNA damage, and enabling the phenotypic switch towards
drug resistance. Therefore, having observed that ATM and AMPK activate TFAM, a key regulator
of mitochondrial biogenesis, and cisplatin resistance (Chapters 4 and 5), in this chapter, we tested
whether PGC1α plays an intermediary role in this signaling cascade.

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Figure 6.2. Regulation of mitochondrial biogenesis by PGC1α.
a) The diagram illustrates the regulatory network of the PGC1α feed-forward loop controlled by
AMPK, highlighting the complex interplay among signaling entities, transcriptional co-activators,
repressors, and various transcription factors. Figure was taken from McGee, S. L. et al, Clin Sci (Lond)
(2010)(263). b) Control of mitochondrial formation involves the triggering of various signaling
cascades, including those involving AMPK, SIRT1, CREB, and MAPK, which are linked to the
enhancement of PGC1α gene activity. PGC1α is the principal cofactor that orchestrates the creation of
mitochondria, as it stimulates both Nrf1 and Nrf2. This activation, in turn, elevates the levels of TFAM,
which is crucial for both the transcription and replication of mitochondrial DNA. Figure was taken from
Roque, W. et al, Int J Mol Sci (2020)(264).

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Results
6.1 Cisplatin increases PGC1α and TFAM levels, whereas PGC1α inhibition decreases
TFAM levels
We first investigated the effect of cisplatin treatment on PGC1α expression, as well as on
Nrf1 and Nrf2, known coactivators of TFAM(89, 93, 188). Cisplatin treatment significantly
increased the mRNA levels of PGC1α, as well as of Nrf1 and Nrf2 in a panel of BC cell lines (Fig.
6.3a), suggesting that cisplatin exposure triggers a coordinated upregulation of PGC1α and its
coactivators, potentially leading to enhanced TFAM activity and mitochondrial biogenesis.
Next, to assess the role of PGC1α in TFAM regulation, we employed both a
pharmacological inhibitor and siRNA-mediated knockdown to inhibit PGC1α. We utilized SR18292, a recently developed small molecule inhibitor that selectively acetylates and suppresses the
gluconeogenic transcriptional activity of PGC1α(267, 268). SR-18292 facilitates the interaction of
PGC1α with the acetyltransferase enzyme GCN5, leading to an increase in the acetylation marks
that inhibit PGC1α activity(267, 269). SR-18292 inhibition of PGC1α was validated by decreased
expression of genes associated with gluconeogenic function (PCK1, G6PC) and mitochondrial
electron transport chain (COX5A, ATP5G1)(Fig. 6.3b)(267, 270, 271).
Additionally, we used siRNA-mediated knockdown of PGC1α as an orthogonal strategy
of PGC1α’s inhibition. Depletion of PGC1α by siRNA KD yielded a significant knockdown (~80-
85%) of PGC1α and resulted in significantly lower gene expression of TFAM in BC cells, a trend
also observed in the resistant T24-R2 cell line (Fig.6.3c).

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Figure 6.3. Investigation of cisplatin’s effect on PGC1α, Nrf1, Nrf2, and PGC1α inhibition.
a) Expression of PGC1⍺, Nrf1, Nrf2, (qRT-PCR) after treatment with cisplatin (10μM, 24 hrs), compared
to untreated cells, normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p values
compare treated cells to respective untreated cells. b) Gene expression of PGC1α-regulated gluconeogenic
(PCK1, G6PC) and mitochondrial electron transport chain associated genes (COX5A, ATP5G1), in BC
cell lines treated with SR-18292 (40μM, 24 hrs) relative to untreated cells, normalized to β-Actin. Graph
displays mean ± SEM values in n=3 replicates; p values comparing treated cells to respective untreated
cells. c) Expression (qRT-PCR) of PGC1α and TFAM after PGC1⍺ KD (24 hrs), relative to sc-siRNA
treatment; normalized to β-Actin. Graph displays mean ± SEM values in n=3 replicates; p values compare
PGC1⍺-siRNA to sc-siRNA treatment. Statistical significance determined using Student’s two-tailed ttest, *p < 0.01, **p < 0.001.

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As expected, SR-18292 reduced TFAM expression, confirming its potent inhibitory effect
on PGC1α. Additionally, it diminished the cisplatin-triggered rise in TFAM protein levels across
both sensitive (T24 and UM-UC-3) and resistant (T24-R2) BC cells (6.4a). These results were
mirrored by PGC1α siRNA KD, which resulted in decreased PGC1α levels and blunted cisplatininduced upregulation of TFAM in T24, UM-UC-3 and resistant T24-R2 BC cells (Fig. 6.4a). These
observations implicate PGC1α activity in the TFAM increases seen with cisplatin treatment,
supporting its role in modulating mitochondrial biogenesis and OxPhos.
To extend our observations from BC cell lines to more clinically relevant models, we
treated both CTC-1044 and CK9151 PDOs with SR-18292. This pharmacological inhibitor was
used in PDOs rather than PGC1α-siRNA, because knocking down gene expression in PDOs is
more complicated and requires greater amounts of transfection reagents which can have toxic
effects. As expected, SR-18292 significantly abrogated the cisplatin-induced increases in TFAM
protein levels in both PDOs (Fig 6.4b).

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Lending further supporting to the hypothesized sequential signaling cascade, inhibiting
downstream components did not affect protein expression or activity of upstream components (Fig.
6.5). In presence of cisplatin, while KU-55933 affected the phosphorylation status of AMPK and
H2AX (used a control of DNA damage), AMPK inhibition did not have any considerable effects
on p-ATM and γH2AX levels, while it did lower p-AMPK levels successfully. Similarly, SRFigure 6.4. Effect of PGC1⍺ inhibition on TFAM levels.
a) Protein levels of PGC1⍺ and TFAM after treatment with PGC1⍺ KD (24 hrs) or inhibition (SR-18292
40μM, 24 hrs), alone or in combination with cisplatin (10μM, 24 hrs), with accompanying fold change
based on densitometry graphs generated using Image J; normalized to ⍺-Tubulin loading control; blot
representative of 3 independent experiments. b) Protein levels of PGC1⍺ and TFAM after treatment
with cisplatin (50μM, 24 hrs), or SR-18292 (50μM, 24 hrs), or both, , with accompanying fold change
based on densitometry graphs generated using Image J; normalized to ⍺-Tubulin loading control, blot
representative of 2 independent experiments.

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18292 did not have any significant effect on p-ATM, p-AMPK or γH2AX. These findings support
the proposed linear hierarchy within the signaling cascade, where targeting downstream
components (PGC1α, AMPK) did not exert any significant feedback inhibition on upstream
components (ATM, AMPK).
Figure 6.5. Inhibiting the proposed players of the nuclear-mitochondrial signaling cascade did
not inhibit the hypothesized ‘upstream’ proteins in the cascade.
Western blots showing the effect of ATM inhibition (KU-55933; 10μM, 24 hrs), AMPK inhibition
(Compound C; 5μM, 24 hrs) and PGC1⍺ inhibition (SR-18292; 40μM, 24 hrs), in presence of cisplatin
(10μM, 24 hrs), on protein levels of ‘upstream’ proteins in the hypothesized signaling cascade, with
accompanying fold change based on densitometry graphs generated using Image J; normalized to
GAPDH loading control, n=2.
on protein levels of signaling mediators in PDOs, with accompanying fold change based on
densitometry graphs generated using Image J; normalized to GAPDH and ⍺-Tubulin loading controls,
n=2.

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6.2 PGC1α inhibition synergizes with cisplatin to reduce cell survival in BC cells and PDOs
We next evaluated the effect of targeting PGC1α in combination with cisplatin, on the
survival of BC cells. Cell viability assays revealed that both PGC1α knockdown (Fig. 6.6a, CDI
0.59 for UM-UC-3; CDI 0.56 for T24; CDI 0.61 for T24-R2) and SR-18292 treatment (Fig. 6.6a,
CDI 0.63 for UM-UC-3; CDI 0.63 for T24; CDI 0.62 for T24-R2) significantly synergized with
cisplatin to reduce the survival of BC cells. This synergistic effect was also observed in the
cisplatin-resistant T24-R2 cells, indicating that PGC1α inhibition could re-sensitize resistant cells
to cisplatin treatment.
SR-18292 and cisplatin’s synergism was also recapitulated in PDOs, as we recorded
significant CDI values (CDI < 0.7), for both PDO lines (Fig. 6.6b, CDI 0.55 for CK9151; CDI
0.54 for CTC-1044). Therefore, this provides compelling evidence that PGC1α plays a critical role
in mediating cisplatin resistance through its regulation of TFAM and mitochondrial function.

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Figure 6.6. AMPK inhibition synergizes with cisplatin in BC cells and PDOs.
a) Cell viability after treatment with PGC1⍺ KD (24 hrs) or inhibition (SR-18292; 40μM, 24 hrs), alone
or in combination with cisplatin (10μM, 24 hrs), calculated relative to sc-siRNA transfected untreated
respective cells, recorded by trypan blue exclusion assay; coefficient of drug interaction (CDI)<0.7
indicates significant synergism, n=3. Graph displays mean ± SEM values in n=3 replicates, p values
compare the respective values indicated in the graph. b) Cell viability after treatment with PGC1⍺
inhibition (SR-18292; 50μM, 24 hrs), alone or in combination with cisplatin (50μM, 24 hrs), calculated
relative to untreated respective cells, recorded by trypan blue exclusion assay; CDI<0.7 indicates
significant synergism and CDI>1.0 indicates significant antagonism. Graph displays mean ± SEM
values in n=3 replicates, p values compare the respective values indicated in the graph. Statistical
significance determined using Student’s two-tailed t-test, *p < 0.01; **p < 0.001.

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6.3 PGC1α’s cancer promoting role is recapitulated in TCGA database analysis.
We explored the clinical relevance of PGC1α expression in bladder cancer (BC) and other
malignancies. Utilizing data from The Cancer Genome Atlas (TCGA), we investigated the
association between PGC1α expression and patient outcomes. In TCGA-PANCAN’s bladder
cancer subset, survival analysis revealed a significant correlation between higher PGC1α
expression and reduced survival rates (n=212, p<0.01). Notably, higher PGC1α expression in BC
patients also correlated significantly with increased metastasis (n=340, p<0.05).
Further analysis across diverse cancer types within the TCGA-PANCAN dataset also
revealed a significant association between higher PGC1α expression and worse overall survival
for patients across various malignancies (n=5463, p<0.001). Additionally, comparing PGC1α
expression in different disease stages of BC within the TCGA-PANCAN dataset showcased
significantly higher levels in patients with metastatic, recurrent, and progressive tumors compared
to those with localized disease (n=10906, p<0.001). These observations suggest that elevated
PGC1α expression might be a broader indicator of poor prognosis and disease progression across
various cancer types, perhaps in part due to its role investigated in our work, as a mediator of
cisplatin-induced mitochondrial biogenesis, OxPhos upregulation and drug resistance.

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Figure 6.7. PGC1 gene expression analysis using TCGA.
a) TCGA analysis using PANCAN-Bladder Cancer subset (n=212) was used to calculate the KaplanMeier curve and revealed significant correlation of higher PGC1 (PPARGC1A) expression with
reduced patient survival incidence. TCGA analysis (using PANCAN-Bladder Cancer subset) also
revealed a significant correlation of elevated PGC1 expression with increased metastasis (n=340,
p<0.05). b) TCGA-PANCAN analysis used to calculate the Kaplan-Meier curve, indicated a significant
correlation of increased PGC1 expression with reduced patient survival incidence (n=5463, p<0.001).
TCGA-PANCAN analysis comparing PGC1 expression in locoregional urothelial tumor to metastatic,
recurrent, and progressive disease tumors also revealed significantly elevated PGC1 expression in the
latter (n=10906, p<0.001). TCGA PANCAN and PANCAN-BLCA mRNA-Seq expression cohorts
were used for this analysis, and the results are based upon data generated by the TCGA Research
Network: http://cancergenome.nih.gov/.

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Discussion
Previously, we demonstrated how cisplatin-induced DNA damage triggered mitochondrial
biogenesis and OxPhos upregulation, culminating in drug resistance. This chapter further dissects
the proposed ATM-AMPK-PGC1α-TFAM signaling cascade and delves into the role of PGC1α
in cisplatin resistance in bladder cancer.
The coordinated action of PGC1α, Nrf1, and Nrf2 in TFAM synthesis is well-established.
PGC1α, the primary orchestrator, activates various transcription factors, including Nrf1 and Nrf2,
which subsequently drive TFAM expression. TFAM, imported into mitochondria, stabilizes
mtDNA and enhances the synthesis of electron transport chain subunits, leading to mtDNA
replication and expansion. This orchestrated process, described in previous chapters, forms the
downstream portion of the proposed signaling cascade, ultimately leading to cisplatin resistance.
Our experimental findings provide evidence supporting the central role of PGC1α in this pathway.
Both pharmacological inhibition and siRNA-mediated knockdown of PGC1α significantly
decrease TFAM levels, confirming its regulatory role, and led to significant synergism with
cisplatin to reduce BC cell survival (cell lines and PDOs) and re-sensitize cisplatin-resistant T24-
R2 cells. TCGA data analysis revealed a significant correlation between higher PGC1α expression
and reduced survival rates and worse disease outcomes in BC patients, as well as across various
cancer malignancies.
Prior studies have reported variable PGC1α levels, both high and low, to be associated with
cancer outcomes, perhaps reflecting its function as a node that integrates multiple signals (100,
272). PGC1α may be suppressed early in cancer development due to its potential anti-cancer
effects, with reduced levels indicative of a glycolysis-dominant cancer cell metabolism. As the
cancer advances, PGC1α tends to become more active, contributing to a rise in lipid and fatty acid

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metabolism with an overall metabolic plasticity that likely aids drug resistance and
progression(273-275). In sum, PGC1α in the cancer setting acts not as an ally or adversary, but as
a responsive agent to metabolic and environmental signals, therefore targeting it therapeutically
may not provide enough specificity of downstream targets. The results in Fig 6.5, highlight the
unidirectional effect of inhibiting the mediators in our proposed signaling cascade, supporting the
possibility that targeting downstream effectors like TFAM could offer a more specific therapeutic
approach, minimizing potential off-target effects on broader cellular processes regulated by
upstream kinases like ATM and AMPK, and the broad metabolic transcriptional coactivator
PGC1. However, the lack of upstream inhibition observed here does not preclude the possibility
of complex feedback loops or crosstalk with other signaling pathways under different cellular
conditions or stress stimuli. Exploring these dynamic interactions in more detail could offer
valuable information for optimizing therapeutic strategies. Exploring and optimizing TFAM
inhibitors (or even downstream mitochondrial targeting) for enhanced clinical translation may
constitute a more promising target in combination with current agents, like cisplatin. Their
concurrent use may exert a targeted neutralizing effect on the metabolic switch towards OxPhos,
precluding the adaptive resistant phenotype.

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Chapter 7: Conclusion
Effective treatment of advanced cancers is significantly hindered by the development of
resistance to therapy. This resistance arises not only through the survival and proliferation of cells
with genetic mutations that confer drug resistance but also via phenotypic plasticity, which is the
capacity for cells within the same genetic population to alter their state or phenotype in response
to environmental changes, acting as an evolutionary survival strategy to enhance adaptability in
fluctuating conditions(39, 276). A crucial strategy to counteract drug resistance, especially that
which is not genetically based, involves constraining cells to remain in states that are sensitive to
drugs, thereby improving treatment outcomes and reducing the risk of disease recurrence or
progression. Achieving this requires a comprehensive understanding of the regulatory mechanisms
that control phenotypic plasticity. Our current study, along with a growing body of work,
highlights the crucial role of rapid epigenetic, transcriptional, and metabolic shifts, independent of
genetic alterations, in shaping tumor subpopulations with pro-survival features like enhanced
OxPhos metabolism(26-31, 57-61). This latter finding offers an alternative adaptive strategy to the
canonical Warburg effect, traditionally focused on aerobic glycolysis, and instead underscores the
functional relevance of mitochondria and OxPhos in promoting tumorigenicity and drug resistance
(56, 58-60). Delineating the drivers of these metabolic reprogramming events presents an exciting
opportunity to identify novel therapeutic strategies for re-sensitizing cancer cells to treatment.
In this study, we endeavored to decipher the mechanisms underlying cisplatin-induced
adaptive upregulation of OxPhos in BC. Our findings reveal a novel signaling cascade initiated by
the canonical nuclear DNA damage elicited by cisplatin. This cascade involves sequential
activation of ATM, AMPK, and PGC1α, culminating in the nuclear upregulation of TFAM, the

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master regulator of mitochondrial replication and transcription. Subsequently, TFAM orchestrates
a significant increase in mitochondrial OxPhos, leading to enhanced ATP production that powers
drug efflux pumps. Activation of this signaling axis
(cisplatin→ATM→AMPK→PGC1α→TFAM→OxPhos→ATP→drug efflux) fuels survival and
treatment resistance in BC cell lines and patient-derived organoids (PDOs). Furthermore, high
expression levels of these key proteins correlate with aggressive BC and unfavorable patient
outcomes in publicly available patient databases, highlighting their potential clinical significance.
While the individual roles of ATM, AMPK, and PGC1α in cellular signaling and cancer have been
explored extensively(100, 160, 243, 248, 273, 277-282), to our knowledge, this study is the first
to highlight their coordinated participation in a cisplatin-induced pro-resistance pathway in BC.
This important adaptive signaling pathway merits further study as a new therapeutic avenue
to forestall chemotherapy resistance and improve clinical outcomes in BC and other malignancies.
The results in Figure 6.5, corroborate the theory of a linear hierarchy within the signaling pathway,
where the alteration of elements downstream (like PGC1α, AMPK) did not result in substantial
feedback suppression of elements upstream (such as ATM, AMPK). This one-way influence
strengthens the argument for specifically targeting downstream molecules like TFAM to tailor
more precise therapeutic interventions, thus reducing the possibility of inadvertently affecting
wider cellular functions that are controlled by upstream molecules like ATM and AMPK.
Nevertheless, the absence of observed feedback to upstream components in this study does not
eliminate the potential existence of intricate feedback loops or interactions with other signaling
pathways that may arise under varying cellular conditions or in response to different types of stress.
A deeper investigation into these complex interactions could provide critical insights beneficial
for refining therapeutic approaches.

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It is also crucial to acknowledge that adaptive resistance likely extends beyond the core
proposed signaling mediators investigated in this study. A multitude of other factors, including
mutations, epigenetic modifications, and interactions with other signaling pathways, almost
certainly contribute to the complex resistance phenotype. For example, future investigations
should extend to explore the interaction between ATM and ROS(179), considering mechanisms
by which increased OxPhos activity in and of itself also contributes to DNA damage. It would also
be worthwhile to assess the shuttling of AMPK and ATM between the nucleus and cytoplasm, and
how these factors could affect their interaction and feedback between one another(182, 185, 212,
283-285). ATM expression and activation have been associated with enhanced proliferation and
metastatic potential in cancer cells(155, 278, 286). However, its pleiotropic functions in cell
division and DNA repair render it a challenging therapeutic target. Similarly, AMPK plays a
central role in cellular energy homeostasis, encompassing diverse processes like lipogenesis,
glycolysis, the tricarboxylic acid cycle, cell cycle progression, and mitochondrial dynamics(246,
287), has been implicated in aberrant activation of metabolic pathways, mitochondrial dynamics,
and epigenetic regulation, contributing to neoplastic mechanisms(243, 288). However, its contextdependent nature necessitates careful consideration, as it can exert both pro- and anti-tumor
effects(281, 287, 289, 290). PGC1α is regulated by AMPK and itself acts as a master regulator of
mitochondrial proliferation and metabolism, making it a compelling candidate to bridge the gap
between cisplatin-induced DNA damage signaling upstream (ATM, AMPK) and downstream
mitochondrial activation (TFAM, OxPhos). However, similar to ATM and AMPK, PGC1α
influences a broad spectrum of cellular metabolic pathways, with evidence suggesting both proand anti-neoplastic roles(291-293). To the best of our knowledge, our study presents the first direct
evidence linking cisplatin-induced DNA damage signaling to mitochondrial activation and drug

99
efflux. While targeting the upstream initiators of this cascade (ATM, AMPK, PGC1α) might be
challenging due to their pleiotropic functions, the downstream metabolic consequences offer more
promising avenues for therapeutic intervention. Previous attempts to combine chemotherapy with
global OxPhos inhibition using agents like metformin or phenformin have yielded limited
benefits(294, 295). An alternative strategy that combines chemotherapy with targeted inhibition
of TFAM and other mitochondrial co-regulators might offer greater therapeutic efficacy and
specificity. Numerous studies have demonstrated elevated levels of TFAM across various cancer
forms, corroborating the results from our study(98, 141, 296-300). Therefore, the indispensable
position of TFAM in the growth and resistance of tumors underscores its promise as an attractive
target for cancer therapy and paves the way to elicit the development and preclinical evaluation of
specific TFAM inhibitors or modulators which are crucial for clinical translation.
Mitophagy is yet another mitochondrial function that impacts drug resistance. Mitophagy,
the selective degradation of mitochondria by autophagy, plays a complex and multifaceted role in
cancer(301). It has been shown to potentially promote tumor progression by protecting cancer cells
from chemotherapy-induced apoptosis and supporting the survival of CSCs, the small
subpopulation of cancer cells with the ability to self-renew and initiate tumor growth in the face
of cancer therapy(302, 303). In early tumorigenesis, mitophagy can act as a tumor suppressor by
removing damaged mitochondria that generate excessive ROS. However, once tumors are
established, mitophagy can switch roles and promote tumor progression by protecting the tumor
cells against chemotherapy-induced apoptosis(304). In response to chemotherapy-induced
mitochondrial dysfunction, Pink1 recruits Parkin to the outer mitochondrial membrane, leading to
targeted degradation of damaged mitochondria through mitophagy and preserving the host cell
overall(305, 306). Interestingly, Parkin is linked to TFAM and boosts TFAM's role in

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mitochondrial transcription(307, 308). This suggests that parkin plays a part in controlling
mitochondrial transcription/replication beyond its involvement in the ubiquitin-mediated protein
degradation pathway in dividing cells, therefore further dissection of mitophagy’s interaction with
mitochondrial biogenesis and resultantly promoting drug resistance, offers a promising
exploratory avenue.
Beyond the immediate implications for BC therapy, our findings offer broader insights into
the intricate interplay between the host cell and its resident mitochondria, a symbiotic relationship
forged over a billion years of co-evolution(309, 310). The host cell meticulously regulates
mitochondrial replication, transcription, and function to ensure efficient ATP production, redox
and calcium homeostasis, and proper execution of apoptotic and necrotic signaling(311-313). In
our BC cisplatin model, nuclear DNA damage activates host signaling mediators (e.g. ATM and
its downstream effectors), that not only address the host DNA damage but concurrently elicit a
mitochondrial response (OxPhos, ATP production) that powers drug efflux and promotes survival.
Intriguingly, recent work highlights surprising parallels between the gut microbiome and
mitochondria, both playing crucial roles in host metabolism and longevity(314, 315). These shared
functions, coupled with the established evolutionary link between mitochondria and bacterial
ancestors, suggest a potential for dynamic intercellular communication, a hypothesis supported by
accumulating evidence(316-319). Structural and functional similarities further strengthen this
notion. Notably, mitochondrial membranes mirror bacterial membranes, and shared autophagic
systems for membrane degradation suggest conserved cellular processes(320, 321). Emerging
work delves into the intimate connection between the gut microbiome and mitochondria in disease,
with recent studies implicating microbial metabolites as potential messengers in this intricate
crosstalk(322). Some recent findings have also specifically observed that gut microbiota exert

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regulatory control over key factors in mitochondrial biogenesis, including PGC1α and AMPK,
impacting the expression or activity of these transcriptional co-activators, transcription factors,
and enzymes(323-325). Thus, mitochondria may extend their influence beyond their host cells,
shaping distal cellular and tissue phenotypes and overall physiology through dynamic interactions
with the gut microbiome(326-328).

