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1

의학석사 학위논문

전이 유방암 환자 혈액에서

상피세포 부착 분자 양성 단일세포의

체세포 돌연변이

Somatic mutation profile

of epithelial cell adhesion molecule positive single cells

from blood of metastatic breast cancer patients

2017년 11월

서울대학교 대학원

의학과 외과학 전공

최지혜

2

A thesis of the Master’s degree

Somatic mutation profile of

epithelial cell adhesion molecule positive single cells

from blood of metastatic breast cancer patients

전이 유방암 환자 혈액에서 상피세포 부착 분자 양성 단일세포의

체세포 돌연변이 분석

November 2017

Department of Medicine

Seoul National University Graduate School

Jihye Choi

3

Abstract

Somatic mutation profile of epithelial cell adhesion molecule positive

single cells from blood of metastatic breast cancer patients

Background: Circulating tumor cell (CTC) enumeration provides prognostic information

for chemotherapy in metastatic breast cancer. However, due to its rarity and

heterogeneity, it is difficult to distinguish true CTCs from normal blood cells and perform

genomic analysis on them for use in therapeutic strategies. Most currently available CTC

detection systems consist of an enumeration of putative CTCs without further analysis.

The aim of this study was to evaluate the feasibility of single cell picking and target

sequencing of epithelial cell adhesion molecule (EpCAM)-positive cells for detecting CTCs.

Methods: Whole blood sampled from metastatic breast cancer patients who were newly

diagnosed with metastasis or who had disease progression during palliative treatment

were used for this study. After applying IsoFlux Circulating Tumor Cell Enrichment Kit

(Fluxion, South San Francisco, CA, USA), single CTC candidates were picked from a pool

of EpCAM-positive cells. Genomic DNA from the picked cells was whole genome

amplified and target sequencing was performed using Ion AmpliSeq™ Cancer Hotspot

Panel (Life Technologies, Carlsbad, CA, USA). Target sequencing reads were mapped on

human genome reference (hg19) using BWA-MEM (0.7.10). Single nucleotide variants

(SNVs) were annotated using dbSNP, Human Variome Project 0.2 and COSMIC databases.

Results: A total of 172 EpCAM-positive cells were selected according to size and EpCAM

status from whole blood of 11 patients. The remaining cells were grouped into a pooled

sample for each patient. The mean read depth of the target genes was 13455ⅹ. A mean

8.55 mutations as determined by SNVs listed in the COSMIC database but not in dbSNP

and Variome Data 0.2 were detected in each patient. Cells with multiple mutated genes,

or those with a mutated gene repeatedly observed in another cell from the same patient

were judged to be putative CTCs. At least 2 putative CTCs were detected in 7 patients

while no CTCs were detected in 2 patients. Mutated genes observed in the putative CTCs

were ABL1, AKT1, APC, CDH1, CDKN2A, ERBB2, FGFR3, HRAS, IDH1, JAK2, KDR, NPM1,

RB1, RET, SMARCB1, STK11, and TP53.

4

Conclusions: Potential CTCs were successfully identified by single cell picking and target

sequencing of EpCAM-positive cells from whole blood of metastatic breast cancer

patients. Unique mutations not detected in other single cells and pooled samples can be

used to distinguish putative CTCs from normal cells. Our results implicate an area for

research and validation of CK negative subgroups of CTCs.

…………………………………………………………………………………………………

Keywords: Keywords: Breast neoplasm, Circulating tumor cell, Next generation

sequencing, Liquid biopsy

Student Number : 2015-23217

5

Contents

Abstract………………………………………………………………………………………………………………………………….3

Contents…………………………………………………………………………………………………………………………………5

List of figures ………………………………………………………………………………………………………………………...6

List of tables ………………………………………………………………………………………………………………………….7

Introduction……………………………………………………………………………………………………………………………8

Materials and Methods……………………………………………………………………………………………………........9

Results…………………………………………………………………………………………………………………………………..13

Discussion…………………………………………………………………………………………………………………………….15

References…………………………………………………………………………………………………………………………….18

Figures…………………………………………………………………………………………………………………………………..22

Tables……………………………………………………………………………………………………………………………………30

Abstract in Korean………………………………………………………………………………………………………………..36

6

List of Tables

Table 1……………………………………………………………………………………………………….……….………………..30

Patient Characteristics

Table 2…………………………………………………………………………………………………………….……………………31

Summary of somatic mutation profile

Table 3….………………………………………………………………………………………………………………………………32

Sequenced results of the three patients (Patient #1,#7 and #9)

7

List of Figures

Figure 1 …………………………………………………………………………………………………………………………….22

Experimental study with MCF cell lines

Figure 2………………………………………………………………………………………………………………………………23

Manually captured cells with EpCAM antibodies attached

Figure 3………………………………………………………………………………………………………………………………24

Hotspot variant gene mutations identified from different isolated single MCF cells and

pooled cells

Figure 4………………………………………………………………………………………………………………………………25

Schematic study flow

Figure 5………………………………………………………………………………………………………………………………26

A heat map of 172 individual picked cells from 11 blood samples

Figure 6………………………………………………………………………………………………………………………………28

Characteristics of the somatic mutations observed in putative CTCs

8

1. Introduction

Circulating tumor cells are tumor cells that shed from primary tumors spreading

through the bloodstream. Because they are considered to be responsible for metastasis,

previous studies have demonstrated that CTC enumeration provides prognostic

information for metastatic breast cancer patients (1–3). However, its rarity and

heterogeneity keeps it difficult to characterize the detected CTCs individually and utilize

the information for therapeutic strategies.

