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Evaluating the impact of long non-coding RNAs on tumor mutational burden in cancer

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Evaluating the impact of long non-coding RNAs on tumor mutational burden in cancer

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Content Evaluating the impact of long non-coding RNAs on tumor
mutational burden in cancer

By

Tianchun Xue

A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA

In Partial Fulfilment of the Requirement for the Degree
MASTER OF SCIENCE
(BIOCHEMISTRY AND MOLECULAR MEDICINE)

August 2021

Copyright 2021 Tianchun Xue
ii
Dedication

To my dearest parents Jiwu Xue and Yang Song for their unconditional love and
support.

iii
Acknowledgements

First, I would like to express my heartfelt gratitude to my advisor Dr. Crystal Marconett
for allowing me to be part of her laboratory and giving me opportunity to learn and complete my
thesis. Her passion, patience, encouragement and guidance towards my research project, thesis
writing, and presentation is much appreciated.

Second, I would like to extend my thanks to all the members of Dr. Marconett’s lab and
Dr. Offringa’s lab for their generous help during my research. In particularly, I would like to
thank Jonathan Castillo for working together on our TMB paper, Daniel Mullen and Lars St.
Pierre for giving me suggestions and answering my bioinformatics-related questions and Ziben
Zhou for providing emotional support.

Then, I would like to extend my thanks the members of my committee, Dr. Ite Offringa
and Dr. Suhn Kyohng Rhie, for their time and kindness. I would like to especially thank Dr. Ite
Offringa for her ever care and involvement throughout my two-year master’s program.

Lastly, I would like to thank my family, for giving birth to me and supporting me
financially and spiritually throughout my life. Thank you, I love you so much and I’m really
happy and proud to be your daughter.

iv

Table of Contents
Dedication ....................................................................................................................................... ii

Acknowledgements ........................................................................................................................ iii

List of Figures ................................................................................................................................ vi

Abstract ......................................................................................................................................... vii

CHAPTER I: Introduction .............................................................................................................. 1
1.1 Lung adenocarcinoma ........................................................................................................... 1
1.2 Tumor mutational burden ..................................................................................................... 4
1.3 DNA Repair .......................................................................................................................... 6
1.4 LncRNA and LINC00261 ..................................................................................................... 8

CHAPTER II: Methods and Materials .......................................................................................... 12
2.1 High dimensional data analysis .......................................................................................... 12
2.2 Common gene pattern analysis ........................................................................................... 12
2.3 Correlation analysis ............................................................................................................ 13
2.4 Pathway Analysis ................................................................................................................ 13
2.5 Analysis of mutational signatures and mutational patterns ................................................ 14
2.6 Mediation analysis .............................................................................................................. 15

CHAPTER III: Results ................................................................................................................. 16
3.1 Evaluating the effect of DDR-associated lncRNA on TMB ............................................... 16
3.1.1 Determining the spectrum of lncRNA correlated to tumor mutational burden across
cancer types ........................................................................................................................... 16
3.1.2 Common pathways in LUAD and STAD include DDR signaling .............................. 17
3.2 Evaluating the effect of LINC00261 and interacting partners on TMB in multiple epithelial
cancers ....................................................................................................................................... 20
3.2.1 LINC00261 is anti-correlated to TMB in LUAD and STAD ...................................... 20
3.2.2 Genes correlated to LINC00261 expression were enriched for DDR-related pathways
in LUAD and STAD ............................................................................................................. 22
3.2.3 LINC00261 silencing is associated predominantly with increased C>A transversion in
LUAD ................................................................................................................................... 24
v
3.2.4 DDR-associated COSMIC signatures were present in both the LINC00261-expressing
group and silenced group ...................................................................................................... 25
3.2.5 LINC00261 acts as a partial mediator of the nucleotide excision repair (NER)
components’ effect on TMB, and its effect on TMB is completely mediated by CENPA ... 27

Chapter IV: Discussion ................................................................................................................. 29

Supplements .................................................................................................................................. 32

References ..................................................................................................................................... 37

vi
List of Figures
Figure 1: Ten Leading Cancer Types for the Estimated New Cancer Deaths by Sex, United
States, 2021. .................................................................................................................................... 2
Figure 2: Significantly mutated genes in lung adenocarcinomas ................................................... 3
Figure 3: Single oncogenic driver paradigm of lung adenocarcinoma molecular classification. ... 4
Figure 4: Different types of mutation in TMB. ............................................................................... 5
Figure 5: Somatic mutations per sample are plotted for each sample and cancer type. ................. 5
Figure 6: Evolution of TMB as an immunotherapy biomarker. ..................................................... 6
Figure 7: DNA repair pathways ...................................................................................................... 7
Figure 8: MEG3 as tumor suppressor and MALAT1 as oncogene .................................................. 9
Figure 9: TP53TG1 prevents the activation of oncogenes ............................................................ 10
Figure 10: LINC00261 acts locally on expression of FOXA2 via recruitment of SMAD3 to the
FOXA2/LINC00261 transcriptional locus ..................................................................................... 11
Figure 11: Genes correlated to TMB across ten cancer types. ..................................................... 17
Figure 12: The comparison of TMB-correlated gene pattern in LUAD and STAD ..................... 18
Figure 13: Pathway enrichment of TMB-correlated genes in LUAD (Top) and STAD (Bottom)19
Figure 14: LINC00261 expression was negatively correlated to TMB in LUAD and STAD. ..... 21
Figure 15: LINC00261 was negatively correlated to TMB in smokers in LUAD. ....................... 22
Figure 16: Pathway enrichment of LINC00261-correlated genes in LUAD and STAD .............. 23
Figure 17: The effect of LINC00261 expression on mutational pattern. ...................................... 25
Figure 18: Counts of different signatures and comparison to COSMIC signatures. .................... 26
Figure 19: The mutation pattern of signature 1 in LINC00261-expressing and silenced group. .. 27
Figure 20: Diagram for mediation analysis between factors and TMB. ....................................... 28
vii
Abstract

