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의학박사 학위논문

Next Generation Sequencing Based Multi-Gene Mutation Analysis in

Patients with Myelodysplastic Syndrome and Idiopathic Cytopenia

of Undetermined Significance

골수형성이상증후군과 원인불명 혈구감소증 환자에서

차세대염기서열분석법을 이용한 다중 유전자 변이 분석

2015 년 2 월

서울대학교 대학원

임상의과학과

김 선 영

A thesis of the Degree of Doctor of Philosophy

골수형성이상증후군과 원인불명 혈구감소증 환자에서

차세대염기서열분석법을 이용한 다중 유전자 변이 분석

Next Generation Sequencing Based Multi-Gene Mutation Analysis in

Patients with Myelodysplastic Syndrome and Idiopathic Cytopenia

of Undetermined Significance

February 2015

The Department of Clinical Medical Sciences,

Seoul National University

College of Medicine

Seon Young Kim

ABSTRACT

Introduction: Myelodysplastic syndrome (MDS) is a heterogeneous group of

myeloid neoplasms characterized by cytopenias that are associated with

impaired hematopoietic differentiation, and have a risk of progression to acute

myeloid leukemia (AML). The development of high-throughput DNA

sequencing technologies has uncovered many important new somatic

mutations underlying the pathogenesis of MDS. Knowledge of the genetic

basis of MDS allows more accurate diagnoses and better treatment choices.

However, data on the genetic abnormalities of Korean MDS patients remain

scarce. The aim of this study was to investigate the mutational profiles of

Korean MDS patients using multi-gene panels based on next-generation

sequencing.

Methods: A total of 162 patients diagnosed with different MDS subtypes and

36 patients with idiopathic cytopenia of undetermined significance (ICUS),

were enrolled in this study. The MDS patients were diagnosed with the

following subtypes: 28 refractory cytopenia with unilineage dysplasia

(RCUD); 6 refractory anemia with ring sideroblasts (RARS); 29 refractory

cytopenia with multilineage dysplasia (RCMD); 64 refractory anemia with

excess blasts (RAEB); 14 MDS-unclassifiable (MDS-U); 5 myelodysplastic

syndrome/myeloproliferative neoplasm, unclassifiable (MDS/MPN), and 16

AML evolved from previous MDS (MDS-AML). Targeted sequencing of 87

selected genes was performed, and the putative mutations were analyzed

compared to a normal reference control population.

Results: Overall, 136/162 MDS patients (84.0%) and 25/36 (69.4%) ICUS

i

patients harbored at least one mutation. The 10 most frequently mutated genes

were ASXL1, TP53, U2AF1, DNMT3A, TET2, RUNX1, BCOR, FAT4, SRSF2,

and NOTCH1. The most frequently mutated genes in ICUS patients were

NFKBIE (mutated in 3 patients, 8.6%) and BCOR, BRD4, NOTCH1, and

SAMHD1 (each mutated in 2 patients, 5.7%). The most frequent target

functions of the mutated genes were transcription, RNA splicing, and DNA

methylation. Interestingly, novel mutations in FAT4 were frequently found in

our population. The number of mutations increased in patients with advanced

stages of MDS (RAEB or AML from previous MDS). The mutations were

associated with distinct cytogenetic abnormalities such as TP53 and del(5q).

When mutational profiles were incorporated into prognostic models, the

presence of NRAS, TP53, WT1, and LRP1B mutations were associated with

poor prognosis.

Conclusions: This is the first large-scale study on mutational profiles of

Korean MDS patients. Korean MDS patients presented with mutations in

previously reported MDS-related genes. However, there were significant

differences in the mutation distributions which may represent ethnic

differences. The molecular profiling of target genes can improve diagnostic

accuracy for ICUS and MDS and can assist better subclassification of

prognostic groups of MDS patients.

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

Keywords: Myelodysplastic syndrome, Next generation sequencing,

Somatic mutation

Student number: 2013-30820 ii

CONTENTS

Abstract ......................................................................................... i

Contents ....................................................................................... iv

List of tables and figures ............................................................ vi

List of abbreviations ................................................................... ix

1. Introduction ............................................................................. 1

2. Materials and Methods ......................................................... 10

2.1. Patients ............................................................................. 10

2.2. Bone marrow histopathologic examination .................. 14

2.3. Conventional cytogenetic analysis and FISH ............... 14

2.4. Targeted sequencing ........................................................ 15

2.5. Sequencing data processing and variant calling

process .............................................................................. 18

2.6. Statistical analysis ............................................................ 20

3. Results..................................................................................... 21

3.1. Summary of targeted sequencing................................... 21

3.2. Characteristics of mutational profiles ........................... 23

3.3. Clonal evolution patterns of genetic mutations in

iii

sequential samples .......................................................... 41

3.4. Usefulness of mutational analysis of the diagnosis of

challenging cases ............................................................. 44

3.5. Correlation of gene mutations and cytogenetic

lesions ............................................................................... 47

3.6. Development of prognosis model using mutational

profiles ............................................................................. 52

3.7. Prediction of treatment response ................................... 60

4. Discussion ............................................................................... 64

References .................................................................................. 70

Abstract in Korean .................................................................... 84

iv

LIST OF TABLES

Table 1 Baseline patient characteristics ...................................... 13

Table 2 Gene panel for targeted sequencing ............................... 16

Table 3 List of candidate variants detected in MDS patients ...... 33

Table 4 List of candidate variants detected in ICUS patients ..... 40

Table 5 Results of mutational analysis in patients for whom

the diagnosis of MDS was challenging ......................... 45

Table 6 Correlations among genes, cytogenetic abnormalities,

and pathways ................................................................. 50

Table 7 Multivariate Cox analysis of the overall survival and

progression-free survival among MDS patients ........... 59

Table 8 Patient characteristics according to response to

hypomethylating therapy ............................................... 61

Table 9 Association of the mutations with responses to

hypomethylating agents ................................................ 63

v

LIST OF FIGURES

Figure 1 Summary of data processing pipeline algorithms

used for variant calls of somatic mutations ................. 19

Figure 2 Comparison of allele frequency distributions

between candidate somatic mutations and known

database-registered SNPs ............................................ 22

Figure 3 Frequency of the mutations identified in the multi-

gene sequencing panel ................................................. 25

Figure 4 Distribution of the mutations in selected genes

among MDS patients. .................................................. 27

Figure 5 Number of mutations detected in patients with

different MDS subtypes .............................................. 28

Figure 6 Mutation diagrams for frequently mutated genes ......... 32

Figure 7 Variant allele fractions of mutated genes ...................... 42

Figure 8 Clonal evolution patterns in MDS patients .................. 43

Figure 9 Pairwise correlations among genes and cytogenetic

abnormalities ............................................................... 48

Figure 10 Pairwise correlations among involved pathways ....... 49

vi

Figure 11 Overall survival of MDS patients stratified

according to genetic mutations ................................... 54

Figure 12 Progression-free survival of MDS patients

stratified according to genetic mutations .................... 55

Figure 13 Kaplan-Meier survival curves comparing between

overall survival of patients with mutated and wild

type genes .................................................................... 56

Figure 14 Overall survival of MDS patients according to risk

groups .......................................................................... 57

Figure 15 Overall survival of MDS patients according to

gene mutation groups and IPSS-R risk groups ........... 58

vii

LIST OF ABBREVIATIONS

AML acute myeloid leukemia

BM bone marrow

FISH fluorescence in situ hybridization

ICUS idiopathic cytopenia of undetermined significance

IPSS International Prognostic Scoring System

IPSS-R idiopathic cytopenia of undetermined significance

MDS myelodysplastic syndrome

MDS-U myelodysplastic syndrome, unclassifiable

NGS next generation sequencing

PB peripheral blood

RA refractory anemia

RAEB refractory anemia with excess blasts

RARS refractory anemia with ring sideroblasts

RCMD refractory cytopenia with multilineage dysplasia

RCUD refractory cytopenia with unilineage dysplasia

WHO World Health Organization

viii

1. INTRODUCTION

Myelodysplastic syndrome (MDS) is a clonal disorder of hematopoietic cells

that is characterized by ineffective hematopoietic differentiation [1]. In MDS,

hematopoietic cells in the bone marrow (BM) and peripheral blood (PB) show

dysplastic morphologies and fail to produce functionally intact terminally

differentiated cells, thus resulting in cytopenias. In approximately 30% of

MDS patients, the disease ultimately transformed to acute myeloid leukemia

(AML), and comprise a subset of AML with a poor prognosis. Because of the

refractory cytopenias, substantial proportion of patients is transfusion

dependent. In a previous survey, of lower-risk MDS patients, 57% received

red blood cell (RBC) transfusions, and 37% received platelet transfusions; and

of high-risk MDS patients, 88% received RBC transfusions and 58% received

platelet transfusions [2, 3].

MDS includes idiopathic conditions in addition to secondary or therapy-

related disorders. For a large portion of patients with MDS, the exact

etiologies are unclear. However, increasing incidence of MDS patients with

advanced age suggests that an accumulation of genetic and epigenetic

mutations in hematopoietic progenitor cells is associated with disease

occurrence. The annual incidence of MDS in the Western countries is

approximately 5 per 100,000 persons in the general population. However, the

incidence increases to >20 per 100,000 persons among persons over the age of

70 [1]. MDS incidences in data from Korean National Cancer Registry were

1.7 per 100,000 persons per year for general population and 9.2 per 100,000

- 1 -

persons per year for patients over the age of 70. In addition, some rare genetic

disorders predispose patients to MDS; they include inherited BM failure

syndromes such as Fanconi anemia, and dyskeratosis congenita. It has long

been recognized that certain cytotoxic agents used for cancer treatment cause

DNA damage thus resulting in therapy-related MDS.

MDS is among the most challenging myeloid neoplasms to diagnose and

classify and it comprises a group of myeloid neoplasms that is heterogeneous

in presentation and pathogenesis [1, 4]. MDS is diagnosed based on

peripheral cytopenias, morphologic examination of BM dysplasia and

increase of blasts, and cytogenetics. The recommended cytopenia thresholds

for defining MDS are hemoglobin < 10 g/dL, absolute neutrophil count (ANC)

< 1.8×109/L, and platelet count < 100×109/L [4]. However, blood counts

above these thresholds do not exclude a diagnosis of MDS if definitive

dysplasia, an increase in blasts, and/or cytogenetic abnormalities are present

[5]. The morphological classification of MDS is based on the type and degree

of dysplasia, the presence of ring sideroblasts, and the percentage of blasts in

the BM and PB [4, 6]. To qualify as significant, the percentage of dysplastic

cells must reach ≥ 10% in one or more hematopoietic cell lineages [4, 5, 7]. A

threshold of 20% blasts in the PB or BM is used to distinguish MDS from

AML. Meanwhile, morphologic assessment of dysplasia may be subjective

and may depend on personal experiences and preferences. Previous studies

have revealed that inter-observer agreement in the assessment of dysplsia is

unsatisfactory in routine practice [8, 9]. In addition, assessment of the degree

of dysplasia may be difficult depending on the quality of the smear

- 2 -

preparations. In MDS with hypoplastic BM or fibrotic changes, assessment of

dysplasia may be impossible due to the difficulty in obtaining aspirated

samples of adequate quality. Therefore, it can be difficult to distinguish

between MDS and aplastic anemia or primary myelofibrosis [10, 11].

Moreover, morphologically dysplastic changes are reported to be present in

non-clonal cytopenias and in healthy subjects [7, 11, 12]. Secondary dysplasia

can be caused by nutritional deficiencies, toxins, medications, growth factor

therapy, inflammation or infection [6]. A diagnosis of MDS may be

particularly difficult when the disease is in the early stage and shows no

robust morphologic markers (such as ring sideroblasts) and no overt increase

in the percentage of blasts.

Even for patients for whom the diagnosis of MDS is clear, it may

nonetheless be difficult to subclassify MDS. According to the World Health

Organization (WHO) classification revised in 2008, MDS is classified into

refractory cytopenias with unilineage dysplasia (RCUD), refractory anemia

with ring sideroblasts (RARS), refractory cytopenia with multilineage

dysplasia (RCMD), refractory anemia with excess blasts-1 (RAEB-1), RAEB-

2, myelodysplastic syndrome-unclassified (MDS-U), and MDS associated

with isolated del(5q). The specific subclassification depends on the

characteristics of the cytopenias, dysplasias, the blast percentages in PB and

BM, and cytogenetic abnormalities [4]. RCUD presents as unicytopenia or

bicytopenia with unilineage dysplasia and includes refractory anemia (RA),

refractory neutropenia (RN), and refractory thrombocytopenia (RT). RARS is

characterized by anemia, dysplasia in erythroid lineage, and ≥ 15% ring

- 3 -

sideroblasts in BM erythroid precursors. RCMD is characterized by one or

more cytopenias combined with dysplasias in ≥ 2 myeloid lineages. RABE is

characterized by an increase of blasts, with 5-19% myeloblasts in the BM or

2-19% blasts in the PB. When an MDS patient presents with findings

incompatible with any of the subclassifications described above, the patient is

classified as MDS-U. For patients with unequivocal dysplasia in < 10% of the

cells in myeloid lineages and with no increase of blasts but with cytogenetic

abnormalities that are considered as presumptive evidence of MDS [4].

Cytogenetic abnormalities are observed in about half of MDS patients [13,

14]. Balanced translocations are very rare in MDS, while deletions or

amplifications of large chromosomal segments are frequently detected in

MDS patients by cytogenetic analyses such as G-banding karyotyping or

fluorescence in situ hybridization (FISH). The most common chromosomal

abnormalities in MDS is the deletion of chromosome 5, which is found in

about 15% of cases, del(7q)/monosomy 7 (about 10% of cases), and trisomy 8

(about 10% cases). Cytogenetic abnormalities are important as prognostic

markers as well as for the diagnosis of MDS. The International Prognostic

Scoring System (IPSS) which was developed by the International Working

Group (IWG) on MDS in 1997 has been the international standard for risk

assessment of MDS. Cytogenetics plays a decisive role in the IPSS scoring in

addition to blast counts and the degree of cytopenias [15]. In 2012, a more

comprehensive cytogenetic scoring system which uses 19 cytogenetic

categories was proposed [14], and the cytogenetic subgroups were included in

scoring of the revised IPSS (IPSS-R) [16].

- 4 -

When a patient has unexplained persistent cytopenia and when a diagnosis of

MDS cannot be established (as neither dysplasia in >10% of cells of the

myeloid lineage nor without cytogenetic abnormalities can be shown), this

condition can be termed “idiopathic cytopenia of undetermined significance

(ICUS)” [17-20]. In addition, BM dysplasia without definitive evidence of

MDS in the normal blood count or with only mild cytopenia is described as

idiopathic dysplasia of uncertain significance (IDUS) [18, 20]. When patients

with ICUS or IDUS are followed, some patients progress to overt MDS,

which indicates neoplastic conditions in at least some patients [20]. For a

diagnosis of ICUS, there should be substantial cytopenia that is recorded for

at least 6 months after diagnosis. A hematologic follow-up similar to that for

low-risk MDS should be performed because of the risk of developing MDS or

other myeloid malignancies. Because the number of reported cases of ICUS

was small, little is known about this condition [20]. In a few cases, clonal

abnormalities in hematopoietic cells have been detected using FISH or

molecular studies [17, 20, 21]. In some patients with ICUS, aberrant myeloid

cell phenotypes have been detected by flow cytometry [22].

Since the introduction of massively parallel next generation sequencing

(NGS) technology, the genetic mutations underlying MDS have been rapidly

uncovered [23]. The NGS technologies has made high-throughput and

multiplexed sequencing of whole genomes, whole exomes, or specific panels

of genes possible. The genetic abnormalities of MDS were explored using

whole genome or whole exome technology [24-26]. Recently, studies on

large-scaled MDS patients have been performed using multi-gene panels

- 5 -

targeting frequently mutated genes [27-29]. The genes that are typically

mutated in MDS can be classified according to the functions of the proteins

they encode [30]. The commonly mutated class of genes in MDS encodes

splicing factors (SF3B1, U2AF1, ZRSR2, SRSF2, SF3A1, PRPF40B, U2AF2)

[24, 25, 31], DNA methylation (TET2, DNMT3A, IDH1/2), chromatin

modification (ASXL1, ATRX, and EZH2) [32-40], transcription regulation

(RUNX1, and ETV6) [34, 41], tyrosine kinase signaling (JAK2, NRAS/KRAS,

PTPN11, MPL, and CBL/CBLB) [42-46], DNA repair (TP53) [47, 48], and

components of the cohesion complex (STAG2) [49]. In previous studies,

mutations were detected in about 70% of MDS patients. Most frequent

mutations were detected up to approximately 20% of MDS, with majority of

mutations with low incidence [50]. Many of MDS patients typically have 2 to

3 driver oncogenic mutations at presentation, and many background mutations

[51]. Frequently mutated genes in MDS are also frequently mutated in other

myeloid neoplasms, including AML, myeloproliferative neoplasms (MPN),

and MDS/MPN overlapping syndromes, such as chronic myelomonocytie

leukemia (CMML), with different frequencies of specific genes [51, 52].