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Chapter 8: Materials and Methods
Cell Culture
Human bladder cancer cell lines J82 (RRID: CVCL_0359), UM-UC-3 (RRID: CVCL_1783), RT4 (RRID: CVCL_0036) and T24 (RRID: CVCL_0554) were purchased from ATCC (Manassas,
VA). J82, UM-UC-3 and RT-4 cells were maintained at 37°C, 5% CO2 in DMEM (Corning, Cat#
10-013-CV) supplemented with 10% heat-inactivated fetal bovine serum (Omega, Cat# FB-02),
100 U/ml penicillin and 100μg/ml streptomycin (Gibco, Cat# 15140122). T24 cells were cultured
in RPMI 1640 (Corning, Cat#10-040-CV) supplemented and cultured as described for other BC
cells. T24-R2 (generously provided by Dr. Seok-Soo Byun, Seoul National University College of
Medicine, South Korea) is a cisplatin-resistant derivative cell line of T24 established through serial
desensitization of T24 cells and developed to resist 6.6μM cisplatin(123). T24-R2 cells were
maintained at 37°C, 5% CO2 in DMEM supplemented with 10% heat-inactivated fetal bovine
serum, 100 U/ml penicillin, 100μg/ml streptomycin and 6.6μM cisplatin (Sigma-Aldrich, Cat#
CAS 15663-27-1). All cell lines were authenticated using 9-marker STR profiling (IDEXX
BioAnalytics, Columbia, MO, USA) within the past 6 months. Interspecies contamination test
(IDEXX BioAnalytics, Columbia, MO, USA) and mycoplasma evaluation (MycoAlert
mycoplasma detection kit, Lonzo, Basel, Switzerland) were negative.
Human Bladder Cancer Organoid Culture
Freshly resected tissue was obtained with informed consent (IRB#HS-11-00054) from patients
undergoing transurethral resection of bladder tumor (TURBT) as part of their routine care. The
human bladder tissue was examined by a faculty pathologist to confirm presence of tumor prior to
further processing. Subsequently, at the Goldkorn Lab the tissue was cut into 1–2 mm pieces with

103
a surgical blade and digested with collagenase (1 mg/mL of collagenase
from Clostridium histolyticum: Sigma Aldrich, Cat# C9891) in Adv DMEM/F-12 (ThermoFisher,
Cat# 12634028) with ROCK inhibitor (Y-27632, 10μM) for 30 mins at 37 °C. The incubation was
repeated once, after which the cell suspension was filtered through a 100-μm strainer. Cells were
collected by centrifugation and resuspended in ∼35μL of BME (Cultrex, Cat# 3533-001-02) per
well and plated into two to four individual wells of a prewarmed 24-well plate, which was then
inverted for 15 minutes in a 37°C, 5% CO2 incubator. When the BME was solidified, human
bladder organoid media (BOM) was added based on a modified 6A Media recipe provided by the
NCI PDMR with working concentrations of: Advanced DMEM/F12 (1X) (Invitrogen, Cat#:
12634-028), HEPES (10mM) (Invitrogen, Cat# 15630080), GlutaMax Supplement (1X) (Life
Technologies, Cat#: 35050061), Primocin (0.1 mg/mL) (InvivoGen, Cat#: Ant-pm-2), L-WRN
Conditioned Media (50%) (Sigma-Aldrich, Cat# SCM105), N-acetylcysteine (1.25 mM) (Sigma,
Cat# A9165-5G), Nicotinamide (10 mM)(Sigma, Cat# N0636-100G), B-27 Supplement (1X) (Life
Technologies, Cat# 17504044), N-2 Supplement (1X)( Life Technologies, Cat# 17502048), Y27632 dihydrochloride (10μM) (Tocris, Cat# 1254), FGF10 (100 ng/mL) (Peprotech Cat# 100-
26), FGF7 (25 ng/mL) (Peprotech, Cat# 100-19), FGF2 (12.5 ng/mL) (Peprotech, Cat# 100-18B)
and A83-01 (5μM) (Tocris, Cat# 2939). The media was filtered through 0.22-μm filter and
prepared fresh each week. Several reagents have short half-lives, instructions for media
preparation should be followed to ensure the best outcome. The PDOrg models 772611-094-RV3-organoid [Sample Number: CK9151] used in this study was developed by NCI PDMR. Human
bladder organoids were passaged biweekly and were sheared through a glass pipet. ROCK
inhibitor (Y-27632, 10μM) was added to the media after passaging, to prevent cell death.

104
Organoids were frozen in freezing media (50% FBS, 10% DMSO, and 40% Adv DMEM/F-12)
and could be recovered efficiently with high degree of viability.
Histology
Organoids and tissue were fixed in 4% paraformaldehyde for 3 hrs, dehydrated, and paraffinembedded according to standard histology procedures. Sections were stained with H&E and the
following antibodies: TFAM (CST, 7495S, RRID: AB_10841294), Keratin 5 (AF138 COVANCE
160P-100, RRID:AB_291581), Ki-67 (Monosan MONX10283, RRID:AB_1833494), Keratin 20
(KS20.8 Dako M7019, RRID:AB_2133718), TP63 (4A4 Abcam ab735, RRID:AB_305870), and
Uroplakin III (AU1 Progen 690108, RRID:AB_2904126)(329), according to the manufacturer’s
protocols, at the Department of Pathology and Laboratory Medicine at Keck School of Medicine,
USC. Images were acquired using Zeiss Axio Observer.A1 and Axio Imager.Z1 microscopes.
Control staining for TFAM was done on human colon tissue.
Treatment with Pharmacological Agents
Cisplatin treatment: J82, UM-UC-3 cells, T24 cells and RT-4 cells were seeded 24 hrs prior to
10μM cisplatin treatment (Sigma-Aldrich, Cat# CAS 15663-27-1), or transfected for 48 hrs prior
to 10μM cisplatin treatment. CK9151 and CTC-1044 BC PDOs were treated with 50μM cisplatin
for 24 hrs. Cells were cultured at 37 °C, 5% CO2 for 24 hrs of treatment.

105
Phenformin treatment: J82, UM-UC-3 cells, T24 cells and RT-4 cells were seeded for 24 hrs prior
to treatment with phenformin 20μM (Sigma Aldrich, Cat# P7045) for 24 hrs, either alone or in
combination with 10μM cisplatin. Cells were cultured at 37 °C, 5% CO2.
SR-18292 treatment: UM-UC-3 cells, T24 cells and T24-R2 cells were seeded 24 hrs prior to 40μM
SR-18292 treatment (Selleck Chem, Cat# S1092). CK9151 and CTC-1044 BC PDOs were treated
with 50μM SR-18292 for 24 hrs. Cells were cultured at 37 °C, 5% CO2 for 24 hrs of treatment.
KU-55933 treatment: UM-UC-3 cells, T24 cells and T24-R2 cells were seeded 24 hrs prior to
10μM KU-55933 treatment (Selleck Chem, Cat# S1092) for 24 hrs. CK9151 and CTC-1044 BC
PDOs were treated with 20μM KU-55933 for 24 hrs. Cells were cultured at 37 °C, 5% CO2 for 24
hrs of treatment.
Compound C/ Dorsomorphin treatment: UM-UC-3 cells, T24 cells and T24-R2 cells were seeded
24 hrs prior to 5μM Compound C treatment (Sigma Aldrich, Cat# 171260) for 24 hrs. CK9151
and CTC-1044 BC PDOs were treated with 15μM Compound C for 24 hrs. Cells were cultured at
37 °C, 5% CO2 for 24 hrs of treatment.
AICAR treatment: UM-UC-3 cells, T24 cells and T24-R2 cells were seeded 24 hrs prior to 0.1mM
AICAR treatment (Sigma Aldrich, Cat# A9978). CK9151 and CTC-1044 BC PDOs were treated
with 0.5mM AICAR for 24 hrs. Cells were cultured at 37 °C, 5% CO2 for 24 hrs of treatment.

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Trypan Blue Exclusion Survival Assay
J82, UM-UC-3, RT-4, T24 or T24-R2 cells were seeded in a 6-well plate (3x105
cells/well). After
24 hrs the cells were treated in triplicates with the respective control vehicles and pharmacological
reagents (as mentioned under ‘Treatment with pharmacological agents’), alone or in combination
with cisplatin. Other experiments included treating scrambled control siRNA-, TFAM siRNA-,
PGC1 siRNA-, or AMPK1-siRNA transfected T24, T24-R2, UM-UC-3 BC cells, with
Cisplatin (10μM for 24 hrs), alone or in combination. After respective treatments, the cells were
trypsinized and counted using trypan blue exclusion viability assay on a Nikon Eclipse TS100
Inverted Routine Microscope and Life Technologies Countess II FL Automated Cell Counter. Cell
counts were used to calculate a coefficient of drug interaction (CDI) to evaluate synergy, where
CDI = [combination/control]/[(cisplatin/control) x (phenformin or target siRNA alone/control)];
CDI < 1.0 indicates synergy and CDI < 0.7 indicates significant synergy.
For BC PDOs (CK9151 and CTC-1044), wells were treated in triplicate with cisplatin alone (50μM
for 24 hrs), pharmacological reagents (as mentioned under ‘Treatment with pharmacological
agents’), and TFAM KD (48 hrs transfection) alone or in combination, and then digested with
Dispase II (0.75mg/ml) (ThermoFisher, Cat# 17105-041) for 2 hrs, followed by 15 mins incubation
with TrypLE (Gibco, Cat# 12-605-010). Carefully recovered single cells were then used for
counting live cells using trypan blue exclusion assay. Cell counts were used to calculate a
coefficient of drug interaction (CDI) to evaluate synergy.

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Mitochondrial DNA qPCR
mitochondrial DNA was harvested using a DNeasy kit (Qiagen, Cat#69504), and the presence of
mitochondrially encoded tRNA-Leu was assessed by PCR (forward primer, 5’-
GATGGCAGAGCCCGGTAATCGC-3’; reverse primer, 5’-
TAAGCATTAGGAATGCCATTGCG-3’). Presence of the nuclear genome was confirmed by
PCR against a region of the X chromosome (forward primer, 5’-GAAGGTGAAGGTCGGAGTC3’; reverse primer, 5’-GAAGATGGTGATGGGATTTC-3’). Quantitative PCR was performed in
triplicate using the MyiQ single-color real-time PCR detection system (Bio-Rad) for 45 cycles at
94 °C for 30 s and 60 °C for 30 s followed by 72 °C for 60 s. The Bio-Rad iQ5 optical system
software version 2.0 was used to analyze the results.
Quantitative Reverse Transcription-PCR Assays
Total RNA was extracted from cells using the RNeasy Micro Kit (Qiagen, Cat# 74004) and was
reverse-transcribed into single-stranded cDNAs using the qScript cDNA Synthesis Kit (Quanta
BioSciences, Cat# 95047-025). RT-qPCR was performed using Perfecta SYBR Supermix-IQ
(Quanta BioSciences, Cat# 95053-500). The primer sequences used are given in Table 8.1.
Quantitative RT-PCR was performed in triplicate using the MyiQ single-color real-time PCR
detection system (Bio-Rad) for 40 cycles at 95 °C for 15 s and 52 °C for 30 s followed by 72 °C
for 30 s. The Bio-Rad iQ5 optical system software version 2.0 was used to analyze the results and
was normalized by β-actin as the internal controls.

108
Western Blotting
Whole cell lysates were extracted from human BC cells and BC PDOs using RIPA lysis buffer
(Sigma-Aldrich, Cat# R0278), and total protein concentrations were determined by the BCA
Protein Assay Kit (Bio-Rad, Hercules, CA, USA). BC PDOs were treated with Dispase II
(0.75mg/ml) for 1.5 hrs prior to RIPA lysis for 45 min on ice. Proteins were separated on 4–20%
Tris-Glycine gradient or 3-8%, Tris-Acetate gels, respectively, (Invitrogen, Cat# EC6021BOX,
EA0375BOX) and transferred to PVDF membranes using the iBlot Dry Blotting System
(ThermoFisher Scientific, Cat# IB24002). Membranes were blocked in an Odyssey blocking
buffer (LI-COR, Lincoln, NE, USA) or 5% non-fat milk, respectively, and incubated with primary
antibodies overnight at 4 °C. The details of the antibodies used are given in Table 8.2. The sample
protein concentration used in for all western blots ranged from 30-60μg/sample respectively for
different proteins’ detection.
The protein ladders used were PageRuler™ Prestained Protein Ladder (10-180 kDa) (Thermo
Scientific™, Cat# 26616) and Spectra™ Multicolor High Range Protein Ladder (Thermo
Scientific™, Cat# 26625). Blots were incubated with respective secondary antibodies (Table 8.2),
for 1 hr at room temperature and imaged using an LI-COR Odyssey® DLx Imaging System.
Digital images were processed and analyzed using LI-COR ImageStudio Version 5.0 software.
Image J was used to quantify protein expression.
siRNA Transient Transfection
BC cell lines:
TFAM stealth siRNA (Invitrogen, Cat# 1299001, Assay ID: HSS144251), negative siRNA
controls were purchased from Invitrogen. AMPK1 siRNA (h) (Santa Cruz Biotech, Cat# sc-

109
29673), and negative control siRNA-A (Santa Cruz Biotech, Cat# sc-37007) were purchased from
Santa Cruz Biotech. PGC1 siRNA TriFECTa Kit DsiRNA oligo (hs.Ri.PPARGC1A.13.3) (IDT,
Reference# 438937295) and negative control siRNA (IDT, Reference# 438937298) were
purchased from IDT.
To introduce TFAM, PGC1 or AMPK1 siRNA duplex into T24 and T24-R2 cells, Lipofectin
reagent (Invitrogen, Cat# 18292037) and Optimem-I (Gibco, Cat# 31985062) were used to
transfect cells at 70% confluence in a 12-well plate per manufacturer’s protocol, with 40pmol
TFAM siRNA duplex, or 50pmol PGC1 or AMPK1 siRNA duplex. For UM-UC-3 cells,
Lipofectamine 3000 (Invitrogen, Cat# L3000015) reagent and Optimem-I (Gibco, Cat# 31985062)
was used to transfect cells at 70% confluence in a 12-well plate per manufacturer’s protocol, with
the same respective siRNA duplex concentrations as for T24 cells. 10pmol of scrambled siRNA
negative control was used with the same respective reagents for each cell line. The transfection
was done for 48 hrs for TFAM KD, and for 24 hrs for PGC1 or AMPK1 KD before collecting
the cells for total RNA. qRT-PCR was used to measure transfection efficiency.
PDOs:
Human bladder organoids (CK9151 and CTC-1044) were transfected with Lipofectamine
RNAiMAX Transfection Reagent (ThermoFisher, Cat# 13778030) as follows: After 14-21 days
of culture, PDOs were transfected with respective siRNAs: 300pmol TFAM siRNA duplex. siRNA
complexes were formed using Optimem-I and RNAiMAX mix, incubated for 20 mins, and then
mixed in human bladder organoid media (BOM) with 10% normal/dialyzed FBS (Gibco). In all
conditions antibiotics were omitted as recommended by the manufacturer to preserve viability
during the transfection process. 500μl of formed siRNA complex medium was then bathed over

110
35μl BME contained within a single well of a 24-well plate and incubated for 72 hrs, before
collection. Dispase II (0.75mg/ml) treatment was done for 1.5 hrs prior to total RNA extraction.
qRT-PCR was used to measure transfection efficiency(330).
Measurement of Oxygen Consumption Rate (OCR)
OCR was measured using Seahorse XF Cell Mito Stress Test (Agilent, Santa Clara, CA, USA) at
the Translational Research Laboratory Core, University of Southern California. Cells were seeded
at 104
cells per well in 6 replicates in a Seahorse XF 96 Cell Culture Microplate. Assays were
initiated by replacing the growth medium with 180 μL XF assay medium (specially formulated,
unbuffered Dulbecco's modified Eagle's medium for XF assays; Seahorse Bioscience)
supplemented with 1 mM pyruvate, 2 mM glutamine, and 10 mM glucose (pH 7.4). Cells were
kept in a non–CO2-incubator for 60 mins at 37 °C before placement in the Analyzer. Cells were
treated with 1 μM oligomycin; 1 μM tri-fluorocarbonylcyanide phenylhydrazone (FCCP); and 0.5
μM mixture including rotenone and antimycin A according to the manufacturer instructions. All
readings were normalized to DNA concentration using Hoechst dye. Average values from 6
replicates were plotted. OCR data was generated using the mitochondrial stress test by Seahorse
WAVE Software (Agilent) and was analyzed using GraphPad Prism Software. Error bars indicate
standard deviation around the mean (SEM).
Non-descanned Multiphoton Fluorescence Lifetime Imaging Microscopy (FLIM)
Fluorescence lifetime images were acquired with a Leica SP8 DIVE FALCON inverted
microscope coupled to a SpectraPhysics Insight X3 ultrafast tunable infrared laser. For image
acquisition the following settings were used: 740 nm excitation wavelength at an average 0.6 mW

111
laser power, 512 × 512 pixels image format, 12.6 μs/pixel dwell time, acquired from 425 nm to
475 nm on Leica hybrid detectors, and FLIM data was collected in 7 integrated frame repetitions
per optical section using the Leica FLIM/FCS module. We detect some autofluorescence within
the hydrogel and we therefore masked out the hydrogel signal from our spheroid FLIM
measurements using the region of interest (ROI) tool within the Leica FLIM/FCS software. All
FLIM Phasor data were filtered using the Complex Wavelet Filter(331) at a threshold of 1.
Multi-Drug Resistance (MDR) Efflux
Fluorimetric MDR Assay kit (Abcam, Cat# ab112142) was used to investigate the effect of TFAM
knockdown +/- Cisplatin, on the activity of major drug-efflux proteins MDR1/P-gp and MRP1
according to the manufacturer's instructions. T24, T24-R2, UM-UC-3 cells were transfected with
TFAM siRNA (as mentioned above) for 48 hrs before being recollected and reseeded into 96-well
black microculture plates: T24 (20K cells/well), T24-R2 (20K cells/well), UM-UC-3 (25K
cells/well). Cisplatin 10μM (or verapamil 50μM as a positive control) was added 6 hrs post
reseeding. Fluorescence (Ex/Em=490/525nm) was measured 24 hrs later using a plate reader. The
experiments were performed in quadruplicates and normalized to cell density using Janus green
stain (Abcam, cat# (ab111622). The results are expressed as relative florescence units compared
to untreated cells and data were analyzed using Student’s t-test with p < 0.05 as the significance
criterion when two groups were compared.

112
ATP Detection Assay
Luminescent ATP detection assay kit (Abcam, Cat# ab113849) was used to quantify total cellular
ATP levels according to manufacturer’s protocol. T24, T24-R2, UM-UC-3 cells were transfected
with TFAM siRNA (as mentioned above) for 48 hrs before being recollected and reseeded into
96-well black microculture plates: T24 (20K cells/well), T24-R2 (20K cells/well), UM-UC-3 (25K
cells/well). Cisplatin 10μM (or Oligomycin 10nM as a positive control) was added 6 hrs post
reseeding. Luminescence was measured 24 hrs later using a plate reader. The experiments were
performed in quadruplicates and normalized to the number of viable cells using Janus green stain
(Abcam, cat# ab111622). The results are expressed as final ATP concentration in media (μM)
using an ATP standard concentration curve (per manufacturer’s instructions), compared to
untreated cells and data were analyzed using Student’s t-test with p < 0.05 as the significance
criterion when two groups were compared.
FACS Sorting
UM-UC-3 and T24 cells were trypsinized, counted, and resuspended in prewarmed 10% FBS
DMEM media at a concentration of 1x106/mL. Hoechst 33342 (Sigma-Aldrich) was added at a
concentration of 5μg/mL, incubated for 2 hrs in 37°C. Parallel sample aliquots were prepared in
the presence of 50 μmol/L verapamil (Sigma-Aldrich), an ATP-binding cassette transporter family
inhibitor, at room temperature for 10 minutes before adding the Hoechst 33342 dye. Cells were
washed and resuspended in ice-cold DMEM. 7-AAD used to discriminate dead cells was added at
a final concentration of 2μg/ml. Samples were incubated for at least 5 min on ice before FACS
analysis and sorting (FACSAria and FACSLSR-II, BD Biosciences, both equipped with UV
lasers). SP and NSP were gated based on comparison with verapamil control and sorted as needed

113
by FACS Aria I. At least 10 000 events were recorded. All samples were performed in technical
triplicates and experiments were repeated in two biological replicates.
Immunofluorescence Microscopy
150K cells were seeded per well in 12-well plates over 18mm #1.5 thick precision coverslips
(Neuvitro, #NCO768385). After treatment, cells were fixed with pre-warmed 10% formalin for 15
min at 37°C, permeabilized in 0.1% Triton X-100 in 1X PBS at room temperature for 15 minutes
and blocked in 2% Bovine Serum Albumin for 1 hour at room temperature. Primary antibody
incubations were performed overnight with appropriate antibodies at 4°C. The primary antibodies
used in this study are provided in Table 8.3. Samples were then washed 3 times with 1X PBS and
incubated with secondary antibodies for 45 minutes at room temperature before being washed 3
more times with 1X PBS. Coverslips were mounted on microscopy slides (VWR, Catalog #16004-
368) using VectaShield Antifade Mounting Medium containing DAPI (Catalog #H-1200-10
Vector Laboratories, Burlingame, CA, USA) and sealed with clear nail polish. After primary
antibody incubation, samples were washed 3X with PBS followed by an incubation for 45 minutes
at room temperature with the secondary antibodies (provided in Table 8.3). Representative cells
were selected and imaged on a Zeiss 800 Axio Imager.Z2 upright laser scanning confocal
microscope (USC Stem Cell Optical Imaging Facility), using an EC Plan-Neofluar 40x/1.30 Oil
lens. 1024 x 1024 pixel images were acquired using GaAsP-PMT detectors. For z-stack imaging,
slices were acquired with 1μm intervals. Image analysis was then performed uniformly using
ImageJ.

114
Schematic Figures
Schematics were created using BioRender.com (publication licenses available).
Statistical Analysis
All data shown graphically has error bars representing standard deviation around the mean (SEM).
No data were excluded from the analyses. Significance levels for comparison between groups were
determined with unpaired two-tailed Student’s t test, using GraphPad Prism version 10.0.2 or
Excel version 16.63.1 (Microsoft). Exact p values and statistical tests for each result are stated in
the figures.

