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의학박사 학위논문

Genomic analysis of

ovarian clear cell carcinomas and uterine

corpus endometrial carcinomas using next

generation sequencing

차세대 염기서열 분석을 활용한 난소 투명세포

암과 자궁내막 암의 유전체 분석 연구

2018년 8월

서울대학교 대학원

의과학과 의과학 전공

이지원

차세대 염기서열 분석을 활용한 난소 투명세포

암과 자궁내막 암의 유전체 분석 연구

지도 교수 김종일

이 논문을 의학박사 학위논문으로 제출함

2018년 8월

서울대학교 대학원

의과학과 의과학 전공

이지원

이지원의 의학박사 학위논문을 인준함

2018년 8월

위 원 장 (인)

부위원장 (인)

위 원 (인)

위 원 (인)

위 원 (인)

Genomic analysis of

ovarian clear cell carcinomas and

uterine corpus endometrial carcinomas

using next generation sequencing

by

Ji won Lee

A thesis submitted to the Department of Medicine in partial

fulfillment of the requirements for the Degree of Doctor of

Philosophy in Biomedical Science at Seoul National

University College of Medicine

August 2018

Approved by Thesis Committee:

Professor Chairman

Professor Vice chairman

Professor

Professor

Professor

i

Abstract

Genomic analysis of

ovarian clear cell carcinomas and

uterine corpus endometrial

carcinomas using next generation

sequencing

Ji Won Lee

Major in Biomedical Science

Department of Biomedical Science

Seoul National University Graduate School

Gynecologic Cancer, occurred in reproductive organs, including the

cervix, ovaries, uterus, fallopian tubes, vagina and vulva, is one of the

cancers that women have most often been affected. Especially, ovarian

cancer and uterine corpus cancer of gynecologic cancers are in the 10

leading types of estimated new cancer cases and death in 2017. In

accordance with the era of precision medicine, reliable genetic

diagnosis is essential for providing individualized treatment of the

cancers. Recently, many groups including The Cancer Genome Atlas

ii

have been researched for the genetic profiling of this cancers to broaden

the understanding of gynecologic cancer. However, in the cases of

ovarian clear cell carcinoma (OCCC) and uterine corpus endometrial

carcinoma (UCEC), only small genomic studies have been reported,

and the genetic understandings of these cancers are not fully elucidated

until now.

In the first chapter, comprehensive genomic characterization of OCCCs

was performed and analyzed via whole exome sequencing (WES) with

blood samples and fresh cancer tissues. The samples were collected

from the fifteen patients with ovarian clear cell carcinoma (OCCC) from

2012 to 2016, and stored at the Seoul National University Hospital

Human Biobank. The sequencing data of the fresh OCCC tissues was

characterized by analyzing genomic alterations (somatic mutations and

somatic copy number variations). A median of 178 exonic mutations

(111-25,798) and a median of 343 somatic copy number variations (43-

1,820) were found per tumor sample. In all, 54 somatic mutations

including PIK3CA, ARID1A, and KRAS were found in the 15 Korean

OCCCs. Copy number amplifications in NTRK1, MYC, and GNAS and

copy number deletions in TET2, TSC1, BRCA2, and SMAD4 were

frequently detected in 15 OCCCs. The somatically altered pathways

were associated with proliferation and survival pathways (including the

PI3K/AKT, ERBB2, and TP53 pathways) in 87% of OCCCs, and with

iii

chromatin remodeling pathway in 47% of OCCCs. No significant

differences in frequencies of genetic alterations were detected between

OCCC with endometriosis and OCCC groups without endometriosis.

In the second chapter, clinical characteristics and corresponding

genomic data were analyzed via sequencing data of 370 patients with

UCEC from The Cancer Genome Atlas (TCGA) database, and factors

associated with survival outcomes were also investigated. The analyses

suggest that the LYL1 gene amplification was observed in 22 patients

(5.9%) with UCEC. Patients with LYL1 gene amplification were

significantly older at the time of diagnosis and more often were marked

by non-endometrioid, high-grade, or advanced disease. Multivariate

analyses, adjusted for tumor histologic type, grade, and stage, did not

confirm LYL1 gene amplification as an independent prognostic factor for

either progression-free survival (PFS) or overall survival (OS) clearly.

However, in survival analyses, PFS and OS rates (3-year PFS: 34.4%

vs. 79.9%, P=0.031; 5-year OS: 25.1% vs. 84.9%, P=0.014) were

observed in the amplification subset. Cancer-related genes (MYC,

CDK6, and ERBB2) increased in patients with LYL1 amplification were

discovered. MAPK, WNT, and cell cycle pathways were also

significantly enriched in LYL1 gene amplified patients.

iv

These results improved the current understanding of gynecologic cancer,

and allows more efficient diagnostic strategy for ovarian clear cell

carcinoma and uterine corpus endometrial carcinoma.

* The first chapter was published in Gynecologic Oncology [1], and the

second chapter was published in BMC cancer [2].

----------------------------------------------------------------------------------------------

Keywords: Gynecologic cancer; Ovarian clear cell carcinoma; Uterine

corpus endometrial carcinoma; Whole exome sequencing; Copy

number variation; Somatic mutation; The Cancer Genome Atlas

Student number: 2015-30608

v

CONTENTS

Abstract………………………………………………………………………i

Contents………………………………………………………………….....v

List of tables……………….……………………………………………….vi

List of figures………………………………………………………………vi

List of Abbreviations…………………………………………………,…xii

General introduction…………………………....………………………...1

Ovarian cancer...............................................................................1

Uterine cancer................................................................................3

Next generation sequencing………………………………...............5

Cancer genomics...........................................................................7

Genomic characteristics of ovarian clear cell carcinoma.............10

Genomic characteristics of uterine corpus endometrial

carcinomas...................................................................................12

Objectives of this study.................................................................13

Chapter 1. Genomic landscape of ovarian clear cell carcinoma via

next generation sequencing.............................................................15

Introduction……………………………………………………………16

vi

Materials and Method………………………………………………..18

Results…………………………………………………………….......23

Discussion………………………………………………………….....45

Chapter 2. Idnetification of LYL1 gene amplification as an

independent prognostic factor of uterine corpus endometrial

cancer via next generation sequencing data analysis of The Cancer

Genome Atlas.....................................................................................59

Introduction……………………………………………………………60

Materials and Method………………………………………………..63

Results………………………………………………………………...65

Discussion………………………………………………………........83

General discussion............................................................................90

General conclusion...........................................................................93

Reference………………………………………………………………….95

Abstract in Korean…………………………………………………..….111

vii

LIST OF TABLES

Table 1-1. Primer design for validation with Sanger sequencing…….22

Table 1-2. Clinicopathologic characteristics of the patients…………..28

Table 1-3. Summary of somatic mutations and copy number

variations.............................................................................................29

Table 1-4. Significant somatic mutations in 15 Korean patients with

OCCC……………………………………………………………………….30

Table 2-1 Cliicopathologic characteristics of patients..........................73

Table 2-2. Factors associated with survival outcomes in patients with

survival outcomes in patients with uterine corpus endometrial

carcinoma............................................................................................76

Table 2-3. Factors associated with survival outcomes in patients with

endometriod histologic type of uterine caner......................... .............77

viii

LIST OF FIGURES

Figure 1-1. Landscape of somatic mutations in 15 Korean OCCCs…32

Figure 1-2. The validations of somatic mutations……………………...33

Figure 1-3. Somatic mutations and CNVs pathways in OCCCs.…..…34

Figure 1-4. Landscape of somatic copy number variations in 15 Korean

OCCCs……………………………………………………………………...35

Figure 1-5. Arm level CNVs and focal level CNVs……………....…......36

Figure 1-6. Copy number variation analysis for drug targetable

genes...................................................................................................37

Figure 1-7. Somatic copy number variations in chromosome arm

level…………………………………………………………………..……..38

Figure 1-8. Comparison of mutation burden and CNV burden between

endometriosis-associated and non-endometriosis-associated

OCCCs……………………………………………...………………………39

Figure 1-9. Comparison of somatic mutations between endometriosis-

associated and non-endometriosis-associated OCCCs in frequently

mutated genes……………………………………………………………..40

Figure 1-10. Comparison of somatic mutations between endometriosis-

associated and non-endometriosis-associated OCCCs in somatically

altered pathways…………………………………………..……………….41

ix

Figure 1-11. Comparisons of somatic copy number variations between

endometriosis-associated and non-endometriosis-associated OCCCs in

amplified and deleted chromosomes…………………………………….42

Figure 1-12. Comparisons of somatic copy number variations between

endometriosis-associated and non-endometriosis-associated OCCCs in

oncogenes and tumor suppressor genes………………………………..43

Figure 1-13. Comparisons of somatic copy number variations between

endometriosis-associated and non-endometriosis-associated OCCCs in

somatically-altered pathways……………………………………………..44

Figure 1-14. The alteration frequency of RFX3 (Regulatory Factor X3)

in TCGA………………………………………………....………………….51

Figure 1-15. The alteration frequency of MST1R(Macrophage

stimulating protein receptor) in TCGA……………………………………52

Figure 1-16. The alteration frequency of MED12 (Mediator Complex

Subunit 12) in TCGA……..............................................................……53

Figure 1-17. The alteration frequency of GPC3(Glypican 3) in TCGA..54

Figure 1-18. Associations between tumor stage and somatically-

mutated genes…………………………………………………....………..55

Figure 1-19. Associations between somatic genomic alterations and

clinical features………………………………………………………….....56

x

Figure 1-20. Mutation signature difference between patient #1 and other

patients……………………………………………………..……......….….57

Figure 1-21. APOBEC signature in Patient #13………………………...58

Figure 2-1. Frequencies of LYL1 gene amplification in various cancer

types....................................................................................................69

Figure 2-2. Frequencies of copy number variations............................70

Figure 2-3. Correlations between amplification frequencies and

mortality...............................................................................................71

Figure 2-4. Correlations between deletion frequencies and mortality..72

Figure 2-5. Overall survival of patients with uterine corpus endometrial

carcinoma............................................................................................74

Figure 2-6. Progression-free survival of patients with uterine corpus

endometrial carcinoma.........................................................................75

Figure 2-7. Enrichment analysis by LYL1 gene status.........................78

Figure 2-8. Expression levels of enriched DEGs.................................79

Figure 2-9. Significant gene networks.................................................80

Figure 2-10. Gene set enrichment analysis according to histologic types

and TCGA classes...............................................................................81

Figure 2-11. Enriched gene list of cancer-related and cell proliferation

pathways.............................................................................................82

xi

Figure 2-12. Gene expression between LYL1 amplified patients and

non- LYL1 amplified patients................................................................86

Figure 2-13. Pearson’s correlation between LYL1 amplification and

oncogene expression...........................................................................87

Figure 2-14. The genomic alteration frequency of LYL1 gene..............88

Figure 2-15. Survival analysis in high-grade serous ovarian cancer....89

xii

LIST OF ABBREVIATIONS

EOC: epithelial ovarian cancer

EMS: endometriosis

OCCC: ovarian clear cell carcinoma

UCEC: uterine corpus endometrial cancer

PDS: primary debulking surgery

BMI: body mass index

FIGO: and International Federation of Gynecology and Obstetrics

CNV: copy number variation

TCGA: the cancer genome analysis

NGS: the next generation sequencing

PFS: progression-free survival

OS: overall survival

MSI: microsatellite instability

DEG: differentially expressed gene

GSEA: gene set enrichment analysis

KEGG: kyoto encyclopedia of genes and genomes

GATK: genome analysis toolkit

xiii

WGS: Whole-genome sequencing

WES: Whole-exome sequencing

RNA-seq: RNA sequencing

Gb: Giga base

SNV: Single nucleotide variant

Indel: Short insertion/deletion

CNV: Copy number variation

SV: Structural variation

TSG: Tumor suppressor gene

DEG: Differentially expressed gene

TCGA: The cancer genome atlas

Mb: Mega base

SCNA: Somatic copy number alteration

PCR: Polymerase chain reaction

GATK: Genome analysis toolkit

rlog: Regularized log

FPKM: Fragment per kilo base per million

１

General Introduction

1. Ovarian cancer

Ovarian cancer is the most lethal gynecologic cancer in the United State

and Korea [3, 4]. Ovarian cancer is the fifth type of estimated deaths at

the United State, and the tenth type of estimated new cases and the

eighth of estimated deaths at Korea in 2017 [4-6]. Although

carcinogenesis and cancer classification in the past is based on their

morphology, recently, genetic profiling and molecular pathways in

characterizing carcinogenesis became also one of main method for

classification [7, 8]. Ovarian cancer can be subdivided into four types;

epithelial, germ cell, sex cord-stromal, and unspecified [9, 10]. Epithelial

type is over 90% of ovarian cancers [9, 11]. The ovarian epithelial

cancers can divide type I tumors into three groups; endometriosis-

related tumors (clear cell carcinoma and seromucinous carcinomas),

low-grade serous carcinomas, and mucinous carcinomas and malignant

Brenner tumors [10]. Type II tumors might be divided into high-grade

serous carcinomas, undifferentiated carcinomas, carcinosarcomas, and

primary peritoneal carcinomas [10]. Based on previous studies, high

grade serous carcinoma, which is the most of type I, mainly is deficient

in homologous recombination DNA damage repair, and amplifies the

CCNE1 gene [12]. ERBB2, KRAS, BRAF and MEK pathway are

activated in Low grade serous carcinoma [13, 14]. Activation of PIK3CA

２

and inactivation of ARID1A chromatin remodeling are expected as main

cause of ovarian clear cell carcinoma [15]. Endometrioid carcinomas

were associated with inactivation of PTEN and activations of WNT-β-

catenin pathway [16]. Mucinous carcinomas were related with the

features such as KRAS pathway activation and frequent TP53 mutation

[17]. Additionally, Type II carcinomas exhibit a high proliferative activity,

and are sensitive to carboplatin/paclitaxel therapy in the beginning, most

type I carcinomas is stable, and are resistant at chemotherapy [10].

