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Molecular signature of aggressive disease and clonal diversity revealed by single-cell copy number analysis of prostate cancer cells across multiple disease states

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Molecular signature of aggressive disease and clonal diversity revealed by single-cell copy number analysis of prostate cancer cells across multiple disease states

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Content Molecular Signature of Aggressive
Disease and Clonal Diversity Revealed
by Single-Cell Copy Number Analysis of
Prostate Cancer Cells Across Multiple
Disease States
By
Paymaneh D. Malihi
_____________________________________________________________
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the Requirements for the
Degree of DOCTOR OF PHILOSOPHY
Molecular Biology
May 2020
Table of Contents
Acknowledgments……………………………………………………………………………….iv
Glossary……………………………………………………………………………………...…...v
Clinical Motivation………………………………………………………………………………1
Chapter 1. Single-cell analysis of circulating rare cells in organ-confined prostate cancer
patients and comparison to prostate tissue touch preparation………………………………….….2
1.1 Abstract…………………………………………………………………………………….….3
1.2: Introduction………………………………………………………………………………...…4
1.3: Material and Methods………………………………………………………………………...6
1.4: Results and Discussion……………………………………………………………………….8
1.5: Conclusion……………………………………………………………………………….….12
1.6: Figures……………………………………………………………………………………....14
Chapter 2. Clonal diversity revealed by morphoproteomic and copy number profiles of single
prostate cancer cells at diagnosis………………………………………………………………...28
This chapter is published as:
Malihi, P .D., Morikado, M., Welter, L., Liu, S.T., Miller, E.T., Cadaneanu, R.M., Knudsen, B.S.,
Lewis, M.S., Carlsson, A., Velasco, C.R. and Kolatkar, A., 2018. Clonal diversity revealed by
morphoproteomic and copy number profiles of single prostate cancer cells at diagnosis.
Convergent Science Physical Oncology, 4(1), p.015003.
2.1 Abstract……………………………………………………………………………………...29
2.2 Introduction………………………………………………………………………………….30
2.3 Material and Methods ……………………………………………………………………….32
2.4: Results and Discussion…………………………………………………………………...…35
2.5: Conclusion…………………………………………………………………………………..39
2.6: Figures……………………………………………………………………………………....41
Chapter 3. Single-cell analysis of circulating tumor cells reveals genomic instability and loss of
tumor suppressors PTEN, RB1, and TP53 as distinctive features of aggressive prostate cancer..52
This chapter is submitted for publication.
Malihi, P .D.*, Graf, R.P .*, Rodriguez, A.E., Ramesh, N., Lee, J., Sutton, R., Jiles, R. , Velasco,
C.R., Sei, E., Kolatkar, A., Logothetis, C.J., Navin, N., Corn, P .G., Aparicio, A.M., Dittamore, R.,
Hicks, J., Kuhn, P . and Zurita, A. J. 2019. Single-cell circulating tumor cell analysis reveals
genomic instability as a distinctive aggressive prostate cancer feature. Nature Communications.
3.1 Abstract…………………………………………………………………………………...….53
3.2 Introduction……………………………………………………………………………….….54
3.3 Material and Methods ……………………………………………………………………….55
3.4: Results and Discussion…………………………………………………………………..….58
3.5: Conclusion……………………………………………………………………………….….62
3.6: Figures……………………………………………………………………………………....63
Future Prospective……………………………………………………………………………...81
References……………………………………………………………………………………….82
Acknowledgments
I am reminiscing back to the past six years and thinking about what I want to put down on this
page as I hear my four-month-old son crying in the background. If someone told me six years
ago that today I would be a proud mom of a baby boy, be living with my soulmate and best
friend, my husband, will be surrounded by nothing but love from friends and family, AND will
be getting my PhD, I would have not believed a single word they said.
So, I want to take this moment to thank those who got me here, because I did not do any of this
by myself.
First and foremost, I want to thank my mother. She is the strongest and most brave woman I
know. I cannot ever thank her enough for putting her fears aside to bring my sister and I to
America. For working long hours and multiple jobs to make sure we had everything we ever
wanted or needed. For setting an example that it is never too late to pursue your dreams and
when you do, aim high. For her continuous support in every way possible. I am now excited to
see her flourish into the most amazing grandmother any child could ask for. My job is for Liam
to understand how lucky he is to call her Banoo.
My husband, Dmitry, where would I be without you. You are the reason I am who I am today. I
had no idea what I was missing in life until I met you. You are my number one fan, my
cheerleader, and I know I can count on you 1000%. You came into my life six years ago and
completely changed my perception of myself, life, and happiness. You’re my best friend and the
word “love” barely described how I feel about you, because I feel so much more.
My friends. This section can be the length of this dissertation alone. I am so lucky to be
surrounded by friends whom I call family. I love every one of you so much and could not
imagine life without you. You have all provided me with so much support, and I am forever
grateful. I am so honored that my son will be calling you his aunts and uncles.
My Kuhn-Hicks lab family, thank you for your support. I would not have survived my Ph.D.
journey without you. Peter and Jim, thank you for your mentorship and constant push to be
better. I am forever grateful for everything you have done for me during this journey.
I want to end this section by thanking my son, Liam. My heart belongs to you. I never
understood what that cheesy line meant until I met you and realized it's not so cheesy. I love your
smiles, your cries, your confused face when you notice something for the first time, and your big
eyes looking at everything with curiosity. I love you so much and I dedicate this work to you,
especially since you helped me write the last chapter.
Cheers
Glossary
ADT Androgen Deprivation Therapy
AR Androgen Receptor
A VPC Aggressive Variant Prostate Cancer
BMA Bone Marrow Aspirate
BMTP Bone Marrow Tissue Touch preps
CD31 Common Antigen 31
CD45 Common Antigen 45
cfDNA cell-free DNA
CK Cytokeratin
CNA Copy Number Alterations
CRC Circulating Rare Cell
CRPC Castration Resistant Prostate Cancer
CTC Circulating Tumor Cell
ctDNA cell-free Tumor DNA
DAPI 4′,6-diamidino-2-phenylindole
DRC Disseminating Rare Cell
DTC Disseminating Tumor Cell
GI Genomic Instability
HD-SCA High Definition-Single Cell Assay
HR Hazard Ratio
HRD Homologous Recombination Deficiency
IMC Imaging Mass Cytometer
KM Kaplan-Meier
LOD Limit of Detection
LST Large-Scale Transition
MTC Metastatic Tumor Cell
NCCN National Comprehensive Cancer Network
OCPC Organ Confined Prostate Cancer
PB Peripheral Blood
PSA Prostate Specific Serum
PSMA Prostate Specific Membraine Antigen
PTTP Prostate Tissue Touch Preps
RP Radical Prostatectomy
SDOM Standard Deviation of Mean
UMAP Uniform Manifold Approximation
Vim Vimentin
WBC White Blood Cell
Clinical Motivation
Prostate cancer has displayed an increase in incidence with a gradual increase in mortality in the
last two decades[1]. This disease is mainly characterized by heterogeneous growth patterns
ranging from slow-growing tumors to highly metastatic and fast-growing lesions. Genetic
alterations associated with prostate cancer has been the focus of current research, and many have
been identified as associated with aggressive disease phenotype. However, many of these studies
are based on tissue collected at the time of diagnosis with lack of any tissue from repeat biopsy
or metastatic tissue, animal models, and prostate cancer cell lines. Lack of access to metastatic
tissue has hindered molecular, pathological, and clinical characterization of heterogeneity in
prostate cancer.
Understanding therapeutically relevant heterogeneity in solid tumors leads to the introduction of
targeted therapy. This is most notable in breast cancer (ER and HER2 overexpression), lung
cancer (EGFR mutations and EML4-ALK translocations), melanoma (BRAF mutations), and
colon cancers (RAS mutations)[2-7]. However, the prognostic model in prostate cancer is based
on clinical features, which in turn continues to impact clinical decision making[8, 9]. With the
availability of new therapeutics with diverse mechanisms, there is a need for molecular markers
that would target more clearly defined patient subpopulations and better identify responders.
Preclinical and clinical studies have identified candidate molecular markers for the classification
of aggressive disease phenotype using prostate cancer cell lines, patient tumor-derived
xenografts, and solid biopsy tissue specimens from primary and metastatic sites [10-20]. The
most consistently found alterations are: loss in copy number of PTEN and RB1, loss in copy
number as well as missense mutations of TP53, gain in chromosome 8q (MYC, NCOA2, EZH2,
and more), loss of MYC-N on chromosome 2. These alterations have been associated with
adverse prognosis and are enriched in highly aggressive tumors [10, 11, 13-15, 18, 20, 21].
Identification of these molecular biomarkers of aggressive disease in circulating tumor cells and
metastatic tumor cells may lead to the stratification of high-risk and fast progressor subgroups at
earlier stages. Liquid biopsy resulting in the assessment of circulating and metastatic tumor cells
has the potential to provide a non-invasive approach for studying tumors in real-time and at
multiple time points allowing for early recognition of aggressive disease. Longitudinal sampling
thorough liquid biopsy may identify the rise of aggressive clonal populations during treatment
course before any clinical symptoms of failed therapy. This would allow for modification of
treatment plan prior to physical indications of progression or drug resistance.
The following studies focus on the use of non-invasive liquid biopsy to identify and characterize
circulating rare cells in peripheral blood and bone marrow aspirate of localized, de novo
metastatic, and aggressive variant prostate cancer patients. We hope to use this platform to
identify molecular signatures of aggressive disease that would allow for selection of high-risk
patients for early intervention and intensified treatment.
1
Chapter 1.
Single-cell analysis of circulating rare
cells in organ-confined prostate cancer
patients and comparison to prostate tissue
touch preparation
In Collaboration with Dr. Kenneth Pienta at Johns
Hopkins Hospital, Baltimore, Maryland
2
1.1 Abstract
As the second leading cause of cancer in men, 90% of prostate cancer patients are diagnosed at
organ-confined stages, where radical prostatectomy (RP) is the “golden standard” of treatment.
However, deferring intervention is becoming a more popular option for low-risk patients, with a
15-year progression-free survival of 95%. High-risk patients greatly benefit from RP, with a 5-
year progression-free survival of 85%, while the decision to proceed with RP is not so candid for
intermediate-risk patients. Using the high definition-single cell assay (HD-SCA) workflow,
circulating rare cells (CRCs) from peripheral blood (PB) and disseminating rare cells (DRCs)
from bone marrow aspirate (BMA), and single cells from prostate tissue touch preparations
(PTTP) will be analyzed for morphological, genomic, and proteomic profiling. We hypothesize
that by using morpho-proteo-genomic parameters we may develop markers for a more accurate
stratification of organ-confined prostate cancer (OCPC) patients, offering personalized treatment
for each individual. Preliminary data shows 89% (n=32) of PB samples positive for CRCs and
0.03% (n=1) of BMA are positive for DRCs, as defined by ≥5 cells/mL threshold. The incidence
of CRCs in PB and DRCs in BMA did not correlate with serum prostate-specific antigen (PSA)
levels, Gleason score, or other clinical parameters. Low levels of genomic aberrations were
observed in all CRCs (0.02%, n=220). Proteomic analysis identified Vim/CD31 positivity and
lack of prostate-specific markers in these rare cells. Single-cell analysis of PTTPs revealed
genomic aberrations associated with aggressive disease such as loss of PTEN, RB1, TP53, N-
MYC, CDH1, CDH11 and gain in chromosome 8q associated with MYC, NCOA2, and EZH2.
In conclusion, given the absence of CRCs in normal donors, the presence of these cells at such
high levels is related to disease and further characterization may shine light on their cell lineage
and how they may impact treatment response and precision medicine.
3
1.2 Introduction
Prostate cancer is the most common urological malignancy in American men with an estimated
180,000 new cases in 2019 and mortality rates as high as 60-90% [1, 22, 23]. Risk stratification
groups low-, intermediate-, and high-risk as proposed originally by D’Amico et al. in 1998 were
subsequently adopted by the National Comprehensive Cancer Network (NCCN) [23]. Organ-
confined prostate cancer (OCPC) patients stratified as low-risk based on anatomical
classification and guidelines are recommended conservative management of the disease [24, 25].
On the other end of the disease spectrum, high-risk patients are treated with potent systematic
and radical therapies [25-28]. No standard treatment recommendations exist for patients who
harbor disease outside of the extreme ends due to tremendous heterogeneity.
Biological and clinical heterogeneity observed within these populations (low-, intermediate-,
high-risk) are not well captured via an anatomical classification. Thus, a more accurate
stratification system could lead to better treatment planning that may significantly affect a
patient’s quality of care and overall survival. The development of a more precise stratification in
prostate cancer has been hindered by the lack of access to tissue needed for clinical, pathological,
and molecular correlation and biomarker development both at diagnosis and progression.
Liquid biopsies through analysis of circulating rare cells (CRCs) and disseminated rare cells
(DRCs) from peripheral blood (PB) and bone marrow aspirate (BMA), respectively, provides a
minimally-invasive opportunity to serially characterize tumor heterogeneity at single-cell
resolution relative to individual patient tumor characteristics [9, 29, 30].
Among the multiple CTC technologies available, most of which primarily rely on enrichment
methods based on protein marker, cell size, or density-specific selection, the high definition-
single cell assay (HD-SCA) workflow is remarkable in that it allows for direct and unbiased cell
identification and inclusive of heterogeneous CRC and DRC populations [31-33].
The HD-SCA workflow enables protein expression, subcellular protein localization, and genomic
alteration via copy number aberration (CNA) analysis in addition to the enumeration of CTCs.
Furthermore, this platform can be used for single-cell solid biopsy analysis of prostate tissue
tumor touch preps (PTTP) prepared using fresh tissue following core biopsy or radical
prostatectomy. Such single-cell analysis of liquid and solid biopsy will allow for evaluation of
candidate molecular markers associated with disease progression, aggressiveness, and/or
therapeutic response.
Current efforts in preclinical and clinical studies have established several candidate molecular
markers of aggressive phenotype or therapeutic resistance in prostate cancer. Detection of
androgen-receptor splice variant 7 (AR-V7) in circulating tumor cells has been associated with
resistance to enzalutamide and abiraterone [34, 35]. Platinum-based and taxane-based
chemotherapy such as cabazitaxel has been shown to be beneficial for a subset of metastatic
prostate cancer patients with clinical criteria for aggressive variant prostate cancer (A VPC) [11,
4
36, 37]. Recent studies have shown that A VPC patient tumor samples are enriched for loss of two
or more tumor suppressor genes TP53, RB1, and PTEN thus providing a molecular signature for
accurate stratification of these patients and thus intensified therapy [10, 16, 36, 38-42].
Here we propose an assessment of liquid and solid biopsy from OCPC patients using the HD-
SCA workflow for presence of candidate molecular markers of disease progression,
aggressiveness, and/or therapeutic response. Identification of such markers and their use in a
non-invasive biopsy may lead to early detection of prostate cancer, early identification of
aggressive phenotype and use of intensified treatment, and over-treatment prevention for low-
risk disease.
5
1.3 Material and Methods
Specimen collection and HD-SCA sample preparation
Samples from prostate cancer patient harboring organ-confined prostate cancer confirmed via CT
scan were collected at Johns Hopkins Hospital. Clinical parameters such as prostate serum
antigen (PSA), Gleason score, clinical stage, and patient demographics were provided by the
clinical site (Table 1). Peripheral blood and bone marrow aspiration were collected at the time of
radical prostatectomy (RP) and placed into 10-ml Cell-Free DNA BCT Streck tubes (STRECK,
Omaha, USA, Cat#62790315) (Figure 1). Post RP, the prostate is section into multiple pieces by
a pathologist who identifies and imprints different sections of the tissue onto a glass slide. All
touch preparation slides were air-dried, blocked with 7% BSA in PBS, and stored in -80°C until
processing. The slides are then stained and imaged manually for identification of both location
and quality of prostate tissue imprints. Identified clusters of tissue are separated into single cells
for genomic analysis. A standard percutaneous bone marrow aspiration was performed at the
right posterior iliac crest. Samples were shipped overnight to the Kuhn laboratory at the
University of Southern California, Los Angeles, CA, USA. Upon arrival of PB and BMA
samples in Streck tubes, red blood cells were lysed, and remaining cells were plated on a custom-
made adhesive glass slide (Marienfeld, Lauda-Königshofen, Germany) as a monolayer of 3.0 x
10
6
nucleated cells (Figure 2) [33]. Unstained slides were covered with coverslips and stored at
-80°C before analysis.
Detailed sample processing procedures have been described previously[43]. Briefly, two slides
per patient were stained using four fluorescent markers. Cell nuclei were identified using DAPI
(4',6-Diamidino-2-Phenylindole, Dihydrochloride, Cat#D1306, Invitrogen, Waltham, MA).
Epithelial cells were identified using a mix of cytokeratin (CK) 19 (1:100; Dako, Carpinteria,
USA, Cat#M0888) and pan-cytokeratin antibodies (1:100; Sigma-Aldrich, St. Louis, USA,
Cat#C2562) with an Alexa Fluor 555 secondary antibody (Invitrogen, Carlsbad, USA,
Cat#A21127). An anti-CD45 Alexa Fluor 647–conjugated antibody (1:125; Biorad, Hercules,
USA, Cat#MCA87A647X) was used as a leukocyte exclusion marker. An androgen receptor
(AR) rabbit monoclonal antibody (1:250; Cell Signaling Technology, Danvers, USA, Cat#5153)
and Alexa Fluor 488 secondary antibody (Invitrogen, Carlsbad, USA, Cat#A11034) were used
for evaluation of AR levels in cells[31, 33].
Candidate cell imaging and morphological analysis
PB and BMA slides were imaged at 10X magnification using automated high-throughput
microscopy. Candidate cells were computationally identified and semi-manually classified using
morphology and DAPI+/CK+/CD45- staining criteria as previously established (Figure 2) [44,
45]. PTTPs were manually imaged at 10X and 40X magnification. AR expression and
localization were reviewed and quantified using average fluorescent intensity within a fixed-size
circle centered around the cell[46]. The threshold for CK intensity and AR positivity was
defined as a signal of more than six standard deviations over the mean signal intensity (SDOM)
6
observed in the surroundings leukocytes (background). For morphometric analysis of candidate
cells, nuclear roundness was measured using DAPI intensity within a fixed-size circle centered
around the cell using image object features from EBImage R script.
Single-cell next-generation sequencing and analysis
Single cells from PB, BMA, and PTTP were isolated and underwent genomic amplification as
previously described (Figure 3) [47, 48]. In short, tumor cells were isolated off the slides using a
robotic micromanipulator system and placed in individual tubes for whole genome amplification
(Sigma-Aldrich, St. Louis, USA, Cat#WGA4). Prior to cell capture, PTTP slides were incubated
with dispase type II (1:1000; ThermoFisher, Waltham, USA, Cat#17105041) and collagenase
(1:1000; ThermoFisher, Waltham, USA, Cat#17018029) in PBS at 37°C for 30 minutes. PTTP
slides were washed with PBS twice for 3 minutes, and individual cells or cell clusters from
PTTPs were isolated and extracted in the same manner. Following DNA purification, 50ng of
DNA was sonicated to 200 bp fragments in AFA fiber pre-slit snap-cap microtubes (Covaris,
Woburn, USA, Cat# 520077) with the Covaris S2 using the following setting: intensity of 5, 10%
duty cycle, 200 cycles per burst, 3-minute treatment time, and temperature less than 7°C.
Sonicated DNA was used for library construction with the DNA Ultra Library Prep Kit and
Multiplex Oligos for Illumina (New England Biolabs, Ipswich, USA, Cat#E7370 and E7600).
Copy number alteration (CNA) profiles and heatmaps were created as previously described, with
R unsupervised hierarchical clustering using the Ward method and Euclidian distance to
distinguish subclones[47, 48].
Single-cell targeted proteomic analysis
Previously identified candidate cells from PB slides were subjected to protein analysis using
Fluidigm Hyperion Imaging Mass Cytometer (IMC) (Fluidigm, San Francisco, USA). Slides
were washed in PBS to remove cell media before secondary staining. Slides were stained with 21
markers of the 40 available channels to detect leukocyte, epithelial, endothelial, and prostate cell
protein expressions (Table 1). Slides were blocked using 1% BSA with 0.2mg/mL mouse IgG Fc
fragment (Thermofisher, Waltham, USA, Cat#31205) in PBS for 60 minutes at 37°C. MaxPar
TM