With the acknowledgement of heterogeneity observed in tumor cells in recent years,

the need for single cell analysis has increased (4),(5). CTCs, like tumor cells, have

heterogenic molecular and genetic properties. Single cell analysis could give deeper

understanding about their origin, evolution and role in metastasis (1),(6). Traditionally,

CTCs were defined as epithelial cell adhesion molecule (EpCAM) positive, cytokeratin (CK)

positive and CD45 negative cells but there is increasing evidence that there are

subgroups of CTCs that may not bear these markers (7),(8). CTCs that undergo epithelial

mesenchymal transition(EMT) may lose epithelial antigens and have more aggressiveness

being invasive and attaining self-renewal capacity (9). These subgroups of CTCs may be

more clinically challenging.

Nevertheless, most currently available CTC detection systems, such as CellSearch ™

consist of enumeration of putative CTCs without further analysis and are more oriented

to utilize cells expressing traditionally defined biomarkers for CTCs.

Apostream ™ is a revolutionary system that isolates CTCs using dielectrophoresis field

flow. CTCs are distinguished according to morphological and electrical properties,

indepent of EpCAM positivity and they still maintain cell viability enabling downstream

single cell analysis after isolation (10). Yet Apostream™ is not currently widely available in

Korea.

Herein, we introduce a non –automated technique, feasible in single cell isolation and

target sequencing of EpCAM positive cells for the discovery of CK negative putative CTCs

from blood of metastatic breast cancer patients.

9

2. Materials and Methods

2.1 Control cell line experiment

Before conducting this study, MCF-7 cell line was tested as control since the mutations

of MCF-7 are well announced. Study flow chart is drawn in Figure 1.

MCF-7 cells were obtained from UNIST (Ulsan, Korea), and were routinely maintained in

RPMI-1640 medium (Gibco®; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal

calf serum and 1% antibiotics at 37˚C in humidified, concentrated CO2 (5%) atmosphere.

The MCF-7 cells used in the study were all in the same generation. After enrichment with

Isoflux system, EpCAM positive cells were isolated by single cell picking method. We

performed analysis on four picked MCF-7 cells respectively (#1~#4). REPLI-g kit (Qiagen)

was used for lysis in cells #1 and #2 and Genomiphi V2 kit (GE Healthcare Life Sciences)

was fused for cells #3 and#4. After whole genome amplification, common Hotspot

variant gene mutations of MCF were successfully identified on Comprehensive cancer

panel (Thermo Fisher Scientific, Inc.). With the remaining pooled cells – which we think

consisted of more than ten thousand cells- purity enhancement and whole genome

amplification with NGS DNA kit was performed. Figure 3 shows common hotspot variant

gene mutations identified from different isolated single MCF cells and pooled cells.

Among the three picked cells analyzed, we excluded cells that had poor quality control

(#3 and #4). However, the gene mutations identified in the two picked cells (#1 and #2)

and those in the pooled celled was highly concordant. This result proved the feasibility of

applying this technique into further studies. Non-shared mutations suggest

heterogeneity within the MCF cell line.

2.2 Patient characteristics and blood samples

Figure 4 depicts overall schematic study flow. A total of eleven breast cancer patients

who were diagnosed with new metastasis after adjuvant chemotherapy or who had

progression of disease during palliative chemotherapy from July 2014 to Feb 2015 at

Seoul National University Hospital were included in this study. Six milliliters of whole

10

blood sampled from each patient were collected in EDTA tubes. Right after blood

collection, EDTA tubes were stored at a nitrogen tank and kept for analysis. Analysis was

done at UNIST (The Genomics Institute, UNIST, Ulsan).

All blood samples had been obtained before initiation of chemotherapy and stored in

Seoul National University Hospital Biobank after written, informed consent from patient.

The study was approved by the institutional review board of Seoul National University

Hospital. Clinico-pathological information of the patients was obtained from the medical

records. Among 6mls of blood, 3mls were analyzed with Isoflux system, and the

remaining 3mls of blood were analyzed for non-automated technique.

2.3 Cell enrichment and isolation

For enrichment of CTC candidates, IsoFlux Circulating Tumor Cell Enrichment Kit (Fluxion,

South San Francisco, CA, USA) was applied. The kit contains cocktails of antibodies and

reagents for immunofluorescence staining of cells to define CTCs as Cytokeratin positive,

CD45 negative, nucleated and intact cells. Using microfluidics, the target cells having

antibodies with magnetic core attached, are attracted towards the magnetic field and

therefore isolated on a disk.

However, our initial attempt to select CTCs using IsoFlux system was not successful. To

verify whether or not if it is caused by internal quality factor, we asked other laboratories

to find out that using the same system, others have produced successful results in

capturing CTCs in many cancer types including lung cancer, colon cancer previously.

Accordingly, we assumed that there might be CTCs with different nature, expressing or

not expressing common biomarkers at the same time and therefore managed to select

EpCAM positive cells, and if the cells were EpCAM positive, they were stained for CK

sequentially. However, in 9 out of 11 patients, cells were negative for CK. Therefore,

instead of the original plan, cells with relatively larger size and higher EpCAM-positivity

were isolated from enriched pools by micromanipulation. In each individual, 14 to 16

cells were isolated according to 1) size in sequence and 2) the number of EpCAM

antibodies attached on each cell. Size selection was based on the size of white blood

cells since they usually are the smallest. After enrichment, picking was done with manual

pipetting with 2 microliter pipet under visual guidance. The remaining cells were grouped

11

into pooled sample for each patient as a control. Picked singles cells were transferred to

a PCR tube containing cell lysis buffer to be prepared for the following step. Pictures of

captured cells with EpCAM antibodies attached are shown in Figure 2.

2.3.1 Immunoflruorescence staining

Blood tubes were processed to recover the peripheral blood mononuclear cell fraction.