However understanding of ovarian cancer is not perfect, and has several

barriers due to limited understanding of disease etiology, tumor immune

micro-environment, or the mechanism of disease becoming refractory

to treatment. Many strategies for detection and treatment have been

researched, and developed. Especially, recent research for inhibitors of

the PARP and VEGF pathways have shown remarkable progress in

improving of progression-free survival in ovarian cancer patients [18].

For example, recently, EZH2 and PARP inhibitors, sensitive to BRCA1

gene and BRCA2 gene inactivating somatic mutations of ARID1A, are

major research topic in uterine and ovarian carcinomas area [18, 19].

Moreover many research groups including the cancer genome atlas

(TCGA) focus on genetic profiling for broader understanding and better

precision medicine for gynecologic cancer [7, 20, 21]. However, in spite

of recent efforts for the genomic characteristics, the molecular

characteristics of some ovarian cancers are still not fully revealed yet.

３

2. Uterine cancer

Uterine cancer is the most common gynecologic cancer in developed

countries and the second most common in developing countries [22, 23].

Additionally, in the United States, the estimated cases were 26.0 per

100,000 women per year, and the estimated deaths were 4.6 per

100,000 women per year from 2013 to 2015 [24, 25]. Uterine cancer can

be divided into two different types; Uterine sarcomas, which is detected

in the muscle layer or connective tissue of the uterus, and Endometrial

carcinomas, which is identified in the endometrium of uterus. Moreover,

risk factor and symptoms for both types are different [26]. Uterine

sarcomas are developed by the risk factors such as radiation therapy

history, retinoblastoma history, and diagnosed twice in white race

women [27]. Endometrial carcinomas, which are the most of uterine

cancer, are formed by the risk factors such as age, obesity, high number

of menstrual cycle, no prior pregnancies, Lynch syndrome history,

estrogen replacement therapy history, and Tamoxifen treated history

[27]. Patients with uterine endometrial cancers and uterine sarcomas

can have experience such as bleeding not related to menstruation,

postmenopausal bleeding, and unusual vaginal discharge without any

visible blood [27]. The symptoms of endometrial cancer are painful

urination, pain during intercourse, mass in the pelvic area, and

unintentional weight loss. Uterine sarcoma symptoms include frequent

４

urination, pain in the abdomen, mass in the vagina, and feeling full at all

times [27]. However the treatments (chemotherapy after surgery) are

same because the cause is unknown. Recently, to broaden

understanding of the uterine cancer, TCGA analyzed Uterine Corpus

Endometrial Carcinomas, and Identified four subtypes; 1) POLE

ultramutated, 2) Microsatellite instability hypermutated, 3) Copy number

low and 4) Copy number high [20, 21]. The Uterine Corpus Endometrial

Carcinomas were characterized by frequent mutations in TP53,

extensive copy number alterations, DNA methylation changes, and

frequent mutations in PTEN and KRAS [20]. Their analysis mainly

covered characterizing by analyzing genomic alterations and dividing

subtypes in UCEC. Therefore, finding genomic targets for the novel

molecular diagnostic and the effective precision medicine with their data

will be great benefit to concur uterine cancers.

５

3. Next generation sequencing.

Next generation sequencing (NGS) is one of the most advanced

genome analysis techniques in the biological sciences of the last 10

years [28]. Sanger sequencing or the second generation sequencing is

considered as gold standard for researching human genetics, however

it is difficult to analyze the whole human genome because of the cost

and the period of analyses [29]. Next generation sequencing has been

developed since Dr. Sydney Brenner discovered the basic concepts of

NGS in 1992, and, recently, A human genome can be analyzed in 3 days

with $1000 via NGS technology [30]. Recently many researching

projects including HapMap, 1000 Genome Project, ENCODE and

modENCODE are researched by analyzing sequencing data of the

human genome via NGS [31, 32]. Additionally, NGS plays an important

roles in developing the genetic diagnosis and precision medicines of

cancer [33]. There are various types of NGS methods, and the different

NGS methods should be applied to different project according to

research objectives. To discover the sequence of gene from novel

species, de novo sequencing is usually performed. Over 10,000 species

are discovered by de novo sequencing until now, and more species are

researching via Genome 10K project [32]. However de novo sequencing

should be improved, for research levels, the limitation which the whole

gene of the unknown species cannot be assembled because of technical

６

issues such as sequencing read size and difficulty in detect repeated

sequence. For cancer genetics, re-sequencing technologies such as

whole-genome sequencing (WGS), whole-exome sequencing (WES),

RNA sequencing (RNA-seq), whole bisulfite sequencing, and CHIP

sequencing are used according to purposes of analyses [34]. WGS

generates the sequencing data of entire genome, and is used for

identification of the precise breakpoint of CNA and SV. However WGS

is usually not used for clinical researches because expensive cost and

big data size are required to perform whole genome sequencing. WES

covers the coding regions of gene (exome), approximately 1% of

genome, and is the most accurate and effective method for identification

of genomic alterations (SNV and indel) with clinical implications, and

copy number alteration in exome region [35, 36]. RNA sequencing

covers the whole transcriptomes of genes, and is applied to discovery

of novel splicing, novel transcripts in unannotated protein coding genes,

RNA-editing, differentially expressed gene, and fusion genes [37].

Whole bisulfite sequencing is developed for identifying the role of

epigenetics in cancer, and detect DNA methylation, epigenetic mark [38].

CHIP sequencing is the method to discover chromatin

immunoprecipitation enriched methylated DNA [38]. Genomic analyses

via NGS technology allow us to handle personalized management of

patients via their molecular characteristics, and to broaden genetic

understanding of cancers.

７

4. Cancer genomics

Cancer is a disease caused by certain changes to genes [39]. The

genetic change in cancer can be divided into two types. The one change

is the genetic change inherited from the parents. Such changes, called

germline mutation, can be found in every cell of the offspring [39]. The

other changes, that cause cancer, can be acquired by DNA damages

via exposure to carcinogenic substance such as tobacco smoke and

radiation. These genetic changes, called somatic mutation, cannot

inherit to the offspring [39]. Cancers are generally made by the somatic

mutations in oncogene or tumor suppressor gene (TSG). In the past, the

researchers can focus on only already well known site such as BRCA1

and TP53 by Sanger sequencing. However, recently, with privilege of

the development of next generation sequencing technology,

researchers could extensively discover frequently mutated somatic

mutations which can be main cause of cancer [39]. They also identified

actionable mutations in oncogene of cancer via WGS and WES.

Furthermore, recently they could discover molecular subtypes,

differentially expressed genes (DEGs) between those subtypes, fusion

gene, which can be drivers in cancers via RNA-seq, the most powerful

NGS technology in cancer genomics [40]. Additionally, the researchers

can study epigenetic alterations in cancer genome via bisulfite

sequencing or chip sequencing. Lately, The Cancer Genome Atlas

８

(TCGA) has researched for many cancer genomics study since 2005

though next generation sequencing, and broaden the knowledge on

mutational characteristics of cancer by WGS and WES. TCGA also

allows us to know the underlying mechanism of cancer by using RNA-

seq and microarray. In general, cancer genomics via NGS discovered

driver mutations such as oncogenes and TSGs after filtering somatic

mutation in cancers because there are various numbers of somatic

mutations in cancer, but all somatic mutation didn’t play a role in cancer

[41]. The driver alterations can be found by identify single-nucleotide

polymorphisms (SNP), copy number variation (CNV) and the others

such as fusion genes, transcriptome function, and methylation of genes

[41]. Although synonymous SNPs, which is the most in human body, are

not contributed to cancer formation, non-synonymous SNPs, which can

be driver mutations, play the most important roles in cancer [41]. The

somatic CNVs are also important genetic events for tumorigenesis and

the aggressiveness of cancer. CNVs can be divided into two types; focal

and arm-level SCNA [42, 43]. Focal SCNA usually contribute the

deletions or amplifications of gene level region such as TSG or

oncogene, and the arm-level SCNAs how the aneuploidy of the

chromosome arms, and could be applied as independent prognostic

factor [43]. Additionally, it is important to find the different source of

mutational processes of cancer via mutational signature, measured with

six nucleotide substitution subtypes: C>A, C>G, C>T, T>A, T>C, and

T>G, because cancers are made by several exogenous and

９

endogenous mutational processes which generate somatic mutations

such as tobacco smoking, ultraviolet, and DNA replication deficiency

[44]. Therefore, the mutational signature analysis is also one of the most

powerful methods for understanding the mechanism of cancer genomics.

１０

5. Genomic characteristics of ovarian clear cell carcinoma

Ovarian clear cell carcinoma (OCCC), resistant to chemotherapy, and

associated with endometriosis, is the fourth ranked type of all epithelial

ovarian carcinomas [45]. Although the genomic researches of OCCC

were important for developments of precision medicines, a few

Japanese groups have researched for OCCCs with only targeted

sequencing from FFPE samples [45]. Previous studies reported that

PIK3CA, ARID1A, CTNNB1, CSMD3, LPHN3, LRP1B, TP53, KRAS,

PPP2R1A, and PTEN were frequently mutated in OCCC, and minorly

MLL3, ARID1B, and PIK3R1 were altered in OCCC [46]. MET gene

amplification was also reported in previous OCCC studies [47]. Previous

researches were shown that frequently mutated or amplified/deleted

genes were involved in the KRAS pathway, MYC pathways, and the

critical chromatin remodeling complex pathway via integration of the

genomic analyses [47, 48]. However previous studies couldn’t

discovered the genomic profiling fully because OCCC is rarer than other

cancers, and it is difficult to obtain the fresh tumor samples. The all of

the previous studies were genomic analyses from formalin-fixed

paraffin-embedded (FFPE) samples, which can be detected artifact of

genomic alterations, of OCCC [49, 50]. Moreover, CNVs, another

important genomic alterations which can drives the aggressive cancer,

were discovered limited regions of CNV. To full understanding the

１１

genomic events and the tumor genesis mechanisms of OCCCs, further

studies are needed with fresh tissues and better next generation

sequencing such as WES, WGS and RNA sequencing.

１２

6. Genomic characteristics of uterine corpus endometrial

carcinomas

Uterine endometrial cancer is one of the most common cancers of the

female pelvic site, and arise in the cells of the inner lining of the uterus.

43,000 women with uterine corpus endometrial carcinomas in the United

States have been diagnosed and about 8,000 of them have died of

endometrial cancer [25]. However risk factors for uterine cancer don’t

have cleared. Therefore TCGA analyzed 548 cases, and characterize

UCEC with next generation sequencing data [20]. They discovered four

subtypes (POLE ultramutated, Microsatellite instability hypermutated,

Copy number low and Copy number high) by analyzing the status of

genomic alterations in UCEC. TCGA identified that PTEN, PIK3CA, TTN,

ARID1A, PIK3R, TP53, MUC4, MUC16, KMT2D, CSMD3 were

frequently mutated in UCECs, and characterized by frequency of TP53

mutations, somatic CNVs and DNA methylations [20]. Although, as

TCGA suggested, type and burdens of genomic alteration were

significant for characterizing the UCECs, only stages and grades of

prognostic factors for survival have reported as prognostic factors in

UCECs. If genetic factors were investigated in relation to prognostic

factors via TCGA sequencing data, they will be good benefits to develop

prognostic markers for diagnostics and therapeutic medicines for cancer

therapy.

１３

7. Objectives of this studies

Although there have been incredible efforts in deciphering the molecular

characteristics of OCCCs, there was many obstacles such as difficulty

of obtaining fresh tumor samples and the limitation of next generation

sequencing technology, and it was difficult to understanding of genomic

alterations and the mechanism in OCCCs. In first chapter, 15 fresh

OCCC tissues and whole exome sequencing were applied to discover

the genomic alterations (SNP and CNV), and to establish the genomic

landscape of OCCC. OCCC is also well known to associations with

endometriosis which can affect mutation frequency. Additionally, the

frequencies of somatic mutations and somatic copy number variants

between in patients with endometriosis and in patients without

endometriosis were tested by various statistical methods.

Genomic profiling of uterine corpus endometrial carcinomas is analyzed

fully via next generation sequencing data by TCGA. However they

focused on only genomic characterizing of UCECs and genomic

alteration analyses, and researched for very small parts of the genomic

risk factors with clinical features. Therefore, in second chapter, various

statistical analyses between clinicopathologic features and genomic

alterations were analyzed for an independent prognostic factor.

