Spliceosome mutations exhibit specific associations with epigenetic modifiers and proto-oncogenes mutated in myelodysplastic syndrome

by Syed A. Mian, Alexander E. Smith, Austin G. Kulasekararaj, Aytug Kizilors, Azim M. Mohamedali, Nicholas C. Lea, Konstantinos Mitsopoulos, Kevin Ford, Erick Nasser, Thomas Seidl, and Ghulam Mufti

Haematologica 2013 [Epub ahead of print]

Citation: Mian SA, Smith AE, Kulasekararaj AG, Kizilors A, Mohamedali AM, Lea NC, Mitsopoulos K, Ford K, Nasser E, Seidl T, and Mufti G. Spliceosome mutationsexhibit specific associations with epigenetic modifiers and proto-oncogenes mutated in myelodysplastic syndrome. Haematologica. 2013; 98:xxx doi:10.3324/haematol.2012.075325

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1 

 

Spliceosome mutations exhibit specific associations with epigenetic

modifiers and proto-oncogenes mutated in myelodysplastic syndrome

Syed A. Mian1, Alexander E. Smith1, Austin G. Kulasekararaj1,2, Aytug Kizilors2,

Azim M. Mohamedali1, Nicholas C. Lea2, Konstantinos Mitsopoulos 3, Kevin Ford1,

Erick Nasser1, Thomas Seidl,1 and Ghulam J. Mufti1,2

SAM and AES contributed equally to this manuscript

1King’s College London School of Medicine, Department of Haematological Medicine,

London, SE5 9NU, UK; 2 Kings College Hospital, Department of Haematology, London SE5

9RS,UK, and 3Kings College Hospital, Department of Histopathology, London SE5 9RS,UK.

3. ICR Breakthrough Breast Cancer

Correspondence

Ghulam J. Mufti, King’s College London, Department of Haematological Medicine,

The Rayne Institute 123 Coldharbour Lane, London, SE5 9NU, United Kingdom.

Phone: international +44.203.3463080. Fax: international +44.203.3463514.

E-mail: ghulam.mufti@kcl.ac.uk 

DOI: 10.3324/haematol.2012.075325

2

ABSTRACT

Background

Recent identification of acquired mutations in key components of the spliceosome machinery

strongly implicates abnormalities of mRNA splicing in the pathogenesis of myelodysplastic

syndromes. However, questions remain as to how these aberrations functionally combine

with the growing list of mutations in genes involved in epigenetic modification and cell

signalling/transcription regulation identified in these diseases.

Design and Methods

In this study, amplicon sequencing was used to perform a mutation screen in 154

myelodysplastic syndrome patients using a 22-gene panel, including commonly mutated

spliceosome components (SF3B1, SRSF2, U2AF1, ZRSR2), and a further 18 genes known to

be mutated in myeloid cancers.

Results

Sequencing of 22-gene panel revealed that 76% (n=117) of the patients had mutations in at

least one of the genes, with 38% (n=59) having splicing gene mutations and 49% (n=75)

patients harbouring more than one gene mutation. Interestingly, single and specific epigenetic

modifier mutations tended to coexist with SF3B1 and SRSF2 mutations (p<0.03).

Furthermore, mutations in SF3B1 and SRSF2 were mutually exclusive to TP53 mutations

both at diagnosis and time of disease transformation. Moreover, mutations in FLT3, NRAS,

RUNX1, CCBL and C-KIT were more likely to co-occur with splicing factor mutations

generally (p<0.02), particularly in SRSF2 mutants (p<0.003) and were significantly

associated with disease transformation (p<0.02). Patients with SF3B1 and TP53 mutations

had varying impacts on overall survival with hazard ratios of 0.2 (p<0.03, 95% CI, 0.1- 0.8)

and 2.1 (p<0.04, 95% CI, 1.1- 4.4), respectively. Moreover, patients with splicing factor

mutations alone had a better overall survival than those with epigenetic modifier mutations,

or cell signalling/transcription regulator mutations with and without coexisting mutations of

splicing factor genes, with worsening prognosis (p<0.001).

Conclusion

These findings suggest that splicing factor mutations are maintained throughout disease

evolution with emerging oncogenic mutations adversely impacting on patient outcome,

implicating spliceosome mutations as founder mutations in myelodysplastic syndrome.

DOI: 10.3324/haematol.2012.075325

3

Introduction

Myelodysplastic syndrome (MDS) comprise a heterogeneous group of clonal hematopoietic

stem-cell disorders with diverse phenotypes, characterized by varying severity of ineffective

haematopoiesis, bone marrow dysplasia, rate of progression to acute myeloid leukaemia

(AML), overall survival (OS) and response to therapy 1-3

. Cytogenetic abnormalities are

detected in up to 60% of patients where the type and complexity of these aberrations

correlates with progression, leukaemia transformation and response to therapy2,4

.

Furthermore, application of high-density single nucleotide polymorphism (SNP) arrays has

led to the enhanced detection of smaller chromosomal aberrations, including micro deletions

or uniparental disomy (UPD) with an overall loss of heterozygosity (LOH)5-7

.

Recent studies have reported mutations involving multiple components of the mRNA splicing

machinery including SF3B1, SRSF2, U2AF1, ZRSR2, PRPF40B, U2AF65 and SF1 in patients

with MDS, myeloproliferative disorder (MPN) and AML8-19

. Moreover, the most frequently

mutated spliceosome component in MDS, SF3B1 (30% of cases), is aberrant in 70-85% of

refractory anaemia with ringed sideroblasts (RARS) cases and is highly associated with the

presence of ringed sideroblasts8,12

. Fundamentally however, the influence of such SF3B1

mutations is not just in myeloid tissue and RARS, but has now been observed in chronic

lymphocytic leukaemia (CLL) and lymphoid tissue20-22

, suggesting that genetic background

plays an important role in the functional manifestation of spliceosome aberrations.

Over the past decade a number of novel gene mutations have been identified that are

associated with MDS including genes involved in epigenetic regulation (TET223,24

,

DNMT3A25,26

, IDH1/226,27

, ASXL128,29

and EZH230,31

), suggesting an underlying genomic

instability or aberrant transcription regulation in the evolution of this disease. Moreover, the

occurrence in MDS of known oncogenic mutations or mutations in genes involved in cell

signalling/transcription regulation has also been extensively studied in recent years, including

TP53 (8%)32

, NRAS/KRAS, RUNX1 (9%)33,34

, FLT3 (6%)35,36

, ETV6 (3%)34

and CCBL

(2.3%)34,37

. In fact, around 80% of MDS patients have defects in one or more of these

‘epigenetic’ or ‘oncogenic’ factors. A recent study by Bejar et al. showed that mutations in 5

genes (TP53, RUNX1, EZH2, ASXL1 and ETV6) are independent predictors of poor overall

survival in patients with MDS34

. Data from this study and elsewhere have shown that TP53

remains the only gene with a statistically robust prognostic impact in MDS. However,

aberrations in DNMT3A38

and FLT339

which have previously been attributed prognostic

significance were not analysed in this study. In an another study, Thol et al studied various

epigenetic, cell cycle/apoptotic genes and spliceosome components in 193 patients with

MDS, and found SRSF2 mutations were associated with RUNX1 and IDH1 mutations while

as U2AF1 mutations were associated with ASXL1 and DNMT3A mutations. In addition to

this, SRSF2 mutations were associated with poor OS and more frequent AML progression11

.

However, several genes including FLT3, CCBL, JAK2, TET2 and EZH2 which are frequently

mutated in MDS were not analysed in these patients. Therefore, to gain a better

understanding of spliceosome aberrations and how they interact with other coexisting

mutations, as well as to determine their prognostic significance in isolation or in combination

with other mutations, we performed a comprehensive mutation screen in 154 MDS patients.

DOI: 10.3324/haematol.2012.075325

4

Design and Methods

Clinical data and patient samples

Eight MDS patients (5 RARS, 1 RARS-T, 1 RCMD-RS and 1 tMDS) with >50% ringed

sideroblasts were initially selected for whole-exome sequencing. An additional 146 MDS

patients were selected for mutation analysis of the splicing pathway genes, epigenetic

modifiers and other cell signalling/transcription regulator genes, followed by validation of the

candidate mutations. Patients with MDS seen at King’s College Hospital from June 2004 to

June 2011 were enrolled in this study. All patients had provided written informed consent in

accordance to National Research Ethics Protocol (KCLPR060 PR029). Demographic and

clinical characteristics of the studied patients are detailed in Table 1. All patients were risk

stratified according to IPSS categories. The clinical variables, FAB, WHO subtype and the

prognostic risk of all patients were ascertained at the time of sample collection. The median

follow-up was 21.4 months (range, 1-83months). The cohort was followed up to January

2012 for disease progression, and survival. The survival data for patients who underwent

allogeneic haematopoietic stem cell transplant (HSCT) (n=35, 22%) were censored on the

day of transplant and the treatments received by other patients are annotated in

supplementary table 1.

DNA was extracted from CD34+ cells (n=8), CD34-CD3+ (n=18), CD34-CD235+ (n=1),

CD34-CD235+CD71+ (n=3), CD34-CD235- (n=3), CD34-CD3+CD4+ (n=3), CD34-CD19+

(n=3), Skin (n= 27), buccal swab (n=3) and bone marrow total nucleated cells (n=154) using

QIAamp DNA extraction kit (Qiagen) according to the manufacturer’s protocol. Out of 154

cases, whole-genome-amplified DNA was used in 40 cases for mutation screening.

Amplicon Sequencing

Mutation screening for SF3B1, SRSF2, U2AF1, ZRSR2, TP53, FLT3, DNMT3A, ASXL1,

EZH2, NRAS, KRAS, JAK2, CCBL, RUNX1, CEBPA, BRAF, MPL, NPM1, IDH1, IDH2, C-

KIT and TET2 was performed on bone marrow total nucleated cell DNA using the Roche GS

FLX platform as described previously40

. We sequenced all coding exons for ZRSR2, TP53,

DNMT3A, EZH2, RUNX1, CEBPA and TET2. This included mutation data of TET2 for 142

cases that was available from our previously published study40

. Mutational hotspots were

specifically sequenced for SF3B1, SRSF2, U2AF1, ASXL1, CCBL, FLT3, NRAS, KRAS, JAK2

exon12/14, IDH1, IDH2, BRAF, MPL, C-KIT and NPM1 in all cases. Gene hotspot regions

were selected based on the previously published data and frequency of the mutations shown

in COSMIC database (Supplementary table 2). In addition, all coding exons of SF3B1 were

sequenced for all 24 RARS/RCMD-RS patients. SRSF2, U2AF1 and ZRSR2 genes were

amplified by using polymerase chain reaction (PCR) primers published previously41

. PCR

primers for all the genes are shown in supplementary table 3. PCR and sequencing

methodology has been described previously40

. The average sequencing coverage across all

genes was 200X and >90% of the coding regions had a coverage of >100X. This coverage

enabled us to reliably detect mutant clones down to ≥5-10% mutant allele burden, defined as

the proportion of sequence reads containing the mutation. All mutations were confirmed

through independent PCR and GS FLX sequencing/Sanger sequencing experiments. For data

relating to samples at/prior to transformation to AML, where 454-amplicon sequencing data

was not available, relative peak intensity from Sanger sequencing was used to estimate

mutant allele burden. The acquired status of novel mutations was also confirmed in 48/54

cases for whom constitutional source of DNA was available: skin biopsy (n=27), CD3+ T-

cells (n=18) and Buccal swab (n=3) (Supplementary table 6).

DOI: 10.3324/haematol.2012.075325

5

Exome sequencing

Eight patients with >50% ringed sideroblasts were selected for whole-exome sequencing,

using DNA from CD34+ cells. The exomic regions of the genome were enriched using

Agilent SureSelect Human All Exon Kit and paired end sequencing was performed using

Illumina HiSeq 2000 (version 3 chemistry) (See Supplementary methods).

Statistical analysis

Statistical calculations were performed using SPSS version 17.0 (SPSS Inc.) as described in

supplementary methods. A p-value of ≤0.05 was considered statistically significant.

Results

Somatic mutations in MDS

Whole-exome sequencing (Illumina) using CD34+ cells from 8 RARS patients initially

revealed mutations in splicing factors 3b subunit 1 (SF3B1) in 7/8 cases (see supplementary

results; supplementary table 4 and 5).