LeucoSep tubes (Greiner Bio-One, Monroe, NC) were prepared by adding 15 ml of Ficoll-

Paque Plus (GE Healthcare, Pittsburgh, PA). The peripheral blood mononuclear cell

fraction was recovered and resuspended in 1 ml of binding buffer (CTC Enrichment Kit;

Fluxion Biosciences Inc). Immunomagnetic beads preconjugated with anti-EpCAM

antibodies (CTC Enrichment Kit; Fluxion Biosciences Inc) were added directly to the

sample and incubated for 2 hours at 4°C with passive mixing on a rotator.

Immunofluorescence staining was performed using anti-cytokeratin (CK), anti-CD45, and

Hoechst 33342 (nucleus) (CTC Enumeration Kit; Fluxion Biosciences Inc). Recovered CTCs

were fixed with phosphate-buffered saline (PBS) buffer containing 1.8% formaldehyde,

washed, and blocked with 1% goat sera in PBS. Cells were stained with rabbit polyclonal

anti-human CD45 antibody followed by goat anti-rabbit antibody conjugated with Cy3.

After permeabilization with 0.1% Triton X-100, cells were then stained with anti-CK

(fluorescein isothiocyanate). For CK staining, we used antibody clone CK3-6H5, a

pancytokeratin-specific antibody likely to recognize all simple epithelium CK. CK3-6H5

crossblocks antibodies known to be specific for cytokeratins 7 and 8.

2.4 DNA amplification and target sequencing

Genomic DNA from the picked cells was whole genome amplified with Isoflux NGS DNA

Kit (Fluxion, South San Francisco, CA, USA) and target sequencing was performed using

Ion AmpliSeq™ Cancer Hotspot Panel (Life Technologies, Carlsbad, CA, USA) covering

about 2,800 COSMIC mutations in 50 cancer genes. Twenty nanograms of DNA were

used to generate libraries using the IonAmpliseq library preparation kit v2.0 (Life

Technologies) according to the manufacturer's protocol. Genomic DNA was amplified

using the aforementioned WGA protocol and the amplicons were purified using the

Agencourt AM-Pure XP kit (Beckman Coulter, Inc., Brea, CA, USA), followed by end repair

and ligation using Ion Xpress™ Barcode Adapters kit (catalog no., 4471250; Thermo

Fisher Scientific, Inc.). The median fragment size and concentration of the final amplicon

library were detected using a BioAnalyzer 2100 with Agilent High Sensitivity DNA kit

12

(Agilent Technologies, Inc., Santa Clara, CA, USA).The amplicon library was diluted to 10

pM with TE buffer and 5 μl of the library was used for automatic PCR; the Ion

OneTouch™ system (catalog no. 4474779; Invitrogen; Thermo Fisher Scientific, Inc.)

performed emulsion PCR reactions using Ion PGM™ Template OT2 200 kit following the

manufacturer's protocol. The following cycling conditions were used: 80˚C for 3 min; 18

cycles of (99˚C for 20 sec, 58˚C for 30 sec, 72˚C for 1 min, 99˚C for 20 sec, 56˚C for 30 sec

and 70˚C for 1 min); and 10 cycles of (99˚C for 20 sec, and 58˚C for elongated durations

from 3-20 min) with heat cover at 85˚C.

2.5 Database

The raw reads of the 183 Target Exome Sequencing samples were mapped to hg19. Two

mapping data was generated. One was generated by TMAP program, which was used to

call SNV using the Torrent Variant Caller program. Another was generated by the mem of

a BWA-0.7.10 (11) program followed by the IndelRealigner of a GATK-3.3-0 program that

realigned reads mapped to indel regions. Variant calling was performed with SAM tools

(12) and GATK Unified Genotyper (13). The SNV data was generated at UNIST. Single

nucleotide variants (SNVs) were annotated and compared to, using validated mutations

listed in the dbSNP (14), Human Variome Project Data 0.2 (15), and COSMIC (Catalog of

Somatic Mutations in Cancer) Cell lines project databases (10).

2.6 Definition of potential CTCs

Initially selected cells according to size and EpCAM positivity were put to target

sequencing. To be considered as potential CTC, a cell should have multiple mutated

genes within one cell and/or have a mutated gene repeatedly observed in another cell

from the same patient. More than two gene mutations within a cell were regarded as

having multiple mutated genes. If more than two cells from a same patient shared a

same mutation, it could be inferred that the mutation is more likely to reflect the

mutational status of the tumor than those that do not, therefore considered as a putative

CTC.

13

2.7 Genomic profiling of primary tumor and metastatic site

Formalin fixed paraffin embedded (FFPE) samples of primary tumor was available in

patient #1, #7 and #9. FFPE from metastatic site was available in patient #7 and #9.

Patient #7 had liver biopsied and #9 had lung biopsied. Target sequencing was

performed using Ion AmpliSeq™ Cancer Hotspot Panel (Life Technologies, Carlsbad, CA,

USA).

3. Results

3.1 Patient Characteristics

Characteristics of the 11 patients are listed in Table1. Two patients had stage IV disease

at diagnosis. Most of the patients had more than two visceral metastases. Four patients

had been diagnosed with triple negative breast cancer. Patients were aged 49 year in

average.

3.2 Putative CTCs and their mutational profile

A total of 172 EpCAM-positive cells were selected according to size and EpCAM status

from the whole blood of 11 patients with breast cancer metastasis. Seven to 14

mutations were found in each individual. At least 2 putative CTCs were detected in 7

patients while no CTCs were detected in 2 patients. In nine patients only CK negative CTC

candidates were identified. The mean read depth of the target genes was 13455ⅹ. Mean

10.54 SNVs were detected in a cell and 164.91 SNVs in per patient. A mean of 8.55

mutations as determined by SNVs listed in the COSMIC database but not in dbSNP and

Variome Data 0.2 were detected in each patient.