１４

It is expected that these studies will allow us to have more intensive

understanding of molecular genetic information of gynecologic cancer,

and will make us discover genomic targets for the novel molecular

diagnostic and the effective precision medicine.

１５

CHAPTER 1

Genomic landscape of ovarian clear cell

carcinoma via next generation

sequencing

１６

Introduction

Ovarian cancer is the most lethal gynecologic cancer in the United State,

Korea, and even developing contries [23]. In Korea, the incidence rate

of ovarian cancer has been gradually increasing and is expected to

reach 2.5% (2,618) of new cancer cases and 3.8% (1,168) of all cancer

deaths among women in 2017 [5, 51]. Of the histologic types, the

majority (90%) of ovarian cancers are epithelial ovarian cancers (EOCs),

which are further grouped into different histologic subtypes [52].

Ovarian clear cell carcinoma (OCCC), which is the fourth ranked type of

all epithelial ovarian carcinomas, was resistant to chemotherapy, and

related with endometriosis Additionally OCCC has a poorer prognosis

than other histologic EOC subtypes, such as serous or endometrioid

adenocarcinomas [53]. OCCC is associated with endometriosis (EMS),

which is a common benign condition in reproductive-age women [54,

55]. Interestingly, OCCC is more common in East Asian women than in

Western women: it accounts for 24% of EOCs in Japan but only a small

portion of EOCs in Western countries [56]. In Korea, OCCC is the fourth

most common histologic subtype, which accounts for 10.3% of EOCs,

and the incidence of OCCC has increased markedly across all age

groups since 1999 [57].

Although, in previous period, carcinogenesis and cancer classification

１７

are identified by their morphology, molecular pathways in characterizing

carcinogenesis were used for classification. Additionally,in accordance

with the era of precision medicine, it is obvious that reliable genetic

diagnosis is essential for providing individualized treatment for patients

with OCCC. Although the genomic researches of OCCC were important

for developments of precision medicines, a few Japanese groups have

researched for OCCCs with targeted sequencing from FFPE samples.

Previous studies reported that PIK3CA, ARID1A, CTNNB1, CSMD3,

LPHN3, LRP1B, TP53, KRAS, PPP2R1A, and PTEN were frequently

mutated in OCCC, and minorly MLL3, ARID1B, and PIK3R1 were

altered in OCCC. MET gene amplification was also reported in previous

OCCC studies. In OCCC, both a clinical approach, considering the

presence of underlying EMS, and a genomic approach, such as those

conducted by The Cancer Genome Analysis (TCGA) Group, may be

necessary [7]. However, the low incidence of OCCC hinders such

integrative genomic analyses. To date, only small genomic studies of

OCCC have been reported from some East Asian countries; the

genomic landscape of Korean OCCC has not yet been investigated.

The aim of this study was to obtain whole exome sequencing (WES)

data of Korean OCCC via the next generation sequencing (NGS)

technique. Genomic profiles were compared between EMS-associated

OCCC (EMS-OCCC) and Non-EMS-OCCC.

１８

MATERIALS AND METHODS

Study population

At our institution, Seoul National University Hospital (SNUH), patients

scheduled to undergo surgery for gynecologic cancer have been

routinely asked whether they will donate their biological samples (e.g.,

blood samples, cancer tissue samples) for research purposes since

June 2012. Blood samples and cancer tissues are obtained before

surgery and at the time of surgery, respectively, from those patients who

provide informed consent. The cancer tissues undergo gross

examination and frozen section procedures. In this step, the

pathologists ascertain necrotic portions of the tumor, which are ruled out

from banking. Only viable portions of the tumor are selected and cut in

the form of a 1 cm3 sized cube. These biospecimens are then stored at

the SNUH Human Biobank.

For the present study, we searched relevant patients from the SNUH

Ovarian Cancer Cohort to identify those who met the following inclusion

criteria: 1) older than 18 years; 2) diagnosed with OCCC between June

2012 and December 2016; 3) underwent primary debulking surgery

(PDS); 4) agreed to donate their biological samples and provided

informed consent; and 5) blood and cancer tissue samples were stored

simultaneously at the SNUH Human Biobank. Patients with following

conditions were excluded: 1) diagnosis of any malignancy other than

１９

ovarian cancer; 2) neoadjuvant chemotherapy or targeted therapy

before surgery; 3) insufficient clinical data or lost to follow-up; and 4)

severe co-morbidities, such as end-stage renal disease, uncontrolled

diabetes mellitus, or long-term corticosteroid use.

Of the 15 patients with OCCC who met these criteria, 5 were

pathologically diagnosed with OCCC arising in EMS (EMS-OCCC group)

and the other 10 were pathologically diagnosed with OCCC that did not

arise in EMS (Non-EMS-OCCC group) [58]. By reviewing their medical

records, we collected information about clinicopathologic characteristics,

which we compared between the two groups.

Whole exome sequencing via next generation sequencing

technique

Both blood and cancer samples underwent preparation, DNA library

construction, and quality control analysis before sequencing. All blood

and cancer tissue samples passed quality control. The Illumina HiSeq

2000 system was used to obtain WES data. To determine “somatic

mutation”, 20% of 0.2 g of frozen cancer tissue samples were

sequenced with a depth of x150. To determine “germline mutation”, 500-

600 μl of buffy coat blood samples were sequenced with a depth of x100.

Both somatic and germline genomic data underwent mutation analysis,

including the identification of single nucleotide polymorphisms (SNPs)

and insertion or deletion mutations (INDELs), as well as copy number

２０

variation (CNV) analysis via MuTect [59], Indelocator, and ExomCNV

[60]. Cosmic, dbSNP, and Clinvar databases were searched to

determine whether the detected mutations were previously assigned ID

numbers. We also performed in silico analyses with SIFT and Polyphen2

to predict whether the observed mutations were likely to be deleterious

or not: SIFT (deleterious, sift ≤0.05; tolerated, sift >0.05); PolyPhen2

(probably damaging, PP2 ≥0.957; possibly damaging, 0.453≤ PP2

<0.957; benign, PP2 <0.453).

Validation with Sanger sequencing

Somatic mutations, which discovered by exome sequencing, were

validated by Sanger sequencing. PCR amplification was performed at

following conditions: 95°C for 5 minutes; 45 cycles of 95°C for 30

seconds, 60°C for 30seconds and 72°C for 1 minute; and 72°C for 7

minutes. Primers for the identified somatic mutations are presented in

Table 1-1. Sanger sequencing was run on ABI PRISM 3730XL Analyzer

(Applied Biosystems, Foster City, CA, USA) and variant sequences

were analyzed using Variant Reporter Software Version 1.1 (Applied

Biosystems).

Statistical Analysis

Statistical analyses were performed to evaluate differences between

２１

groups with respect to patient characteristics. Student’s t-test and Mann-

Whitney U-test were used to compare continuous variables. Pearson’s

chi-squared test, Fisher’s exact test, and Kruskal-Wallis test were used

to compare categorical variables. Pearson correlation coefficients were

calculated between patient characteristics and somatically-mutated

genes. R statistical software (version 2.12; R Foundation for Statistical

Computing, Vienna, Austria; ISBN 3-900051-07-0; http://www.R-

project.org) was used for the statistical analyses. A P value < 0.05 was

considered statistically significant.