Some somatic mutations present close association with MDS phenotypes,

such as the association of the SF3B1 mutation with RARS [53]. Recent

studies in genetic changes on MDS have demonstrated an important role of

components of splicing machinery in the pathogenesis of MDS [50].

Splicesome component mutations were found to be an notably common (45%-

85%) group of mutated genes in MDS, and they occurred in a mutually

exclusive manner [23]. Different mutations of spliceosome components seem

- 6 -

to have different effects on MDS phenotypes. SF3B1 mutations were found to

be present in 10-20% of MDS patients, while this mutation is extremely

frequent in RARS [23]. U2AF1 mutations were detected in approximately

5%-10% of MDS, with slightly increased occurrence in higher-risk MDS [23,

31]. SRSF2 is frequently mutated n CMML (28%-47%), and less frequently in

MDS (10-13%) [23]. Genes involved in epigenetic regulations are frequently

mutated in MDS. TET2 plays as a major role in DNA methylation and

epigenetic gene regulation. TET2 is frequently affected by uniparental disomy

and microdeletions, and frequently mutated in various myeloid neoplasms [32,

33]. The mutation frequency is reported to be 15-27% in MDS and up to 44%

in CMML [23]. Somatic mutations in specific genes, such as TP53, RUNX1,

and NRAS were reported to be associated with adverse clinical features,

including increase of BM blast percentages and severe thrombocytopenia [34].

The mutations of TP53 gene occur in more frequently in higher-risk MDS,

and are associated with a worse outcome, in addition to NRAS, EZH2, ETV6,

and ASXL1 [34, 48, 54]. The identification of characteristic mutations can be

helpful in the diagnosis, and can also predict prognosis, which may improve

the current prognosis assessing systems. The studies on biology of MDS have

revealed that, multiple chromosomal and molecular abnormalities occur

before MDS becomes clinically apparent. MDS progresses from lower- to

higher-risk subtypes, and eventually to AML [3]. With recent studies on

molecular events during MDS to secondary AML, clonal evolution patterns of

MDS have been revealed [26].

MDS patients are grouped into those with lower-risk and higher-risk on the

- 7 -

basis of WHO classification and IPSS score. Patients with lower-risk MDS

may be observed without treatment or may be treated with erythropoiesis-

stimulating agents, lenalidomide, or hypomethylating agents (azacitidine and

decitabine). Higher-risk MDS patients have a median life expectancy of < 2

years, therefore, they are immediately treated with hypomethylating agents,

cytotoxic chemotherapy, or allogeneic hematopoietic stem cell transplantation.

Hypomethylating agents are shown to prolong overall survival in higher-risk

MDS, and to improve blood counts. Meanwhile, only about 40% to 50% of

patients treated with either azacitidine or decitabine respond to these agents,

and complete responses occur in only 10% to 15% of treated patients [55].

Current studies presented that genetic mutations were associated with

responses to the hypomethylating agents. Patients with TET2 mutations

presented increased responses for hypomethylating agents [55].

Previous studies on the phenotypes and cytogenetics of Asian patients with

MDS found differences in the clinical presentations and chromosomal

abnormalities of Asian and Western MDS patients. The median age of the

diagnosis was 60 years, which is about 10 years younger than that of the

reported in the Western patients with median ages of around 70 years [56].

The survival of Asian patients is reported to be shorter compared with that of

Western patients [57]. In addition, the prognostic factors of Asian people are

somewhat different from Western people. The quantitative blood counts were

less significant prognostic factors in Asian people rather the granulocytic

dysplasia were prognostic factors predicting overall survival [57]. The genetic

events that lead to MDS are heterogeneous and racial differences may exist.

- 8 -

However, data on the genetic abnormalities of Korean MDS patients remain

scarce. Therefore, the aim of this study is to investigate the mutational profiles

of Korean MDS patients using a multi-gene panel based on NGS technology.

- 9 -

2. MATERIALS AND METHODS

2.1. Patients

A total of 162 patients diagnosed with different MDS subtypes and 36 patients

with ICUS were enrolled in this study. BM samples from the patients were

sent for diagnosis to Seoul National University Hospital between November

2004 and January 2014. The patients were diagnosed according to the 2008

WHO classification criteria [4]. The MDS patients were diagnosed with the

following subtypes: 28 RCUD (24 RA, 2 RT, and 2 RN), 6 RARS, 29 RCMD,

22 RAEB-1, 42 RAEB-2, 14 MDS-U, 16 AML evolved from previous MDS

(MDS-AML) and 5 myelodysplastic syndomre/myeloproliferative neoplasm,

unclassifiable (MDS/MPN). BM cells were obtained for further analysis at

various times during the patients’ follow-up periods. In total, BM cells from

127 patients (77.9%) were obtained at the initial diagnostic work-up or during

the follow-up period without any treatment. When a patient underwent

repeated BM testing due to undetermined diagnosis at first BM testing and if

the patient was diagnosed as MDS in repeated BM testing, the patient was

classified into the MDS group according to the final diagnosis. The ICUS

group in this study was composed of patients who never diagnosed as MDS

up to the last follow-up time. The following laboratory and clinical data were

obtained for each patient: dates of diagnosis and therapy initiation, age, sex,

peripheral blast counts, hemoglobin level, platelet counts, WBC counts, ANC

counts, and conventional G-banding cytogenetic results. For MDS patients,

the IPSS [15] and IPSS-R risk categorizations were assessed as previously

- 10 -

described [16]. Among MDS patients, 86 patients received treatment with

DNA hypomethylating agents (azacitidine for 49 patients and decitabine for

31 patients, and 3 patients received decitabine after azacitidine therapy). In

addition, 42 patients underwent allogeneic hematopoietic stem cell

transplantation. Treatment responses were assessed according to the IWG

response criteria revised in 2006 [58]. Among patients who underwent

treatment with hypomethylating agents and for whom response assessment

was possible, patients who achieved a complete response (CR), marrow

complete response (mCR), partial response (PR), or hematologic

improvement (HI) were considered as responders. Otherwise, patients who

presented with stable disease (SD) or progressive disease (PD) were classified

as non-responders. This study complied with the Declaration of Helsinki. All

BM samples were collected with informed consent, and the study was

reviewed and approved by the Institutional Review Board (IRB) of Seoul

National University College of Medicine (IRB No. 1311-091-535).

- 11 -

Table 1. Baseline patient characteristics

Parameter MDS (n = 162) ICUS (n = 36)

Sampling time

Without treatment 127 (77.9) 36 (100)

After chemotherapy or hypomethylating therapy 36 (22.1) 0

Male/Female (% male) 101/61 (62.4) 13/23 (36.1)

Age (year) 63 (17-86) 66 (31-82)

< 60 67 (41.4) 14 (38.9)

≥ 60 95 (58.6) 22 (61.1)

MDS subtype (WHO, 2008)

RCUD 28 (17.3) 0

RA 24 (14.8) 0

RT 2 (1.2) 0

RN 2 (1.2) 0

RARS 6 (3.7) 0

RCMD 29 (17.9) 0

RAEB-1 22 (13.6) 0

RAEB-2 42 (25.9) 0

MDS-U 14 (8.6) 0

MDS/MPN 5 (3.1) 0

MDS-AML 16 (9.9) 0

Cytogenetics

Normal or –Y only 73 (45.1) 36 (100)

Complex karyotype 34 (21.0) 0

Other abnormalities 55 (34.0) 0

del(5q)/ –5 26 (16.1) 0

del(7q)/ –7 27 (16.7) 0

Trisomy 8 25 (15.4) 0

del(20q) 15 (9.3) 0

Cytogenetic risk group

Very good 10 (6.2) 0

Good 76 (46.9) 36 (100)

Intermediate 38 (23.5) 0

- 12 -

Poor 14 (8.6) 0

Very poor 24 (14.8) 0

IPSS risk group

Low 17 (10.5) NA

Intermediate-1 73 (45.1) NA

Intermediate-2 50 (30.9) NA

High 22 (13.6) NA

IPSS-R risk group

Very low 7 (4.3) NA

Low 34 (21.0) NA

Intermediate 40 (24.7) NA

High 45 (27.8) NA

Very high 36 (22.2) NA

Treatment

No treatment 45 (27.8) 36 (100)

Azacitidine 49 (30.1) 0

Decitabine 31 (19.0) 0

Azacitidine and decitabine 3 (1.8) 0

Transplantation 42 (25.9) 0

Outcome

Follow-up months 19.3 (0.2-217.7) 23.4 (12.5-186.9)

Leukemic transformation 54 (33.3) 0

Expired 106 (65.4) 6 (16.7) Data are presented as the median (range) for continuous variables and the number of cases

(percentage) for categorical variables unless otherwise indicated.

Abbreviations: AML, acute myeloid leukemia; BM, bone marrow; ICUS, idiopathic cytopenia

of undetermined significance; IPSS, International Prognostic Scoring System; IPSS-R,

International Prognostic Scoring System, revised; MDS, myelodysplastic syndrome;

MDS/MPN, myelodysplastic syndrome/myeloproliferative neoplasm; MDS-U, myelodysplastic

syndrome, unclassifiable; NA, not available; RA, refractory anemia; RAEB, refractory anemia

with excess blasts; RARS, refractory anemia with ring sideroblasts; RCMD, refractory

cytopenia with multilineage dysplasia; RCUD, refractory cytopenia with unilineage dysplasia;

RN, refractory neutropenia; RT, refractory thrombocytopenia; WHO, World Health

Organization

- 13 -

2.2. Bone marrow histopathological examination

Hematopathologists reviewed the Wright-stained BM smears and the

hematoxylin and eosin (H&E)-stained sections of BM trephine biopsies. For

all patients, immunohistochemical (IHC) staining was performed to detect

reticulin, collagen, CD34, CD117, and CD61 in BM sections (all antibodies

from Dako, Glostrup, Denmark).

2.3. Conventional cytogenetic and FISH analysis

Cytogenetic studies using standard G-banding techniques on heparinized BM

samples were performed as part of the diagnostic work-up. At least 20

metaphases were analyzed whenever possible. Clonal abnormalities were

defined as two or more cells with the same chromosomal gain or structural

rearrangement or at least three cells with the same chromosomal deletion.

Karyotypes were recorded according to the International System for Human

Cytogenetic Nomenclature (ISCN) 2013 [59].

Fluorescence in situ hybridization (FISH) was performed for most cases (n =

158). The following commercial FISH probes were used: LSI EGR1 (5q31)

probe, LSI D7S522 (7q31) probe, CEP 8 probe, BCR/ABL dual-color, dual

translocation probe, LSI D20S108 (20q12) probe, and LSI 1p32/1q25 probe

(all from Abbott Molecular, Des Plaines, IL, USA). The slides were stained

with the FISH probes and were counter-stained with DAPI. Tthe fluorescence

signals were analyzed by fluorescent microscopy (Zeiss, Göttingen, Germany).

The FISH results were recorded according to the ISCN 2013 [59].

- 14 -

2.4. Targeted sequencing

We selected 87 genes for targeted sequencing (Table 2). The panel included

either of genes that were published as mutational targets in MDS (n = 49) [27-

29] or genes that were found to be mutated in other myeloid and lymphoid

malignancies (n = 11 and n = 27) [60-64]. To construct a sequencing library,

genomic DNA (gDNA) was extracted from BM cells that had been separated

from the buffy coat of BM aspirates. The aspirates had been drawn and

then placed into tubes that were anticoagulated with sodium

ethylenediaminetetraacetate (EDTA). After RBCs were lysed and the samles

were washed, the cell pellets were resuspended in RNAlater solution and

stored at −80°C until genetic analysis. Total gDNA was extracted using the

QIAamp DNA Blood Mini Kit (Qiagen, Valencia, CA, USA), according to

the manufacturer’s instructions. gDNA shearing, standard library production

and hybridization were performed by Celemics Inc. (Seoul, Korea). The final

gDNA quality was assessed by analyzing the samples with the Agilent 2200

TapeStation System (Agilent, Santa Clara, CA, USA). We prepared and

sequenced a total target length of 259 kb region using the paired-end 150 bp

rapid-run sequencing mode on an Illumina Hiseq 2500 platform (Illumina,

San Diego, CA, USA) following the manufacturer’s instructions.

- 15 -

Table 2. Gene panel for targeted sequencing

Genes NCBI Id. Position Pathway/Ontology ASXL1 171023 20q11.1 Chromatin modification ATM 472 11q22.3 DNA repair ATRX 546 Xq21.1 Chromatin modification BARD1 580 2q35 DNA repair BCOR 54880 Xp11.14 Transcription BIRC3 330 11q22.2 Receptor/Kinases BRAF 673 7q34 RAS pathway BRCC3 79184 Xq28 DNA repair BRD2 6046 6p21.3 Transcription BRD4 23476 19p13.1 Other CARD6 84674 5p13.1 Other CBL 867 11q23.3 RAS pathway CCND1 595 11q13.3 Cell cycle CDKN2A 1029 9p21 Cell cycle CEBPA 1050 19q13.1 Transcription CHD2 1106 15q26.1 Other CSF1R 1436 5q32 Receptor/Kinases CSF3R 1441 1p34.3 Receptor/Kinases DAP3 7818 1q22 Other DDX3X 1654 Xp11.4 Other DIS3 22894 13q22.1 Other DNMT3A 1788 2p23 DNA methylation EEF1E1 9521 6p24.3 Other EGR2 1959 10q21.3 Transcription ETV6 2120 12p13.2 Transcription EZH2 2146 7q35-36 Chromatin modification FAM46C 54855 1p12 Other FAT4 79633 4q28.1 Other FBXW7 55294 4q31.3 Receptor/Kinases FLT3 2322 13q12 Receptor/Kinases GATA1 2623 Xp11.23 Transcription GATA2 2624 3q21.3 Transcription HIST1H1E 3008 6p22.2 Other IDH1 3417 2q33.3 DNA methylation IDH2 3418 15q26.1 DNA methylation IKZF1 10320 7p13 Transcription ITPKB 3707 1q42.12 Signaling JAK2 3717 9p24 Receptor/Kinases KIAA0355 9710 19q13.11 Other KIT 3815 4q12 Receptor/Kinases KLHL6 89857 3q27.1 Other KRAS 3845 12p12.1 RAS pathway LAMB4 22798 7q31.1 Other LRP1B 53353 2q21.2 Other MAPK1 5594 22q11.22 Signal/Kinase

- 16 -

MED12 9968 Xq13.1 Other MPL 4352 1p34.2 Receptor/Kinases MYD88 4615 3p22.2 Signaling NF1 4763 17q11.2 RAS pathway NFKBIE 4794 6p21.1 Other NOTCH1 4851 9q34.3 Receptor/Kinases NPM1 4869 5q35 Transcription NRAS 4893 1p13.2 RAS pathway PHF6 84295 Xq26.2 Transcription PLEKHG5 57449 1p36.31 Other POLG 5428 15q25 Other POT1 25913 7q31.33 Other PRKD3 23683 2p22.2 Signaling PRPF40B 25766 12q13.12 Splicing PTEN 5728 10q23.3 Other PTPN11 5781 12q24.1 RAS pathway RAD21 5885 8q24.11 Cohesin RB1 5925 13q14 Cell cycle RIPK1 8737 6p25.2 Other RUNX1 861 21q22.3 Transcription SAMHD1 25939 20q11.23 Other SCRIB 23513 8q24.3 Other SETBP1 26040 18q12.3 Other SF1 7536 11q13.1 Splicing SF3A1 10291 22q12.2 Splicing SF3B1 23451 2q33.1 Splicing SH2B3 10019 12q24.12 Signaling SMARCA2 6595 9p24.3 Other SMC1A 8243 Xp11.22 Cohesin SMC3 9126 10q25.2 Cohesin SRSF2 6427 17q25.1 Splicing STAG2 10735 Xq25 Cohesin TCF12 6938 15q21.3 Transcription TET2 54790 4q24 DNA methylation TGM7 116179 15q15.2 Other TP53 7157 17p13.1 Transcription U2AF1 7307 21q22.3 Splicing U2AF2 11338 19q13.42 Splicing WT1 7490 11p13 Transcription XPO1 7514 2p15 Other ZMYM3 9203 Xq13.1 Other ZRSR2 8233 Xp22.1 Splicing

- 17 -

2.5. Sequencing data processing and variant calling process

A schematic diagram describing the data processing pipeline and the

algorithm used for variant calling of somatic mutations is shown in Figure 1.

The raw data were mapped to the reference genome (hg19) with the Burrows-

Wheeler Aligner (BWA, v0.62). PCR duplicate reads were removed using

Picard 1.98 and variants were called using “UnifiedGenotyper” in GATK 2.7-

2. Variants flagged as “LowQual” and low depth (<10) were excluded. We

annotated variants with ANNOVAR. We selected candidate mutations

according to the criteria described in Figure 1. All synonymous variants were

discarded. Missense SNVs that were reported in public databases including

dbSNP, ESP6500, and the 1000 genomes project with a frequency of greater 0.