We next utilized a 22-gene amplicon sequencing panel for genes known to be mutated in

MDS for an additional 146 MDS patients comprising: splicing factor genes SF3B1, SRSF2,

U2AF1 and ZRSR2; genes implicated in epigenetic regulation TET2, IDH1/2, ASXL1, EZH2

and DNMT3A; and known oncogenes/genes involved in cell signalling/transcription

regulation TP53, FLT3, NRAS, KRAS, RUNX1, CCBL, C-KIT, JAK2, MPL, CEBPA, BRAF

and NPM. Mutations in any one or more of these genes were detected in 76% (n=117) of the

cohort, with 38% (n=59) of the patients having splicing factor mutations and 49% (n=75) of

patients harbouring more than one mutation (figure 1 and supplementary table 6). Known

single nucleotide polymorphisms (SNPs) and insertion/deletion variants listed in national

Center for Biotechnology informations SNP database (dbSNP, build 135) and previously

reported as germ line were excluded from further analysis. Novel mutations present in 48/54

cases which had not been previously reported in the literature were confirmed as acquired by

their absence in paired constitutional DNA. The remaining 7 novel variants (3 stop codons, 1

frame-shift mutation, 1 in-frame-shift deletion and 2 splice-site mutation) detected in 6

patients were also included in the analysis although constitutional source of DNA was not

available (supplementary table 6). The nature of these mutations makes them unlikely to be

benign inherited variants. The reminder of the mutations identified in this study had

previously been reported in literature as acquired mutations.

Spliceosome gene mutations - frequency and clinical correlates

Sequencing of the splicing factor genes revealed 61 mutations in 38% (59 of 154) of MDS

patients (table 1 and supplementary table 6), comprising SF3B1 16% (n=24), SRSF2 13%

(n=20), U2AF1 10% (n=15) and ZRSR2 1% (n=2). WHO subgroups for mutations of

splicing factor genes included, RARS/RCMD-RS (20/24, 83%), CMML or MDS/MPN (9/14,

64%), sAML (6/15, 40%), RAEB-1/2 (13/49, 27%), RA/RCMD (10/40, 25%), but were

uncommon in tMDS (1 of 12). Importantly, splicing factor mutations were common in

DOI: 10.3324/haematol.2012.075325

6

low/int-1 IPSS (36/68, 53%) compared to int-2/high risk IPSS categories (12/63, 19%,

p<0.002). Furthermore, with the exception of one case, all patients with isolated splicing

factor mutations and no additional coexisting mutations (n=21) belonged to low/int-1 IPSS.

Patients with complex karyotypes were less likely to harbour any splicing factor mutations as

compared with good and intermediate risk IPSS cytogenetic groups (13 % vs 47% and 44%,

p<0.002).

Overall, 24 of 154 (16 %) MDS patients had a somatic mutation of SF3B1, however the

frequency of SF3B1 mutation was significantly higher in the RARS/RCMD-RS (20/24, 83%)

as compared to other WHO categories (4/130, 3%, p<0.001). Although there were no

significant differences in patients between the groups with respect to age, sex, blast

percentage and neutrophil count, SF3B1 mutations correlated strongly with lower

haemoglobin (median-8.9 vs 10.1 g/dl, p<0.006), higher platelet count (median-296 vs 102 x

109/l, p<0.001), low/int-1 risk IPSS score (31% vs 0%, p<0.001), normal cytogenetics (24%

vs 0%, p<0.002), transfusion dependency (21% vs 9%, p<0.03) and a decreased likelihood of

leukaemic progression (4% vs 15%, p<0.02) when compared with wild type SF3B1.

Among the 154 MDS patients, 20 had SRSF2 mutations (13%) and showed a significantly

high neutrophil count (median-11 vs 2.8 x 109/l, p<0.001) and higher haemoglobin levels

(median 10.9 vs 9.8 g/dl, p<0.03) compared to patients with wild type SRSF2. There was no

difference in platelet count or transfusion dependency rate between the groups. SRSF2

mutations were more frequently seen in patients with MDS/MPN or CMML (50%) and

RAEB-1/2 (14%), but was absent in low risk IPSS including patients with ringed

sideroblasts. Interestingly, both patients with isochromosome17q (n=2) had mutations of the

SRSF2 gene which maps to 17q25.1. There was significant difference in rates of leukaemic

progression in patients with mutant SRSF2 compared with wild type (50% vs 24%, p<0.02),

with two thirds of patients with co-existing SRSF2 and cell signalling/transcription regulator

mutations progressing to AML.

U2AF1 mutations were detected in 15 (10%) patients with clustering in male patients

(12/15, 80%) and was also associated with lower haemoglobin (median-9 vs 10 g/dl, p<0.05)

when compared to wild type U2AF1, but no significant differences in age, platelets,

neutrophils, transfusion dependence, IPSS score or WHO subtype, were observed between

the two groups. ZRSF2 mutations were detected in only two (1%) MDS patients.

Interestingly, splicing factor mutations were largely mutually exclusive to each other, with

only 2 patients having two separate spliceosome gene mutations, one with mutations in

SF3B1 and U2AF1 whilst the other patient had mutations in SF3B1 and ZRSR2 genes.

Splicing factor mutations- Type, site and allele burden

All SF3B1 mutations were non-synonymous amino acid substitutions with an average mutant

allele burden of 41% (n=24), indicative of a heterozygous state. Amino acids affected were

K700E(n=11), H662Q(n=4), K666Q/R(n=2), E622D(n=2), D781G(n=1) and R625C(n=1)

and clustered in the protein c-terminal HEAT motifs implicated in snRNP stabilization within

the U2 snRNP complex of the major spliceosome42

.

A majority of SRSF2 mutations were similarly non-synonymous amino acid substitutions,

with a heterozygous profile and an average mutant allele burden of 37.5% (n=20), comprising

P95H/L/R (n=16) changes as previously reported9. Importantly, a novel 24 base pair deletion

in SRSF2 causing the frameshift mutation Y93fsX121 was detected in 4 patients, which

would be predicted to cause loss of protein function.

Contrastingly, U2AF1 mutations had a lower average mutant allele burden of 26.7% (n=15)

and were exclusively S34F (n=4) or Q157P/R/H (n=11) amino acid changes, found within the

DOI: 10.3324/haematol.2012.075325

7

amino- and the carboxyl-terminal zinc finger motifs respectively, flanking the U2AF

homology motif (UHM) domain. On the other hand, ZRSR2 mutations had a much higher

average mutant allele burden of 60.5% (n=2), were distinct in nature and did not cluster in the

same protein domain, comprising in-frame deletion (S439_R440del) and a frame-shift

deletion E133Gfs11X.

Prevalence of mutations in Epigenetic modifiers and Cell Signalling /Transcription

regulators

The overall frequency of mutations of genes involved in epigenetic regulation (TET2, ASXL1,

DNMT3A, IDH2, EZH2 and IDH1), cell signalling/transcription regulators (FLT3, RUNX1,

NRAS, C-KIT, CCBL, JAK2 and MPL) and mutations in tumour suppressor gene TP53 were

52% (n=80), 18% (n=28) and 12 % (n=19), respectively (Table 1 and Table 2). Interestingly,

mutations predicted to effect epigenetic regulation were detected in nearly half of the MDS

patients, with TET2 mutations being the most frequent in 22% (n=34) of the cohort. The

frequency of other mutations was ASXL1 (17%, n=26), DNMT3A (10%, n=15), IDH2 (8%,

n=13), EZH2 (7%, n=11) and IDH1 (1%, n=2). Although mutations in epigenetic modifiers

clustered in female patients (64% vs 25%, p<0.05), there was no differences in the WHO

subtypes, IPSS score, transfusion dependency or leukaemic transformation rate when

compared with cases wild type for genes involved in epigenetic regulation. Furthermore,

mutations in genes involved in cell signalling/transcription regulation were detected in 18%

of MDS patients with mutations of NRAS and RUNX1 each present in 6% (n=9 each), CCBL

(4%, n=6), FLT3 (3%, n=4) and JAK2 (2%, n=3). C-KIT and MPL mutations were detected in

one patient each. Overall patients with these mutations, with the exception of JAK2 and MPL,

were associated with high risk MDS, increased blast count, transfusion dependency and

increased likelihood of leukaemic transformation when compared to their wild type

counterparts (p<0.01). No BRAF, NPM and KRAS mutations were found in our cohort of

patients.

Mutual exclusivity of TP53 with spliceosome components

TP53 mutations were detected in 12% (19/154) of cases. TP53 mutations were infrequent in

patients with splicing factor mutations (5%, 3/59) compared to patients with wild type

splicing factor genes (17%, 16/99, p<0.04). However, among patients with splicing factor

mutations, all three TP53 mutations were observed exclusively in those who had mutations in

spliceosome component U2AF1 (20% vs 0%, p<0.01).

Coexistence and exclusivity of splicing factor mutations with commonly mutated

epigenetic modifiers and Cell signalling/transcription regulating genes

Of the 59 patients with spliceosome mutations, 16 (27%) had isolated splicing factor

mutations, whilst 28 (48%) and 15 (25%) had mutations in epigenetic modifiers and cell

signalling/transcription regulator mutations respectively, including 8 patients with coexisting

mutations from all 3 mutation classes (Figure 1; Supplementary figure 1A and 1B) (table 2).

Regardless of disease subtypes, MDS cases with non-SF3B1 splicing factor mutations had

significantly more mutations in other genes screened here (mean 2.35 mutations/case), than in

patients with SF3B1 mutations (mean 1.85 mutation/ case, p<0.03).

Furthermore, mutations of epigenetic modifiers were associated with mutant SRSF2

compared to wild type (70% vs 50%, p<0.07), which was predominantly due to the presence

DOI: 10.3324/haematol.2012.075325

8

of more TET2 mutations with mutant SRSF2 than wild type (40% vs 25%, p<0.04). Patients

with splicing factor mutations were less likely to have multiple epigenetic modifier mutations

(14%, n=5) compared to patients with wild type splicing factors (33%, n=15) (p<0.03),

although if present, multiple mutations of epigenetic modifiers more often coexisted with

U2AF1 (n=4) rather than SF3B1 or SRSF2 (n=1) mutations. DNMT3A mutations were less

likely to be seen with SRSF2 mutations compared with other spliceosome mutations (0% vs

15%, p<0.08), whilst ASXL1 mutations were more likely to co-occur with SF3B1 than other

splicing factor mutations (23% vs 4%, p<0.07), indicating non-random mutation associations

and tolerances. A trend towards an association between IDH2 and U2AF1 was observed,

when compared with other splicing mutations (p<0.06).

Significantly, for all MDS cases, mutations of genes involved in cell signalling/transcription

regulation (FLT3, NRAS, CCBL, RUNX1, JAK2, MPL and C-KIT) clustered with splicing

factor mutations (27% vs 13%, P<0.02). When splicing factor mutations were looked at

individually, these non-TP53 mutations co-existed frequently with SRSF2 mutations (50% vs

13%, p<0.003), compared to MDS patients with wild-type SRSF2. Furthermore, when

considering all 59 cases of splicing factor mutants alone, mutations of FLT3, NRAS, CCBL,

RUNX1, JAK2, MPL, C-KIT and TP53 were significantly less often associated with SF3B1

mutations compared with other splicing factor alteration cases (8% vs 34%, p<0.009). SRSF2

mutations co-existed with mutations of cell signalling/transcription regulation genes,

especially with alterations of NRAS (p<0.04) and FLT3 (p<0.03), compared with non-SRSF2

splicing factor mutations (40% vs 13%, p<0.02). Furthermore, NRAS mutations (n=9) were

mutually exclusive to aberrations of epigenetic modifiers IDH2 (n=13) and EZH2 (n=11).

CEBPA mutations co-existed significantly with mutant SF3B1 than with other splicing factor

mutations (21% vs 0%, p<0.008).

The average mutant allele burden for SF3B1 and SRSF2 mutations was 41% (24 cases) and

37.5% (20 cases), respectively. Coexisting point mutations present alongside with these

splicing factor mutations had an average mutant allele burden of 35% and 37.5%,

respectively, where the mutant allele burden of epigenetic modifiers (38% and 39%) was

higher than cell signalling/transcription regulator mutations (27.5% and 28.75) for SF3B1 and

SRSF2, respectively. Contrastingly, U2AF1 mutations had a lower average allele burden of

26.7% (15 cases) with 38.8% average mutation allele burden of other coexisting mutations

where the burden of the cell signalling/transcription regulator mutations (45%) was higher

than epigenetic modifier mutations (33%). This seemed to be due to the U2AF1/TP53 mutant

cases which had a relatively higher TP53 than U2AF1 mutant allele burden in 2/3 cases.