metal-labeled antibody cocktail was prepared in 0.1% Tween and 1% BSA in PBS with
antibodies from Fluidigm according to the manufacturer’s dilutions. Antibody cocktail was
added to each slide for 90 minutes at room temperature and washed with PBS twice for 3
minutes. IR-193 DNA intercalator (Fluidigm, San Francisco, USA, Cat#201192A) was added to
slides for 30 minutes at room temperature. Slides were washed with PBS twice for 3 minutes and
dipped in ddH2O for 5 seconds to remove the salt. Slides were dried for 2 hours and stored at
room temperature until IMC runs.
Laser ablation with time-of-flight detection and analysis was performed using the IMC. A 400
µm x 400 µm region of interest around the cell of interest was ablated aerosolizing a 1 µm
2
area/
pulse (200 Hz), followed by ionization and quantification in the CyTOF Helios instrument. Ion
7
mass data is collected, resulting in the construction of 1 µm
2
images. This region includes the
tumor cells of interest and surrounding white blood cells as reference (Figure 3).
A four-level scoring system was developed where 0 is below the limit of detection (LOD), 1 is at
limit of detection, and 2-3 is above. The LOD for each marker was set as equal to signal to noise
ratio (S/N) ≥ 3 or standard deviation of mean (SDOM) > 3.3. A score of 1 was given to cells
exceeding the LOD, a score of 2 was given to signals with S/N of 7-20 or SDOM > 6, and finally
a score of 3 was assigned to signals of S/N > 20 or SDOM > 12.
8
1.4 Results and Discussion
Circulating and disseminating rare cells: Enumeration, morphometric, and clinical
parameters
A total of 36 patients have consented to peripheral blood (PB) and bone marrow aspirate (BMA)
collection prior to RP. All patients were enrolled at the time of diagnosis with localized prostate
cancer and are treatment naïve. Patient demographics and clinical data are summarized in Table
2. Of the 36 patients analyzed, 32 (89%) were positive for CRCs, as defined by a threshold of ≥5
CRC/mL (Figure 4A). Within these positive samples, 30 (93%) contained clusters of CRCs
defined as two or more cells grouped (Figure 4B). There was no correlation between CRC
enumeration and presence or size of CRC clusters with clinical staging, Gleason score, or PSA
(Figure 4C, 4D, and 5).
Morphometric parameters such as nuclear roundness and mean CK intensity were measured at
the single-cell level, and an averaged signal from the surrounding white blood cells (WBCs)
were used as control for these measurements. No correlation was observed when comparing
these morphometric measurements to clinical parameters (Figure 6A and 6B). In 2015, AUA
introduced a new grading system for prostate cancer patients that classifies Gleason score based
on the pattern of the sum of the individual scores (i.e. 3+4 vs 4+3) and the histological pattern of
the tissue specimen. When including this “grade group” into the analysis with CRC enumeration
and morphological measurements, no correlation was observed (Figure 6C).
Several autopsy data have shown that approximately 80% of patients who die from prostate
cancer have metastases in the bone [49-51]. Given that this is a primary metastatic site for
prostate cancer, a BMA analysis can lead to the detection of micrometastasis which can initiate
intensified treatment where it is needed. Analysis of BMA for DRCs in matched samples for
these patients showed a low frequency of positive samples with only one (0.03%) patient having
a positive sample based on threshold of ≥5 DRC/mL (data not shown). Given that these patients
are diagnosed with organ-confined disease with no evidence of metastasis, a negative BMA
sample further confirms the clinical diagnosis.
Genomic landscape of CTCs in localized disease
Single-cell copy number genomic analysis was performed on 220 CRCs from a total of 14
patients. CNA profiles with 5K bins and median centered data were generated for all sequenced
cells (Figure 7). There are specific copy number alterations that are hallmark of prostate cancer
including AR gain on chromosome X [42], PTEN loss on chromosome 10 [18, 38, 52], ERG-
TMPRSS2 fusion [53], RB1 loss on chromosome 13 [18, 38], TP53 loss on chromosome 17 [18,
38], and MYC gain on chromosome 8 [42, 52]. From the cells analyzed to date, only 4 (0.02%)
harbored alterations. These alterations included PTEN loss on chromosome 10 in 2 (0.001%) of
CRCs and TP53 deletion on chromosome 17 in 1 (0.005%) of CRCs, however, both alterations
9
are part of a large chromosomal loss and not a focal gene alteration and therefore include loss of
other genes as well. All other CRCs analyzed harbored copy number neutral profiles (Figure 7).
For all positive samples described above, the androgen receptor (AR) signal was below the
threshold of detection (data not shown). AR protein overexpression is not the only mean of
detecting AR driven cancer development in prostate cancer. AR gene amplification or missense
or nonsense mutations are also possible [42]. CNA profiles of CRCs revealed no amplification of
AR on chromosome X (Figure 7).
The 0.02% of CRCs with few CNA profiles speak to the level of genomic instability and disease
development in organ-confined patients. Given the absence of genomically altered cells in
normal donor blood, as previously published by Kuhn laboratory [54, 55], we hypothesize that
CRCs with minor genomic alterations are disease-related and are being shed into the circulation
due to inflammation in the prostate and/or tumor growth damaging nearby tissue.
Genomic landscape of PTTPs
PTTPs were selected from six additional patients for single-cell genomic profiling. PB and BMA
analysis for these patients are not included in the data presented above. Each PTTP was analyzed
at a single cell level for number of aberrated cells from all cells picked and sequenced, number of
clones present for all aberrated cells, presence of A VPC molecular signature (loss of PTEN, RB1,
and/or TP53), AR amplification on chromosome X, and any other cancer-specific genomic
alterations across the entire genome (Table 3).
A VPC molecular signature was detected in one of the six samples. Majority of the aberrated cells
in this samples displayed the loss of all three TSGs, with a few cells carrying only two (Figure
8). No AR amplification was detected in this patient; however, additional cancer-specific
alterations such as loss of MYC-N, a gain of 8q (MYC and NCOA2), loss of BRCA1, and loss of
CDH1 and CDH11 were detected (Figure 8). The detection of the A VPC signature in this patient
at diagnosis may have altered treatment strategy and led to intensified treatment upfront.
Two patients displayed a loss in RB1, and one displayed a loss in PTEN. Loss of these tumor
suppressor genes may lead to increased genomic instability which may indicate development of
aggressive disease in these patients. Overall, gain in AR on chromosome X was not detected in
any of these patients. Cancer-specific alterations such as loss of MYC-N, gain of 8q (EZH2,
MYC, NCOA2), loss of CDH1 and CDH11, loss of APC, loss of MAP2K1, or loss of BRCA1
and BRCA2 were observed in these patients [42]. These alterations have been associated with
more aggressive disease and risk of progression in prostate cancer and may be targeted for future
therapeutics.
Single-cell genomic alterations of PTTPs reveal that even at early stages of the disease, prostate
cancer’s genomic landscape may include alterations that are associated with a higher risk in
disease progression, genomic instability, aggressive phenotype, and drug resistance. In the
10
absence of DRCs and aberrated CRCs, single-cell genomic analysis of PTTPs may be
informative for clinicians and patients during treatment decision-making. Observation of the
A VPC molecular signature is one genomic signature that can lead to intensified treatment.
11
Proteomic profiling of CRCs in localized disease
Using the imaging mass cytometer (IMC) by Fluidigm, a panel of 21 target proteins were
analyzed on two patient samples [56]. As seen in Table 2, protein targets on the panel included
epithelial-specific proteins such as Epithelial Cell Adhesion Molecule (EpCAM), Epithelial
cadherin (E-Cadherin), and CK 8/18, endothelial and mesenchymal markers such as vimentin
(Vim), CD31, CD44, and CD66, prostate-specific markers such as PSA and prostate-specific
membrane antigen (PSMA), AR N-terminus, AR C-terminus, and AR-v7, and leukocyte specific
markers such as CD45, CD3, CD8a, and more (Table 2).
The analysis was completed on two patients, each with Gleason score of 9 (4+5) and 7 (3+4) as
seen in Figure 9A. Using an already established scoring system each target from the panel of 21
was assessed for each CRC single cell or cluster. None of the CRCs analyzed from the two
patients expressed epithelial or prostate-specific protein markers. However, all 12 single-cell or
clusters of CRCs analyzed expressed Vim and 9/12 (75%) expressed CD31. Three out of 12
(25%) were HLA-DR positive, and all three of these cells are also Vim+/CD31+ (Figure 9B).
Vim is a type III intermediate filament protein and major cytoskeletal component in
mesenchymal cells. Because of this, Vim is often used as a marker of mesenchymal-derived cells
or cells undergoing an epithelial-to-mesenchymal transition (EMT) during both disease
development and metastatic progression. CD31 is found on the surface of platelets, monocytes,
neutrophils, and some types of T-cells, and makes up a large portion of endothelial cell
intercellular junctions.
HLA-DR is an MHC class II cell surface receptor encoded by the human leukocyte antigen
complex, and its primary function is to present peptide antigens, potentially foreign in origin, to
the immune system to elicit responses that eventually lead to the production of antibodies against
the same peptide antigen.
Expression of Vim alone on 3/12 (0.25%) of analyzed cells point to these CRCs being
mesenchymal in origin. These CK and Vim positive cells may be undergoing epithelial-to-
mesenchymal transition (EMT) in order to leave the primary tumor. Expression of pan-CK and
Vim has been observed in human primary cell lines in breast, lung, and prostate cancer and in
CTCs found in mouse prostate models [57].
Six CK positive CRCs express both Vim and CD31. Vim expression points to mesenchymal cell
lineage while CD31 expression points to endothelial cell lineage, such as cells making up the
vasculature. Currently, there are no studies that have detected these CK/VIM/CD31 positive
CRCs in liquid, solid biopsy, or animal model samples. The main hallmark in cancer initiation
and development is alteration of the protein profile expression in defected cells. These CK/Vim/
CD31 positive cells may be traces of abnormal cells arisen during disease development and may
be lost during natural cycle of the disease.
12
Investigation of the origin of mesenchymal/endothelial cells in PB
Absent of epithelial and prostate-specific markers and present of Vim+/CD31+ markers in CRCs
initiated a set of investigational experiments to assess the origin of these Vim+/CD31+ cells in
PB. Initial assessment of surgical workup of a patient revealed a few variables to be tested
(Figure 10). These variables included effects of anesthesia, enema 24-hours prior to surgery, and
mechanical damage to arteries during the blood draw. Additionally, a blood draw was collected
24-72 hours prior to surgery during clinical visit as well as on the day of surgery prior to surgery
prep. Ten fasting normal donor samples and ten kidney/bladder cancer patient samples were also
collected as controls.
Using the HD-SCA workflow, each variable was tested for presence of CRCs in PB (Figure 11).
Samples collected before and after administration of anesthesia showed the same frequency of
CRC/mL. Therefore, anesthesia does not pose any stress on the body or the prostate that would
induce shedding of rare cells. For mechanical damage to arteries multiple blood tubes (total of
two) were collected during each draw. If there were any damage to the arteries during collection,
more cells would be present in tube one than tube two. Both draw one and two showed the same
frequency of CRC positivity.
Due to the nature of the procedure, an enema may apply pressure to the already inflamed prostate
and therefore cause shedding of cells into the circulation. Patients had an enema prior to surgery
as standard procedure. These samples were compared to samples collected from patients without
enema prior to surgery and showed the same frequency of CRC/mL per sample. Analysis of PB
from twelve kidney/bladder cancer patients also showed high levels of CRCs in samples.
However, PB samples from ten healthy fasting donors showed absent of these CRCs. Presence of
these rare cells in the circulation is isolated to cancer patients only and is not present in all
samples collected at this clinical site.
Comparison of PB collected 24-72 hours prior to surgery (Days-before-surgery), and PB
collected on the day of the surgery (Day-of-surgery) revealed a difference in CRC levels.
Samples collected prior to day of surgery lacked any level of CRCs where samples collected on
the day of surgery had high levels of CRCs present (Figure 11). Given the absence of these cells
24 hours prior to surgery, an unknown clinical variable may be causing stress-induced shedding
of epithelial cells into the circulation.
13
1.5 Conclusion
Liquid biopsy analysis allows a minimally-invasive approach for longitudinal sampling of
circulating cancer cells at diagnosis and over the course of treatment. There are several
technologies available for analysis of liquid biopsy; however, HD-SCA workflow has several
advantages over many others. The HD-SCA workflow allows for an unbias approach for
detection and sampling of circulating tumor in both PB and BMAs. Multi-omics analysis of
identified candidate rare cells in PB of OCPC patients revealed presence of CK positive cells and
clusters that are genomically neutral, express Vim and CD31, and carry no prostate-specific
protein markers such as PSA and PSMA. CK, Vim, and CD31 expression in additional to copy
number neutral genomic profiles of these rare circulating cells may point to endothelial cell
lineage for these cells, perhaps involved in tumor vasculature. The tumor microenvironment
plays an essential role in disease initiation, development, and progression in prostate cancer.
These detected CRCs might not carry copy number alterations. However, mutational analysis has
not been done on these cells and may hold additional information. Additionally, a more extensive
proteomic panel may shine light on the exact origin of these cells and perhaps reveal new
candidate rare cell populations that have not been reported previously. Addition of proteins
involved in EMT, epithelial, endothelial, and mesenchymal cell lineage may be helpful in this
search.
Lack of DRCs in BMA samples of these patients further confirms their organ-confined clinical
diagnosis. This does not speak on the presence or absence of micrometastasis in these patients.
We can increase our assay’s sensitivity for the detection of micrometastasis by increasing our
sample volume. We currently testing approximately 1 ml of PB or BMA. We can increase this
volume by analyzing more slides per patient. We can parallel this analysis with clinical data in
rate of progression and presence of bone metastasis based on CT Scan and validated the
sensitivity of our DRC detection in organ-confined setting.
Single-cell analysis of solid biopsy samples reveals the presence of both genomically altered and
copy number neutral clones of cells. These genomically altered cells include multiple clones
within each patient sample. Detection of cancer-specific alterations and molecular markers
associated with aggressive phenotype in these patients may impact a patient’s treatment plan.
A VPC molecular signature was detected in one patient and based on multiple clinical trials,
presence of this signature is associated with the most aggressive form of prostate cancer which
will either not respond or respond for a very short time to standard therapy. However, these
patients are responsive to platinum-based chemotherapy, and cabazitaxel which are both
intensified therapies kept for as a last resort treatment option[18, 36-38]. Detection of such
molecular signature at diagnosis can lead to intensified treatment upfront. Other molecular
markers such as loss of PTEN, RB1, gain in 8q, Loss of BRCA1 and BRCA2 is associated with
highly aggressive disease and may be treated with therapies targeting DNA repair pathways or
intensified chemotherapy[58, 59].
14
In conclusion, the HD-SCA workflow allows for detected of rare cells in the circulation that are
otherwise absent in normal individuals. CRCs in these OCPC patients are not only present
frequently but also at a very high concentration, and further investigation and protein profiling of
these cells are necessary to investigate both cell lineage and role at this stage of the disease. Any
morphometric, genomic, and proteomic signature discovered through this work can in
combination with already established stratification system lead to a more accurate assessment of
risk at diagnosis and impact treatment.
15
1.6 Figures