5% were filtered as polymorphisms. In addition, an in-house database of

Korean polymorphisms from the sequence data of 273 normal Koreans was

used as a private database to filter polymorphisms. We filtered out SNVs that

were found in more than 2 persons in the Korean database (allele fraction ≥ 0.

2%). Among the filtered SNVs with low depth, we rescued SNV variants, that

are known from previous MDS studies to be hotspot mutations [27, 29], using

the criterion of > 5 total reads with supporting reads. Mapping errors were

confirmed by visual inspection with the IGV browser.

- 18 -

.fastq data from HiSeq 2500

Processing and alignment to human reference genome (hg19) using BWA

Remove duplicate reads using Picard MarkDuplicates

Eliminate low-quality data using the following criteria: • Reads with < 10 mapping quality • Bases with < 20 base quality

Detect SNV and indel information

Eliminate low-quality data using the following criteria: • Reads with < 10 mapping quality • Variants with < 0.01 allele frequency

Remove synonymous SNVs

Remove known missense SNVs registered in the following databases: • dbSNP • 1000 genome (2012/04) with ≥ 0.005 allele frequency • ESP 6500 with ≥ 0.005 allele frequency • In-house Korean Exome database with ≥ 0.002 allele frequency

Rescue candidate mutations registered in COSMIC V60 database

Remove ambiguous missense SNVs predicted to be “benign” by Polyphen

Figure 1. Summary of data-processing pipeline algorithm used for variant

calls of somatic mutations

- 19 -

2.6. Statistic analysis

The χ2 test and Fisher’s exact test were used to compare categorical variables.

Pairwise correlations between gene mutations and cytogenetic lesions were

evaluated using Fisher’ exact test for all pairs of mutated genes and frequent

cytogenetic abnormalities. The results were adjusted for multiple testing using

the Benjamini-Hochberg method. Progression-free survival (PFS) was

calculated from date of diagnosis to date of progression to AML, relapse, or

death. Overall survival (OS) was calculated from date of diagnosis to date of

death from any cause. Survival curves for PFS and OS were constructed using

the Kaplan-Meier method, and differences among survival curves were

analyzed using the log-rank test. Cox proportional hazards regression analysis

was used to develop a multivariate model of prognostic factors when

considering those factors that were associated with survival. Statistical

analyses were performed using SPSS version 17.0 (SPSS Inc., Chicago, IL,

USA). P values < 0.05 were considered statistically significant.

- 20 -

3. RESULTS

3.1. Summary of targeted sequencing

The mean depth of the sequencing data for the 87 target genes was 185 reads

(range, 50-478 reads) across all samples (n = 198). More than 95% of the

target sequences (258,861 nucleotides covering the exons of 87 genes) were

covered with > 20 independent reads. After exclusion of sequencing errors

and known or possible polymorphisms, a total of 329 single nucleotide

variants (SNVs) and insertion/deletions (indels) were found in 74 genes in

MDS patients, and 22 SNVs and indels were found in 28 genes as probable

somatic genetic changes in ICUS patients (Figure 2). Twenty-four of the 87

target genes (27.6%) were mutated in more than 5 MDS patients (>3% of

MDS patients), and 5 of the 87 genes (5.8%) were mutated in more than 2

ICUS patients (>5% of ICUS patients).

- 21 -

A

B

Figure 2. Comparison of allele frequency distributions between candidate

somatic mutations and known database-registered SNPs in (A) 162 MDS

patients and (B) 36 ICUS patients

- 22 -

3.2. Characteristics of mutation profiles

A total of 136 of the 162 MDS patients (84.0%) and 25 of the 36 ICUS

patients (69.4%) harbored at least one mutation. A median of 2 mutations

(range 0-8) were detected per sample in MDS patients, and a median of 1

mutation (range, 0-3) was detected per sample in ICUS patients. The 10 most

frequently mutated genes in MDS patients were ASXL1, TP53, U2AF1,

DNMT3A, TET2, RUNX1, BCOR, FAT4, SRSF2, and NOTCH1 (Figure 3A).

The most frequently mutated genes in ICUS patients were NFKBIE (mutated

in 3 patients, 8.6%), and BCOR, BRD4, NOTCH1, and SAMHD1 (each

mutated in 2 patients, 5.7%) (Figure 3B). The most frequent target functions

of the mutated genes were DNA methylation (ASXL1), DNA repair (TP53),

and RNA splicing (Figure 3C). The mutated genes were classified according

to their functional categories, including RNA splicing, DNA methylation,

chromatin modification, transcription, receptor/kinase, RAS pathway, other

signaling, DNA replication cycle, and cohesion pathway. Among these

functional groups, the most frequently mutated genes were involved in

transcription (observed in 42% of MDS patients). In addition, genes

associated with receptor/kinases, splicing, chromatin modification, and DNA

methylation were frequently mutated (Figure 3 and 4). The number of

mutations was associated with MDS subtypes. A greater number of mutations

were observed in patients with RAEB and AML that had evolved from

previous MDS. Lower number of gene mutations were found in RCUD

patients (Figure 5).

- 23 -

Many of the mutated genes have been previously implicated in MDS. The

specific mutation sites previously reported to be frequently involved in MDS

were also frequently mutated in this study. The most frequently observed

mutation was ASXL1 p.G646fs*12 mutation (n = 10) (Figure 6). In addition,

mutations were frequently found in newly identified genes which were noted

in other hematologic malignancies and in other cancers, including FAT4,

NOTCH1, SETBP1, and, ATM (Tables 3 and 4).

- 24 -

A

- 25 -

B

C

Figure 3. Frequency of the mutations identified in the multi-gene sequencing

panel. (A) Frequencies of mutated genes in 162 MDS patients, (B)

frequencies of mutated genes in 36 ICUS patients, and (C) proportion of

involved pathways according to MDS subtypes.

- 26 -

A

B

Figure 4. Distribution of the (A) mutations in selected genes and (B) involved

pathways among 162 MDS patients.

- 27 -

Figure 5. Number of mutations detected in patients with different MDS

subtypes

- 28 -

A

B

C

- 29 -

D

E

F

- 30 -

G

H

I

- 31 -

J

K

L

Figure 6. Mutation diagrams for frequently mutated genes in MDS patients.

Genes are include (A) ASXL1, (B) TP53, (C) TET2, (D) U2AF1, (E) DNMT3A,

(F) BCOR, (G) RUNX1, (H) FAT4, (I) NOTCH1, (J) SRSF2, (K) LRP1B, and

(L) SF3B1.

- 32 -

Table 3. List of candidate variants detected in 162 patients with

myelodysplastic syndrome

Pat ID MDS type Chr Start position

Gene Type of mutation

Amino acid changes

P044 RCMD 1 6528506 PLEKHG5 missense NM_001265592:exon21:c.C2627T:p.T876M P130 RARS 1 6530351 PLEKHG5 missense NM_001265592:exon17:c.C1982T:p.T661M P005 AML 1 6531618 PLEKHG5 missense NM_001265592:exon13:c.C1448G:p.P483R P108 RARS 1 6534132 PLEKHG5 missense NM_001265592:exon8:c.G769A:p.G257R P126 MDSMPN 1 6557043 PLEKHG5 missense NM_001265592:exon1:c.49G>C: P126 MDSMPN 1 6557043 PLEKHG5 missense NM_001265592:exon1:c.49G>C: P047 RAEB 1 36933434 CSF3R missense NM_000760:exon14:c.C1853T:p.T618I P101 RAEB 1 36933434 CSF3R missense NM_000760:exon14:c.C1853T:p.T618I P101 RAEB 1 36933434 CSF3R missense NM_000760:exon14:c.C1853T:p.T618I P102 RCMD 1 36933434 CSF3R missense NM_000760:exon14:c.C1853T:p.T618I P034 RARS 1 36937223 CSF3R missense NM_000760:exon10:c.G1096A:p.G366R P160 RCUD 1 36937223 CSF3R missense NM_000760:exon10:c.G1096A:p.G366R P059 AML 1 43815009 MPL missense NM_005373:exon10:c.G1544C:p.W515S P098 RCUD 1 43818306 MPL missense NM_005373:exon12:c.T1771G:p.Y591D P002 RCUD 1 43818307 MPL missense NM_005373:exon12:c.A1772G:p.Y591C P002 RCUD 1 43818307 MPL missense NM_005373:exon12:c.A1772G:p.Y591C P060 RAEB 1 43818309 MPL nonsense NM_005373:exon12:c.C1774T:p.R592X P137 RAEB 1 115256529 NRAS missense NM_002524:exon3:c.A182G:p.Q61R P007 AML 1 115258747 NRAS missense NM_002524:exon2:c.G35A:p.G12D P052 RAEB 1 115258747 NRAS missense NM_002524:exon2:c.G35A:p.G12D P102 RCMD 1 115258747 NRAS missense NM_002524:exon2:c.G35A:p.G12D P004 RAEB 1 115258748 NRAS missense NM_002524:exon2:c.G34T:p.G12C P024 RAEB 1 155695233 DAP3 missense NM_001199850:exon4:c.T229G:p.Y77D P142 AML 1 226836415 ITPKB missense NM_002221:exon3:c.T1990C:p.F664L P086 RCMD 1 226924961 ITPKB missense NM_002221:exon2:c.G199A:p.E67K P087 RCUD 1 226924961 ITPKB missense NM_002221:exon2:c.G199A:p.E67K P011 MDSU 1 226924982 ITPKB missense NM_002221:exon2:c.C178G:p.P60A P085 RCUD 1 226924982 ITPKB missense NM_002221:exon2:c.C178G:p.P60A P120 AML 1 226924982 ITPKB missense NM_002221:exon2:c.C178G:p.P60A P157 RCMD 1 226924982 ITPKB missense NM_002221:exon2:c.C178G:p.P60A P057 RCUD 1 226925011 ITPKB missense NM_002221:exon2:c.C149T:p.P50L P119 RAEB 2 25457180 DNMT3A missense NM_175629:exon23:c.G2707A:p.A903T P007 AML 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P024 RAEB 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P045 RCUD 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645C:p.R882P P048 RAEB 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P070 RAEB 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P070 RAEB 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P152 AML 2 25457242 DNMT3A missense NM_175629:exon23:c.G2645A:p.R882H P147 RAEB 2 25462024 DNMT3A missense NM_175629:exon20:c.T2383C:p.W795R P100 RCMD 2 25463248 DNMT3A missense NM_175629:exon19:c.C2245T:p.R749C P040 RCUD 2 25463307 DNMT3A missense NM_175629:exon19:c.G2186A:p.R729Q P057 RCUD 2 25464576 DNMT3A missense NM_175629:exon17:c.G1937A:p.G646E P149 RAEB 2 25467449 DNMT3A missense NM_175629:exon14:c.G1627T:p.G543C P149 RAEB 2 25467449 DNMT3A missense NM_175629:exon14:c.G1627T:p.G543C P016 RCMD 2 25471064 DNMT3A frameshift NM_175629:exon7:c.697delC:p.P233fs P085 RCUD 2 25536826 DNMT3A missense NM_175629:exon2:c.G28A:p.G10R P016 RCMD 2 37513472 PRKD3 missense NM_005813:exon5:c.G758T:p.W253L P078 RAEB 2 141055448 LRP1B missense NM_018557:exon84:c.G12896A:p.C4299Y P112 RAEB 2 141130580 LRP1B missense NM_018557:exon69:c.C10765T:p.P3589S P152 AML 2 141274484 LRP1B missense NM_018557:exon50:c.A8123T:p.E2708V P118 RCMD 2 141291688 LRP1B missense NM_018557:exon47:c.G7664C:p.G2555A P057 RCUD 2 141356349 LRP1B missense NM_018557:exon43:c.C7045G:p.L2349V P036 RCUD 2 141458125 LRP1B missense NM_018557:exon41:c.C6493T:p.R2165W P137 RAEB 2 141458125 LRP1B missense NM_018557:exon41:c.C6493T:p.R2165W

- 33 -

P137 RAEB 2 141458125 LRP1B missense NM_018557:exon41:c.C6493T:p.R2165W P093 AML 2 141607712 LRP1B missense NM_018557:exon29:c.C4898G:p.A1633G P065 RAEB 2 141625795 LRP1B missense NM_018557:exon26:c.C4207T:p.R1403C P047 RAEB 2 141660510 LRP1B missense NM_018557:exon23:c.G3745A:p.G1249S P136 RCMD 2 198265476 SF3B1 missense NM_012433:exon18:c.A2681T:p.D894V P136 RCMD 2 198265476 SF3B1 missense NM_012433:exon18:c.A2681T:p.D894V P004 RAEB 2 198266834 SF3B1 missense NM_012433:exon15:c.A2098G:p.K700E P016 RCMD 2 198266834 SF3B1 missense NM_012433:exon15:c.A2098G:p.K700E P114 RCMD 2 198266834 SF3B1 missense NM_012433:exon15:c.A2098G:p.K700E P144 RCUD 2 198266834 SF3B1 missense NM_012433:exon15:c.A2098G:p.K700E P111 RCUD 2 198267371 SF3B1 missense NM_012433:exon14:c.C1986A:p.H662Q P075 AML 2 209113112 IDH1 missense NM_005896:exon4:c.G395A:p.R132H P105 RCUD 2 209113112 IDH1 missense NM_005896:exon4:c.G395A:p.R132H P095 AML 2 209113113 IDH1 missense NM_005896:exon4:c.C394T:p.R132C P158 RCMD 2 215593494 BARD1 missense NM_000465:exon11:c.C2240T:p.P747L P195 MDSU 3 38180217 MYD88 missense NM_002468:exon1:c.G65C:p.G22A P119 RAEB 3 128200145 GATA2 missense NM_032638:exon6:c.C1160T:p.T387I P120 AML 3 183225953 KLHL6 missense NM_130446:exon3:c.C803T:p.P268L P101 RAEB 4 55599320 KIT missense NM_000222:exon17:c.G2446T:p.D816Y P005 AML 4 55599321 KIT missense NM_000222:exon17:c.A2447T:p.D816V P071 AML 4 55604658 KIT missense NM_000222:exon21:c.C2866T:p.R956W P124 RCMD 4 106155688 TET2 frameshift NM_001127208:exon3:c.589_589delinsAT: P124 RCMD 4 106155688 TET2 frameshift NM_001127208:exon3:c.589_589delinsAT: P110 MDSU 4 106155931 TET2 nonsense NM_001127208:exon3:c.C832T:p.Q278X P054 RCUD 4 106156895 TET2 missense NM_001127208:exon3:c.A1796G:p.Q599R P054 RCUD 4 106156895 TET2 missense NM_001127208:exon3:c.A1796G:p.Q599R P038 RAEB 4 106157692 TET2 frameshift NM_001127208:exon3:c.2593_2593delinsAT: P075 AML 4 106158043 TET2 nonsense NM_001127208:exon3:c.A2944T:p.K982X P120 AML 4 106158043 TET2 nonsense NM_001127208:exon3:c.A2944T:p.K982X P075 AML 4 106158173 TET2 missense NM_001127208:exon3:c.T3074G:p.I1025S P075 AML 4 106158344 TET2 missense NM_001127208:exon3:c.A3245T:p.E1082V P117 RAEB 4 106164773 TET2 missense NM_001127208:exon6:c.G3641A:p.R1214Q P153 RCMD 4 106164778 TET2 nonsense NM_001127208:exon6:c.C3646T:p.R1216X P155 RAEB 4 106180777 TET2 nonsense NM_001127208:exon7:c.A3805T:p.R1269X P014 RCUD 4 106190824 TET2 missense NM_001127208:exon9:c.T4102G:p.F1368V P016 RCMD 4 106190860 TET2 missense NM_001127208:exon9:c.C4138T:p.H1380Y P111 RCUD 4 106193861 TET2 frameshift NM_001127208:exon10:c.4323_4323delinsTG: P011 MDSU 4 106193892 TET2 nonsense NM_001127208:exon10:c.C4354T:p.R1452X P026 RAEB 4 126238810 FAT4 missense NM_024582:exon1:c.C1244G:p.P415R P113 RAEB 4 126238810 FAT4 missense NM_024582:exon1:c.C1244G:p.P415R P113 RAEB 4 126238810 FAT4 missense NM_024582:exon1:c.C1244G:p.P415R P153 RCMD 4 126238810 FAT4 missense NM_024582:exon1:c.C1244G:p.P415R P003 AML 4 126240743 FAT4 missense NM_024582:exon1:c.C3177A:p.D1059E P077 RCUD 4 126241026 FAT4 missense NM_024582:exon1:c.G3460A:p.E1154K P078 RAEB 4 126242164 FAT4 missense NM_024582:exon1:c.A4598G:p.N1533S P082 RAEB 4 126242209 FAT4 missense NM_024582:exon1:c.C4643A:p.T1548K P082 RAEB 4 126242209 FAT4 missense NM_024582:exon1:c.C4643A:p.T1548K P082 RAEB 4 126242209 FAT4 missense NM_024582:exon1:c.C4643A:p.T1548K P075 AML 4 126242287 FAT4 missense NM_024582:exon1:c.C4721T:p.T1574I P107 MDSU 4 126336542 FAT4 missense NM_024582:exon5:c.G6424T:p.V2142F P158 RCMD 4 126337730 FAT4 missense NM_024582:exon6:c.G6971A:p.R2324Q P019 RCUD 4 126370977 FAT4 missense NM_024582:exon9:c.C8806A:p.Q2936K P004 RAEB 4 126389846 FAT4 missense NM_024582:exon11:c.G12079T:p.A4027S P125 RCMD 4 126400925 FAT4 missense NM_024582:exon14:c.G12503A:p.R4168H P125 RCMD 4 126400925 FAT4 missense NM_024582:exon14:c.G12503A:p.R4168H P125 RCMD 4 126400925 FAT4 missense NM_024582:exon14:c.G12503A:p.R4168H P013 RCUD 4 126408582 FAT4 missense NM_024582:exon16:c.C12899T:p.A4300V P149 RAEB 4 126412373 FAT4 missense NM_024582:exon17:c.G14396A:p.R4799H P149 RAEB 4 126412373 FAT4 missense NM_024582:exon17:c.G14396A:p.R4799H P045 RCUD 4 153332513 FBXW7 missense NM_033632:exon2:c.G443C:p.S148T P117 RAEB 5 40852404 CARD6 missense NM_032587:exon3:c.G970A:p.D324N P075 AML 5 149459788 CSF1R missense NM_001288705:exon3:c.T419A:p.L140Q