A heat map of 172 individual picked cells from 11 blood samples is listed in Figure 6.

A cell that has multiple mutated genes within itself and/or has a mutated gene

repeatedly observed in another cell from the same patient were regarded as a putative

14

CTC. Therefore for patient No.1, five putative CTCs were identified. #1 CTC had multiple

mutations (VHL and HRAS) #2 CTC had mutation on HRAS only but this was repeatedly

observed in #1 CTC, therefore was considered as a putative CTC. Likewise #3 and #4 CTC

had shared CDH1 mutation in common which was not observed in the pooled cells. #5

CTC had multiple mutations (STK1 and SMARCB1). (Red box indicates mutation in

putative CTC. Yellow box, mutation in putative non-tumor cell and blue indicates absence

of mutation.)

Summary of somatic mutation profiles of the patients are listed in Table2. Mutated

genes observed in the putative CTCs were ABL1, AKT1, APC, CDH1, CDKN2A, ERBB2,

FGFR3, HRAS, IDH1, JAK2, KDR, NPM1, RB1, RET, SMARCB1, STK11, and TP53. (Figure 5)

Out of the 17 mutations identified, TP53, CDH1 and ABL1 were the top 3 commonly

mutated genes in the putative CTCs.

3.3 Genomic profiling of primary tumor and metastatic tissue

CTCs, primary tissue and metastatic tissue of patient #1, #7 and #9 were analyzed by

target sequencing. Unfortunately, sequencing coverage was insufficient to reveal

significant correlation between CTCs and tissue profiling. Whether this may be due to the

condition of FFPE samples or due to purity issue is not clear. However, in the assumption

that the data is credible, the result may have been due to CTC evolution during disease

progression. Primary tumors and metastases can be heterogeneous, not always

displaying the same biological markers (16). Due to low numbers of FFPE cases, the result

remains inconclusive. Further studies with larger numbers of samples are warranted.

Sequencing results of the three patients are listed in Table 3.

15

4. Discussion

Enumeration of CTCs is a validated prognostic factor in metastatic breast cancer (17). Its

pivotal role in understanding the biology of tumor cell dissemination and clonal

evolution is addressed in the era of precision medicine (18).

In earlier studies, most of the analysis of CTCs has been performed in bulk or nucleic

acids levels due to technical difficulty (19),(20),(21). However, bulk analysis may result in

incomplete understanding about tumor heterogeneity only by representing average, but

not cell to cell variations of the tumor (22). In consequence, single cell genomic analysis

has shown much progression in the last few years (23). Through single CTC profiling, the

limitation proposed by leukocyte contamination can be overcome enabling the study of

CTC heterogeneity(24). Single CTC profiling may reflect real-time status of tumor

environment such as additional genomic characteristics acquired over time that might be

different from those of the primary tumor (25),(26). However the capture and detection

of CTCs are challenging because they are rarely present in the blood, as low as one CTC

per 106-107 leukocytes (27).

Among many CTC detection systems, the CellSearch™ system- which is currently the

only FDA approved system- is widely in use (18) and many further platforms are

dependent on EpCAM positive, CK positive and CD45 negative cell enumeration to

confirm an epithelial phenotype (28).

But there are increasing evidence that there are phenotypes of CTCs lacking expression

of these markers. Biological mechanisms, such as EMT, may result in a change in the

spectrum of markers through epithelial marker shedding and perhaps indicate a more

aggressive phenotype clinically. Though CK negative CTCs were first reported in 2011

taking EMT into consideration (29), current CTC capture systems are likely to miss

populations of cells undergoing EMT (30). In consequence, there are relatively few studies

on CK negative CTCs in breast cancer

The result of this study also supports the presence of an EpCAM positive, CK negative

putative CTC in breast cancer and opens up an area for research and validation of CK

negative subgroups of CTCs. Whether these CTCs really are undergoing EMT is left to be

verified in further studies. In one study, a dual-color immunocytochemistry approach

using antibodies to PanCK, E-cadherin and anti-vimentin were additionally performed on

16

CK negative cells to evaluate EMT on CTCs (8).

Another problem of the conventional CTC detecting systems is the challenges brought

against the culture of isolated CTCs. Often immunomagnetic isolation procedure

associated with CTC isolation involves chemical and mechanical stress that hinders cell

culture thereby limiting further downstream analysis. A recently highlighted system-

Apostream™ – is independent of EpCAM positivity and suggested to be a better option

for further single cell-level analysis but is not yet widely accessible.

In this study, we demonstrated the feasibility of single cell picking and target

sequencing of epithelial cell adhesion molecule (EpCAM)-positive cells for detecting CTCs.

The method was approved to be technically achievable both in the experimental studies

and in identifying putative CTCs. This non-automated technique may be used as an

alternative option in laboratories facing similar confrontations.

Through application of a hotspot panel, we intended to compensate for amplification

bias generated by whole genome amplification (31),(32). Mutations that are known to be

frequent in breast cancer were sought and matched to using the hotspot panel. In our

study, among the 17 identified mutations, TP53, CDH1 and ABL1 were the most

commonly mutated genes in the putative CTCs. This is somewhat consistent in that

TP53 and PIK3CA are known to be the most frequently mutated genes in breast cancer

whereas a large number of other genes are less commonly mutated (33). This suggests

that genomic analysis was successful. However, whether if all the 17 identified mutations

are true private mutations or not is not clear. In contrast to our results, much less

mutations have been reported in other studies. In one study using Cell search system

and Ampliseq cancer hotspot panel, mutations were detected in 4 genes (PIK3CA, ESR1,

TP53, and KRAS) and only 1 or 2 mutations were detected in the majority of samples. (34)

Another study reported similar results using the same platform (35). Private CTC

mutations, mutations that are observed exclusively in single CTCs are not easy to

differentiate from sequencing artifacts unless whole genome analysis of the primary or

metastatic tissue is performed. To address this issue, in a consensus-based manner, we

opted for unique multiple mutations and/or repeated mutations from a single patient

that was not observed in the pooled cells (36).