Figure 1: Study workflow for the collection of samples and downstream analysis. For each
patient on the study, PB, BMA, and PTTP post-radical prostatectomy was collected and shipped
to Kuhn laboratory at University of Southern California for HD-SCA analysis.
16

Figure 2: HD-SCA platform for morphometric, genomic, and proteomic profiling of liquid
biopsy. PB, BMA, and solid tissue touch prep samples were collected. PB and BMA were
initially spun down for plasma extraction. Next, they undergo red blood cell lysis before plating
approximately 3 million nucleated cells on each slide. Prepared slides are stored at -80°C until
needed for fluorescent antibody staining. Touch preps are blocked with BSA, air-dried and stored
in -80°C until staining. PB and BMA stained slides are first morphometrically profiled using
automated digital microscopy at 10X magnification, followed by classification by a technical
analyst. Touch preps are imaged manually at both 10X and 40X magnification. Location of tissue
imprints is recorded. Identified tumor cells and tissue imprints are processed for genomic copy
number alteration or targeted protein analysis via imaging mass cytometry.
17

Figure 3A: Single-cell and cfDNA genomic preparation for next-generation sequencing and
CNA analysis. Single cells are extracted from slides with a robotic micromanipulator prior to
whole genome amplification and DNA purification followed by Illumina DNA library
preparation for sequencing. CfDNA is extracted from plasma, and Illumina DNA libraries are
constructed similarly to single cells. CNA profiles are created using the human genome as
reference where copy number is calculated then displayed as the ratio to the median. 3B:
Targeted proteomic analysis via IMC. Slides previously labeled with fluorescent antibodies are
stained with 21 metal-conjugated antibodies. Regions of interest are laser ablated with plasma
ionization, and ions are detection using Cytometry by Time of Flight (CyTOF) technology.
Rasterized images are generated from ion count data, and protein expression on tumor cells are
scored for expression levels.
18

Table 1: Patient Demographics. Clinical and demographics such as age, race, clinical stage,
serum PSA levels at time of diagnosis, and diagnostic Gleason score was collected for all 36
patients.
19

Table 2: Targets and descriptions of 21 metal-conjugated antibodies for IMC analysis.
Markers are subdivided into epithelial, endothelial, leukocyte, and prostate-specific panels for
classification of cell type and origin.
20

Figure 4: Enumeration of CRCs and correlation to clinical parameters. 4A: Enumeration of
CRCs in PB arranged from highest concentration (CRC/mL) to lowest. 4B: Number of CRCs
found within each cluster group 1, 2, 3-5, 6-10, and 11+. 4C and 4D: Clinical staging and
Gleason score for each patient. Patients are sorted based on CTC/mL count.
21

Figure 5: CRC enumeration and serum PSA levels. Enumeration of CRCs in PB arranged
from highest concentration (CRC/mL) to lowest compared to serum PSA levels (ng/mL). Normal
serum PSA levels are marked at 4 ng/mL. Two outlier PSA concentrations are marked by the
green circles. Patients are sorted based on CTC/mL count.
22

Figure 6: Morphological measurements of CRCs. 6A: Nuclear roundness measurement of
each CRC within each patient. 6B: Mean CK intensity for each CRC in each patient. 6C:
Clinical rick group for each patient assigned based on combination of PSA levels, clinical
staging, and Gleason score. Patients are sorted based on Risk Group from lowest to highest.
23

Figure 7: Copy number alteration profiles for CRCs in PB. A total of 220 CRCs from 14
patients were picked and sequenced. Using low depth sequencing copy number alteration
profiled was generated for each CRC. Blue indicates deletion, red amplification, and white
represent a copy number neutral profile for each cell. Cells from the same patient are indicated
by the same color.
24

Table 3: Summary of genomic alterations found in prostate tissue touch preps. For each of
the six patient touch preps analyzed, Gleason score, number of aberrated cells, number of cells
sequenced, number of clones present, presence of A VPC signature, AR genomic amplification,
and other cancer-specific aberrations status is summarized.
25

Figure 8: Example of CNA profile for cancer cell picked and sequenced from PTTP.
Example of CNA profile from a tumor cell picked form PTTP from patient no.1. Alterations
discovered includes the A VPC molecular signature and other cancer-specific alterations such as
loss of CDH1 and CDH11, Gain of chromosome 8q, loss of MYC-N, and many others.
26

Figure 9: Imaging mass cytometer data for two PTTPs. 9A: HD-SCA images of CRC single
cells and clusters detected in PB of two patients with Gleason score 4+5 and 3+4. 9B: Heatmap
indicating the levels of protein expression for each marker stained and analyzed using the
imaging mass cytometer. Prostate-specific markers are boxed in blue, epithelial cell markers are
boxed in green, and endothelial and mesenchymal markers are boxed in purple.
27

Figure 10: Clinical timeline for a patient 24-hours prior to radical prostatectomy. Major
clinical events such as enema, anesthesia, blood draws, and more are marked on a 24-hour
timeline as variables to be tested for CRC/mL levels.
28