- 34 -

P075 AML 5 149459788 CSF1R missense NM_001288705:exon3:c.T419A:p.L140Q P142 AML 5 149459890 CSF1R missense NM_001288705:exon3:c.G317A:p.R106Q P097 RAEB 5 170818746 NPM1 missense NM_002520:exon4:c.G295T:p.V99F P007 AML 5 170837543 NPM1 frameshift NM_002520:exon11:c.859_859delinsCTCTG: P149 RAEB 5 170837543 NPM1 frameshift NM_002520:exon11:c.859_859delinsCTCTG: P091 AML 5 170837544 NPM1 frameshift NM_002520:exon11:c.860_860delinsTCTGC: P031 RCMD 6 3105961 RIPK1 missense NM_003804:exon8:c.G1252T:p.D418Y P058 RCUD 6 26157091 HIST1H1E missense NM_005321:exon1:c.C473T:p.A158V P122 RAEB 6 26157105 HIST1H1E missense NM_005321:exon1:c.G487C:p.A163P P085 RCUD 6 32942456 BRD2 missense NM_001199455:exon2:c.C247A:p.H83N P153 RCMD 6 32944075 BRD2 missense NM_001199455:exon5:c.C659A:p.S220Y P076 RAEB 6 32944646 BRD2 missense NM_001199455:exon6:c.C1133T:p.A378V P106 MDSU 6 32948443 BRD2 missense NM_001199456:exon12:c.C2213G:p.S738C P136 RCMD 6 44233136 NFKBIE missense NM_004556:exon1:c.G365T:p.S122I P136 RCMD 6 44233136 NFKBIE missense NM_004556:exon1:c.G365T:p.S122I P029 RAEB 6 44233361 NFKBIE frameshift NM_004556:exon1:c.140delG:p.R47fs P004 RAEB 7 50450292 IKZF1 missense NM_001220766:exon4:c.A215G:p.N72S P126 MDSMPN 7 50450292 IKZF1 missense NM_001220766:exon4:c.A215G:p.N72S P126 MDSMPN 7 50450292 IKZF1 missense NM_001220766:exon4:c.A215G:p.N72S P047 RAEB 7 50467813 IKZF1 missense NM_001220776:exon4:c.C343G:p.L115V P034 RARS 7 107678014 LAMB4 missense NM_007356:exon30:c.G4498A:p.E1500K P009 RAEB 7 107704289 LAMB4 missense NM_007356:exon22:c.G2978A:p.R993Q P144 RCUD 7 107704289 LAMB4 missense NM_007356:exon22:c.G2978A:p.R993Q P071 AML 7 107748134 LAMB4 frameshift NM_007356:exon6:c.533_533delinsAGT: P152 AML 7 107748258 LAMB4 missense NM_007356:exon6:c.C409T:p.R137W P121 RCMD 7 107752345 LAMB4 missense NM_007356:exon4:c.C239T:p.P80L P121 RCMD 7 107752345 LAMB4 missense NM_007356:exon4:c.C239T:p.P80L P153 RCMD 7 124487041 POT1 missense NM_001042594:exon11:c.G568A:p.V190I P155 RAEB 7 124503636 POT1 missense NM_015450:exon8:c.C314T:p.T105M P082 RAEB 7 140501331 BRAF missense NM_004333:exon6:c.T741G:p.F247L P082 RAEB 7 140501331 BRAF missense NM_004333:exon6:c.T741G:p.F247L P082 RAEB 7 140501331 BRAF missense NM_004333:exon6:c.T741G:p.F247L P114 RCMD 7 140507845 BRAF missense NM_004333:exon5:c.G626A:p.G209D P005 AML 7 148506219 EZH2 missense NM_152998:exon18:c.A2007G:p.I669M P035 RCMD 7 148506443 EZH2 missense NM_152998:exon17:c.G1937A:p.R646H P031 RCMD 7 148507497 EZH2 nonsense NM_152998:exon16:c.C1825T:p.Q609X P060 RAEB 7 148508788 EZH2 missense NM_152998:exon15:c.G1744A:p.V582M P100 RCMD 7 148511148 EZH2 missense NM_152998:exon14:c.G1622A:p.C541Y P072 MDSMPN 7 148524295 EZH2 frameshift NM_152998:exon6:c.572delT:p.M191fs P005 AML 8 144885863 SCRIB missense NM_015356:exon23:c.C3368A:p.A1123D P103 RCUD 8 144895225 SCRIB missense NM_015356:exon7:c.G617A:p.R206Q P044 RCMD 8 144896237 SCRIB missense NM_015356:exon2:c.A211C:p.I71L P129 AML 9 2110397 SMARCA2 missense NM_001289396:exon24:c.A3436C:p.S1146R P139 RCMD 9 5073770 JAK2 missense NM_004972:exon14:c.G1849T:p.V617F P042 RCUD 9 21971043 CDKN2A missense NM_001195132:exon2:c.C315A:p.D105E P057 RCUD 9 21974501 CDKN2A missense NM_058197:exon1:c.C326T:p.A109V P028 RAEB 9 21974808 CDKN2A missense NM_001195132:exon1:c.A19T:p.S7C P028 RAEB 9 21974808 CDKN2A missense NM_001195132:exon1:c.A19T:p.S7C P075 AML 9 139390794 NOTCH1 missense NM_017617:exon34:c.C7397T:p.T2466M P195 MDSU 9 139391268 NOTCH1 missense NM_017617:exon34:c.G6923T:p.C2308F P013 RCUD 9 139391715 NOTCH1 missense NM_017617:exon34:c.G6476A:p.R2159H P156 RAEB 9 139391840 NOTCH1 missense NM_017617:exon34:c.C6351A:p.N2117K P132 RAEB 9 139397763 NOTCH1 missense NM_017617:exon27:c.A5038G:p.I1680V P102 RCMD 9 139399344 NOTCH1 missense NM_017617:exon26:c.T4799C:p.L1600P P159 RCMD 9 139401390 NOTCH1 missense NM_017617:exon23:c.C3679T:p.P1227S P135 RCUD 9 139405215 NOTCH1 missense NM_017617:exon17:c.C2630T:p.P877L P149 RAEB 9 139407526 NOTCH1 missense NM_017617:exon15:c.C2414T:p.T805M P149 RAEB 9 139407526 NOTCH1 missense NM_017617:exon15:c.C2414T:p.T805M P122 RAEB 9 139418211 NOTCH1 missense NM_017617:exon3:c.A361G:p.T121A P046 RAEB 10 64573097 EGR2 missense NM_000399:exon2:c.C1301T:p.S434L P101 RAEB 11 32417907 WT1 frameshift NM_000378:exon6:c.1094_1094delinsTCGGC: P088 AML 11 32417910 WT1 nonsense NM_000378:exon6:c.C1091A:p.S364X

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P069 RCMD 11 32421514 WT1 missense NM_000378:exon5:c.G1027C:p.G343R P147 RAEB 11 32439199 WT1 frameshift NM_000378:exon4:c.874_874delinsTG: P159 RCMD 11 32449530 WT1 missense NM_000378:exon3:c.G844T:p.A282S P033 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P089 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P089 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P089 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P113 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P113 RAEB 11 32450067 WT1 missense NM_000378:exon2:c.C745A:p.P249T P018 RARS 11 102195846 BIRC3 missense NM_001165:exon2:c.A606T:p.R202S P034 RARS 11 102207763 BIRC3 missense NM_001165:exon9:c.C1745G:p.A582G P037 RAEB 11 108117781 ATM missense NM_000051:exon8:c.A992G:p.K331R P041 MDSU 11 108122697 ATM missense NM_000051:exon11:c.T1741G:p.L581V P041 MDSU 11 108122697 ATM missense NM_000051:exon11:c.T1741G:p.L581V P041 MDSU 11 108122697 ATM missense NM_000051:exon11:c.T1741G:p.L581V P001 RAEB 11 108139269 ATM missense NM_000051:exon18:c.G2771A:p.R924Q P009 RAEB 11 108163474 ATM missense NM_000051:exon30:c.G4565A:p.G1522D P159 RCMD 11 108186778 ATM missense NM_000051:exon42:c.C6136G:p.L2046V P002 RCUD 11 108203493 ATM missense NM_000051:exon53:c.G7793A:p.R2598Q P002 RCUD 11 108203493 ATM missense NM_000051:exon53:c.G7793A:p.R2598Q P153 RCMD 11 108216615 ATM missense NM_000051:exon58:c.G8564A:p.S2855N P117 RAEB 11 119148891 CBL missense NM_005188:exon8:c.T1111C:p.Y371H P036 RCUD 11 119148967 CBL missense NM_005188:exon8:c.G1187A:p.C396Y P105 RCUD 11 119149376 CBL nonsense NM_005188:exon9:c.C1384T:p.R462X P002 RCUD 12 11992111 ETV6 frameshift NM_001987:exon3:c.201_208del:p.67_70del P050 RCMD 12 25398284 KRAS missense NM_004985:exon2:c.G35C:p.G12A P050 RCMD 12 25398284 KRAS missense NM_004985:exon2:c.G35C:p.G12A P072 MDSMPN 12 25398284 KRAS missense NM_004985:exon2:c.G35T:p.G12V P090 RAEB 12 25398284 KRAS missense NM_004985:exon2:c.G35C:p.G12A P121 RCMD 12 25398284 KRAS missense NM_004985:exon2:c.G35T:p.G12V P146 RCMD 12 25398285 KRAS missense NM_004985:exon2:c.G34T:p.G12C P123 RCMD 12 50025689 PRPF40B missense NM_001031698:exon4:c.C275T:p.A92V P160 RCUD 12 50027231 PRPF40B missense NM_001031698:exon8:c.T481C:p.W161R P081 MDSMPN 12 50036366 PRPF40B missense NM_001031698:exon21:c.C2026T:p.R676C P155 RAEB 12 50036372 PRPF40B missense NM_001031698:exon21:c.C2032T:p.R678C P043 RAEB 12 50036378 PRPF40B missense NM_001031698:exon21:c.G2038A:p.V680M P158 RCMD 12 111885281 SH2B3 missense NM_005475:exon6:c.T1169C:p.L390P P119 RAEB 12 111885970 SH2B3 missense NM_005475:exon8:c.C1592T:p.S531L P038 RAEB 12 112884198 PTPN11 missense NM_002834:exon2:c.G133C:p.V45L P015 RAEB 12 112888165 PTPN11 missense NM_002834:exon3:c.G181A:p.D61N P060 RAEB 12 112888165 PTPN11 missense NM_002834:exon3:c.G181C:p.D61H P054 RCUD 12 112888166 PTPN11 missense NM_002834:exon3:c.A182G:p.D61G P075 AML 12 112888198 PTPN11 missense NM_002834:exon3:c.G214A:p.A72T P075 AML 12 112888198 PTPN11 missense NM_002834:exon3:c.G214A:p.A72T P120 AML 12 112888198 PTPN11 missense NM_002834:exon3:c.G214A:p.A72T P113 RAEB 12 112926885 PTPN11 missense NM_002834:exon13:c.C1505T:p.S502L P003 AML 13 28592642 FLT3 missense NM_004119:exon20:c.G2503C:p.D835H P065 RAEB 13 28592642 FLT3 missense NM_004119:exon20:c.G2503C:p.D835H P078 RAEB 13 28623550 FLT3 missense NM_004119:exon8:c.C1007T:p.P336L P128 MDSU 13 28623550 FLT3 missense NM_004119:exon8:c.C1007T:p.P336L P044 RCMD 13 48936961 RB1 frameshift NM_000321:exon8:c.729_729delinsTA: P100 RCMD 13 49030386 RB1 missense NM_000321:exon19:c.C1861A:p.R621S P098 RCUD 13 73337738 DIS3 missense NM_014953:exon16:c.A1978G:p.N660D P041 MDSU 13 73355467 DIS3 missense NM_001128226:exon2:c.T256C:p.W86R P156 RAEB 13 73355467 DIS3 missense NM_001128226:exon2:c.T256C:p.W86R P145 RAEB 15 43574189 TGM7 missense NM_052955:exon9:c.G1204A:p.A402T P093 AML 15 57484369 TCF12 missense NM_003205:exon7:c.G404A:p.R135K P127 MDSU 15 57545653 TCF12 missense NM_003205:exon15:c.G1382A:p.R461Q P181 RCMD 15 57565310 TCF12 missense NM_003205:exon18:c.G1756A:p.E586K P138 RAEB 15 89860052 POLG missense NM_001126131:exon23:c.C3650T:p.A1217V P138 RAEB 15 89860052 POLG missense NM_001126131:exon23:c.C3650T:p.A1217V P138 RAEB 15 89860052 POLG missense NM_001126131:exon23:c.C3650T:p.A1217V

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P138 RAEB 15 89860052 POLG missense NM_001126131:exon23:c.C3650T:p.A1217V P052 RAEB 15 89870557 POLG missense NM_001126131:exon7:c.C1274A:p.A425D P123 RCMD 15 89871702 POLG missense NM_001126131:exon6:c.C1235T:p.P412L P105 RCUD 15 89871722 POLG missense NM_001126131:exon6:c.G1215C:p.E405D P009 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P048 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P070 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P070 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P118 RCMD 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P149 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P149 RAEB 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P005 AML 17 7572986 TP53 nonsense NM_001126112:exon11:c.C1123T:p.Q375X P020 RAEB 17 7573996 TP53 missense NM_001126112:exon10:c.T1031C:p.L344P

P133 RAEB 17 7574009 TP53 frameshift NM_001126112:exon10:c.1006_1018del:p.336_340del