Another strong point of our study is that we analyzed relatively large numbers of CTC

candidates. More than a hundred and fifty EpCAM positive cells were analyzed at single

17

cell level basis thereby presenting more reliability on our results. Using various methods,

including microfluidic transcriptional profiling, targeted mutation detection, and next-

generation sequencing, considerable heterogeneity had been reported in single CTCs

from breast cancer patients (16). In these studies, numbers of CTC candidates that were

analyzed ranged from 14 to 500 (16), (37), (38).

Our limitation, like those in other studies, includes the challenges encountered by CTC

enrichment and single CTC isolation efficiency. Though the genomic profiling of CTC

candidates and primary tumor site biopsy did not conform well in our study, there is still

chance of CTC heterogeneity and evolution in explaining this result (39). Therefore, to

verify CTCs, genomic profiling of corresponding primary tumor and metastatic site biopsy

in larger cohorts as well as detailed immune-histochemical studies are warranted to

verify the CTCs and investigate their role in disease progression.

In future studies, it is of our interest to find out whether acquired information of single

CK negative putative CTCs will actually correlate with breast cancer prognosis in real

clinical setting

5. Conclusion

Potential CTCs were successfully identified by single cell picking and target sequencing

of EpCAM-positive cells from whole blood of metastatic breast cancer patients. Unique

mutations not detected in other single cells and pooled samples can be used to

distinguish putative CTCs from normal cells. Genomic profiling of corresponding primary

tumor and metastatic site biopsy in larger cohort is warranted. Our results implicate an

area for research and validation of CK negative subgroups of CTCs.

18

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22

Figure1. Experimental study with MCF cell lines

23

Figure 2. Captured cells with EpCAM antibodies attached

24

Figure 3. Hotspot variant gene mutations identified from different isolated

single MCF cells and pooled cells

25

Figure 4. Schematic study flow

26

Figure 5. A heat map of 172 individual picked cells from 11 blood samples.

Gene Name Position

ALK 29432607

ALK 29443675

IDH1 209113152

ERBB4 212530161

VHL 10188269

VHL 10191461

PIK3CA 178921540

FGFR3 1807856

FGFR3 1808889

KDR 55946130

KDR 55946161

KDR 55972974

APC 112173894

APC 112173943

APC 112175813

NPM1 170837514

EGFR 55221881

MET 116339679

MET 116340185

JAK2 5073832

CDKN2A 21970956

CDKN2A 21970957

CDKN2A 21971101

ABL1 133747505

ABL1 133747507

NOTCH1 139399380

RET 43613852

RET 43615687

PTEN 89717601

FGFR2 123257970

HRAS 533903

ATM 108117839

ATM 108123581

ATM 108225629

HNF1A 121432013

RB1 49033847

RB1 49039169

RB1 49039229

AKT1 105241484

AKT1 105246476

AKT1 105246497

CDH1 68847249

CDH1 68847286

TP53 7578421

TP53 7579409

TP53 7579410

TP53 7579419

TP53 7579420

ERBB2 37881031

ERBB2 37881329

SMAD4 48584605

STK11 1207066

STK11 1220320

STK11 1220517

STK11 1220519

STK11 1220599

STK11 1221247

JAK3 17954222

SMARCB1 24143264

SMARCB1 24145510

61 2 3 4 5

27

Gene Name Position

ALK 29432607

ALK 29443675

IDH1 209113152

ERBB4 212530161

VHL 10188269

VHL 10191461

PIK3CA 178921540

FGFR3 1807856

FGFR3 1808889

KDR 55946130

KDR 55946161

KDR 55972974

APC 112173894

APC 112173943

APC 112175813

NPM1 170837514

EGFR 55221881

MET 116339679

MET 116340185

JAK2 5073832

CDKN2A 21970956

CDKN2A 21970957

CDKN2A 21971101

ABL1 133747505

ABL1 133747507

NOTCH1 139399380

RET 43613852

RET 43615687

PTEN 89717601

FGFR2 123257970

HRAS 533903

ATM 108117839

ATM 108123581

ATM 108225629

HNF1A 121432013

RB1 49033847

RB1 49039169

RB1 49039229

AKT1 105241484

AKT1 105246476

AKT1 105246497

CDH1 68847249

CDH1 68847286

TP53 7578421

TP53 7579409

TP53 7579410

TP53 7579419

TP53 7579420

ERBB2 37881031

ERBB2 37881329

SMAD4 48584605

STK11 1207066

STK11 1220320

STK11 1220517

STK11 1220519

STK11 1220599

STK11 1221247

JAK3 17954222

SMARCB1 24143264

SMARCB1 24145510

7 8 9 10 11

Figure 5. A heat map of 172 individual picked cells from 11 blood samples.

Red indicates mutation in putative CTC. Yellow, mutation in putative non-tumor cell and blue

indicates absence of mutation. Note that, for patient No.1, five putative CTCs were identified. #1

CTC had multiple mutations (VHL and HRAS) #2 CTC had mutation on HRAS only but this was

repeatedly observed in #1 CTC, therefore was considered as a putative CTC. Likewise #3 and #4

CTC had shared CDH1 mutation in common which was not observed in the pooled cells. #5 CTC

had multiple mutations (STK1 and SMARCB1)

28

Figure 6. Characteristics of the somatic mutations observed in putative CTCs

geneName strand chrNo positionReference

NT

Altered

NT

Function

location

Function

Effect

NT

ChangeAA Change COSMIC ID

ALK - chr2 29432607 A G INTRON . . . .