ZRSR2 mutation allele burden was 60.5% (2 cases) on average.

Sequential acquisition of cell signalling/transcription regulating gene mutations in

SF3B1 mutant clones with disease transformation

Only 2 of 24 patients with RARS/RCMD-RS and SF3B1 mutations evolved to AML at

variable time points after diagnosis (Patient UPN RC060337, 4 months; patient UPN

RC090006, 36 months). To investigate the contributions of SF3B1 and coexisting mutations

in disease evolution we screened relevant sequential samples for both these cases.

Sequencing analysis of the samples taken from patient UPN RC060337 (A) and UPN

RC090006 (B) at the time of diagnosis (A) and at presentation to our institute (B, 24 months

after diagnosis) revealed SF3B1 mutation with a mutant allele burden of 39% and 42%,

respectively (exampled for patient A in Figure 2 and supplementary figure 2). A TET2

mutation was detected at diagnosis, with a mutant allele burden of 60%, in patient A (figure

DOI: 10.3324/haematol.2012.075325

9

2) and a RUNX1 mutation in patient B. However, patient B transformed to AML and

underwent allogeneic HSCT in morphological remission, following induction chemotherapy.

He relapsed with AML and lost his donor chimerism, shortly after HSCT. The SF3B1 and

RUNX1 mutation burden was maintained at the same level at the RARS stage and also at

AML phase after HSCT in patient B. Likewise, SF3B1 and TET2 mutations were maintained

at the same mutant allele burden at transformation in patient A. Interestingly, at the time of

transformation to AML, patient A also acquired a mutation in RUNX1 (F163Y) with a mutant

allele burden of ≈30%, and a FLT3-ITD (F590_W603dupInsP) with a mutant allele burden of

≈50% (Figure 2 and Supplementary figure 2). Following intensive chemotherapy, patient (A)

attained a transient morphological remission but relapsed promptly with FLT3-ITD and

RUNX1 mutant allele burden increasing to ≈80% and ≈45% respectively. Importantly, the

mutation allele burden of SF3B1 and TET2 genes remained constant at around 40% to 50%

throughout the disease duration, which as a heterozygous mutation would occur in a majority

of sample cells. As such, RUNX1 and FLT3 mutations seemed to have evolved from the

SF3B1/TET2 clonal population and coexist in the same cells (Supplementary figure 2).

Prognostic significance of mutations

The median OS of the entire cohort was 34.4 months [(95% confidence interval (CI), (16.9 to

51.9 months)]. Univariate analysis revealed that patients with SF3B1 mutations had a better

OS [Not Reached (NR) vs 24.2 months, p<0.003] and progression free survival (PFS) (NR vs

40.3, p<0.02) than wild type SF3B1 (Figure 3A and 3B), whilst none of the other splicing

factor gene mutations had an impact on either outcome measure. The median OS of patients

with either of splicing factor mutations was significantly better than wild type spliceosome

(NR vs 24.2 months, p<0.03), but no difference in PFS was seen between the groups (Figure

3C and 3D).

On a univariate model, there was no significant difference in either OS or PFS in patients

with other individual mutations compared to wild type, except for TP53 and NRAS

(Supplementary figure 3A-3D). Among patients with epigenetic modifier mutations (n=80), a

proportion also had coexisting splicing factor mutations (n= 36) and 44 patients had

epigenetic modifier mutations alone. A trend towards a better survival was seen in the group

of patients with epigenetic modifier mutations with splicing gene mutations compared with

the rest (p<0.06), although the distribution of mutations of genes involved in cell signaling

/transcription regulation and TP53 mutations were evenly distributed in both groups

(Supplementary figure 4A and 4B).

Interestingly, patients harboring both mutations of splicing factor and of genes involved in

cell signaling/transcription regulation or TP53 (n=15) had an extremely poor OS (15.8

months vs NR, p<0.009) and PFS (12.5 months vs NR, P<0.001) when compared with

spliceosome mutations without mutations of genes involved in cell signaling/transcription

regulation (n=44), (Figure 4A and 4B) although this group had only 3 patients with TP53

mutation.

Multivariable analysis using variables: age at sample, WHO category, medullary blast count,

IPSS cytogenetic groups, transfusion dependency status, and SF3B1, NRAS, TP53 mutations,

revealed that NRAS mutations did not have impact on OS or PFS. Patients with SF3B1 and

TP53 mutations had varying impacts on OS with hazard ratios (HR) of 0.2 (p<0.03, 95% CI,

0.1- 0.8) and 2.1 (p<0.04, 95% CI, 1.1- 4.4), respectively. None of the analyzed genotypes,

including SF3B1 and TP53, impacted on PFS in multivariable model (supplementary Table

7).

DOI: 10.3324/haematol.2012.075325

10

Discussion

In our study, 76% of MDS patients had mutations of the genes screened in this study, with

nearly 50% of patients having more than one mutation. Splicing factor genes, SF3B1, SRSF2,

U2AF1 and ZRSR2 were mutated in 38% of patients. Although collectively these mutations

were found in a wide spectrum of MDS subtypes, some were strongly associated with

specific disease features. SF3B1 mutations for example were strongly correlated with high

levels of ringed sideroblasts and were therefore found in 80% of RARS/RCMD-RS cases, as

reported in other studies8. Conversely, SRSF2 and U2AF1 mutations were often seen in

advanced forms of MDS such as RAEB and CMML respectively, which fitted well with a

higher number of coexistent mutations in genes involved in cell signalling/transcription

regulation (e.g. NRAS, FLT3 and RUNX1) with known oncogenic functions.

Occurrence of SF3B1 mutations has been linked to significantly better OS and in some

instances longer, leukaemia-free and event-free survival in RARS8. Similarly we show a

beneficial independent prognostic impact for SF3B1 mutations on outcome, especially OS. In

accordance with this observation is an absence of coexistent TP53 aberrations, which is a

strong predicator of poor OS, according to data presented both here and elsewhere 34

.

Furthermore, data from a cohort of 317 MDS patients indicated no influence of SF3B1

mutations on OS or time to leukaemic progression19

. In addition to this, previous studies have

also linked mutations of U2AF114,16

and SRSF214

with an increased risk to AML progression

and/or shorter OS. However, another study by Thol et al demonstrated only SRSF2 mutations

were associated with shorter OS as well as time to AML progression and not mutations in

U2AF111

.We could only demonstrate a correlation of SRSF2 mutations with progression to

AML, but no impact on OS. U2AF1 mutations similarly did not have any impact on the

outcome in our study. Although NRAS and TP53 mutations had impact on outcome, only

TP53 retained significance in a multivariate model.

Within the spliceosome mutant group, SF3B1 seemed to be the strongest driver of a

beneficial effect and the only spliceosome factor independently attributed with better OS and

PFS. Such a model possibly suggests that splicing factor mutations are early disease events

and define a ‘founder’ disease clone that subsequently develops additional, increasingly

deleterious mutations during the natural course of the disease, clonal evolution and disease

progression. Furthermore, as particular splicing factor mutations differ in their patterns of

association with other mutations, this suggests a hierarchy of tolerated mutational load and

limitation to particular paths of disease evolution. For example, a dearth of co-existent TP53

mutations throughout spliceosome mutant cases and mutual exclusivity of TP53 mutations

with SF3B1 and SRSF2 mutants at the time of diagnosis and disease transformation further

supports a restrictive pattern of genetic insults. Furthermore, a restrictive pattern of additional

coexisting mutations previously linked with disease transformation, such as FLT3 and

RUNX1, are seen alongside SF3B1 mutations, although this might be expected in low risk

disease such as RARS/RCMD-RS, which make up the majority of SF3B1 mutants. However,

it is important to note that SF3B1 mutant cases which gained such oncogenic mutations

during transformation maintained a constant SF3B1 mutant allele burden, indicating

evolution within the same disease clone population.

Interestingly, SRSF2 mutants, which are present both in low and high risk disease cases,

similarly did not coexist with TP53 mutations here, but rather FLT3, NRAS or RUNX1 mutant

oncogenes, as was the case for transformed SF3B1 mutants. Conversely, U2AF1 mutations

were found to coexist with TP53 mutations here, although the U2AF1 mutation burden was

significantly lower than that of TP53 in 2/3 of these cases, where TP53 mutation burden was

high and consistent with LOH on chromosome 17p confirmed by metaphase cytogenetics in

DOI: 10.3324/haematol.2012.075325

11

one of these cases. Furthermore, FLT3 and NRAS mutations were independently more likely

to occur with SRSF2 mutations, accentuating the leukaemic transformation rate in these

patients, but were less likely to occur with SF3B1 mutant cases. The questions therefore

remains, are such mutual exclusivities driven at a molecular level and due to a lethal

combination of genetic lesions and do mutations in different spliceosome components carry

different weights in terms of biological importance and different thresholds of additional

insults that a particular mutant clone can endure?

Significantly, mutations of epigenetic modifiers also seemed to fall in with particular splicing

factor mutations. For example, ASXL1 mutation was less likely to coexist with SF3B1

mutations compared to SRSF2 and U2AF1 mutations cases, whilst DNMT3A mutations were

not found with SRSF2 but coexisted with SF3B1 mutants (4/24). Again this highlights the

importance of genetic background and how particular mutation combinations may attain an

acceptable biochemical equilibrium and stable disease. It is of particular interest that only

single epigenetic modifier mutations seemed to co-occur with SF3B1 and SRSF2 mutations,

which would fit the theory that such mutations represent early events in disease where

numerous clonal genetic lesions or separate disease clones have not yet evolved. This is again

highlighted in the case of TET2 mutations, reasoned as early disease events and which do not

add a prognostic value in MDS24,40

, which were more likely to be found in MDS cases with

mutant spliceosome in our patient cohort, especially SRSF2 mutations.

Mutation analyses performed on serial samples in patients who were initially diagnosed with

RARS and subsequently transformed to AML, also provides us with clear evidence that

SF3B1 mutations are an early ancestral event. In these cases, the SF3B1 mutant clone

survives various treatments during the course of the disease, acquiring additional oncogenic

mutations such as FLT3 and/or RUNX1, enabling the disease to evolve. Supporting this

theory, splicing factor mutants in isolation have a better OS in MDS cases generally, which

seemingly worsens as additional mutant genes are added to the genetic makeup. Hence, the

contribution of SF3B1 mutations in disease transformation or progression to AML may be

limited, but they may instead provide a favourable environment or sufficient pressure for

other more destabilizing mutations to occur.

We initially performed whole-exome sequencing on CD34+ cells as such progenitors are

likely to provide the reservoir of myeloid mutations, as has been previously reported40,43

. The

fact that SF3B1 mutations are present in CD34+ and in differentiated CD235+CD71+ cells,

but not in T or B-cells in MDS, further reinforces their importance and link to a clonal

advantage through the course of myeloid disease, particularly in RARS.

Functional characterization of spliceosome mutations and their contribution to

myelodysplasia is still unclear8. However, a recent study by Visconte et al has implicated the

role of SF3B1 aberrations in the formation of ring sideroblasts in MDS44

. Furthermore, a

majority of splicing factor mutations are heterozygous and often clustered in particular

protein functional domains, indicating an altered gain-of-function and underlining biological

significance. In in-vitro knockdown experiments of spliceosome components, SF3B1 and

U2AF1 have been linked to aberrant cell cycle characteristics, cell cycle arrest and increased

apoptosis, where aberrant splicing of cell-cycle genes has been noted9,45

. Furthermore, a

study by Yoshida et al has reported that overexpression of mutant U2AF1 gene in mice leads

to reduced reconstitution capacity of haematopoietic stem cells9. However, heterozygous

knockout of SF3B1 in mice elsewhere was not linked to altered splicing activity itself but

rather altered interactions with polycomb proteins leading to deregulation of gene

expression46

. This finding adds further weight to the fundamental role aberrations of the

splicing machinery and epigenetic modifiers play in MDS. It is noteworthy that functional

studies to date have not as yet arrived at a consensus in identifying definitive functional

DOI: 10.3324/haematol.2012.075325

12

pathways affected by splicing factor mutations, even in RARS where SF3B1 mutation

dominate. This again implies interplay of several factors driving the MDS clone, where

spliceosome mutations are perhaps fuelling subtle biochemical changes which can be built

upon in the course of disease evolution.