115
Table 8.1. Primer sequences used for qRT-PCR analysis.
β-Actin F: 5'-ACAGAGCCTCGCCTTTGCC-3'
R: 5'-GATATCATCATCCATGGTGAGCTGG-3'
TFAM
F: 5'-AGCTCAGAACCCAGATGCAA-3'
R: 5'-TCAGGAAGTTCCCTCCAACG-3'
PRKAA1/AMPK⍺1
F: 5'-TCAGGAAGATTGTATGCAGGCCCA-3'
R: 5'-TTCATGGGATCCACCTGCAGCATA-3'
PRKAA2/AMPK⍺2
F: 5'-TTTGTGGCACCCTCCCATTTGATG-3'
R: 5'-AGAACAGGAACGCTGAGGTGTTGA-3'
PGC1α
F: 5'-GCTTTCTGGGTGGACTCAAGT-3'
R: 5'-GAGGGCAATCCGTCTTCATCC-3'
Nrf1 F: 5'-AGGAACACGGAGTGACCCAA-3'
R: 5'-TGCATGTGCTTCTATGGTAGC-3'
Nrf2 F: 5'-CAGCGACGGAAAGAGTATGA-3'
R: 5'-TGGGCAACCTGGGAGTAG-3'
PCK1 F: 5'-CATATGCTGATCCTGGGCATAAC-3'
R: 5'-CAAACTTCATCCAGGCAATGT C-3'
G6PC F: 5'-ACACCGACTACTACAGCAACAG -3'
R: 5'-CCTCGAAAGATAGCAAGAGTAG-3'
COX5A
F: 5'-GCCAGATATAGATGCCTGGGA-3'
R: 5'-ACAACCTCTAGGATACGAACTGT-3'
ATP5G1
F: 5'-CTGTTGTACCAGGGGTCTAATCA-3'
R: 5'-GTGGGAAGTTGCTGTAGGAAG-3'
mT-ND5
F: 5'-GTACCCACGCCTTCTTCAAA-3'
R: 5'-GCTAATGCTAGGCTGCCAAT-3'
mT-CO2
F: 5'-AACGATCCCTCCCTTACCAT-3'
R: 5'-TCGATTGTCAACGTCAAGGA-3'
MOTS-c
F: 5'-AGC GCA AGT ACC CAC GTA AA-3'
R: 5'-AGG GCC CTG TTC AAC TAA GC-3'
Humanin
F: 5'-ACT TTG CAA GGA GAG CCA AA-3'
R: 5'-GCT ATC ACC AGG CTC GGT AG-3'
PCNA
F: 5'-TTTGGTGCAGCTCACCCTG-3'
R: 5'-CGCGTTATCTTCGGCCCTTA-3'
Ki-67
F: 5'-TCCTTTGGTGGGCACCTAAGACCTG-3'
R: 5'-TGATGGTTGAGGTCGTTCCTTGATG-3'

116
Table 8.2. Antibodies used in Western blots.
Protein Target Species
Reactivity
Size
(kDa)
Dilution Company Catalog# RRID
GAPDH Mouse 37 1:5000 Invitrogen 437000 AB_2532218
β-Actin Mouse 45 1:5000 CST 3700S AB_2242334
α-Tubulin Mouse 55 1:4000 Invitrogen 62204 AB_1965960
ATM Rabbit 350 1:750 CST 2873T AB_2062659
Phospho-ATM
(Ser1981)
Mouse 350 1:500 CST 4526T AB_2062663
AMPKα Rabbit 62 1:750 CST 2532S AB_330331
Phospho-AMPKα
(Thr172)
Rabbit 62 1:400 CST 2535T AB_331250
PGC1α Mouse ~37 1:500 SCBT sc-518025 AB_2890187
TFAM Rabbit 24 1:1000 CST 7495S AB_10841294
H2AX Rabbit 15 1:1000 CST 7631T AB_10860771
Phospho-H2AX Rabbit 15 1:1000 CST 9718T AB_2118009
Acetyl-CoA
Carboxylase
Rabbit 280 1:500 CST 3676T AB_2219397
Phospho-AcetylCoA Carboxylase
(Ser79)
Rabbit 280 1:500 CST 3661S AB_330337
p53 Rabbit 53 1:400 CST 9282T AB_331476
Phospho-p53 (Ser15) Rabbit 53 1:400 CST 9284T AB_331464
Goat anti-Rabbit IgG
Secondary Antibody
DyLight 800
- - 1:5,000 Invitrogen SA5-
35571
AB_2556775
Goat anti-Mouse
IgG Secondary
Antibody IRDye
680RD
- - 1:5,000 LI-COR 926-
68070
AB_10956588
Table 8.3. Antibodies used in Immunofluorescence imaging.
Protein Target Species
Reactivity
Dilution Company Catalog# RRID
TOMM20 Mouse 1:1000 ThermoFisher 66777-1- IG AB_2882123
TFAM Rabbit 1:500 CST 7495S AB_10841294
Phospho-H2AX (Ser139) Rabbit 1:500 CST 9718T AB_2118009
Goat anti-Rabbit Alexa
Fluor 488
- 1:1000 ThermoFisher A-11008 AB_143165
Goat anti-Mouse Alexa
Fluor 568
- 1:1000 ThermoFisher A-11004 AB_2534072

117
Bibliography
1. Teoh JY, Huang J, Ko WY, Lok V, Choi P, Ng CF, et al. Global Trends of Bladder
Cancer Incidence and Mortality, and Their Associations with Tobacco Use and Gross Domestic
Product Per Capita. Eur Urol. 2020;78(6):893-906. Epub 20200921. doi:
10.1016/j.eururo.2020.09.006. PubMed PMID: 32972792.
2. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin.
2023;73(1):17-48. doi: 10.3322/caac.21763. PubMed PMID: 36633525.
3. Kiriluk KJ, Prasad SM, Patel AR, Steinberg GD, Smith ND. Bladder cancer risk from
occupational and environmental exposures. Urol Oncol. 2012;30(2):199-211. doi:
10.1016/j.urolonc.2011.10.010. PubMed PMID: 22385990.
4. Cumberbatch MGK, Jubber I, Black PC, Esperto F, Figueroa JD, Kamat AM, et al.
Epidemiology of Bladder Cancer: A Systematic Review and Contemporary Update of Risk
Factors in 2018. Eur Urol. 2018;74(6):784-95. Epub 20180926. doi:
10.1016/j.eururo.2018.09.001. PubMed PMID: 30268659.
5. Tran L, Xiao JF, Agarwal N, Duex JE, Theodorescu D. Advances in bladder cancer
biology and therapy. Nat Rev Cancer. 2021;21(2):104-21. Epub 20201202. doi: 10.1038/s41568-
020-00313-1. PubMed PMID: 33268841; PubMed Central PMCID: PMC10112195.
6. Osterman CK, Milowsky MI. New and Emerging Therapies in the Management of
Bladder Cancer. F1000Res. 2020;9. Epub 20200916. doi: 10.12688/f1000research.26841.1.
PubMed PMID: 32983413; PubMed Central PMCID: PMC7495213.
7. Halaseh SA, Halaseh S, Alali Y, Ashour ME, Alharayzah MJ. A Review of the Etiology
and Epidemiology of Bladder Cancer: All You Need To Know. Cureus. 2022;14(7):e27330.
Epub 20220727. doi: 10.7759/cureus.27330. PubMed PMID: 36042998; PubMed Central
PMCID: PMC9411696.
8. Gandhi J, Chen JF, Al-Ahmadie H. Urothelial Carcinoma: Divergent Differentiation and
Morphologic Subtypes. Surg Pathol Clin. 2022;15(4):641-59. Epub 20221013. doi:
10.1016/j.path.2022.07.003. PubMed PMID: 36344181; PubMed Central PMCID:
PMC9756812.
9. Grabe-Heyne K, Henne C, Mariappan P, Geiges G, Pöhlmann J, Pollock RF.
Intermediate and high-risk non-muscle-invasive bladder cancer: an overview of epidemiology,
burden, and unmet needs. Front Oncol. 2023;13:1170124. Epub 20230602. doi:
10.3389/fonc.2023.1170124. PubMed PMID: 37333804; PubMed Central PMCID:
PMC10272547.
10. Golla V, Lenis AT, Faiena I, Chamie K. Intravesical Therapy for Non-muscle Invasive
Bladder Cancer-Current and Future Options in the Age of Bacillus Calmette-Guerin Shortage.
Rev Urol. 2019;21(4):145-53. PubMed PMID: 32071562; PubMed Central PMCID:
PMC7020281.

118
11. Hayashi T, Fujita K, Hayashi Y, Hatano K, Kawashima A, McConkey DJ, et al.
Mutational Landscape and Environmental Effects in Bladder Cancer. Int J Mol Sci. 2020;21(17).
Epub 20200823. doi: 10.3390/ijms21176072. PubMed PMID: 32842545; PubMed Central
PMCID: PMC7503658.
12. Patel VG, Oh WK, Galsky MD. Treatment of muscle-invasive and advanced bladder
cancer in 2020. CA Cancer J Clin. 2020;70(5):404-23. Epub 20200807. doi: 10.3322/caac.21631.
PubMed PMID: 32767764.
13. Lee HW, Kwon WA, Nguyen NT, Phan DTT, Seo HK. Approaches to Clinical Complete
Response after Neoadjuvant Chemotherapy in Muscle-Invasive Bladder Cancer: Possibilities and
Limitations. Cancers (Basel). 2023;15(4). Epub 20230219. doi: 10.3390/cancers15041323.
PubMed PMID: 36831665; PubMed Central PMCID: PMC9953905.
14. Ranasinghe R, Mathai ML, Zulli A. Cisplatin for cancer therapy and overcoming
chemoresistance. Heliyon. 2022;8(9):e10608. Epub 20220914. doi:
10.1016/j.heliyon.2022.e10608. PubMed PMID: 36158077; PubMed Central PMCID:
PMC9489975.
15. T.B P, B PV, S G, J B, E K, J H-c, et al., editors. LBA6 - EV-302/KEYNOTE-A39:
Open-label, randomized phase III study of enfortumab vedotin in combination with
pembrolizumab (EV+P) vs chemotherapy (Chemo) in previously untreated locally advanced
metastatic urothelial carcinoma (la/mUC). ESMO Congress 2023; 2023; Madrid, Spain: Annals
of Oncology (2023).
16. van Ginkel N, Hermans TJN, Meijer D, Boormans JL, Voortman J, Mertens L, et al.
Survival outcomes of patients with muscle-invasive bladder cancer according to pathological
response at radical cystectomy with or without neo-adjuvant chemotherapy: a case-control
matching study. Int Urol Nephrol. 2022;54(12):3145-52. Epub 20220823. doi: 10.1007/s11255-
022-03339-6. PubMed PMID: 35997906; PubMed Central PMCID: PMC9606088.
17. Teply BA, Kim JJ. Systemic therapy for bladder cancer - a medical oncologist's
perspective. J Solid Tumors. 2014;4(2):25-35. doi: 10.5430/jst.v4n2p25. PubMed PMID:
25404954; PubMed Central PMCID: PMC4232954.
18. Powles T, Bellmunt J, Comperat E, De Santis M, Huddart R, Loriot Y, et al. Bladder
cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up. Ann Oncol.
2022;33(3):244-58. Epub 20211130. doi: 10.1016/j.annonc.2021.11.012. PubMed PMID:
34861372.
19. Wangpaichitr M, Theodoropoulos G, Nguyen DJM, Wu C, Spector SA, Feun LG, et al.
Cisplatin Resistance and Redox-Metabolic Vulnerability: A Second Alteration. Int J Mol Sci.
2021;22(14). Epub 20210709. doi: 10.3390/ijms22147379. PubMed PMID: 34298999; PubMed
Central PMCID: PMC8304747.
20. Wang X, Zhang H, Chen X. Drug resistance and combating drug resistance in cancer.
Cancer Drug Resist. 2019;2(2):141-60. Epub 20190619. doi: 10.20517/cdr.2019.10. PubMed
PMID: 34322663; PubMed Central PMCID: PMC8315569.

119
21. Tabassum DP, Polyak K. Tumorigenesis: it takes a village. Nat Rev Cancer.
2015;15(8):473-83. Epub 20150702. doi: 10.1038/nrc3971. PubMed PMID: 26156638.
22. Salgia R, Kulkarni P. The Genetic/Non-genetic Duality of Drug 'Resistance' in Cancer.
Trends Cancer. 2018;4(2):110-8. Epub 20180203. doi: 10.1016/j.trecan.2018.01.001. PubMed
PMID: 29458961; PubMed Central PMCID: PMC5822736.
23. Greene JM, Gevertz JL, Sontag ED. Mathematical Approach to Differentiate
Spontaneous and Induced Evolution to Drug Resistance During Cancer Treatment. JCO Clin
Cancer Inform. 2019;3:1-20. doi: 10.1200/CCI.18.00087. PubMed PMID: 30969799; PubMed
Central PMCID: PMC6873992.
24. Bell CC, Gilan O. Principles and mechanisms of non-genetic resistance in cancer. Br J
Cancer. 2020;122(4):465-72. Epub 20191213. doi: 10.1038/s41416-019-0648-6. PubMed PMID:
31831859; PubMed Central PMCID: PMC7028722.
25. Shlyakhtina Y, Moran KL, Portal MM. Genetic and Non-Genetic Mechanisms
Underlying Cancer Evolution. Cancers (Basel). 2021;13(6). Epub 20210318. doi:
10.3390/cancers13061380. PubMed PMID: 33803675; PubMed Central PMCID: PMC8002988.
26. He K, Xu T, Goldkorn A. Cancer cells cyclically lose and regain drug-resistant highly
tumorigenic features characteristic of a cancer stem-like phenotype. Mol Cancer Ther.
2011;10(6):938-48. Epub 20110425. doi: 10.1158/1535-7163.MCT-10-1120. PubMed PMID:
21518726; PubMed Central PMCID: PMC3112267.
27. He K, Xu T, Xu Y, Ring A, Kahn M, Goldkorn A. Cancer cells acquire a drug resistant,
highly tumorigenic, cancer stem-like phenotype through modulation of the PI3K/Akt/βcatenin/CBP pathway. Int J Cancer. 2014;134(1):43-54. Epub 20130805. doi: 10.1002/ijc.28341.
PubMed PMID: 23784558.
28. Xu T, Li HT, Wei J, Li M, Hsieh TC, Lu YT, et al. Epigenetic plasticity potentiates a
rapid cyclical shift to and from an aggressive cancer phenotype. Int J Cancer.
2020;146(11):3065-76. Epub 20200222. doi: 10.1002/ijc.32904. PubMed PMID: 32017074;
PubMed Central PMCID: PMC7448727.
29. Xu T, Junge JA, Delfarah A, Lu YT, Arnesano C, Iqbal M, et al. Bladder cancer cells
shift rapidly and spontaneously to cisplatin-resistant oxidative phosphorylation that is trackable
in real time. Sci Rep. 2022;12(1):5518. Epub 20220401. doi: 10.1038/s41598-022-09438-9.
PubMed PMID: 35365706; PubMed Central PMCID: PMC8976067.
30. Lu YT, Xu T, Iqbal M, Hsieh TC, Luo Z, Liang G, et al. FOXC1 Binds Enhancers and
Promotes Cisplatin Resistance in Bladder Cancer. Cancers (Basel). 2022;14(7). Epub 20220328.
doi: 10.3390/cancers14071717. PubMed PMID: 35406487; PubMed Central PMCID:
PMC8996937.
31. Hodara E, Mades A, Swartz L, Iqbal M, Xu T, Bsteh D, et al. m6A epitranscriptome
analysis reveals differentially methylated transcripts that drive early chemoresistance in bladder
cancer. NAR Cancer; 2023.

120
32. Marine JC, Dawson SJ, Dawson MA. Non-genetic mechanisms of therapeutic resistance
in cancer. Nat Rev Cancer. 2020;20(12):743-56. Epub 20201008. doi: 10.1038/s41568-020-
00302-4. PubMed PMID: 33033407.
33. Villa Martín P, Muñoz MA, Pigolotti S. Bet-hedging strategies in expanding populations.
PLoS Comput Biol. 2019;15(4):e1006529. Epub 20190418. doi: 10.1371/journal.pcbi.1006529.
PubMed PMID: 30998676; PubMed Central PMCID: PMC6490941.
34. Carja O, Plotkin JB. The evolutionary advantage of heritable phenotypic heterogeneity.
Sci Rep. 2017;7(1):5090. Epub 20170711. doi: 10.1038/s41598-017-05214-2. PubMed PMID:
28698577; PubMed Central PMCID: PMC5505965.
35. Deshmukh S, Saini S. Phenotypic Heterogeneity in Tumor Progression, and Its Possible
Role in the Onset of Cancer. Front Genet. 2020;11:604528. Epub 20201130. doi:
10.3389/fgene.2020.604528. PubMed PMID: 33329751; PubMed Central PMCID:
PMC7734151.
36. Pisco AO, Huang S. Non-genetic cancer cell plasticity and therapy-induced stemness in
tumour relapse: 'What does not kill me strengthens me'. Br J Cancer. 2015;112(11):1725-32.
Epub 20150512. doi: 10.1038/bjc.2015.146. PubMed PMID: 25965164; PubMed Central
PMCID: PMC4647245.
37. Hammerlindl H, Schaider H. Tumor cell-intrinsic phenotypic plasticity facilitates
adaptive cellular reprogramming driving acquired drug resistance. J Cell Commun Signal.
2018;12(1):133-41. Epub 20171130. doi: 10.1007/s12079-017-0435-1. PubMed PMID:
29192388; PubMed Central PMCID: PMC5842196.
38. Jolly MK, Kulkarni P, Weninger K, Orban J, Levine H. Phenotypic Plasticity, BetHedging, and Androgen Independence in Prostate Cancer: Role of Non-Genetic Heterogeneity.
Front Oncol. 2018;8:50. Epub 20180306. doi: 10.3389/fonc.2018.00050. PubMed PMID:
29560343; PubMed Central PMCID: PMC5845637.
39. Qin S, Jiang J, Lu Y, Nice EC, Huang C, Zhang J, et al. Emerging role of tumor cell
plasticity in modifying therapeutic response. Signal Transduct Target Ther. 2020;5(1):228. Epub
20201007. doi: 10.1038/s41392-020-00313-5. PubMed PMID: 33028808; PubMed Central
PMCID: PMC7541492.
40. Emmons MF, Faião-Flores F, Smalley KSM. The role of phenotypic plasticity in the
escape of cancer cells from targeted therapy. Biochem Pharmacol. 2016;122:1-9. Epub
20160625. doi: 10.1016/j.bcp.2016.06.014. PubMed PMID: 27349985; PubMed Central
PMCID: PMC5124530.
41. Holohan C, Van Schaeybroeck S, Longley DB, Johnston PG. Cancer drug resistance: an
evolving paradigm. Nat Rev Cancer. 2013;13(10):714-26. doi: 10.1038/nrc3599. PubMed PMID:
24060863.
42. Emran TB, Shahriar A, Mahmud AR, Rahman T, Abir MH, Siddiquee MF, et al.
Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and

121
Therapeutic Approaches. Front Oncol. 2022;12:891652. Epub 20220623. doi:
10.3389/fonc.2022.891652. PubMed PMID: 35814435; PubMed Central PMCID: PMC9262248.
43. Tsou SH, Chen TM, Hsiao HT, Chen YH. A critical dose of doxorubicin is required to
alter the gene expression profiles in MCF-7 cells acquiring multidrug resistance. PLoS One.
2015;10(1):e0116747. Epub 20150130. doi: 10.1371/journal.pone.0116747. PubMed PMID:
25635866; PubMed Central PMCID: PMC4312059.
44. Wang J, Seebacher N, Shi H, Kan Q, Duan Z. Novel strategies to prevent the
development of multidrug resistance (MDR) in cancer. Oncotarget. 2017;8(48):84559-71. Epub
20170712. doi: 10.18632/oncotarget.19187. PubMed PMID: 29137448; PubMed Central
PMCID: PMC5663620.
45. Zhang M, Liu E, Cui Y, Huang Y. Nanotechnology-based combination therapy for
overcoming multidrug-resistant cancer. Cancer Biol Med. 2017;14(3):212-27. doi:
10.20892/j.issn.2095-3941.2017.0054. PubMed PMID: 28884039; PubMed Central PMCID:
PMC5570599.
46. Borgo C, Ruzzene M. Role of protein kinase CK2 in antitumor drug resistance. J Exp
Clin Cancer Res. 2019;38(1):287. Epub 20190705. doi: 10.1186/s13046-019-1292-y. PubMed
PMID: 31277672; PubMed Central PMCID: PMC6612148.
47. Bukowski K, Kciuk M, Kontek R. Mechanisms of Multidrug Resistance in Cancer
Chemotherapy. Int J Mol Sci. 2020;21(9). Epub 20200502. doi: 10.3390/ijms21093233. PubMed
PMID: 32370233; PubMed Central PMCID: PMC7247559.
48. Zhang Z, Tan Y, Huang C, Wei X. Redox signaling in drug-tolerant persister cells as an
emerging therapeutic target. EBioMedicine. 2023;89:104483. Epub 20230222. doi:
10.1016/j.ebiom.2023.104483. PubMed PMID: 36827719; PubMed Central PMCID:
PMC9982619.
49. Cantor JR, Sabatini DM. Cancer cell metabolism: one hallmark, many faces. Cancer
Discov. 2012;2(10):881-98. Epub 20120925. doi: 10.1158/2159-8290.CD-12-0345. PubMed
PMID: 23009760; PubMed Central PMCID: PMC3491070.
50. Jang M, Kim SS, Lee J. Cancer cell metabolism: implications for therapeutic targets. Exp
Mol Med. 2013;45:e45. Epub 20131004. doi: 10.1038/emm.2013.85. PubMed PMID: 24091747;
PubMed Central PMCID: PMC3809361.
51. Phan LM, Yeung SC, Lee MH. Cancer metabolic reprogramming: importance, main
features, and potentials for precise targeted anti-cancer therapies. Cancer Biol Med.
2014;11(1):1-19. doi: 10.7497/j.issn.2095-3941.2014.01.001. PubMed PMID: 24738035;
PubMed Central PMCID: PMC3969803.
52. Rodríguez-Enríquez S, Marín-Hernández Á, Gallardo-Pérez JC, Pacheco-Velázquez SC,
Belmont-Díaz JA, Robledo-Cadena DX, et al. Transcriptional Regulation of Energy Metabolism
in Cancer Cells. Cells. 2019;8(10). Epub 20191009. doi: 10.3390/cells8101225. PubMed PMID:
31600993; PubMed Central PMCID: PMC6830338.

122
53. Chen X, Chen S, Yu D. Metabolic Reprogramming of Chemoresistant Cancer Cells and
the Potential Significance of Metabolic Regulation in the Reversal of Cancer Chemoresistance.
Metabolites. 2020;10(7). Epub 20200716. doi: 10.3390/metabo10070289. PubMed PMID:
32708822; PubMed Central PMCID: PMC7408410.
54. Gnocchi D, Nikolic D, Paparella RR, Sabbà C, Mazzocca A. Cellular Adaptation Takes
Advantage of Atavistic Regression Programs during Carcinogenesis. Cancers (Basel).
2023;15(15). Epub 20230803. doi: 10.3390/cancers15153942. PubMed PMID: 37568758;
PubMed Central PMCID: PMC10416974.
55. Warburg O, Wind F, Negelein E. THE METABOLISM OF TUMORS IN THE BODY. J
Gen Physiol. 1927;8(6):519-30. doi: 10.1085/jgp.8.6.519. PubMed PMID: 19872213; PubMed
Central PMCID: PMC2140820.
56. Liberti MV, Locasale JW. The Warburg Effect: How Does it Benefit Cancer Cells?
Trends Biochem Sci. 2016;41(3):211-8. Epub 20160105. doi: 10.1016/j.tibs.2015.12.001.
PubMed PMID: 26778478; PubMed Central PMCID: PMC4783224.
57. Afonso J, Santos LL, Morais A, Amaro T, Longatto-Filho A, Baltazar F. Metabolic
coupling in urothelial bladder cancer compartments and its correlation to tumor aggressiveness.
Cell Cycle. 2016;15(3):368-80. Epub 20151204. doi: 10.1080/15384101.2015.1121329. PubMed
PMID: 26636903; PubMed Central PMCID: PMC4943695.
58. Lehuédé C, Dupuy F, Rabinovitch R, Jones RG, Siegel PM. Metabolic Plasticity as a
Determinant of Tumor Growth and Metastasis. Cancer Res. 2016;76(18):5201-8. Epub
20160901. doi: 10.1158/0008-5472.CAN-16-0266. PubMed PMID: 27587539.
59. Fu Y, Liu S, Yin S, Niu W, Xiong W, Tan M, et al. The reverse Warburg effect is likely
to be an Achilles' heel of cancer that can be exploited for cancer therapy. Oncotarget.
2017;8(34):57813-25. Epub 20170525. doi: 10.18632/oncotarget.18175. PubMed PMID:
28915713; PubMed Central PMCID: PMC5593685.
60. Cannino G, Ciscato F, Masgras I, Sánchez-Martín C, Rasola A. Metabolic Plasticity of
Tumor Cell Mitochondria. Front Oncol. 2018;8:333. Epub 20180824. doi:
10.3389/fonc.2018.00333. PubMed PMID: 30197878; PubMed Central PMCID: PMC6117394.
61. Valle S, Alcalá S, Martin-Hijano L, Cabezas-Sáinz P, Navarro D, Muñoz ER, et al.
Exploiting oxidative phosphorylation to promote the stem and immunoevasive properties of
pancreatic cancer stem cells. Nat Commun. 2020;11(1):5265. Epub 20201016. doi:
10.1038/s41467-020-18954-z. PubMed PMID: 33067432; PubMed Central PMCID:
PMC7567808.
62. Gray LR, Tompkins SC, Taylor EB. Regulation of pyruvate metabolism and human
disease. Cell Mol Life Sci. 2014;71(14):2577-604. Epub 20131221. doi: 10.1007/s00018-013-
1539-2. PubMed PMID: 24363178; PubMed Central PMCID: PMC4059968.
63. du Plessis SS, Agarwal A, Mohanty G, van der Linde M. Oxidative phosphorylation
versus glycolysis: what fuel do spermatozoa use? Asian J Androl. 2015;17(2):230-5. doi:

123
10.4103/1008-682X.135123. PubMed PMID: 25475660; PubMed Central PMCID:
PMC4650467.
64. Yetkin-Arik B, Vogels IMC, Nowak-Sliwinska P, Weiss A, Houtkooper RH, Van
Noorden CJF, et al. The role of glycolysis and mitochondrial respiration in the formation and
functioning of endothelial tip cells during angiogenesis. Sci Rep. 2019;9(1):12608. Epub
20190830. doi: 10.1038/s41598-019-48676-2. PubMed PMID: 31471554; PubMed Central
PMCID: PMC6717205.
65. Di Donato S. Disorders related to mitochondrial membranes: pathology of the respiratory
chain and neurodegeneration. J Inherit Metab Dis. 2000;23(3):247-63. doi:
10.1023/a:1005684029429. PubMed PMID: 10863941.
66. Vercellino I, Sazanov LA. The assembly, regulation and function of the mitochondrial
respiratory chain. Nat Rev Mol Cell Biol. 2022;23(2):141-61. Epub 20211007. doi:
10.1038/s41580-021-00415-0. PubMed PMID: 34621061.
67. Zhao Z, Mei Y, Wang Z, He W. The Effect of Oxidative Phosphorylation on Cancer
Drug Resistance. Cancers (Basel). 2022;15(1). Epub 20221222. doi: 10.3390/cancers15010062.
PubMed PMID: 36612059; PubMed Central PMCID: PMC9817696.
68. Liu Y, Sun Y, Guo Y, Shi X, Chen X, Feng W, et al. An Overview: The Diversified Role
of Mitochondria in Cancer Metabolism. Int J Biol Sci. 2023;19(3):897-915. Epub 20230116. doi:
10.7150/ijbs.81609. PubMed PMID: 36778129; PubMed Central PMCID: PMC9910000.
69. Lenaz G, Genova ML. Kinetics of integrated electron transfer in the mitochondrial
respiratory chain: random collisions vs. solid state electron channeling. Am J Physiol Cell
Physiol. 2007;292(4):C1221-39. Epub 20061011. doi: 10.1152/ajpcell.00263.2006. PubMed
PMID: 17035300.
70. Luo Y, Ma J, Lu W. The Significance of Mitochondrial Dysfunction in Cancer. Int J Mol
Sci. 2020;21(16). Epub 20200805. doi: 10.3390/ijms21165598. PubMed PMID: 32764295;
PubMed Central PMCID: PMC7460667.
71. Hammoudi N, Ahmed KB, Garcia-Prieto C, Huang P. Metabolic alterations in cancer
cells and therapeutic implications. Chin J Cancer. 2011;30(8):508-25. doi:
10.5732/cjc.011.10267. PubMed PMID: 21801600; PubMed Central PMCID: PMC4013402.
72. Pavlova NN, Thompson CB. The Emerging Hallmarks of Cancer Metabolism. Cell
Metab. 2016;23(1):27-47. doi: 10.1016/j.cmet.2015.12.006. PubMed PMID: 26771115; PubMed
Central PMCID: PMC4715268.
73. Di Gregorio J, Petricca S, Iorio R, Toniato E, Flati V. Mitochondrial and metabolic
alterations in cancer cells. Eur J Cell Biol. 2022;101(3):151225. Epub 20220413. doi:
10.1016/j.ejcb.2022.151225. PubMed PMID: 35453093.
74. Makena MR, Ranjan A, Thirumala V, Reddy AP. Cancer stem cells: Road to therapeutic
resistance and strategies to overcome resistance. Biochim Biophys Acta Mol Basis Dis.