P145 RAEB 17 7576541 TP53 missense NM_001126113:exon10:c.C1037T:p.S346L P023 RCMD 17 7577094 TP53 missense NM_001126113:exon8:c.C844T:p.R282W P026 RAEB 17 7577096 TP53 missense NM_001126113:exon8:c.A842G:p.D281G P158 RCMD 17 7577120 TP53 missense NM_001126113:exon8:c.G818A:p.R273H P084 RAEB 17 7577141 TP53 missense NM_001126113:exon8:c.G797T:p.G266V P129 AML 17 7577538 TP53 missense NM_001126113:exon7:c.G743A:p.R248Q P097 RAEB 17 7577539 TP53 missense NM_001126113:exon7:c.C742T:p.R248W P130 RARS 17 7577560 TP53 missense NM_001126113:exon7:c.T721G:p.S241A P032 RAEB 17 7577568 TP53 missense NM_001126113:exon7:c.G713A:p.C238Y P032 RAEB 17 7577568 TP53 missense NM_001126113:exon7:c.G713A:p.C238Y P137 RAEB 17 7577574 TP53 missense NM_001126113:exon7:c.A707G:p.Y236C P137 RAEB 17 7577574 TP53 missense NM_001126113:exon7:c.A707G:p.Y236C P142 AML 17 7578203 TP53 missense NM_001126113:exon6:c.G646A:p.V216M P017 AML 17 7578406 TP53 missense NM_001126113:exon5:c.G524A:p.R175H P151 RAEB 17 7578517 TP53 missense NM_001126113:exon5:c.C413T:p.A138V P143 RAEB 17 7578518 TP53 missense NM_001126113:exon5:c.G412C:p.A138P P003 AML 17 29486068 NF1 missense NM_000267:exon3:c.C245T:p.S82F P126 MDSMPN 17 29497003 NF1 nonsense NM_000267:exon5:c.C574T:p.R192X P017 AML 17 29508730 NF1 frameshift NM_000267:exon7:c.657_657delinsAT: P126 MDSMPN 17 29528493 NF1 missense NM_000267:exon11:c.T1250A:p.I417N P046 RAEB 17 29546080 NF1 missense NM_000267:exon14:c.C1585T:p.L529F P097 RAEB 17 29562747 NF1 missense NM_000267:exon28:c.G3827A:p.R1276Q P082 RAEB 17 74732364 SRSF2 missense NM_001195427:exon2:c.G545A:p.R182Q P082 RAEB 17 74732364 SRSF2 missense NM_001195427:exon2:c.G545A:p.R182Q P082 RAEB 17 74732364 SRSF2 missense NM_001195427:exon2:c.G545A:p.R182Q P009 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P048 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284T:p.P95L P062 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P075 AML 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P113 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284T:p.P95L P113 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284T:p.P95L P118 RCMD 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P121 RCMD 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P122 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P123 RCMD 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P124 RCMD 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P124 RCMD 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P155 RAEB 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284T:p.P95L P135 RCUD 18 42281681 SETBP1 missense NM_001130110:exon2:c.A370G:p.I124V P048 RAEB 18 42456620 SETBP1 missense NM_001130110:exon4:c.A631T:p.M211L P155 RAEB 18 42531526 SETBP1 missense NM_015559:exon4:c.G2221A:p.E741K P029 RAEB 18 42531647 SETBP1 missense NM_015559:exon4:c.C2342T:p.T781I P146 RCMD 18 42531907 SETBP1 missense NM_015559:exon4:c.G2602T:p.D868Y P086 RCMD 18 42643500 SETBP1 missense NM_015559:exon6:c.C4628G:p.P1543R P087 RCUD 18 42643500 SETBP1 missense NM_015559:exon6:c.C4628G:p.P1543R P058 RCUD 18 42643545 SETBP1 missense NM_015559:exon6:c.A4673T:p.Q1558L P056 RAEB 19 15354212 BRD4 missense NM_058243:exon14:c.G2668C:p.V890L

- 37 -

P114 RCMD 19 15376437 BRD4 missense NM_058243:exon5:c.G577A:p.V193I P048 RAEB 19 33792408 CEBPA nonsense NM_001287435:exon1:c.C871T:p.Q291X P047 RAEB 19 33793117 CEBPA frameshift NM_001287435:exon1:c.161_162del:p.54_54del P101 RAEB 19 33793137 CEBPA frameshift NM_001287435:exon1:c.142delA:p.I48fs P101 RAEB 19 33793137 CEBPA frameshift NM_001287435:exon1:c.142delA:p.I48fs P125 RCMD 19 33793170 CEBPA missense NM_001287435:exon1:c.C109G:p.P37A P125 RCMD 19 33793170 CEBPA missense NM_001287435:exon1:c.C109G:p.P37A P125 RCMD 19 33793170 CEBPA missense NM_001287435:exon1:c.C109G:p.P37A P149 RAEB 19 33793252 CEBPA frameshift NM_001287435:exon1:c.27_27delinsCG: P157 RCMD 19 33793254 CEBPA missense NM_001287435:exon1:c.C25T:p.P9S P160 RCUD 19 33793296 CEBPA missense NM_004364:exon1:c.G25T:p.A9S P007 AML 19 33793320 CEBPA missense exon1:c.A1G:p.M1V: P093 AML 19 34818931 KIAA0355 missense NM_014686:exon6:c.C979T:p.R327W P109 AML 19 34843853 KIAA0355 missense NM_014686:exon14:c.A3206G:p.Q1069R P076 RAEB 19 56172515 U2AF2 missense NM_001012478:exon5:c.G446A:p.R149Q P146 RCMD 19 56173940 U2AF2 missense NM_001012478:exon6:c.T559G:p.L187V P078 RAEB 19 56185361 U2AF2 missense NM_001012478:exon12:c.G1343T:p.R448L P011 MDSU 20 30954215 ASXL1 missense NM_015338:exon2:c.T86G:p.M29R P105 RCUD 20 31021211 ASXL1 nonsense NM_015338:exon11:c.C1210T:p.R404X P042 RCUD 20 31021530 ASXL1 missense NM_015338:exon11:c.T1529C:p.L510P P139 RCMD 20 31022334 ASXL1 missense NM_015338:exon12:c.G1819A:p.G607S P146 RCMD 20 31022378 ASXL1 frameshift NM_015338:exon12:c.1863_1876del:p.621_626del P012 MDSU 20 31022382 ASXL1 nonsense NM_015338:exon12:c.C1867T:p.Q623X P122 RAEB 20 31022435 ASXL1 frameshift NM_015338:exon12:c.1920_1930del:p.640_644del P009 RAEB 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P049 RAEB 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P058 RCUD 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P072 MDSMPN 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P076 RAEB 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P113 RAEB 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P121 RCMD 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P124 RCMD 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P126 MDSMPN 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P126 MDSMPN 20 31022441 ASXL1 frameshift NM_015338:exon12:c.1926_1926delinsAG: P062 RAEB 20 31022487 ASXL1 nonsense NM_015338:exon12:c.G1972T:p.G658X P080 RAEB 20 31022592 ASXL1 nonsense NM_015338:exon12:c.C2077T:p.R693X P019 RCUD 20 31023931 ASXL1 missense NM_015338:exon12:c.C3416A:p.T1139K P136 RCMD 20 31024313 ASXL1 frameshift NM_015338:exon12:c.3798_3798delinsTAA: P136 RCMD 20 31024313 ASXL1 frameshift NM_015338:exon12:c.3798_3798delinsTAA: P130 RARS 20 31024480 ASXL1 missense NM_015338:exon12:c.C3965T:p.P1322L P143 RAEB 20 31024480 ASXL1 missense NM_015338:exon12:c.C3965T:p.P1322L P060 RAEB 20 31024635 ASXL1 frameshift NM_015338:exon12:c.4120_4120delinsGT: P068 RAEB 20 35521341 SAMHD1 frameshift NM_015474:exon16:c.1875delA:p.P625fs P078 RAEB 20 35526925 SAMHD1 missense NM_015474:exon14:c.T1526A:p.M509K P140 MDSU 20 35533852 SAMHD1 missense NM_015474:exon12:c.G1325A:p.R442Q P005 AML 21 36164460 RUNX1 missense NM_001754:exon9:c.T1415C:p.L472P P129 AML 21 36164460 RUNX1 missense NM_001754:exon9:c.T1415C:p.L472P P121 RCMD 21 36171600 RUNX1 nonsense NM_001754:exon8:c.C965G:p.S322X P038 RAEB 21 36206774 RUNX1 frameshift NM_001754:exon7:c.723_738del:p.241_246del P075 AML 21 36252866 RUNX1 nonsense NM_001754:exon5:c.C496T:p.R166X P128 MDSU 21 36252876 RUNX1 missense NM_001754:exon5:c.G486T:p.R162S P060 RAEB 21 36252878 RUNX1 missense NM_001754:exon5:c.A484G:p.R162G P082 RAEB 21 36252959 RUNX1 missense NM_001754:exon5:c.G403A:p.G135S P082 RAEB 21 36252959 RUNX1 missense NM_001754:exon5:c.G403A:p.G135S P082 RAEB 21 36252959 RUNX1 missense NM_001754:exon5:c.G403A:p.G135S P072 MDSMPN 21 36252994 RUNX1 frameshift NM_001754:exon5:c.368_368delinsGGA: P011 MDSU 21 36259159 RUNX1 missense NM_001754:exon4:c.C332A:p.T111N P121 RCMD 21 36259159 RUNX1 missense NM_001754:exon4:c.C332A:p.T111N P080 RAEB 21 36259172 RUNX1 missense NM_001754:exon4:c.C319A:p.R107S P064 RAEB 21 36259192 RUNX1 missense NM_001754:exon4:c.C299T:p.S100F P056 RAEB 21 36259210 RUNX1 missense NM_001754:exon4:c.G281T:p.S94I P015 RAEB 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P

- 38 -

P089 RAEB 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P089 RAEB 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P089 RAEB 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P102 RCMD 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P105 RCUD 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P006 RARS 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101A:p.S34Y P008 MDSU 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P033 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101A:p.S34Y P052 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P052 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P054 RCUD 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101A:p.S34Y P054 RCUD 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101A:p.S34Y P069 RCMD 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P076 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P079 MDSU 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101A:p.S34Y P119 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P128 MDSU 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P146 RCMD 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P156 RAEB 21 44524456 U2AF1 missense NM_001025203:exon2:c.C101T:p.S34F P027 RAEB 22 30733705 SF3A1 missense NM_005877:exon12:c.G1925C:p.R642P P045 RCUD 22 30736350 SF3A1 missense NM_005877:exon9:c.G1210A:p.A404T P002 RCUD X 15827344 ZRSR2 nonsense NM_005089:exon7:c.C460T:p.Q154X P002 RCUD X 15827344 ZRSR2 nonsense NM_005089:exon7:c.C460T:p.Q154X P003 AML X 39913508 BCOR splicing NM_001123383:exon14:c.4717+1G>C: P158 RCMD X 15838370 ZRSR2 nonsense NM_005089:exon10:c.C868T:p.R290X P153 RCMD X 15838378 ZRSR2 nonsense NM_005089:exon10:c.T876G:p.Y292X P121 RCMD X 39914664 BCOR frameshift NM_001123383:exon12:c.4596_4596delinsGGC: P121 RCMD X 39914664 BCOR frameshift NM_001123383:exon12:c.4596_4596delinsGGC: P076 RAEB X 39921394 BCOR nonsense NM_001123383:exon10:c.G4324T:p.E1442X P112 RAEB X 39923700 BCOR missense NM_001123383:exon7:c.C3391T:p.R1131W P094 RCUD X 39932171 BCOR nonsense NM_001123383:exon4:c.C2428T:p.R810X P128 MDSU X 39932302 BCOR frameshift NM_001123383:exon4:c.2296_2297del:p.766_766del P132 RAEB X 39932471 BCOR missense NM_001123383:exon4:c.C2128T:p.R710C P122 RAEB X 39933151 BCOR missense NM_001123383:exon4:c.C1448T:p.P483L P123 RCMD X 39933151 BCOR missense NM_001123383:exon4:c.C1448T:p.P483L P024 RAEB X 39934071 BCOR frameshift NM_001123383:exon4:c.525_528del:p.175_176del P095 AML X 39934135 BCOR missense NM_001123383:exon4:c.T464C:p.I155T P066 RCMD X 48650778 GATA1 missense NM_002049:exon4:c.G647A:p.R216Q P004 RAEB X 48652221 GATA1 missense NM_002049:exon6:c.C892T:p.R298W P149 RAEB X 53432009 SMC1A missense NM_006306:exon13:c.C2131T:p.R711W P117 RAEB X 53441941 SMC1A missense NM_006306:exon2:c.G287A:p.R96H P078 RAEB X 70341430 MED12 nonsense NM_005120:exon7:c.C865T:p.Q289X P136 RCMD X 70354629 MED12 missense NM_005120:exon35:c.G4794C:p.M1598I P136 RCMD X 70354629 MED12 missense NM_005120:exon35:c.G4794C:p.M1598I P028 RAEB X 70468137 ZMYM3 missense NM_005096:exon11:c.G1850A:p.R617H P028 RAEB X 70468137 ZMYM3 missense NM_005096:exon11:c.G1850A:p.R617H P122 RAEB X 76939415 ATRX missense NM_138270:exon8:c.A1219G:p.K407E P009 RAEB X 123179180 STAG2 frameshift NM_006603:exon7:c.629_629delinsCA: P113 RAEB X 123185196 STAG2 frameshift NM_006603:exon12:c.1148_1148delinsAT: P054 RCUD X 123215314 STAG2 missense NM_006603:exon27:c.C2860T:p.R954C P059 AML X 123217371 STAG2 missense NM_006603:exon28:c.A3025G:p.K1009E P158 RCMD X 123220440 STAG2 nonsense NM_006603:exon29:c.C3097T:p.R1033X P062 RAEB X 123220475 STAG2 nonsense NM_006603:exon29:c.C3132G:p.Y1044X P078 RAEB X 123224580 STAG2 nonsense NM_006603:exon30:c.C3433T:p.Q1145X P059 AML X 154305494 BRCC3 missense NM_001018055:exon4:c.G245A:p.R82H P124 RCMD X 154327651 BRCC3 missense NM_024332:exon8:c.A610C:p.K204Q P124 RCMD X 154327651 BRCC3 missense NM_024332:exon8:c.A610C:p.K204Q

- 39 -

Table 4. List of candidate variants detected in 36 patients with idiopathic

cytopenia of undetermined significance

Sample ID

Disease category

Chr Start position

Gene Type of mutation

Amino acid changes

P191 ICUS 1 6531121 PLEKHG5 missense NM_001265592:exon14:c.G1558A:p.E520K P175 ICUS 2 141598619 LRP1B missense NM_018557:exon30:c.G4982A:p.R1661H P192 ICUS 2 209113112 IDH1 missense NM_005896:exon4:c.G395A:p.R132H P180 ICUS 2 215632347 BARD1 missense NM_000465:exon6:c.T1427G:p.V476G P173 ICUS 4 106156862 TET2 nonsense NM_001127208:exon3:c.C1763G:p.S588X P190 ICUS 6 26156976 HIST1H1E missense NM_005321:exon1:c.G358C:p.A120P P165 ICUS 6 44233361 NFKBIE frameshift NM_004556:exon1:c.140delG:p.R47fs P171 ICUS 6 44233361 NFKBIE frameshift NM_004556:exon1:c.140delG:p.R47fs P162 ICUS 6 44233428 NFKBIE missense NM_004556:exon1:c.G73A:p.D25N P192 ICUS 8 144893165 SCRIB missense NM_015356:exon11:c.T1184G:p.M395R P179 ICUS 9 5044421 JAK2 nonsense NM_004972:exon5:c.G369A:p.W123X P180 ICUS 9 21974501 CDKN2A missense NM_058197:exon1:c.C326T:p.A109V P197 ICUS 9 139399480 NOTCH1 missense NM_017617:exon26:c.G4663A:p.E1555K P166 ICUS 9 139402504 NOTCH1 missense NM_017617:exon21:c.C3413T:p.T1138I P164 ICUS 10 112361545 SMC3 missense NM_005445:exon24:c.T2795C:p.L932P P194 ICUS 11 102195846 BIRC3 missense NM_001165:exon2:c.A606T:p.R202S P193 ICUS 11 108201015 ATM missense NM_000051:exon50:c.G7382A:p.R2461H P196 ICUS 11 119156112 CBL missense NM_005188:exon11:c.C1777T:p.R593W P177 ICUS 13 49039470 RB1 missense NM_000321:exon23:c.C2455G:p.L819V P193 ICUS 15 90631934 IDH2 missense NM_002168:exon4:c.G419A:p.R140Q P193 ICUS 17 74732959 SRSF2 missense NM_001195427:exon1:c.C284A:p.P95H P168 ICUS 19 15349722 BRD4 missense NM_058243:exon19:c.G3852T:p.E1284D P172 ICUS 19 15349764 BRD4 missense NM_058243:exon19:c.G3810C:p.E1270D P187 ICUS 19 56173940 U2AF2 missense NM_001012478:exon6:c.T559G:p.L187V P162 ICUS 20 35533852 SAMHD1 missense NM_015474:exon12:c.G1325A:p.R442Q P178 ICUS 20 35580009 SAMHD1 missense NM_015474:exon1:c.C38T:p.P13L P192 ICUS 21 36259308 RUNX1 frameshift NM_001754:exon4:c.183_183delinsCG: P187 ICUS 21 44514777 U2AF1 missense NM_001025203:exon6:c.A470C:p.Q157P P182 ICUS 22 30733705 SF3A1 missense NM_005877:exon12:c.G1925C:p.R642P P186 ICUS X 15838385 ZRSR2 nonsense NM_005089:exon10:c.C883T:p.R295X P198 ICUS X 39914677 BCOR nonsense NM_001123383:exon12:c.C4583G:p.S1528X P187 ICUS X 39933244 BCOR frameshift NM_001123383:exon4:c.1355delC:p.A452fs P183 ICUS X 48652248 GATA1 missense NM_002049:exon6:c.C919A:p.R307S P162 ICUS X 53432018 SMC1A missense NM_006306:exon13:c.C2122G:p.L708V

- 40 -

3.3. Clonal evolution patterns of genetic mutations in sequential

samples

During the progression of MDS, clonal evolution can be observed in the

various tiers of driver and passenger mutations. The variant allele fraction

(VAF) data were used to identify clonal mutations, which are found in all

tumor cells, and subclonal mutations, which are found in a fraction of cells

(Figure 7). When the mutations were compared among the 12 patients for

whom sequential sampling was performed after disease progression, some

preexisting mutations and cytogenetic abnormalities presented similar VAFs,

while some other mutations showed expanded clonal sizes (Figure 8). In

addition, some patients presented novel mutations after disease progression;

such mutations tended to have smaller VAFs than the preexisting mutations.