２２

Table 1-1. Primer design for validation with Sanger sequencing

Ref

Seq

#9 ARID1A chr1:27106421 T A 5_ARID1A_F 5_ARID1A_RCCGAGATGTTGGCGAGTGTA 363

#1 ARID1A chr1:27023237 A C 1_ARID1A_F 1_ARID1A_RTTGTTGTCCGCCATGTTGTT 325

#1 ARID1A chr1:27023891 G A 2_ARID1A_F 2_ARID1A_RCAGACAATGGCAGCTCCC 647

#1 ARID1A chr1:27100129 C T 4_ARID1A_F 4_ARID1A_RGCCTTGGGTGGAGAACTGAT386

#2 ARID1A chr1:27056178 CCTCAGCCA C 3_ARID1A_F 3_ARID1A_RGTATAAGAGAGGCCGCCCAT386

#3 ARID1A chr1:27056181 C T 3_ARID1A_F 3_ARID1A_RGTATAAGAGAGGCCGCCCAT386

#7 ARID1A chr1:27100207 C T 4_ARID1A_F 4_ARID1A_RGCCTTGGGTGGAGAACTGAT386

#8 ARID1A chr1:27023743 CG C 2_ARID1A_F 2_ARID1A_RCAGACAATGGCAGCTCCC 647

#1 ARID2 chr12:46246393 C T 11_ARID2_F 11_ARID2_RTGGCACAGCAACCATTGT 380

#14 ARID2 chr12:46230701 TAC T 10_ARID2_F 10_ARID2_RTTGAAATCAACAGGGTCCAGT559

#4 ERBB2 chr17:37868583 G T 12_ERBB2_F 12_ERBB2_RTACCCATCAAAGCTCTCCGG 337

#1 ERBB2 chr17:37882870 C A 13_ERBB2_F 13_ERBB2_RTAGAAGGTGCTGTCCAAGGG399

#12 GPC3 chrX:132887723 G T 41_GPC3_F 41_GPC3_RCCAGGTTTCCAAGTCACTGC 316

#13 GPC3 chrX:132888189 C A 42_GPC3_F 42_GPC3_RGGGACCTTAATCACCACAGC 330

#11 KRAS chr12:25398284 C A 9_KRAS_F 9_KRAS_RAAGCGTCGATGGAGGAGTTT544

#10 KRAS chr12:25398284 C A 9_KRAS_F 9_KRAS_RAAGCGTCGATGGAGGAGTTT544

#1 KRAS chr12:25398240 G A 9_KRAS_F 9_KRAS_RAAGCGTCGATGGAGGAGTTT544

#1 LRP1B chr2:141299405 A G 16_LRP1B_F 16_LRP1B_RGGTGATAGTTAAATCTGGGCCAG381

#1 LRP1B chr2:141660725 G A 17_LRP1B_F 17_LRP1B_RGGGCAAAGCAAACTATACTCCC496

#1 LRP1B chr2:142567941 G A 19_LRP1B_F 19_LRP1B_RTGTGTGTTTCAGCTGAGTGG 337

#9 LRP1B chr2:141806579 C A 18_LRP1B_F 18_LRP1B_RTTGTGCCAGTTAAACGGTGG 436

#13 MED12 chrX:70352744 C G 44_MED12_F 44_MED12_RGTGTTTCCATCCCACAGCAG 370

#3 MED12 chrX:70346843 G A 43_MED12_F 43_MED12_RGAGTGTGAGGAAGTGCATGC386

#4 MST1R chr3:49940820 G A 25_MST1R_2F 25_MST1R_2RCCTCTAGGGTCCCAGCTCG 383

#14 MST1R chr3:49940343 C T 24_MST1R_F 24_MST1R_RTTCCTGCATGACCTAGAGCC 400

#4 PIK3CA chr3:178917478 G A 21_PIK3CA_F 21_PIK3CA_RTGAGGTGAATTGAGGTCCCT 366

#5 PIK3CA chr3:178936082 G A 22_PIK3CA_F 22_PIK3CA_RCGTATCACCAACAGCAGGG 730

#2 PIK3CA chr3:178936082 G A 22_PIK3CA_F 22_PIK3CA_RCGTATCACCAACAGCAGGG 730

#6 PIK3CA chr3:178916876 G A 20_PIK3CA_F 20_PIK3CA_RAGAAAGGGACAACAGTTAAGCT422

#1 PIK3CA chr3:178952018 A G 23_PIK3CA_2F 23_PIK3CA_2RTGCTGTTCATGGATTGTGCA 484

#3 PIK3CA chr3:178936082 G A 22_PIK3CA_F 22_PIK3CA_RCGTATCACCAACAGCAGGG 730

#9 PPP2R1A chr19:52715983 G A 15_PPP2R1A_F 15_PPP2R1A_RAGGGAGAGGAGAGGAACAGT462

#2 PPP2R1A chr19:52715983 G A 15_PPP2R1A_F 15_PPP2R1A_RAGGGAGAGGAGAGGAACAGT462

#11 PPP2R1A chr19:52715982 C T 15_PPP2R1A_F 15_PPP2R1A_RAGGGAGAGGAGAGGAACAGT462

#6 PTEN chr10:89692904 C G 7_PTEN_F 7_PTEN_RAAATTCTCAGATCCAGGAAGAGG300

#1 PTEN chr10:89624271 A C 6_PTEN_F 6_PTEN_RCCAGGCAAGAGTTCCGTCTA 364

#1 PTEN chr10:89720853 G A 8_PTEN_F 8_PTEN_RTTGACGCTGTGTACATTGGG 375

#5 RFX3 chr9:3263078 C T 39_RFX3_F 39_RFX3_RATGCTACGCTCAGATGTCGA 381

#13 RFX3 chr9:3330444 G C 40_RFX3_F 40_RFX3_RATGCGAAACTTGCCATGTTG 335

#4 SYNE1 chr6:152646355 T A 30_SYNE1_F 30_SYNE1_RCCCTTGTCTCCTCTCTTCCG 254

#12 SYNE1 chr6:152779915 C T 37_SYNE1_F 37_SYNE1_RGCTATGAACGTTCCCTGAGC 493

#1 SYNE1 chr6:152454491 G A 26_SYNE1_F 26_SYNE1_RCTCCACGTTTGATGCTCAGG 478

#1 SYNE1 chr6:152510441 A C 27_SYNE1_F 27_SYNE1_RTGCCATGATGTGCCTCTAGA 406

#1 SYNE1 chr6:152539486 G A 28_SYNE1_2F 28_SYNE1_2RCCACTTGCCCTTTTACCAGAC 427

#1 SYNE1 chr6:152642987 C G 29_SYNE1_F 29_SYNE1_RACAGTGTTGAGGAAGTGTCTT340

#1 SYNE1 chr6:152675831 T G 31_SYNE1_2F 31_SYNE1_2RGCTAACCCATGCAAGTGTGA 953

#1 SYNE1 chr6:152683401 G T 32_SYNE1_F 32_SYNE1_RCCCTTGCTTACTGGAGTGGA 356

#1 SYNE1 chr6:152688444 T G 33_SYNE1_F 33_SYNE1_RGACTTGCCTCGTATCTGTGC 364

#1 SYNE1 chr6:152706918 G A 34_SYNE1_F 34_SYNE1_RAGGCCTGTTGTCTTACCTGA 376

#1 SYNE1 chr6:152711468 C A 35_SYNE1_F 35_SYNE1_RATTGCAATAGGGCCCAGAGT399

#1 SYNE1 chr6:152754967 G A 36_SYNE1_F 36_SYNE1_RCCTTTGGACCCAGCAATGTT 505

#1 SYNE1 chr6:152832214 T C 38_SYNE1_F 38_SYNE1_RGTGCTTGTTTGTTTCGGTGC 543

#14 TP53 chr17:7578260 C T 14_TP53_F 14_TP53_RTTTCTTTGCTGCCGTCTTCC 516

#15 TP53 chr17:7578427 T A 14_TP53_F 14_TP53_RTTTCTTTGCTGCCGTCTTCC 516

Alt seq, alternate sequence; Ref seq, reference sequence.

GCCACTGACAACCACCCTTA

GGTGGATGGAGACGGGAAAT

GTGTAGAATCAGTGAGGCATCA

CCAGGAGACCTCAGTACTGC

GGGACCGAACAGCATTTGAT

TTCCTGAGCTCCTTCTGCAA

GCCACTGACAACCACCCTTA

CAGGACTTGTAAGAGCAGTGG

AAGAATTATGGGCCGGGTCC

GGCGTGCCCCTATAATCCTA

TGATGGGTAAAGAGAATGAGGAG

GAGCTGAGACCGCACCAC

GCTTATGTGCACTGCCCTAG

GAGCCATTTCCATCCTGCAG

TACCAGGACCAGAGGAAACC

ACGGTTGAGGTCACTAAGCA

CCATTGAGTTGCCGATCAGA

TGTTAAAGCCCGTCCACTCA

TGAATGAGCCTGGTTTGCAC

CTTTGTCTACGAAAGCCTCTCT

TGGTTCTTTCCTGTCTCTGAAA

TAAACCTCATGCGGACCTGT

TAAACCTCATGCGGACCTGT

TAAACCTCATGCGGACCTGT

TTTACCACAGTTGCACAATATCC

GTGTCTGTGTCACCGGGAG

GCAGGGCACTAGGATCATCT

AGGAATGTTTGCTGCCTTTG

TGGTTCTTTCCTGTCTCTGAAA

TGGTTCTTTCCTGTCTCTGAAA

CAGAACAATGCCTCCACGAC

TGAACAGCCAAATCTTGAACACT

ACCGTCTACATCCAGCTTCC

CGAATGGCATGAGAACCTT

ACCAACCAGAAGAACTTGAGT

AGGTTGGAAGTTGACTCCCA

ACGAGCCCTTCTATCCTGTG

TCCCTTGGGACTGTCTAGAC

ACCAAGCAGTACGTTCTCCA

CCTGGGTTCATTAGCTGGGT

GGACCCTGACATACTCCCAA

GGACCCTGACATACTCCCAA

GGACCCTGACATACTCCCAA

AATTAGGGTTCTGAGGCGGG

CCACGACAGCACTATCCCTA

CCAACCACCAGTACAACTCC

TACAGGAGGCTTCAAATGCG

CGAAAGCTGGGCATTAACGA

CCACCCTGTCCTATCCTTCC

Size

GCCACCGGAACATCAAGATC

GGGAAAGGAGCTGCAGGA

CCAACCACCAGTACAACTCC

CCACGACAGCACTATCCCTA

AATTAGGGTTCTGAGGCGGG

Table 1. Primer design for validation with Sanger sequencing.

Sample Gene Position Alt seq Forward primer Sequence Reverse primer Sequence

２３

RESULTS

Characteristics of 15 Korean patients with OCCC

The patients’ clinicopathologic characteristics are depicted in Table 1-2.

Their median age was 51.1 years. The numbers of patients with stage I,

II, and III disease were 9, 2, and 4, respectively. All patients underwent

PDS, which was followed by adjuvant chemotherapy in all except two

patients. During the median observation period of 23.4 months, two

patients had a recurrence and received second-line chemotherapy. Of

these, one patient eventually died despite treatment at 19.0 months after

diagnosis. Clinicopathologic factors, such as age, body mass index

(BMI), parity, initial serum CA-125 levels, and International Federation

of Gynecology and Obstetrics (FIGO) stage, were not statistically

different between the EMS-OCCC and Non-EMS-OCCC groups.

Landscape of somatic mutations in Korean OCCCs

To detect somatic mutation, WES was performed on frozen tissues of

15 Korean OCCCs with paired blood samples. The 15 Korean OCCCs

in this study displayed a median of 178 somatic exonic mutations (range,

111-25,798) in each tumor sample, identified by NGS analysis (Figure

1-1A, Table 1-3). The number of somatic mutations was not associated

with accompanying endometriosis, tumor stage, patient age, or patient

BMI (Figure 1-1B). However, PIK3CA mutations and PTEN mutations

２４

were positively associated with disease stage (P=0.004 and P=0.03,

respectively). The frequencies of significant somatic mutations were as

follows: PIK3CA (40%), ARID1A (40%), KRAS (20%), PPP2R1A (20%),

SYNE1 (20%), RFX3 (13%), MED12 (13%), GPC3 (13%), MST1R

(13%), TP53 (13%), ARID2 (13%), LRP1B (13%), PTEN (13%), and

ERBB2 (13%) (Figure 1-1C). C to T transition was the most frequent

somatic mutations in each OCCC (C to T ratio: 34%-57%) (Figure 1-

1D).

Somatic mutations (26 known and 27 unpublished) in Korean

OCCCs

In total, 54 somatic mutations (3 nonsense variants and 51 missense

variants) were discovered across 14 genes in 15 Korean OCCCs by

NGS WES; the predicted functional impact, resulting from the

corresponding amino acid substitutions, was analyzed using SIFT and

PolyPhen2. 53 of 54 somatic mutations were verified by Sanger

sequencing: PIK3CA (6/6), ARID1A (8/8), KRAS (3/3), PPP2R1A (3/3),

SYNE1 (13/13), RFX3 (2/2), MED12 (2/2), GPC3 (1/2), MST1R (2/2),

TP53 (2/2), ARID2 (2/2), LRP1B (4/4), PTEN (3/3), and ERBB2 (2/2)

(Figure 1-2). Of the validated 53 mutations, 27 were not reported in the

COSMIC database, dbSNP, or Clinvar (Table 1-4). Of the newly

identified somatic mutations, 19 were designated “deleterious” in SIFT

and “probably damaging” or “possibly damaging” in Polyphen2: ARID1A

２５

(pLeu2011Gln, pAsn115His, pLeu1309Phe), ERBB2 (pGly344Cys,

pPhe976Leu), GPC3 (pPro273His), LRP1B (pSer2444Pro, pLeu38Phe),

MED12 (pLeu1489Val), MST1R (pAla234Thr), PTEN (pArg335Gln),

RFX3 (pAla488Thr, pGln97Glu), and SYNE1 (pAsn5174Ile,

pArg8641Trp, pAsn3630Thr, pPhe3401Leu, pAsp3294Ala, pThr112Ala)

(Table 1-4).

Somatically-altered pathway in Korean OCCCs

Frequent somatic mutations in biological pathways play an important

role in tumorigenesis and tumor progression (15). To discover key

altered pathways in Korean OCCCs, we screened published papers on

mutational spectrum for mutated genes and manually searched the

KEGG pathway database for frequently and potentially important

mutated genes. We found that significantly altered pathways were

associated with proliferation and survival (including the PI3K/AKT

pathway, TP53 pathway, and ERBB2 pathway) in 87% of the 15 Korean

OCCCs and with chromatin remodeling in 47% of OCCCs (Figure 1-3).

Landscape of somatic copy number variations

A median of 343 somatic CNVs (range, 43-1,820) were discovered per

tumor sample using ExomeCNV tool (Table 1-3). CNV profiles were

variable across 15 Korean OCCCs (Figure 1-4). Somatic CNVs were

２６

also detected in the key mutated pathways consisting of somatic

mutations (TP53 pathway: patients #3, #11, and #13; PI3K/AKT

pathway: patients #13 and #15; MAPK pathway: patients #3, #9, #10,

#13, and #14) (Figure 1-5). Focal level gains of oncogenic genes,

including NTRK1 (33%) in the 1q chromosome region, MYC (40%) in

the 8q chromosome, and GNAS (47%) in the 20q chromosome, were

frequently observed in 15 Korean OCCCs (Figure 1-6). Focal level

losses of TET2 (73%) in the 4 chromosome, TSC1 (67%) in the 9q

chromosome, BRCA2 (60%) in the 13q chromosome and SMAD4 (47%)

in the 18q chromosome were frequently detected (Figure 1-6). Arm-

level gains and losses most frequently involved the 8q chromosome

(47%) and the 4q chromosome (80%), respectively (Figure 1-5, Figure

1-7).

EMS-OCCC vs. Non-EMS-OCCC

To identify differences between EMS-OCCC and Non-EMS-OCCC,

somatic genetic alterations (somatic mutation: total burden and

frequently mutated genes; somatic CNVs: total burden, oncogenes and

tumor suppressor genes), as well as somatically-altered pathways

(somatic mutations: TP53 pathway, PI3K/AKT pathway, ERBB2

pathway, and chromatin remodeling pathway; somatic CNVs: TP53

pathway, PI3K/AKT pathway, and MAPK pathway) were compared. No

significant differences in the frequency of genetic alterations were

２７

detected between the EMS-OCCC and Non-EMS-OCCC groups

(Figure 1-8, 1-9, 1-10, 1-11, 1-12,1-13).

２８

Table 1-2. Clinicopathologic characteristics of the patients.

All EMS-OCCC Non-EMS-OCCC

(n=15) (n=5) (n=10)

Age at diagnosis, years

Median (range) 51.1 (28.9-71.4) 51.3 (42.7-55.5) 49.8 (28.9-71.4) 0.953

BMI, kg/m2

Median (range) 21.5 (16.9-28.3) 21.5 (19.5-23.1) 21.6 (17.0-28.3) 0.513

Parity

Mean±SD 1.3±0.9 1.2±0.8 1.4±1.0 0.594

Menopause 10 (66.7) 4 (80.0) 6 (60.0) 0.6

Comorbidities

Hypertension 1 (6.7) 0 1 (10.0) 1

Diabetes 2 (13.3) 0 2 (20.0) 0.524

Dyslipidemia 2 (13.3) 0 2 (20.0) 0.524

Alcohol intake 3 (20.0) 0 3 (30.0) 0.505

Smoking 0 0 0 N/A

CA-125 at diagnosis, IU/ml

Median (range) 79.9 (7.7-1067.0) 447.5 (15.5-1067.0) 63.7 (7.7-269.8) 0.165

FIGO stage 0.839

I 9 (60.0) 3 (60.0) 6 (60.0)

II 2 (13.3) 1 (20.0) 1 (10.0)

III 4 (26.7) 1 (20.0) 3 (30.0)

Residual tumor at PDS 0.333

None 14 (93.3) 4 (80.0) 10 (100.0)

RT < 1 cm 1 (6.7) 1 (20.0) 0

Adjuvant chemotherapy

None 2 (13.3) 0 2 (20.0) 0.524

Paclitaxel-Carboplatin 12 (80.0) 5 (100.0) 7 (70.0)

Irinotecan-Cisplatin 1 (6.7) 0 1 (10.0)

Observation period, months 0.44

Median 23.4 48.9 20.3

Recurrence 2 (13.3) 1 (20.0) 1 (10.0) 1

Progression free survival, months

Median 23.4 30.1 19.4 0.808*

Death 1 (6.7) 0 1 (10.0) 1

Values are n (%) unless otherwise specified.

EMS, endometriosis; EMS-OCCC, endometriosis associated ovarian clear cell carcinoma; BMI, body mass index; CA-125;

cancer antigen 125; FIGO, International Federation of Gynecology and Obstetrics 2014; N/A, not applicable; PDS, primary

debulking surgery; RT, residual tumor; SD, standard deviation.