Lung cancer is the leading cause of cancer deaths in the US and worldwide. Among lung
cancer, non-small cell lung cancer (NSCLC) represents the majority of new lung cancer cases,
and lung adenocarcinoma (LUAD) is the most common subtype within NSCLC. However, there
are around 30% of LUADs lack a known cancer driver. Here, I focused on a novel non-coding
RNA that appears to regulate the cellular response to DNA damage, LINC00261. I utilized the
Cancer Genome Atlas (TCGA) database to investigate the relationship between LINC00261
expression and tumor mutational burden (TMB) in multiple cancer types. Although there are
hundreds of lncRNAs associated with TMB, LINC00261 is significantly negatively correlated to
TMB in LUAD and STAD. The expression of LINC00261 contributes to a
transversion/transition switch and is related to some crucial DNA damage response-related genes
in LUAD. In addition, DDR-associated COSMIC signatures are present in both the LINC00261-
expressing group and silenced group. In conclusion, LINC00261 plays an important role in
protecting the LUAD patients from the high mutational burden and helps to make a precision
treatment decision.
1
CHAPTER I: Introduction
1.1 Lung adenocarcinoma
Lung cancer is the leading cause of cancer death among males and females in the United
States, accounting for 22% of all estimated cancer mortality in 2021(Siegel, Miller, Fuchs, & Jemal,
2021). There are two major clinical categories of lung cancer: small cell lung carcinoma and non-
small cell carcinoma (NSCLC). NSCLC accounts for ~ 85% of all newly diagnosed lung cancers
(Schabath & Cote, 2019). Among non-small cell lung cancer, lung adenocarcinoma (LUAD),
which arises from the alveolar epithelium (Xu et al., 2012) is the largest histological subtype,
occurring in ~40% of NSCLC cases (Schabath & Cote, 2019). Environmental exposure to inhaled
chemicals, including smoking, are major risk factors for lung cancer (de Groot & Munden, 2012),
including exposure to secondhand smoke (Hori, Tanaka, Wakai, Sasazuki, & Katanoda, 2016),
and are thought to induce carcinogenesis by inducing DNA damage (Leanderson & Tagesson,
1992).

2
Figure 1: Ten Leading Cancer Types for the Estimated New Cancer Deaths by Sex, United States, 2021.
(Siegel et al., 2021)

Although smoking is undoubtedly a major factor contributing to the incidence of lung
cancer, approximately 10% to 25% of all lung cancers occur in never-smokers (Ferlay et al., 2010;
Naoki et al., 2002). Mutations in oncogenic drivers occur in a large percentage of lung
adenocarcinoma cases, including smoking-associated LUAD with KRAS mutations (Lee et al.,
2016), never-smoking LUAD associated with EGFR (Lynch et al., 2004), ELM4-ALK (Soda et al.,
2007), BRAF (Marchetti et al., 2011), ROS1 (Bergethon et al., 2012) among others. Additionally,
inactivation of tumor suppressors can also affect key DNA repair pathways, resulting in the
accumulation of mutations and formation of lung adenocarcinoma. TP53 is the most frequently
mutated gene in lung adenocarcinoma, accounting for ~70% of patients (Ding et al., 2008). p53,
encoded by TP53, plays a critical role in regulating cell cycle and cell death, and hence function
as a tumor suppressor (Vazquez, Bond, Levine, & Bond, 2008). Now, several p53-based cancer
therapies have been developed, including restoration of p53 function (W. W. Zhang et al., 1994),
inhibition of p53-MDM2 interaction (Vassilev et al., 2004), targeting p53 protein family
(Kravchenko et al., 2008), elimination of mutant p53 (Li et al., 2011) and p53-based vaccines
(Speetjens et al., 2009).
3

Figure 2: Significantly mutated genes in lung adenocarcinomas
(Ding et al., 2008)

However, despite this progress, ~30% of LAUD tumors still lack a known genetic
alteration (Figure 3, Skoulidis & Heymach, 2019). And patients who do have these mutations
develop resistance at a high rate which blunts the impact of these drugs on overall patient survival
(Del Re et al., 2017; Leonetti et al., 2019). Therefore, there remains a critical lack of understanding
of the molecular mechanisms underlying carcinogenesis which is needed to develop enhanced
precision medicine tools and improve patient survival outcomes.

4

Figure 3: Single oncogenic driver paradigm of lung adenocarcinoma molecular classification.
(Skoulidis & Heymach, 2019)

1.2 Tumor mutational burden
Tumor mutational burden (TMB) is determined by whole exome genome sequencing
carried out on tumor DNA and matching normal DNA. It is the tumor number of gene mutations
existing in cancer cells, including single nucleotide variation (SNV), which refers to somatic single
nucleotide changes from one base to another, and insertions/deletions (indel), which refers to a
stretch of nucleotides inserted or deleted from the “control” genome (Chan et al., 2019).
5

Figure 4: Different types of mutation in TMB.
(Nesta, Tafur, & Beck, 2020)
SNV: Single nucleotide variation. Indel: Insertion/deletion.

TMB rates vary across and within cancer types. For example, skin cutaneous melanoma
(SKCM) has the highest mean level of tumor mutations per tumor, though this varies wildly within
SKCM cases (Yan, Wu, Yu, Zhu, & Cang, 2020). This is followed by lung squamous cell
carcinoma (LUSC), bladder urothelial carcinoma (BLCA), lung adenocarcinoma (LUAD),
esophageal carcinoma (ESCA) and other cancer types (Figure 5, Bailey et al., 2018).