The emergent mutations following disease progression included ASXL1, ETV6,

IDH2, KIT, KRAS, NRAS, POLG, PTPN11, SMC1A, SRSF2, STAG2, and WT1.

- 41 -

Figure 7. Variant allele fractions of mutated genes

- 42 -

Figure 8. Clonal evolution patterns in MDS patients for whom sequential

sampling was performed during disease progression

- 43 -

3.4. Usefulness of mutational analysis for diagnosis of challenging

cases

Among the included MDS cases, 29 of the 162 patients (17.9%) were difficult

to diagnose using morphologic dysplasia assessment alone, as dysplasia was

equivocal in an approximate range of 5% to 10% (depending on the observer,

assessment could yield dysplasia levels of 10% or less than 10%). These

variability of the dysplasia levels was in part due to the poor-quality of BM

slides, which may be severely diluted by peripheral blood or have too short-

length of BM biopsy sections for proper evaluation. Among these 29 patients,

8 (27.6%) were diagnosed with referring on cytogenetic abnormalities (Table

5). The observed cytogenetic abnormalities included del(20q) in 4 cases,

del(7q) in 2 cases, and 1 case each of del(1p), and +15. The patients in all

these cases showed somatic gene mutations in U2AF1, ASXL1, FAT4, CBL,

LRP1B, DNMT3A, EZH2, RB1, FAT4, TET2, and TCF12. Twelve other

patients were diagnosed with MDS based on the results of repeated BM

testing. For 6 of these patients, repeated testing was performed after disease

progression, and the other 5 performed repeat testing within short-term period

(1 to 6 months after the initial diagnosis) to clarify the diagnosis. Remaining

10 patients were presumptively diagnosed with MDS according to their

correlation with clinical manifestations. For these patients, a discussion of BM

morphologies led us to conclude that > 10% of dysplasias were present in at

least 1 myeloid lineage. Among these 10 patients, 7 patients showed

mutations in our mutational analysis.

- 44 -

Table 5. Results of mutational analysis in patients for whom the diagnosis of

MDS was challenging.

Case No BM diagnosis Cytogenetics Mutated genes

Patients diagnosed with referring on cytonetics

P001 MDS-U 46,XY,15pstk+,del(20)(q12q13.3)[10]/46,XY,15pstk+[10] U2AF1*

P012 MDS-U 46,XY,der(1;7)(q10;p10),1qh+[1]/

46,XY,1qh+[20].nuc ish(D7Z1X2,D7S522X1)[10/200]

ASXL1*

P019 Iron deficiency anemia (preliminary dx) → RCUD

46,XY,del(20)(q12q13.3)[3]/46,XY[17] ASXL1*, FAT4

P036 Immune thrombocytopenia (preliminary dx) → RCUD 46,XY,del(20)(q11.2)[20] CBL*, LRP1B

P100 Inadequate specimen, Suspicious of MDS

46,XY,del(20)(q11.2)[12]/45,idem,-Y[8]

DNMT3A*, EZH2, RB1*

P107 MDS-U 46~47,XX,del(1)(p22p34),-20,marX1~2[19]/46,XX[1] FAT4

P110 MDS-U 46,XY,del(7)(q22q32)[1]/46,XY[3]

.nuc ish(D7Z1X2,D7S522X1)[10/200]

TET2

P163 ICUS (preliminary dx) → Suspicious RCMD 47,XX,+15[9]/46,XX[11] TCF12

Patients diagnosed with repeated BM testing after disease progression

P035 Possibly ICUS or MDS-U → (20 months after BM tests) RCMD

46,XY[20] → (20 months)

46,XY[20] No mutation → (20

months) EZH2*

P040 Possibly drug-induced neutropenia → (12 months) RAEB-1

No mitosis → (12 months) 47,XY,dup(1)(q32q42),t(9;22)(p22

;q11.2),+22[3]/46,XY[18]

DNMT3A* → (12 months) NT

P054

Previous AA, improved states → (11 months) Suspicious of RCUD, possibly

secondary dysplasia → (4 months) RAEB-1

46,XY,inv(9)(p12q13)[20] → (11 months) 46,XY,inv(9)(p12q13)[20]

→ (4 months) 46,XY,inv(9)(p12q13)[20]

TET2, U2AF1* → (11 months) TET2,

U2AF1*, PTPN11*, STAG2 → (4 months) NT

P058 Possibly AA or hypoplastic MDS → (23 months) RCUD

46,XY[17] → (23 months) 46,XY[19]

ASXL1*, HIST1H1E, SETBP1 → (23 months) NT

P063 Hypoplastic marrow, Possibly AA → (61 months) RCMD

46,XY[2] → (61 months) 46,X,-Y,+15[10]/46,XY,+8,-

17[2]/46,XY[8]

No mutation → (61 months) No mutation

P103 Inadequate specimen, possibly AA or MDS → (28 months) MDS-U

46,XX[10] → (28 months) 47,XX,+8[3]/48,idem,+21[16]/46,

XX[1]

SCRIB → (28 months) NT

- 45 -

Patients diagnosed with repeated BM testing with short-term follow-up

P011 ICUS or possibly RCMD → (6 months) MDS-U

46,XY[20] → (6 months) 46,XY[21]

ASXL1, ITPKB, RUNX1, TET2*→ (6

months) NT

P057 Anemia of chronic disease or possibly MDS → (3 months) RCUD

46,XY[20] → (3 months) 46,XY[23]

NT → (3 months) DNMT3A*, ITPKB,

LRP1B

P087 Suspicious of RCUD → (6 months) RCUD

46,XX[20] → (6 months) 46,XX[20]

NT → (6 months) ITPKB, SETBP1

P105 Possibly anemia of chronic disease or ICUS → (1 month) RCUD

46,XY[20] → (1 months) 46,XY[20]

ASXL1*, CBL*, IDH1*, POLG, U2AF1* → (1

months) NT

P106 Possibly hemolytic anemia → (1 month) MDS-U

46,XY[20] → (1 months) 46,XY[22]

BRD2 → (1 months) NT

Patients diagnosed with clinical correlation and after discussion

P014 Possibly RCUD 46,XX,9qh-[20] TET2

P018 Possibly RARS or ICUS 46,XY[14] BIRC3

P073 Possibly RCUD or AA 46,XX[19] No mutation

P085 Suspicious of hypoplastic MDS or possibly AA 46,XY[20] BRD2, DNMT3A,

ITPKB

P094 Suspicious of hypoplastic MDS or possibly AA 46,XY[20] BCOR

P098 Suspicious of hypoplastic MDS or possibly AA 45,X,-Y[3]/46,XY[17] DIS3, MPL*

P111 Possibly RCUD 46,XY[20] SF3B1*, TET2

P127 Possibly MDS-U 46,XX[20] TCF12

P135 Possibly RCUD or lymphoproliferative disorder 46,XY[21] NOTCH1, SETBP1

P154 Suspicious of hypoplastic MDS or possibly AA 46,XX[20] No mutation

*Mutations that are registered in COSMIC database as oncogenic mutations

Abbreviations: AA, aplastic anemia; BM, bone marrow; dx, diagnosted; NT, not tested

- 46 -

3.5. Correlations among gene mutations and cytogenetic lesions

Pairwise correlations among gene mutations and cytogenetic abnormalities

were investigated to determine whether certain genes have a tendency toward

concurrent or exclusive mutation (Figure 9). There was a significant

correlation with the false discovery rate (0.2) in 40 pairs of mutated genes and

in combinations of mutated genes and cytogenetic abnormalities. The most

significant correlations were found between TP53 mutations and the 5q and

7q deletions, and 17p abnormalities (Figure 9, and Table 6). These

correlations were accord with previously reported data [27]. U2AF1 mutations

showed positive correlation with the trisomy 8. When the correlations among

mutated genes were investigated, the correlations were less significant (Figure

9). Some genes showed a tendency to co-occur, including PTPN1 and TET2,

IDH2 and SRSF2, IDH2 and DNMT3A, EZH2 and RUNX1, ASXL1 and

SRSF2, and CEBPA and IDH2. The strongest positive correlation was found

between CSF3R and CEBPA. When the involved pathways were compared,

genes involved in cohesion pathway and genes involved in DNA repair/cell

cycle showed strongest correlations (Figure 10, and Table 6).

- 47 -

Figure 9. Pairwise correlations among genes and cytogenetic abnormalities

found in more than 4 patients. Correlation coefficients with adjusted q-value <

0.2 are shown as colored boxes. Significant correlations were found between

TP53 mutations and del(5q), del(7q)/−7, and abnormalities of 17p.

- 48 -

Figure 10. Pairwise correlations among involved pathways. Correlation

coefficients with adjusted q-value < 0.2 are presented as colored boxes.

- 49 -

Table 6. Correlations among genes, cytogenetic abnormalities, and pathways

Parameters Correlation coefficients q-value

Between genetic mutations and cytogenetics

Mutated genes Chromosomal abnormalities

TP53 abn 17p 0.595 <0.001 TP53 del(5q)/ –5 0.564 <0.001 TP53 del(7q)/ –7 0.495 <0.001 TP53 abn 12p 0.387 <0.001 TP53 –18 0.359 <0.001 PLEKHG5 abn 16q 0.313 <0.001 U2AF1 +8 0.279 <0.001 LRP1B abn 17p 0.240 0.002 PLEKHG5 abn 3q 0.236 0.003 TP53 abn 3q 0.228 0.004 NF1 del(5q)/ –5 0.214 0.006 RUNX1 –18 0.194 0.013 KRAS abn 9q 0.175 0.026 Between mutated genes Gene 1 Gene 2 CEBPA CSF3R 0.454 <0.001 PTPN11 TET2 0.384 <0.001 IDH2 SRSF2 0.377 <0.001 DNMT3A IDH2 0.326 <0.001 EZH2 RUNX1 0.319 <0.001 ASXL1 SRSF2 0.302 <0.001 CEBPA IDH2 0.289 <0.001 PRPF40B SRSF2 0.236 0.003 CEBPA DNMT3A 0.234 0.003 ASXL1 KRAS 0.227 0.004 U2AF1 WT1 0.224 0.004 KRAS RUNX1 0.222 0.005 BCOR SRSF2 0.220 0.005 ASXL1 RUNX1 0.214 0.006 ITPKB SETBP1 0.211 0.007 LAMB4 SRSF2 0.207 0.008 FAT4 STAG2 0.207 0.008 SRSF2 STAG2 0.207 0.008 SF3B1 TET2 0.183 0.020 CSF3R NRAS 0.175 0.026 CSF3R PRPF40B 0.175 0.026 NF1 PLEKHG5 0.175 0.026 NF1 TP53 0.172 0.029 NRAS U2AF1 0.172 0.029 PLEKHG5 TP53 0.172 0.029

- 50 -

PTPN11 RUNX1 0.172 0.029 CEBPA FAT4 0.165 0.036 SETBP1 SRSF2 0.165 0.036 Between involved pathways Pathway 1 Pathway 2 Cohesin DNA repair/Cell cycle 0.259 0.001 Other signaling DNA methylation 0.229 0.003 Cohesin Splicing 0.221 0.005 DNA methylation Splicing 0.189 0.016 Others Cohesin 0.148 0.060 DNA repair/Cell cycle DNA methylation 0.136 0.084 Abbreviations: abn, abnormality

- 51 -

3.6. Development of a prognosis model using mutational profiles

We investigated the prognostic significance of the genetic lesions we

uncovered in MDS patients alone and in association with other prognostic

factors. In the univariate analysis, the statistically significantly higher hazard

ratio (HR) for OS was observed in patients with TP53 mutations (HR of 2.42,

P < 0.001). Patients with mutations in NPM1 SAMHD1, IKZF1, TP53, NRAS,

WT1, IDH2, NOTCH1, NF1, ZRSR2, and LRP1B tended to have poor OS but

higher HR were not statistically significant (Figure 11). Otherwise, several

genes were associated with better OS but were not statistically significant

(these genes included SF3B1, DIS3, LAMB4, TCF12, ATM, CDKN2A, BRD2,

and SETBP1). When we investigated the association between PFS and the

mutational profiles, we found that mutations in IKZF1, SAMHD1, WT1,

NPM1, TP53, SRSF2, and LRP1B were associated with a shorter PFS (Figure

12). We classified MDS patients as follows: patients having at least 1

mutation that presented strong to weak association to poor prognosis (group 3

mutations: those with > 1.5 HRs for OS, including mutations in TP53,

SAMHD1, NPM1, IKZF1, NRAS, WT1, IDH2, NOTCH1, NF1, ZRSR2, and

LRP1B; Figure 13); patients having mutations that presented tendency to

better prognosis (group 1 mutations: those with < 0.7 HRs, including

mutations in SF3B1, LAMB4, TCF12, ATM, CSF3R, BRD2, and SETBP1)

only; and patients having mutations in other genes (group 2 mutations) or no

mutations. MDS patients could be stratified according to IPSS-R risk scores

(Figure 14). When the same IPSS-R risk groups were further stratified by the

mutation groups, very low-, low-, intermediate-, and high-risk groups of

- 52 -

patients showed separated OS according to the mutation groups (Figure 15).

Patients belonging to very high-risk group showed very poor prognosis

irrespective of mutation groups. Multivariate analysis was performed for

mutation groups or presence of specific mutations adjusted for patients’ age

and IPSS-R risk groups (Table 7). Presence of the group 3 mutations was

independent risk factors in comparison with group 1 mutations (HR, 3.98; P <

0.001). The analysis of the presence of specific mutations revealed that TP53

and NRAS mutations were independent indicators for shorter OS (HR, 2.59, P

= 0.002; and HR 4.10, P = 0.008, respectively). In addition, presence of TP53,

NRAS, WT1, and LRP1B mutations were independent markers for disease

progression.

- 53 -

Figure 11. Overall survival of MDS patients, stratified according to genetic

mutations

- 54 -

Figure 12. Progression-free survival of MDS patients, stratified according to

genetic mutations

- 55 -

Figure 13. Kaplan-Meier survival curves comparing between overall survival

of patients with mutated and wild type genes including (A) TP53, (B)

SAMHD1, (C) NPM1, (D) IKZF1, (E) NRAS, (F) WT1, (G) IDH2, (H)

NOTCH1, and (I) NF1

- 56 -

A

B

C

D

Figure 14. Overall survival of MDS patients, stratified according to (A) IPSS

cytogenetic risk group, (B) IPSS-R new cytogenetic risk group, (C) IPSS risk

group, and (D) IPSS-R risk group.

.

- 57 -

A

B

C

D

Figure 15. Overall survival (OS) of MDS patients, stratified according to

gene mutation groups [group 3 mutations: those with > 1.5 hazard ratios (HRs)

for OS; group 2 mutations: HRs for OS between 0.7 to 1.5; group 1 mutations:

HRs for OS < 0.7] and IPSS-R risk groups (A) very low and low risk group,

(B) intermediate risk group, (C) high risk group, and (D) very high risk group.

- 58 -

Table 7. Multivariate Cox analysis of the overall survival (OS) and

progression-free survival (PFS) of 162 MDS patients according to IPSS-R

risk stratification and mutational profiles OS PFS

Risk factors HR 95% CI P HR 95% CI P

Risk stratification according to mutation groups

Age, <60 vs. ≥ 60 yrs 1.86 1.23-2.80 0.003 1.71 1.16-2.52 0.007

IPSS-R risk group, Low vs. Intermediate 1.34 0.72-2.52 0.361 1.74 0.94-3.21 0.078

IPSS-R risk group, Low vs. High 1.99 1.16-3.41 0.013 2.92 1.72-4.96 <0.001

Mutation group1 vs group 2 1.74 0.83-3.63 0.141 1.46 0.75-2.86 0.268

Mutation group1 vs group 3 3.98 1.84-8.61 <0.001 4.12 2.04-8.32 <0.001

Presence of specific mutations as risk factors

Age, <60 vs. ≥ 60 yrs 2.03 1.35-3.07 <0.001 1.84 1.23-2.76 0.003

IPSS-R risk group, Low vs. Intermediate 1.28 0.70-2.34 0.617 1.81 0.98-3.35 0.058

IPSS-R risk group, Low vs. High 2.29 1.35-3.89 0.002 3.43 2.01-5.87 <0.001

TP53 mutated vs. normal 2.59 1.42-4.73 0.002 2.55 1.43-4.56 0.002

NRAS mutated vs. normal 4.10 1.45-11.56 0.008 3.80 1.33-10.87 0.013

WT1 mutated vs. normal NS NS NS 4.28 1.78-10.25 0.001

LRP1B mutated vs. normal NS NS NS 2.42 1.16-5.06 0.019

Specific mutations were included in the multivariate model and stepwise selection

procedure if they were found in minimum 5 patients.