ALK - chr2 29443675 C T CDS MISSENSE cGc/cAc R1181H .

IDH1 - chr2 209113152 G A CDS MISSENSE Cgg/Tgg R119W COSM1015578

ERBB4 - chr2 212530161 G A CDS SILENT ggC/ggT G586 .

VHL + chr3 10188269 C T CDS MISSENSE Cca/Tca P138S COSM1566375

VHL + chr3 10191461 G A INTRON . . . .

PIK3CA + chr3 178921540 C T CDS MISSENSE gCa/gTa A341VCOSM1420785

COSM29613

FGFR3 + chr4 1807856 G A CDS MISSENSE Gcc/Acc A639T .

FGFR3 + chr4 1808889 G A CDS MISSENSE gGc/gAc G774D .

KDR - chr4 55946130 G A CDS MISSENSE aCa/aTa T1350I .

KDR - chr4 55946161 C T CDS MISSENSE Gcc/Acc A1340T .

KDR - chr4 55972974 T G CDS MISSENSE caA/caC Q472H COSM149673

APC + chr5 112173894 A G CDS MISSENSE gAa/gGa E868G .

APC + chr5 112173943 A G CDS SILENT gcA/gcG A884 .

APC + chr5 112175813 G A CDS MISSENSE Gct/Act A1508T .

NPM1 + chr5 170837514 T C INTRON . . . .

EGFR + chr7 55221881 G A INTRON . . . .

MET + chr7 116339679 G A CDS MISSENSE Gga/Aga G181R .

MET + chr7 116340185 C T CDS SILENT agC/agT S349 .

JAK2 + chr9 5073832 T C INTRON . . . .

CDKN2A - chr9 21970956 C G CDS SILENT gcG/gcC A134 .

CDKN2A - chr9 21970957 G C CDS MISSENSE gCg/gGg A134G COSM13612

CDKN2A - chr9 21971101 G A CDS MISSENSE gCc/gTc A86V COSM12495

ABL1 + chr9 133747505 T C INTRON . . . .

ABL1 + chr9 133747507 C T INTRON . . . .

NOTCH1 - chr9 139399380 C G CDS MISSENSE aGc/aCc S1588T .

RET + chr10 43613852 G A CDS SILENT ctG/ctA L772 .

RET + chr10 43615687 G A INTRON . . . .

PTEN + chr10 89717601 A G INTRON . . . COSM5932

FGFR2 - chr10 123257970 C T INTRON . . . .

HRAS - chr11 533903 G A CDS SILENT tgC/tgT C51 .

ATM + chr11 108117839 A G CDS SILENT gcA/gcG A350COSM1561135

COSM1561136

ATM + chr11 108123581 A G CDS MISSENSE Agt/Ggt S614G .

ATM + chr11 108225629 T A INTRON . . . .

HNF1A + chr12 121432013 C T CDS SILENT Ctg/Ttg L254 .

29

RB1 + chr13 49033847 C T CDS SILENT Cta/Tta L662 .

RB1 + chr13 49039169 T C CDS SILENT taT/taC Y749 .

RB1 + chr13 49039229 G A CDS SILENT ttG/ttA L769 .

AKT1 - chr14 105241484 G A CDS SILENT Ctg/Ttg L166 .

AKT1 - chr14 105246476 G A CDS MISSENSE Ccg/Tcg P42S .

AKT1 - chr14 105246497 A G CDS MISSENSE Ttc/Ctc F35L COSM48226

CDH1 + chr16 68847249 G A CDS MISSENSE Gtc/Atc V391I .

CDH1 + chr16 68847286 C G CDS MISSENSE gCc/gGc A403G .

TP53 - chr17 7578421 G C CDS MISSENSE aCg/aGg T170R COSM44552

TP53 - chr17 7579409 A G CDS MISSENSE cTg/cCg L93P .

TP53 - chr17 7579410 G A CDS SILENT Ctg/Ttg L93

COSM1564165

COSM1564164

COSM1564163

COSM43812

TP53 - chr17 7579419 A G CDS MISSENSE Tcc/Ccc S90P

COSM1735386

COSM1735387

COSM1735385

COSM1735384

TP53 - chr17 7579420 G A CDS SILENT ccC/ccT P89 .

ERBB2 + chr17 37881031 G C CDS MISSENSE gGc/gCc G787A .

ERBB2 + chr17 37881329 C T CDS MISSENSE Ctc/Ttc L841F .

SMAD4 + chr18 48584605 T C CDS MISSENSE Tac/Cac Y260H .

STK11 + chr19 1207066 G A CDS MISSENSE Ggg/Agg G52R .

STK11 + chr19 1220320 C A INTRON . . . .

STK11 + chr19 1220517 T G INTRON . . . .

STK11 + chr19 1220519 G A INTRON . . . .

STK11 + chr19 1220599 C T CDS MISSENSE gCg/gTg A206V .

STK11 + chr19 1221247 G A CDS MISSENSE gGg/gAg G257E .

JAK3 - chr19 17954222 A G CDS SILENT ctT/ctC L129 .

SMARCB1 + chr22 24143264 C G CDS MISSENSE Ctt/Gtt L166V .

SMARCB1 + chr22 24145510 C T CDS MISSENSE Cat/Tat H177Y .