Figure 5: Somatic mutations per sample are plotted for each sample and cancer type.
(Bailey et al., 2018)

6
TMB together with PD-L1 and CTLA-4 expression levels are used clinically to determine
which patients may benefit from immunotherapy (Büttner et al., 2019). Figure 5 shows the
development of TMB as an immunotherapy biomarker over last several years. TMB-based assays
are currently being considered by the FDA for approval as companion diagnostics for immune
checkpoint blockade agents (Chan et al., 2019). Therefore, understanding the key mechanisms that
trigger the level of TMB can be helpful toward clinical treatment.

Figure 6: Evolution of TMB as an immunotherapy biomarker.
(Chan et al., 2019)

1.3 DNA Repair
The accumulation of mutations in tumors can occur when DNA repair mechanisms are
dysregulated (Parikh et al., 2019). DNA damage is an alteration in the chemical structure of DNA,
such as bulky adduct addition, depurination, single strand vs double strand breaks, etc. Cells
respond to DNA damage through several DNA damage response (DDR) pathways, which initiate
several coordinated cellular processes to effect DNA repair. There are at least five major DNA
repair pathways active at different stages of the cell cycle, and activation of each is dependent on
7
the type of DNA damage being repaired: base excision repair (BER) used for removing small, non-
helix-distorting base lesions from the genome; nucleotide excision repair (NER) repair used for
removing bulky DNA lesions induced by ultraviolet light (UV); mismatch repair (MMR) used for
correcting spontaneous base-base mis-pairs and small insertions or deletions that occur during
DNA replication and recombination; homologous recombination (HR) used for accurately
repairing harmful breaks that occurred on both strands of DNA; and non-homologous end joining
(NHEJ) used for direct ligation of break ends without the need for a homologous strand (Chatterjee
& Walker, 2017).

Figure 7: DNA repair pathways
(Pottenger et al., 2019)

Chemotherapy is a part of the standard of care for treatment of many cancers and works by
inducing high enough levels of DNA damage that the cancer cells can no longer effectively
8
replicate, and instead undergo apoptosis (Dasari & Tchounwou, 2014). As sensitivity to the overall
level of DNA damage is necessary for these therapeutics to be effective, it is extremely pressing
that we understand the factors that participate in DNA repair to know which tumors will and will
not respond to a given chemotherapeutic. In addition, when genes involved with DDR are
dysregulated, DNA repair is inhibited. Therefore, the expression and function of DDR factors are
a major contributor to elevated TMB levels. It is essential to study genes that could be correlated
to TMB so that we can discover biomarkers of clinical indication for treatments targeting cancers
with high mutational loads.

1.4 LncRNA and LINC00261
While many of the proteins in DNA damage detection and repair have been characterized,
relatively little is known about how functional RNAs participate in DNA fidelity. Long non-coding
RNAs (lncRNA) are defined as transcripts longer than 200 nucleotides. They exert their cellular
function as mature RNA by interacting with proteins and/or DNA as critical components of cellular
regulation (Castillo, Stueve, & Marconett, 2017). There are many lncRNAs with previously
characterized roles in LUAD, with both tumor suppressor or oncogene function (Castillo et al.,
2017). For instance, MEG3 is a tumor suppressor, which activates p53 directly or indirectly by
suppressing MDM2 leading to activation of p53 downstream targets and inhibition of subsequent
proliferation (Zhou et al., 2007). MALAT1 is an oncogene first identified in LUAD which enhances
the expression level of known pro-angiogenic factors VEGF-A and FGF2, leading to abnormally
high pro-angiogenesis (Z. C. Zhang et al., 2017).
9

Figure 8: MEG3 as tumor suppressor and MALAT1 as oncogene
(Z. C. Zhang et al., 2017; Zhou et al., 2007)

LncRNAs are also known to participate in DNA repair. TP53TG1 is stimulated by p53
upon DNA damage-induced double-strand breaks and binds to the DNA/RNA binding protein
YBX1 to prevent its activation of oncogenes (Wu & Wang, 2017).

10
Figure 9: TP53TG1 prevents the activation of oncogenes
(Wu & Wang, 2017)

LINC00261 is a lncRNA our lab has previously identified to be a tumor suppressor in
LUAD that alters activation of the DDR response (Shahabi et al., 2019). LINC00261 is normally
expressed in alveolar epithelial cells, the primary epithelial tissue in the region of lung where this
cancer arises (Sergeeva, Korinfskaya, Kurochkin, & Zatsepin, 2019; Shahabi et al., 2019), and it
is epigenetically silenced in LUAD (Shahabi et al., 2019). Elevated expression of LINC00261 in
LUAD was found to significantly improve overall survival (Fan et al., 2016; Shahabi et al., 2019).
Previous studies have reported that LINC00261 acts locally on expression of FOXA2 via
recruitment of SMAD3 to the FOXA2/LINC00261 transcriptional locus during mouse lung
development as well as in embryonic stem cell differentiation toward an endodermal lineage (Jiang,
Liu, Liu, Zhang, & Zhang, 2015; Mather et al., 2021). Our lab has reported an alternate mechanism,
namely that LINC00261 blocks cellular proliferation by arresting the G2/M phase of the cell cycle
and that expression of LINC00261 acts as a tumor suppressor in vivo (Shahabi et al., 2019). The
reintroduction of LINC00261 into LUAD cell line H522, which lacks endogenous expression, was
able to initiate the DDR phosphorylation cascade, including ATM phosphorylation and CHK2
phosphorylation (Shahabi et al., 2019).

11

Figure 10: LINC00261 acts locally on expression of FOXA2 via recruitment of SMAD3 to the FOXA2/LINC00261
transcriptional locus
(Mather et al., 2021)

Therefore, my thesis was designed to test the relationship between LINC00261 and TMB
in primary LUAD, the effect of LINC00261 on individual mutation types, and the influence of
DDR factors on this relationship in lung adenocarcinoma.