It is now becoming clear that splicing regulation and global genomic epigenetic marks are

intricately linked, where epigenetic ‘annotation’ of intron/exon boundaries influences the rate

of transcription or recruitment of splicing effector proteins to particular histone

modifications47-50

. It therefore seems likely that as our knowledge grows of how the splicing

machinery and cellular epigenetic modifiers communicate in the control of gene expression

patterns, we shall move towards a fuller understanding of the functional consequences of

their dysregulation in leukaemia.

Acknowledgements

We would like to acknowledge the Leukemia Lymphoma Research Fund (UK) for supporting

S.A.M and King’s College London for funding the Kings College Hemato-oncology Tissue

Bank. We would like to acknowledge Nigel B. Westwood and Rajani Chelliah for assisting

with sample processing, tissue separation and clinical data.

Authorship and Disclosures

S.A.M and A.E.S contributed equally and were involved with all aspects of the study's

design, execution, analysis, and manuscript preparation; G.J.M contributed to design,

analysis, and manuscript preparation as well as providing project leadership; A.G.K

contributed to clinical data, statistical analysis and manuscript preparation. A.K, A.A.M,

N.C.L, T.S and E.N contributed to experiments and analysis; K.F contributed to the study

design and manuscript preparation. K.M contributed to analysis.

The authors declare no competing financial interests.

DOI: 10.3324/haematol.2012.075325

13

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DOI: 10.3324/haematol.2012.075325

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Tables and figure

Table 1: Clinical characteristics of patients studied. Patients were stratified by presence or absence of

splicing factor mutations SF3B1, SRSF2 and U2AF1. Cytogenetics failed in 4 patients in this cohort. P

values with statistical significance are highlighted in bold. **Epigenetic modifiers-TET2, ASXL1, DNMT3A,

IDH2, EZH2 and IDH1; ** Cell signalling/transcription regulators -FLT3, RUNX1, NRAS, CKIT, CCBL,

JAK2 and MPL; **TP53. n- Represents number of patients; % - Represents percentage of the patients.

Patient Characteristics

Overall

SF3B1

Mutant

SF3B1

Wild Type

P value

SRSF2

Mutant

SRSF2

Wild Type

P value

U2AF1

Mutant

U2AF1

Wild Type

P value

154 24 (16%) 130 (84%)

20 (13%) 134 (87%) 15 (10%) 139(90%)

Age, years

0.42

0.16

0.8

Median 65.5 65.2 63.1

66.9 63.1 62.8 63.5

Range 17-85 35-83 17-85

51-82 17-85 48-75 17-85

Sex

0.98

0.5

0.38

Male [n (%)] 104

(67%) 16 (66%) 88 (68%)

12 (60%) 92(69%) 12 (80%) 92(66%)

WHO category *

<0.001

<0.001

0.2

RA/RCMD [n (%)] 40 (26%) 0 (0%) 40 (31%)

6 (30%) 34 (25%) 4 (27%) 36 (26%)

RARS/RCMD-RS [n (%)] 24 (16%) 20(83%) 4 (3%)

0 (0%) 24 (18%) 0 (0%) 24 (17%)

RAEB -1/2 [n (%)] 49 (32%) 1 (4%) 48 (37%)

7 (35%) 42 (32%) 5 (33%) 44 (32%)

s AML [n (%)] 15 (10%) 2 (8%) 13 (10%)

0 (0%) 15 (11%) 4 (27%) 11 (8%)

t MDS/AML [n (%)] 12 (8%) 0 (0%) 12 (9%)

0 (0%) 12 (9%) 1 (7%) 11 (8%)

CMML &MPD/MDS-U [n (%)] 14 (9%) 1 (4%) 13 (10%)

7 (35%) 7 (5%) 1 (7%) 13 (9%)

Bone marrow blasts

0.19

0.39

0.27

Median (%) 5 1 6

8.5 4 9 4

Range 0-80 0-19 0-72

0-19 0-80 0-59 0-80

IPSS cytogenetic risk group 151

<0.001

0.15

0.46

Good [n (%)] 90 22 (96%) 68(53%)

14 (70%) 76 (58%) 7 (50%) 83(61%)

Intermediate [n (%)] 17 1 (4%) 16 (13%)

4 (20%) 13(11%) 3 (21%) 14 (11%)

Poor N (%) 44 0 (0%) 44 (34%)

2 (10%) 42 (31%) 4 (29%) 40 (28%)

Transfusion dependency

0.04

0.3

0.19

Yes [n (%)] 80 (51%) 17 (71%) 63 (47%)

8 (40%) 72 (52%) 10 (66%) 70 (49%)

Progression to AML

0.02

0.02

0.4

Yes [n (%)] 44 (28%) 2 (8%) 42 (31%)

10 (50%) 34 (24%) 3 (20%) 41 (29%)

Co-existing mutations **

Epigenetic Modifier mutations [n (%)] 80 (52%) 14 (58%) 66 (51%) 0.5 14 (70%) 66 (49%) 0.07 9 (60%) 71 (54%) 0.6

Cell Signalling/Transcription regulator

mutations [n (%)] 28 (18%) 3 (12%) 25 (19%) 0.5 10 (50%) 18 (13%) 0.03 2 (13%) 26 (19%) 0.9

TP53 mutations [n (%)] 19(12%) 0 (0%) 19 (15%) 0.04 0 (0%) 19 (14%) 0.07 3 (20%) 16 (12%) 0.4

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Table 2: Summary of mutations coexisting with mutant and wildtype spliceosome components in 117

patients. Mutations were grouped according to their functional relevance; splicing factor mutations , SF3B1,

SRSF2, U2AF1 and ZRSR2; epigenetic modifier mutations, TET2, IDH1/2, ASXL1, EZH2, and DNMT3A;

Cell signalling/transcription regulator mutations , FLT3, NRAS, C-KIT, RUNX1, CCBL, JAK2 and MPL;

mutations in tumour suppressor TP53. The top half of the table compares mutations in the grouped genes.

The bottom half of the table indicates individual gene mutations coexisting with splicing factor mutations,

37 patients were wildtype for all genes present in our panel screen. Table cells indicate number of patients

within mutant (white cells) or non-mutant (shaded cells) spliceosome groups followed by the percentage

where appropriate.

Mutant

Spliceosome

SF3B1

SRSF2

U2AF1

ZRSR2

Wild Type

Spliceosome With

Other Mutations

Total mutant cases 59 24 20 15 2 58

Cases with additional co-existing

non-splice factor mutations 48 (81%) 19 (79%) 17 (85%) 12 (80%) 2 (100%) 27 (47%)

Cases with >1 additional co-existing

non-splice factor mutations 15 (25%) 3 (13%) 7 (35%) 5 (33%) 0 5 (9%)

Cases with co-existing TP53 mutations 3 (5%) 0 0 3 (20%) 0 16 (28%)

Cases with co-existing Cell Signalling

/Transcription regulator mutations 16 (27%) 3 (13%) 10 (50%) 2 (13%) 1 (50%) 12 (21%)

Cases with co-existing Epigenetic

modifier mutations 36 (61%) 14 (58%) 14 (70%) 9 (60%) 1 (50%) 44 (76%)

CO-EXISTING GENE MUTATIONS

TP53 3 (5%) 0 0 3 (20%) 0 16 (28%)

FLT3 3 (5%) 0 3 (15%) 0 0 1 (2%)

NRAS 5 (8%) 0 4 (20%) 1 (7%) 0 4 (7%)

RUNX1 4 (7%) 1 (4%) 2 (10%) 1 (7%) 0 5 (9%)

CCBL 3 (5%) 1 (4%) 2 (10%) 0 0 3 (5%)

C-KIT 1 (2%) 0 1 (5%) 0 0 0

JAK2 2 (3%) 1 (4%) 1 (5%) 0 0 1 (2%)

MPL 1 (2%) 0 0 0 1 (50%) 0

ASXL1 9 (15%) 1 (4%) 5 (25%) 3 (20%) 0 17 (29%)

DNMT3A 6 (10%) 4 (17%) 0 2 (13%) 0 9 (16%)

EZH2 2 (3%) 0 1 (5%) 1 (7%) 0 9 (16%)

TET2 17 (29%) 6 (25%) 8 (40%) 3 (20%) 0 17 (30%)

IDH2 7 (12%) 3 (13%) 1 (5%) 4 (27%) 1 (50%) 6 (11%)

IDH1 1 (2%) 0 0 1 (7%) 0 1 (2%)

CEBPA 5 (8%) 5 (21%) 0 0 0 1 (2%)

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Figure Legends

Figure 1: Distribution of all mutations detected in our patient cohort. Top row represents 117 mutated MDS

cases where the shade of the bar indicates the cytogenetic risk groups according to the inset key. Underlying

rows represent individual gene mutations denoted by coloured bars and specified on the left hand side. Bars

with black strips indicate nonsense mutations, including splice-site mutations, while bars without stripes

represent missense mutations. ● Indicates mutations with <10% mutant allele burden.

Figure 2: Clonal evolution and disease progression in RARS patient. Sequencing analysis of sequential

samples in patient A (UPN RC060337) with an SF3B1 mutation. Three horizontal rows represent samples

collected at different time points: diagnosis, transformation to AML and disease progression after a short

duration of remission, respectively. Accumulation of the oncogenic mutations and changes in mutant allele

burden levels are seen through disease progression along with increase in the blast count. Sanger sequencing

was used to confirm/ determine the mutation status throughout the experiment. 454 sequencing confirmed

the mutation level differences at start and end points of the experiment. WT-Wildtype

Figure 3A-3D: Overall survival (A) and progression free survival (B) for patients with SF3B1 mutations

(n=24) compared with wild type SF3B1 (n=130). Overall survival (C) and progression free survival (D) for

patients with spliceosome mutations (n=59) compared with patients without splicing factor mutations

(n=99).

Figure 4A-4B: Overall survival (A) and progression free survival (B) for patients with spliceosome

mutations (n=59), stratified according to patients with splicing factor mutations (SF) co-existing with

mutations of genes involved in cell signalling/transcription regulation (CS/TR) or TP53 mutations versus

splicing factor mutations co-existing with epigenetic modifier (EM) mutations versus patients with splicing

factor mutations alone.

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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Supplementary Appendix

Supplementary Methods

Exome sequencing

Eight patients (5 RARS, 1 RARS-T, 1 RCMD-RS and 1 tMDS) with >50% ringed sideroblasts were selected for whole-exome

sequencing, using DNA from CD34+ cells in all cases and paired constitutional source [skin (n=3 ), CD3+ T-cells (n=3 ) and CD34-

CD235+ (n=1)] (Supplementary Table 3). The exomic regions of the genome were enriched using Agilent SureSelect Human All Exon

Kit covering approximately 50Mb of the genome. This was followed by paired-end sequencing with a read length of 75bp using Illumina

HiSeq 2000 and version 3 chemistry. The base calls generated by the Real Time Analysis (RTA) or the Off-Line Basecaller (OLB)

software were de-multiplexed and converted to fastq format using Casava 1.8. Alignment (NCBI37/Hg19) and variant calling for SNPs

and Indels was performed using both Casava 1.8 (Illumina), and BWA/GATK according to the Broad Institute best practices. Annovar

software1 was used to perform functional annotation of all variants and also to remove SNPs reported in the 1000 Genomes Project,

dbSNP132, and genomic super duplications databases. For Casava, remaining variants with a quality score of >QSNP 25 and with a

mutant read depth of > 1 (total read depth > 6) were passed for further analysis. A repeat sequencing run was also used to filter out

sequencing artefacts where candidate mutations supported in both runs were passed. For GATK, variants were selected that passed

standard filtering, Additionally, variations present in available paired skin and CD3+ T-cell samples at greater than 20% or 50% the level

found in CD34+ tissue were designated germline and excluded from subsequent analysis of acquired mutations. Data from both pipelines

were largely in agreement for filter passed variants and variant depth >1. Any discrepancies between the two pipelines at this level were

later confirmed to be artefactual calls.