Figure 11: CRC enumeration for all tested clinical parameters. Comparison of CRC
enumeration for each tested variable including pre- and post-anesthesia, multiple blood draws
(draw 1 and 2), with (pre- or post-anesthesia draws) and without an enema, days-before-surgery
(24-72 hours prior), day-of-surgery, kidney/bladder samples, and fasting healthy donors. CRC
levels are separated by less than 5 CRC/mL, between 5 and 20 CRC/mL, and more than 20 CRC/
mL.
29
Chapter 2.
Clonal diversity revealed by
morphoproteomic and copy number
profiles of single prostate cancer cells at
diagnosis
In collaboration with Dr. Isla Garraway at Veterans
Affairs Hospital, Los Angeles, California
This chapter is published as:
Malihi, P.D., Morikado, M., Welter, L., Liu, S.T., Miller, E.T.,
Cadaneanu, R.M., Knudsen, B.S., Lewis, M.S., Carlsson, A., Velasco,
C.R. and Kolatkar, A., 2018. Clonal diversity revealed by
morphoproteomic and copy number profiles of single prostate cancer
cells at diagnosis. Convergent Science Physical Oncology, 4(1),
p.015003.
30
2.1 Abstract
Tumor heterogeneity is prevalent in both treatment-naïve and end-stage metastatic castration-
resistant prostate cancer (PCa) and may contribute to the broad range of clinical presentation,
treatment response, and disease progression. To characterize molecular heterogeneity associated
with de novo metastatic PCa, multiplatform single cell profiling was performed using High
Definition Single Cell Analysis (HD-SCA). HD-SCA enabled morphoproteomic and
morphogenomic profiling of single cells from touch preparations of tissue cores (prostate and
bone marrow biopsies) as well as liquid samples (peripheral blood and bone marrow aspirate).
Morphology, nuclear features, copy number alterations, and protein expression were analyzed.
Tumor cells isolated from prostate tissue touch preparation (PTTP) and bone marrow touch
preparation (BMTP) as well as metastatic tumor cells (MTCs) isolated from bone marrow
aspirate were characterized by morphology and cytokeratin expression. Although peripheral
blood was examined, circulating tumor cells were not definitively observed. Targeted proteomics
of PTTP, BMTP, and MTCs revealed cell lineage and luminal prostate epithelial differentiation
associated with PCa, including co-expression of EpCAM, PSA, and PSMA. Androgen receptor
expression was highest in MTCs. Hallmark PCa copy number alterations, including PTEN and
ETV6 deletions and NCOA2 amplification, were observed in cells within the primary tumor and
bone marrow biopsy samples. Genomic landscape of MTCs revealed to be a mix of both primary
and bone metastatic tissue. This multiplatform analysis of single cells reveals several clonal
origins of metastatic PCa in a newly diagnosed, untreated patient with polymetastatic disease.
This case demonstrates that real-time molecular profiling of cells collected through prostate and
bone marrow biopsies is feasible and has the potential to elucidate the origin and evolution of
metastatic tumor cells. Altogether, biological and genomic data obtained through longitudinal
biopsies can be used to reveal the properties of PCa and can impact clinical management.
31
2.2 Introduction
Despite early detection and aggressive intervention, prostate cancer (PCa) is the third-leading
cause of death among men in United States, with an estimated 160,000 new cases and
approximately 26,000 annual deaths in 2017[60, 61]. Multiple therapeutic agents have been
shown to improve overall survival in end-stage metastatic castrate-resistant PCa (mCRPC).
Additionally, recent clinical trials have provided evidence that use of combination therapies,
including docetaxel, with first-line androgen deprivation therapy (ADT), significantly increases
overall survival in de novo metastatic patients[62, 63]. However, not all patients respond to
combination therapy, and it is unknown whether the efficacy of combinatorial approaches can be
optimized based upon biological and genomic features of the tumor.
Intra-patient spatiotemporal molecular profiling has the potential to provide treatment response
signatures, insight into heterogeneity, and prognostic information for patients with metastatic
PCa. While standard-of-care assessment of primary and metastatic tissue biopsies determines
tumor type and grade, origin information on heterogeneity, clonality, and likelihood of treatment
response is limited.
Comprehensive assessment of the liquid biopsies that can yield circulating tumor cells (CTCs),
disseminated tumor cells (DTCs), metastatic tumor cells (MTCs), and cell-free DNA (cfDNA)
has the potential to greatly improve prognostication and prediction of treatment efficacy, as well
as provide in depth glimpse of tumor heterogeneity and clonality, especially when integrated
with single-cell analysis. CTCs refer to cancer cells identified in routine peripheral blood (PB)
samples whereas MTCs and DTCs refer to cancer cells found in bone marrow aspirate (BMA) of
patients with or without clinical bone metastasis, respectively[33]. Many new technological
advances allow for high-resolution single cell analysis and when combined with rare cell
detection, provide clinical insights that can impact management.
We have previously developed and technically validated the High Definition Single Cell
Analysis (HD-SCA) workflow for enumeration, morphoproteomic, and morphogenomic
characterization of rare cells in order to identify and quantify cellular heterogeneity[30, 32, 43,
64, 65]. The workflow utilizes single-cell profiling to characterize CTCs, DTCs, and MTCs
followed by genomic and proteomic characterization that can be correlated with morphology
data[31, 43, 65].
CfDNA extraction from a liquid biopsy prior to HD-SCA sample processing allows for genomic
assessment of circulating tumor DNA (ctDNA) found in both PB and BMA. The same
multiplatform single-cell analysis can be used for genomic and proteomic characterization of
primary and metastatic tissue preparation[32]. Additionally, as demonstrated by our recently
published data, the HD-SCA platform can be adapted to the fluid form of BMA, allowing
characterization and comparison of circulatory and bone marrow cancer cells[33]. Carlsson et al.
demonstrated the value of non-guided BMA as a feasible and cost-effective procedure for
32
longitudinal sampling during cancer progression and treatment similar to current clinical practice
in the liquid malignancies[66].
In this report, we used the HD-SCA workflow to characterize cancer cells in a newly diagnosed
de novo polymetastatic prostate cancer patient. Single-cell morphoproteomic and
morphogenomic data allowed direct comparison of primary and metastatic tumor cells, including
isolates from PB and BMA, as well as cells sampled from diagnostic biopsy tissue cores from
bone marrow and prostate. The clinical utility of liquid biopsy (CTC, DTC, MTC, and/or
ctDNA) lies in the ability to illuminate indicators of treatment response/resistance that may guide
therapeutic selection. This report demonstrated that genomic and proteomic data collected from
the HD-SCA workflow has the potential to provide information on heterogeneity, clonality, and
marker expression that may translate into improved prognostication and treatment response in
patients harboring lethal PCa.
33
2.3 Material and Methods
Specimen collection and HD-SCA sample preparation
The patient is a 73-year-old African-American male diagnosed with de novo polymetastatic PCa
via prostate needle biopsy (PNBX) within the Greater Los Angeles Veterans’ Affairs Healthcare
System. Peripheral blood and bone marrow aspiration/biopsy were collected at the time of
diagnostic PNBX and placed into 10-ml Cell-Free DNA BCT Streck tubes (STRECK, Omaha,
USA, Cat#62790315). Approximately 12 prostate tissue cores were obtained in a random fashion
from the right and left base, mid-gland, and apex via transrectal ultrasound guidance and prostate
tissue touch preparation (PTTP) was performed prior to formalin fixation and paraffin
embedding (FFPE). PTTP were generated by gently rolling each core onto a glass slide. A
standard percutaneous bone marrow biopsy and aspiration were performed at the right posterior
iliac crest. The bone marrow core was gently rolled onto a glass slide to prepare bone marrow
touch preparation (BMTP). Samples were shipped overnight to the Kuhn laboratory at the
University of Southern California, Los Angeles, CA, USA. Upon arrival of PB and BMA
samples in Streck tubes, red blood cells were lysed, and remaining cells were plated on a custom-
made adhesive glass slide (Marienfeld, Lauda-Königshofen, Germany) as a monolayer of 3.0 x
10
6
nucleated cells[33]. Unstained slides were covered with coverslips and stored at -80°C before
analysis (Figure 12). All touch preparation slides were air dried, blocked with 7% BSA in PBS,
and stored in -80°C until processing.
Detailed sample processing procedures have been described previously[43]. Briefly, two slides
per patient were stained using four fluorescent markers. Cell nuclei were identified using DAPI
(4',6-Diamidino-2-Phenylindole, Dihydrochloride, Cat#D1306, Invitrogen, Waltham, MA).
Epithelial cells were identified using a mix of cytokeratin (CK) 19 (1:100; Dako, Carpinteria,
USA, Cat#M0888) and pan-cytokeratin antibodies (1:100; Sigma-Aldrich, St. Louis, USA,
Cat#C2562) with an Alexa Fluor 555 secondary antibody (Invitrogen, Carlsbad, USA,
Cat#A21127). An anti-CD45 Alexa Fluor 647–conjugated antibody (1:125; Biorad, Hercules,
USA, Cat#MCA87A647X) was used as a leukocyte exclusion marker. An androgen receptor
(AR) rabbit monoclonal antibody (1:250; Cell Signaling Technology, Danvers, USA, Cat#5153)
and Alexa Fluor 488 secondary antibody (Invitrogen, Carlsbad, USA, Cat#A11034) were used
for evaluation of AR levels in cells[31, 33] (Figure 12).
Candidate cell imaging and morphological analysis
PB and BMA slides were imaged at 10X magnification using automated high-throughput
microscopy. Candidate cells were computationally identified and semi-manually classified using
morphology and DAPI+/CK+/CD45- staining criteria as previously established (Figure 12)[44,
45]. PTTP and BMTP were manually imaged at 10X and 40X magnification. AR expression and
localization were reviewed and quantified using average fluorescent intensity within a fixed-size
circle centered around the cell[46]. The threshold for AR positivity was defined as a signal more
than 6 standard deviations over the mean signal intensity (SDOM) observed in the surroundings
34
leukocytes (background). For morphometric analysis of candidate cells, nuclear area and nuclear
circularity was measured using DAPI intensity within a fixed-size circle centered around the cell
using image object features from EBImage R script.
Single-cell next-generation sequencing and analysis
Single cells from PB, BMA, PTTP, and BMTP were isolated and underwent genomic
amplification as previously described[47, 48] (Figure 13A). In short, tumor cells were isolated
off the slides using a robotic micromanipulator system and placed in individual tubes for whole
genome amplification (Sigma-Aldrich, St. Louis, USA, Cat#WGA4). Prior to cell capture, PTTP
and BMTP slides were incubated with dispase type II (1:1000; ThermoFisher, Waltham, USA,
Cat#17105041) and collagenase (1:1000; ThermoFisher, Waltham, USA, Cat#17018029) in PBS
at 37°C for 30 minutes. PTTP and BMTP slides were washed with PBS twice for 3 minutes, and
individual cells or cell clusters from PTTP and BMTP were isolated and extracted in the same
manner. Following DNA purification, 50ng of DNA was sonicated to 200 bp fragments in AFA
fiber pre-slit snap-cap microtubes (Covaris, Woburn, USA, Cat# 520077) with the Covaris S2
using the following setting: intensity of 5, 10% duty cycle, 200 cycles per burst, 3-minute
treatment time, and temperature less than 7°C. Sonicated DNA was used for library construction
with the DNA Ultra Library Prep Kit and Multiplex Oligos for Illumina (New England Biolabs,
Ipswich, USA, Cat#E7370 and E7600). Copy number alteration (CNA) profiles and heatmaps
were created as previously described, with R unsupervised hierarchical clustering using the Ward
method and Euclidian distance to distinguish subclones[47, 48].
CfDNA next-generation sequencing and analysis
PB and BMA were fractionated by centrifugation at 2,000g for 10 min at RT, 2 ml of plasma
were collected from each sample, and the removed plasma volume was reconstituted to its
original concentration with 1xPBS. Plasma was spun at 14,000g for 10 min at RT and
supernatant was stored at -80°C for future analysis. CfDNA was extracted with the QIAamp
Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany, Cat# 55114) according to the
manufacturer’s instructions. Illumina libraries were constructed from 5ng cfDNA using the
NEBNext Ultra II DNA Library Prep Kit and Multiplex Oligos for Illumina according to the
manufacturer’s instructions (New England Biolabs, Ipswich, USA, Cat#E7370 and E7600) as
seen in Figure 13A. CNA profiles were created as described under single-cell next-generation
sequencing and analysis.
Single-cell targeted proteomic analysis
Previously identified candidate cells from BMA, PTTP, and BMTP slides were subjected to
protein analysis using Fluidigm Hyperion Imaging System (IMC) (Fluidigm, San Francisco,
USA). Slides were washed in PBS to remove cell media before secondary staining. Slides were
stained with 21 markers of the 40 available channels to detect leukocyte, epithelial, endothelial,
and prostate cell protein expressions (Table 4). Slides were blocked using 1% BSA with 0.2mg/
35
mL mouse IgG Fc fragment (Thermofisher, Waltham, USA, Cat#31205) in PBS for 60 minutes
at 37°C. MaxPar
TM
metal-labeled antibody cocktail was prepared in 0.1% Tween and 1% BSA in
PBS with antibodies from Fluidigm according to the manufacturer’s dilutions. Antibody cocktail
was added to each slide for 90 minutes at room temperature and washed with PBS twice for 3
minutes. IR-193 DNA intercalator (Fluidigm, San Francisco, USA, Cat#201192A) was added to
slides for 30 minutes at room temperature. Slides were washed with PBS twice for 3 minutes and
dipped in ddH2O for 5 seconds to remove salt. Slides were dried for 2 hours and stored at room
temperature until IMC runs.
Laser ablation with time-of-flight detection and analysis was performed using the IMC as seen in
Figure 13B. A 400 µm x 400 µm region of interest around the cell of interest was ablated
aerosolizing a 1 µm
2
area/pulse (200 Hz), followed by ionization and quantification in the
CyTOF Helios instrument. Ion mass data is collected, resulting in the construction of 1 µm
2