12
CHAPTER II: Methods and Materials
2.1 High dimensional data analysis
The TCGA (The Cancer Genome Atlas) data (April 7, 2020) of CESC, ESCA, STAD,
LUAD, BLCA, UCEC, SKCM, HNSC, LUSC, COAD were downloaded from the GDC (Genomic
Data Commons) repository using the “TCGAbiolinks” package (version 2.16.0) (Colaprico et al.,
2016) in R (version 4.0.0). TMB was obtained from the TCGA mutation format (MAF) files. The
“stringr” package was used to substring the barcodes of MAF and expression data (FPKM-UQ).
Log2 fold change of expression data of tumor samples was used to calculate the Pearson
correlation to tumor TMB levels, and FDR (false discovery rate)-corrected p-value was applied to
account for multiple comparisons being performed. Significantly correlated genes were selected
by setting an FDR-corrected p-value cutoff as 0.05. The positive or negative value of Pearson
correlation coefficient determines the positive or negative of the correlation. LncRNA versus
mRNA classification was annotated using “gencode.v34.LncRNAs”, which was downloaded from
the GENCODE website and imported in R by the “rtracklayer” package (version 1.47.0)
(Lawrence, Gentleman, & Carey, 2009). Heatmaps were generated with the “Pheamap” package
(version 1.0.12) (Kolde, 2019) in R, using ward. D2 as the clustering method and colored by their
correlation values and gene types. Specifically, genes included in the heatmap were significantly
correlated to TMB in LUAD and at least one other cancer type among the ten cancer types tested.
Pie graphs were generated by excel (version 16.49).

2.2 Common gene pattern analysis
Genes expressed in both LUAD and STAD were selected and their Pearson correlation to
TMB was calculated to determine if the two cancers have similar TMB-related gene pattern. Log10-
13
transformed FDR-corrected p-values indicate the significance of correlation, with significant
cutoff as ±1.3. Adjusted p-values of genes in LUAD and STAD were shown as a starburst plot
generated by the “ggplot2” package (version 3.3.0) (Wickham, 2016) in R, colored by their
different types of correlation.

2.3 Correlation analysis
Smoking status was obtained from TCGA-LUAD clinical data. smoking status “1” (non-
smokers) was defined as never smokers, “2” (current smokers) was classified into current smokers,
“3” (Current Reformed Smoker for > 15 years) & “4” (Current Reformed Smoker for <=15 years)
were classified into former smokers. The “stringr” package was used to substring the barcodes of
MAF and expression data (FPKM). Samples were divided into LINC00261-expressing or silenced
group at FPKM = 3. Box plots were generated by the ‘ggplot2’ package (version 3.3.0) (Wickham,
2016) in R.

2.4 Pathway Analysis
QIAGEN Ingenuity Pathways Analysis (version 01-12) was used to determine pathway
enrichment with Benjamini-Hochberg correction on significantly correlated mRNAs (BH-
corrected p-value <= 0.05) for the statistical likelihood of enrichment. TMB-correlated genes were
selected to analyze in which pathway they participated. LINC00261-correlated genes were selected
through the correlation analysis on TANRIC. Bar plots were generated by the “ggplot2” package
(version 3.3.0) (Wickham, 2016) in R as well as to visualize the enriched pathways with a
significance threshold of -log 10 BH corrected p-value set to 1.3 (-log 10 BH of 0.05).

14
2.5 Analysis of mutational signatures and mutational patterns
The “MutSignature” package (version 2.1.3) (Fantini, Vidimar, Yu, Condello, & Meeks,
2020) in R was used to perform de novo extraction of mutational signatures from LUAD MAF file
(non-SNV was filtered out), annotating by hg38 in the “BSgenome.Hsapiens.UCSC.hg38”
package (version 1.4.3) (Team, 2020) in R. Counts of each signature in each sample were plotted
by colors. Then the extracted signatures were compared with COSMIC signatures and visualized
as a heatmap. The “maftools” package (version 2.4.12) (Mayakonda, Lin, Assenov, Plass, &
Koeffler, 2018) in R was used to classify SNPs in LUAD MAF files into transition and
transversions mutations. Summarized data was visualized as a box plot showing the overall
distribution of six different mutation types. Then, the entire dataset was subset into two groups
based on LINC00261-expressing or silenced (above and below FPKM = 3), and the fraction of
individual mutation types was then compared between the two groups. Box plots were generated
by the “ggplot2” package (version 3.3.0) (Wickham, 2016) in R; and a t-test was used to determine
significance (p-value <= 0.05).
15
2.6 Mediation analysis
TMB generated from LUAD MAF files, together with the matched expression data
(FPKM) from the TCGA-LAUD were used to perform the mediation analysis in R. Mediation
analysis was done using Baron & Kenny’s steps (Baron & Kenny, 1986). In short, a linear model
was used to calculate the p-value, with a significance cutoff <= 0.05. Once the relationship between
the candidate upstream regulator and downstream mediator was determined, mediate() in the
‘mediation’ package was used (version 4.5.0) (Tingley, Yamamoto, Hirose, Keele, & Imai, 2014)
in R. Bootstrapping (sims=1000) was used to test the significance of the mediation effect (p-value
<= 0.05).