PCR and Sanger sequencing were used to validate selected candidate mutations. ExoSAP-IT purification kit was used for purification of

PCR products. Sequencing was performed using Big Dye Terminator V 3.1 kit, according to the manufactures protocol. Sanger

sequencing was performed using an ABI3010xl (Applied Biosystems) and the sequencing results were analysed by SeqScape software.

Statistical analysis

The characteristics of the study population were studied with appropriate statistical methods (Mann-Whitney test for continuous variables

and Fisher’s exact test for categorical variables) comparing patients with specific mutation versus cases without mutation. Clinical

characteristics, survival and time to progression to AML were updated to January 2012 and measured from time of sample collection

(N=123) or diagnosis (N=31) at Kings College Hospital. The median disease duration prior to sample analysis for 123 patients was 11

months (range 1-119 months), during this period 29 patients showed disease progression (upstaging of WHO category) of which 10 were

treated with either with intensive chemotherapy or 5-azacitidine.

The Kaplan-Meier estimate was used to evaluate time to survival and time to progression. The log-rank test was used to assess potential

differences in outcome between subgroups. A p-value of ≤0.05 was considered statistically significant.

Supplementary Results

Exome sequencing reveals common SF3B1 mutations in RARS

Whole exome sequencing (Illumina) was performed on CD34+ cells from 8 patients all of whom had >50% ringed sideroblasts,

comprising 5 RARS, 1 RARS-T, 1 RCMD-RS and 1 tMDS patient (supplementary table 3). Paired constitutional DNA from skin (3

cases) or CD3+ T cells (3 cases) was similarly sequenced. An average of 8Gbp of sequence data was generated per patient exome and

processed using both the Casava 1.8 (Illumina) and Broad Institute ‘Best practise’ pipelines, supplemented with additional software tools

as detailed in materials and methods. Functional variants not found in DbSNP132 or 1000 genomes databases were subsequently collated.

Aberrations in one particular gene, splicing factor 3b subunit 1 (SF3B1), a component of the major and minor spliceosomes, initially stood

out due to its high frequency across all patients (7 of 8 cases). Importantly, TP53 mutation was observed in a single case with tMDS who

had wildtype SF3B1. Furthermore, mutations in epigenetic factors including TET2 and DNMT3A were found in 3 cases with SF3B1

aberrations. Validation of these selected mutations in available paired skin biopsy or CD3+ T-cell samples by Sanger sequencing led to

DOI: 10.3324/haematol.2012.075325

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the confirmation of the acquired nature of these mutations. For 6 patients with paired samples, an additional 61 patient specific mutation

candidates (10 per patient exome on average) were identified and selectively validated via sanger sequencing (supplementary table 4 and

5).

SF3B1 mutations persist through differentiation

Initially CD34+ cells (n=8) were subjected to exome sequencing and subsequent screening/confirmation of SF3B1 mutations was

performed on total bone marrow cells. We also quantified the mutation load in CD34+, total CD3+ T-cells, CD3+CD4+ T-cells , CD19+

B-cells, CD235+CD71+ erythroblasts and CD34-CD3-CD235- for 3 additional patients in whom SF3B1 mutations were detected in total

bone marrow cells. CD235+CD71+ erythroblasts and CD34-CD3-CD235- cells showed approximately the same SF3B1 mutant allele

burden (according to Sanger sequencing) as seen for paired total bone marrow and CD34+ cells. Importantly, no SF3B1 mutation was

detected above background in CD3+ T-cells, CD3+CD4+ T-cells or CD19+ B-cells according to Sanger sequencing (3/3 mutant

patients), indicating that the mutant clone has a growth advantage only in the myeloid lineage in MDS. Mutant SF3B1 was not detected

in any available paired skin biopsy samples, confirming the acquired nature of these mutations.

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Supplementary Table and Figures

Supplementary Table 1: Treatments received by 154 patients. † denotes other treatments which include;

lenalidomide, thalidomide, cyclosporine and Antithymocyte globulin (ATG). * includes 13 patients who had HSCT

after receiving 5-azacitidine.

Treatment No of Cases

5-Azacytidine 68

Other Treatments† 14

HSCT* 35

No treatment 37

Supplementary Table 2: Genes screened for hotspot mutations. Mutation frequency within the hotspot regions was obtained from Cosmic

database (Welcome Trust Sanger Institute, http://www.sanger.ac.uk/genetics/CGP/cosmic/).

Gene Total Frequency of

Mutations in MDS

Hot Spot Regions

Sequenced Previous Publications

COSMIC Database

(Welcome Trust Sanger Institute, http://www.sanger.ac.uk/genetics/CGP/cosmic/)

SF3B1 30% Exon 12 - 16 Yoshida, et al Nature (2011); Papaemmanuil, et al. NEJM (2011)

Shows 99% of SF3B1 somatic mutations in haematopoietic neoplasms within this region (out of 344 reported mutant cases; 2668 sequenced samples)

SRSF2 15% Exon 1 Yoshida, et al. Nature (2011) Shows 100% of SRSF2 somatic mutations in haematopoietic neoplasms within this region (out of

279 reported mutant cases; 1872 sequenced samples)

ASXL1 15% Exon 12 Gelsi-Boyer, et al. BJH (2009); Carbuccia, et al. LEukemia (2009);

Metzeler, et a. Blood (2011)

Shows 99% of ASXL1 somatic mutations in haematopoietic neoplasms within this region (out of

472 reported mutant cases; 3590 sequenced samples)

U2AF1 12% Exon 2, 6 Yoshida, et al. Nature (2011) Shows 99% of U2AF35 somatic mutations in haematopoietic neoplasms within this region (out of 96 reported mutant cases; 1518 sequenced samples)

FLT3 6% Exon14, 15

Nakao, et al. Leukemia (1996);

Thiede, et al. Blood (2002);

Jiang et al. Blood (2004); Loriaux et al. Blood (2008)

Shows 100% of FLT3 somatic mutations in haematopoietic neoplasms within this region (out of

11816 reported mutant cases; 51551 sequenced samples)

NRAS 3.6% Exon 2, 3

Hirai, et al. Nature (1987);

Bacher, et al. Haematologica (2007);

Tyner, et al. Blood (2008)

Shows 99% of NRAS somatic mutations in haematopoietic neoplasms within this region (out of 676 reported mutant cases; 6473 sequenced samples)

JAK2 3% Exon12,14 Baxter et al. Lancet (2005); Kralovics, et al. NEJM (2005)

Shows 100 % of JAK2 somatic mutations in haematopoietic neoplasms within this region (out of 32218 reported mutant cases; 76213 sequenced samples)

CCBL 2.3% Exon 7, 8, 9 Sanada, et al. (2009); Makishima, et al. (2009)

Shows 99% of CCBL somatic mutations in haematopoietic neoplasms within this region (out of 163 reported mutant cases; 3577 sequenced samples)

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Gene Total Frequency of

Mutations in MDS

Hot Spot Regions

Sequenced Previous Publications

COSMIC Database

(Welcome Trust Sanger Institute, http://www.sanger.ac.uk/genetics/CGP/cosmic/)

IDH2 2.1% Exon 4 Mardis, et al. NEJM (2009) Shows 100 % of IDH2 somatic mutations in haematopoietic neoplasms within this region (out of 905 reported mutant cases; 13777 sequenced samples)

NPM1 1.8% Exon 12 Falini, et al. NEJM (2005) Shows ≥ 97 % of NPM1 somatic mutations in haematopoietic neoplasms within this region (4181

reported mutant cases; 14167 sequenced samples)

IDH1 1.4 Exon 4 Mardis, et al. NEJM (2009) Shows 100 % of IDH1 somatic mutations in haematopoietic neoplasms within this region (out of 778 reported mutant cases; 16301 sequenced samples)

KRAS 0.9% Exon 2, 3 Tyner, et al. Blood (2008) Shows >97% of KRAS somatic mutations in haematopoietic neoplasms within this region (out of

152 reported mutant cases ; 3716 sequenced samples)

BRAF 0.5% Exon 11, 15 Shows 100 % of BRAF somatic mutations in haematopoietic neoplasms within this region (6 reported mutant cases; 581 sequenced samples)

MPL Rare Exon 10

Pardanani et al. Blood (2006);

Beer, et al. Haematologica (2010);

Patnaik et al. Leukemia (2010)

Shows ≥ 97 % of MPL somatic mutations in haematopoietic neoplasms within this region (534 reported mutant cases; 14202 sequenced samples)

C-KIT Rare Exon 17 Bowen, et al. Leukemia (1993);

Lorenzo, et al. Leukemia Research (2006)

Shows 40 % of C-KIT somatic mutations in haematopoietic neoplasms within this region (1672

reported mutant cases; 7734 sequenced samples)

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Supplementary Table 3: Primers for 22 genes used for 454-sequencing. Primer names reflect exons being sequenced. Universal sequencing

primers for 2nd

round PCR and Sanger sequencing are in lower case.