images. This region includes the tumor cells of interest and surrounding white blood cells as
reference. For BMA, this section encompasses the MTC of interest and the surrounding WBCs
totaling approximately 500 cells. For BMTP and PTTP, the ablated area may contain hundreds to
thousands of cells, depending on the size of the imprint.
A four-level scoring system was developed where 0 is below limit of detection (LOD), 1 is at
limit of detection, and 2-3 is above. The LOD for each marker was set as equal to signal to noise
ratio (S/N) ≥ 3 or standard deviation of mean (SDOM) > 3.3. A score of 1 was given to cells
exceeding the LOD, a score of 2 was given to signals with S/N of 7-20 or SDOM > 6, and finally
a score of 3 was assigned to signals of S/N > 20 or SDOM > 12.
36
2.4 Results and Discussion
Single cell morphometric analysis of identified tumor cells
To examine intra-patient heterogeneity, samples were collected from a patient diagnosed with de
novo polymetastatic PCa. During a routine examination in 2016, the patient had a prostate
specific antigen (PSA) level of 234 ng/mL triggering a diagnostic workup for PCa. A bone scan
demonstrated widespread metastatic disease involving skull, bilateral ribs, multilevel lumbar
spine, and pelvic bone. Diagnostic PNBX revealed high-grade PCa with a Gleason score of 8
(4+4) in 9 of 12 biopsy cores. Immunohistochemistry (IHC) staining of FFPE PNBX and bone
marrow biopsy core revealed adenocarcinoma that expressed AR and PSA (Figure 14).
For the HD-SCA workflow, PB, BMA, and bone marrow tissue (BMT) were collected at the time
of PNBX. Following processing and staining as described in Materials and Methods, we
identified and categorized candidate cells from PB and BMA using a semi-automated reporting
system. Candidate cells were evaluated based on DAPI
+
/CK
+
/CD45
-
criteria and marked as
CTCs in PB and MTCs in BMA[43, 65]. Enumeration of tumor cells revealed a significantly
high count of MTCs and lack of CTCs. Counts for CTCs in PB and MTCs in BMA were 0 cells/
mL and 3673 cells/mL, respectively (Figure 15A). The HD-SCA threshold for a positive sample
is ≥1 cell per case based on previous studies, making BMA positive for this patient (23, 24).
MTCs were detected as both single cells and clusters of 2-5 cells (25%), 6-10 cells (3%), and
11+ cells (1%) (Figure 15A).
AR expression was observed in both BMTP and PTTP via IHC analysis (Figure 14). Single-cell
morphometric analysis of MTCs revealed AR expression localized to the nucleus (Figure 15B).
Despite observed heterogeneity in AR expression, AR staining was confined to the nucleus as
expected, regardless of expression levels (Figure 15B). To further confirm nuclear localization,
detailed confocal images were acquired (Figure 16A). We observed marked heterogeneity across
MTCs in nuclear area, nuclear circularity, CK stain intensity SDOM, and AR expression SDOM
(Figure 16B), revealing several MTC subclones. Collectively, these data demonstrate that tumor
heterogeneity can be detected at a morphometric level using measurements such as protein
expression and nuclear features.
Single cell and cfDNA genomic analysis using copy number alteration
To explore tumor heterogeneity on a molecular level, we selected a total of 73 single cells for
CNA analysis from 3 sample types: BMA (n=22), BMTP (n=17), and PTTP (n=32). This
analysis included white blood cells (WBC) as controls (2 from BMA). CNA profiles of tumor
cells in prostate tissue (PT) and BMT showed prostate-specific alterations across the genome as
well as clonality, which we analyzed further (Figure 17). Table 5 summarizes all observed
alterations with their frequency in MTCs, BMTP, and PTTP and their function and implications
in cancer. We define clonality as two or more cells that share two or more genomic alterations.
Alterations of tumor suppressor genes such as PTEN, RB1, and TP53 have been shown to
37
contribute to tumor and metastatic development as well as invasiveness[42, 67]. PTEN deletion
was observed in 77% (17/22) and 34% (11/32) of MTCs and PTTP, respectively. Partial
chromosome 13 loss was observed in 23% (4/17) of BMTP, resulting in at least hemizygous loss
of RB1. TP53 loss was observed in 68% (15/22) of MTCs and 82% (14/17) of BMTP. No TP53
deletion was observed in PT; thus, TP53 loss may have been acquired after the cancer cells left
the PT and settled in the BM. Research has shown that in the absence of or dysregulation of
TP53, DNA damage and spindle damage occur, resulting in polyploidy in tumor cells[68]. 64%
(14/22) of MTCs have 4 copies of each chromosome and are thus polyploid. Regardless of this
increase in DNA content, they remain clonal and share common alterations with the rest of the
tumor cells.
Genomic alterations affecting DNA repair pathways like ATM, BRCA1, BRCA2, CDK2, and
MLH1 have been shown to affect sensitivity to platinum-based treatment in metastatic PCa
patients[58]. Investigation of these alterations in this patient revealed genomic changes in DNA
repair pathway genes. Deletions affecting ATM were found in 77% (17/22), 94% (16/17), and
47% (15/32) of MTCs, BMTP, and PTTP, respectively. Next, we investigated BRCA1 and
detected a deletion in 9% (2/22), 29% (5/17), and 9% (3/32) of MTCs, BMTP, and PTTP,
respectively. We found partial chromosome 13 loss in 23% (4/17) of BMTP, resulting in at least
hemizygous loss of BRCA2. CDK2 gene deletions were also detected at frequencies of 36%
(8/22), 23% (4/17), and 9% (3/32) of MTCs, BMTP, and PTTP, respectively. MLH1 deletion was
only detected in BMTP and PTTP at frequencies of 2% (3/17) and 19% (6/32), respectively.
Collectively, these data demonstrate multiple genomic alterations associated with DNA damage
repair. These alterations were identified in a subset of tumor cells and required single-cell
analysis for detection. Furthermore, we found the frequency of tumor cells harboring genomic
aberrations to be increased in bone marrow tumor cells relative to prostate resident cells. This
genomic heterogeneity and low frequency of alterations would have been overlooked in bulk
analysis, demonstrating the power of single-cell sequencing.
Next, we focused on PCa specific alterations, such as in chromosome Xq12 where the AR gene
resides. We did not observe amplification of the AR gene locus in any sample but noted a whole-
chromosome gain of the X chromosome in 6% (1/17) and 3% (1/32) of BMTP and PTTP,
respectively. In addition, NCOA2 (AR co-regulator) amplification was detected in 68% (15/22),
100% (17/17), and 28% (9/32) of MTCs, BMTP, and PTTP, respectively[42]. MYC
amplification was observed in 12% (2/17) and 3% (1/32) of BMTP and PTTP, respectively[42].
All cells with MYC amplification have NCOA2 amplification as well. Consistent with published
data showing that NCOA2 and MYC on 8q13 and 8q24 function as driver oncogenes, we
observed an increased frequency of amplifications of these genes in MTCs compared to primary
tumors[42, 69]. Thus, our observation of AR expression in MTCs and BMTP is likely linked to
NCOA2 and MYC gene amplification in most of these cells. AR plays a major role in disease
initiation as well as progression and is a target of first-line therapy in PCa. AR overexpression in
PCa is well documented, and AR gene amplification or mutation are linked to castration-resistant
38
prostate cancer, which is believed to be a result of treatment pressure as opposed to natural
disease evolution[42, 70]. Consequently, samples collected from a metastatic hormone-naïve
PCa patient may present a higher expression of AR but not a genomic amplification, as seen
here.
Other alterations observed include a loss in ERCC3, a DNA helicase that is involved in DNA
damage repair. Studies have shown that an age-related decline in this gene leads to declining
repair capacity in cells and may lead to development of cancer[71]. The gain in NFκB2 observed
in this patient has been demonstrated to contribute to tumorigenesis by uncoupling the normal
mode of regulation in immune regulation and inflammation[72]. We also detected deletion of
transcription factor ETV6 in 45% (10/22), 41% (7/17), and 6% (2/32) of MTCs, BMTP, and
PTTP, respectively. Studies have shown that ETV6 is deleted in 25% of prostate cancers[53].
One of the most common forms of alterations in PCa is a loss of chromosome 8p encoding
NKX3.1, a transcription factor suppressing cell growth in PT[73]. In this patient, NKX3.1 loss
was observed in 73% (16/22), 94% (16/17), and 50% (16/32) of MTCs, BMTP, and PTTPs,
respectively. Other observed gene deletions important for PCa initiation and progression include
MEEK1, KRAS, and BCL2. Amplifications of the MET tyrosine kinase, BRAF threonine/serine
kinase, and the methyltransferase and transcription suppressor EZH2 were also observed across
MTCs, BMTP, and PTTP.
Comparison of PTTP, BMA, and BMTP CNAs across sample types enabled construction of a
phylogenetic tree demonstrating heterogeneity as well as common patterns of CNAs between
tumor cells in prostate and bone marrow (Figure 17). Clones of tumor cells can be divided into
three groups: clonal cells consisting of PCa hallmark genomic alterations residing in the PT
(clone 1), tumor cells with a minimal number of CNAs (clone 2), and tumor cells similar to clone
1 and with additional CNAs residing only in the bone marrow (clones 3).
Amplification of chromosome 10p and loss of chromosome 16p distinguish two subclones
contained in clone 3. Within the primary and metastatic tumor cells additional sub-clonal
populations were identified: a subclone of cells with a deletion in chromosome 2, a subclone
with focal deletions in chromosome 5, a subclone with an amplification in chromosome 9, and a
subclone with a deletion in chromosome 16. This heatmap also demonstrates the development of
subclones just within prostate cells and those exclusive to the metastatic site. CNAs only in BM
(MTCs and BMTP) included a deletion in chromosome 1 and amplifications in chromosomes 4,
10, and 14. An alteration unique to PTTP included the deletion in chromosome 4.
A single dominant clone was identified in all three compartments (BMA, BMTP, and PTTP) and
was traced back to a few cells (9/32) in the primary tumor (Figure 18). Based on genomic
profiles, these cells did not share all of the CNAs of the clonal BM population (Figure 17). At its
origin in the primary tumor, the clonal population included losses in chromosomes 8p, 10, 11q,
and 14q. MTCs in the BM descending from this clone gained additional alterations such as
amplifications in 4q, 10q, and 14q as well as losses in 1p and 17p. In addition, MTCs from the
39
BM aspirate gained additional alterations seen in the BM biopsy, such as gain in 10p. These
changes can be tracked from PT to BMT. Further examination revealed subclones in the PTTP
cells that have developed independently; for example, cells in PTTP with a gain in chromosome
9q were not found in the BM cells. Similarly, subclones in MTCs with a loss in chromosome 16p
were observed that were not otherwise found in BMTP cells.
The HD-SCA genomic analysis extends beyond rare cells. Plasma was isolated from both PB and
BMA for CNA analysis of cfDNA. Analysis of cfDNA from PB plasma revealed CNA profiles
with common genomic alterations observed in both MTCs and BMTP (Figure 19). A detailed
analysis revealed that the profile is more closely related to tumor cells in the MTC than the
BMTP sample. Deletions in chromosomes 1, 6, 10, 11, and 14 as well as amplifications in
chromosomes 4, 8, and 10 in PB ctDNA matched with those found in the clonal population
(Figure 19). For ctDNA from BMA, CNA comparison revealed features present in both BMTP
and MTCs. Such features included deletions in chromosomes 6 and amplifications in
chromosomes 8, 10, and 11. Using in-house software, the fraction of tumor DNA to normal DNA
in plasma can be estimated by comparing the amplitude of amplification and deletions of cfDNA
to those observed in a single tumor cell where the gains and losses fall in integer steps. PB
cfDNA consisted of 75% (3:1 ratio of tumor: normal) ctDNA whereas BMA cfDNA consisted of
50% ctDNA (1:1 ratio of tumor: normal). Thus, the data demonstrate that analysis of cfDNA in
the liquid biopsy enables detection of genomic alterations that are present in both primary and
metastatic tissue.
Targeted proteomics reveals origin of cells in BM
To generate single-cell targeted proteomic data we selected MTCs, PTTP, and BMTP tissue
imprints for analysis. We have previously validated a panel of 21 protein markers including a
comprehensive leukocyte, epithelial, endothelial, and prostate protein panel for analysis (Table
4). A total of 13 MTCs immobilized on a glass slide, 12 BMTP tissue imprints, and 19 PTTP
tissue imprints were stained and laser ablated for analysis. Each region of interest was scanned
for ablation by the Fluidigm Hyperion Imaging System using a highly focused, pulsed laser that
atomizes and ionizes a 1x1µm region and the resulting ions are introduced into the inductively
coupled plasma time-of-flight mass spectrometer[74, 75]. The ion count for each pulse is
reconstructed into a 1µm
2
images, and ion count for each protein is scored. The scores for each
single MTC as well as BMTP and PTTP tissue imprints are demonstrated as a heatmap in Figure
20A.
Targeted proteomics of MTCs, BMTP, and PTTP revealed co-expression of epithelial cellular
adhesion molecule (EpCAM), PSA, and prostate specific membrane antigen (PSMA) in a
majority of cells. E-Cadherin was expressed in 23% (3/13), 67% (8/12), and 100% (19/19) of
MTCs, BMTP, and PTTP, respectively. CK8/18 was expressed in 38% (5/13), 100% (12/12), and
84% (16/19) of MTCs, BMTP, and PTTP, respectively. Evaluation of these markers in MTCs,
PTTP, and BMTP revealed epithelial and prostatic cellular origin consistent with luminal PCa.
40
Expression level of PSA and PSMA varied both within and between tissue sections. Expression
of EpCAM, PSA, and PSMA was consistent across all 3 sample types, but expression of CK8/18
and E-Cadherin was mainly observed in BMTP and PTTP. Such observation may be due to
microenvironmental pressures. Once MTCs reach their final destination in the bone marrow, that
new microenvironment may lead to higher expression of CK8/18 and E-Cadherin. Examination
of EpCAM also revealed higher expression in PTTP than BMTP, suggesting that once MTCs
have established a BM metastasis, moderate EpCAM expression may be sufficient[76].
The prostate consists of both epithelial and stromal cells where the stromal cells express
mesenchymal markers such as Vimentin[77]. We observed a mix of stromal and epithelial cells
in some of the PTTP sections as seen by both EpCAM and Vimentin expression in different parts
of the tissue (Figure 20B). Expansion of our proteomic panel to include proteins such as
NKX3.1, ERG, ATM, PTEN, and other markers could help further stratify the subclones of
tumor cells. Heterogeneity of protein expression between each cell or cell cluster was visible
even within each sample type in the same patient, revealing the power of single-cell data to study
variability.
41
2.5 Conclusion
Assessment of liquid and solid biopsy from a patient with de novo polymetastatic PCa using
single-cell technology revealed tumor heterogeneity, protein expression variability, and genomic
clonality at the intra and inter-biopsy level. Such high-content extraction is feasible with a single
cell biology research workflow that preserves morphology and molecular integrity following
preservation of cells, identification, and enumeration. This preservation and maintenance of
identity through the workflow enables genomic and proteomic analysis using CNA and protein
expression, which can be correlated through the morphology. A detailed study of the dominant
clonal population revealed modifications such as amplifications in NCOA2 and MYC and
deletions in ETV6, PTEN, and TP53, among others. Genomic clonality was demonstrated both
within and between each biopsy source (MTCs, BMTP, and PTTP). Tumor heterogeneity via
protein expression has also been demonstrated in this patient. Proteomic analysis data shows
heterogeneity in expression level of EpCAM, PSA, and PSMA within and across all 3 sample
types.
Deletions in genes such as BRCA1, BRCA2, CDK2, and MLH1 revealed dysfunction in the
DNA repair pathway. Many of these genes are candidate targets for therapeutics, and prior
knowledge of such gene alterations may be relevant when it comes to choosing the optimal
combination therapy. For other therapeutic targets such as PTEN and MYC, having knowledge
of both genomic and proteomic status of these genes may lead to optimal combination therapy
and personalized medicine for each patient.
We also identified specific gene alterations that are found only in BM while some are shared
across all three compartments (BMA, BMTP, PTTP). Similar to Klein et al., our results show
that once cells leave the primary tumor and enter the circulation and BM, they acquire
independent alterations[78]. Using our single-cell analysis, we were able to demonstrate the
same independent evolution in MTCs and BMTP. Such details can be revealed through single-
cell study approaches where non-dominant molecular features are detected rather than masked by
the dominant population, as is the case in bulk sampling. Information on such detailed features of
each compartment (PT vs BM) may influence therapeutic recommendation in the future.
We used liquid biopsy as a source for both cellular and acellular components, including cfDNA
extraction prior to sample preparation leading to CNA analysis for both PB and BMA liquid
biopsies. PB and BMA ctDNA analysis revealed a CNA profile similar to that of the dominant
clonal population. CfDNA analysis may result in evaluation and close monitoring of tumor
evolution and progression of the dominant clone when CTCs and/or MTCs are not available for
analysis.
Cell lineage of MTCs and BMTP cells can be traced back to the primary tumor using genomic
data[79, 80]. We have observed genomic heterogeneity not only between but also within each
compartment. Detailed evaluation of individual CNAs revealed the rise of several small sub-
clonal population of cells within each compartment, demonstrating independent evolution at each
42
site. Tumor lineage of the clonal dominant population revealed that this population started in PT,
gained additional alterations as an MTC, and evolved with additional genomic modifications
once metastasized. The genomic heterogeneity and clonal subpopulations within each
compartment in this single patient were vast.
In conclusion, these results demonstrate the high content characterization that can be obtained
using a high definition single cell analysis workflow for single-cell profiling of liquid and solid
biopsies. Using previously published data as validation, BMA has been demonstrated to be a
feasible and applicable approach opening new opportunities for analysis in advanced prostate
cancer[33]. BMAs are easier and more cost-effective to obtain when compared to image-guided
biopsies and can be collected repeatedly during treatment cycles and progression. Longitudinal
sampling and assessment provide critical insight to predict therapy response and monitor disease
progression. Using morphology to integrate genomics and proteomics of single cells throughout
the workflow maintains the single cell identity of CTCs, MTCs, and cells isolated from solid
tissues. This viable, longitudinal approach has the potential to support treatment decisions and
monitor progression, which are goals of precision medicine. Future studies include
comprehensive investigation of a broader cohort of both treatment-naïve organ-confined PCa
patients as well as additional metastatic cases to further characterize the biology of metastatic
tumors.
43
2.6 Figures