(NT: nucleotide, AA: aminoacid, CDS: coding sequence)

30

Table 1. Patient Characteristics

Patient

numberGender Age

Location

of Primary

tumor

Metastasis

/ Locationpstage

Tumor

cell type

1 F 52Lt. breast -

> Rt. Breastliver, lung

T4N1M0(2006.05.25, Lt.) -

>T1N0M0(20010.02.18, Rt.)-

>liver (2013.11) -> pleural

effusion, lung(2014.07)

IDCa, Lt. (ER/PR/HER2 -/-/-),

IDCa, Rt. (ER/PR/HER2 +/+/-)

2 F 45 Lt. breast LungypT2N3(2013.06.27) ->

lung(2014.02)DCIS, Lt. (ER/PR/HER2 -/-/+)

3 F 46 Rt. Breast bone, liverypT2N1M0->bone,

liver(2014.12.09)IDCa, Rt. (ER/PR/HER2 -/-/-)

4 F 36 Rt. Breastlung, mediastinal LN,

bone

cT3N1M1(2010) -> palliative

TM ypT2NxM1(2014.05.28)

IDCa & ILCa, Rt. (ER/PR/HER2

+/-/-)

5 F 66 Rt. Breast bone, lungypT2N1M0(2006.07.12) ->

axillary LN (2007.07.10)IDCa, Rt. (ER/PR/HER2 +/-/+)

6 F 38

Rt. (2007),

Lt. (2012)

Recur in Lt.

(2014.11.14)

brain, multiple Lt. SCN,

skin, Lt. pelvic bone

Rt. pT1N2(4/16) - 2007-01-10

Lt. pT2N1(3/17) - 2012-06-01

Lt. ypT2N1(1/1) - 2014-11-14

IDCa, both (ER/PR/HER2 -/-/-)

7 F 47 Rt. breast bone, brain, liver ypT3N2(7/21) IDCa, Rt. (ER/PR/HER2 +/+/-)

8 F 58 Rt. breast liver pT2N1(2/19)IDCa, Rt. (ER/PR/HER2 -/-/2+,

FISH -)

9 F 38Lt breast

2014-12-05bone, lung

cT3N1M1(2014) -> palliative

TM ypT2NxM1(2014.12.03)IDCa, Lt. (ER/PR/HER2 +/+/-)

10 F 47 2011-12-28 bone, lung, liver pT2N2(5/10) IDCa, Rt. (ER/PR/HER2 +/+/-)

11 F 63Rt breast

2010-07-08lung, liver ypT2 N3 IDCa, Rt. (ER/PR/HER2 +/+/-)

31

Table 2. Summary of somatic mutation profiles

Sample

No.

No. of

single

cells

No. of

SNVs

No.

of

mutations

No. of

Putative CTCs

Mutations

observed in

Putative CTCs

Position Amino acid

change

1 16 146 10 5

VHL 10188269 P138S

HRAS 533903 C51

CDH1 68847286 A403G

STK11 1221247 G257E

SMARCB1 24143264 L166V

2 16 160 8 2

APC 112173943 A884

RB1 49033847, 49033847 L662, L769

TP53 7579409 L93P

ERBB2 37881031 G787A

3 16 173 8 4 TP537579409,

7579410L93P,L93

4 15 134 12 6

IDH1 209113152 R119W

KDR 55946130 T1350I

NPM1 170837514 -

ABL1133747505,

133747507-

CDH1 68847286 A403G

5 16 155 7 1

NPM1 170837514 -

CDKN2A21970956,

21970957A134, A134G

TP53 7579419 S90P

6 16 203 7 1 TP53 7579410 L93

7 16 179 7 3AKT1

105246497,

105241484L166, F35L

TP53 7579409 L93P

8 15 169 14 3CDH1 68847286 A403G

TP53 7579409 L93P

9 14 144 5 0 - - -

10 16 179 9 2

FGFR3 1808889 G774D

KDR 55972974 Q472H

JAK2 5073832 A86V

CDKN2A 21971101 -

RET 43615687 -

11 16 172 7 0 -- -

(Note: More detailed description of somatic mutation profiles are listed in Figure 5)

32

Table 3. Sequencing results of the three patients

Formalin fixed paraffin embedded (FFPE) samples of primary tumor was available in patient #1, #7 and #9. FFPE from metastatic site was

available in patient #7 and #9. Patient #7 had liver biopsied and #9 had lung biopsied. Target sequencing was performed using Ion AmpliSeq™

Cancer Hotspot Panel (Life Technologies, Carlsbad, CA, USA).