* Log-rank test.

Characteristics P

２９

All

EM

S-O

CC

CN

on-E

MS

-OC

CC

(n=

15)

(n=

5)

(n=

10)

Som

atic m

uta

tions

M

edia

n (

range)

178 (

111-2

5,7

98)

168 (

154-2

07)

184 (

111-2

5,7

98)

0.3

07

Som

atic C

NV

s

Media

n (

range)

343 (

43-1

,820)

221 (

135-4

05)

345 (

43-1

,820)

0.1

9

Tab

le 3

. S

um

mary

of

so

mati

c m

uta

tio

ns a

nd

co

py n

um

be

r v

ari

ati

on

s.

Chara

cte

rist

ics

P

Tab

le 1

-3. S

um

ma

ry o

f so

mati

c m

uta

tio

ns a

nd

co

py n

um

ber

vari

ati

on

s

３０

Ref

Seq

#9

AR

ID1

Ach

r1:2

71

06

42

1T

AM

isse

nse

NM

_0

06

01

54

c60

32

T>

Ap

Leu

20

11

Gln

#1

AR

ID1

Ach

r1:2

70

23

23

7A

CM

isse

nse

NM

_0

06

01

54

c34

3A

>C

pA

sn1

15

His

#1

AR

ID1

Ach

r1:2

70

23

89

1G

AM

isse

nse

NM

_0

06

01

54

c99

7G

>A

pA

la3

33

Th

r

#1

AR

ID1

Ach

r1:2

71

00

12

9C

TM

isse

nse

NM

_0

06

01

54

c39

25

C>

Tp

Leu

13

09

Ph

e

#2

AR

ID1

Ach

r1:2

70

56

17

8C

CT

CA

GC

CA

CF

ram

esh

ift

NM

_0

06

01

54

c11

75

_1

18

2d

elC

TC

AG

CC

Ap

Pro

39

2fs

#3

AR

ID1

Ach

r1:2

70

56

18

1C

TN

on

sen

seN

M_

00

60

15

4c1

17

7C

>T

pG

ln3

93

*

#7

AR

ID1

Ach

r1:2

71

00

20

7C

TN

on

sen

seN

M_

00

60

15

4c4

00

3C

>T

pA

rg1

33

5*

#8

AR

ID1

Ach

r1:2

70

23

74

3C

GC

Fra

mes

hif

tN

M_

00

60

15

4c8

54

del

Gp

Gly

28

5fs

#1

AR

ID2

chr1

2:4

62

46

39

3C

TM

isse

nse

NM

_1

52

64

12

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e

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85

83

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Mis

sen

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44

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s

#1

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BB

2ch

r17

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88

28

70

CA

Mis

sen

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00

44

48

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92

8C

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76

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PC

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32

88

77

23

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sen

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00

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71

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His

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PC

3ch

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88

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89

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00

11

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71

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18

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e

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RA

Sch

r12

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39

82

84

CA

Mis

sen

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03

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>T

pG

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al

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Sch

r12

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39

82

84

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Mis

sen

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33

60

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pG

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al

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KR

AS

chr1

2:2

53

98

24

0G

AM

isse

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NM

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33

36

03

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Tp

His

27

Ty

r

#1

LR

P1

Bch

r2:1

41

29

94

05

AG

Mis

sen

seN

M_

01

85

57

2c7

33

0T

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pS

er2

44

4P

ro

#1

LR

P1

Bch

r2:1

41

66

07

25

GA

Mis

sen

seN

M_

01

85

57

2c3

53

0C

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er1

17

7L

eu

#1

LR

P1

Bch

r2:1

42

56

79

41

GA

Mis

sen

seN

M_

01

85

57

2c1

12

C>

Tp

Leu

38

Ph

e

#9

LR

P1

Bch

r2:1

41

80

65

79

CA

No

nse

nse

NM

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18

55

72

c17

65

G>

Tp

Glu

58

9*

#1

3M

ED

12

chrX

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35

27

44

CG

Mis

sen

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M_

00

51

20

2c4

46

5C

>G

pL

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48

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al

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ME

D1

2ch

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03

46

84

3G

AM

isse

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NM

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05

12

02

c27

10

G>

Ap

Glu

90

4L

ys

#4

MS

T1

Rch

r3:4

99

40

82

0G

AM

isse

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NM

_0

02

44

72

c22

3C

>T

pA

rg7

5C

ys

#1

4M

ST

1R

chr3

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94

03

43

CT

Mis

sen

seN

M_

00

24

47

2c7

00

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23

4T

hr

#4

PIK

3C

Ach

r3:1

78

91

74

78

GA

Mis

sen

seN

M_

00

62

18

2c3

53

G>

Ap

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11

8A

spD

elet

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us

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bab

ly D

amag

ing

CO

SM

75

1V

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ied

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lera

ted

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ssib

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ing

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35

88

75

39

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ifie

d

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ably

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agin

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12

46

71

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ifie

d

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ably

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agin

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ifie

d

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eter

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agin

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To

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ted

Ben

ign

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SM

38

94

88

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37

75

18

98

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d

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OS

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94

52

59

Ver

ifie

d

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ably

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20

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21

91

35

29

Ver

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d

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eter

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20

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21

91

35

29

27

62

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Del

eter

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41

85

00

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Ver

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d

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eter

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Dam

agin

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OS

M3

87

16

44

Ver

ifie

d

Ver

ifie

d

CO

SM

90

77

23

rs3

87

90

68

46

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ifie

d

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SM

51

44

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ifie

d

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eter

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ibly

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agin

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erif

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eter

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rob

ably

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agin

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OS

M1

60

21

37

Ver

ifie

d

Ver

ifie

d

Del

eter

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sP

rob

ably

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agin

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erif

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db

SN

P I

DC

lin

var

ID

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lid

ati

on

by

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ng

er s

equ

enci

ng

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eter

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ably

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agin

g

Gen

eP

osi

tio

nT

ype

of

Alt

era

tio

nN

M n

um

ber

cDN

A c

ha

ng

eA

A c

ha

ng

eP

ati

ent

Alt

seq

Tab

le 4

. S

ign

ific

an

t so

mati

c m

uta

tio

ns i

n 1

5 K

ore

an

pati

en

ts w

ith

OC

CC

SIF

TP

oly

ph

en2

Co

smic

ID

Tab

le 1

-4. S

ign

ific

an

t so

mati

c m

uta

tio

ns in

15 K

ore

an

pati

en

ts w

ith

OC

CC

３１

Ref

Seq

#5

PIK

3C

Ach

r3:1

78

93

60

82

GA

Mis

sen

seN

M_

00

62

18

2c1

62

4G

>A

pG

lu5

42

Ly

s

#2

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3C

Ach

r3:1

78

93

60

82

GA

Mis

sen

seN

M_

00

62

18

2c1

62

4G

>A

pG

lu5

42

Ly

s

#6

PIK

3C

Ach

r3:1

78

91

68

76

GA

Mis

sen

seN

M_

00

62

18

2c2

63

G>

Ap

Arg

88

Gln

#1

PIK

3C

Ach

r3:1

78

95

20

18

AG

Mis

sen

seN

M_

00

62

18

2c3

07

3A

>G

pT

hr1

02

5A

la

#3

PIK

3C

Ach

r3:1

78

93

60

82

GA

Mis

sen

seN

M_

00

62

18

2c1

62

4G

>A

pG

lu5

42

Ly

s

#9

PP

P2

R1

Ach

r19

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71

59

83

GA

Mis

sen

seN

M_

01

42

25

5c5

48

G>

Ap

Arg

18

3G

ln

#2

PP

P2

R1

Ach

r19

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71

59

83

GA

Mis

sen

seN

M_

01

42

25

5c5

48

G>

Ap

Arg

18

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ln

#1

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PP

2R

1A

chr1

9:5

27

15

98

2C

TM

isse

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NM

_0

14

22

55

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7C

>T

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83

Trp

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PT

EN

chr1

0:8

96

92

90

4C

GM

isse

nse

NM

_0

00

31

44

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8C

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30

Gly

#1

PT

EN

chr1

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96

24

27

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CM

isse

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NM

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31

44

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Arg

15

Ser

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EN

chr1

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97

20

85

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AM

isse

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NM

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00

31

44

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Ap

Arg

33

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ln

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RF

X3

chr9

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63

07

8C

TM

isse

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NM

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01

28

21

16

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46

2G

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88

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r

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FX

3ch

r9:3

33

04

44

GC

Mis

sen

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M_

00

12

82

11

61

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9C

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NE

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r6:1

52

64

63

55

TA

Mis

sen

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M_

18

29

61

3c1

55

21

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Tp

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51

74

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YN

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chr6

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27

79

91

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TM

isse

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NM

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82

96

13

c25

45

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Ap

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84

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hr

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NE

1ch

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52

45

44

91

GA

Mis

sen

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M_

18

29

61

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59

21

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Tp

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86

41

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NE

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52

51

04

41

AC

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sen

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M_

18

29

61

3c2

32

47

T>

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74

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et

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NE

1ch

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52

53

94

86

GA

Mis

sen

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M_

18

29

61

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20

97

C>

Tp

Ala

73

66

Val

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SY

NE

1ch

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52

64

29

87

CG

Mis

sen

seN

M_

18

29

61

3c1

59

52

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Val

53

18

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SY

NE

1ch

r6:1

52

67

58

31

TG

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sen

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M_

18

29

61

3c1

08

89

A>

Cp

Asn

36

30

Th

r

#1

SY

NE

1ch

r6:1

52

68

34

01

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Mis

sen

seN

M_

18

29

61

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02

03

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Ap

Ph

e34

01

Leu

#1

SY

NE

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52

68

84

44

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29

61

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29

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la

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52

70

69

18

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Mis

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18

29

61

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54

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84

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al

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52

71

14

68

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Mis

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18

29

61

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70

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52

75

49

67

GA

Mis

sen

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M_

18

29

61

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42

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>T

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47

5L

eu

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SY

NE

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52

83

22

14

TC

Mis

sen

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M_

18

29

61

3c3

34

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Th

r11

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P5

3ch

r17

:75

78

26

0C

TM

isse

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NM

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00

54

65

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9G

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al1

97

Met

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P5

3ch

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78

42

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AM

isse

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NM

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00

54

65

c50

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>T

pH

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68

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AA

, am

ino

aci

d; A

lt s

eq,

alte

rnat

e se

qu

ence

; R

ef s

eq,

refe

ren

ce s

equ

ence

.

Cli

nva

r ID

Va

lid

ati

on

by

Sa

ng

er s

equ

enci

ng

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ha

ng

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A c

ha

ng

eS

IFT

Po

lyp

hen

2C

osm

ic I

Dd

bS

NP

ID

Pa

tien

tG

ene

Po

siti

on

Alt

seq

Typ

e o

f A

lter

ati

on

NM

nu

mb

er

Tab

le 4

. (C

on

t.)

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M4

48

01

Ver

ifie

d

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M4

37

79

Ver

ifie

d

Del

eter

iou

sP

rob

ably

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agin

gV

erif

ied

Del

eter

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sB

enig

nC

OS

M3

02

11

18

Ver

ifie

d

To

lera

ted

Ben

ign

Ver

ifie

d

To

lera

ted

Pro

bab

ly D

amag

ing

CO

SM

27

75

55

rs3

68

83

23

47

Ver

ifie

d

Del

eter

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sP

rob

ably

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agin

gV

erif

ied

Del

eter

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sP

rob

ably

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agin

gV

erif

ied

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eter

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enig

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lera

ted

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ign

Ver

ifie

d

To

lera

ted

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ign

Ver

ifie

d

Del

eter

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sB

enig

nC

OS

M5

86

95

29

Ver

ifie

d

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eter

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ably

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agin

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erif

ied

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ted

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ign

Ver

ifie

d

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eter

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ibly

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agin

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erif

ied

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lera

ted

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ssib

ly D

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ing

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ifie

d

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eter

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sP

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ibly

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agin

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erif

ied

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eter

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sP

oss

ibly

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agin

gV

erif

ied

Del

eter

iou

sB

enig

nC

OS

M5

27

5V

erif

ied

Del

eter

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sP

rob

ably

Dam

agin

gC

OS

M5

21

9rs

12

19

09

22

43

62

83

7V

erif

ied

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M5

12

11

Ver

ifie

d

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M5

12

53

Ver

ifie

d

Del

eter

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sP

rob

ably

Dam

agin

gC

OS

M5

12

53

Ver

ifie

d

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M7

60

rs1

21

91

32

73

40

60

9V

erif

ied

To

lera

ted

Pro

bab

ly D

amag

ing

CO

SM

77

1rs

39

75

17

20

25

46

34

Ver

ifie

d

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M7

46

rs1

21

91

32

87

Ver

ifie

d

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M7

60

rs1

21

91

32

73

40

60

9V

erif

ied

Del

eter

iou

sP

rob

ably

Dam

agin

gC

OS

M7

60

rs1

21

91

32

73

40

60

9V

erif

ied

Table

1-4

. (C

ont.)