Figure 12: HD-SCA platform for morphoproteogenomic profiling of liquid biopsy. PB and
BMA samples are initially spun down for plasma extraction. Next, they undergo red blood cell
lysis before plating approximately 3 million nucleated cells on each slide. Prepared slides are
stored at -80°C until needed for fluorescent antibody staining. Stained slides are first
morphometrically profiled using automated digital microscopy at 10X magnification, followed
by classification by a technical analyst. Identified tumor cells are then re-imaged at 40X
magnification and proceed for genomic copy number alteration or targeted protein analysis via
imaging mass cytometry.
44

Figure 13A: Single cell and cfDNA genomic preparation for next-generation sequencing
and CNA analysis. Single cells are extracted from slides with a robotic micromanipulator prior
to whole genome amplification and DNA purification followed by Illumina DNA library
preparation for sequencing. CfDNA is extracted from plasma and Illumina DNA libraries are
constructed similarly to single cells. CNA profiles are created using the human genome as
reference where copy number is calculated then displayed as the ratio to the median. 13B:
Targeted proteomic analysis via IMC. Slides previously labeled with fluorescent antibodies are
stained with 21 metal-conjugated antibodies. Regions of interest are laser ablated with plasma
ionization and ions are detection using Cytometry by Time of Flight (CyTOF) technology.
Rasterized images are generated from ion count data, and protein expression on tumor cells are
scored for expression levels.
45

Figure 14: IHC staining of FFPE PT from diagnostic prostate needle biopsy and BMT from
bone marrow biopsy. IHC staining for AR and PSA were performed on both PT and BMT
samples. Samples were imaged using a light microscope at 10 and 20X objective. Staining
results were reported as positive or negative for each marker.
46

Figure 15A: MTC enumeration in BMA. Total counts are reflected by cluster group size.
The y-axis reflects the number of cells within each cluster group and the x-axis reflects each
cluster group category including single cells, cluster of 2 cells, cluster of 3-5 cells, cluster of
6-10 cells, and clusters of 11 and more cells. Overall, 3673 cells/mL were detected in BMA.
15B: AR expression heterogeneity in MTCs. Each row shows both composite and individual
DAPI, CK, CD45, and AR channel for each cell. DAPI is shown in blue, CK in red, AR in white,
and CD45 in green. AR expression was localized in the nucleus in both single cells and cell
clusters despite differences in expression levels. Other single cells or cell clusters had no AR at
all, showing heterogeneity in MTCs.
47

Figure 16A: Confocal images of MTCs. Nuclear localization and heterogeneity of AR
expression are shown in three different clusters of MTCs. DAPI is shown in blue, CK in red, AR
in white, and CD45 in green. Some clusters were entirely AR-positive or AR-negative while
others presented mixed expression within the cluster. 16B: Morphometric analysis of MTCs in
BMA. A wide range of nuclear area, nuclear circularity, CK intensity SDOM, and AR intensity
SDOM was displayed, highlighting the ability to detect tumor heterogeneity through
morphometric measurements. The distribution of cells is shown along the y-axis for nuclear area,
nuclear circularity, CK intensity SDOM, and AR intensity SDOM.
48

Figure 17: Heatmap and phylogenic tree of CNAs across the entire population of cells from
MTC, PTTP, and BMTP. Sample type and clones are identified using color key. Three clones
were identified: clone 1 consisting of prostate cells with hallmark alterations, clone 2 with few
CNAs, and clone 3 of bone marrow specimens with additional alterations from that of clone 1.
Key genes such as MYC, NCOA2, PTEN, and TP53 are highlighted by chromosome location
across clone and sample type.
49

Figure 18: Cell lineage of dominant clone starting from the primary tumor. Cells in the bone
marrow gained additional alterations, such as losses in chromosomes 1 and 17 as well as
amplifications in 4q, 10q, and 14q as they evolved from PT. A subpopulation within the MTC
was distinguished by the addition of a loss in 16p, and bone marrow touch preparation cell
exhibited a 10p gain. Deviations from the original dominant population are highlighted with
amplifications in red and deletions in blue. Through this analysis, the lineage of the cancer cells
can be tracked from PT to MTCs to BMT. DAPI is shown in blue, CK in red, AR in white, and
CD45 in green.
50

Figure 19: CNA profile comparison of PB and BMA ctDNA to MTCs and BMTP cells.
Genomic alterations in cfDNA analysis match CNA of dominant clonal population in BMTP and
MTCs. Common amplifications across profile types are highlighted in red while common
deletions are shown in blue.
51

Figure 20A: Four-level scoring heatmap for IMC analysis of single MTC and tissue
imprints of BMTP and PTTP. Each protein is scored by ion count, and the limit of detection is
determined by a signal-to-noise (S/N) ratio ≥ 3 or a standard deviation of the mean (SDOM)
above 3.3. For each marker, below the limit of detection is a 0, above the limit of detection is a 1,
a S/N ratio between 7 and 20 or SDOM > 6 is a 2, and a S/N ratio above 20 or SDOM above 12
is a 3. 20B: Composite images comparing fluorescent imaging to IMC analysis of all three
sample types. The first panel on the left shows fluorescent images with DAPI in blue, CK in red,
and CD45 in green. All subsequent panels are IMC-generated composite images showing DAPI
in blue followed by two different markers in red and green. EpCAM confirmed epithelial
character of most of the cells while PSA and PSMA verified the prostatic source of the samples.
Vimentin (VIM) expression in regions of PTTP highlighted the stromal cells of the prostate.
52

Table 4: Targets and descriptions of 21 metal-conjugated antibodies for IMC analysis.
Markers are subdivided into epithelial, endothelial, leukocyte, and prostate-specific panels for
classification of cell type and origin.
53