Genes Primer Sequence

CMPL_F gtagtgcgatggccagtAGCCTGGATCTCCTTGGTGAC

CMPL_R cagtgtgcagcgatgacCGAGTCCCAGAGGTGACGT

JAK2_E12F gtagtgcgatggccagtCAGAACGAATGGTGTTTCTGA

JAK2_E12R cagtgtgcagcgatgacCCAATGTCACATGAATGTAAATCAA

JAK2_E14F gtagtgcgatggccagtTCCTCATCTATAGTCATGCTGAAA

JAK2_E14R cagtgtgcagcgatgacCTGACACCTAGCTGTGATCCTG

IDH1_F gtagtgcgatggccagtGCGTCAAATGTGCCACTATC

IDH1_R cagtgtgcagcgatgacTTCATACCTTGCTTAATGGGTGT

IDH2_F gtagtgcgatggccagtAATTTTAGGACCCCCGTCTG

IDH2_R cagtgtgcagcgatgacTGTGGCCTTGTACTGCAGAG

FLT3/KTD_F gtagtgcgatggccagtCCGCCAGGAACGTGCTTG

FLT3/KTD_R cagtgtgcagcgatgacGCAGCCTCACATTGCCCC

FLT3/ITD_F gtagtgcgatggccagtCAATTTAGGTATGAAAGCCAGCTA

FLT3/ITD_R cagtgtgcagcgatgacCTTTCAGCATTTTGACGGCAACC

CKIT_F gtagtgcgatggccagtGTTTTCTTTTCTCCTCCAACC

CKIT_R cagtgtgcagcgatgacGGACTGTCAAGCAGAG

NPM1_F gtagtgcgatggccagtATTTCTTTTTTTTTTTTTCCAGGCTATTCAAG

NPM1_R cagtgtgcagcgatgacGGTAGGGAAAGTTCTCACTCTGC

NRAS2_F gtagtgcgatggccagtGATGTGGCTCGCCAATTAA

NRAS2_R cagtgtgcagcgatgacGATTGTCAGTGCGCTTTTCC

NRAS3_F gtagtgcgatggccagtATAGGCAGAAATGGGCTTGAATA

NRAS3_R cagtgtgcagcgatgacGTATTGGTCTCTCATGGCACTGTA

KRAS2_F gtagtgcgatggccagtGCCTGCTGAAAATGACTGAA

KRAS2_R cagtgtgcagcgatgacGAATGGTCCTGCACCAGTAA

KRAS3_F gtagtgcgatggccagtCCAGACTGTGTTTCTCCCTTC

KRAS3_R cagtgtgcagcgatgacAAAGAAAGCCCTCCCCAGT

BRAF_11_F gtagtgcgatggccagtTTTTCTGTTTGGCTTGACTTGA

BRAF_11_R cagtgtgcagcgatgacACTTGTCACAATGTCACCACA

BRAF_15_F gtagtgcgatggccagtTGCTTGCTCTGATAGGAAAATG

BRAF_15_R cagtgtgcagcgatgacTGGAAAAATAGCCTCAATTCTTA

CCBL_E7_F gtagtgcgatggccagtACACCACGTTGCCCTTTTAG

CCBL_E7_R cagtgtgcagcgatgacAAGCTTGTGTCCAGTGATATGG

CCBL_E8_F gtagtgcgatggccagtGGAAACAAGTCTTCACTTTTTCTGT

CCBL_E8_R cagtgtgcagcgatgacAAAAAAGTCGCTGTTTAGATCCGTA

CCBL_E9_F gtagtgcgatggccagtTGCATCTGTTACTATCTTTTGCTTC

CCBL_E9_R cagtgtgcagcgatgacCTCACAATGGATTTTGCCA

DNMT3A_E2_F gtagtgcgatggccagtGCCTCCAAAGACCACGATAA

DNMT3A_E2_R cagtgtgcagcgatgacCGGCTGTCATCACATAGGG

DNMT3A_E3_F gtagtgcgatggccagtGGTGGGGGCATATTACACAG

DNMT3A_E3_R cagtgtgcagcgatgacTCCCTGCAGGACATACATCA

DNMT3A_E4a_F gtagtgcgatggccagtAGAATTTCAGAGCGGTCAATG

DNMT3A_E4a_R cagtgtgcagcgatgacCAGCCATTTTCCACTGCTCT

DNMT3A_E4b_F gtagtgcgatggccagtATTACCCAATGGGGACTTGG

DNMT3A_E4b_R cagtgtgcagcgatgacAAGCAGACCTTTAGCCACGA

DNMT3A_E5_F gtagtgcgatggccagtGAACAGCTAAACGGCCAGAG

DNMT3A_E5_R cagtgtgcagcgatgacCTTCCCACAGAGGGATGTGT

DNMT3A_E6_F gtagtgcgatggccagtCATTGTGTTTGAGGCGAGTG

DNMT3A_E6_R cagtgtgcagcgatgacTAGCCTGAAGGGGAAACTGA

DNMT3A_E7_F gtagtgcgatggccagtTTCCTGGAGAGGTCAAGGTG

DNMT3A_E7_R cagtgtgcagcgatgacTGGAGAGAGGAGAGCAGGAC

DNMT3A_E8_F gtagtgcgatggccagtTCTTGCCTCATTCAGATGGA

DNMT3A_E8_R cagtgtgcagcgatgacCCTGGGATCAAGAACCTTCC

DNMT3A_E9_F gtagtgcgatggccagtCTCCAGGGCTGAGACTGACT

DNMT3A_E9_R cagtgtgcagcgatgacACCTGCACTCCAACTTCCAG

DNMT3A_E10_F gtagtgcgatggccagtCCTGTGCCACCCTCACTACT

DNMT3A_E10_R cagtgtgcagcgatgacCTCCCTAAGCATGGCTTTCC

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Genes Primer Sequence

DNMT3A_E11_F gtagtgcgatggccagtAGGTGGGAACAAGTTGGAGA

DNMT3A_E11_R cagtgtgcagcgatgacAGAGCTGGCGTCAGAGGAG

DNMT3A_E12_F gtagtgcgatggccagtTAGTTGGCCTGCTTCTGGAG

DNMT3A_E12_R cagtgtgcagcgatgacGAGTCCCACACCCTGAAGAC

DNMT3A_E13_F gtagtgcgatggccagtAGATGATGGCGTTCGAGACT

DNMT3A_E13_R cagtgtgcagcgatgacTGGACACAGTCAGCCAGAAG

DNMT3A_E14_F gtagtgcgatggccagtCTCTGTGAGGCCAGGTGTG

DNMT3A_E14_R cagtgtgcagcgatgacAGGTGTGCTACCTGGAATGG

DNMT3A_E15F gtagtgcgatggccagtACCAGGGCTGAGAGTCTCCT

DNMT3A_E15_R cagtgtgcagcgatgacAGGCTCCTAGACCCACACAC

DNMT3A_E16F gtagtgcgatggccagtCAGGGTGTGTGGGTCTAGGA

DNMT3A_E16_R cagtgtgcagcgatgacAAGCTTCCCCTTTGGGATAA

DNMT3A_E17_F gtagtgcgatggccagtGACTTGGGCCTACAGCTGAC

DNMT3A_E17_R cagtgtgcagcgatgacCAAAATGAAAGGAGGCAAGG

DNMT3A_E18_F gtagtgcgatggccagtCAACTTGGTCCCGTTCTTGT

DNMT3A_E18_R cagtgtgcagcgatgacCAAGGAGGAAGCCTATGTGC

DNMT3A_E19_F gtagtgcgatggccagtGACAGCTATTCCCGATGACC

DNMT3A_E19_R cagtgtgcagcgatgacGCTCCACAATGCAGATGAGA

DNMT3A_E20_F gtagtgcgatggccagtCAGCTTGTGGAATGTGGCTA

DNMT3A_E20_R cagtgtgcagcgatgacCACTATGGGTCATCCCACCT

DNMT3A_E21_F gtagtgcgatggccagtATGACGTGTGTGCGTGATTT

DNMT3A_E21_R cagtgtgcagcgatgacCCACACTAGCTGGAGAAGCA

DNMT3A_E22_F gtagtgcgatggccagtTTTGGTAGACGCATGACCAG

DNMT3A_E22_R cagtgtgcagcgatgacCAGGACGTTTGTGGAAAACA

DNMT3A_E23_F gtagtgcgatggccagtTCCTGCTGTGTGGTTAGACG

DNMT3A_E23_R cagtgtgcagcgatgacTTTTTCTCTTCTGGGTGCTGA

DNMT3A_E24_F gtagtgcgatggccagtTGAAGGAGTATTTTGCGTGTG

DNMT3A_E24_R cagtgtgcagcgatgacCAGAAAACCCCTCTGAAAAGA

ASXL1_E12_1F gtagtgcgatggccagtGTTCACACAGTCCCACCAGAAA

ASXL1_E12_1R cagtgtgcagcgatgacACTCTCTATGGCAGTGGTGACCT

ASXL1_E12_2F gtagtgcgatggccagtGTGGACCCTCGCAGACATTAAA

ASXL1_E12_2R cagtgtgcagcgatgacACTTGGGAGGCATCTCCTAGC

ASXL1_E12_3F gtagtgcgatggccagtGTGGACTCACAGATGGGCTAGG

ASXL1_E12_3R cagtgtgcagcgatgacACCCAAGCCCTAATTCGTCATC

ASXL1_E12_4F gtagtgcgatggccagtGTAATCCTCACCGACTGATTGC

ASXL1_E12_4R cagtgtgcagcgatgacACTGCTTCAGAGTCTCCGTTGA

ASXL1_E12_5F gtagtgcgatggccagtGTTCACTCTGGACTGTGCCATC

ASXL1_E12_5R cagtgtgcagcgatgacACGCAGCAACTGCATCACAAGT

ASXL1_E12_6F gtagtgcgatggccagtGTTCCCAGATTCCCTACTGCTG

ASXL1_E12_6R cagtgtgcagcgatgacACCTGGATGGAGGGAGTCAAAA

ASXL1_E12_7F gtagtgcgatggccagtGTAAGGCAGTCCCAAGTTTTGA

ASXL1_E12_7R cagtgtgcagcgatgacACGTTTGTGCTTGGTCCCAACT

ASXL1_E12_8F gtagtgcgatggccagtGTGATGCCTCTTCCTGCTGAGA

ASXL1_E12_8R cagtgtgcagcgatgacACAGCTGGTGGAACTCAGTTGG

ASXL1_E12_9F gtagtgcgatggccagtGTCTTCTCTCCCCTCCCAACTC

ASXL1_E12_9R cagtgtgcagcgatgacACACAGAGCTTTGAGGGTCCAA

RUNX1_E3_F gtagtgcgatggccagtAGCTGTTTGCAGGGTCCTAA

RUNX1_E3_R cagtgtgcagcgatgacCATCCCAAGCTAGGAAGACCGA

RUNX1_E4_F gtagtgcgatggccagtGTATAACATCCCTGATGTCTGCA

RUNX1_E4_R cagtgtgcagcgatgacCCATGAAACGTGTTTCAAGC

RUNX1_E5_F gtagtgcgatggccagtATGTTCAGGCCACCAACCTCAT

RUNX1_E5_R cagtgtgcagcgatgacGACATGGTCCCTGAGTATACCAG

RUNX1_E6_F gtagtgcgatggccagtCGAGTCTATGTTGGGGTGAGGGGA

RUNX1_E6_R cagtgtgcagcgatgacGAAACCCCAGTTGGTCTGGGA

RUNX1_E7B_F gtagtgcgatggccagtATTTGAACAAGGGCCACTCAT

RUNX1_E7B_R cagtgtgcagcgatgacCTCAGCTGCAAAGAATGTGT

RUNX1_E8A_F gtagtgcgatggccagtGGAAGAGCTGTGGCCTCCGCAA

RUNX1_E8A_R cagtgtgcagcgatgacTACAGGTGGTAGGAGGGCGAGCTG

RUNX1_E8B_F gtagtgcgatggccagtTCGTCGCAAGCGCAGGGAGG

RUNX1_E8B_R cagtgtgcagcgatgacGGGCTTGTCGCGAACAGGAGG

CEBPA_1F gtagtgcgatggccagtGTTCGCCATGCCGGGAGAACTCTAAC

DOI: 10.3324/haematol.2012.075325

31

Genes Primer Sequence

CEBPA_CEBPA AC cagtgtgcagcgatgacACCCTGCTGCCGGCTGTGCTGGAAC

CEBPA_PP4F gtagtgcgatggccagtGTCTTCAACGACGAGTTCCTGGCCGA

CEBPA_PP4R cagtgtgcagcgatgacACAGCTGCTTGGCTTCATCCTCCT

CEBPA_PP5F gtagtgcgatggccagtGTCCGCTGGTGATCAAGCAGGA

CEBPA_PP5R cagtgtgcagcgatgacACCCGGTACTCGTTGCTGTTCT

CEBPA_CEBPA C gtagtgcgatggccagtGTCAAGGCCAAGAAGTCGGTGGACA

CEBPA_PP6RBN cagtgtgcagcgatgacACGGAGCAGGCCAGGCTTTCAG

P53_E2_F gtagtgcgatggccagtCTGGATCCCCACTTTTCCTC

P53_E2_R cagtgtgcagcgatgacTCCCACAGGTCTCTGCTAGG

P53_E3_F gtagtgcgatggccagtAGCGAAAATTCATGGGACTG

P53_E3 _R cagtgtgcagcgatgacGGGGACTGTAGATGGGTGAA

P53_E4A_F gtagtgcgatggccagtCCTGGTCCTCTGACTGCTCT

P53_E4A_R cagtgtgcagcgatgacTTCTGGGAAGGGACAGAAGA

P53_E4B_F gtagtgcgatggccagtGTCCAGATGAAGCTCCCAGA

P53_E4B_R cagtgtgcagcgatgacCAGGCATTGAAGTCTCATGG

P53_E5_F gtagtgcgatggccagtTTTCAACTCTGTCTCCTTCCTCTT

P53_E5_R cagtgtgcagcgatgacAGCCCTGTCGTCTCTCCAG

P53_E6_F gtagtgcgatggccagtCAGGCCTCTGATTCCTCACT

P53_E6_R cagtgtgcagcgatgacCTTAACCCCTCCTCCCAGAG