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CHAPTER III: Results
3.1 Evaluating the effect of DDR-associated lncRNA on TMB
3.1.1 Determining the spectrum of lncRNA correlated to tumor
mutational burden across cancer types
Tumor mutational burden (TMB) is used clinically to determine which patients may benefit
from immunotherapy (Büttner et al., 2019), as presentation of neoantigens on the tumor surface is
required for immune cells to recognize the tumor for targeted cell death. Therefore, it is essential
to understand the molecular mechanisms that result in elevated TMB. Expression data and
mutational data were collected from 10 cancer types in the TCGA database that are known to have
high overall TMB levels, including cervical squamous cell carcinoma and endocervical
adenocarcinoma (CESC), esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), lung
adenocarcinoma (LUAD), bladder urothelial carcinoma (BLCA), uterine corpus endometrial
carcinoma (UCEC), skin cutaneous melanoma (SKCM), head and neck squamous cell carcinoma
(HNSC), lung squamous cell carcinoma (LUSC), colon adenocarcinoma (COAD), (Bailey et al.,
2018). Correlation between gene expression and TMB was performed in each of the above cancer
types using the Pearson method (Yarchoan et al., 2019). Then, of those 691 genes with significant
positive or negative correlations to TMB (p <= 0.05) in LUAD and at least one another cancer
type, were visualized using supervised clustering (Figure 1A). Of the 691 genes, 598 were
classified as mRNAs and the remaining 93 were classified as lncRNAs (Figures 1B, 1C). Among
those lncRNAs where functional annotation was available, one lncRNA, TP53TG1, was known to
be associated with the DNA damage response pathway (Figure 1B), and another 5 lncRNAs, who
17
were suspected to function in DDR pathway: LINC01985, LINC00659, FBXO30-DT, AC011773.1,
AL135905.1.

Figure 11: Genes correlated to TMB across ten cancer types.
(A) Supervised clustering heatmap of coloration values between gene expression and TMB across cancer types. The depth of colors
represented the level of correlation; red = positively correlated, blue = negatively correlated. The 691 TMB-correlated genes were
divided into mRNAs (green) and lncRNAs (yellow). (B)The percentage of genes negatively or positively correlated to TMB. 37%
genes were positively correlated to TMB, 53% genes were negatively correlated to TMB. (C) The percentage of each kind of gene.
87% of genes were mRNA, and 12% of genes were lncRNAs. Among all lncRNAs, only TP53TG1 functions in DDR pathway. 5
lncRNAs, whose next-door genes participate in DDR pathways, were defined as having a suspected function in DDR. The rest of
the 87 lncRNAs were not characterized for function in the DDR pathway. The analyses were performed with R package pheatmap;
pie graphs were generated using Excel (version 16.49).

3.1.2 Common pathways in LUAD and STAD include DDR signaling
Both LUAD and STAD are epithelial-based cancer types, and they clustered together,
suggesting that they share a similar TMB-correlated gene pattern (Figure 1A). To explore the
similarity in this pattern, the intersect TMB-correlated genes of the two cancer types were selected.
There were 800 dual-positively correlated genes, made up of 771 mRNAs and 29 lncRNAs, and
18
1864 dual-negatively correlated genes, consisting of 1664 mRNAs and 300 lncRNAs, including
LINC00261 and TP53TG1 (Figure 2).

Figure 12: The comparison of TMB-correlated gene pattern in LUAD and STAD
Starburst plot showing the shared gene pattern in LUAD and STAD. This plot's horizontal and vertical lines were the thresholds
for significance (Adjusted P = ± 1.3). Red points = dual-positively TMB-correlated genes, blue points = dual-negatively TMB-
19
correlated genes, dark grey points = TMB-correlated genes with opposite correlation values in LUAD and STAD, light grey points
= genes which were not significantly correlated to TMB in LUAD and STAD. LINC00261 was marked as the orange point,
TP53TG1 was marked as the pink point. The analyses were performed with the R package ggplot2.

Moreover, the significantly positively and negatively TMB-correlated mRNAs in these two
cancers were extracted to perform pathway analysis. Genes positively correlated to TMB were
found to be enriched for DDR-associated pathways, including cell cycle control, mismatch repair,
and nucleotide excision repair, among others (Figure 3A, 3B); genes negatively correlated to TMB
were enriched for immune-related pathways including antigen signaling, neuroinflammation
signaling, T&B cell signaling, among others (Figure 3C, 3D).

Figure 13: Pathway enrichment of TMB-correlated genes in LUAD (Top) and STAD (Bottom)
(A) Bar plot showing the pathways positively TMB-correlated genes enriched for in LUAD. (B) Bar plot showing the pathways
positively TMB-correlated genes enriched for in STAD. (C) Bar plot showing the pathways negatively TMB-correlated genes
enriched for in LUAD. (D) Bar plot showing the pathways negatively TMB-correlated genes enriched for in STAD. DDR-related
pathways are labeled with red star marks, immune-related pathways are labeled with blue star marks. Dotted lines are the significant
threshold (-Log 10 BH P = 1.3). The analyses were performed using IPA (version 01-12) and the R package ggplot2.

20
3.2 Evaluating the effect of LINC00261 and interacting partners on TMB in
multiple epithelial cancers
3.2.1 LINC00261 is anti-correlated to TMB in LUAD and STAD
Previous findings indicated that LINC00261 acts as a tumor suppressor in LUAD (Shahabi
et al., 2019), therefore we set out to test the relationship between LINC00261 expression and TMB.
Correlation analysis was performed across 9 epithelial-based cancer types, including: Lung
adenocarcinoma (LUAD), Stomach adenocarcinoma (STAD), Lung Squamous Cell Carcinoma
(LUSC), Bladder Urothelial Carcinoma (BLCA), Colon adenocarcinoma (COAD), Liver
hepatocellular carcinoma (LIHC), Pancreatic adenocarcinoma (PAAD), Prostate adenocarcinoma
(PRAD), Rectum adenocarcinoma (READ) (Supplemental Figure 1). LINC00261 is expressed in
normal lung from the alveolar epithelium, which is the origin of LUAD (Sergeeva et al., 2019;
Shahabi et al., 2019), and was epigenetically silenced (Shahabi et al., 2019). Therefore, the tumor
samples were classified into two groups based on whether LINC00261 was expressing or silenced.
Among all 9 cancer types, LINC00261 was negatively correlated to TMB only in LUAD and
STAD (Figures 4A, 4B). When LINC00261 was expressing, overall TMB was lower, indicating
the expression level of LINC00216 may protect LUAD and STAD patients from high TMB.