124
2020;1866(4):165339. Epub 20181124. doi: 10.1016/j.bbadis.2018.11.015. PubMed PMID:
30481586.
75. Zhou HM, Zhang JG, Zhang X, Li Q. Targeting cancer stem cells for reversing therapy
resistance: mechanism, signaling, and prospective agents. Signal Transduct Target Ther.
2021;6(1):62. Epub 20210215. doi: 10.1038/s41392-020-00430-1. PubMed PMID: 33589595;
PubMed Central PMCID: PMC7884707.
76. Prasad S, Ramachandran S, Gupta N, Kaushik I, Srivastava SK. Cancer cells stemness: A
doorstep to targeted therapy. Biochim Biophys Acta Mol Basis Dis. 2020;1866(4):165424. Epub
20190226. doi: 10.1016/j.bbadis.2019.02.019. PubMed PMID: 30818002.
77. Yadav UP, Singh T, Kumar P, Sharma P, Kaur H, Sharma S, et al. Metabolic Adaptations
in Cancer Stem Cells. Front Oncol. 2020;10:1010. Epub 20200625. doi:
10.3389/fonc.2020.01010. PubMed PMID: 32670883; PubMed Central PMCID: PMC7330710.
78. Mai Y, Su J, Yang C, Xia C, Fu L. The strategies to cure cancer patients by eradicating
cancer stem-like cells. Mol Cancer. 2023;22(1):171. Epub 20231018. doi: 10.1186/s12943-023-
01867-y. PubMed PMID: 37853413; PubMed Central PMCID: PMC10583358.
79. Shen YA, Chen CC, Chen BJ, Wu YT, Juan JR, Chen LY, et al. Potential Therapies
Targeting Metabolic Pathways in Cancer Stem Cells. Cells. 2021;10(7). Epub 20210713. doi:
10.3390/cells10071772. PubMed PMID: 34359941; PubMed Central PMCID: PMC8304173.
80. Peiris-Pagès M, Martinez-Outschoorn UE, Pestell RG, Sotgia F, Lisanti MP. Cancer stem
cell metabolism. Breast Cancer Res. 2016;18(1):55. Epub 20160524. doi: 10.1186/s13058-016-
0712-6. PubMed PMID: 27220421; PubMed Central PMCID: PMC4879746.
81. Shin MK, Cheong JH. Mitochondria-centric bioenergetic characteristics in cancer stemlike cells. Arch Pharm Res. 2019;42(2):113-27. Epub 20190215. doi: 10.1007/s12272-019-
01127-y. PubMed PMID: 30771209; PubMed Central PMCID: PMC6399179.
82. Zhu X, Chen HH, Gao CY, Zhang XX, Jiang JX, Zhang Y, et al. Energy metabolism in
cancer stem cells. World J Stem Cells. 2020;12(6):448-61. doi: 10.4252/wjsc.v12.i6.448.
PubMed PMID: 32742562; PubMed Central PMCID: PMC7360992.
83. Garimella SV, Gampa SC, Chaturvedi P. Mitochondria in Cancer Stem Cells: From an
Innocent Bystander to a Central Player in Therapy Resistance. Stem Cells Cloning. 2023;16:19-
41. Epub 20230823. doi: 10.2147/SCCAA.S417842. PubMed PMID: 37641714; PubMed
Central PMCID: PMC10460581.
84. Pendleton KE, Wang K, Echeverria GV. Rewiring of mitochondrial metabolism in
therapy-resistant cancers: permanent and plastic adaptations. Front Cell Dev Biol.
2023;11:1254313. Epub 20230913. doi: 10.3389/fcell.2023.1254313. PubMed PMID: 37779896;
PubMed Central PMCID: PMC10534013.
85. Song IS, Jeong YJ, Jeong SH, Heo HJ, Kim HK, Bae KB, et al. FOXM1-Induced PRX3
Regulates Stemness and Survival of Colon Cancer Cells via Maintenance of Mitochondrial

125
Function. Gastroenterology. 2015;149(4):1006-16.e9. Epub 20150617. doi:
10.1053/j.gastro.2015.06.007. PubMed PMID: 26091938.
86. García-Heredia JM, Carnero A. Role of Mitochondria in Cancer Stem Cell Resistance.
Cells. 2020;9(7). Epub 20200715. doi: 10.3390/cells9071693. PubMed PMID: 32679735;
PubMed Central PMCID: PMC7407626.
87. Mukherjee S, Bhatti GK, Chhabra R, Reddy PH, Bhatti JS. Targeting mitochondria as a
potential therapeutic strategy against chemoresistance in cancer. Biomed Pharmacother.
2023;160:114398. Epub 20230209. doi: 10.1016/j.biopha.2023.114398. PubMed PMID:
36773523.
88. Finck BN, Kelly DP. PGC-1 coactivators: inducible regulators of energy metabolism in
health and disease. J Clin Invest. 2006;116(3):615-22. doi: 10.1172/JCI27794. PubMed PMID:
16511594; PubMed Central PMCID: PMC1386111.
89. Ventura-Clapier R, Garnier A, Veksler V. Transcriptional control of mitochondrial
biogenesis: the central role of PGC-1alpha. Cardiovasc Res. 2008;79(2):208-17. Epub 20080422.
doi: 10.1093/cvr/cvn098. PubMed PMID: 18430751.
90. Li YR, Fang Y, Lyu Z, Zhu Y, Yang L. Exploring the dynamic interplay between cancer
stem cells and the tumor microenvironment: implications for novel therapeutic strategies. J
Transl Med. 2023;21(1):686. Epub 20231002. doi: 10.1186/s12967-023-04575-9. PubMed
PMID: 37784157; PubMed Central PMCID: PMC10546755.
91. Kim M, Bakyt L, Akhmetkaliyev A, Toktarkhanova D, Bulanin D. Re-Sensitizing Cancer
Stem Cells to Conventional Chemotherapy Agents. Int J Mol Sci. 2023;24(3). Epub 20230120.
doi: 10.3390/ijms24032122. PubMed PMID: 36768445; PubMed Central PMCID:
PMC9917165.
92. Dominy JE, Puigserver P. Mitochondrial biogenesis through activation of nuclear
signaling proteins. Cold Spring Harb Perspect Biol. 2013;5(7). Epub 20130701. doi:
10.1101/cshperspect.a015008. PubMed PMID: 23818499; PubMed Central PMCID:
PMC3685894.
93. Gureev AP, Shaforostova EA, Popov VN. Regulation of Mitochondrial Biogenesis as a
Way for Active Longevity: Interaction Between the Nrf2 and PGC-1α Signaling Pathways. Front
Genet. 2019;10:435. Epub 20190514. doi: 10.3389/fgene.2019.00435. PubMed PMID:
31139208; PubMed Central PMCID: PMC6527603.
94. Quan Y, Xin Y, Tian G, Zhou J, Liu X. Mitochondrial ROS-Modulated mtDNA: A
Potential Target for Cardiac Aging. Oxid Med Cell Longev. 2020;2020:9423593. Epub
20200326. doi: 10.1155/2020/9423593. PubMed PMID: 32308810; PubMed Central PMCID:
PMC7139858.
95. Cantó C, Auwerx J. AMP-activated protein kinase and its downstream transcriptional
pathways. Cell Mol Life Sci. 2010;67(20):3407-23. Epub 20100717. doi: 10.1007/s00018-010-
0454-z. PubMed PMID: 20640476; PubMed Central PMCID: PMC3622821.

126
96. Kang I, Chu CT, Kaufman BA. The mitochondrial transcription factor TFAM in
neurodegeneration: emerging evidence and mechanisms. FEBS Lett. 2018;592(5):793-811. Epub
20180215. doi: 10.1002/1873-3468.12989. PubMed PMID: 29364506; PubMed Central PMCID:
PMC5851836.
97. Brüser C, Keller-Findeisen J, Jakobs S. The TFAM-to-mtDNA ratio defines innercellular nucleoid populations with distinct activity levels. Cell Rep. 2021;37(8):110000. doi:
10.1016/j.celrep.2021.110000. PubMed PMID: 34818548.
98. Kunkel GH, Chaturvedi P, Tyagi SC. Mitochondrial pathways to cardiac recovery:
TFAM. Heart Fail Rev. 2016;21(5):499-517. doi: 10.1007/s10741-016-9561-8. PubMed PMID:
27166683; PubMed Central PMCID: PMC4985491.
99. Gabrielson M, Björklund M, Carlson J, Shoshan M. Expression of mitochondrial
regulators PGC1α and TFAM as putative markers of subtype and chemoresistance in epithelial
ovarian carcinoma. PLoS One. 2014;9(9):e107109. Epub 20140922. doi:
10.1371/journal.pone.0107109. PubMed PMID: 25243473; PubMed Central PMCID:
PMC4170973.
100. Mastropasqua F, Girolimetti G, Shoshan M. PGC1α: Friend or Foe in Cancer? Genes
(Basel). 2018;9(1). Epub 20180122. doi: 10.3390/genes9010048. PubMed PMID: 29361779;
PubMed Central PMCID: PMC5793199.
101. Kotrys AV, Szczesny RJ. Mitochondrial Gene Expression and Beyond-Novel Aspects of
Cellular Physiology. Cells. 2019;9(1). Epub 20191219. doi: 10.3390/cells9010017. PubMed
PMID: 31861673; PubMed Central PMCID: PMC7017415.
102. Mishmar D, Levin R, Naeem MM, Sondheimer N. Higher Order Organization of the
mtDNA: Beyond Mitochondrial Transcription Factor A. Front Genet. 2019;10:1285. Epub
20191220. doi: 10.3389/fgene.2019.01285. PubMed PMID: 31998357; PubMed Central
PMCID: PMC6961661.
103. Dasari S, Tchounwou PB. Cisplatin in cancer therapy: molecular mechanisms of action.
Eur J Pharmacol. 2014;740:364-78. Epub 20140721. doi: 10.1016/j.ejphar.2014.07.025. PubMed
PMID: 25058905; PubMed Central PMCID: PMC4146684.
104. Campbell CT, Kolesar JE, Kaufman BA. Mitochondrial transcription factor A regulates
mitochondrial transcription initiation, DNA packaging, and genome copy number. Biochim
Biophys Acta. 2012;1819(9-10):921-9. Epub 20120321. doi: 10.1016/j.bbagrm.2012.03.002.
PubMed PMID: 22465614.
105. Diaz-Ruiz R, Rigoulet M, Devin A. The Warburg and Crabtree effects: On the origin of
cancer cell energy metabolism and of yeast glucose repression. Biochim Biophys Acta.
2011;1807(6):568-76. Epub 20100908. doi: 10.1016/j.bbabio.2010.08.010. PubMed PMID:
20804724.

127
106. Zheng J. Energy metabolism of cancer: Glycolysis versus oxidative phosphorylation
(Review). Oncol Lett. 2012;4(6):1151-7. Epub 20120920. doi: 10.3892/ol.2012.928. PubMed
PMID: 23226794; PubMed Central PMCID: PMC3506713.
107. Jia D, Park JH, Jung KH, Levine H, Kaipparettu BA. Elucidating the Metabolic Plasticity
of Cancer: Mitochondrial Reprogramming and Hybrid Metabolic States. Cells. 2018;7(3). Epub
20180313. doi: 10.3390/cells7030021. PubMed PMID: 29534029; PubMed Central PMCID:
PMC5870353.
108. Garofano L, Migliozzi S, Oh YT, D'Angelo F, Najac RD, Ko A, et al. Pathway-based
classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities.
Nat Cancer. 2021;2(2):141-56. Epub 20210111. doi: 10.1038/s43018-020-00159-4. PubMed
PMID: 33681822; PubMed Central PMCID: PMC7935068.
109. Papadaki S, Magklara A. Regulation of Metabolic Plasticity in Cancer Stem Cells and
Implications in Cancer Therapy. Cancers (Basel). 2022;14(23). Epub 20221130. doi:
10.3390/cancers14235912. PubMed PMID: 36497394; PubMed Central PMCID: PMC9741285.
110. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al.
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide
expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. Epub 20050930. doi:
10.1073/pnas.0506580102. PubMed PMID: 16199517; PubMed Central PMCID: PMC1239896.
111. Kanehisa M, Goto S, Sato Y, Kawashima M, Furumichi M, Tanabe M. Data,
information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res.
2014;42(Database issue):D199-205. Epub 20131107. doi: 10.1093/nar/gkt1076. PubMed PMID:
24214961; PubMed Central PMCID: PMC3965122.
112. Klein K, He K, Younes AI, Barsoumian HB, Chen D, Ozgen T, et al. Role of
Mitochondria in Cancer Immune Evasion and Potential Therapeutic Approaches. Front Immunol.
2020;11:573326. Epub 20201016. doi: 10.3389/fimmu.2020.573326. PubMed PMID: 33178201;
PubMed Central PMCID: PMC7596324.
113. Giddings EL, Champagne DP, Wu MH, Laffin JM, Thornton TM, Valenca-Pereira F, et
al. Mitochondrial ATP fuels ABC transporter-mediated drug efflux in cancer chemoresistance.
Nat Commun. 2021;12(1):2804. Epub 20210514. doi: 10.1038/s41467-021-23071-6. PubMed
PMID: 33990571; PubMed Central PMCID: PMC8121950.
114. Basu A, Krishnamurthy S. Cellular responses to Cisplatin-induced DNA damage. J
Nucleic Acids. 2010;2010. Epub 20100808. doi: 10.4061/2010/201367. PubMed PMID:
20811617; PubMed Central PMCID: PMC2929606.
115. Han XJ, Shi SL, Wei YF, Jiang LP, Guo MY, Wu HL, et al. Involvement of
mitochondrial dynamics in the antineoplastic activity of cisplatin in murine leukemia L1210
cells. Oncol Rep. 2017;38(2):985-92. Epub 20170628. doi: 10.3892/or.2017.5765. PubMed
PMID: 28677814.

128
116. Lee S, Lee JS, Seo J, Lee SH, Kang JH, Song J, et al. Targeting Mitochondrial Oxidative
Phosphorylation Abrogated Irinotecan Resistance in NSCLC. Sci Rep. 2018;8(1):15707. Epub
20181024. doi: 10.1038/s41598-018-33667-6. PubMed PMID: 30356107; PubMed Central
PMCID: PMC6200737.
117. Lentz SI, Edwards JL, Backus C, McLean LL, Haines KM, Feldman EL. Mitochondrial
DNA (mtDNA) biogenesis: visualization and duel incorporation of BrdU and EdU into newly
synthesized mtDNA in vitro. J Histochem Cytochem. 2010;58(2):207-18. doi:
10.1369/jhc.2009.954701. PubMed PMID: 19875847; PubMed Central PMCID: PMC2803709.
118. Park D, Lee S, Min KT. Techniques for investigating mitochondrial gene expression.
BMB Rep. 2020;53(1):3-9. doi: 10.5483/BMBRep.2020.53.1.272. PubMed PMID: 31818361;
PubMed Central PMCID: PMC6999826.
119. Fernandez-Vizarra E, Zeviani M. Mitochondrial disorders of the OXPHOS system. FEBS
Lett. 2021;595(8):1062-106. Epub 20201218. doi: 10.1002/1873-3468.13995. PubMed PMID:
33159691.
120. Lee C, Yen K, Cohen P. Humanin: a harbinger of mitochondrial-derived peptides?
Trends Endocrinol Metab. 2013;24(5):222-8. Epub 20130208. doi: 10.1016/j.tem.2013.01.005.
PubMed PMID: 23402768; PubMed Central PMCID: PMC3641182.
121. Lee C, Kim KH, Cohen P. MOTS-c: A novel mitochondrial-derived peptide regulating
muscle and fat metabolism. Free Radic Biol Med. 2016;100:182-7. Epub 20160520. doi:
10.1016/j.freeradbiomed.2016.05.015. PubMed PMID: 27216708; PubMed Central PMCID:
PMC5116416.
122. Masoud R, Reyes-Castellanos G, Lac S, Garcia J, Dou S, Shintu L, et al. Targeting
Mitochondrial Complex I Overcomes Chemoresistance in High OXPHOS Pancreatic Cancer.
Cell Rep Med. 2020;1(8):100143. Epub 20201117. doi: 10.1016/j.xcrm.2020.100143. PubMed
PMID: 33294863; PubMed Central PMCID: PMC7691450.
123. Lee S, Yoon CY, Byun SS, Lee E, Lee SE. The role of c-FLIP in cisplatin resistance of
human bladder cancer cells. J Urol. 2013;189(6):2327-34. Epub 20130109. doi:
10.1016/j.juro.2013.01.003. PubMed PMID: 23313194.
124. Yevshin I, Sharipov R, Kolmykov S, Kondrakhin Y, Kolpakov F. GTRD: a database on
gene transcription regulation-2019 update. Nucleic Acids Res. 2019;47(D1):D100-D5. doi:
10.1093/nar/gky1128. PubMed PMID: 30445619; PubMed Central PMCID: PMC6323985.
125. Picca A, Lezza AM. Regulation of mitochondrial biogenesis through TFAMmitochondrial DNA interactions: Useful insights from aging and calorie restriction studies.
Mitochondrion. 2015;25:67-75. Epub 20151003. doi: 10.1016/j.mito.2015.10.001. PubMed
PMID: 26437364.
126. Araujo LF, Siena ADD, Plaça JR, Brotto DB, Barros II, Muys BR, et al. Mitochondrial
transcription factor A (TFAM) shapes metabolic and invasion gene signatures in melanoma. Sci

129
Rep. 2018;8(1):14190. Epub 20180921. doi: 10.1038/s41598-018-31170-6. PubMed PMID:
30242167; PubMed Central PMCID: PMC6155108.
127. Zanoni M, Cortesi M, Zamagni A, Arienti C, Pignatta S, Tesei A. Modeling neoplastic
disease with spheroids and organoids. J Hematol Oncol. 2020;13(1):97. Epub 20200716. doi:
10.1186/s13045-020-00931-0. PubMed PMID: 32677979; PubMed Central PMCID:
PMC7364537.
128. Hockney S, Parker J, Turner JE, Todd X, Todryk S, Gieling RG, et al. Next generation
organoid engineering to replace animals in cancer drug testing. Biochem Pharmacol.
2023;213:115586. Epub 20230508. doi: 10.1016/j.bcp.2023.115586. PubMed PMID: 37164297.
129. Xu S, Tan S, Guo L. Patient-Derived Organoids as a Promising Tool for Multimodal
Management of Sarcomas. Cancers (Basel). 2023;15(17). Epub 20230830. doi:
10.3390/cancers15174339. PubMed PMID: 37686615; PubMed Central PMCID:
PMC10486520.
130. The NCI Patient-Derived Models Repository (PDMR), NCI-Frederick, Frederick
National Laboratory for Cancer Research, Frederick, MD. . Available from:
https://pdmr.cancer.gov/ .
131. Boettcher M, McManus MT. Choosing the Right Tool for the Job: RNAi, TALEN, or
CRISPR. Mol Cell. 2015;58(4):575-85. doi: 10.1016/j.molcel.2015.04.028. PubMed PMID:
26000843; PubMed Central PMCID: PMC4441801.
132. Jin P, Jiang J, Zhou L, Huang Z, Nice EC, Huang C, et al. Mitochondrial adaptation in
cancer drug resistance: prevalence, mechanisms, and management. J Hematol Oncol.
2022;15(1):97. Epub 20220718. doi: 10.1186/s13045-022-01313-4. PubMed PMID: 35851420;
PubMed Central PMCID: PMC9290242.
133. Fiorillo M, Ózsvári B, Sotgia F, Lisanti MP. High ATP Production Fuels Cancer Drug
Resistance and Metastasis: Implications for Mitochondrial ATP Depletion Therapy. Front Oncol.
2021;11:740720. Epub 20211015. doi: 10.3389/fonc.2021.740720. PubMed PMID: 34722292;
PubMed Central PMCID: PMC8554334.
134. Cao Y, Chen E, Wang X, Song J, Zhang H, Chen X. An emerging master inducer and
regulator for epithelial-mesenchymal transition and tumor metastasis: extracellular and
intracellular ATP and its molecular functions and therapeutic potential. Cancer Cell Int.
2023;23(1):20. Epub 20230207. doi: 10.1186/s12935-023-02859-0. PubMed PMID: 36750864;
PubMed Central PMCID: PMC9903449.
135. Choi YH, Yu AM. ABC transporters in multidrug resistance and pharmacokinetics, and
strategies for drug development. Curr Pharm Des. 2014;20(5):793-807. doi:
10.2174/138161282005140214165212. PubMed PMID: 23688078; PubMed Central PMCID:
PMC6341993.

130
136. Chen SH, Chang JY. New Insights into Mechanisms of Cisplatin Resistance: From
Tumor Cell to Microenvironment. Int J Mol Sci. 2019;20(17). Epub 20190824. doi:
10.3390/ijms20174136. PubMed PMID: 31450627; PubMed Central PMCID: PMC6747329.
137. Lugones Y, Loren P, Salazar LA. Cisplatin Resistance: Genetic and Epigenetic Factors
Involved. Biomolecules. 2022;12(10). Epub 20220924. doi: 10.3390/biom12101365. PubMed
PMID: 36291573; PubMed Central PMCID: PMC9599500.
138. El-kott AF, El-baz MA, Mokhtar AA. Proliferating cell nuclear antigen (PCNA)
overexpression and microvessel density predict survival in the urinary bladder carcinoma. Int
Urol Nephrol. 2006;38(2):237-42. doi: 10.1007/s11255-005-4760-6. PubMed PMID: 16868689.
139. Tian Y, Ma Z, Chen Z, Li M, Wu Z, Hong M, et al. Clinicopathological and Prognostic
Value of Ki-67 Expression in Bladder Cancer: A Systematic Review and Meta-Analysis. PLoS
One. 2016;11(7):e0158891. Epub 20160713. doi: 10.1371/journal.pone.0158891. PubMed
PMID: 27410033; PubMed Central PMCID: PMC4943634.
140. Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, et al. Visualizing
and interpreting cancer genomics data via the Xena platform. Nat Biotechnol. 2020;38(6):675-8.
doi: 10.1038/s41587-020-0546-8. PubMed PMID: 32444850; PubMed Central PMCID:
PMC7386072.
141. Gao W, Wu M, Wang N, Zhang Y, Hua J, Tang G, et al. Increased expression of
mitochondrial transcription factor A and nuclear respiratory factor-1 predicts a poor clinical
outcome of breast cancer. Oncol Lett. 2018;15(2):1449-58. Epub 20171124. doi:
10.3892/ol.2017.7487. PubMed PMID: 29434836; PubMed Central PMCID: PMC5774493.
142. Jiang X, Wang J. Down-regulation of TFAM increases the sensitivity of tumour cells to
radiation via p53/TIGAR signalling pathway. J Cell Mol Med. 2019;23(7):4545-58. Epub
20190506. doi: 10.1111/jcmm.14350. PubMed PMID: 31062473; PubMed Central PMCID:
PMC6584511.
143. Wang W, Jiang CF, Yin HS, Gao S, Yu BP. Targeting mitochondrial transcription factor
A sensitizes pancreatic cancer cell to gemcitabine. Hepatobiliary Pancreat Dis Int.
2023;22(5):519-27. Epub 20230321. doi: 10.1016/j.hbpd.2023.03.006. PubMed PMID:
37002014.
144. Lin Y, Yang B, Huang Y, Zhang Y, Jiang Y, Ma L, et al. Mitochondrial DNA-targeted
therapy: A novel approach to combat cancer. Cell Insight. 2023;2(4):100113. Epub 20230722.
doi: 10.1016/j.cellin.2023.100113. PubMed PMID: 37554301; PubMed Central PMCID:
PMC10404627.
145. Boland ML, Chourasia AH, Macleod KF. Mitochondrial dysfunction in cancer. Front
Oncol. 2013;3:292. Epub 20131202. doi: 10.3389/fonc.2013.00292. PubMed PMID: 24350057;
PubMed Central PMCID: PMC3844930.