P53_E7_F gtagtgcgatggccagtTCATCTTGGGCCTGTGTTATC

P53_EX7_R cagtgtgcagcgatgacAGTGTGCAGGGTGGCAAG

P53_EX8_F gtagtgcgatggccagtGCCTCTTGCTTCTCTTTTCC

P53_EX8_R cagtgtgcagcgatgacAACTGCACCCTTGGTCTCC

P53_EX9_F gtagtgcgatggccagtGGAGACCAAGGGTGCAGTTA

P53_EX9_R cagtgtgcagcgatgacGAAAACGGCATTTTGAGTGTT

P53_EX10_F gtagtgcgatggccagtACTTCTCCCCCTCCTCTGTT

P53_EX10_R cagtgtgcagcgatgacGAAGGCAGGATGAGAATGGA

P53_EX11_F gtagtgcgatggccagtTGTCATCTCTCCTCCCTGCT

P53_EX11_R cagtgtgcagcgatgacCAAGGGTTCAAAGACCCAAA

EZH2_E2_F gtagtgcgatggccagtGGTGATCATATTCAGGCTGGT

EZH2_E2_R cagtgtgcagcgatgacTTGAACTTAGGAGGGGAAAAA

EZH2_E3_F gtagtgcgatggccagtTTTCTCCTTTCCTCTCCTTCA

EZH2_E3_R cagtgtgcagcgatgacTCCTCCCAATAACCAAACAAA

EZH2_E4_F gtagtgcgatggccagtTGGGTAGGCAGCATCTCTTT

EZH2_E4_R cagtgtgcagcgatgacCTGTCTTGATTCACCTTGACAAT

EZH2_E5_F gtagtgcgatggccagtTCTGGAGAACTGGGTAAAGACA

EZH2_E5_R cagtgtgcagcgatgacGCCCCTTTTTCCAAGAGAAG

EZH2_E6_F gtagtgcgatggccagtGCTATGCCTGTTTTGTCCAAG

EZH2_E6_R cagtgtgcagcgatgacGCTGTAATGGCTACACAGAATCC

EZH2_E7_F gtagtgcgatggccagtTGGGTAGAGAAAATGAAAGATCAA

EZH2_E7_R cagtgtgcagcgatgacGCAAGATTGCCTCAAAGGAA

EZH2_E8_F gtagtgcgatggccagtCATCAAAAGTAACACATGGAAACC

EZH2_E8_R cagtgtgcagcgatgacAGCACTCTCCAAGCTGCTTTA

EZH2_E9_F gtagtgcgatggccagtCCAGTGGAACTGGAAGAGTGA

EZH2_E9_R cagtgtgcagcgatgacACCTCCACCAAAGTGCAAAG

EZH2_E10_F gtagtgcgatggccagtTGATGTTGACATTTTTCATTTCG

EZH2_E10_R cagtgtgcagcgatgacCAGTAAAACCCAGTTATTAGACGTG

EZH2_E11_F gtagtgcgatggccagtTCCAATCATTTCTTGACCAGTG

EZH2_E11R cagtgtgcagcgatgacTTTTCTTTGTTTGGACAACGAGT

EZH2_E12F gtagtgcgatggccagtCTGTCCTCATGGCTCTGTGA

EZH2_E12R cagtgtgcagcgatgacGCCTTGCCTGCAGTGTCTAT

EZH2_E13F gtagtgcgatggccagtTTGTAGCTTCCCGCAGAAAT

EZH2_E13R cagtgtgcagcgatgacCGTCCTCCATTCAAATTGGT

EZH2_E14F gtagtgcgatggccagtCATCTCCCTGGATTCATTGG

EZH2_E14R cagtgtgcagcgatgacGCAATTGCATCAAAGCAACA

EZH2_E15F gtagtgcgatggccagtGTACAGCCCTTGCCACGTAT

EZH2_E15R cagtgtgcagcgatgacAAGGCAATCCTGACATTTGC

EZH2_E16F gtagtgcgatggccagtTCCATTTTCACCCTCCTTTTT

EZH2_E16R cagtgtgcagcgatgacCATTTCCAATCAAACCCACA

EZH2_E17F gtagtgcgatggccagtTATTCACTCTGTGCGCTTTTG

EZH2_E17R cagtgtgcagcgatgacCCTCCCAGCTCTGAAACATAC

DOI: 10.3324/haematol.2012.075325

32

Genes Primer Sequence

EZH2_E18F gtagtgcgatggccagtAGGCAAACCCTGAAGAACTG

EZH2_E18R cagtgtgcagcgatgacGGACTGAAAAGGGAGTTCCA

EZH2_E19F gtagtgcgatggccagtCCGTCTTCATGCTCACTGAC

EZH2_E19R cagtgtgcagcgatgacAAAAACCCTCCTTTGTCCAGA

EZH2_E20F gtagtgcgatggccagtGCCATCCAGCGGACATCTCC

EZH2_E20R cagtgtgcagcgatgacCTAAATTGCCCACAGTACTCGAGG

U2AF1_ex2f gtagtgcgatggccagtTCATGCTGCTGACATATTCC

U2AF1_ex2r cagtgtgcagcgatgacCACACTTATGAACACAAATGGAAA

U2AF1_ex6f gtagtgcgatggccagtCCTGAGTGTGTATATCTCTCTCTGAT

U2AF1_ex6r cagtgtgcagcgatgacTGGGTTGGAAGGAGACATTT

SRSF2_ex1f gtagtgcgatggccagtGTCGGCGACGTGTACATCC

SRSF2_ex1r cagtgtgcagcgatgacCGCGGACCTTTGTGAGGT

ZRSR2_ex1 f gtagtgcgatggccagtCGTTTCAAGTCCCACGCT

ZRSR2_ex1r cagtgtgcagcgatgacTTCCTGCCACCACATCCT

ZRSR2_ex2f gtagtgcgatggccagtCTGCATTGTAGCCGCTGA

ZRSR2_ex2r cagtgtgcagcgatgacGCTGGAGTGGACAGAGCAA

ZRSR2_ex3f gtagtgcgatggccagtTTGACCAAGGATTTGCAGC

ZRSR2_ex3r cagtgtgcagcgatgacGACTGGTACTGGTTAGTAAAGGTTGA

ZRSR2_ex4f gtagtgcgatggccagtTGTGGATTAATGCCCATTTC

ZRSR2_ex4r cagtgtgcagcgatgacCCAACCTCCCAAGATAGGC

ZRSR2_ex5f gtagtgcgatggccagtTGTGCGCTGTATGTGAAATG

ZRSR2_ex5r cagtgtgcagcgatgacCCCAAACTCTGACATGCCT

ZRSR2_ex6f gtagtgcgatggccagtTGTGTGCGTGTGTGTGTTTT

ZRSR2_ex6r cagtgtgcagcgatgacCCACGAAACTAACATTACTGGAAC

ZRSR2_ex7f gtagtgcgatggccagtTCATGGGTTTTTACTCCACCA

ZRSR2_ex7r cagtgtgcagcgatgacCCTCTCCCAAAAGGGGAA

ZRSR2_ex8f gtagtgcgatggccagtCCACCATGCCTGGTCTAAAG

ZRSR2_ex8r cagtgtgcagcgatgacTGTGTCCCAGCTCTCTTGTG

ZRSR2_ex9f gtagtgcgatggccagtGGGGAATGTTAGCCTGGA

ZRSR2_ex9r cagtgtgcagcgatgacCAGGAAGACATCCACAAGCA

ZRSR2_ex10f gtagtgcgatggccagtCAGTGAACTTGGTGGTCCTACA

ZRSR2_ex10r cagtgtgcagcgatgacACTGGGTTTCCCCCAAAG

ZRSR2_ex11Af gtagtgcgatggccagtTTCGGAAAAGGATAAAGTAGCA

ZRSR2_ex11Ar cagtgtgcagcgatgacCTTCCCCCTGTGACGACTAC

ZRSR2_ex11Bf gtagtgcgatggccagtAACCCTAGTCCAGACCACTCC

ZRSR2_ex11Br cagtgtgcagcgatgacGCCTTCTATCCGAGTATGTAGCA

SF3B1 1f gtagtgcgatggccagtAGCCCCCAGCTATTTTTCTC

SF3B1 1r cagtgtgcagcgatgacTGTAAGAGGAGGACGCCATT

SF3B1 2f gtagtgcgatggccagtGAAATGCATTGTGTTGGGAGT

SF3B1 2r cagtgtgcagcgatgacTAAACCAGATGGCTGCAACA

SF3B1 3f gtagtgcgatggccagtTGAAGGAGGGCTTAGACATCA

SF3B1 3r cagtgtgcagcgatgacTGGGAACTCAGACATTCACTTTT

SF3B1 4f gtagtgcgatggccagtGGCATGTATTAAACATTTGTGCTT

SF3B1 4r cagtgtgcagcgatgacAATTCAGAAGCATGCCAAAAA

SF3B1 5f gtagtgcgatggccagtGCAGGGCAGATAAATCAGTTG

SF3B1 5r cagtgtgcagcgatgacTGGGGGTAAGATTCTTTCTCAG

SF3B1 6f gtagtgcgatggccagtGGAAGTGATTGCGCTAATGG

SF3B1 6 r cagtgtgcagcgatgacCTATGGCAACCCAAGCAGAC

SF3B1 7f gtagtgcgatggccagtTTCTGTGTGGGTGTGTGAAA

SF3B1 7r cagtgtgcagcgatgacATACGTGTCCACCCAGGAAT

SF3B1 8 f gtagtgcgatggccagtAATTGTGGTTTTACTCACTCTTTCTTT

SF3B1 8 r cagtgtgcagcgatgacAACAATTATGTCCAATGAGACAGTTC

SF3B1 9 f gtagtgcgatggccagtAAATTTAAGTCTTGGTTTGCGTTT

SF3B1 9 r cagtgtgcagcgatgacTCCTAAATACCACCTCATTCAAA

SF3B1 10 f gtagtgcgatggccagtTGCAAATATTGTTCATTATGCTGTT

SF3B1 10 r cagtgtgcagcgatgacAAAAATGTTAAGGGAAGTTGAAATG

SF3B1 11 f gtagtgcgatggccagtAATGACCAGCCATCTGGAAA

SF3B1 11 r cagtgtgcagcgatgacTGACAATTTAACAAACTGGGACTT

SF3B1 12 f gtagtgcgatggccagtGAAACCACACCTATTACTCTGCTC

SF3B1 12 r cagtgtgcagcgatgacAAGGAAAAGGTCTAGGAGAATATGT

SF3B1 13 f gtagtgcgatggccagtCATGAGCATTTCATCAGTAATTG

DOI: 10.3324/haematol.2012.075325

33

Genes Primer Sequence

SF3B1 13 r cagtgtgcagcgatgacGTAGCCAGACCAGCAGCCTA

SF3B1 14 f gtagtgcgatggccagtCCAACTCATGACTGTCCTTTCTT

SF3B1 14 r cagtgtgcagcgatgacGGGCAACATAGTAAGACCCTGT

SF3B1 15 f gtagtgcgatggccagtTTGGGGCATAGTTAAAACCTG

SF3B1 15 r cagtgtgcagcgatgacTTCAAGAAAGCAGCCAAACC

SF3B1 16 f gtagtgcgatggccagtGTATCCGCCAACACAGAGGA

SF3B1 16 r cagtgtgcagcgatgacTGTTAGAACCATGAAACATATCCA

SF3B1 17 f gtagtgcgatggccagtTTCTCTTCATTTCAGGTCAGTTG

SF3B1 17 r cagtgtgcagcgatgacTCCATCTCCTTTCATAATCAAGC

SF3B1 18 f gtagtgcgatggccagtTGCTTTATTTCCTTGGAAAAGC

SF3B1 18 r cagtgtgcagcgatgacGCAATGTGCCATAATAGTTTTCAT

SF3B1 19 f gtagtgcgatggccagtGCAACAGATGTTTGGGTGGT

SF3B1 19 r cagtgtgcagcgatgacTTTGGGGAAGAAGTAAGAATTTG

SF3B1 20 f gtagtgcgatggccagtTGTCATGAAGACTTGTCAAGAGG

SF3B1 20 r cagtgtgcagcgatgacAACAAAAACCTCCCAACTCC

SF3B1 21 f gtagtgcgatggccagtATCTGGGGCTTTCTCTTTCC

SF3B1 21 r cagtgtgcagcgatgacATTGAATACAAAGTGGCCAAA

SF3B1 22 f gtagtgcgatggccagtTCATGTTTTTAGAACTGAATTTGC

SF3B1 22 r cagtgtgcagcgatgacTCAGACCATGCCTCAAAAGA

SF3B1 23 f gtagtgcgatggccagtCAGCTTGTTGACCCATTTGTT

SF3B1 23 r cagtgtgcagcgatgacTTCACGATGTTCTAAAATGAAGGA

SF3B1 24 f gtagtgcgatggccagtCCGCATCTTAAAGGACTTTTT

SF3B1 24 r cagtgtgcagcgatgacATGCATGCAGGGCTTAAAAC

SF3B1 25 f gtagtgcgatggccagtCCCCAAGGGAAATAATTTTGA

SF3B1 25 r cagtgtgcagcgatgacAGGTGTGAAGTAGCTGTGCATT

DOI: 10.3324/haematol.2012.075325

34

PA

TIE

NT

ID

WH

O D

IAG

NO

SIS

GE

NE

NC

BI

RE

FE

RE

NC

E

SE

QU

EN

CE

ID

CH

RO

MO

SO

ME

CH

RO

MO

SO

ME

LO

CA

TIO

N

RE

F B

AS

E

MU

TA

NT

BA

SE

BA

SE

S U

SE

D

MU

TA

NT

RE

AD

S

QS

NP

CO

DO

N C

HA

NG

E

CO

NF

IRM

ED

SO

MA

TIC

/

GE

RM

LIN

E

GS

K P

IPE

LIN

E

CO

NS

TIT

UT

ION

AL

DN

A S

OU

RC

E

RC-06-0256 MDS RARS AGXT2L1 NM_001146590 4 109680914 G A 30 11 104 T103I Yes Somatic No Skin