３２

Figure 1-1. Landscape of somatic exonic mutations in 15 Korean

OCCCs. (A) The numbers of somatic mutations, for each patient with

OCCC, are shown. (B) Key clinical features (endometriosis, stage, age,

and body mass index) are presented. (C) Names of significantly mutated

genes (left), distribution of mutations across 15 OCCCs (middle), and

frequency of significantly mutated genes (right) are shown. (D) The

mutational signature of each OCCC is displayed.

３３

Figure 1-2. The validations of somatic mutations. 53 of 54 somatic

mutations are validated by sanger sequencing.

３４

Figure 1-3. Somatic mutations and CNVs pathways in OCCCs.

Somatically-altered pathways are plotted with both somatic mutations

and somatic CNVs in chromatin remodeling-related, and cell

proliferation and survival-related pathways.

３５

Figure 1-4. Landscape of somatic copy number variations in 15

korean OCCCs. A Circos plot is displayed, showing the somatic copy

number gains and losses in each OCCC, in order from outermost ring

(#1 patient-#15 patient).

３６

Figure 1-5. Arm level CNVs and focal level CNVs. CNVs in key

biological pathways are plotted.

３７

Figure 1-6. Copy number variation analysis for drug targetable

genes. Frequency of copy number gains (red) and copy number losses

(blue) are shown across the genome. Genes of interest are labeled with

frequency of CNVs.

３８

Figure 1-7. Somatic copy number variations in chromosome arm

level. Arm-level somatic copy number variations are displayed by each

OCCC case (left, middle). Frequency of copy number gains (red) and

copy number losses (blue) are shown (right).

３９

Figure 1-8. Comparisons of mutation burden and CNV burden

between between endometriosis-associated and non-

endometriosis-associated OCCCs. Mutation burden and CNV burden

were ploted across endometriosis status.

４０

Figure 1-9. Comparisons of somatic mutations between

endometriosis-associated and non-endometriosis-associated

OCCCs in frequently mutated genes. Names of significantly mutated

genes (left), distribution of mutations across 15 OCCCs (middle), and

frequency of mutated genes, with P values for differences between the

two groups (right) are displayed.

４１

Figure 1-10. Comparisons of somatic mutations between

endometriosis-associated and non-endometriosis-associated

OCCCs in somatically altered pathways. Names of significantly

mutated genes in somatically-altered pathways (left), distribution of

mutations across 15 OCCCs (middle), and frequency of mutated genes,

with P values for differences between the two groups (right) are

displayed.

４２

Figure 1-11. Comparisons of somatic copy number variations

between endometriosis-associated and non-endometriosis-

associated OCCCs in amplified and deleted chromosomes. Names

of representative genes in amplified and deleted chromosomes (left),

distribution of mutations across 15 OCCCs (middle), and frequency of

mutated genes, with P values for differences between the two groups

(right) are displayed.

４３

Figure 1-12. Comparisons of somatic copy number variations

between endometriosis-associated and non-endometriosis-

associated OCCCs in oncogenes and tumor suppressor genes.

Names of representative genes in oncogenes and tumor suppressor

genes (left), distribution of mutations across 15 OCCCs (middle), and

frequency of mutated genes, with P values for differences between the

two groups (right) are displayed.

４４

Figure 1-13. Comparisons of somatic copy number variations

between endometriosis-associated and non-endometriosis-

associated OCCCs in somatically-altered pathways. Names of

amplified and deleted genes in somatically copy number-altered

pathways (left), distribution of mutations across 15 OCCCs (middle),

and frequency of mutated genes, with P values for differences between

the two groups (right), are displayed.

４５

DISCUSSION

In the present study, we successfully characterized the genomic

landscape of 15 Korean patients with OCCC. This cancer featured

complex genomic alterations. To our knowledge, this is the first report

of an NGS WES study in Korean patients with OCCC, as well as the first

attempt to compare genomic profiles of OCCC according to the

presence or absence of EMS. As TCGA Research Network emphasizes,

genomic analyses and identification of alterations will provide new

therapeutic approaches and allow subtype-stratified care, with the

anticipation that this will improve cancer outcomes [61]

It is well known that the different histologic types of EOC exhibit a distinct

mutation spectrum, reflecting their different etiology and lineage. In

contrast with high-grade serous carcinomas, OCCCs have few TP53

mutations, but they do contain recurrent ARID1A and PIK3CA mutations

[62, 63] .NGS WES data of the current study showed similar results, as

the proportions of ARID1A, PIK3CA, and TP53 mutations in the Korean

OCCCs were 40%, 40%, and 13%, respectively. Mutations in ARID1A,

a tumor suppressor gene, are found in many human cancers, including

OCCCs. A Japanese group also reported that ARID1A mutations

accounted for 17% of Japanese OCCCs [45]. Recently, ATR inhibitors

were suggested as candidates for synthetic lethal therapy in ARID1A-

deficient tumors [64]. In the current era of precision medicine, ATR

４６

inhibitors may open new treatment choices for individuals with ARID1A-

mutated OCCCs.

Associations between EMS and OCCC have been reported in previous

studies. EMS, a common benign gynecologic disorder that occurs in

5%–10% of reproductive-age women, seems to increase the risk for

EOCs, especially OCCC or endometrioid carcinoma [65, 66].The exact

molecular mechanisms, underlying malignant transformation of EMS,

have not been fully elucidated [66]. Nevertheless, mutations in ARID1A,

and PI3K/AKT pathway alterations, such as mutations in PIK3CA, are

considered an early event in the transformation of EMS into OCCC [67].

OCCCs arising in the presence of EMS, and those arising in the

absence of EMS, may develop from different processes of

carcinogenesis. However, previous comparisons between EMS-OCCC

and Non-EMS-OCCC have focused only on treatment response or

survival outcomes. Therefore, we compared genomic profiles between

the two groups in the current study, and the results showed no

significant differences in the frequency of genetic alterations between

EMS-OCCC and Non-EMS-OCCC groups.

Moreover, the present study represents the most precise and

comprehensive genomic analysis in Korean OCCCs in several ways.

First, fresh frozen tissue samples, rather than formalin-fixed paraffin-

embedded (FFPE) samples, were used in this study. Despite the use of

optimization methods, the yield rate of good quality DNA in FFPE

４７

samples is known to be low, compared with the rate in fresh frozen

tissue samples [68]. The quality of DNA extraction in FFPE samples is

negatively affected by defects or artifacts that occur during sample

storage and preparation [69].In the current study, the median storage

period of fresh frozen samples was 23.1 (range, 2.2-60.8) months, and

although some samples with relatively long storage periods were

included, NGS WES data were successfully obtained in all 15 patients.

Another advantage of using fresh frozen tissue is that a sufficient

amount of DNA, required for WES, is retained easily. However, fresh

frozen tissue samples may not always be available in some clinical

settings.

Second, we used the WES technique, rather than targeted sequencing,

for NGS DNA analyses. Compared with a designed cancer panel,

consisting of selected-cancer-related genes, WES covers a wider area

of the genome and, thereby, more readily enables identification of novel

mutations. Of the 27 novel somatic mutations we identified in Korean

OCCCs, MST1R (pAla234Thr), GPC3 (pPro273His), MED12

(pLeu1489Val), and RFX3 (pAla488Thr, pGln97Glu) could only be

discovered using WES because these genes are not usually included in

a cancer panel. MED12 (a component of the transcriptional Mediator

complex), MST1R, GPC3, and RFX3 were reported as cancer-related

genes in previous studies and in The Cancer Genome Altas database

(Figure 1-14 - 1-17). In the recent exome sequencing study of Japanese

group, list of the significantly mutated genes and their frequencies were

４８

different from the current study. However, cell proliferation and survival

related genetic alterations (MYC amplifications, RB1 deletions, ARID1A

mutations and PIK3CA mutations) were detected in both studies [70].

Moreover, PIK3CA mutations and PTEN mutations were positively

associated with disease stage (P=0.004 and P=0.03, respectively)

(Figure 1-18). However the frequencies of somatic genomic alterations

were no associations with clinical features (Figure 1-19).

Lastly, the WES technique, used in the current study, provided

additional analyses of copy number alterations. Because structural

variations, producing large genomic rearrangements in the human

genome, are known to play a key role in some diseases, confirmation of

somatic CNVs in the current study was meaningful [71]. Identification of

CNVs also helped to determine somatically-altered pathways more

precisely. CNV analyses identified amplification of oncogenic genes

(NTRK1, MYC, and GNAS) and deletion of tumor suppressor genes

(BRCA2 and ATM), which were not detected by somatic mutation

analysis.

Interestingly, somatic mutations in TP53 (13%; 2/15), and somatic CNVs

in BRCA2 (60%; 9/15) and TP53 (26%; 4/15), were more common in

this study than in previously reported studies. Both TP53 and BRCA2

genes are well-known tumor suppressor genes. As the TP53 pathway is

related to cancer cell proliferation and survival, it is thought that although

their frequency is low, when somatic mutations do occur in TP53, they

４９

will play an important role in tumorigenesis of OCCC.

Patient #1 showed higher number of somatic mutations compared to

other patients. We analyzed the relative proportions of the six different

possible base-pair substitutions to discover mutational processes. As

the results, C to A transversions, known to be related with tobacco

smoking [44, 72], were more frequently observed in patient #1 (31%)

than in others (12%) (Figure 1-20).

Patient #13 showed APOBEC signatured mutation pattern, which were

well known as key player in mutagenesis in cervical, bladder, head and

neck, breast, and lung cancer type (Figure 1-21). However the

APOBEC signatured mutation were

The current study has several limitations. First, the size of the study

population in this retrospective case-control study was small (n=15).

Second, expression of somatically-mutated genes was not investigated.

Whole transcriptome sequencing or microarray analyses to discover the

effects of genetic alterations would improve the accuracy and

completeness of genomic profiling. Despite these limitations, the current

study was the first to use NGS WES for the characterization of Korean

OCCCs. Frequently mutated genes, as well as novel

(unpublished/unreported) deleterious somatic mutations, were

discovered. Through the evaluation of somatic exonic mutations and

CNVs, we were able to plot somatically-altered pathways in OCCC. By

５０

analyzing cancer tissue samples and their matched blood samples of

individual OCCC patients, integrative somatic and genomic analyses

were performed.

In conclusion, the present study successfully characterized the genomic

landscape of 15 patients with OCCC. We identified potential therapeutic

targets in most tumors for the treatment of OCCC. Additional larger

studies, including whole transcriptome sequencing to determine the

effects of genetic alterations, are warranted.

５１

Figure 1-14. The alteration frequency of RFX3 (Regulatory Factor

X3) in TCGA. The alteration frequencies of RFX3 were shown across

cancers which were researched in TCGA

５２

Figure 1-15. The alteration frequency of MST1R (Macrophage

stimulating protein receptor) in TCGA. The alteration frequencies of

MST1R were shown across cancers which were researched in TCGA

５３

Figure 1-16. The alteration frequency of MED12 in TCGA. The

alteration frequencies of MED12 were shown across cancers which

were researched in TCGA

５４

Figure 1-17. The alteration frequency of GPC3 (Glypican 3) in

TCGA. The alteration frequencies of GPC3 were shown across

cancers which were researched in TCGA

５５

Figure 1-18. Associations between tumor stage and somatically-

mutated genes. Pearson correlation coefficients (positive (red) /

negative (blue)) between tumor stage and somatically-mutated genes

are displayed (top). P values for differences between patients with and

without gene mutations are shown (bottom).

５６

Figure 1-19. Association between somatic genomic alterations and

clinical features.

５７

Figure 1-20. Mutation signature difference between patient #1 and

other patients. the relative proportions of the six different possible

base-pair substitutions were ploted across patient #1 and other patients

５８

Figure 1-21. APOBEC signature in Patient #13. Mutational spectrum

was shown across substitution subtypes.

５９

CHAPTER 2

Idnetification of LYL1 gene amplification as

an independent prognostic factor of uterine

corpus endometrial cancer via next

generation sequencing data analysis of The

Cancer Genome Atlas

６０

INTRODUCTION

Uterine corpus endometrial cancer (UCEC) imposes a global burden in

both the United States and Korea [73]. In United States, it is the most

common gynecologic cancer, and 61,380 new cases were identified in

2017 [74]. In Korea, the incidence of UCEC has been increasing and

2.5% (2,578) of new cancer cases is expected in 2017 [5, 51]. However

genomic understanding of UCEC is not clear because the cause is

unknown [16]. However recently, to broaden understanding of the

uterine cancer, TCGA analyzed Uterine Corpus Endometrial

Carcinomas, and Identified four subtypes; POLE ultramutated,

Microsatellite instability hypermutated, Copy number low and Copy

number high [19]. The Uterine Corpus Endometrial Carcinomas were

characterized by frequent mutations in TP53, extensive copy number

alterations, DNA methylation changes, and frequent mutations in PTEN

and KRAS. However their analysis mainly covered characterizing by

analyzing genomic alterations and dividing subtypes in UCEC [19-21].