21

Figure 14: LINC00261 expression was negatively correlated to TMB in LUAD and STAD.
(A) Bar plot showing the counts of TMB in LINC00261-expressing and silenced groups in LUAD (P = 9.5e-10). (B) Bar plot
showing the counts of TMB in LINC00261-expressing and silenced groups in STAD (P = 7.4e-05). White boxes are the
LINC00261-expressing group, and grey boxes are the LINC00261-silenced group. Cutoff: FPKM = 3. The analyses were performed
with the R package ggplot2.

Smoking is a known risk factor for lung cancer (Hecht, 1999). To determine if the
relationship between LINC00261 and TMB was affected by tobacco smoking status, LUAD tumor
samples were divided into three groups, according to their length of smoking history. As shown in
figure 5, the correlation between LINC00261 and TMB was much more significant in smoker
groups, especially in former smokers, than never smokers.
22

Figure 15: LINC00261 was negatively correlated to TMB in smokers in LUAD.
Box plot showing the comparison of the LINC00261-TMB correlation within the three different smoking groups. White boxes are
the LINC00261-expressing group, and grey boxes are the LINC00261-silenced group. The p-value for the current smokers’ group
was 0.09202, for the former smokers’ group was 5.443e-14, for the non-smokers’ group was 0.4672. The analyses were performed
with the R package ggplot2.

3.2.2 Genes correlated to LINC00261 expression were enriched for
DDR-related pathways in LUAD and STAD
Once we found that LINC00261 is negatively correlated to TMB in both LUAD and STAD,
we set out to explore the potential pathways it may participate in. To do so, LINC00261-correlated
mRNAs were selected through TANRIC correlation analysis, to perform the pathway analysis. For
those genes whose expression was positively or negatively correlated to LINC00261, among the
top 10 pathways in enriched in LUAD, 9 pathways were downregulated DDR-related pathways,
including cell cycle control, ATM signaling, mismatch repair and so on. In STAD, genes correlated
23
to LINC00261 expression were only enriched for G2/M DNA damage checkpoint regulation
(Figure 6).

Figure 16: Pathway enrichment of LINC00261-correlated genes in LUAD and STAD
(A) Bar plot showing the pathways LINC00261-correlated genes enriched for in LUAD. (B) Bar plot showing the pathways
positively LINC00261-correlated genes enriched for in STAD. Red bar = Pathway consisting of upregulated genes. Blue bar =
Pathway consisting of downregulated genes. Purple bar = Pathway consisting of both upregulated and downregulated genes. DDR-
related pathways are labeled with red star marks, immune-related pathways are labeled with blue star marks. Dotted lines indicate
the significance threshold (-Log 10 BH P = 1.3). The analyses were performed with the software IPA and R package ggplot2.

24
3.2.3 LINC00261 silencing is associated predominantly with increased
C>A transversion in LUAD
After observing that the expression of LINC00261 could influence the number of mutations,
I set out to determine if LINC00261 affected specific types of mutations. LUAD tumor samples
were again separated into LINC00261-expressing or silenced groups. The frequency of transition
and transversion mutations was then calculated for each group. As showed figure 7A, we observed
a different level of C > A transversions and C > T transitions between the expressing and silenced
groups. To illustrate this, those two mutation types were isolated and I performed an unpaired t-
test. I found that silencing of LINC00261 leads to a significantly higher C > A transversions in
LUAD (Figure 7B, P = 0.0027). I also separated the tumor samples based on
KRAS/BRAF/XPC/XPA expression level; however, this switch did not exhibit in those groups
(Supplemental Figure 2).

25
Figure 17: The effect of LINC00261 expression on mutational pattern.
(A) Box plot showing the percentage of 6 types of mutation, including C>T, C>A, C>G, T>C, T>G, T>A. (B) Bar plot showing
the percentage of C>T and C>A mutations in LINC00261-expressing and silenced group. White boxes are the LINC00261-
expressing group, and grey boxes are the LINC00261-silenced group. The p-value for C>A was 0.0027, for C>T was 0.00074. The
analyses were performed with the R package maftools and ggplot2.

3.2.4 DDR-associated COSMIC signatures were present in both the
LINC00261-expressing group and silenced group
Having determined that LINC00261 silencing is significantly associated with increased
C >A transversion and decreased C >T transition, we set out to determine if this was characteristic
of loss of DDR function in LUAD. To do that, we utilized the COSMIC mutation signature
database, which has collected mutation signatures based on molecular origins across cancer types
(Tate et al., 2019). To determine if LINC00261 loss was similar to loss of DDR function observed
in COSMIC signatures 3, 6, 15, 20 and 26, a nonnegative matrix factor (NMF) analysis was
performed, and the top four signatures were selected. Then, the activity of each signature was
extracted from raw mutation counts and the comparison of signatures and COSMIC signatures
were visualized. As shown in figures 8A and 8B, signature 1 was the most prominent signature in
both the LINC00261-expressing and silenced groups. After comparing with the COSMIC database,
signature 1 acts in a similar manner with COSMIC 4, which had been found in LUAD, exhibiting
transcriptional strand bias for C>A mutations and associating with NER. Also, the signature 1
showed some similarity with COSMIC 3, which is associated with failure of DNA double-strand
break-repair by homologous recombination. In addition, signature 3 was relatively similar to
COSMIC 6, which was associated with defective DNA mismatch repair. Signature 4 had a strong
signal in LINC00261-silenced group and was very sample-dependent. Moreover, I spit LUAD
samples based on the expression level of KRAS/BRAF/XPC/XPA and extracted signatures, the
results showed that there was no such a difference of signature 4 in BRAF/XPC/XPA groups as I
26
observed in LINC00261 groups. However, KRAS-high group shared a similar signature pattern
with LINC00261-silenced group (Supplemental Figure 3).