Table 5: Summary of observed CNAs. Gene functions, alteration types, and implications are
displayed with the frequencies of occurrence in MTCs, BMTP, and PTTP.
54
Chapter 3.
Single-cell analysis of circulating tumor
cells reveals genomic instability and loss
of tumor suppressors PTEN, RB1, and
TP53 as distinctive features of aggressive
prostate cancer
In Collaboration with Dr. Amado Zurita-Saavedra at
MD Anderson Cancer Center, Houston, Texas
This chapter will be published as:
Malihi, P.D.*, Graf, R.P.*, Rodriguez, A.E., Ramesh, N., Lee, J., Sutton,
R., Jiles, R. , Velasco, C.R., Sei, E., Kolatkar, A., Logothetis, C.J.,
Navin, N., Corn, P.G., Aparicio, A.M., Dittamore, R., Hicks, J., Kuhn, P.
and Zurita, A. J. 2019. Single-cell circulating tumor cell analysis reveals
genomic instability as a distinctive aggressive prostate cancer feature.
Nature Communications. Submitted 11/25/2019.
55
3.1 Abstract
Aggressive variant prostate cancer (A VPC) represents a clinical subset distinguished by therapy
resistance and poor prognosis, linked to combined losses of the tumor suppressor genes (TSG)
PTEN, RB1, and TP53. Circulating tumor cells (CTC) provide a minimally-invasive opportunity
for more precise characterization and detection of A VPC.
Objective: To evaluate the incidence and clinical significance of compound (2+) TSG losses and
genomic instability in prostate cancer CTC, and to expand the set genomic biomarkers relevant
to A VPC.
Design, setting, and participants: Genomic analysis at single-cell resolution of chromosomal
copy number alterations (CNA) in CTC from 62 patients with and without A VPC.
Outcome measurements and statistical analysis: The associations between single-CTC genomics
and clinical features, PFS and OS, were evaluated.
Results and limitations: 257 individual CTC were sequenced from 47 patients (1-22 CTC/
patient). Twenty patients (42.6%) had concurrent 2+TSG losses in at least 1 CTC in association
with poor survival and increased genomic instability, inferred by high large-scale transitions
scores (LST). Higher LST in CTC was independent of CTC enumerated, clinically more
indicative of aggressive behavior than co-occurring TSG losses, and molecularly associated to
gains in chromosomal regions including PTK2, Myc and NCOA2, increased AR expression, and
BRCA2 loss. In 57 patients with matched cell-free tumor DNA (ctDNA) data, CTC was more
frequently detectable and evaluable for CNA analysis (in 73.7% vs 42.1%, respectively).
Limitations include number of patients and CTC sequenced.
Conclusions: Single-cell sequencing of CTC can be used to detect A VPC behavior through high
LST/increased genomic instability and combined losses of PTEN, RB1, and TP53. Single CTC
sequencing is a sensitive tool to profile prostate cancer heterogeneity and identify candidate
molecular drivers of progression.
Patient summary: The clinical behavior of advanced prostate cancer varies considerably. We
analyzed the DNA of single tumor cells in blood from patients with advanced prostate cancer and
identified molecular characteristics linked to aggressiveness. Our findings suggest single CTC
genomic analysis may help recognize prostate cancer patients in need of intensified treatment.
56
3.2 Introduction
Prostate cancer is associated with considerable clinical heterogeneity and molecular
diversity[81-87]. Tumor volume, anatomical distribution, and line of treatment constitute the
foundation of the clinical classification of advanced disease, but these factors do not fully
capture the heterogeneity observed. Developing a more precise biology-based stratification has
been hindered by the difficulty in reliably accessing the tumor tissue needed for molecular
correlation and biomarker development, in most cases limited to bone.
Although metastatic castration-resistant prostate cancer (mCRPC) responds in a majority of
cases to novel androgen receptor (AR) signaling inhibitors, resistance eventually develops[81,
88, 89]. In 20-30% of the patients, the disease directly fails to respond. AR-inhibition-refractory
and several resistant forms tend to present atypically, occasionally with neuroendocrine features,
and to behave aggressively, resulting in poor prognosis [90, 91]. Since those aggressive variant
prostate cancer (A VPC) patients may benefit from intensified treatments, a number of clinical
criteria were proposed to facilitate recognition[90]. However, the clinical presentations may be
difficult to distinguish, and treatment options remain suboptimal, making molecular
characterization and more precise identification priorities for the field[90-93].
Preclinical and clinical studies have established candidate molecular markers to classify
A VPC[91-93]. Genomic aberrations affecting the tumor suppressor genes (TSG) PTEN, RB1,
and TP53 are among the most frequently enriched in mCRPC[82-84, 94]. TSG loss-of-function
has been linked to adverse prognosis[83, 94-99]. In particular, two-hit RB1 loss is frequently
found in neuroendocrine prostate cancer, often together with concurrent alterations in PTEN and/
or TP53[91-93]. Indeed, RB1-loss promotes lineage plasticity, anti-androgen blockade
indifference, and a switch to a neuroendocrine transcriptional program in PTEN-deficient
preclinical models in cooperation with TP53 loss[100-103]. Moreover, the presence of at least
two of those cooperative TSG alterations may indicate benefit from carboplatin in A VPC[104].
Circulating tumor cells (CTC) provide a minimally-invasive opportunity to characterize tumor
heterogeneity at single-cell resolution. The Epic Sciences CTC platform (EsCTC) is inclusive of
heterogeneous CTC populations[105, 106], and allows for single CTC genomic characterization
via low-pass whole-genome analysis of copy number alterations (CNA), thus enabling
assessment of focal gains/losses of chromosomal regions and genome-wide estimations of
genomic instability (GI) such as large-scale transitions (LST)[107]. LST reflect chromosomal
breakpoints between adjacent DNA segments, and their measurement has been used as a
functional surrogate for homologous recombination deficiency (HRD)[107, 108].
Here we report on the application of the EsCTC workflow for the analysis of single CTC in
blood samples collected from mCRPC patients with clinically-defined A VPC and non-A VPC
“conventional” mCRPC. Our main objectives were to evaluate the incidence and clinical
significance of the TSG previously linked to A VPC in single CTC, and under the premise that
57
CTC in this setting provides a faithful representation of the tumor clones driving progression, to
refine the set of genomic biomarkers associated to aggressive prostate cancer behavior and poor
prognosis.
3.3 Material and Methods
Patients
Blood samples were collected immediately before starting treatment from participants on trial
NCT01505868 evaluating the efficacy of cabazitaxel plus carboplatin relative to cabazitaxel
alone. Three sub-cohorts were evaluated (Supplementary Fig. 1): i. CellSearch®: samples with
matched EsCTC and CellSearch® draws; ii. CTC-sequenced: at least 1 sequenced CTC; and iii.
circulating cell-free tumor DNA (ctDNA)-matched (n=57): evaluated for both EsCTC and
ctDNA. Disease volume and rate of progression definitions are available in the Supplementary
material.
CTC collection and analysis
Blood (7.5 mL) was collected in Streck tubes and sent to the Kuhn-Hicks laboratory for
processing within 24 hours, as previously described [105, 109]. CTCs were identified at Epic
Sciences’ facilities in two slides/patient corresponding to a median 0.9 mL (Table 6), as cells
containing an intact nucleus, without CD45 expression/leukocyte (WBC) nuclear morphology,
and with cytokeratin or AR N-terminus expression (Fig. 21A-B). CTC clusters were defined as
>1 CTC sharing a boundary. AR expression was calculated from AR-fluorescence intensity
compared to neighboring WBC. Clinical laboratory scientists conducted final QC of CTC
classification.
CTC isolation, genome amplification, and next-generation sequencing
We followed previously described methods for CTC relocation, isolation, and genomic
sequencing [105, 107]. If <12 CTC were present, all were picked and sequenced. If 12 were
present, CTCs were picked at random within a sample based on sequencing plate availability.
Genome-wide CNA analysis was performed using the Epic Sciences single-cell pipeline (Fig.
21C; Supplementary material). Each CNA profile was given an LST score based on the number
of chromosomal breaks between adjacent regions of at least 10 Mb across the entire
genome[107]. Gene-based CNA analysis was performed as described previously[96], focusing
on 578 cancer genes (Roche Sequencing).
ctDNA extraction and analysis of copy number
DNA was extracted from plasma (median 3 mL [1–4.5 mL]). WBCs were used to extract
matched-control genomic DNA. The plasma DNA was next size-selected (<1000bp) to enrich for
ctDNA. QC for ctDNA at 160bp was then performed and quantified on TapeStation (Agilent).
Samples with ctDNA >2 ng were used for low-input barcoded next-generation sequencing library
≥
58
construction using highly-efficient DNA ligases (KAPA HyperPlus). The barcoded libraries were
enriched by PCR and used for both whole-genome sparse sequencing (0.1X) for CNA profiling
at high resolution (220kb) (Supplementary material).
Statistical analyses
Statistical analysis was done in R v3.4.1. Descriptive statistics were used to evaluate
demographic and clinical characteristics at the time of blood draw. Wilcoxon rank-sum tests were
utilized to compare differences between patient groups with continuous measures. The
probability of survival over time was assessed using Kaplan-Meier (KM) estimation. The
probability of survival over time was assessed using Kaplan-Meier (KM) estimation. Differences
in time-to-event outcomes between groups were measured with the log-rank method, hazard
ratios (HR) were obtained from a univariable Cox model, and the p-value from the logrank test
within the coxph function, all in the R package ‘survival.’ Dimensionality reduction was
conducted with the R package ‘umap’ without parameter alteration. With the exception of KM
curves (R package ‘survminer’), data visualizations were created using R package ‘ggplot2’. For
all analyses, p<0.05 was considered significant, without correction for multiple comparisons
unless noted. All statistical analyses were exploratory and not prospectively-declared under
NCT01505868.
Supplementary:
Patients
High disease volume referred to >10 focal bone metastases or equivalent and/or tumor mass >4
cm at any site, and/or extension to at least three organ sites with one lesion at least 2 cm in
diameter. Low disease volume included 4 bone metastases with or without extension to lymph
nodes up to 2 cm in diameter. An accelerated rate of progression applied to patients with
worsening performance status, pain or other symptoms related to tumor growth in the six weeks
prior to the blood specimen collection, and/or development of >2 new metastatic lesions in a
single site or new non-nodal organ site extension in the previous three months.
CTC isolation, genome amplification, and next-generation sequencing
FASTQ files were aligned to hg38 human reference genome from the UCSC Genome database.
BAM files were filtered for MAPQ 30 reads followed by discrete analyses for genome-wide
profiling, individual gene copy number changes, and LST. A CNA-neutral profile carries an LST
score near zero, and CTC with low LST score carry few chromosomal breaks.
Genome-wide profiling: hg38 human genome was divided into ~3000 1Mbp bins and counted
across bins for each cell. Read counts were normalized against WBC controls, and the circular
binary segmentation algorithm ‘DNAcopy’ was used to segment DNA copy number data (log2
normalized ratio, sample/reference) and identify abnormal copy number. For individual gene
≤
59
copy number changes: reads were counted for each gene and each sample and normalized against
the total sequencing reads for the particular sample. Normalized reads were compared to
reference WBC and z-scores were calculated for each gene. The significant cutoff for calling
gene gain or loss was -3 < Z-score of > 3.
ctDNA extraction and analysis of copy number
Multiplexed reads sequenced using Illumina’s HiSeq4000 (76bp paired-end) were demultiplexed
using Illumina’s bcltofastq software and stored in individual FASTQ files while allowing one
barcode mismatch. The demultiplexed sequencing data were processed using the ‘variable
binning’ pipeline. The individual FASTQ files were aligned to human genome assembly NCBI
Build 37 (hg19/NCBI37) using Bowtie2 (2.2.6) alignment software. The aligned reads stored as
SAM files were converted to BAM files and sorted using SAMtools (1.2). PCR duplicates were
marked and removed using SAMtools. The reads were counted using variable bin sizes at an
average genomic resolution of 220 kb. Unique normalized read counts were segmented using the
circular binary segmentation (CBS) method from R Bioconductor ‘DNAcopy’ package followed
by MergeLevels to join adjacent segments with non-significant differences in segmented ratios.
The parameters used for CBS segmentation were alpha = 0.0001 and undo.prune = 0.05. Default
parameters were used for MergeLevels, which removed erroneous chromosome breakpoints.
Statistical analyses
Statistical analysis was done in R v3.4.1. Descriptive statistics were used to evaluate
demographic and clinical characteristics at the time of blood draw. Wilcoxon rank-sum tests were
utilized to compare differences between patient groups with continuous measures. The
coefficient of determination was utilized to compare the fit of linear regressions. The probability
of survival over time was assessed using Kaplan-Meier (KM) estimation. Differences in time-to-
event outcomes between groups were measured with the log-rank method, hazard ratios (HR)
were obtained from a univariable Cox model, and the p-value from the logrank test within the
coxph function, all in the R package ‘survival.’ Time-dependent ROC analyses were conducted
with R package ‘survivalROC.’ Dimensionality reduction was conducted with the R package
‘umap’ without parameter alteration. Power estimations for predictive biomarker associations
made use of the method by Peterson and George 1993 for estimating the power of an interaction
with a time-to-failure outcome. With the exception of KM curves (R package ‘survminer’), data
visualizations were created using R package ‘ggplot2’. For all analyses, p<0.05 was considered
significant, without correction for multiple comparisons unless noted. All statistical analyses
were exploratory and not prospectively-declared under NCT01505868.
60
3.4 Results and Discussion:
CTC in relation to clinical parameters and CellSearch® comparison
Peripheral blood samples were collected from 62 mCRPC patients prospectively stratified as
A VPC (29 patients) [90] or conventional mCRPC (33 patients) before starting chemotherapy on
trial NCT01505868 (Table 6; Supplementary Fig. 1). Using EsCTC, CTC were detectable in 49
patients (79.0%). Of those, enumeration, AR expression, and genomic sequencing data were
available from 47. No association was observed between CTC number (as single cells or CTC
clusters) and site of metastasis, performance status, tumor load, or clinically-defined A VPC
characteristics (Supplementary Fig. 2A-B). CellSearch® comparison showing a positive
correlation between the two CTC enumeration methods and higher detection sensitivity for
EsCTC is available in Table 6 and the Supplementary material.
TSG-based signature through single-cell genomics
Genome-wide CNA profiles were used to calculate copy number for individual gene regions. A
candidate molecular signature defined as loss of at least two of the three (2+) TSG PTEN, RB1,
and TP53 in single CTC was evaluated first. A total of 257 CTCs were individually sequenced
across the 47 patients (1–22 CTC/patient) (Fig. 22A). A patient qualified as TSG-signature
positive if at least one CTC had concurrent loss of 2+TSG. PTEN, RB1, or TP53 loss
individually was observed in at least 1 CTC in 21/47 patients (44.7%), concomitant 2+TSG loss
in 20/47 patients (42.6%), and all-3TSG loss in 10/47 patients (21.3%) (Fig. 22A;
Supplementary Table 6 shows single-cell data per patient). Loss of 2+TSG on the same CTC was
associated with shorter median PFS and OS, even if the differences did not reach statistical
significance (Fig. 22B-C; Supplementary Fig. 4A-B shows all-3TSG loss). Patients with at least
3 CTC sequenced demonstrated similar results (Supplementary Fig. 4C-D).
TSG-based signature in single CTC versus ctDNA
We sought to compare TSG loss detection in DNA from CTC v ctDNA. In 57 patients with
analysis of matched samples available for CTC and ctDNA analysis, 24 (42.1%) had sufficient
ctDNA for CNA assessment. CTC/mL was higher in patients with sufficient v insufficient ctDNA
(Table 7). The 2+TSG signature was positive in 12/57 patients (21.0%) by ctDNA and in 20/57
patients (35.1%) by EsCTC. Concordance in methods was most common in patients with high
CTC burden (Supplementary Fig. 5A-B). In 20 matched samples with analytically sufficient
ctDNA and sequenced CTC, concordance for the 2+TSG signature was observed in 14 (70%, 5
positive and 9 negative) (Table 7). Three patients (5.3%) had evaluable CNA by ctDNA, but no
CTC detected, while 23 patients (40.4%) had CTC detected and sequenced but insufficient
ctDNA (Supplementary Fig. 5A-B).
AR-protein expression in CTC is multifactorial
61
High-resolution CTC imaging before genomic analysis allowed for comparison of AR-protein
expression intensity to CNA profiles. We found that the relationship between AR-gene z-score
and AR-protein expression could be patient-specific (Supplementary Fig. 6A), suggesting that
factors other than the AR-gene could contribute to AR-protein expression. Across all patient CTC
in the cohort, there was a relationship between AR-gene gain status and AR-protein expression
(Supplementary Fig. 6B). However, the degree of AR-protein expression on individual CTC
became more resolved when gains in additional genes, such as MYC and NCOA2, were factored
in (Supplementary Fig. 6C), as well as gains in the AR-gene enhancer region (Supplementary
Fig. 6D), suggesting an additive, multifactorial effect of other genes on AR-protein expression in
individual CTC.
Genomic instability as measured by LST in single CTC is associated with aggressive
prostate cancer behavior
For every patient, the median LST across all CTC was determined and used as a GI score (Figure
22A and Supplementary Table 6). CTC/mL and GI score per patient showed no discernable
correlation (Supplementary Fig. 7A-B). However, loss of any TSG and, to a lesser degree,
median AR expression, were associated with higher GI in single CTC (Table 8). Higher GI
across patient CTC was also observed in those with clinical A VPC, accelerated progression pre-
therapy, PFS <4 months, and OS <12 months. Conversely, those with merely higher tumor load
did not see an association of similar magnitude (Table 8). Time-dependent ROC analyses for 12-
month survival showed a trend for greater AUC when CTC/mL, GI score and TSG-presence in
CTC were assessed in combination compared to each individually (Supplementary Fig. 8).
High-dimensional gene-gene relationships between CTC and patients’ prognosis
For each gene region altered, incidence among all 257 CTC sequenced and GI score for the CTC
carrying the alteration were calculated (Table 9). Two regions on chromosomes 8q (gain) and
13q (loss) had ≥3 gene alterations associated with high GI. Genes in the regions lost in 13q
include BRCA2 and RB1 (which in 23% of the CTC did not coincide in intact or loss status).
The most common alteration (44%) was a gain corresponding to the 100kb region spanning
PTK2, which encodes Focal Adhesion Kinase (FAK).
To more broadly assess the relationship between common CNA, GI, prior therapy, and survival,
we performed dimensionality reduction on the z-scores of the >20% incidence genes in all
sequenced CTC, projecting these gene-gene relationships into 2-dimensional space. These
projections of gene-gene relationships did not identify trends between CTC from patients
previously treated with abiraterone/enzalutamide v not (Supplementary Fig. 9A). However, the
CTC from patients with >2 years survival clustered in a dense region associated with lower GI
and lower incidence of TSG loss, broadly suggesting relationships between these genomic
alterations and prognosis (Supplementary Fig. 9B-D).
Supplementary: CellSearch® comparison
62
Clinical CTC enumeration based on CellSearch® was available from 47 of the 62 patients.
Regression analysis showed a positive correlation between the two CTC enumeration methods
(r2 = 0.73, p <0.0001). V olumetrically normalized analysis of CTC enumeration in matched
samples demonstrated that EsCTC detected CK+ CTCs at a higher concentration than
CellSearch® in almost every patient (median 3.9/mL v 0.93/mL, p = 0.016) (Supplementary Fig.
3A-B).
Discussion:
In this study, we extended the classification of patients prospectively identified as either clinical
A VPC or conventional mCRPC to liquid biopsies through the application of the EsCTC
technology for single CTC genomic analysis. We found a compound TSG loss-based molecular
signature previously related to A VPC [93, 95] and linked it to other specific CNA observed in
association with increased GI in single CTC relative to patients’ outcomes, thus effectively
expanding the set of chromosomal alterations associated to aggressive prostate cancer behavior.
Our findings support the feasibility of single CTC genomics to contribute information of
biological significance and clinical relevance beyond CTC enumeration, with a depth that bulk
tissue or ctDNA analyses may not attain.
In line with detection rates reported in tissue-based studies [91-93, 95, 96], we identified the
candidate TSG-based molecular A VPC signature, defined as concomitant loss of at least 2 of the
3 TSG in at least 1 CTC, in 32.2% of the 62 initial patients. We next compared TSG status in the
CTC v ctDNA in matched blood samples and found a general degree of concordance (70%) that
lends credence to both techniques for gene CNA assessment in liquid biopsy. Still, only 42% of
the patients in the ctDNA cohort had sufficient ctDNA for CNA analyses, whereas 77% had CTC
detected and sequenced, demonstrating increased detection sensitivity through CTC (at least with
the methods we used). An important caveat is that only a median 3 mL plasma and 0.9/mL blood
were respectively used for ctDNA and CTC analyses, likely underestimating CTC/ctDNA
detection rates.
As our group has reported that the presence of the candidate A VPC signature in tumor tissue and/
or ctDNA predicts for the benefit from carboplatin[104], we tried to evaluate whether the same
relationship could be discerned through CTC. Unfortunately, our cohort did not have the
requisite sample size power to detect predictive biomarker effects less than an exceptionally high
treatment-specific interaction hazard ratio (Supplementary Table 7). Regardless, we found the
presence of 2+TSG on a single CTC associated with shorter survival outcomes, and incremental
loss of TSG to be directly proportional to poorer prognosis, consistent with established biology
of more aggressive oncogenic behavior and empirical correlations to poor outcome seen by other
groups [93, 95].
Our ability to simultaneously evaluate AR protein and gene resulted in the finding of patients
with mixed patterns of AR expression and amplification status in CTC. A probable explanation
63
for AR-expressing mCRPC cases with no or low-level AR amplification is the presence of newly
described tandem duplications in the enhancer region upstream of AR [85, 86]. The concordance
between AR protein and gene status across CTC may have clinical significance and should be
further examined. Our single CTC genomic analysis also uncovered increased AR expression in
relation to gains in the chromosomal regions containing Myc and NCOA2, a nuclear coactivator
of AR [82, 110].
We sought to expand on the catalog of genomic alterations related to aggressive mCRPC
behavior that are resolvable through liquid biopsy, with an eye toward disease evolution
monitoring. Our analyses led to a broad surrogate of GI in LST, a genome-wide measure of DNA
scarring that has been linked to HRD, with HRD linked to the sensitivity of different cancer
types to platinum drugs and PARP inhibitors. Clinically, we found higher GI scores as
independent of CTC number, more clearly related to measures of aggressiveness than to tumor
bulk, and additive to prognostic models. Molecularly, co-occurring 2+TSG losses were all
positively correlated with increasing GI but had less sensitivity for detecting poor prognosis. The
chromosomal regions most frequently altered in CTC with higher GI included those affecting
genes linked to HRD (loss in 13q13.1 [BRCA2]), increasing AR activity (gain in 8q12-13
[NCOA2] and loss in 16q23-24 [CBFA2T3]), lineage plasticity (loss in 13q14.2 [RB1]), and cell
proliferation and metastasis (gain in 8q24 [Myc, PTK2, EXT1]). While GI via LST appears to be
a global measure of prostate cancer aggressiveness in CTC, PTK2 gain, MYC gain, and TP53
loss were the specific gene alterations most clearly associated to poor prognosis (Supplementary
Table 8).
We used dimensionality reduction in an exploratory attempt to further refine the definition of
aggressiveness through molecular means. UMAP applied to the 32 genes most commonly altered
in the cohort revealed gene-gene associations that suggest a continuum of GI related to
increasing TSG losses and worse survival. Studies with larger number of patients and sample
volumes available will enable greater resolution of genomic heterogeneity and more refined
subclonal assessments of biological and disease classification significance. Yet even with our
limitations in patient number and CTC sequenced, this study represents the most comprehensive
single CTC sequencing dataset tied to clinically-relevant outcomes completed to date in mCRPC.
64
3.5 Conclusion
Broadly, our single CTC genomic analysis suggests an increasing continuum of gene alterations
or altered chromosomal regions associated to GI towards aggressiveness in mCRPC, and
representative of still not well characterized, discrete paths to progression that are likely linked to
distinct molecularly-defined A VPC categories. PTEN, RB1, and TP53 seem critical components
of those paths, and based on this study, GI through the determination of LST in CTC is a
hallmark of prostate cancer aggressiveness. Serial evaluation of liquid biopsies from larger
numbers of similarly-treated patients will be needed to untangle clinical and molecular
heterogeneity and the biological paths to progression.
65
3.6 Figures