Abbreviations: CI, confidence interval; HR, hazard ratio; NS, not significant;

- 59 -

3.7. Prediction of treatment response

Among the 83 MDS patients who were treated with hypomethylating agents,

the treatment responses were assessed in 73 patients (including 42 patients

who were treated with azacitidine and 31 patients who were treated with

decitabine). Among them, 35 patients (48.0%) were responders, while 38

patients (52.0%) were non-responders. When the clinical and laboratory

characteristics of responders and non-responders were compared, there were

no specific characteristics that significantly differed between the two groups

(Table 8). When the associations between specific gene mutations and

responses to hypomethylating agents were investigated, none of the genes

showed an association (Table 8). However, patients with TET2 and RUNX1

mutations tended to respond less (odds ratio 0.25, and 0.27, respectively) (not

significant; P = 0.226 and 0.117, respectively). The presence of TP53 and

ASXL1 mutations were also not significantly associated with responses to

hypomethylating agents. When the number of mutations were investigated,

the presence of ≥ 2 mutations was associated with a poorer response when

compared to having ≤ 1 mutation present (odds ratio, 0.24; P = 0.005).

- 60 -

Table 8. Patient characteristics according to response to hypomethylating

therapy in 73 MDS patients.

Parameter Total Non-responders Responders P value

N 73 38 (52) 35 (48)

Male/Female (% male) 50/23 (68) 24/14 (63) 26/9 (26) 0.307

Age (years) 61 (17-86) 60 (18-86) 63 (17-86) 0.330

< 60 31 (42) 18 (47) 13 (37) 0.377

≥ 60 42 (58) 20 (53) 22 (63)

MDS subtypes

RCUD 10 (14) 5 (13) 5 (14) 0.869

RARS 2 (3) 0 (0) 2 (6)

RCMD 14 (19) 7 (18) 7 (20)

RABE 34 (47) 19 (50) 15 (43)

MDS-U 7 (10) 4 (11) 3 (9)

MDS-MPN 2 (3) 1 (3) 1 (3)

MDS-AML 4 (5) 2 (5) 2 (6)

Cytogenetics

Normal or –Y alone 32 (44) 17 (45) 15 (43) 0.371

Complex karyotype 18 (25) 7 (18) 11 (31)

Other abnormalities 23 (31) 14 (37) 9 (26)

Cytogenetics risk group

Very good 2 (3) 1 (3) 1 (3) 0.636

Good 34 (47) 17 (48) 17 (49)

Intermediate 16 (22) 10 (26) 6 (17)

Poor 7 (10) 5 (13) 2 (6)

Very poor 14 (19) 5 (13) 9 (26)

IPSS risk group

Low 6 (8) 2 (5) 4 (11) 0.945

Intermediate-1 31 (42) 18 (47) 13 (37)

Intermediate-2 31 (42) 16 (42) 15 (43)

High 5 (7) 2 (5) 3 (9)

IPSS-R risk group

- 61 -

Very low 2 (3) 0 (0) 2 (6) 0.820

Low 10 (14) 5 (13) 5 (14)

Intermediate 19 (26) 11 (29) 8 (23)

High 24 (33) 14 (37) 10 (29)

Very high 18 (25) 8 (21) 10 (29)

Treatment

Azacitidine 42 (58) 21 (55) 21 (60) 0.683

Decitabine 31 (42) 17 (45) 14 (40)

Transplantation 22 (30) 13 (34) 9 (26) 0.429

Outcome

Follow-up months 21.3 (4.8-105.9) 17.1 (4.8-105.9) 23.7 (6.0-73.1) 0.091

Leukemic transformation 25 (34) 14 (37) 11 (31) 0.626

Expired 51 (70) 32 (84) 25 (71) 0.187 Data are presented as the median (range) for continuous variables and the number of cases

(percentage) for categorical variables unless otherwise indicated.

Abbreviations: AML, acute myeloid leukemia; BM, bone marrow; IPSS, International

Prognostic Scoring System; IPSS-R, International Prognostic Scoring System, revised; MDS,

myelodysplastic syndrome; MDS/MPN, myelodysplastic syndrome/myeloproliferative

neoplasm; MDS-U, myelodysplastic syndrome, unclassifiable; NA, not available; RA,

refractory anemia; RAEB, refractory anemia with excess blasts; RARS, refractory anemia with

ring sideroblasts; RCMD, refractory cytopenia with multilineage dysplasia; RCUD, refractory

cytopenia with unilineage dysplasia; RN, refractory neutropenia; RT, refractory

thrombocytopenia; WHO, World Health Organization

- 62 -

Table 9. Association of the presence of specific gene mutations and the

number of mutations with the response to hypomethylating agents.

Parameter Odds ratio 95% CI P value

Presence of specific mutations

TET2-mut vs TET2-WT 0.25 (0.01-1.80) 0.226

RUNX1-mut vs RUNX1-WT 0.27 (0.04-1.21) 0.117

DNMT3A-mut vs DNMT3A-WT 0.71 (0.09-4.52) 0.714

TP53-mut vs TP53-WT 1.42 (0.35-6.19) 0.627

ASXL1-mut vs ASXL1-WT 1.65 (0.48-6.12) 0.434

Number of mutations

≥ 1 vs 0 mutation 0.38 (0.11-1.21) 0.111

≥ 2 vs ≤ 1 mutation 0.24 (0.09-0.63) 0.005

≥ 3 vs ≤ 2 mutation 0.45 (0.16-1.24) 0.131

≥ 4 vs ≤ 3 mutation 0.67 (0.20-2.09) 0.491 Abbreviations: CI, confidence interval; mut, mutated; WT, wild type

- 63 -

4. DISCUSSION

MDS is a heterogeneous spectrum of neoplasms that originates from

hematopoietic stem cells and is characteristically associated with peripheral

cytopenias due to ineffective hematopoiesis. MDS is morphologically defined

by dysplasias of one or more hematopoietic cell lineages, and sometimes by

an increased percentage of blasts in the PB and BM [50]. Due to the

heterogeneous characteristics of MDS and the rather ambiguous criteria for

assessing morphological dysplasias, the diagnosis and classification of MDS

can be difficult. There are many non-neoplastic conditions that can cause

peripheral cytopenias and transient dysplastic features in hematopoietic cells.

Therefore, it is important to confirm the neoplastic features of hematopoietic

cells for the diagnosis of MDS. As the cytogenetic abnormalities of MDS

have been extensively studied using conventional karyotyping and molecular

cytogenetic techniques such as FISH, cytogenetic results have become a key

component of the diagnosis, classification, and prognostication of MDS [14,

16, 50]. However, cytogenetic abnormalities are detected in about half of the

de novo MDS patients [65]. Therefore, cytogenetic evaluation yields only

limited information for the remaining patients.

The molecular basis for MDS has only recently been explored with newly

introduced NGS technology. Using high-throughput sequencing, the list of

genes involved in the molecular pathogenesis of MDS is rapidly expanding

[25, 27-29, 34], and their clinical implications are beginning to be revealed.

Mutations in approximately 40-50 genes have been reported in studies based

- 64 -

on large patient cohorts [27, 29, 66]. The frequently mutated gene lists and

their mutation frequencies were consistent among such studies. The top 10

frequently mutated genes include SF3B1, TET2, SRSF2, ASXL1, DNMT3A,

RUNX1, U2AF1, and TP53. The top 4 genes (SF3B1, TET2, SRSF2, and

ASXL1) were found to be mutated in >10% of MDS patients [27, 29, 66].

Therefore, the predominantly affected pathways involved in MDS

pathogenesis are likely to be RNA splicing and DNA methylation [66].

In a large cohort of Korean patients, we investigated the mutational profiles

both of frequently mutated genes in MDS and of other genes involved in the

oncogenesis of other hematologic malignancies. This is the first study to

investigated multi-gene mutation profiles in Korean MDS patients. Although

the ethnic distributions of previous large cohorts of MDS patients were not

clearly indicated, the research groups leading these studies, and probably

many of the patients were primarily Western people (some Japanese patients

were also included) [27-29, 34]. The molecular pathogenesis of MDS is

heterogeneous, and ethnic differences may be present.

The results of the present study of the mutational profiles of Korean

patients indicate similarities to and differences from previous studies [25, 27-

29, 34]. Korean patients with MDS also presented with recurrent mutations in

previously known genes, such as ASXL1, TP53, U2AF1, DNMT3A, RUNX1,

and TET2. However, there were some notable differences in the distribution

of mutational profiles. SF3B1 is a frequently mutated gene (up to 30% of

MDS patients in previous studies) [27, 29], but it was not very frequently

mutated in our study (less than 5% of total patients). Because SF3B1

- 65 -

mutations were previously reported with a high frequency in RARS or RCMD

with ring sideroblasts but with lower frequencies in other subtypes of MDS

[27, 29], the relative rarity of RARS patients included in this study seemed to

be associated with a lower incidence of SF3B1 mutation. This difference may

reflect the ethnic differences of Korean patients compared with other

populations. In addition, the frequencies of TP53 mutations were higher than

in other studies. Previous studies on Korean patients with MDS reported that

Korean patients tended to present in more advanced disease stages and were

associated with complex karyotype abnormalities; del(5q) with a complex

karyotype was more frequent than isolated del(5q) [67]. These clinical and

cytogenetic differences might be associated with the higher incidence of TP53

mutations found in our study. In addition, novel genes, which were not

significantly noted in previous studies, were presented as highly involved

genes in this study. Interestingly, U2AF1 presented high frequencies, with

increased FAT4 was previously reported to be mutated in

myeloproliferative neoplasms [68] and other non-hematologic malignancies,

such as colon cancer [69]. The mutated loci in FAT4 were disperse and

showed no main hotspots in this study or in other previous studies. Learning

the significance of these mutations will require further investigation.

We also investigated the mutational profiles of ICUS patients, whose

diagnosis of malignancy or benign disease is ambiguous. Therefore,

confirming the presence of clonal abnormalities may be important for the

diagnosis of these patients. Candidate variants were present in about 70% of

ICUS patients with a median low frequency of 1 mutation per patient. Three

- 66 -

patients presented with known oncogenic mutations. However, most of the

candidate variants were previously unknown, and they require further

clarification to learn their significance. Screening for these variants in ICUS

patients and subsequently elucidating their clinical significance in association

with the clinical course may facilitate the establishment of clear diagnostic

criteria for a currently ambiguous condition that can only be reported as ICUS.

In addition, we investigated the role of molecular testing in the diagnosis of

MDS in challenging cases. Because the dysplasia is assessed only by the

morphologic examination, it is subjective to determine, and extremely

dependent on the quality of slides. Previous studies, on the reproducibility of

diagnosis of MDS by 2008 WHO classification showed that low concordance

rates for assessment of unilineage dysplasia [8, 9]. The 10% threshold for

dysplasia is arbitrary, and quite low value, which made the reproducibility of

diagnosis RCUD quite low. In this study, 29 patients presented equivocal

dysplasia levels around 5% to 10%, which may cause debates in the diagnosis

of MDS among observers. Cytogenetic tests can assist in the diagnosis of

these challenging cases. In this study 8 cases were diagnosed as MDS with

referring to the concurrent cytogenetic abnormalities. Most of these 8 patients

did not present the recurrent chromosomal abnormalities which define

presumptive MDS, and 4 of them presented del(20q), which cannot be

definite evidence of MDS alone. However, upon the confirmation of presence

of clonal abnormalities, and presence of cytopenias, the debate over dysplasia

could be resolved and the diagnosis of MDS can be made. In other cases,

repeated BM testing was performed and final diagnosis of MDS was made. In

- 67 -

these challenging cases, molecular abnormalities were found in 26/29 patients,

and half of those were known oncogenic mutations. Therefore, molecular

testing can provide additional informative and subjective diagnostic clues for

MDS in addition to cytogenetics in many challenging cases.

Previous studies on genetic abnormalities of MDS showed mutational

profiles can be important prognostic factors, and will be incorporated in the

current prognostic models, IPSS-R risk models. Many studies reported that

the mutations of TP53 gene were associated with a worse outcome, in

addition to NRAS, EZH2, ETV6, and ASXL1 [34, 48, 54]. In this study, TP53

and NRAS mutations were associated with poor prognosis, which was

consistent finding with previous reports. Meanwhile, neither EZH2 nor ASXL1

presented significant association with poor prognosis in this study, although

patients with EZH2 mutations tended to have higher HR. Otherwise, novel

mutations LRP1B was associated with disease progression and tended to show

poor OS. In a previous study on ovarian cancer, LRP1B deletion was

associated with chemotherapy resistance [70]. However, the significance of

this gene is not well noted in hematologic malignancies. Because most of the

patients having LRP1B mutation have multiple other mutations, mutations in

LRP1B seem to occur late stage of clonal evolution process in MDS, and may

reflect the course of disease progression.

In summary, Korean patients with MDS also presented with mutations in

previously reported MDS-related genes. However, there were significant

differences in the mutation distribution that are possibly associated with

ethnic differences. The molecular profiling of target genes can improve the

- 68 -

diagnostic accuracy for ICUS and MDS and can permit more accurate

subclassification of prognostic groups among patients with MDS.

- 69 -

REFERENCES

1. Cazzola M and Malcovati L. Myelodysplastic syndromes--coping with ineffective

hematopoiesis. The New England journal of medicine 2005;352:536-8.

2. Sekeres MA, Schoonen WM, Kantarjian H, List A, Fryzek J, Paquette R, et al.

Characteristics of US patients with myelodysplastic syndromes: results of six cross-

sectional physician surveys. Journal of the National Cancer Institute

2008;100:1542-51.

3. Sekeres MA. Myelodysplastic syndromes: it is all in the genes. Journal of clinical

oncology: official journal of the American Society of Clinical Oncology

2012;30:774-6.

4. Swerdlow SH, Campo E, Harris NL, et al, eds. World Health Organization

Classification of Tumours of Haematopoietic and Lymphoid Tissues. In. Lyon,

France: IARC Press, 2008.

5. Verburgh E, Achten R, Louw VJ, Brusselmans C, Delforge M, Boogaerts M, et al.

A new disease categorization of low-grade myelodysplastic syndromes based on

the expression of cytopenia and dysplasia in one versus more than one lineage

improves on the WHO classification. Leukemia : official journal of the Leukemia

Society of America, Leukemia Research Fund, UK 2007;21:668-77.

6. Vardiman JW, Thiele J, Arber DA, Brunning RD, Borowitz MJ, Porwit A, et al. The

2008 revision of the World Health Organization (WHO) classification of myeloid

neoplasms and acute leukemia: rationale and important changes. Blood

2009;114:937-51.

7. Parmentier S, Schetelig J, Lorenz K, Kramer M, Ireland R, Schuler U, et al.

Assessment of dysplastic hematopoiesis: lessons from healthy bone marrow donors.

Haematologica 2012;97:723-30.

- 70 -

8. Font P, Loscertales J, Benavente C, Bermejo A, Callejas M, Garcia-Alonso L, et al.

Inter-observer variance with the diagnosis of myelodysplastic syndromes (MDS)

following the 2008 WHO classification. Annals of hematology 2013;92:19-24.

9. Font P, Loscertales J, Soto C, Ricard P, Novas CM, Martin-Clavero E, et al.

Interobserver variance in myelodysplastic syndromes with less than 5 % bone

marrow blasts: unilineage vs. multilineage dysplasia and reproducibility of the

threshold of 2 % blasts. Annals of hematology 2014.

10. Bennett JM and Orazi A. Diagnostic criteria to distinguish hypocellular acute

myeloid leukemia from hypocellular myelodysplastic syndromes and aplastic

anemia: recommendations for a standardized approach. Haematologica

2009;94:264-8.

11. Della Porta MG, Travaglino E, Boveri E, Ponzoni M, Malcovati L, Papaemmanuil

E, et al. Minimal morphological criteria for defining bone marrow dysplasia: a

basis for clinical implementation of WHO classification of myelodysplastic

syndromes. Leukemia : official journal of the Leukemia Society of America,

Leukemia Research Fund, UK 2014.

12. Vardiman JW. Hematopathological concepts and controversies in the diagnosis

and classification of myelodysplastic syndromes. Hematology / the Education

Program of the American Society of Hematology American Society of Hematology

Education Program 2006199-204.

13. Germing U, Strupp C, Kuendgen A, Isa S, Knipp S, Hildebrandt B, et al.

Prospective validation of the WHO proposals for the classification of

myelodysplastic syndromes. Haematologica 2006;91:1596-604.

14. Schanz J, Tuchler H, Sole F, Mallo M, Luno E, Cervera J, et al. New

comprehensive cytogenetic scoring system for primary myelodysplastic syndromes

(MDS) and oligoblastic acute myeloid leukemia after MDS derived from an

- 71 -

international database merge. Journal of clinical oncology : official journal of the

American Society of Clinical Oncology 2012;30:820-9.