Figure 18: Counts of different signatures and comparison to COSMIC signatures.
The order of signatures was consistent in LINC00261-expressing group and silenced group. (A) & (B) show the counts of four
signatures. (C) & (D) show the similarity of extracted signatures and COSMIC signatures. The depth of color represents the level
of similarity. The analyses were performed with the R package MutSignature.

To determine if the major signature (Sign.01) was the same one in the two groups, the
contents of this signature were investigated. As shown in figure 9, the biases of each mutation type
were similar. To verify this, the Pearson correlation was calculated. As a result, the signature 1, 2
27
and 3 in the two groups was the same (P < 2.2e-16), indicating that the signature 1, 2, and 3
presented in LUAD and was independent on LINC00261 expression level. Only signature 4, was
different in the two groups.

Figure 19: The mutation pattern of signature 1 in LINC00261-expressing and silenced group.
Bar plot showing the profile of extracted mutational signature in LINC00261-expressing (top 4 panels) and silenced (bottom 4
panels) groups in LUAD. X-axis is the fraction of each mutation types. Y-axis contains different mutation types in different genome
context. The analysis was performed with the R package MutSignature.

3.2.5 LINC00261 acts as a partial mediator of the nucleotide excision
repair (NER) components’ effect on TMB, and its effect on TMB is
completely mediated by CENPA
Dysregulation of the DNA damage response can result in the accumulation of DNA
mutations (Parikh et al., 2019). To understand how LINC00261’s effect on DDR function could
influence the accumulation of TMB, several factors were investigated, including oncogenes with
a known influence on TMB as positive controls, known DDR factors, and NER factors. Mediation
analysis was performed using the Baron & Kenny method (Baron & Kenny, 1986), which contains
three sets of regression variables: X → Y, X → M, and X + M → Y. A linear model was used to
28
calculate the significance of mediation (FDR-corrected p value cutoff <= 0.05). Through mediation
analysis, CENPA, as a member of non-homologous ending joining (NHEJ) pathway, was found to
completely mediate the effect of LINC00261 on TMB, which indicated that LINC00261 may first
act on CENPA, which then results in reduced. TMB. Other DDR factors, including ATM, ATR and
NER factors, including GTF2H4, XPB, XPD, DDB1 and DDB2 as well as oncogenes, including
KRAS and BRAF, were tested for their relationship between TMB and LINC00261 expression
levels. Many worked independently on TMB (Figure 10, red circles). In contrast, GTF2H3, XPC
and XPA, which were also NER factors and TOP2A, as a crucial member in the p53 pathway,
partially mediated the relationship between LINC00261 and TMB.

Figure 20: Diagram for mediation analysis between factors and TMB.
The arrow represents two factors correlated to each other; red arrow represents positively correlated; black represents negatively
correlated.

LINC00261
KRAS
ATM
BRAF
ATR
CENPA
TOP2A
XPC
TMB
WorkindependentlyonTMB
Fullymediate
Partiallymediate
XPB
XPD
XPA
GTF2H3
GTF2H4
DDB1 DDB2
Positivelycorrelated
Negativelycorrelated
29
Chapter IV: Discussion
Mutation occurs when DNA damage response is dysregulated, and tumor mutational
burden is a measurement that quantifies the total number of mutations in tumor cells. TMB has
been identified as a predictive biomarker of immunotherapy response (Alexandrov et al., 2013).
Therefore, understanding the molecular mechanisms underlying elevated TMB can be useful
clinically to identify biomarkers to indicate if specific therapies that depend on functional DDR
will be effective for a given patient. To this end, we identified 598 mRNAs correlated to TMB in
LUAD and one other high-TMB cancer type, including GTSE1, which responds to DNA damage,
and DCLRE1B, which is involved in repairing interstrand cross-links. However, some well-known
DDR factors, such as ATM, ATR, were not correlated to TMB across all cancer types. We speculate
that these known DDR members affect mutation accumulation by being themselves mutated,
instead of having altered expression levels. We also identified 93 lncRNAs correlated to TMB in
LUAD and at least one other cancer type. Among those lncRNAs, TP53TG1 was negatively
correlated to TMB and was previously characterized as a member of DDR signaling. In addition,
LUAD and STAD clustered together in the genome-wide TMB-correlation analysis, suggesting
that they shared similar TMB-correlated gene patterns. After visualizing the genes expressed in
both LUAD and STAD, TMB-correlated genes, we found that many genes in LUAD and STAD
showed the same direction of correlation to TMB. Moreover, pathway analysis showed that the
positively correlated genes in both cancer types were enriched for DDR signaling pathways,
whereas the negatively correlated genes to TMB were enriched for immune-related pathways.
Therefore, our findings identified both mRNA and lncRNA genes correlated to TMB, which upon
further mechanistic interrogation may become useful biomarkers to determine which patients may
benefit from immunotherapy.
30
LINC00261 is a known tumor suppressor and participates in DDR signaling in LUAD
(Shahabi et al., 2019). We found that LINC00261 was negatively correlated to TMB in both LUAD
and STAD, suggesting that the loss of LINC00261 expression disrupts efficient DDR in multiple
cancer types, leading to an increase in TMB. The anti-correlation was stronger in smokers in
LUAD, perhaps because tobacco smoking is known to induce DNA mutations (Alexandrov et al.,
2016). mRNAs correlated to LINC00261 were engaged in several DDR pathways. Performing
mediation analysis to determine what genes may interact with LINC00261 to affect TMB, we
found that with regards to the relationship between CENPA (a crucial non-homologous end-joining
factor), LINC00261 and TMB. Therefore, LINC00261 may first act on CENPA, which then
increased TMB accumulation. Functional experiments need to be done to validate this hypothesis.
In addition, I found that the expression level of LINC00261 influences the individual
mutation types. Transversion mutations of C>A occurred significantly more frequently in tumors
where LINC00261 was silenced. The mutational signature is the unique combination of mutation
types in a tumor or group of tumors. COSMIC Mutation Signatures is a catalogue of curated
reference mutational signatures. COSMIC group 4, which is associated with smoking, was the
predominant signature (Sign.01) in both LINC00261-expressing and silenced groups. However,
subsetting the tumor samples by smoking status did not remove the effect observed of LINC00261
silencing, indicating that LINC00261 may act independently on TMB from smoking.
Taken together, my results suggest that LINC00261 could be pursued as a biomarker to
help make clinical decisions. Limitations of this analysis include the fact that they were generated
using only the TCGA database. I pursued numerous other study sets (EGAD00001004793,
ORIEN), however data transfer issues (EGAD00001004793), and lack of sample number for
significance calculations (ORIEN) hampered further validation in independent data sets. In the
31
future, we plan to use the EGAD00001004793 set to validate the effect of LINC00261 on TMB in
LUAD. In addition, in vivo and in vitro experimentation will need to be done to characterize the
functional role of LINC00261 as well as other DDR genes on TMB, as well as what effect they
have on patient response to the chemotherapies. We will also look into the epigenetics status
around LINC00261 through DNA methylation or with the histone data from AECs, to discover the
mechanism for its silencing in LUAD.