131
146. Shi Y, Lammers T. Combining Nanomedicine and Immunotherapy. Acc Chem Res.
2019;52(6):1543-54. Epub 20190523. doi: 10.1021/acs.accounts.9b00148. PubMed PMID:
31120725; PubMed Central PMCID: PMC7115879.
147. S Allemailem K, Almatroudi A, Alsahli MA, Aljaghwani A, M El-Kady A, Rahmani
AH, et al. Novel Strategies for Disrupting Cancer-Cell Functions with Mitochondria-Targeted
Antitumor Drug-Loaded Nanoformulations. Int J Nanomedicine. 2021;16:3907-36. Epub
20210609. doi: 10.2147/IJN.S303832. PubMed PMID: 34135584; PubMed Central PMCID:
PMC8200140.
148. Lu T, Zhang Z, Bi Z, Lan T, Zeng H, Liu Y, et al. TFAM deficiency in dendritic cells
leads to mitochondrial dysfunction and enhanced antitumor immunity through cGAS-STING
pathway. J Immunother Cancer. 2023;11(3). doi: 10.1136/jitc-2022-005430. PubMed PMID:
36858460; PubMed Central PMCID: PMC9980377.
149. Rocha CRR, Silva MM, Quinet A, Cabral-Neto JB, Menck CFM. DNA repair pathways
and cisplatin resistance: an intimate relationship. Clinics (Sao Paulo). 2018;73(suppl 1):e478s.
Epub 20180906. doi: 10.6061/clinics/2018/e478s. PubMed PMID: 30208165; PubMed Central
PMCID: PMC6113849.
150. Oliver TG, Mercer KL, Sayles LC, Burke JR, Mendus D, Lovejoy KS, et al. Chronic
cisplatin treatment promotes enhanced damage repair and tumor progression in a mouse model
of lung cancer. Genes Dev. 2010;24(8):837-52. doi: 10.1101/gad.1897010. PubMed PMID:
20395368; PubMed Central PMCID: PMC2854397.
151. Tchounwou PB, Dasari S, Noubissi FK, Ray P, Kumar S. Advances in Our
Understanding of the Molecular Mechanisms of Action of Cisplatin in Cancer Therapy. J Exp
Pharmacol. 2021;13:303-28. Epub 20210318. doi: 10.2147/JEP.S267383. PubMed PMID:
33776489; PubMed Central PMCID: PMC7987268.
152. Zimmermann A, Zenke FT, Chiu LY, Dahmen H, Pehl U, Fuchss T, et al. A New Class
of Selective ATM Inhibitors as Combination Partners of DNA Double-Strand Break Inducing
Cancer Therapies. Mol Cancer Ther. 2022;21(6):859-70. doi: 10.1158/1535-7163.MCT-21-0934.
PubMed PMID: 35405736; PubMed Central PMCID: PMC9381122.
153. Huang C, Filippone NR, Reiner T, Roberts S. Sensors and Inhibitors for the Detection of
Ataxia Telangiectasia Mutated (ATM) Protein Kinase. Mol Pharm. 2021;18(7):2470-81. Epub
20210614. doi: 10.1021/acs.molpharmaceut.1c00166. PubMed PMID: 34125542.
154. Studstill CJ, Mac M, Moody CA. Interplay between the DNA damage response and the
life cycle of DNA tumor viruses. Tumour Virus Res. 2023;16:200272. Epub 20231031. doi:
10.1016/j.tvr.2023.200272. PubMed PMID: 37918513; PubMed Central PMCID:
PMC10685005.
155. Weber AM, Ryan AJ. ATM and ATR as therapeutic targets in cancer. Pharmacol Ther.
2015;149:124-38. Epub 20141213. doi: 10.1016/j.pharmthera.2014.12.001. PubMed PMID:
25512053.

132
156. Phan LM, Rezaeian AH. ATM: Main Features, Signaling Pathways, and Its Diverse
Roles in DNA Damage Response, Tumor Suppression, and Cancer Development. Genes (Basel).
2021;12(6). Epub 20210530. doi: 10.3390/genes12060845. PubMed PMID: 34070860; PubMed
Central PMCID: PMC8228802.
157. Vadivel Gnanasundram S, Bonczek O, Wang L, Chen S, Fahraeus R. p53 mRNA
Metabolism Links with the DNA Damage Response. Genes (Basel). 2021;12(9). Epub
20210920. doi: 10.3390/genes12091446. PubMed PMID: 34573428; PubMed Central PMCID:
PMC8465283.
158. Deng S, Vlatkovic T, Li M, Zhan T, Veldwijk MR, Herskind C. Targeting the DNA
Damage Response and DNA Repair Pathways to Enhance Radiosensitivity in Colorectal Cancer.
Cancers (Basel). 2022;14(19). Epub 20221005. doi: 10.3390/cancers14194874. PubMed PMID:
36230796; PubMed Central PMCID: PMC9561988.
159. Wang X, Yu J, Wang J. Neural Tube Defects and Folate Deficiency: Is DNA Repair
Defective? Int J Mol Sci. 2023;24(3). Epub 20230122. doi: 10.3390/ijms24032220. PubMed
PMID: 36768542; PubMed Central PMCID: PMC9916799.
160. Stucci LS, Internò V, Tucci M, Perrone M, Mannavola F, Palmirotta R, et al. The ATM
Gene in Breast Cancer: Its Relevance in Clinical Practice. Genes (Basel). 2021;12(5). Epub
20210513. doi: 10.3390/genes12050727. PubMed PMID: 34068084; PubMed Central PMCID:
PMC8152746.
161. Baretić D, Pollard HK, Fisher DI, Johnson CM, Santhanam B, Truman CM, et al.
Structures of closed and open conformations of dimeric human ATM. Sci Adv.
2017;3(5):e1700933. Epub 20170510. doi: 10.1126/sciadv.1700933. PubMed PMID: 28508083;
PubMed Central PMCID: PMC5425235.
162. Jansma M, Linke-Winnebeck C, Eustermann S, Lammens K, Kostrewa D, Stakyte K, et
al. Near-Complete Structure and Model of Tel1ATM from Chaetomium thermophilum Reveals a
Robust Autoinhibited ATP State. Structure. 2020;28(1):83-95.e5. Epub 20191115. doi:
10.1016/j.str.2019.10.013. PubMed PMID: 31740028.
163. Dupré A, Boyer-Chatenet L, Gautier J. Two-step activation of ATM by DNA and the
Mre11-Rad50-Nbs1 complex. Nat Struct Mol Biol. 2006;13(5):451-7. Epub 20060423. doi:
10.1038/nsmb1090. PubMed PMID: 16622404.
164. Shibata A, Jeggo PA. ATM's Role in the Repair of DNA Double-Strand Breaks. Genes
(Basel). 2021;12(9). Epub 20210831. doi: 10.3390/genes12091370. PubMed PMID: 34573351;
PubMed Central PMCID: PMC8466060.
165. Uziel T, Lerenthal Y, Moyal L, Andegeko Y, Mittelman L, Shiloh Y. Requirement of the
MRN complex for ATM activation by DNA damage. EMBO J. 2003;22(20):5612-21. doi:
10.1093/emboj/cdg541. PubMed PMID: 14532133; PubMed Central PMCID: PMC213795.
166. Bian L, Meng Y, Zhang M, Li D. MRE11-RAD50-NBS1 complex alterations and DNA
damage response: implications for cancer treatment. Mol Cancer. 2019;18(1):169. Epub

133
20191126. doi: 10.1186/s12943-019-1100-5. PubMed PMID: 31767017; PubMed Central
PMCID: PMC6878665.
167. Wu ZH, Shi Y, Tibbetts RS, Miyamoto S. Molecular linkage between the kinase ATM
and NF-kappaB signaling in response to genotoxic stimuli. Science. 2006;311(5764):1141-6.
doi: 10.1126/science.1121513. PubMed PMID: 16497931.
168. Hinz M, Stilmann M, Arslan S, Khanna KK, Dittmar G, Scheidereit C. A cytoplasmic
ATM-TRAF6-cIAP1 module links nuclear DNA damage signaling to ubiquitin-mediated NF-κB
activation. Mol Cell. 2010;40(1):63-74. doi: 10.1016/j.molcel.2010.09.008. PubMed PMID:
20932475.
169. Hadian K, Krappmann D. Signals from the nucleus: activation of NF-kappaB by
cytosolic ATM in the DNA damage response. Sci Signal. 2011;4(156):pe2. Epub 20110118. doi:
10.1126/scisignal.2001712. PubMed PMID: 21245467.
170. Barroso-González J, Auclair S, Luan S, Thomas L, Atkins KM, Aslan JE, et al. PACS-2
mediates the ATM and NF-κB-dependent induction of anti-apoptotic Bcl-xL in response to DNA
damage. Cell Death Differ. 2016;23(9):1448-57. Epub 20160304. doi: 10.1038/cdd.2016.23.
PubMed PMID: 26943323; PubMed Central PMCID: PMC5072422.
171. Shimura T, Sunaga K, Yamazaki M, Honoka N, Sasatani M, Kamiya K, et al. Nuclear
DNA damage-triggered ATM-dependent AMPK activation regulates the mitochondrial radiation
response. Int J Radiat Biol. 2024:1-11. Epub 20240103. doi: 10.1080/09553002.2023.2295297.
PubMed PMID: 38166485.
172. Fu X, Wan S, Lyu YL, Liu LF, Qi H. Etoposide induces ATM-dependent mitochondrial
biogenesis through AMPK activation. PLoS One. 2008;3(4):e2009. Epub 20080423. doi:
10.1371/journal.pone.0002009. PubMed PMID: 18431490; PubMed Central PMCID:
PMC2329593.
173. Alexander A, Cai SL, Kim J, Nanez A, Sahin M, MacLean KH, et al. ATM signals to
TSC2 in the cytoplasm to regulate mTORC1 in response to ROS. Proc Natl Acad Sci U S A.
2010;107(9):4153-8. Epub 20100216. doi: 10.1073/pnas.0913860107. PubMed PMID:
20160076; PubMed Central PMCID: PMC2840158.
174. Sanli T, Rashid A, Liu C, Harding S, Bristow RG, Cutz JC, et al. Ionizing radiation
activates AMP-activated kinase (AMPK): a target for radiosensitization of human cancer cells.
Int J Radiat Oncol Biol Phys. 2010;78(1):221-9. Epub 20100707. doi:
10.1016/j.ijrobp.2010.03.005. PubMed PMID: 20615625.
175. Yee SW, Chen L, Giacomini KM. The role of ATM in response to metformin treatment
and activation of AMPK. Nat Genet. 2012;44(4):359-60. Epub 20120328. doi: 10.1038/ng.2236.
PubMed PMID: 22456732; PubMed Central PMCID: PMC3359140.
176. Zou Y, Wang Q, Li B, Xie B, Wang W. Temozolomide induces autophagy via
ATM‑AMPK‑ULK1 pathways in glioma. Mol Med Rep. 2014;10(1):411-6. Epub 20140415. doi:
10.3892/mmr.2014.2151. PubMed PMID: 24737504.

134
177. Szewczuk M, Boguszewska K, Kaźmierczak-Barańska J, Karwowski BT. The role of
AMPK in metabolism and its influence on DNA damage repair. Mol Biol Rep.
2020;47(11):9075-86. Epub 20201018. doi: 10.1007/s11033-020-05900-x. PubMed PMID:
33070285; PubMed Central PMCID: PMC7674386.
178. Yang W, Wang L, Wang F, Yuan S. Roles of AMP-Activated Protein Kinase (AMPK) in
Mammalian Reproduction. Front Cell Dev Biol. 2020;8:593005. Epub 20201119. doi:
10.3389/fcell.2020.593005. PubMed PMID: 33330475; PubMed Central PMCID: PMC7710906.
179. Xie X, Zhang Y, Wang Z, Wang S, Jiang X, Cui H, et al. ATM at the crossroads of
reactive oxygen species and autophagy. Int J Biol Sci. 2021;17(12):3080-90. Epub 20210722.
doi: 10.7150/ijbs.63963. PubMed PMID: 34421351; PubMed Central PMCID: PMC8375236.
180. Pu W, Chu X, Guo H, Huang G, Cui T, Huang B, et al. The activated
ATM/AMPK/mTOR axis promotes autophagy in response to oxidative stress-mediated DNA
damage co-induced by molybdenum and cadmium in duck testes. Environ Pollut. 2023;316(Pt
2):120574. Epub 20221106. doi: 10.1016/j.envpol.2022.120574. PubMed PMID: 36351481.
181. Luo L, Huang W, Tao R, Hu N, Xiao ZX, Luo Z. ATM and LKB1 dependent activation
of AMPK sensitizes cancer cells to etoposide-induced apoptosis. Cancer Lett. 2013;328(1):114-
9. Epub 20120905. doi: 10.1016/j.canlet.2012.08.034. PubMed PMID: 22960274; PubMed
Central PMCID: PMC3521637.
182. Li R, Luo X, Zhu Y, Zhao L, Li L, Peng Q, et al. ATM signals to AMPK to promote
autophagy and positively regulate DNA damage in response to cadmium-induced ROS in mouse
spermatocytes. Environ Pollut. 2017;231(Pt 2):1560-8. Epub 20170928. doi:
10.1016/j.envpol.2017.09.044. PubMed PMID: 28964605.
183. Jeon SM. Regulation and function of AMPK in physiology and diseases. Exp Mol Med.
2016;48(7):e245. Epub 20160715. doi: 10.1038/emm.2016.81. PubMed PMID: 27416781;
PubMed Central PMCID: PMC4973318.
184. Jeong HY, Kang JM, Jun HH, Kim DJ, Park SH, Sung MJ, et al. Chloroquine and
amodiaquine enhance AMPK phosphorylation and improve mitochondrial fragmentation in
diabetic tubulopathy. Sci Rep. 2018;8(1):8774. Epub 20180608. doi: 10.1038/s41598-018-
26858-8. PubMed PMID: 29884802; PubMed Central PMCID: PMC5993726.
185. Afinanisa Q, Cho MK, Seong HA. AMPK Localization: A Key to Differential Energy
Regulation. Int J Mol Sci. 2021;22(20). Epub 20211010. doi: 10.3390/ijms222010921. PubMed
PMID: 34681581; PubMed Central PMCID: PMC8535671.
186. Taherzadeh-Fard E, Saft C, Akkad DA, Wieczorek S, Haghikia A, Chan A, et al. PGC1alpha downstream transcription factors NRF-1 and TFAM are genetic modifiers of Huntington
disease. Mol Neurodegener. 2011;6(1):32. Epub 20110519. doi: 10.1186/1750-1326-6-32.
PubMed PMID: 21595933; PubMed Central PMCID: PMC3117738.
187. Onishi Y, Ueha T, Kawamoto T, Hara H, Toda M, Harada R, et al. Regulation of
mitochondrial proliferation by PGC-1α induces cellular apoptosis in musculoskeletal

135
malignancies. Sci Rep. 2014;4:3916. Epub 20140129. doi: 10.1038/srep03916. PubMed PMID:
24472748; PubMed Central PMCID: PMC7365312.
188. Chen L, Qin Y, Liu B, Gao M, Li A, Li X, et al. PGC-1α-Mediated Mitochondrial
Quality Control: Molecular Mechanisms and Implications for Heart Failure. Front Cell Dev Biol.
2022;10:871357. Epub 20220527. doi: 10.3389/fcell.2022.871357. PubMed PMID: 35721484;
PubMed Central PMCID: PMC9199988.
189. Abu Shelbayeh O, Arroum T, Morris S, Busch KB. PGC-1α Is a Master Regulator of
Mitochondrial Lifecycle and ROS Stress Response. Antioxidants (Basel). 2023;12(5). Epub
20230510. doi: 10.3390/antiox12051075. PubMed PMID: 37237941; PubMed Central PMCID:
PMC10215733.
190. Rogakou EP, Pilch DR, Orr AH, Ivanova VS, Bonner WM. DNA double-stranded breaks
induce histone H2AX phosphorylation on serine 139. J Biol Chem. 1998;273(10):5858-68. doi:
10.1074/jbc.273.10.5858. PubMed PMID: 9488723.
191. Olive PL, Banáth JP. Kinetics of H2AX phosphorylation after exposure to cisplatin.
Cytometry B Clin Cytom. 2009;76(2):79-90. doi: 10.1002/cyto.b.20450. PubMed PMID:
18727058.
192. Zhao H, Huang X, Halicka HD, Darzynkiewicz Z. Detection of Histone H2AX
Phosphorylation on Ser-139 as an Indicator of DNA Damage. Curr Protoc Cytom.
2019;89(1):e55. doi: 10.1002/cpcy.55. PubMed PMID: 31237414.
193. Sears CR, Cooney SA, Chin-Sinex H, Mendonca MS, Turchi JJ. DNA damage response
(DDR) pathway engagement in cisplatin radiosensitization of non-small cell lung cancer. DNA
Repair (Amst). 2016;40:35-46. Epub 20160303. doi: 10.1016/j.dnarep.2016.02.004. PubMed
PMID: 26991853; PubMed Central PMCID: PMC4852375.
194. Podhorecka M, Skladanowski A, Bozko P. H2AX Phosphorylation: Its Role in DNA
Damage Response and Cancer Therapy. J Nucleic Acids. 2010;2010. Epub 20100803. doi:
10.4061/2010/920161. PubMed PMID: 20811597; PubMed Central PMCID: PMC2929501.
195. Hickson I, Zhao Y, Richardson CJ, Green SJ, Martin NM, Orr AI, et al. Identification and
characterization of a novel and specific inhibitor of the ataxia-telangiectasia mutated kinase
ATM. Cancer Res. 2004;64(24):9152-9. doi: 10.1158/0008-5472.CAN-04-2727. PubMed PMID:
15604286.
196. Li Y, Yang DQ. The ATM inhibitor KU-55933 suppresses cell proliferation and induces
apoptosis by blocking Akt in cancer cells with overactivated Akt. Mol Cancer Ther.
2010;9(1):113-25. Epub 20100106. doi: 10.1158/1535-7163.MCT-08-1189. PubMed PMID:
20053781.
197. Harris BRE, Zhang Y, Tao J, Shen R, Zhao X, Cleary MP, et al. ATM inhibitor KU55933 induces apoptosis and inhibits motility by blocking GLUT1-mediated glucose uptake in
aggressive cancer cells with sustained activation of Akt. FASEB J. 2021;35(4):e21264. doi:
10.1096/fj.202001415RR. PubMed PMID: 33715230.

136
198. Canman CE, Wolff AC, Chen CY, Fornace AJ, Kastan MB. The p53-dependent G1 cell
cycle checkpoint pathway and ataxia-telangiectasia. Cancer Res. 1994;54(19):5054-8. PubMed
PMID: 7923116.
199. Banin S, Moyal L, Shieh S, Taya Y, Anderson CW, Chessa L, et al. Enhanced
phosphorylation of p53 by ATM in response to DNA damage. Science. 1998;281(5383):1674-7.
doi: 10.1126/science.281.5383.1674. PubMed PMID: 9733514.
200. Shiloh Y. ATM and related protein kinases: safeguarding genome integrity. Nat Rev
Cancer. 2003;3(3):155-68. doi: 10.1038/nrc1011. PubMed PMID: 12612651.
201. Nissanka N, Moraes CT. Mitochondrial DNA damage and reactive oxygen species in
neurodegenerative disease. FEBS Lett. 2018;592(5):728-42. Epub 20180109. doi: 10.1002/1873-
3468.12956. PubMed PMID: 29281123; PubMed Central PMCID: PMC6942696.
202. Sarkar A, Gandhi V. Activation of ATM kinase by ROS generated during ionophoreinduced mitophagy in human T and B cell malignancies. Mol Cell Biochem. 2021;476(1):417-
23. Epub 20200929. doi: 10.1007/s11010-020-03917-1. PubMed PMID: 32996079; PubMed
Central PMCID: PMC7867566.
203. Dengler F. Activation of AMPK under Hypoxia: Many Roads Leading to Rome. Int J
Mol Sci. 2020;21(7). Epub 20200331. doi: 10.3390/ijms21072428. PubMed PMID: 32244507;
PubMed Central PMCID: PMC7177550.
204. Urushihara Y, Hashimoto T, Fujishima Y, Hosoi Y. AMPK/FOXO3a Pathway Increases
Activity and/or Expression of ATM, DNA-PKcs, Src, EGFR, PDK1, and SOD2 and Induces
Radioresistance under Nutrient Starvation. Int J Mol Sci. 2023;24(16). Epub 20230815. doi:
10.3390/ijms241612828. PubMed PMID: 37629008; PubMed Central PMCID: PMC10454868.
205. Mihaylova MM, Shaw RJ. The AMPK signalling pathway coordinates cell growth,
autophagy and metabolism. Nat Cell Biol. 2011;13(9):1016-23. Epub 20110902. doi:
10.1038/ncb2329. PubMed PMID: 21892142; PubMed Central PMCID: PMC3249400.
206. Herzig S, Shaw RJ. AMPK: guardian of metabolism and mitochondrial homeostasis. Nat
Rev Mol Cell Biol. 2018;19(2):121-35. Epub 20171004. doi: 10.1038/nrm.2017.95. PubMed
PMID: 28974774; PubMed Central PMCID: PMC5780224.
207. Hardie DG. AMP-activated protein kinase: an energy sensor that regulates all aspects of
cell function. Genes Dev. 2011;25(18):1895-908. doi: 10.1101/gad.17420111. PubMed PMID:
21937710; PubMed Central PMCID: PMC3185962.
208. Sun Y, Connors KE, Yang DQ. AICAR induces phosphorylation of AMPK in an ATMdependent, LKB1-independent manner. Mol Cell Biochem. 2007;306(1-2):239-45. Epub
20070905. doi: 10.1007/s11010-007-9575-6. PubMed PMID: 17786544.
209. Lim CT, Kola B, Korbonits M. AMPK as a mediator of hormonal signalling. J Mol
Endocrinol. 2010;44(2):87-97. Epub 20090722. doi: 10.1677/JME-09-0063. PubMed PMID:
19625456.