Therefore, finding genomic targets for the novel molecular diagnostic

and the effective precision medicine with their data will be great benefit

to concur uterine cancers. The Cancer Genome Atlas (TCGA) Research

Network reported integrated genomic and transcriptomic profiling of 373

UCEC patients [20]. Moreover, this group classified UCEC into four

categories listed from good to poor prognosis: (1) POLE (ultramutated);

６１

(2) microsatellite instability (MSI) (hypermutated); (3) copy-number low;

and (4) copy-number high. Especially, copy-number high group included

most of the serous and serous-like endometrioid tumors, and shares

genomic features with ovarian serous carcinomas. Since then,

researchers has designed clinical trials of UCEC on post-surgical

adjuvant treatment based on these molecular classification In

accordance with the era of precision medicine, discovery of reliable

genetic changes is essential to provide individualized treatment for the

patients with UCEC [20, 75]. Not widely known genes, such as LYL1

gene, can be identified as a novel prognostic indicator or as a potential

target for therapeutic drugs. LYL1 gene is located on the short (p) arm

of chromosome 19 at position 13, and encodes a protein possibly

associated with blood vessel maturation and hematopoiesis. As a

member of basic helix-loop-helix transcription factor family, LYL1 gene

is known to regulate cell proliferation and differentiation [76]. A

chromosomal aberration involving LYL1 causes a form of T-cell acute

lymphoblastic leukemia.

Interestingly, somatic amplifications in LYL1 gene are frequently

observed in UCSC than any other cancers: the second among cancers

of The Cancer Genome Atlas (TCGA). However, its impact on UCSC

has not been evaluated in previous studies. Thus, in this study, we

aimed to elucidate whether genetic alterations in LYL1 gene, such as

amplification status, affect survival outcomes in UCEC using data from

TCGA database.

６２

MATERIALS AND METHODS

Data acquisition

We downloaded the UCEC patients’ genomic alteration data and the

corresponding clinicopathologic information from the TCGA data portal

(https://tcgadata.nci.nih.gov/tcga/tcgaDownload.jsp) and the cBioPortal

for Cancer Genomics (http://www.cbioportal.org/). The Illumina Genome

Analyzer was utilized as the platform for DNA sequencing (Illumina Inc,

San Diego, CA). This study was conducted in compliance with the TCGA

publication guideline and publication policy

(http://cancergenome.nih.gov/publications/publicationguidelines).

Study population

In total, 370 UCEC patients were included in this study. We collected

patients’ clinicopathologic characteristics including age, underlying

comorbidities, International Federation of Gynecology and Obstetrics

(FIGO) stage, histologic type, tumor grade, and treatment of UCSC,

such as surgery, radiation, and chemotherapy. Information about

microsatellite instability (MSI) of the tumor was also collected. Patients

were assigned to the LYL1 amplification group and non-amplification

group according to the LYL1 gene alteration status.

６３

Bioinformatic analysis

LYL1 gene alteration status, especially amplification status, was

examined by using the cBioPortal for Cancer Genomics

(http://www.cbioportal.org/). The level 3 data of patients with UCEC and

raw reads (htseq counts) for differentially expressed gene analysis were

obtained from the FireBrowse (http://firebrowse.org). The Kyoto

Encyclopedia of Genes and Genomes (KEGG) pathway analysis of

gene expression data [77] was performed though the Gene Set

Enrichment Analysis (GESA) method [78], and the enrichment pathways

were visualized by the NetworkAnlyst (http://www.networkanalyst.ca)

[79]. In visualization of gene network analyses, the Search Tool for the

Retrieval of Interacting Genes/Proteins (STRING) database was used

with confidence score of 400 to 1,000 [80]. Differentially expressed

genes were identified by the R package ‘DEseq2’ [81].

Statistical Analysis

Statistical analyses were performed to evaluate differences in

clinicopathologic characteristics between the two groups. Student’s t-

test and Mann-Whitney U-test were used to compare continuous

variables. Pearson’s chi-squared test and Fisher’s exact test were used

to compare categorical variables.

６４

We calculated PFS as the time interval between the date of initial

diagnosis and the date of disease progression. Overall survival (OS)

was calculated as the time interval between the date of initial diagnosis

to the date of cancer-related death or end of the study. Survival analyses

were conducted using the Kaplan-Meier method with log-rank test.

Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated

using Cox proportional hazards regression models. We used SPSS

software version 21.0 (SPSS Inc., Chicago, IL, USA) for survival

analyses. All other statistical analyses were performed using the R

statistical software version 2.12.1 (R Foundation for Statistical

Computing, Vienna, Austria; ISBN 3-900051-07-0; http://www.R-

project.org). A P value <0.05 was considered statistically significant.

６５

RESULTS

Somatic copy number variations in uterine corpus endometrial

carcinoma

Frequencies of somatic amplifications in LYL1 gene according to cancer

types of TCGA are depicted in Figure. 2-1. UCEC was the second-

ranked cancer type with high LYL1 gene amplification. In genomic

alteration analyses, chromosome 1q, 3q, 8q, 17q, and 19p were

frequently amplified among 370 patients with UCEC (Figure. 2-2). LYL1

gene on 19p arm was amplified in 5.9% (22/370) of the UCEC patients.

Additionally, LYL1 gene was one of the 15 mostly amplified oncogenes

and deleted tumor suppressor genes filtered by Gene family analysis in

Gene Set Enrichment Analysis (Figure. 2-3 and 2-4).

Characteristics of the patients with uterine corpus endometrial

carcinoma

Patients’ clinicopathologic characteristics are presented in Table 1. The

mean age of patients was 63 years. Of the 370 UCEC patients, 304

(82.2%), 52 (14.1%), and 14 (3.8%) were endometrioid, serous, and

mixed adenocarcinoma types, respectively. Patients in LYL1

amplification group were significantly older at diagnosis of UCEC, and

showed more aggressive cancer features compared to those in LYL1

６６

non-amplification group: advanced-stage disease (FIGO stage III-IV)

(P=0.003), grade 3 disease (P<0.001), and serous histologic type

(P<0.001) were more common. Proportions of four UCEC categories

from TCGA classification were also different between the two groups:

72.7% of patients in LYL1 amplification group were copy-number high

category, whereas only 12.1% in LYL1 non-amplification group showed

this category (P<0.001). In terms of adjuvant treatment, patients who

received chemotherapy were more common in LYL1 amplification group

than in non-amplification group (50.0% vs. 28.4%, P=0.032) (Table 2-1).

Comparisons of survival outcomes between the two groups and

identification of prognostic factors

During the median 23.9 months of observation period (range, 0.5-191.7

months), 5 patients in LYL1 amplification group and 34 in LYL1 non-

amplification group died of the disease. In survival analysis, LYL1

amplification group showed poorer PFS and OS: 3-year PFS rate, 34.4%

vs. 79.9% (P=0.031) and 5-year OS rate, 25.1% vs. 84.9% (P=0.014)

(Figure. 2-5 and 2-6).

LYL1 amplification was significantly associated with poor OS in

univariate analysis (P=0.019) (Table 2-2). However, after adjusting

variables including histologic type, grade, and FIGO stage, LYL1 status

was not confirmed as a prognostic factor for OS; Only advanced stage

disease (FIGO stage III-IV) was an independent poor prognostic factor

６７

(adjusted HR, 3.509; 95% CI, 1.734-7.101; P<0.001). Table 2 also

presents factors associated with PFS. In univariate analysis, LYL1

amplification was associated with poor PFS (P=0.037). However, the

statistical significance of LYL1 status on survival outcome disappeared

in multivariate analysis. Advanced stage disease (FIGO stage III-IV)

was identified as an independent poor prognostic factor for PFS

(adjusted HR, 3.581; 95% CI, 1.981-6.473; P<0.001).

Next, we performed subgroup analyses for each histologic type. In

UCEC patients who had endometrioid histologic type (n=304), both

survival curves for PFS and OS were not different between the LYL1

amplification and non-amplification groups: P values for PFS and OS,

0.070 and 0.323, respectively. Multivariate analyses revealed that LYL

amplification as an independent poor prognostic factor for PFS in this

subgroup with borderline statistical significance (adjusted HR, 4.093; 95%

CI, 0.926-18.012; P=0.063) (Table 2-3).

Differentially expressed genes in LYL1 amplified tumors

To discover the clinical significance of LYL1 gene amplification in UCEC

patients, we performed the GESA pathway analysis with 993 genes with

increased expression in the LYL1 amplification group. As the results, we

identified that MAPK signaling pathway, WNT signaling pathway, cell

cycle pathway, and cancer-related pathway were significantly

upregulated in this group (P<0.001, P=0.002, P=0.004, and P<0.001,

６８

respectively) (Figure. 2-7 and 2-8). From 993 differentially expressed

genes, 384 cancer-related genes were filtered by using STRING

database, and enriched on MAPK signaling pathway, WNT signaling

pathway, cell cycle pathway, and cancer-related pathway. MYC, CDK6,

PPKACA, ERBB2 genes were frequently interacted with other cancer-

related genes (Figure 2-9). We also conducted GSEA according to

histologic types and TCGA classes (Figure 2-10). Among the four TCGA

classes, only the high copy number group showed LYL1 amplifications,

and cell proliferation pathway was significantly enriched in this group.

Compared to endometrioid type, cancer-related and cell proliferation

pathways and genes were more commonly enriched in serous type

(Figure 2-11).

６９

Figure 2-1. Frequencies of LYL1 gene amplification in various

cancer-types. Frequencies of LYL1 genetic alterations are shown

across cancer-types.

７０

Figure 2-2. Frequencies of copy number variations. The

amplifications and deletions of copy number variations are shown

across chromosomes.

７１

Figure 2-3. Correlations between amplification frequencies and

mortality. Correlations between amplification frequencies and mortality

are shown across top 15 oncogenes.

７２

Figure 2-4. Correlations between deletion frequencies and

mortality. Correlations between deletion frequencies and mortality are

shown across top 15 tumor suppressor genes.

７３

Table 2-1. Cliicopathologic characteristics of patients.

７４

Figure 2-5. Overall survival of patients with uterine corpus

endometrial carcinoma. Overall survival of patients are shown by LYL1

gene status.

７５

Figure 2-6. Progression-free survival of patients with uterine

corpus endometrial carcinoma. Progression-free survival of patients

are shown by LYL1 gene status.

７６

Tab

le 2

-2. F

acto

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wit

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ith

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om

etr

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om

a

７７

Tab

le 2

-3.

Facto

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cia

ted

wit

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ival

ou

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n p

ati

en

ts w

ith

en

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nd

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a

７８

Figure 2-7. Enrichment analysis by LYL1 gene status. Significantly

enriched pathways are shown in upregulated 993 DEGs.

７９

Figure 2-8. Expression levels of enriched DEGs. Expression levels

of enriched DEGs are shown across LYL1 amplification.

８０

Figure 2-9. Significant gene networks. Gene networks bearing

simplified KEGG pathway annotations and grouped process-wise by

commonest term prevailing in network

８１

Figure 2-10. Gene set enrichment analysis according to histologic

types and TCGA classes. Gene set enrichment score were shown by

histologic type.

８２

Figure 2-11. Enriched gene list of cancer-related and cell

proliferation pathways. Cancer-related and cell proliferation genes are

shown according to the two histologic types; serous and endometrioid.

８３

DISCUSSION

In this study, we investigated whether LYL1 gene amplification affect

survival outcomes in UCEC through the analysis of TCGA database.

Patients in LYL1 amplification group showed poorer survival outcomes

compared to those in non-amplification group. To date, previous studies

have tried to find out novel biomarkers predicting survival outcomes in

various cancer types. For example, some researchers investigated

impact of altered expression of specific genes, such as homeobox gene

family, L1CAM and MYC, on prognosis of cancers using TCGA database

[7, 82, 83]. LYL1 gene, a basic helix-loop-helix transcription factor, is

known as an oncogene in human and mouse cancers, and shows many

associations with cancer-related properties such as angiogenesis [84-

86]. By genetic and epigenetic modulations, LYL1 gene plays its role as

a regulator for cell proliferation and differentiation [76]. Both in vivo and

in vitro experiments demonstrated that LYL1 gene interacts with several

oncogenes, such as MYC, TAL1, TAL2 and LMO2 [87, 88]. To elucidate

the role of LYL1 gene amplification in UCEC, we analyzed TCGA

expression data, and discovered that overexpressed cancer-related

genes are enriched on MAPK signaling, WNT signaling, and cell cycle

pathways in UCEC patients who had the LYL1 gene amplification.

Especially, MYC, CDK6, PPKACA, and ERBB2, the well-known

oncogenes and cancer markers, were overexpressed in LYL1

８４

amplification groups, and MYC and ERBB2 were reported associations

with uterine cancers in previous studies [89-93].