Figure 21: Epic Sciences enrichment-free CTC detection and single-cell genomics overview.
(A) Workflow for enrichment-free CTC isolation and genomics. All nucleated cells from a blood
sample are plated onto microscope slides, and CTC are identified by automated fluorescence
microscopy, then individually isolated and sequenced. (B) Three example images of CTC are
shown with all four individual channels shown, plus a merge panel. The top two CTC do not
overexpress AR N-term, the bottom CTC overexpressed AR N-term. (C) Representative
immunofluorescent images of CTC and corresponding genome-wide CNA profiles obtained from
patient 180. Disease in this patient is characterized by high clonality, harboring CNA events
extensively described in late-stage prostate cancer such as AR-gain and PTEN loss. All CTC
shown also had AR protein overexpression (not shown in composite). As expected, a CNA
neutral profile was observed from a patient’s WBC used as reference control.
66

Figure 22: PTEN, RB1, TP53 loss on Single CTC. n = 47 cohort with CTCs detected and
sequenced (A) Per patient, progression-free survival is shown alongside subclonal percent of
alterations of PTEN, RB1, and TP53, the presence of any two or all three of the alterations
present in at least one CTC detected, and large-scale transitions per patient across CTC (median,
max, std deviation). KM curves visualizing (B) PFS and (C) OS of patients by the presence or
absence of at least one CTC with loss of two or more of PTEN, RB1, and/or TP53 in the same
CTC.
67

Table 6: Patient demographics and clinical characteristics of cohorts used in analyses.
68

Table 7: Comparison of A VPC molecular signature in CTC and ctDNA. Assessment of
PTEN, RB1, and TP53 CNA and A VPC molecular signature in CTC and ctDNA of 57 patients
with or without sufficient cfDNA. * Includes only patient-matched specimens with detectable
and sequenced CTC and with sufficient ctDNA for sequencing (n = 20).
69

Table 8: CTC enumeration and median LST associations with clinical features and
outcome. The median CTC/mL, median AR (+) CTC/mL, and median of the median LST across
CTC within each patient sample are shown by groups indicated. p-values reflect Wilcoxon Rank-
Sum tests.
70

Table 9: Chromosomal and gene alterations associated with high genomic instability. List of
gene alterations with associate incidence rate within the CTC population sequenced, Median LST
associated with CTC harboring that alteration, and p-value. Chromosomal regions associated
with each gene are also listed.
71

Supp Figure 1: CONSORT diagram of patient samples from clinical trial NCT01505868
used in this study. There are three discrete groups utilized: Those with matched CellSearch data,
those with available sequenced CTC, and those with matched ctDNA data.
72

Supp Figure 2: Non-enrichment CTC enumeration by clinical parameters. (A) CTC
enumeration by sites of metastasis present. (B) CTC enumeration by clinical parameters of
disease severity for all n = 62 patients. Indicated p-values are from Wilcoxon rank sum tests.
73

Supp Figure 3: A comparison of enrichment and enrichment-free CTC detection
techniques. From the subset of patient samples with matched Epic and CellSearch® data,
volumetrically normalized* comparisons of CTC enumeration are shown in (A) matched bar plot
(B) CTC/mL scatter plot, dashed line indicates linear correspondence.
*2 slides were tested per Epic sample (median 0.9 mL blood) vs. 7.5mL of blood tested per
CellSearch® sample.
**one outlier point excluded from linear regression analysis
74

Supp Figure 4: KM curves visualizing: (A) PFS and (B) OS of the subset of patients with 1 or
more CTC sequenced by the presence or absence of at least one CTC with loss of PTEN, RB1,
and TP53 in the same CTC. (C) PFS and (D) OS of the subset of patients with 3 or more CTC
sequenced by the presence or absence of at least one CTC with loss of two or more of PTEN,
RB1, and/or TP53 in the same CTC.
75

Supp Figure 5: CTC and ctDNA comparison. A comparison of CTC enumeration by ctDNA
status and the presence of (A) ≥2 or (B) all 3 of the tumor suppressor genes PTEN, RB1, TP53.
76

Supp Figure 6: Influence of AR gene gain on AR protein expression in single CTCs is multi-
factorial. The relationship between AR gene z-score and AR protein (expressed as a signal to
noise of surrounding white blood cells on a slide) is shown as a scatter plot, with every CTC
colored by the patient of origin for (A) a select few with notable divergent relationships between
AR gene and protein in single CTCs. (B) The relationship between single-CTC AR protein and
AR gene gain, as well as (C) the co-occurrence of AR, NCOA2, and MYC gain, and (D) the co-
occurrence of AR gene and AR enhancer region gain. Dashed line indicates pre-specified
analytical cutoffs for AR gene gain.
77

Supp Figure 7: (A) Comparison of median LST to the number of CTC detected. (B)
Comparison of median LST to number of CTC sequenced.
78

Supp Figure 8: Prognostic features of CTC enumeration, large scale transitions, and A VPC
molecular signature. CTC/mL, genomic instability, and A VPC molecular signature in CTC were
evaluated as additive features to prognosticate survival.
79

Supp Figure 9: High dimensionality single-CTC genomic associations. The 257 single-
sequenced CTC from 47 patients were profiled using UMAP projections. The projections are
colored by (A) the prior treatment history from the patients of origin, (B) the survival of patients
of origin (C) the LST score per CTC, and (D) the number of tumor suppressor genes altered
encompassing PTEN, RB1, TP53.
80

Suppl Table 1: Single CTC genomics. Number of CTC with single and compound TSG losses,
and median LST, per individual case.
81

Suppl Table 2: Predictive Biomarker Power Assessments. (A) Equation 1 of Chan et al. 2016
Oncotarget was utilized to estimate the power to detect a treatment-specific interaction (Ballman
2015 JCO) between biomarker positive (molecular A VPC) and negative patients given the
existing parameters for number of patients, number of death events percent of samples tested
from arm A vs. B, the biomarker positivity rate, and an alpha error of 0.05 (2-sided). HRR
indicates the Hazard Ratio Ratio or the hazard ratio of Arm A vs. B in the biomarker positive
group relative to hazard ratio of Arm A vs. B in the biomarker negative group. An HRR of 3, for
instance, would indicate a group where biomarker positive patients have 3X greater difference in
survival between two drug classes relative to biomarker negative patients. (B) the same as (A)
except with PFS instead of OS.
82

Supp Table 2: CTC from patients across the cohort were grouped together by the survival
associated with their patients of origin. The incidence of alterations are shown for those with
0-1, 1-2, and 2+ years of survival.
83
Future Prospective
There have been many key advances in the detection and prevention of prostate cancer. The
current risk stratification system for prostate cancer depends on accurate solid biopsy. However,
systematic biopsy misses 21% to 28% of prostate cancers and under-grades 14% to 17% [111].
There are several new biomarkers (4Kscore, Prostate Health Index, prostate cancer antigen 3 test,
ConfirmMDx) that help identify potential false-negative results. Additionally, molecular
biomarkers (e.g., Decipher, Prolaris, Oncotype DX) that classify tumor aggressiveness have
become available in the clinic. However, none of these advances lead to non-invasive biopsy and
for re-sampling of the primary tissue or metastatic lesions.
Using the non-enrichment, “no cell left behind” HD-SCA platform to identify and characterize
the tumor and rare cells in peripheral blood and bone marrow aspirate of patients may contribute
to future advances in prevention, diagnosis, and treatment of prostate cancer patients.
Using this technology, morphological differences, genomic alterations, targeted proteomic
analysis, and cell-free DNA analysis can be done using a single tube of blood or bone marrow
aspirate. Several technologies can assess each one of these analytes individually; however, none
can evaluate all from a single tube. A single tube of blood can provide a clinician with serum
PSA levels, presence and enumeration of circulating tumor cells, presence of Androgen receptor
AR-v7 protein expression, presence of copy number alterations such as loss of PTEN, RB1,
TP53, BRCA2, and gain in AR, protein expression data on targets such as AR, AR-v7, TP53,
MYC, BRCA2, and copy number alterations in cell-free DNA, especially crucial in cases where
circulating tumor cells are absent.
The information generated using a single tube of blood may have several clinical impacts.
Recent data has linked the presence of nuclear protein AR-v7 in CTCs as a predictive biomarker,
identifying patients who will have better overall survival on taxane chemotherapy instead of
next-generation AR signaling inhibitors [34, 112, 113]. Low serum PSA levels in combination
with the presence of copy number loss of PTEN, TP53, and/ or RB1 in CTCs or ctDNA may
point to presence of aggressive variant prostate cancer, where patients benefit from
carboplatin[11, 18, 36, 37, 90, 104]. The presence of genetic alterations associated with highly
aggressive disease at time of diagnosis may lead to intensified treatment instead of standard
therapy for patients newly diagnosed with localized prostate cancer.
Our goals for projects and data presented in this thesis are to focus on how characterization of
liquid biopsy may impact diagnosis and treatment of patients with end goal of improved quality
of life and treatment response in patients.
84
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91

Abstract (if available)

Abstract Prostate cancer has displayed an increase in incidence with a gradual increase in mortality in the last two decades[1]. This disease is mainly characterized by heterogeneous growth patterns ranging from slow-growing tumors to highly metastatic and fast-growing lesions. Genetic alterations associated with prostate cancer have been the focus of current research, and many have been identified as associated with aggressive disease phenotype. However, many of these studies are based on tissue collected at the time of diagnosis with lack of any tissue from repeat biopsy or metastatic tissue, animal models, and prostate cancer cell lines. Lack of access to metastatic tissue has hindered molecular, pathological, and clinical characterization of heterogeneity in prostate cancer. ❧ Understanding therapeutically relevant heterogeneity in solid tumors leads to the introduction of targeted therapy. This is most notable in breast cancer (ER and HER2 overexpression), lung cancer (EGFR mutations and EML4-ALK translocations), melanoma (BRAF mutations), and colon cancers (RAS mutations)[2-7]. However, the prognostic model in prostate cancer is based on clinical features, which in turn continues to impact clinical decision making[8, 9]. With the availability of new therapeutics with diverse mechanisms, there is a need for molecular markers that would target more clearly defined patient subpopulations and better identify responders. ❧ Preclinical and clinical studies have identified candidate molecular markers for the classification of aggressive disease phenotype using prostate cancer cell lines, patient tumor-derived xenografts, and solid biopsy tissue specimens from primary and metastatic sites [10-20]. The most consistently found alterations are: loss in copy number of PTEN and RB1, loss in copy number as well as missense mutations of TP53, gain in chromosome 8q (MYC, NCOA2, EZH2, and more), loss of MYC-N on chromosome 2. These alterations have been associated with adverse prognosis and are enriched in highly aggressive tumors [10, 11, 13-15, 18, 20, 21]. ❧ Identification of these molecular biomarkers of aggressive disease in circulating tumor cells and metastatic tumor cells may lead to the stratification of high-risk and fast progressor subgroups at earlier stages. Liquid biopsy resulting in the assessment of circulating and metastatic tumor cells has the potential to provide a non-invasive approach for studying tumors in real-time and at multiple time points allowing for early recognition of aggressive disease. Longitudinal sampling thorough liquid biopsy may identify the rise of aggressive clonal populations during treatment course before any clinical symptoms of failed therapy. This would allow for modification of treatment plan prior to physical indications of progression or drug resistance. ❧ The following studies focus on the use of non-invasive liquid biopsy to identify and characterize circulating rare cells in peripheral blood and bone marrow aspirate of localized, de novo metastatic, and aggressive variant prostate cancer patients. We hope to use this platform to identify molecular signatures of aggressive disease that would allow for selection of high-risk patients for early intervention and intensified treatment.

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Asset Metadata

Creator Malihi, Paymaneh D. (author)

Core Title Molecular signature of aggressive disease and clonal diversity revealed by single-cell copy number analysis of prostate cancer cells across multiple disease states

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Molecular Biology

Publication Date 04/20/2020

Defense Date 10/07/2019

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

Tag cancer,circulating tumor cells,copy number alterations,genomics,liquid biopsy,metastatic tumor cells,molecular marker,OAI-PMH Harvest,prostate cancer,proteomics,single cell

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kuhn, Peter (committee chair), Finkel, Steven (committee member), Hicks, James (committee member), Newton, Paul K. (committee member), Nieva, Jorge (committee member)

Creator Email peymaneh87@gmail.com,pmalihi@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-281962

Unique identifier UC11673157

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Legacy Identifier etd-MalihiPaym-8268.pdf

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Document Type Dissertation

Rights Malihi, Paymaneh D.

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Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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