137
210. Ju TC, Lin YS, Chern Y. Energy dysfunction in Huntington's disease: insights from
PGC-1α, AMPK, and CKB. Cell Mol Life Sci. 2012;69(24):4107-20. Epub 20120525. doi:
10.1007/s00018-012-1025-2. PubMed PMID: 22627493.
211. Ornatowski W, Lu Q, Yegambaram M, Garcia AE, Zemskov EA, Maltepe E, et al.
Complex interplay between autophagy and oxidative stress in the development of pulmonary
disease. Redox Biol. 2020;36:101679. Epub 20200811. doi: 10.1016/j.redox.2020.101679.
PubMed PMID: 32818797; PubMed Central PMCID: PMC7451718.
212. Sanli T, Steinberg GR, Singh G, Tsakiridis T. AMP-activated protein kinase (AMPK)
beyond metabolism: a novel genomic stress sensor participating in the DNA damage response
pathway. Cancer Biol Ther. 2014;15(2):156-69. Epub 20131101. doi: 10.4161/cbt.26726.
PubMed PMID: 24100703; PubMed Central PMCID: PMC3928130.
213. Webb BA, Dosey AM, Wittmann T, Kollman JM, Barber DL. The glycolytic enzyme
phosphofructokinase-1 assembles into filaments. J Cell Biol. 2017;216(8):2305-13. Epub
20170623. doi: 10.1083/jcb.201701084. PubMed PMID: 28646105; PubMed Central PMCID:
PMC5551713.
214. Osler ME, Zierath JR. Adenosine 5'-monophosphate-activated protein kinase regulation
of fatty acid oxidation in skeletal muscle. Endocrinology. 2008;149(3):935-41. Epub 20080117.
doi: 10.1210/en.2007-1441. PubMed PMID: 18202133.
215. Rashtchizadeh N, Argani H, Ghorbanihaghjo A, Sanajou D, Hosseini V, Dastmalchi S, et
al. AMPK: A promising molecular target for combating cisplatin toxicities. Biochem Pharmacol.
2019;163:94-100. Epub 20190207. doi: 10.1016/j.bcp.2019.02.006. PubMed PMID: 30738797.
216. Kim WJ. Is 5´-AMP-Activated Protein Kinase Both Jekyll and Hyde in Bladder Cancer?
Int Neurourol J. 2015;19(2):55-66. Epub 20150629. doi: 10.5213/inj.2015.19.2.55. PubMed
PMID: 26126434; PubMed Central PMCID: PMC4490316.
217. Shackelford DB, Shaw RJ. The LKB1-AMPK pathway: metabolism and growth control
in tumour suppression. Nat Rev Cancer. 2009;9(8):563-75. doi: 10.1038/nrc2676. PubMed
PMID: 19629071; PubMed Central PMCID: PMC2756045.
218. Jones RG, Plas DR, Kubek S, Buzzai M, Mu J, Xu Y, et al. AMP-activated protein kinase
induces a p53-dependent metabolic checkpoint. Mol Cell. 2005;18(3):283-93. doi:
10.1016/j.molcel.2005.03.027. PubMed PMID: 15866171.
219. Russell FM, Hardie DG. AMP-Activated Protein Kinase: Do We Need Activators or
Inhibitors to Treat or Prevent Cancer? Int J Mol Sci. 2020;22(1). Epub 20201227. doi:
10.3390/ijms22010186. PubMed PMID: 33375416; PubMed Central PMCID: PMC7795930.
220. Bartel K, Müller R, von Schwarzenberg K. Differential regulation of AMP-activated
protein kinase in healthy and cancer cells explains why V-ATPase inhibition selectively kills
cancer cells. J Biol Chem. 2019;294(46):17239-48. Epub 20191011. doi:
10.1074/jbc.RA119.010243. PubMed PMID: 31604821; PubMed Central PMCID:
PMC6873172.

138
221. Tsogbadrakh B, Ryu H, Ju KD, Lee J, Yun S, Yu KS, et al. AICAR, an AMPK activator,
protects against cisplatin-induced acute kidney injury through the JAK/STAT/SOCS pathway.
Biochem Biophys Res Commun. 2019;509(3):680-6. Epub 20190104. doi:
10.1016/j.bbrc.2018.12.159. PubMed PMID: 30616891.
222. Hardie DG. Molecular Pathways: Is AMPK a Friend or a Foe in Cancer? Clin Cancer
Res. 2015;21(17):3836-40. Epub 20150707. doi: 10.1158/1078-0432.CCR-14-3300. PubMed
PMID: 26152739; PubMed Central PMCID: PMC4558946.
223. Schmitt DL, Curtis SD, Lyons AC, Zhang JF, Chen M, He CY, et al. Spatial regulation of
AMPK signaling revealed by a sensitive kinase activity reporter. Nat Commun. 2022;13(1):3856.
Epub 20220705. doi: 10.1038/s41467-022-31190-x. PubMed PMID: 35790710; PubMed Central
PMCID: PMC9256702.
224. Jäger S, Handschin C, St-Pierre J, Spiegelman BM. AMP-activated protein kinase
(AMPK) action in skeletal muscle via direct phosphorylation of PGC-1alpha. Proc Natl Acad Sci
U S A. 2007;104(29):12017-22. Epub 20070703. doi: 10.1073/pnas.0705070104. PubMed
PMID: 17609368; PubMed Central PMCID: PMC1924552.
225. Cantó C, Auwerx J. PGC-1alpha, SIRT1 and AMPK, an energy sensing network that
controls energy expenditure. Curr Opin Lipidol. 2009;20(2):98-105. doi:
10.1097/MOL.0b013e328328d0a4. PubMed PMID: 19276888; PubMed Central PMCID:
PMC3627054.
226. Penfold L, Woods A, Pollard AE, Arizanova J, Pascual-Navarro E, Muckett PJ, et al.
AMPK activation protects against prostate cancer by inducing a catabolic cellular state. Cell
Rep. 2023;42(4):112396. Epub 20230415. doi: 10.1016/j.celrep.2023.112396. PubMed PMID:
37061917; PubMed Central PMCID: PMC10576838.
227. Ferraro E, Giammarioli AM, Chiandotto S, Spoletini I, Rosano G. Exercise-induced
skeletal muscle remodeling and metabolic adaptation: redox signaling and role of autophagy.
Antioxid Redox Signal. 2014;21(1):154-76. Epub 20140306. doi: 10.1089/ars.2013.5773.
PubMed PMID: 24450966; PubMed Central PMCID: PMC4048572.
228. Borniquel S, García-Quintáns N, Valle I, Olmos Y, Wild B, Martínez-Granero F, et al.
Inactivation of Foxo3a and subsequent downregulation of PGC-1 alpha mediate nitric oxideinduced endothelial cell migration. Mol Cell Biol. 2010;30(16):4035-44. Epub 20100614. doi:
10.1128/MCB.00175-10. PubMed PMID: 20547753; PubMed Central PMCID: PMC2916439.
229. Doan KV, Ko CM, Kinyua AW, Yang DJ, Choi YH, Oh IY, et al. Gallic acid regulates
body weight and glucose homeostasis through AMPK activation. Endocrinology.
2015;156(1):157-68. doi: 10.1210/en.2014-1354. PubMed PMID: 25356824.
230. Rius-Pérez S, Torres-Cuevas I, Millán I, Ortega Á, Pérez S. PGC-1. Oxid Med Cell
Longev. 2020;2020:1452696. Epub 20200309. doi: 10.1155/2020/1452696. PubMed PMID:
32215168; PubMed Central PMCID: PMC7085407.

139
231. Zhou K, Bellenguez C, Spencer CC, Bennett AJ, Coleman RL, Tavendale R, et al.
Common variants near ATM are associated with glycemic response to metformin in type 2
diabetes. Nat Genet. 2011;43(2):117-20. Epub 20101226. doi: 10.1038/ng.735. PubMed PMID:
21186350; PubMed Central PMCID: PMC3030919.
232. Lee MG, Choi Z, Lim NJ, Lim JS, Lee KW, Ko KP, et al. XAF1 directs glioma response
to temozolomide through apoptotic transition of autophagy by activation of ATM-AMPK
signaling. Neurooncol Adv. 2022;4(1):vdac013. Epub 20220207. doi: 10.1093/noajnl/vdac013.
PubMed PMID: 35274103; PubMed Central PMCID: PMC8903238.
233. Handa N, Takagi T, Saijo S, Kishishita S, Takaya D, Toyama M, et al. Structural basis
for compound C inhibition of the human AMP-activated protein kinase α2 subunit kinase
domain. Acta Crystallogr D Biol Crystallogr. 2011;67(Pt 5):480-7. Epub 20110414. doi:
10.1107/S0907444911010201. PubMed PMID: 21543851.
234. Glosse P, Feger M, Mutig K, Chen H, Hirche F, Hasan AA, et al. AMP-activated kinase
is a regulator of fibroblast growth factor 23 production. Kidney Int. 2018;94(3):491-501. Epub
20180531. doi: 10.1016/j.kint.2018.03.006. PubMed PMID: 29861059.
235. Liu XX, Liu FL, Li X, Lu TS, Luo YX, Jian M, et al. Novel role of AMPK in cocaine
reinforcement via regulating CRTC1. Transl Psychiatry. 2022;12(1):530. Epub 20221231. doi:
10.1038/s41398-022-02299-w. PubMed PMID: 36587026; PubMed Central PMCID:
PMC9805446.
236. Tojkander S, Ciuba K, Lappalainen P. CaMKK2 Regulates Mechanosensitive Assembly
of Contractile Actin Stress Fibers. Cell Rep. 2018;24(1):11-9. doi: 10.1016/j.celrep.2018.06.011.
PubMed PMID: 29972773.
237. Kong L, Zhang H, Lu C, Shi K, Huang H, Zheng Y, et al. AICAR, an AMP-Activated
Protein Kinase Activator, Ameliorates Acute Pancreatitis-Associated Liver Injury Partially
Through Nrf2-Mediated Antioxidant Effects and Inhibition of NLRP3 Inflammasome
Activation. Front Pharmacol. 2021;12:724514. Epub 20210831. doi:
10.3389/fphar.2021.724514. PubMed PMID: 34531748; PubMed Central PMCID:
PMC8438129.
238. Hu L, Su L, Dong Z, Wu Y, Lv Y, George J, et al. AMPK agonist AICAR ameliorates
portal hypertension and liver cirrhosis via NO pathway in the BDL rat model. J Mol Med (Berl).
2019;97(3):423-34. Epub 20190205. doi: 10.1007/s00109-019-01746-4. PubMed PMID:
30721324; PubMed Central PMCID: PMC6394556.
239. Kim J, Yang G, Kim Y, Ha J. AMPK activators: mechanisms of action and physiological
activities. Exp Mol Med. 2016;48(4):e224. Epub 20160401. doi: 10.1038/emm.2016.16. PubMed
PMID: 27034026; PubMed Central PMCID: PMC4855276.
240. Scott KE, Wheeler FB, Davis AL, Thomas MJ, Ntambi JM, Seals DF, et al. Metabolic
regulation of invadopodia and invasion by acetyl-CoA carboxylase 1 and de novo lipogenesis.
PLoS One. 2012;7(1):e29761. Epub 20120106. doi: 10.1371/journal.pone.0029761. PubMed
PMID: 22238651; PubMed Central PMCID: PMC3253107.

140
241. Fullerton MD, Galic S, Marcinko K, Sikkema S, Pulinilkunnil T, Chen ZP, et al. Single
phosphorylation sites in Acc1 and Acc2 regulate lipid homeostasis and the insulin-sensitizing
effects of metformin. Nat Med. 2013;19(12):1649-54. Epub 20131103. doi: 10.1038/nm.3372.
PubMed PMID: 24185692; PubMed Central PMCID: PMC4965268.
242. Galic S, Loh K, Murray-Segal L, Steinberg GR, Andrews ZB, Kemp BE. AMPK
signaling to acetyl-CoA carboxylase is required for fasting- and cold-induced appetite but not
thermogenesis. Elife. 2018;7. Epub 20180213. doi: 10.7554/eLife.32656. PubMed PMID:
29433631; PubMed Central PMCID: PMC5811211.
243. Keerthana CK, Rayginia TP, Shifana SC, Anto NP, Kalimuthu K, Isakov N, et al. The
role of AMPK in cancer metabolism and its impact on the immunomodulation of the tumor
microenvironment. Front Immunol. 2023;14:1114582. Epub 20230215. doi:
10.3389/fimmu.2023.1114582. PubMed PMID: 36875093; PubMed Central PMCID:
PMC9975160.
244. Dasgupta B, Seibel W. Compound C/Dorsomorphin: Its Use and Misuse as an AMPK
Inhibitor. Methods Mol Biol. 2018;1732:195-202. doi: 10.1007/978-1-4939-7598-3_12. PubMed
PMID: 29480476.
245. Wang YG, Han XG, Yang Y, Qiao H, Dai KR, Fan QM, et al. Functional differences
between AMPK α1 and α2 subunits in osteogenesis, osteoblast-associated induction of
osteoclastogenesis, and adipogenesis. Sci Rep. 2016;6:32771. Epub 20160907. doi:
10.1038/srep32771. PubMed PMID: 27600021; PubMed Central PMCID: PMC5013406.
246. Garcia D, Shaw RJ. AMPK: Mechanisms of Cellular Energy Sensing and Restoration of
Metabolic Balance. Mol Cell. 2017;66(6):789-800. doi: 10.1016/j.molcel.2017.05.032. PubMed
PMID: 28622524; PubMed Central PMCID: PMC5553560.
247. Chang WH, Lai AG. An integrative pan-cancer investigation reveals common genetic
and transcriptional alterations of AMPK pathway genes as important predictors of clinical
outcomes across major cancer types. BMC Cancer. 2020;20(1):773. Epub 20200817. doi:
10.1186/s12885-020-07286-2. PubMed PMID: 32807122; PubMed Central PMCID:
PMC7433212.
248. Andugulapati SB, Sundararaman A, Lahiry M, Rangarajan A. AMP-activated protein
kinase promotes breast cancer stemness and drug resistance. Dis Model Mech. 2022;15(6). Epub
20220527. doi: 10.1242/dmm.049203. PubMed PMID: 35195687; PubMed Central PMCID:
PMC9150117.
249. Rae C, Mairs RJ. AMPK activation by AICAR sensitizes prostate cancer cells to
radiotherapy. Oncotarget. 2019;10(7):749-59. Epub 20190122. doi: 10.18632/oncotarget.26598.
PubMed PMID: 30774777; PubMed Central PMCID: PMC6366825.
250. Hill JO, Wyatt HR, Peters JC. The Importance of Energy Balance. Eur Endocrinol.
2013;9(2):111-5. Epub 20130823. doi: 10.17925/EE.2013.09.02.111. PubMed PMID: 29922364;
PubMed Central PMCID: PMC6003580.

141
251. Desvergne B, Michalik L, Wahli W. Transcriptional regulation of metabolism. Physiol
Rev. 2006;86(2):465-514. doi: 10.1152/physrev.00025.2005. PubMed PMID: 16601267.
252. Mouchiroud L, Eichner LJ, Shaw RJ, Auwerx J. Transcriptional coregulators: fine-tuning
metabolism. Cell Metab. 2014;20(1):26-40. Epub 20140501. doi: 10.1016/j.cmet.2014.03.027.
PubMed PMID: 24794975; PubMed Central PMCID: PMC4079747.
253. Talukdar PD, Chatterji U. Transcriptional co-activators: emerging roles in signaling
pathways and potential therapeutic targets for diseases. Signal Transduct Target Ther.
2023;8(1):427. Epub 20231113. doi: 10.1038/s41392-023-01651-w. PubMed PMID: 37953273;
PubMed Central PMCID: PMC10641101.
254. Ding Q, Qi Y, Tsang SY. Mitochondrial Biogenesis, Mitochondrial Dynamics, and
Mitophagy in the Maturation of Cardiomyocytes. Cells. 2021;10(9). Epub 20210918. doi:
10.3390/cells10092463. PubMed PMID: 34572112; PubMed Central PMCID: PMC8466139.
255. Swerdlow RH. Role and treatment of mitochondrial DNA-related mitochondrial
dysfunction in sporadic neurodegenerative diseases. Curr Pharm Des. 2011;17(31):3356-73. doi:
10.2174/138161211798072535. PubMed PMID: 21902672; PubMed Central PMCID:
PMC3351798.
256. Thirupathi A, de Souza CT. Multi-regulatory network of ROS: the interconnection of
ROS, PGC-1 alpha, and AMPK-SIRT1 during exercise. J Physiol Biochem. 2017;73(4):487-94.
Epub 20170714. doi: 10.1007/s13105-017-0576-y. PubMed PMID: 28707280.
257. Di W, Lv J, Jiang S, Lu C, Yang Z, Ma Z, et al. PGC-1: The Energetic Regulator in
Cardiac Metabolism. Curr Issues Mol Biol. 2018;28:29-46. Epub 20180201. doi:
10.21775/cimb.028.029. PubMed PMID: 29388552.
258. Suwa M, Nakano H, Kumagai S. Effects of chronic AICAR treatment on fiber
composition, enzyme activity, UCP3, and PGC-1 in rat muscles. J Appl Physiol (1985).
2003;95(3):960-8. Epub 20030530. doi: 10.1152/japplphysiol.00349.2003. PubMed PMID:
12777406.
259. Terada S, Goto M, Kato M, Kawanaka K, Shimokawa T, Tabata I. Effects of lowintensity prolonged exercise on PGC-1 mRNA expression in rat epitrochlearis muscle. Biochem
Biophys Res Commun. 2002;296(2):350-4. doi: 10.1016/s0006-291x(02)00881-1. PubMed
PMID: 12163024.
260. Chen J, Zhou R, Feng Y, Cheng L. Molecular mechanisms of exercise contributing to
tissue regeneration. Signal Transduct Target Ther. 2022;7(1):383. Epub 20221130. doi:
10.1038/s41392-022-01233-2. PubMed PMID: 36446784; PubMed Central PMCID:
PMC9709153.
261. Juszczak F, Caron N, Mathew AV, Declèves AE. Critical Role for AMPK in Metabolic
Disease-Induced Chronic Kidney Disease. Int J Mol Sci. 2020;21(21). Epub 20201027. doi:
10.3390/ijms21217994. PubMed PMID: 33121167; PubMed Central PMCID: PMC7663488.

142
262. Ruderman NB, Xu XJ, Nelson L, Cacicedo JM, Saha AK, Lan F, et al. AMPK and
SIRT1: a long-standing partnership? Am J Physiol Endocrinol Metab. 2010;298(4):E751-60.
Epub 20100126. doi: 10.1152/ajpendo.00745.2009. PubMed PMID: 20103737; PubMed Central
PMCID: PMC2853213.
263. McGee SL, Hargreaves M. AMPK-mediated regulation of transcription in skeletal
muscle. Clin Sci (Lond). 2010;118(8):507-18. Epub 20100126. doi: 10.1042/CS20090533.
PubMed PMID: 20088830.
264. Roque W, Cuevas-Mora K, Romero F. Mitochondrial Quality Control in Age-Related
Pulmonary Fibrosis. Int J Mol Sci. 2020;21(2). Epub 20200118. doi: 10.3390/ijms21020643.
PubMed PMID: 31963720; PubMed Central PMCID: PMC7013724.
265. Fontecha-Barriuso M, Martin-Sanchez D, Martinez-Moreno JM, Monsalve M, Ramos
AM, Sanchez-Niño MD, et al. The Role of PGC-1α and Mitochondrial Biogenesis in Kidney
Diseases. Biomolecules. 2020;10(2). Epub 20200224. doi: 10.3390/biom10020347. PubMed
PMID: 32102312; PubMed Central PMCID: PMC7072614.
266. Luo JS, Ning JQ, Chen ZY, Li WJ, Zhou RL, Yan RY, et al. The Role of Mitochondrial
Quality Control in Cognitive Dysfunction in Diabetes. Neurochem Res. 2022;47(8):2158-72.
Epub 20220604. doi: 10.1007/s11064-022-03631-y. PubMed PMID: 35661963; PubMed Central
PMCID: PMC9352619.
267. Sharabi K, Lin H, Tavares CDJ, Dominy JE, Camporez JP, Perry RJ, et al. Selective
Chemical Inhibition of PGC-1α Gluconeogenic Activity Ameliorates Type 2 Diabetes. Cell.
2017;169(1):148-60.e15. doi: 10.1016/j.cell.2017.03.001. PubMed PMID: 28340340; PubMed
Central PMCID: PMC5398763.
268. Lin H, Sharabi K, Lin L, Ruiz C, Zhu D, Cameron MD, et al. Structure-Activity
Relationship and Biological Investigation of SR18292 (J Med Chem. 2021;64(2):980-90. Epub
20210112. doi: 10.1021/acs.jmedchem.0c01450. PubMed PMID: 33434430; PubMed Central
PMCID: PMC7869975.
269. Miller KN, Clark JP, Anderson RM. Mitochondrial regulator PGC-1a-Modulating the
modulator. Curr Opin Endocr Metab Res. 2019;5:37-44. Epub 20190227. doi:
10.1016/j.coemr.2019.02.002. PubMed PMID: 31406949; PubMed Central PMCID:
PMC6690794.
270. Piccinin E, Villani G, Moschetta A. Metabolic aspects in NAFLD, NASH and
hepatocellular carcinoma: the role of PGC1 coactivators. Nat Rev Gastroenterol Hepatol.
2019;16(3):160-74. doi: 10.1038/s41575-018-0089-3. PubMed PMID: 30518830.
271. Correia-Melo C, Marques FD, Anderson R, Hewitt G, Hewitt R, Cole J, et al.
Mitochondria are required for pro-ageing features of the senescent phenotype. EMBO J.
2016;35(7):724-42. Epub 20160204. doi: 10.15252/embj.201592862. PubMed PMID: 26848154;
PubMed Central PMCID: PMC4818766.

143
272. Tan Z, Luo X, Xiao L, Tang M, Bode AM, Dong Z, et al. The Role of PGC1α in Cancer
Metabolism and its Therapeutic Implications. Mol Cancer Ther. 2016;15(5):774-82. Epub
20160415. doi: 10.1158/1535-7163.MCT-15-0621. PubMed PMID: 27197257.
273. Bost F, Kaminski L. The metabolic modulator PGC-1α in cancer. Am J Cancer Res.
2019;9(2):198-211. Epub 20190201. PubMed PMID: 30906622; PubMed Central PMCID:
PMC6405967.
274. Tennakoon JB, Shi Y, Han JJ, Tsouko E, White MA, Burns AR, et al. Androgens
regulate prostate cancer cell growth via an AMPK-PGC-1α-mediated metabolic switch.
Oncogene. 2014;33(45):5251-61. Epub 20131104. doi: 10.1038/onc.2013.463. PubMed PMID:
24186207; PubMed Central PMCID: PMC4009392.
275. Cormio A, Guerra F, Cormio G, Pesce V, Fracasso F, Loizzi V, et al. The PGC-1alphadependent pathway of mitochondrial biogenesis is upregulated in type I endometrial cancer.
Biochem Biophys Res Commun. 2009;390(4):1182-5. Epub 20091025. doi:
10.1016/j.bbrc.2009.10.114. PubMed PMID: 19861117.
276. Marusyk A, Janiszewska M, Polyak K. Intratumor Heterogeneity: The Rosetta Stone of
Therapy Resistance. Cancer Cell. 2020;37(4):471-84. doi: 10.1016/j.ccell.2020.03.007. PubMed
PMID: 32289271; PubMed Central PMCID: PMC7181408.
277. Bian L, Meng Y, Zhang M, Guo Z, Liu F, Zhang W, et al. ATM Expression Is Elevated
in Established Radiation-Resistant Breast Cancer Cells and Improves DNA Repair Efficiency.
Int J Biol Sci. 2020;16(7):1096-106. Epub 20200204. doi: 10.7150/ijbs.41246. PubMed PMID:
32174787; PubMed Central PMCID: PMC7053315.
278. Shen M, Xu Z, Xu W, Jiang K, Zhang F, Ding Q, et al. Inhibition of ATM reverses EMT
and decreases metastatic potential of cisplatin-resistant lung cancer cells through
JAK/STAT3/PD-L1 pathway. J Exp Clin Cancer Res. 2019;38(1):149. Epub 20190408. doi:
10.1186/s13046-019-1161-8. PubMed PMID: 30961670; PubMed Central PMCID:
PMC6454747.
279. Tan W, Zhong Z, Carney RP, Men Y, Li J, Pan T, et al. Deciphering the metabolic role of
AMPK in cancer multi-drug resistance. Semin Cancer Biol. 2019;56:56-71. Epub 20180924. doi:
10.1016/j.semcancer.2018.09.005. PubMed PMID: 30261277.
280. Park SY, Chung YS, Kim SH. Role of AMPK in Regulation of Oxaliplatin-Resistant
Human Colorectal Cancer. Biomedicines. 2022;10(11). Epub 20221025. doi:
10.3390/biomedicines10112690. PubMed PMID: 36359211; PubMed Central PMCID:
PMC9687437.
281. Zadra G, Batista JL, Loda M. Dissecting the Dual Role of AMPK in Cancer: From
Experimental to Human Studies. Mol Cancer Res. 2015;13(7):1059-72. Epub 20150508. doi:
10.1158/1541-7786.MCR-15-0068. PubMed PMID: 25956158; PubMed Central PMCID:
PMC4504770.