The current study failed to prove LYL1 gene amplification as an

independent prognostic marker for survival of UCEC patients in

multivariate analyses. Only advanced stage disease was identified as a

poor prognostic marker with statistical significance. In this study, more

than three-fourths of patients had early stage disease: 68.6% and 6.5%

were FIGO stage I and II, respectively. According to the Surveillance,

Epidemiology, and End Results data, the 5-year survival for disease with

distant metastasis was only 16.2%, whereas those for disease with

confined to primary site was 95.3%. [94]. We believe that the effect of

stage on survival outcomes was quite considerable, making it difficult to

analyze impact of LYL1 gene amplification in the current study

population. Nevertheless, we could extrapolate the significance of LYL1

amplification status: LYL1 gene amplification can be a novel cancer

maker highlighting overexpression of accompanying oncogenes such as

MYC, PRKACA, ERBB2, and CDK6 from MAPK signaling, WNT

signaling, and cell cycle pathways in UCEC (Figure 2-12), and there are

positive associations between these oncogenes and LYL1 amplification

in 370 UCECs (Figure 2-13). Therefore, LYL1 gene amplification may

be a prognostic indicator in UCEC patients, having potentials to be a

novel target for therapeutic drugs.

The current study has several limitations. First, validations of suggested

８５

LYL1 gene and its association with other genes, as well as with as

genetic mechanism, were not performed, and protein-level expression

was not investigated. Such proteo-genomics studies might discover the

effects of genetic alterations and the accuracy and completeness of

genomic profiling. In addition, further studies to identify the genetic and

epigenetic regulatory mechanisms of LYL1 gene and to evaluate its

efficacy as a prognostic indicator and therapeutic target are warranted.

In UCEC cell lines, LYL1 gene can be overexpressed or inhibited by

siRNA, and the subsequent changes in cell differentiation, proliferation

and death will be evaluated. Experiments within LYL1 gene knocked-out

patient-derived xenograft (PDX) animal models would be one of the

possible methods. Second, sample size of the LYL1 gene amplification

group was small (n=22) to derive fair statistical analyses.

Additionally, as we are very interested in LYL1 gene, we also performed

similar analyses on other malignancies using TCGA database. As the

results, high-grade serous ovarian cancer and UCEC were the top two

malignancies in which LYL1 gene amplifications are frequently detected

(Figure 2-14). Similarly, in high-grade serous ovarian cancer, patients

displaying LYL1 gene amplification showed significantly poorer OS rates

compared to those without LYL1 gene amplification (P=0.013) (Figure

2-15)

８６

Figure 2-12. Gene expression between LYL1 amplified patients and

non- LYL1 amplified patients. Gene expression of oncogenes are

shown according to LYL1 amplified patients and non- LYL1 amplified

patients.

８７

Figure 2-13. Pearson’s correlation between LYL1 amplification and

oncogene expression. The R values and p values are shown across

oncogenes.

８８

Figure 2-14. The genomic alteration frequency of LYL1 gene. The

frequencies of the genomic alteration in LYL1 gene are shown across

various cancers.

８９

Figure 2-15. Survival analysis in high-grade serous ovarian cancer.

Overall survival outcomes are shown by LYL1 gene status.

９０

General discussion

For more exact diagnosis, more efficient management, and more

effective targeted therapeutic strategy against gynecological cancers,

the genomic profiling of OCCCs were analyzed via WES, and the

genomic targets, for the novel molecular diagnostic and the effective

precision medicine, were discovered by analyzing the next generation

sequencing data of OCCC and UCEC.

However, in the first chapter, there were several idiosyncratic points.

First, Patient #1 showed higher number of somatic mutations compared

to other patients. We analyzed the relative proportions of the six different

possible base-pair substitutions to discover mutational processes [95,

96]. As the results, C to A transversions [97-99], known to be related

with tobacco smoking, were more frequently observed in patient #1

(31%) than in others (12%). Second, Patient #13 showed APOBEC

signatured mutation pattern, which were well known as key player in

mutagenesis in cervical, bladder, head and neck, breast, and lung

cancer type [100, 101]. However the APOBEC signatured mutation

should be verified with RNA sequencing data because APOBEC gene

is functionally related to the C to U RNA-editing cytidine deaminase

[102]. Finally, this current study has additional several limitations; 1) the

size of the study population in this retrospective case-control study was

９１

small (n=15) to test associations between group with endometriosis and

without endometriosis. 2) Expression via Whole transcriptome

sequencing or microarray were not investigated to discover the effects

of genetic alterations. 3) The validation of CNVs detected via WES is

not completed.

In the second chapter, there were also several idiosyncratic point. In this

study, there were very small cohort (n=22) with LYL1 amplification in

370 UCECs, and it is difficult to determine if the amplification in LYL1 is

a driver mutation or a passenger mutation. LYL1 gene amplification and

its association with other gene expression were also successfully

revealed. To overcome these limitations, we followed a previously

published method of Raphael’s to identify a driver mutation as follows:

1) recurrence of the gene; 2) prediction of the functional impact of

individual gene mutations; and 3) assessment of combinations of

mutations using pathways and interaction networks. This method has

been widely used in identification of driver mutations from the NGS data

[103]. In addition, we measured Pearson’s correlation coefficients to

evaluate associations between LYL1 amplification and differentially

expressed genes (DEGs). LYL1 gene was the 10th mostly amplified

oncogenes among the 255 oncogenes whose amplifications were

detected in UCEC, and DEG analyses successfully revealed that

expressions of specific genes were increased along the LYL1 gene

９２

amplification. Despite these limitation, the current study used TCGA

database and discovered a novel gene, LYL1, which is associated with

the prognosis of UCEC by analyzing not only bioinformatics data but

also patients’ clinicopathologic data.

Based on this thesis, two studies would improve the accuracy and

completeness of genomic understanding of OCCC and UCEC, and lead

to more efficient diagnosis, management, and targeted therapeutic

strategy against OCCC and UCEC

９３

General conclusion

The objective of this study was to establish more intensive

understanding of molecular genetic information of gynecologic cancer

(Especially, Ovarian clear cell carcinoma and uterine corpus

endometrial carcinoma), and to allow for us to discover genomic targets

for the novel molecular diagnostic and the effective precision medicine.

In the first chapter, the most exact genomic landscape of 15 patients

with OCCC successfully were established from frequently mutated

genes (PIK3CA, ARID1A, KRAS, PPP2R1A, SYNE1, RFX3, MED12,

GPC3, MST1R, TP53, ARID2, LRP1B, PTEN and ERBB2) via the whole

exome sequencing, and the genomic features of OCCC were

characterized with the somatic mutations and the somatic copy number

variations. Additionally, potential therapeutic targets, such as MYC and

BRCA1, in OCCCs were identified. Although the fact that endometriosis

are associated with OCCC, no significant differences in the frequency

of genetic alterations were detected across the EMS- and Non-EMS-

OCCC groups in this study. However, in spite of this study, the

mechanism of OCCC was not clear. Therefore, additional larger studies

such as whole transcriptome sequencing, to determine the effects of

genetic alterations, will be necessary in future.

９４

In the second chapter, LYL1 gene amplification might be associated with

poor survival outcomes in UCEC patients, especially who had

endometrioid histologic type via TCGA expression data and clinical data

(3-year PFS: 34.4% vs. 79.9%, P = 0.031; 5-year OS: 25.1% vs. 84.9%,

P = 0.014). This study also suggest that expressions of cancer-related

genes (MYC, CDK6, PPKACA and ERBB2) are increased in UCEC

patients who had the LYL1 gene amplification, and MAPK, WNT, and

cell cycle pathways were significantly enriched by LYL1 gene

amplification. Therefore, LYL1 gene amplification can be a prognostic

indicator in UCEC, and it may be a new and potential target for

therapeutic drugs.

It is expected that the findings in this thesis would lead to broader

understanding, more exact diagnosis, more efficient management, and

more effective targeted therapeutic strategy for gynecological cancers.

９５

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１０９

국문초록

차세대 염기서열 분석을 활용한 난

소 투명세포 암과 자궁 내막 암의

유전체 분석 연구

서울대학교 대학원 의과학과 의과학 전공

이지원

부인 암 (Gynecologic Cancer)은 자궁 (uterus) 내외, 난소 (ovary),

나팔관 (fallopian tubes), 질 (vagina)그리고 외음부 (vulva) 등 여성의

생식기 (reproductive organs)에서 일어나는 여성 암이다. 특히 부인

암 중 난소 암 (ovarian cancer) 과 자궁암 (uterine corpus

endometrial carcinoma)은 2017 년도에 발생 율과 사망 율이 높은

10 대 암에 속한다. 최근 들어 정밀 의학 시대에 환자 개개인에

맞는 그리고 각각의 암 종에 맞는 암 진단법과 치료제가 요구되고

있다. 특히 The Cancer Genome Atlas 연구 그룹은 최근 10 년 동안

여러 부인 암을 이해하기 위해 여러 암을 모으고 그 암에 대한

유전자 분석을 수행하고 보고 하고 있다. 하지만 난소 투명세포 암

１１０

(ovarian clear cell carcinoma)과 자궁암의 경우에는 아직 더 많은

분석이 필요한 상태이다.

특히 난소 투명세포 암의 경우에는 사망률이 다소 높지만 발병

율이 낮아 아직까지 저장을 위해 변형을 가하지 않은 암 조직을

구하기 어려운 상태이다. 하지만 이번 연구에서는 2012 년도에서

2016 년도 까지 서울대 병원에서 난소 투명세포 암을 진단 받은

15 명의 환자들로부터 암 조직을 모아 서울대병원 인체 자원은행에

보관하였다. 15 개의 암 조직을 Whole exome sequencing (WES)을

수행하여 얻은 sequencing data 를 통해 난소 투명세포 암의 유전자

지도를 구축하였다. 난소 투명세포 암의 차세대 염기서열 분석을

통해 평균 178 개 (111-25,798)의 체세포 돌연변이 (somatic

mutation)와 343 (43-1,820)개의 DNA 복제 수 변이 (CNVs, copy

number variants)를 찾아내었다. 15 명의 난소 투명세포 암

환자들로부터 PIK3CA (40%), ARID1A (40%), KRAS (20%)을 포함한

14 개의 유전자의 반복적 변이를 찾아내었다. DNA 복제 수 변이의

경우는 NTRK1 (33%), MYC (40%), and GNAS (47%)등의 유전자가

반복적으로 증폭되어 있었고, TET2 (73%), TSC1 (67%), BRCA2 (60%),

and SMAD4 (47%)등의 유전자가 반복적으로 소실 되어 있었다.

또한 난소 투명세포 암을 가진 환자들 중 87%는 세포의 증식과

생존관련 경로 (proliferation and survival signaling pathway: PI3K/AKT,

TP53, ERBB2 pathways) 에, 47%는 염색 질 재구성 (chromatin

１１１

remodeling)관련 경로에 유전자 변이를 가지고 있었다. 그밖에 난소

투명세포 암에 관련이 많다고 알려진 자궁내막 증 유무에 따른

유전자변이 차이를 조사했지만 통계학상으로 차이점을 찾지

못하였다.

또한 자궁 암의 경우에는 TCGA 에서 연구가 되어 기본적인 유전체

연구는 이루어져 있고 발병 율이 높아 진단 방법 및 치료제 개발에

대한 아직 실질 적으로 임상에서 쓸 수 있는 임상 데이터들과의

연관된 유전자 연구는 많이 부족한 상태이다. 두 번째 연구에서는

TCGA 로부터 370 명 자궁암 환자들의 시퀀싱 데이터를 받아 임상

정보들과 같이 분석하여 LYL1 유전자 상태 (amplification 혹은 non-

amplification)에 따른 차이점을 분석하였다. LYL1 유전자 증폭은

370명의 환자 중 22명의 환자에서 확인이 되었다. 22명의 환자들은

진단되었을 당시 나이가 많았고 비 자궁내막 모양, 고 등급 상태,

후기 단계 이었다. 다중 분산 분석 (multivariate analysis)에서는

LYL1 유전자 증폭이 독립적 예후 인자로 인정되기는 힘들었지만

무병 생존 분석 (progression-free survival)과 전체 생존 분석 (overall

survival)에서는 생존에 영향을 미치는 것으로 확인 되었다. DEG

(Differentially expressed Gene)분석과 GSE (Gene Set

Enrichment)분석을 통해 LYL1 유전자 증폭에 따라 발현이 증가하는

유전자들을 분석하였고, 생존 분석을 통해 생존에 따른 영향을

미친다는 것을 찾아 내었다. 그리고 LYL1 유전자 증폭이 있는

１１２

환자에서 발현이 올라가는 유전자들을 확인 해 보면 MAPK

((P<0.001), WNT (P=0.002), and cell cycle pathways (P=0.004)에

통계학적으로 관련성을 보였다. 종합하여, 이번 논문은 부인 암

(Gynecologic cancer)에 대한 이해를 높여 주었고, 특히 난소

투명세포 암을 차세대 염기서열 분석을 통한 정확한 유전체 지도를

구축하였다는 점, 처음으로 유전체 복제 수 변이를 확인 하였다는

점과 아직 정확한 진단 법이 많지 않은 자궁암에 있어 효과적인

진단을 할 수 있는 마커를 제시하였다는 점에 의의가 있다.

* 본 논문의 첫 번째 연구는 Gynecologic Oncology 저널에 출판

완료된 내용[1] 이고, 논문의 두 번째 연구는 BMC cancer 저널에

출판된 내용임 [2].

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주요어: 자궁 암; 난소 투명세포 암; 차세대 염기서열 분석; 체세포

돌연변이; DNA 복제 수 변이; 유전자 증폭; 독립적 예후 인자

학번: 2015-30608