15. Greenberg P, Cox C, LeBeau MM, Fenaux P, Morel P, Sanz G, et al. International

scoring system for evaluating prognosis in myelodysplastic syndromes. Blood

1997;89:2079-88.

16. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al.

Revised international prognostic scoring system for myelodysplastic syndromes.

Blood 2012;120:2454-65.

17. Wimazal F, Fonatsch C, Thalhammer R, Schwarzinger I, Mullauer L, Sperr WR,

et al. Idiopathic cytopenia of undetermined significance (ICUS) versus low risk

MDS: the diagnostic interface. Leukemia research 2007;31:1461-8.

18. Valent P and Horny HP. Minimal diagnostic criteria for myelodysplastic

syndromes and separation from ICUS and IDUS: update and open questions.

European journal of clinical investigation 2009;39:548-53.

19. Pavlu J, Emmerson J, Marks AJ, Bain BJ. Idiopathic cytopenia of undetermined

significance and the minimal criteria for a diagnosis of myelodysplastic syndrome.

Leukemia & lymphoma 2011;52:515-6.

20. Valent P, Bain BJ, Bennett JM, Wimazal F, Sperr WR, Mufti G, et al. Idiopathic

cytopenia of undetermined significance (ICUS) and idiopathic dysplasia of

uncertain significance (IDUS), and their distinction from low risk MDS. Leukemia

research 2012;36:1-5.

21. Schroeder T, Ruf L, Bernhardt A, Hildebrandt B, Aivado M, Aul C, et al.

Distinguishing myelodysplastic syndromes (MDS) from idiopathic cytopenia of

undetermined significance (ICUS): HUMARA unravels clonality in a subgroup of

patients. Annals of oncology : official journal of the European Society for Medical

Oncology / ESMO 2010;21:2267-71.

- 72 -

22. Ando K, Tanaka Y, Hashimoto Y, Ohyashiki JH, Sugimori N, Nakao S, et al.

PNH-phenotype cells in patients with idiopathic cytopenia of undetermined

significance (ICUS) with megakaryocytic hypoplasia and thrombocytopenia.

British journal of haematology 2010;150:705-7.

23. Kulasekararaj AG, Mohamedali AM, Mufti GJ. Recent advances in understanding

the molecular pathogenesis of myelodysplastic syndromes. British journal of

haematology 2013;162:587-605.

24. Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D, et al.

Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. The New

England journal of medicine 2011;365:1384-95.

25. Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, et al.

Frequent pathway mutations of splicing machinery in myelodysplasia. Nature

2011;478:64-9.

26. Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, et al. Clonal

architecture of secondary acute myeloid leukemia. The New England journal of

medicine 2012;366:1090-8.

27. Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, Van Loo P, et al.

Clinical and biological implications of driver mutations in myelodysplastic

syndromes. Blood 2013;122:3616-27; quiz 99.

28. Walter MJ, Shen D, Shao J, Ding L, White BS, Kandoth C, et al. Clonal diversity

of recurrently mutated genes in myelodysplastic syndromes. Leukemia : official

journal of the Leukemia Society of America, Leukemia Research Fund, UK

2013;27:1275-82.

29. Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, et al.

Landscape of genetic lesions in 944 patients with myelodysplastic syndromes.

Leukemia : official journal of the Leukemia Society of America, Leukemia

- 73 -

Research Fund, UK 2014;28:241-7.

30. Bejar R, Levine R, Ebert BL. Unraveling the molecular pathophysiology of

myelodysplastic syndromes. Journal of clinical oncology : official journal of the

American Society of Clinical Oncology 2011;29:504-15.

31. Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, et al. Recurrent

mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nature

genetics 2012;44:53-7.

32. Delhommeau F, Dupont S, Della Valle V, James C, Trannoy S, Masse A, et al.

Mutation in TET2 in myeloid cancers. The New England journal of medicine

2009;360:2289-301.

33. Langemeijer SM, Kuiper RP, Berends M, Knops R, Aslanyan MG, Massop M, et

al. Acquired mutations in TET2 are common in myelodysplastic syndromes. Nature

genetics 2009;41:838-42.

34. Bejar R, Stevenson K, Abdel-Wahab O, Galili N, Nilsson B, Garcia-Manero G, et

al. Clinical effect of point mutations in myelodysplastic syndromes. The New

England journal of medicine 2011;364:2496-506.

35. Walter MJ, Ding L, Shen D, Shao J, Grillot M, McLellan M, et al. Recurrent

DNMT3A mutations in patients with myelodysplastic syndromes. Leukemia :

official journal of the Leukemia Society of America, Leukemia Research Fund, UK

2011;25:1153-8.

36. Kosmider O, Gelsi-Boyer V, Slama L, Dreyfus F, Beyne-Rauzy O, Quesnel B, et

al. Mutations of IDH1 and IDH2 genes in early and accelerated phases of

myelodysplastic syndromes and MDS/myeloproliferative neoplasms. Leukemia :

official journal of the Leukemia Society of America, Leukemia Research Fund, UK

2010;24:1094-6.

37. Gelsi-Boyer V, Trouplin V, Adelaide J, Bonansea J, Cervera N, Carbuccia N, et al.

- 74 -

Mutations of polycomb-associated gene ASXL1 in myelodysplastic syndromes and

chronic myelomonocytic leukaemia. British journal of haematology 2009;145:788-

800.

38. Xue Y, Gibbons R, Yan Z, Yang D, McDowell TL, Sechi S, et al. The ATRX

syndrome protein forms a chromatin-remodeling complex with Daxx and localizes

in promyelocytic leukemia nuclear bodies. Proceedings of the National Academy of

Sciences of the United States of America 2003;100:10635-40.

39. Ernst T, Chase AJ, Score J, Hidalgo-Curtis CE, Bryant C, Jones AV, et al.

Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid

disorders. Nature genetics 2010;42:722-6.

40. Nikoloski G, Langemeijer SM, Kuiper RP, Knops R, Massop M, Tonnissen ER, et

al. Somatic mutations of the histone methyltransferase gene EZH2 in

myelodysplastic syndromes. Nature genetics 2010;42:665-7.

41. Christiansen DH, Andersen MK, Pedersen-Bjergaard J. Mutations of AML1 are

common in therapy-related myelodysplasia following therapy with alkylating

agents and are significantly associated with deletion or loss of chromosome arm 7q

and with subsequent leukemic transformation. Blood 2004;104:1474-81.

42. Steensma DP, Dewald GW, Lasho TL, Powell HL, McClure RF, Levine RL, et al.

The JAK2 V617F activating tyrosine kinase mutation is an infrequent event in both

"atypical" myeloproliferative disorders and myelodysplastic syndromes. Blood

2005;106:1207-9.

43. Hirai H, Kobayashi Y, Mano H, Hagiwara K, Maru Y, Omine M, et al. A point

mutation at codon 13 of the N-ras oncogene in myelodysplastic syndrome. Nature

1987;327:430-2.

44. Liu E, Hjelle B, Morgan R, Hecht F, Bishop JM. Mutations of the Kirsten-ras

proto-oncogene in human preleukaemia. Nature 1987;330:186-8.

- 75 -

45. Loh ML, Martinelli S, Cordeddu V, Reynolds MG, Vattikuti S, Lee CM, et al.

Acquired PTPN11 mutations occur rarely in adult patients with myelodysplastic

syndromes and chronic myelomonocytic leukemia. Leukemia research

2005;29:459-62.

46. Sanada M, Suzuki T, Shih LY, Otsu M, Kato M, Yamazaki S, et al. Gain-of-

function of mutated C-CBL tumour suppressor in myeloid neoplasms. Nature

2009;460:904-8.

47. Sugimoto K, Hirano N, Toyoshima H, Chiba S, Mano H, Takaku F, et al.

Mutations of the p53 gene in myelodysplastic syndrome (MDS) and MDS-derived

leukemia. Blood 1993;81:3022-6.

48. Jadersten M, Saft L, Smith A, Kulasekararaj A, Pomplun S, Gohring G, et al.

TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict

disease progression. Journal of clinical oncology : official journal of the American

Society of Clinical Oncology 2011;29:1971-9.

49. Thota S, Viny AD, Makishima H, Spitzer B, Radivoyevitch T, Przychodzen B, et

al. Genetic alterations of the cohesin complex genes in myeloid malignancies.

Blood 2014;124:1790-8.

50. Nybakken GE and Bagg A. The genetic basis and expanding role of molecular

analysis in the diagnosis, prognosis, and therapeutic design for myelodysplastic

syndromes. The Journal of molecular diagnostics : JMD 2014;16:145-58.

51. Cazzola M, Della Porta MG, Malcovati L. The genetic basis of myelodysplasia

and its clinical relevance. Blood 2013;122:4021-34.

52. Malcovati L, Papaemmanuil E, Ambaglio I, Elena C, Galli A, Della Porta MG, et

al. Driver somatic mutations identify distinct disease entities within myeloid

neoplasms with myelodysplasia. Blood 2014;124:1513-21.

53. Cazzola M, Rossi M, Malcovati L. Biologic and clinical significance of somatic

- 76 -

mutations of SF3B1 in myeloid and lymphoid neoplasms. Blood 2013;121:260-9.

54. Thol F, Friesen I, Damm F, Yun H, Weissinger EM, Krauter J, et al. Prognostic

significance of ASXL1 mutations in patients with myelodysplastic syndromes.

Journal of clinical oncology : official journal of the American Society of Clinical

Oncology 2011;29:2499-506.

55. Bejar R, Lord A, Stevenson K, Bar-Natan M, Perez-Ladaga A, Zaneveld J, et al.

TET2 mutations predict response to hypomethylating agents in myelodysplastic

syndrome patients. Blood 2014;124:2705-12.

56. Oguma S, Yoshida Y, Uchino H, Maekawa T, Nomura T, Mizoguchi H. Clinical

characteristics of Japanese patients with primary myelodysplastic syndromes: a co-

operative study based on 838 cases. Anemia Study Group of the Ministry of Health

and Welfare. Leukemia research 1995;19:219-25.

57. Lee JH, Lee JH, Shin YR, Lee JS, Kim WK, Chi HS, et al. Application of

different prognostic scoring systems and comparison of the FAB and WHO

classifications in Korean patients with myelodysplastic syndrome. Leukemia :

official journal of the Leukemia Society of America, Leukemia Research Fund, UK

2003;17:305-13.

58. Cheson BD, Greenberg PL, Bennett JM, Lowenberg B, Wijermans PW, Nimer SD,

et al. Clinical application and proposal for modification of the International

Working Group (IWG) response criteria in myelodysplasia. Blood 2006;108:419-

25.

59. International Standing Committee on Human Cytogenetic Nomenclature, Shaffer

LG, McGowan-Jordan J, Schmid M. ISCN 2013: An International System for

Human Cytogenetic Nomenclature (2013). Basel ; Unionville, CT: Karger; 2013.

60. Chapman MA, Lawrence MS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC, et

al. Initial genome sequencing and analysis of multiple myeloma. Nature

- 77 -

2011;471:467-72.

61. Wang L, Lawrence MS, Wan Y, Stojanov P, Sougnez C, Stevenson K, et al. SF3B1

and other novel cancer genes in chronic lymphocytic leukemia. The New England

journal of medicine 2011;365:2497-506.

62. Maxson JE, Gotlib J, Pollyea DA, Fleischman AG, Agarwal A, Eide CA, et al.

Oncogenic CSF3R mutations in chronic neutrophilic leukemia and atypical CML.

The New England journal of medicine 2013;368:1781-90.

63. Merker JD, Roskin KM, Ng D, Pan C, Fisk DG, King JJ, et al. Comprehensive

whole-genome sequencing of an early-stage primary myelofibrosis patient defines

low mutational burden and non-recurrent candidate genes. Haematologica

2013;98:1689-96.

64. Nangalia J, Massie CE, Baxter EJ, Nice FL, Gundem G, Wedge DC, et al.

Somatic CALR mutations in myeloproliferative neoplasms with nonmutated JAK2.

The New England journal of medicine 2013;369:2391-405.

65. Olney HJ and Le Beau MM. Evaluation of recurring cytogenetic abnormalities in

the treatment of myelodysplastic syndromes. Leukemia research 2007;31:427-34.

66. Rose D, Kohlmann A, Nagata Y, Ogawa S, Haferlach C, Kern W, et al. A robust

molecular pattern for myelodysplastic syndromes in two independent cohorts

investigated by next-generation sequencing can be revealed by comparative

bioinformatic analyses. British journal of haematology 2014;167:278-81.

67. Lee HR, Oh B, Hong DS, Zang DY, Yoon HJ, Kim HJ, et al. Cytogenetic features

of 5q deletion and 5q- syndrome in myelodysplastic syndrome in Korea; marker

chromosomes proved to be chromosome 5 with interstitial deletion by fluorescence

in situ hybridization. Cancer genetics and cytogenetics 2010;203:193-202.

68. Tenedini E, Bernardis I, Artusi V, Artuso L, Roncaglia E, Guglielmelli P, et al.

Targeted cancer exome sequencing reveals recurrent mutations in

- 78 -

myeloproliferative neoplasms. Leukemia : official journal of the Leukemia Society

of America, Leukemia Research Fund, UK 2014;28:1052-9.

69. Yu J, Wu WK, Li X, He J, Li XX, Ng SS, et al. Novel recurrently mutated genes

and a prognostic mutation signature in colorectal cancer. Gut 2014.

70. Cowin PA, George J, Fereday S, Loehrer E, Van Loo P, Cullinane C, et al. LRP1B

deletion in high-grade serous ovarian cancers is associated with acquired

chemotherapy resistance to liposomal doxorubicin. Cancer research 2012;72:4060-

73.

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국문 초록

서론: 골수형성이상증후군은 말초혈액의 범혈구감소증과 골수 내

비효율적인 조혈 및 조혈세포의 형성 이상을 특징으로 하는 클론성

조혈모세포질환으로, 이러한 특성을 공유하는 다양한 질병들의

집단으로 구성되며 결과적으로 골수기능 부전을 보이며 급성

백혈병으로 전환될 수 있는 골수성 혈액종양이다. 최근 대용량

염기서열 분석법이 개발되어 골수형성이상증후군과 관련되어 있는

새로운 유전변이들이 많이 발견되어 진단 및 예후예측에 활용되고

있으나, 한국인에 대하여 많은 연구가 이루어져 있지 않다. 이

연구에서는 다중 유전자 패널을 사용하여 한국인

골수형성이상증후군 환자의 유전 변이 특성을 확인하고 이러한

변이와 임상적 특성 및 예후와의 관계를 규명하고자 하였다.

방법: 대상 환자군으로 162 명의 다양한 골수형성이상증후군 환자와

36 명의 원인불명의 혈구감소증(Idiopathic cytopenia of undetermined

significance; ICUS) 환자들이 이 연구에 포함되었다. 표적유전자

염기서열분석은 87 개의 유전자를 선택하여 시행되었고, 돌연변이

여부의 분석은 정상인 대조군 과의 비교를 통하여 시행되었다.

결과: 전체 162 명의 골수형성이상증후군 환자들 중

134 명(82.2%)에서, 36 명의 ICUS 환자 중 25 명(71.4%)에서 적어도

하나 이상의 돌연변이가 발견되었다. 골수형성이상증후군 환자에서

변이가 발견된 유전자를 빈도가 높은 순서대로 나열하면 ASXL1,

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TP53, FAT4, U2AF1, DNMT3A, RUNX1, TET2, BCOR, SRSF2, and

NOTCH1 이 있었다. 원인불명의 혈구감소증 환자에서 변이가 발견된

유전자는 NFKBIE (3 명에서 발견됨, 8.6%), BCOR, BRD4, NOTCH1,

SAMHD1 (2 명, 5.7%). 유전자의 기능별로 흔하게 연관된 기능을

살펴보았을 때는 전사와 관련된 유전자, DNA 메틸화와 연관된

유전자, RNA splicing 과 연관된 유전자군을 나열할 수 있다. 이

연구에서는 FAT4 유전자의 돌연변이가 새롭게 높은 빈도로

발견되었다. 질환의 진행이 많이 되어 있을수록 돌연변이의 개수가

증가하였으며, NRAS, TP53 과 WT1 등의 돌연변이는 나쁜 예후와

연관되어 있었다.

결론: 이 연구는 한국인 골수형성이상증후군 환자에서 돌연변이

분포 연구를 조사한 최초의 대규모 연구이다. 한국인

골수형성이상증후군 환자에서도 이전에 보고되었던 유전변이들이

높은 빈도로 발견되었다. 하지만 이전의 연구들과 비교하여

돌연변이의 분포에서 차이를 보여 인종적 차이가 있음을 알 수

있었다. 골수형성이상증후군에서의 다중 유전자이상 분석은

원인불명의 혈구감소증과 골수형성이상증후군의 진단의 정확성을

높일 수 있고, 환자 예후 분류에 도움을 줄 수 있다.

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

주요어 : 골수형성이상증후군, 차세대염기서열분석, 유전자 변이

학 번 : 2013-30820

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