144
282. Wang Z, Liu P, Chen Q, Deng S, Liu X, Situ H, et al. Targeting AMPK Signaling
Pathway to Overcome Drug Resistance for Cancer Therapy. Curr Drug Targets. 2016;17(8):853-
64. doi: 10.2174/1389450116666150316223655. PubMed PMID: 25777274.
283. Khan AS, Frigo DE. A spatiotemporal hypothesis for the regulation, role, and targeting of
AMPK in prostate cancer. Nat Rev Urol. 2017;14(3):164-80. Epub 20170201. doi:
10.1038/nrurol.2016.272. PubMed PMID: 28169991; PubMed Central PMCID: PMC5672799.
284. Berthel E, Foray N, Ferlazzo ML. The Nucleoshuttling of the ATM Protein: A Unified
Model to Describe the Individual Response to High- and Low-Dose of Radiation? Cancers
(Basel). 2019;11(7). Epub 20190628. doi: 10.3390/cancers11070905. PubMed PMID: 31261657;
PubMed Central PMCID: PMC6678722.
285. Agostini F, Bisaglia M, Plotegher N. Linking ROS Levels to Autophagy: The Key Role
of AMPK. Antioxidants (Basel). 2023;12(7). Epub 20230710. doi: 10.3390/antiox12071406.
PubMed PMID: 37507945; PubMed Central PMCID: PMC10376219.
286. Shu J, Wang X, Yang X, Zhao G. ATM inhibitor KU60019 synergistically sensitizes
lung cancer cells to topoisomerase II poisons by multiple mechanisms. Sci Rep. 2023;13(1):882.
Epub 20230117. doi: 10.1038/s41598-023-28185-z. PubMed PMID: 36650267; PubMed Central
PMCID: PMC9845372.
287. Hsu CC, Peng D, Cai Z, Lin HK. AMPK signaling and its targeting in cancer progression
and treatment. Semin Cancer Biol. 2022;85:52-68. Epub 20210418. doi:
10.1016/j.semcancer.2021.04.006. PubMed PMID: 33862221; PubMed Central PMCID:
PMC9768867.
288. Steinberg GR, Carling D. AMP-activated protein kinase: the current landscape for drug
development. Nat Rev Drug Discov. 2019;18(7):527-51. doi: 10.1038/s41573-019-0019-2.
PubMed PMID: 30867601.
289. Sadria M, Seo D, Layton AT. The mixed blessing of AMPK signaling in Cancer
treatments. BMC Cancer. 2022;22(1):105. Epub 20220125. doi: 10.1186/s12885-022-09211-1.
PubMed PMID: 35078427; PubMed Central PMCID: PMC8786626.
290. Konieczny P, Adamus T, Sułkowski M, Skrzypek K, Majka M. Impact of AMPK on
cervical carcinoma progression and metastasis. Cell Death Dis. 2023;14(1):43. Epub 20230119.
doi: 10.1038/s41419-023-05583-9. PubMed PMID: 36658117; PubMed Central PMCID:
PMC9852279.
291. Luo X, Liao C, Quan J, Cheng C, Zhao X, Bode AM, et al. Posttranslational regulation of
PGC-1α and its implication in cancer metabolism. Int J Cancer. 2019;145(6):1475-83. Epub
20190326. doi: 10.1002/ijc.32253. PubMed PMID: 30848477; PubMed Central PMCID:
PMC6767394.
292. Vellinga TT, Borovski T, de Boer VC, Fatrai S, van Schelven S, Trumpi K, et al.
SIRT1/PGC1α-Dependent Increase in Oxidative Phosphorylation Supports Chemotherapy

145
Resistance of Colon Cancer. Clin Cancer Res. 2015;21(12):2870-9. Epub 20150316. doi:
10.1158/1078-0432.CCR-14-2290. PubMed PMID: 25779952.
293. Gravel SP. Deciphering the Dichotomous Effects of PGC-1α on Tumorigenesis and
Metastasis. Front Oncol. 2018;8:75. Epub 20180323. doi: 10.3389/fonc.2018.00075. PubMed
PMID: 29629336; PubMed Central PMCID: PMC5876244.
294. Janzer A, German NJ, Gonzalez-Herrera KN, Asara JM, Haigis MC, Struhl K. Metformin
and phenformin deplete tricarboxylic acid cycle and glycolytic intermediates during cell
transformation and NTPs in cancer stem cells. Proc Natl Acad Sci U S A. 2014;111(29):10574-9.
Epub 20140707. doi: 10.1073/pnas.1409844111. PubMed PMID: 25002509; PubMed Central
PMCID: PMC4115496.
295. Tully E, Bharti S, Woo J, Bhujwalla Z, Gabrielson E. Biguanide drugs enhance cytotoxic
effects of cisplatin by depleting aspartate and NAD+ in sensitive cancer cells. Cancer Biol Ther.
2021;22(10-12):579-86. Epub 20211031. doi: 10.1080/15384047.2021.1982599. PubMed PMID:
34720054; PubMed Central PMCID: PMC8726685.
296. Mo M, Peng F, Wang L, Peng L, Lan G, Yu S. Roles of mitochondrial transcription
factor A and microRNA-590-3p in the development of bladder cancer. Oncol Lett.
2013;6(2):617-23. Epub 20130621. doi: 10.3892/ol.2013.1419. PubMed PMID: 24137381;
PubMed Central PMCID: PMC3789041.
297. Qiao L, Ru G, Mao Z, Wang C, Nie Z, Li Q, et al. Mitochondrial DNA depletion,
mitochondrial mutations and high TFAM expression in hepatocellular carcinoma. Oncotarget.
2017;8(48):84373-83. Epub 20170916. doi: 10.18632/oncotarget.21033. PubMed PMID:
29137431; PubMed Central PMCID: PMC5663603.
298. Lin CS, Liu LT, Ou LH, Pan SC, Lin CI, Wei YH. Role of mitochondrial function in the
invasiveness of human colon cancer cells. Oncol Rep. 2018;39(1):316-30. Epub 20171109. doi:
10.3892/or.2017.6087. PubMed PMID: 29138850.
299. Lee H, Park J, Tran Q, Kim D, Hong Y, Cho H, et al. Mitochondrial transcription factor
A (TFAM) is upregulated in glioma. Mol Med Rep. 2017;15(6):3781-6. Epub 20170412. doi:
10.3892/mmr.2017.6467. PubMed PMID: 28440425.
300. Xie D, Wu X, Lan L, Shangguan F, Lin X, Chen F, et al. Downregulation of TFAM
inhibits the tumorigenesis of non-small cell lung cancer by activating ROS-mediated
JNK/p38MAPK signaling and reducing cellular bioenergetics. Oncotarget. 2016;7(10):11609-24.
doi: 10.18632/oncotarget.7018. PubMed PMID: 26820294; PubMed Central PMCID:
PMC4905497.
301. Yun CW, Jeon J, Go G, Lee JH, Lee SH. The Dual Role of Autophagy in Cancer
Development and a Therapeutic Strategy for Cancer by Targeting Autophagy. Int J Mol Sci.
2020;22(1). Epub 20201226. doi: 10.3390/ijms22010179. PubMed PMID: 33375363; PubMed
Central PMCID: PMC7795059.

146
302. Vara-Perez M, Felipe-Abrio B, Agostinis P. Mitophagy in Cancer: A Tale of Adaptation.
Cells. 2019;8(5). Epub 20190522. doi: 10.3390/cells8050493. PubMed PMID: 31121959;
PubMed Central PMCID: PMC6562743.
303. Song C, Pan S, Zhang J, Li N, Geng Q. Mitophagy: A novel perspective for insighting
into cancer and cancer treatment. Cell Prolif. 2022;55(12):e13327. Epub 20221005. doi:
10.1111/cpr.13327. PubMed PMID: 36200262; PubMed Central PMCID: PMC9715364.
304. Guan Y, Wang Y, Li B, Shen K, Li Q, Ni Y, et al. Mitophagy in carcinogenesis, drug
resistance and anticancer therapeutics. Cancer Cell Int. 2021;21(1):350. Epub 20210705. doi:
10.1186/s12935-021-02065-w. PubMed PMID: 34225732; PubMed Central PMCID:
PMC8256582.
305. Vincow ES, Merrihew G, Thomas RE, Shulman NJ, Beyer RP, MacCoss MJ, et al. The
PINK1-Parkin pathway promotes both mitophagy and selective respiratory chain turnover in
vivo. Proc Natl Acad Sci U S A. 2013;110(16):6400-5. Epub 20130318. doi:
10.1073/pnas.1221132110. PubMed PMID: 23509287; PubMed Central PMCID: PMC3631677.
306. Pickrell AM, Youle RJ. The roles of PINK1, parkin, and mitochondrial fidelity in
Parkinson's disease. Neuron. 2015;85(2):257-73. doi: 10.1016/j.neuron.2014.12.007. PubMed
PMID: 25611507; PubMed Central PMCID: PMC4764997.
307. Kuroda Y, Mitsui T, Kunishige M, Shono M, Akaike M, Azuma H, et al. Parkin enhances
mitochondrial biogenesis in proliferating cells. Hum Mol Genet. 2006;15(6):883-95. Epub
20060131. doi: 10.1093/hmg/ddl006. PubMed PMID: 16449237.
308. Wasner K, Smajic S, Ghelfi J, Delcambre S, Prada-Medina CA, Knappe E, et al. Parkin
Deficiency Impairs Mitochondrial DNA Dynamics and Propagates Inflammation. Mov Disord.
2022;37(7):1405-15. Epub 20220423. doi: 10.1002/mds.29025. PubMed PMID: 35460111.
309. Cavalier-Smith T. The simultaneous symbiotic origin of mitochondria, chloroplasts, and
microbodies. Ann N Y Acad Sci. 1987;503:55-71. doi: 10.1111/j.1749-6632.1987.tb40597.x.
PubMed PMID: 3304084.
310. Roger AJ, Muñoz-Gómez SA, Kamikawa R. The Origin and Diversification of
Mitochondria. Curr Biol. 2017;27(21):R1177-R92. doi: 10.1016/j.cub.2017.09.015. PubMed
PMID: 29112874.
311. van der Bliek AM, Sedensky MM, Morgan PG. Cell Biology of the Mitochondrion.
Genetics. 2017;207(3):843-71. doi: 10.1534/genetics.117.300262. PubMed PMID: 29097398;
PubMed Central PMCID: PMC5676242.
312. Youle RJ. Mitochondria-Striking a balance between host and endosymbiont. Science.
2019;365(6454). doi: 10.1126/science.aaw9855. PubMed PMID: 31416937.
313. Galluzzi L, Kepp O, Kroemer G. Mitochondrial regulation of cell death: a
phylogenetically conserved control. Microb Cell. 2016;3(3):101-8. Epub 20160223. doi:
10.15698/mic2016.03.483. PubMed PMID: 28357340; PubMed Central PMCID: PMC5349020.

147
314. Tomtheelnganbee E, Sah P, Sharma R. Mitochondrial function and nutrient sensing
pathways in ageing: enhancing longevity through dietary interventions. Biogerontology.
2022;23(6):657-80. Epub 20220716. doi: 10.1007/s10522-022-09978-7. PubMed PMID:
35842501.
315. Ghosh TS, Shanahan F, O'Toole PW. The gut microbiome as a modulator of healthy
ageing. Nat Rev Gastroenterol Hepatol. 2022;19(9):565-84. Epub 20220425. doi:
10.1038/s41575-022-00605-x. PubMed PMID: 35468952; PubMed Central PMCID:
PMC9035980.
316. Sagan L. On the origin of mitosing cells. J Theor Biol. 1967;14(3):255-74. doi:
10.1016/0022-5193(67)90079-3. PubMed PMID: 11541392.
317. Andersson SG, Zomorodipour A, Andersson JO, Sicheritz-Pontén T, Alsmark UC,
Podowski RM, et al. The genome sequence of Rickettsia prowazekii and the origin of
mitochondria. Nature. 1998;396(6707):133-40. doi: 10.1038/24094. PubMed PMID: 9823893.
318. Fitzpatrick DA, Creevey CJ, McInerney JO. Genome phylogenies indicate a meaningful
alpha-proteobacterial phylogeny and support a grouping of the mitochondria with the
Rickettsiales. Mol Biol Evol. 2006;23(1):74-85. Epub 20050908. doi: 10.1093/molbev/msj009.
PubMed PMID: 16151187.
319. Wang Z, Wu M. An integrated phylogenomic approach toward pinpointing the origin of
mitochondria. Sci Rep. 2015;5:7949. Epub 20150122. doi: 10.1038/srep07949. PubMed PMID:
25609566; PubMed Central PMCID: PMC4302308.
320. Wang Z, Wu M. Phylogenomic reconstruction indicates mitochondrial ancestor was an
energy parasite. PLoS One. 2014;9(10):e110685. Epub 20141015. doi:
10.1371/journal.pone.0110685. PubMed PMID: 25333787; PubMed Central PMCID:
PMC4198247.
321. Degli Esposti M, Chouaia B, Comandatore F, Crotti E, Sassera D, Lievens PM, et al.
Evolution of mitochondria reconstructed from the energy metabolism of living bacteria. PLoS
One. 2014;9(5):e96566. Epub 20140507. doi: 10.1371/journal.pone.0096566. PubMed PMID:
24804722; PubMed Central PMCID: PMC4013037.
322. Gruber J, Kennedy BK. Microbiome and Longevity: Gut Microbes Send Signals to Host
Mitochondria. Cell. 2017;169(7):1168-9. doi: 10.1016/j.cell.2017.05.048. PubMed PMID:
28622501.
323. Imdad S, Lim W, Kim JH, Kang C. Intertwined Relationship of Mitochondrial
Metabolism, Gut Microbiome and Exercise Potential. Int J Mol Sci. 2022;23(5). Epub 20220228.
doi: 10.3390/ijms23052679. PubMed PMID: 35269818; PubMed Central PMCID:
PMC8910986.
324. Borbolis F, Mytilinaiou E, Palikaras K. The Crosstalk between Microbiome and
Mitochondrial Homeostasis in Neurodegeneration. Cells. 2023;12(3). Epub 20230128. doi:
10.3390/cells12030429. PubMed PMID: 36766772; PubMed Central PMCID: PMC9913973.

148
325. Ma L, Zhang L, Li J, Zhang X, Xie Y, Li X, et al. The potential mechanism of gut
microbiota-microbial metabolites-mitochondrial axis in progression of diabetic kidney disease.
Mol Med. 2023;29(1):148. Epub 20231031. doi: 10.1186/s10020-023-00745-z. PubMed PMID:
37907885; PubMed Central PMCID: PMC10617243.
326. Keiran N, Ceperuelo-Mallafré V, Calvo E, Hernández-Alvarez MI, Ejarque M, NúñezRoa C, et al. SUCNR1 controls an anti-inflammatory program in macrophages to regulate the
metabolic response to obesity. Nat Immunol. 2019;20(5):581-92. Epub 20190408. doi:
10.1038/s41590-019-0372-7. PubMed PMID: 30962591.
327. Martínez-Reyes I, Chandel NS. Mitochondrial TCA cycle metabolites control physiology
and disease. Nat Commun. 2020;11(1):102. Epub 20200103. doi: 10.1038/s41467-019-13668-3.
PubMed PMID: 31900386; PubMed Central PMCID: PMC6941980.
328. Clark A, Mach N. The Crosstalk between the Gut Microbiota and Mitochondria during
Exercise. Front Physiol. 2017;8:319. Epub 20170519. doi: 10.3389/fphys.2017.00319. PubMed
PMID: 28579962; PubMed Central PMCID: PMC5437217.
329. Mullenders J, de Jongh E, Brousali A, Roosen M, Blom JPA, Begthel H, et al. Mouse and
human urothelial cancer organoids: A tool for bladder cancer research. Proc Natl Acad Sci U S
A. 2019;116(10):4567-74. Epub 20190220. doi: 10.1073/pnas.1803595116. PubMed PMID:
30787188; PubMed Central PMCID: PMC6410883.
330. Morgan RG, Chambers AC, Legge DN, Coles SJ, Greenhough A, Williams AC.
Optimized delivery of siRNA into 3D tumor spheroid cultures in situ. Sci Rep. 2018;8(1):7952.
Epub 20180521. doi: 10.1038/s41598-018-26253-3. PubMed PMID: 29785035; PubMed Central
PMCID: PMC5962539.
331. Wang P, Hecht F, Ossato G, Tille S, Fraser SE, Junge JA. Complex wavelet filter
improves FLIM phasors for photon starved imaging experiments. Biomed Opt Express.
2021;12(6):3463-73. Epub 20210517. doi: 10.1364/BOE.420953. PubMed PMID: 34221672;
PubMed Central PMCID: PMC8221945.
332. Huo KG, D'Arcangelo E, Tsao MS. Patient-derived cell line, xenograft and organoid
models in lung cancer therapy. Transl Lung Cancer Res. 2020;9(5):2214-32. doi: 10.21037/tlcr20-154. PubMed PMID: 33209645; PubMed Central PMCID: PMC7653147.
333. Ling A, Gruener RF, Fessler J, Huang RS. More than fishing for a cure: The promises
and pitfalls of high throughput cancer cell line screens. Pharmacol Ther. 2018;191:178-89. Epub
20180625. doi: 10.1016/j.pharmthera.2018.06.014. PubMed PMID: 29953899; PubMed Central
PMCID: PMC7001883.
334. Rizzo G, Bertotti A, Leto SM, Vetrano S. Patient-derived tumor models: a more suitable
tool for pre-clinical studies in colorectal cancer. J Exp Clin Cancer Res. 2021;40(1):178. Epub
20210601. doi: 10.1186/s13046-021-01970-2. PubMed PMID: 34074330; PubMed Central
PMCID: PMC8168319.

149
335. Jin J, Yoshimura K, Sewastjanow-Silva M, Song S, Ajani JA. Challenges and Prospects
of Patient-Derived Xenografts for Cancer Research. Cancers (Basel). 2023;15(17). Epub
20230831. doi: 10.3390/cancers15174352. PubMed PMID: 37686627; PubMed Central PMCID:
PMC10486659.
336. Yang H, Sun L, Liu M, Mao Y. Patient-derived organoids: a promising model for
personalized cancer treatment. Gastroenterol Rep (Oxf). 2018;6(4):243-5. Epub 20181009. doi:
10.1093/gastro/goy040. PubMed PMID: 30430011; PubMed Central PMCID: PMC6225812.
337. Prasad M, Kumar R, Buragohain L, Kumari A, Ghosh M. Organoid Technology: A
Reliable Developmental Biology Tool for Organ-Specific Nanotoxicity Evaluation. Front Cell
Dev Biol. 2021;9:696668. Epub 20210923. doi: 10.3389/fcell.2021.696668. PubMed PMID:
34631696; PubMed Central PMCID: PMC8495170.
338. Ji DB, Wu AW. Organoid in colorectal cancer: progress and challenges. Chin Med J
(Engl). 2020;133(16):1971-7. doi: 10.1097/CM9.0000000000000882. PubMed PMID:
32826461; PubMed Central PMCID: PMC7462208.
339. Jian M, Ren L, He G, Lin Q, Tang W, Chen Y, et al. A novel patient-derived organoidsbased xenografts model for preclinical drug response testing in patients with colorectal liver
metastases. J Transl Med. 2020;18(1):234. Epub 20200612. doi: 10.1186/s12967-020-02407-8.
PubMed PMID: 32532289; PubMed Central PMCID: PMC7291745.
340. Foo MA, You M, Chan SL, Sethi G, Bonney GK, Yong WP, et al. Clinical translation of
patient-derived tumour organoids- bottlenecks and strategies. Biomark Res. 2022;10(1):10. Epub
20220310. doi: 10.1186/s40364-022-00356-6. PubMed PMID: 35272694; PubMed Central
PMCID: PMC8908618.
341. Kapałczyńska M, Kolenda T, Przybyła W, Zajączkowska M, Teresiak A, Filas V, et al.
2D and 3D cell cultures - a comparison of different types of cancer cell cultures. Arch Med Sci.
2018;14(4):910-9. Epub 20161118. doi: 10.5114/aoms.2016.63743. PubMed PMID: 30002710;
PubMed Central PMCID: PMC6040128.
342. Prince E, Cruickshank J, Ba-Alawi W, Hodgson K, Haight J, Tobin C, et al. Biomimetic
hydrogel supports initiation and growth of patient-derived breast tumor organoids. Nat Commun.
2022;13(1):1466. Epub 20220318. doi: 10.1038/s41467-022-28788-6. PubMed PMID:
35304464; PubMed Central PMCID: PMC8933543.
343. Lee SH, Hu W, Matulay JT, Silva MV, Owczarek TB, Kim K, et al. Tumor Evolution
and Drug Response in Patient-Derived Organoid Models of Bladder Cancer. Cell.
2018;173(2):515-28.e17. doi: 10.1016/j.cell.2018.03.017. PubMed PMID: 29625057; PubMed
Central PMCID: PMC5890941.
344. Matulay JT, Kamat AM. Advances in risk stratification of bladder cancer to guide
personalized medicine. F1000Res. 2018;7. Epub 20180725. doi:
10.12688/f1000research.14903.1. PubMed PMID: 30109022; PubMed Central PMCID:
PMC6069735.

150
345. Clinton J, McWilliams-Koeppen P. Initiation, Expansion, and Cryopreservation of
Human Primary Tissue-Derived Normal and Diseased Organoids in Embedded ThreeDimensional Culture. Curr Protoc Cell Biol. 2019;82(1):e66. Epub 20180928. doi:
10.1002/cpcb.66. PubMed PMID: 30265443.
Details of figures used from previous publications are provided below, and it is to state that
I am not gaining any financial profits from using these figures:
Figure 1.1 was taken from Zhang M, Liu E, Cui Y, Huang Y. Nanotechnology-based combination therapy
for overcoming multidrug-resistant cancer. Cancer Biol Med. 2017;14(3):212-27. doi:
10.20892/j.issn.2095-3941.2017.0054. PubMed PMID: 28884039; PubMed Central PMCID:
PMC5570599. Link to the license at http://creativecommons.org/licenses/by-nc-sa/4.0/.
Figure 1.2 (a and b) was taken from Liu Y, Sun Y, Guo Y, Shi X, Chen X, Feng W, et al. An Overview:
The Diversified Role of Mitochondria in Cancer Metabolism. Int J Biol Sci. 2023;19(3):897-915. Epub
20230116. doi: 10.7150/ijbs.81609. PubMed PMID: 36778129; PubMed Central PMCID: PMC9910000.
Link to the license at https://creativecommons.org/licenses/by/4.0/, published by Ivyspring International
Publisher.
Figure 2.1 was taken from Xu T, Li HT, Wei J, Li M, Hsieh TC, Lu YT, et al. Epigenetic plasticity
potentiates a rapid cyclical shift to and from an aggressive cancer phenotype. Int J Cancer.
2020;146(11):3065-76. Epub 20200222. doi: 10.1002/ijc.32904. PubMed PMID: 32017074; PubMed
Central PMCID: PMC7448727. License state that “This Agreement between Maheen Iqbal ("You") and
John Wiley and Sons ("John Wiley and Sons") consists of your license details and the terms and conditions
provided by John Wiley and Sons and Copyright Clearance Center.” License Number: 5733341049483 and
License date Feb 20, 2024.
Figure 2.2 and Figure 2.8c was taken from Xu T, Junge JA, Delfarah A, Lu YT, Arnesano C, Iqbal M, et
al. Bladder cancer cells shift rapidly and spontaneously to cisplatin-resistant oxidative phosphorylation that
is trackable in real time. Sci Rep. 2022;12(1):5518. Epub 20220401. doi: 10.1038/s41598-022-09438-9.
PubMed PMID: 35365706; PubMed Central PMCID: PMC8976067. Link to the license at
http://creativecommons.org/licenses/by-nc-sa/4.0/.
Figure 3.1 was taken from Fiorillo M, Ózsvári B, Sotgia F, Lisanti MP. High ATP Production Fuels Cancer
Drug Resistance and Metastasis: Implications for Mitochondrial ATP Depletion Therapy. Front Oncol.
2021;11:740720. Epub 20211015. doi: 10.3389/fonc.2021.740720. PubMed PMID: 34722292; PubMed
Central PMCID: PMC8554334. This is an open-access article distributed under the terms of the Creative
Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted,
provided the original author(s) and the copyright owner(s) are credited and that the original publication in
this journal is cited, in accordance with accepted academic practice.
Figure 4.1. was taken from Rocha CRR, Silva MM, Quinet A, Cabral-Neto JB, Menck CFM. DNA repair
pathways and cisplatin resistance: an intimate relationship. Clinics (Sao Paulo). 2018;73(suppl 1):e478s.
Epub 20180906. doi: 10.6061/clinics/2018/e478s. PubMed PMID: 30208165; PubMed Central PMCID:
PMC6113849. This is an Open Access article distributed under the terms of the Creative Commons License
(http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and
reproduction in any medium or format, provided the original work is properly cited.