33

A. Hot spot sequencing results: Patient#1

Sample

UNIST기호

Variants AKT1 AKT1 AKT1

Variants ALK

Gene ID APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC

ATM

BRAF

CDKN2A CDKN2A

CSF1R CSF1R CSF1R

EGFR EGFR

ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4

FBXW7

FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3

FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3

HRAS HRAS HRAS

JAK3

KDR KDR KDR KDR KDR KDR

KIT

NOTCH1 NOTCH1 NOTCH1 NOTCH1 NOTCH1 NOTCH1 NOTCH1 NOTCH1 NOTCH1

PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA

PIK3CA

PTEN PTEN

PTPN11

SMARCB1 SMARCB1

SMAD4

SMO SMO

STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11

TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53

VHL

#1-6#1-1 #1-2 #1-3 #1-4 #1-5#1 Primary

FFPE slide#1-9 #1-10 #1-11 #1-12 #1-13 #1-16 #1-17 #1-18 #1-19 #1-20 #1-pooled

34

B. Hot spot sequencing results: Patient#7

Sample #2 Primary

UNIST기호 FFPE sl ide

ABL1

AKT1 AKT1 AKT1

ALK

ATM ATM ATM ATM ATM ATM ATM ATM

APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC

EGFR EGFR EGFR

ERBB2

ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4

FGFR2

FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3

FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3

GNAS

HRAS HRAS HRAS

IDH2

JAK2

JAK3

KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT KIT

KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR

MLH1 MLH1

NOTCH1 NOTCH1 NOTCH1

PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA

PIK3CA PIK3CA

PTEN PTEN

RB1 RB1

RET

SMAD4 SMAD4 SMAD4 SMAD4 SMAD4

SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1 SMARCB1

SMO SMO SMO

SRC

STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11

TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53

VHL

#2-pooled#2 Metastasis

FFPE slide#2-7 #2-8 #2-9 #2-11 #2-12 #2-13

Variants Gene

ID

#2-14 #2-15 #2-16 #2-18#2-1 #2-2 #2-3 #2-4 #2-5 #2-6

35

C. Hot spot sequencing results: Patient#9

Sample

UNIST기호

ATM ATM ATM ATM ATM ATM ATM ATM

APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC APC

BRAF

CDH1 CDH1

CSF1R CSF1R

CTNNB1

EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR EGFR

ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4 ERBB4

FBXW7 FBXW7

FGFR2 FGFR2 FGFR2

FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3 FGFR3

FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3 FLT3

GNA11

HRAS HRAS HRAS HRAS HRAS HRAS HRAS HRAS HRAS HRAS HRAS HRAS

IDH2

KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR KDR

KIT

KRAS KRAS

MET MET

NRAS

NOTCH1 NOTCH1 NOTCH1 NOTCH1

NPM1

PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA PDGFRA

PIK3CA PIK3CA

PTEN PTEN PTEN PTEN

RB1 RB1 RB1

RET RET RET RET RET RET RET RET RET RET RET RET

SMAD4

SMARCB1

SMO SMO

STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11 STK11

TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53 TP53

VHL

#3_metastasi

s_DNA

#3_metastasi

s_WGA#3-8 #3-9 #3-10 #3-11 #3-12 #3-13

Variants

Gene ID

#3-14 #3-15 #3-pooled#3_Primary

FFPE#3-1 #3-2 #3-4 #3-5 #3-6 #3-7

36

요약 (국문초록)

전이 유방암 환자 혈액에서 상피세포 부착 분자 양성 단일세포의

체세포 돌연변이 분석을 통한 순환종양세포의 발견 가능성

배경: 전이성 유방암 환자에서 순환종양세포 집계는 항암요법의 예후예측에 도움을 준다.

그러나 순환종양세포의 희귀성과 이질성 때문에 정상 혈액세포에서 순환종양세포를 구

별해 내고 유전정보 분석을 하기가 쉽지 않다. 현재 활용중인 대부분의 순환종양세포

검출 시스템은 세포 검출 및 집계는 하지만 그 이후의 분석을 하지는 않고 있다. 이

연구를 통해 EpCAM 양성인 세포 중에서 단일 세포 채집과 타겟 시퀀싱을 통해 순환

종양세포의 검출할 수 있는지 그 가능성을 검토하기로 하였다.

방법: 새로 전이 진단을 받거나 고식적 치료 중에 질병 진행이 있었던 전이성 유방암환

자의 전혈을 사용하였다. IsoFlux Circulating Tumor Cell Enrichment Kit (Fluxion, South

San Francisco, CA, USA)을 사용하여 EpCAM 양성인 혼합세포군에서 순환종양세포 후보

를 채집하였다. 채집된 세포들 중 단일세포 단위에서 DNA를 증폭하여 타겟 시퀀싱을

Ion AmpliSeq™ Cancer Hotspot Panel (Life Technologies, Carlsbad, CA, USA)을 이용하여

유전자 분석을 시행하였다. 타겟 시퀀싱 분석서열은 BWA-MEM (0.7.10)을 이용해 human

genome reference (hg19)과 비교하였다. 단일 염기 변형체는 dbSNP, Human Variome

Project 0.2 and COSMIC databases을 통해 비교하였다.

결과: 총 11명의 환자에게서 172 EpCaM양성인 세포와 크기가 큰 세포가 채집이 되었다.

채집이 되지 않은 나머지 세포들은 각 환자 별로 따로 분리하여 한데 모았다. 목표 유

전자의 평균 리드 깊이 (read depth)는 13455ⅹ였다. 각 환자들에서 COSMIC 단일 염

기 변형체(SNV) database는 있지만 dbSNP and Variome Data 0.2는 없었던 돌연변이가

평균 8.55개 발견되었다. 단일세포에서 복수의 돌연변이가 발견되거나, 동일 환자의 한

세포에서 발견된 돌연변이가 그 환자의 다른 세포에서도 관찰될 때, 이 세포를 순환종

양세포로 추정하였다. 7 명의 환자에게서 각각 최소 2개 이상의 순환종양세포 후보가

발견되었으며 2명의 환자들에게서는 발견이 되지가 않았다. 후보 순환종양세포에서 발

견된 돌연변이는 ABL1, AKT1, APC, CDH1, CDKN2A, ERBB2, FGFR3, HRAS, IDH1, JAK2,

KDR, NPM1, RB1, RET, SMARCB1, STK11, and TP53 였다.

결론: 전이성 유방암 환자의 전혈에서 EpCAM 양성인 세포의 채집과 타겟 시퀀싱을 통

해 추정 순환종양세포를 성공적으로 분리해 낼 수 있었다. 다른 단일세포나 혼합샘플

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에서 검출되지 않은 독특한 돌연변이는 정상세포에서 추정 순환종양세포를 구별해 내

는데 사용될 수 있다. 또한 연구를 통해 CK 음성인 순환종양세포의 존재 가능성을 확

인할 수 있었다.

…………………………………………………………………………………………………

Keywords: Keywords: Breast neoplasm, Circulating tumor cell, Next generation

sequencing, Liquid biopsy

Student Number : 2015-23217