32
Supplements
Supplemental Figure 1: Correlations between LINC00261 and TMB in LUSC, PRAD, PAAD, COAD,
LIHC, READ, BLCA.
Bar plots showing the counts of TMB in LINC00261-expressing or silenced groups. White boxes are the
LINC00261-expressing group, and grey boxes are the LINC00261-silenced group. Cutoff: FPKM = 3. The
analyses were performed with the R package ggplot2.

33

Supplemental Figure 2: The effect of gene expression on mutational pattern

(A) Bar plot showing the percentage of C>T and C>A mutations in KRAS-high and low groups. White
boxes are the KRAS-high group, and grey boxes are the KRAS-silenced group. KRAS is expressing in all
the samples (FPKM >=3), therefore, the cutoff for high/low is the median of KRAS expression level
(FPKM=8.968972)
(B) Bar plot showing the percentage of C>T and C>A mutations in BRAF-expressing and silenced
groups. White boxes are the BRAF-expressing group, and grey boxes are the BRAF-silenced group.
Cutoff: FPKM=3.
(C) Bar plot showing the percentage of C>T and C>A mutations in XPC-expressing and silenced groups.
White boxes are the XPC-expressing group, and grey boxes are the XPC-silenced group. Cutoff:
FPKM=3.
34
(D) Bar plot showing the percentage of C>T and C>A mutations in XPA-expressing and silenced groups.
White boxes are the XPA-expressing group, and grey boxes are the XPA-silenced group. Cutoff:
FPKM=3.

35

Supplemental Figure 3: Comparison of extracted signatures to COSMIC signatures
36
Heat map showing the similarity of extracted signatures and COSMIC signatures. The depth of color
represents the level of similarity. The analyses were performed with the R package MutSignature. Cutoff
for KRAS-high and KRAS-low group: the median of KRAS expression level in LUAD (FPKM = 8.968972).
Cutoff for BRAF/XPC/XPA-expressing and BRAF/XPC/XPA-silenced groups: FPKM = 3.

37
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Abstract (if available)

Abstract Lung cancer is the leading cause of cancer deaths in the US and worldwide. Among lung cancer, non-small cell lung cancer (NSCLC) represents the majority of new lung cancer cases, and lung adenocarcinoma (LUAD) is the most common subtype within NSCLC. However, there are around 30% of LUADs lack a known cancer driver. Here, I focused on a novel non-coding RNA that appears to regulate the cellular response to DNA damage, LINC00261. I utilized the Cancer Genome Atlas (TCGA) database to investigate the relationship between LINC00261 expression and tumor mutational burden (TMB) in multiple cancer types. Although there are hundreds of lncRNAs associated with TMB, LINC00261 is significantly negatively correlated to TMB in LUAD and STAD. The expression of LINC00261 contributes to a transversion/transition switch and is related to some crucial DNA damage response-related genes in LUAD. In addition, DDR-associated COSMIC signatures are present in both the LINC00261-expressing group and silenced group. In conclusion, LINC00261 plays an important role in protecting the LUAD patients from the high mutational burden and helps to make a precision treatment decision.

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Asset Metadata

Creator Xue, Tianchun (author)

Core Title Evaluating the impact of long non-coding RNAs on tumor mutational burden in cancer

School Keck School of Medicine

Degree Master of Science

Degree Program Biochemistry and Molecular Medicine

Degree Conferral Date 2021-08

Publication Date 07/26/2023

Defense Date 06/09/2021

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

Tag LINC00261,long non-coding RNA,lung adenocarcinoma,OAI-PMH Harvest,tumor mutational burden

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Marconett, Crystal Nicole (committee chair), Offringa, Ite (committee member), Rhie, Suhn Kyong (committee member)

Creator Email tianchun2021@gmail.com,xuet@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC15659363

Unique identifier UC15659363

Legacy Identifier etd-XueTianchu-9921

Document Type Thesis

Format application/pdf (imt)

Rights Xue, Tianchun

Type texts

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

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