151
Figure 4.5a was taken from Vadivel Gnanasundram S, Bonczek O, Wang L, Chen S, Fahraeus R. p53
mRNA Metabolism Links with the DNA Damage Response. Genes (Basel). 2021;12(9). Epub 20210920.
doi: 10.3390/genes12091446. PubMed PMID: 34573428; PubMed Central PMCID: PMC8465283.
Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license
(https://creativecommons.org/licenses/by/4.0/).
Figure 5.1 was taken from Ju TC, Lin YS, Chern Y. Energy dysfunction in Huntington's disease: insights
from PGC-1α, AMPK, and CKB. Cell Mol Life Sci. 2012;69(24):4107-20. Epub 20120525. doi:
10.1007/s00018-012-1025-2. PubMed PMID: 22627493. This Agreement between Maheen Iqbal ("You")
and Springer Nature ("Springer Nature") consists of your license details and the terms and conditions
provided by Springer Nature and Copyright Clearance Center. License Number: 5733351510950 and
License date Feb 20, 2024.
Figure 6.1 was taken from Swerdlow RH. Role and treatment of mitochondrial DNA-related mitochondrial
dysfunction in sporadic neurodegenerative diseases. Curr Pharm Des. 2011;17(31):3356-73. doi:
10.2174/138161211798072535. PubMed PMID: 21902672; PubMed Central PMCID: PMC3351798. This
is a License Agreement between Maheen Iqbal (“User”) and Copyright Clearance Center, Inc. (“CCC”) on
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Marketplace Permissions General Terms and Conditions below, and any Rightsholder Terms and
Conditions which are included. License Number: 1452314-1and License date Feb 20, 2024.
Figure 6.2a was taken from McGee SL, Hargreaves M. AMPK-mediated regulation of transcription in
skeletal muscle. Clin Sci (Lond). 2010;118(8):507-18. Epub 20100126. doi: 10.1042/CS20090533.
PubMed PMID: 20088830. Copyrights state that if the content that you are seeking to re-use is in a Portland
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you must cite the published article and credit the authors when you re-use it (or part of it).
Figure 6.2b was taken from Roque W, Cuevas-Mora K, Romero F. Mitochondrial Quality Control in AgeRelated Pulmonary Fibrosis. Int J Mol Sci. 2020;21(2). Epub 20200118. doi: 10.3390/ijms21020643.
PubMed PMID: 31963720; PubMed Central PMCID: PMC7013724. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons
Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Figure A1a was taken from Foo MA, You M, Chan SL, Sethi G, Bonney GK, Yong WP, et al. Clinical
translation of patient-derived tumour organoids- bottlenecks and strategies. Biomark Res. 2022;10(1):10.
Epub 20220310. doi: 10.1186/s40364-022-00356-6. PubMed PMID: 35272694; PubMed Central PMCID:
PMC8908618. This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons
licence, and indicate if changes were made. To view a copy of this licence,
visit http://creativecommons.org/licenses/by/4.0/.
Figure A1b was taken from Yang H, Sun L, Liu M, Mao Y. Patient-derived organoids: a promising model
for personalized cancer treatment. Gastroenterol Rep (Oxf). 2018;6(4):243-5. Epub 20181009. doi:
10.1093/gastro/goy040. PubMed PMID: 30430011; PubMed Central PMCID: PMC6225812. Published by
Oxford University Press and Sixth Affiliated Hospital of Sun Yat-Sen University This is an Open Access
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Appendix A: Establishing a Repository of BC Patient-Derived Organoids
The establishment of the patient-derived BC organoid repository was spearheaded by Maheen Iqbal, with
guidance from Tong Xu and Amir Goldkorn.
Authors
Maheen Iqbal1
, Tong Xu1
, Sanam Ladi Seyedian2
, Siamak Daneshmand2
, Amir Goldkorn1,3
1Division of Medical Oncology, Department of Medicine, Norris Comprehensive Cancer Center, Keck
School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
2Department of Urology, Keck School of Medicine, University of Southern California, Los Angeles, CA,
90033, USA
3Department of Biochemistry & Molecular Medicine, Norris Comprehensive Cancer Center, Keck School
of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
Author Contributions
M.I. received the tissue samples, processed them for organoid generation, prepared ‘human bladder
organoid media’, passaged, froze, thawed and regenerated organoids, managed PDO repository and
inventory of materials, while T.X. provided assistance if M.I. was not present to overlook any of these
aspects. M.I. and A.G. also procured PDOs from NCI PDMR after complying to the PDMR request form
and NCI Material Transfer Agreement. A.G. and T.X. provided overall guidance. S.L.S. and S.D. provided
the freshly resected patient TURBT and RC samples. A.G. provided overall study supervision.
Introduction
Deciphering the intricate mechanisms driving tumorigenesis and recurrence and
identifying efficacious therapies remain crucial goals in the fight against cancer. However,
limitations associated with existing preclinical models often hinder the ability to accurately
understand biological interactions and to translate them into successful clinical outcomes.
Traditional preclinical models, such as cancer cell lines and patient-derived xenografts (PDXs),
have played a pivotal role in cancer research. Cell lines offer rapid expansion and ease of
manipulation, facilitating high-throughput drug screening. However, their prolonged culture often
leads to genetic and phenotypic drift, compromising their representation of the original tumor(332,
333). PDXs, on the other hand, better preserve the tumor's heterogeneity and microenvironment,
providing a more faithful reflection of the clinical setting. However, their establishment and
maintenance are expensive, time-consuming, and ethically challenging, limiting their widespread

153
application(334, 335). Patient-derived organoids (PDOs) have emerged as a compelling
alternative, bridging the gap between the simplicity of cell lines and the complexity of animal
models. PDOs are three-dimensional (3D) self-organizing structures derived from patient tumors
or stem cells, recapitulating the key histological and genetic features of the original tumor(336).
PDOs, classified as "stem cell-containing self-organizing structures," offer a unique tool for
modeling human tissues in vitro. Notably, bladder tumoroids, a specific type of PDO, recapitulate
key morphological and genetic features of their parental tumors, making them valuable tools for
studying bladder cancer biology and developing personalized treatment approaches. These
miniature replicas harbor the heterogeneity and functional characteristics of the parent tumor,
including diverse cell types, signaling pathways, and drug response profiles. Compared to cell
lines, PDOs retain the genetic and phenotypic diversity of the original tumor, providing a more
accurate representation of patient tumors(337-340) (Fig. A1.a). Unlike 2D cell cultures, PDOs
adopt a 3D architecture, resembling the native tumor microenvironment with stromal cells and
extracellular matrix components. PDOs can be maintained in culture for extended periods,
preserving their tumor characteristics and enabling longitudinal studies of drug response and
resistance mechanisms(341). PDOs derived from individual patients hold great potential for
personalized medicine, enabling tailored treatment strategies based on the unique characteristics
of each patient's tumor (Fig. A1.b). PDOs can also be used to study the development of drug
resistance, providing valuable insights into overcoming this major hurdle in cancer therapy and
the complex microenvironment of PDOs can be leveraged to test and optimize combination
therapies targeting different aspects of the tumor and its microenvironment(342).

154
Cancer, a profoundly heterogeneous disease, presents a formidable
Bladder cancer remains a complex and heterogeneous disease, posing significant
challenges for effective treatment and personalized medicine strategies, and is marked by its
genetic variability, which complicates prognosis and treatment, posing substantial health and
Figure A1. An overview of comparing PDOs to other models and generating PDOs from patient
tumors.
a) The diagram provides a comparison of advantages and disadvantages of utilizing cell lines, patientderived xenografts (PDXs) and patient-derived organoids (PDOs), as models. Figure was taken from
Foo, M. A. et al. Biomark Res (2022)(340). b) The schema shows a generalized flow diagram of PDO
generation. Patient-derived cancer cells are embedded into a matrix for 3D culture in vitro to grow them
into tumor organoids. Figure was taken from Yang, H. et al. Gastroenterol Rep (Oxf) (2018)(336).

155
economic challenges. While the non-muscle invasive bladder cancer (NMIBC) form responds to
treatment, a notable number of cases experience recurrence, necessitating continuous monitoring
to detect tumors that progress to muscle invasion. The majority of muscle-invasive bladder cancer
(MIBC) cases are diagnosed at advanced stages and often exhibit a limited response to treatment,
leading to low survival rates. This situation highlights the urgent need for advancements in bladder
cancer research and the application of new findings in clinical practice(343, 344). Current in vitro
models often fail to capture the full spectrum of genetic and phenotypic diversity observed in
patient tumors, limiting their utility for drug discovery and predicting therapeutic response.
Mullenders and colleagues have recently introduced a bladder organoid culture system that is
applicable to primary human urothelial bladder cancer cases(329). This system utilizes a specially
optimized growth medium that facilitates the long-term growth of organized three-dimensional
structures, which can be preserved, expanded, and further cultured in vitro. Mullenders and their
team observed a range of morphological and histological features across the UBC organoid
collection, highlighting intra-tumoral heterogeneity through the variable expression of markers
such as Ck5 and Ck20(329). Mullenders and their team also reported that organoids could be
successfully created from MIBC patient samples obtained during radical cystectomy with a
success rate of about 50%. The observed failures in establishing some MIBC organoids could
potentially be attributed to the high genetic instability of the cancer cells, which might lead to a
higher rate of apoptosis in organoids derived from cancerous tissue, as suggested in their
hypothesis.
The most common antibodies used for histological staining and characterization of bladder
organoids include the one for basal markers (keratin 5 (Ck5)), luminal marker (keratin 20 (Ck20),
and UpkIII) and intermediate cells’ marker (p63) and a marker to stain for active cycling cells

156
(Ki67). Briefly, the basal subtype contains Ck5+
cells while luminal subtype is characterized by
Ck20+
cells primarily. As with any tumor biopsy tissue, the organoids are also stained with
hematoxylin and eosin (staining nuclei a purplish blue and extracellular matrix and cytoplasm
pink) (Fig. A2).
Figure A2. Characterization of patient-derived organoids (PDOs).
Representative bright-field images of CTC-1044 and CK9151, at 4X and 10X magnification (upper
panel). Histologic stains for H&E, CK5, Ki67, CK20, TP63 and Uroplakin III, at 10X (lower panel).

157
Establishing Patient-Tumor Derived Organoids Repository
To extend our observations from cell lines to a more robust preclinical model, we
established a repository of patient-derived bladder organoids, spearheaded by me as a part of my
doctoral research plan.
This was made possible with the team of urologists at Keck School of Medicine, USC, led
by Dr. Sia Daneshmand and included Dr. Anne K. Schuckman, Dr. Hooman Djaladat, Dr. Monish
Aron and Dr. Sumeet Bhanvadia. Dr. Sanam Ladi Seyedian, a postdoctoral research fellow at
USC, helped in communicating the availability of eligible patients and providing their tumor
tissues to the pathologist at Keck School of Medicine. The team of pathologists at Keck School of
Medicine, including Dr. Andy E. Sherrod, Dr. Sue Ellen Martin and Moli Chen, also provided
valuable assistance in this project. Freshly resected transurethral resection of bladder tumor
(TURBT) and radical cystectomy (RC) tissues were collected with informed consent under an
IRB-approved protocol, disaggregated and digested, then seeded in defined medium and passaged
until organoids reached optimum size, confluency, and morphology For the current study, one of
the laboratory PDO lines (CTC-1044) was used in parallel with another established PDO line
generously provided by the National Cancer Institute Patient-Derived Models Repository (NCI
PDMR, (https://pdmr.cancer.gov(130)). Fig. A2 shows representative images of their
characterization via bright-field images for morphology and via histological stains for
characterization into luminal of basal subtypes (as mentioned before).
Briefly, the prospective cases are screened in advance based on pre-op scans to identify
high volume tumors likely to yield excess tissue- including both TURBT and radical cystectomy
cases and the patients are consented under our IRB-approved protocol (in collaboration with the
team of urologists at Keck School of medicine, USC). The portion received by the lab is ideally

158
away from the invasive edge to minimize any impact on diagnostic readout and is dissected with
minimal cautery, if possible, to avoid cell damage. The collected tissue is transferred to Keck
Pathology in sterile conditions, before being recorded and processed for transfer to our lab. One
received, all information from the tube carrying the tumor is recorded in database and patient
consent forms are saved in a designated folder. The size of the tumor is estimated and noted,
together with its physical appearance. Under sterile conditions of a tissue culture hood, we cut the
tissue pieces further into smaller pieces (1 mm to –2 mm) with a surgical blade, in a 10cm plate,
while keeping the tissue moist with Adv DMEM/F-12 (Fig A3). Next, these tissue pieces are
digested with collagenase (1 mg/mL of collagenase from Clostridium histolyticum) in Adv
DMEM/F-12 and Y-27632 (10 μM) for 30 min at 37 °C, shaking. This incubation is repeated once
more, before filtering the cell suspension through a 100μm strainer. Cells are then collected by
centrifugation and resuspended in appropriate amount of BME (35μL/well) and plated into wells
of a prewarmed 24-well plate, with the plate turned upside-down. After solidification of the BME
(in ~15 mins), the plate is then incubated right-side up at 37°C, 5% CO2, and 750μL/well of
‘human bladder organoid media’ is added on top of these wells. For the most part organoids like
to be seeded densely, estimate based on the size of the pellet (~50x104
cells in 35μL of BME).
This seeding density enables them to reform organoids in about 4-7 days, but they do not become
confluent. The human bladder organoid media’s ingredients and concentrations were optimized by
us over time and are mentioned in Table A1. The bladder organoids are passaged biweekly and
dissociated using Dispase II (at a final concentration of at 0.75 mg/mL, at ~2hrs incubation).
Organoids are frozen in freezing media (per recipe from NCI PDMR
(https://pdmr.cancer.gov(130)), and have been recovered efficiently. These are organoids
sustained over extended durations and closely mimic the histological characteristics of tumors.

159
Table A2 provides a list of all the patient-derived tissues received from urologists at Keck
School of Medicine and highlights the ones that were successfully developed into organoids,
together with the organoids we received from NCI PDMR, after complying to their PDMR request
form and NCI Material Transfer Agreement form.
Discussion
PDOs are three-dimensional (3D) self-organizing structures derived from patient tumors
or stem cells, recapitulating the key histological and genetic features of the original tumor. These
miniature replicas harbor the heterogeneity and functional characteristics of the parent tumor,
including diverse cell types, signaling pathways, and drug response profiles.
The success rate of establishing thriving organoids from bladder cancer patients is limited
by various factors. This includes the quality of tissue provided, which should not have large
necrotic or cauterized sections, as the cell viability should be around 70-90% in at least a million
cells(345). Besides this, the successful propagation of organoids requires modifications and
Figure A3. An example of a patient-derived tumor sample being measured and minced in our
lab, prior to digestion.

160
optimizations to the growth media and culture protocol over time, which also impacts the success
rate. Our lab has been able to develop 4 BC PDO organoid lines from ‘in-house’ patient-derived
tumors at Keck School of Medicine, in addition to 3 organoid lines that have been acquired from
NCI PDMR. Two of these organoid lines have been used in the experiments presented in this
dissertation, one developed from our ‘in-house’ patient-derived tumors (CTC-1044) and the other
from the NCI PDMR bank (CK9151). Upon various treatments and modulations, the results from
these organoid lines mirrored our observations in cell lines and showed overall similar to each
other. The organoid lines developed by me have also been used to for research by other graduate
students in our lab, adding a very useful bank of pre-clinical models to our lab’s disposal.
PDOs can be maintained in culture for extended periods, preserving their tumor
characteristics and enabling longitudinal studies of drug response and resistance mechanisms.
However, despite their significant advantages, further research is needed to optimize PDO culture
methods, standardize protocols, and address scalability challenges. Additionally, integrating PDOs
with other innovative technologies, such as bioengineering and microfluidics, holds immense
promise for further refining their function and predictive power. Hence, the emergence of PDOs
marks a significant advancement in cancer modeling and drug discovery and their unique ability
to bridge the gap between traditional models and the clinical setting offers potential opportunities
for personalized medicine and the development of more effective therapeutics.

161
Table A1. Detailed composition of the ‘human bladder organoid media’.
Item Stock Solution Volume
(for total volume of 500ml)
Working
Concentration
Advanced DMEM/F12
1X - 465.5 mL -
HEPES 1M 5 mL 10 mM
GlutaMax Supplement 100X 5 mL 1X
Primocin 50mg/mL 1 mL 0.1 mg/mL
N-acetylcysteine 500 mM in sterile water
(81.6 mg/mL) 1.25 mL 1.25 mM
Nicotinamide 1M (1.22 g/10 mL)
DPBS 5mL 10 mM
B-27 Supplement 50X 10 mL 1X
N-2 Supplement 100X 5 mL 1X
A83-01 10mM 250μl 5 μM
Y-27632
dihydrochloride
10 mM in Sterile Water
(high grade) (3.2 mg/mL) 500 μL 10 μM
FGF-2 25μg/ml 250μl 12.5 ng/mL
FGF-10 50μg/ml 1ml 100 ng/mL
FGF-7 50μg/ml 250μl 25 ng/mL

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Table A2. List of the patient-derived BC tissues received from USC and the PDOs received
from NCI PDMR.
Tissue resection process
(TURBT, RC, PDMR (PDOrg from
NCI PDMR))
USC ID or
PDMR Sample #
Date
Collected
Location of
collection
Successful
organoid
generation &
freezing (Y/N)
TURBT (OS-11-5 (3D Organoids)) CTC-0962 8/12/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0963 8/28/20 Keck N
TURBT (OS-11-5 (3D Organoids)) CTC-0964 10/13/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0973 10/26/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0974 10/29/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0975 10/29/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0977 11/05/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0978 11/06/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0979 11/09/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0981 11/20/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0982 11/20/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0983 11/30/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0986 12/11/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0987 12/17/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0988 12/18/20 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0990 01/11/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0993 01/14/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-0996 01/20/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1002 02/17/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1003 03/19/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1009 04/08/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1013 04/12/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1020 04/29/21 Norris N

163
TURBT (OS-11-5 (3D Organoids)) CTC-1021 05/03/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1025 05/24/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1026 05/24/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1027 05/24/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1028 05/28/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1044 08/27/21 Norris Y
TURBT (OS-11-5 (3D Organoids)) CTC-1056 09/10/21 Norris Y
TURBT (OS-11-5 (3D Organoids)) CTC-1059 10/18/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1060 10/18/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1072 12/13/21 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1091 04/25/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1096 05/02/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1101 05/23/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1105 05/26/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1109 06/12/22 Norris Y
TURBT (OS-11-5 (3D Organoids)) CTC-1110 06/23/22 Norris N
RC (OS-11-5 (3D Organoids)) CTC-1116 07/20/22 Keck Y
TURBT (OS-11-5 (3D Organoids)) CTC-1122 10/27/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1124 11/07/22 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1131 01/12/23 Norris N
TURBT (OS-11-5 (3D Organoids)) CTC-1136 02/09/23 Norris N
PDMR CK9151 (04/2017) - Y
PDMR CK8771 (08/2016) - Y
PDMR CK11077 (08/2015) - Y

164
Appendix B: References provided for literature review in Fig. 1.3
The comprehensive literature review shown in Figure 1.3, was done completely by Maheen Iqbal, and
served to analyze the intricate cellular signaling crosstalk in relation to TFAM.
Table A3. List of references for the comprehensive literature review in Fig. 1.3.
Ref. No DOI
1 10.1038/sj.cdd.4400874
2 10.1007/s11010-007-9575-6
3 10.1128/MCB.00670-13
4 10.4161/cc.9.3.10556
5 10.1038/cddiscovery.2015.63
6 10.1016/j.celrep.2017.09.026
7 10.3390/cancers12040827
8 10.1016/j.cmet.2012.04.003
9 10.1038/s41573-019-0019-2
10 10.1186/s12943-017-0648-1
11 10.3390/ijms130810478
12 10.3389/fendo.2023.1201198
13 10.1002/jcp.30461
14 10.1152/physrev.00030.2008
15 10.3389/fphys.2022.997619
16 10.1161/CIRCRESAHA.110.223818
17 10.3390/biom10020320
18 10.1002/jcb.25074
19 10.1177/1947601913476949
20 10.1186/1743-7075-11-10
21 10.3803/EnM.2016.31.1.52
22 10.1007/s10863-017-9722-z
23 10.4161/cc.8.17.9544
24 10.1186/s12967-023-03885-2
25 10.3389/fonc.2013.00292
26 10.1016/j.bbabio.2016.03.025
27 10.1186/s13045-022-01317-0
28 10.1007/s10741-016-9561-8
29 10.3389/fonc.2013.00096
30 10.1074/jbc.M002169200
31 10.1038/s41440-020-00539-4
32 10.1016/j.bbamcr.2016.07.007

165
33 10.3389/fonc.2019.01373
34 10.3390/ijms19103267
35 10.1186/s13578-021-00696-0
36 10.1111/acel.12720
37 10.1186/s40478-020-01062-w
38 10.1371/journal.pone.0142438
39 10.1038/s41386-020-0614-2
40 10.1038/onc.2014.164
41 10.1016/j.bbrc.2022.09.078
42 10.1074/jbc.M508805200
43 10.1007/s10741-016-9561-8
44 10.1038/s41419-021-03505-1
45 10.1016/j.bbrc.2018.02.071.
46 10.1016/j.chom.2019.08.004
47 10.3164/jcbn.18-37
48 10.3390/ijms222111444
49 10.1124/pr.110.002907
50 10.1073/pnas.1905902116
51 10.3390/cancers12030569
52 10.1038/s41420-022-01156-5
53 10.1016/j.molmet.2021.101309
54 10.2174/1566524016666160523143937
55 10.1080/14728222.2017.1397133
56 10.1038/s41586-020-2076-4
57 10.1152/ajprenal.00036.2014
58 10.1038/s41418-019-0469-4
59 10.1016/j.cellsig.2014.06.006
60 10.1096/fj.04-2364com
61 10.1038/sj.bjc.6605530
62 10.4049/jimmunol.179.10.7101
63 10.1016/j.celrep.2023.112185
64 10.1080/23723556.2018.1558682
65 10.1016/j.jbc.2023.104866
66 10.1038/s12276-023-00965-7
67 10.2147/CMAR.S289883
68 10.1038/srep40716
69 10.1038/ncomms10553
70 10.1016/j.coemr.2019.02.002
71 10.3389/fgene.2019.00435

166
72 10.1016/j.bbamcr.2019.118610
73 10.1155/2020/9423593
74 10.3390/biom10020347
75 10.1038/s41598-018-33290-5
76 10.1128/MCB.00110-14
77 10.1128/MCB.00221-14
78 10.3390/cells11142193
79 10.1186/s13045-017-0471-6
80 10.3390/ijms24043748
81 10.1152/physiol.00050.2013
82 10.3389/fcell.2020.617762
83 10.1002/embj.201386474
84 10.1007/s00253-020-10614-y
85 doi.org/10.1186/alzrt265
86 10.1016/j.bbamcr.2011.05.001
87 10.1038/cdd.2010.142
88 10.1158/0008-5472.CAN-22-2370
89 10.4103/1673-5374.249218
90 10.1101/gad.262758.115
91 10.1073/pnas.0702596104
92 10.1016/j.taap.2022.116167
93 10.3389/fcell.2022.871357
94 10.1016/j.yjmcc.2014.10.003
95 10.1002/1873-3468.14228
96 10.3390/antiox6040086
97 10.1016/j.bbamcr.2010.03.019
98 10.1152/ajplung.00244.2018
99 10.3390/cells9122658
100 10.3390/cells12091223
101 10.1152/japplphysiol.00900.2007
102 10.1089/ars.2013.5773
103 10.1074/jbc.M807397200
104 10.1016/j.brainresbull.2017.05.001

Abstract (if available)

Abstract Cisplatin comprises the backbone of therapy for locally advanced and metastatic bladder cancer (BC), but efficacy is limited by the emergence of resistance. We recently reported that cisplatin resistance can arise in BC through mutation-independent phenotypic plasticity, marked by a rapid metabolic shift towards oxidative phosphorylation (OxPhos). Based on these findings, we hypothesized that cisplatin could activate mitochondria and induce the transition to drug resistance. To test this hypothesis, we treated BC cell lines and patient-derived organoids (PDOs) with cisplatin and analyzed its effects on signaling from the nucleus to the mitochondria. We found that nuclear DNA damage induced by cisplatin led to sequential activation of ATM, AMPK, and PGC1 α, culminating in upregulation of TFAM, a master activator of mitochondrial replication and transcription. TFAM in turn activated mitochondrial OxPhos, leading to enhanced drug efflux and cisplatin resistance. Our findings highlight a cisplatin-induced nuclear–mitochondrial signaling pathway that mediates a rapid transition to cisplatin resistance. Disruption of this adaptive tumor response may constitute a new therapeutic strategy to enhance treatment efficacy for bladder cancer.

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Asset Metadata

Creator Iqbal, Maheen (author)

Core Title Cisplatin activates mitochondrial oxphos leading to acute treatment resistance in bladder cancer

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Cancer Biology and Genomics

Degree Conferral Date 2024-05

Publication Date 03/27/2024

Defense Date 03/21/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag bladder cancer,cisplatin resistance,metabolic plasticity,OAI-PMH Harvest,organoids

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Farnham, Peggy (committee chair), Goldkorn, Amir (committee member), Graham, Nicholas (committee member)

Creator Email maheeniq@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113858387

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