RC-06-0256 MDS RARS IKBKE NM_001193321 1 206658341 A G 38 11 102 S394G Yes Somatic Yes Skin

RC-06-0256 MDS RARS GAB4 NM_001037814 22 17472858 C T 294 117 599 R128H Yes Somatic No Skin

RC-06-0256 MDS RARS OC90 NM_001080399 8 133036862 C T 41 15 113 A434T Yes Somatic Yes Skin

RC-06-0256 MDS RARS SF3B1 NM_012433 2 198266494 T C 76 27 185 D781G Yes Somatic Yes Skin

RC-06-0256 MDS RARS ARHGAP39 NM_02525 8 145806353 A C 12 5 92 T389G Yes Germline No Skin

RC-06-0256 MDS RARS VWA5B2 NM_138345 3 183954168 T C 10 3 23 L417P No Skin

RC-06-0256 MDS RARS C1orf85 NM_144580 1 156265317 C T 10 7 104 Q40Q No

RC-06-0256 MDS RARS COG5 NM_001161520 7 107194778 T C 26 9 112 R113G Yes

RC-06-0275 MDS RCMD-RS FGD1 NM_004463 X 54492200 G A 22 5 47 R476W Yes Somatic No Skin

RC-06-0275 MDS RCMD-RS TESC NM_001168325 12 117479796 G A 8 2 23 P148S Yes Somatic No Skin

RC-06-0275 MDS RCMD-RS SF3B1 NM_012433 2 198267491 C G 68 30 237 E622D Yes Somatic Yes Skin

RC-06-0275 MDS RCMD-RS NAGS NM_153006 17 42082405 C A 7 3 51 C374A yes Germline Yes Skin

RC-06-0275 MDS RCMD-RS TRIOBP NM_138632 22 38155502 A C 84 40 23 T422P No

RC-06-0275 MDS RCMD-RS LTBP3 NM_021070 11 65325357 G A 14 3 24 A25V No

RC-06-0275 MDS RCMD-RS LTBP3 NM_021070 11 65325358 C G 14 3 20 A25P No

RC-06-0278 MDS RARS PKHD1 NM_170724 6 51947297 C A 150 56 314 L58F Yes Somatic Yes Skin

RC-06-0278 MDS RARS MC2R NM_000529 18 13885474 G A 43 27 253 A15V Yes Somatic Yes Skin

RC-06-0278 MDS RARS PCNT NM_006031 21 47836271 A G 87 35 211 I2147V Yes Somatic Yes Skin

RC-06-0278 MDS RARS SF3B1 NM_012433 2 198267371 G C 89 33 205 H662Q Yes Somatic Yes Skin

RC-06-0278 MDS RARS ATG9B NM_173681 7 150721444 C T 9 4 58 G23R Yes Somatic No Skin

RC-06-0278 MDS RARS FITM2 NM_001080472 20 42935567 C T 50 24 168 A163T Yes Somatic Yes Skin

RC-06-0278 MDS RARS AFF3 NM_001025108 2 100209883 G T 62 12 28 P747H Yes Somatic No Skin

RC-06-0278 MDS RARS TET2 NM_001127208 4 106164862 CT - 18 8 354 1244_1244del Yes Somatic No Skin

RC-06-0278 MDS RARS CEP110 NM_007018 9 123860787 A G 67 17 73 S249G Yes Somatic Yes Skin

RC-06-0278 MDS RARS ZC3H18 NM_144604 16 88694342 G A 78 34 241 G2284A Yes Germline Yes Skin

RC-06-0278 MDS RARS FRMD4A NM_018027 10 13699338 T G 8 5 20 T751P Yes No Skin

RC-08-0010 MDS RARS KIAA1109 NM_015312 4 123109050 G A 92 37 151 G210R Yes Somatic No† CD3+ T-cells

RC-08-0010 MDS RARS SF3B1 NM_012433 2 198266834 T C 94 40 275 K700E Yes Somatic Yes CD3+ T-cells

RC-08-0010 MDS RARS TET2 NM_001127208 4 106157287 AC - 110 46 2232 730_730del Yes Somatic No CD3+ T-cells

RC-08-0010 MDS RARS PPM1D NM_003620 17 58740624 A - 182 76 1812 Q510fs Yes Somatic No CD3+ T-cells

RC-08-0010 MDS RARS AR NM_000044 X 66765212 A C 28 9 97 A224C Yes Germline No† CD3+ T-cells

RC-08-0010 MDS RARS RP1L1 NM_178857 8 10466010 C A 164 47 82 G5598T Yes Germline No CD3+ T-cells

RC-08-0010 MDS RARS CREM NM_001881 10 35468147 A G 67 30 244 A304G Yes Germline No CD3+ T-cells

RC-08-0010 MDS RARS SLC12A6 NM_00104249 15 34543180 C G 69 38 336 G1367C Yes Germline No CD3+ T-cells

RC-08-0010 MDS RARS FBLIM1 NM_001024215 1 16101435 T C 25 13 84 T1034C Yes Germline No CD3+ T-cells

RC-08-0010 MDS RARS FIZ1 NM_032836 19 56104423 A C 7 4 69 L295R

No† CD3+ T-cells

RC-08-0010 MDS RARS COL6A6 NM_001102608 3 130287289 G A 205 99 658 E748K

Yes CD3+ T-cells

RC-08-0010 MDS RARS VAV3 NM_006113 1 108507419 A G 47 26 237 W25R

Yes CD3+ T-cells

RC-08-0010 MDS RARS DONSON NM_017613 21 34960634 G C 17 5 75 P105R

Yes CD3+ T-cells

RC-08-0010 MDS RARS LOC401097 NM_001168214 3 159943822 C G 24 11 117 P134A

Yes CD3+ T-cells

RC-08-0010 MDS RARS SYCP2 NM_014258 20 58461000 C G 39 24 258 V838L

Yes CD3+ T-cells

RC-08-0010 MDS RARS B4GALNT4 NM_178537 11 376345 C T 15 8 121 P431S

Yes CD3+ T-cells

RC-08-0010 MDS RARS PKHD1L1 NM_177531 8 110465039 C T 39 63 182 I2200M

Yes CD3+ T-cells

RC-08-0010 MDS RARS SPTB - 14 65249007 C T 86 20 51 Splicesite 5' C>T

No CD3+ T-cells

Supplementary table 4: Exome data processed through CASAVA pipeline showing a complete list of somatic mutations in 6 MDS

exomes. † SNV present filtered out during the analysis as a result of low quality read or low read numbers.

DOI: 10.3324/haematol.2012.075325

35

PA

TIE

NT

ID

WH

O D

IAG

NO

SIS

GE

NE

NC

BI

RE

FE

RE

NC

E

SE

QU

EN

CE

ID

CH

RO

MO

SO

ME

CH

RO

MO

SO

ME

LO

CA

TIO

N

RE

F B

AS

E

MU

TA

NT

BA

SE

BA

SE

S U

SE

D

MU

TA

NT

RE

AD

S

QS

NP

CO

DO

N C

HA

NG

E

CO

NF

IRM

ED

SO

MA

TIC

/

GE

RM

LIN

E

GS

K P

IPE

LIN

E

CO

NS

TIT

UT

ION

AL

DN

A S

OU

RC

E

RC-08-0182 MDS RARS ATP1A4 NM_144699 1 160143980 C A 59 14 59 H691N Yes Somatic No† CD3+ T-cells

RC-08-0182 MDS RARS MCM3AP NM_003906 21 47666752 G A 111 25 49 Q1447X Yes Somatic No CD3+ T-cells

RC-08-0182 MDS RARS SF3B1 NM_012433 2 198267371 G C 99 40 271 H662Q Yes Somatic Yes CD3+ T-cells

RC-08-0182 MDS RARS GPR112 NM_153834 X 135455180 T C 107 27 67 M2578T Yes Somatic Yes CD3+ T-cells

RC-08-0182 MDS RARS CAMLG NM_001745 5 134076908 C A 69 32 240 C328A Yes Germline No CD3+ T-cells

RC-08-0182 MDS RARS PLEC NM_201381 8 145005769 C T 9 4 63 G2141A Yes Germline No CD3+ T-cells

RC-08-0182 MDS RARS ACAN NM_013227 15 89399738 G A 66 20 63 A1308T

No CD3+ T-cells

RC-08-0182 MDS RARS SAMD11 NM_152486 1 878744 G C 12 8 125 G559A

No† CD3+ T-cells

RC-10-0089 MDS RARS ANXA7 NM_004034 10 75143371 C - 39 10 410 T259fs Yes Somatic No CD3+ T-cells

RC-10-0089 MDS RARS SF3B1 NM_012433 2 198266834 T C 57 22 164 K700E Yes Somatic Yes CD3+ T-cells

RC-10-0089 MDS RARS DNMT3A NM_022552 2 25457242 C T 41 20 175 R882H Yes Somatic Yes CD3+ T-cells

RC-10-0089 MDS RARS SLC7A2 NM_001164771 8 17417899 C T 80 31 212 S494L Yes Somatic Yes CD3+ T-cells

RC-10-0089 MDS RARS IGF1R 15 99192762 T C 10 8 83 Yes No CD3+ T-cells

RC-10-0089 MDS RARS RAPGEF3 NM_001098531 12 48151789 G A 8 4 52 79T Yes Germline Yes CD3+ T-cells

RC-10-0089 MDS RARS ST3GAL3 NM_174964 1 44360114 G A 82 39 289 .G407A Yes Germline No CD3+ T-cells

RC-10-0089 MDS RARS C20orf118 - 20 35507598 T G 21 7 24 Splicesite 3' T>G

No CD3+ T-cells

DOI: 10.3324/haematol.2012.075325

36

Supplementary table 5: Exome data processed through BWA/GATK (Broard Institute best practices pipeline) showing a complete list of somatic mutations in 6

MDS exomes. † SNV filtered out during the analysis as a result of low quality read or low read numbers.

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RC-06-0256 MDS RARS COG5 ENSG00000164597 7 107194778 28 11 311.59 PASS Aga/Gga R113G Yes Skin

RC-06-0256 MDS RARS IKBKE ENSG00000143466 1 206658341 38 11 238.34 PASS Agc/Ggc S479G Yes Skin

RC-06-0256 MDS RARS OC90 ENSG00000253117 8 133036862 41 15 409.99 PASS Gca/Aca A434T Yes Skin

RC-06-0256 MDS RARS SF3B1 ENSG00000115524 2 198266494 74 28 848.12 PASS gAt/gGt D781G Yes Skin

RC-06-0275 MDS RCMD-RS NAGS ENSG00000161653 17 42082405 6 3 63.08 PASS aCg/aAg T125K Yes Skin

RC-06-0275 MDS RCMD-RS IL2RA ENSG00000134460 10 6060053 17 8 225.67 PASS Gtc/Atc V253I NO† Skin

RC-06-0275 MDS RCMD-RS SF3B1 ENSG00000115524 2 198267491 65 28 1621.88 PASS gaG/gaT E622D Yes Skin

RC-06-0278 MDS RARS ENSG00000248602 ENST00000397266 7 150721444 9 4 74.58 PASS - G/R NO Skin

RC-06-0278 MDS RARS MC2R ENSG00000185231 18 13885474 43 27 789.9 PASS gCa/gTa A15V Yes Skin

RC-06-0278 MDS RARS FITM2 ENSG00000197296 20 42935567 48 24 664.06 PASS Gcc/Acc A163T Yes Skin

RC-06-0278 MDS RARS CEP110 ENSG00000119397 9 123860787 63 16 388.87 PASS Agt/Ggt S249G Yes Skin

RC-06-0278 MDS RARS ZC3H18 ENSG00000158545 16 88694342 77 34 1053.25 PASS Gaa/Aaa E762K Yes Skin

RC-06-0278 MDS RARS PCNT ENSG00000160299 21 47836271 81 32 722.23 PASS Ata/Gta I2147V Yes Skin

RC-06-0278 MDS RARS SF3B1 ENSG00000115524 2 198267371 90 33 2937.78 PASS caC/caG H662Q Yes Skin

RC-06-0278 MDS RARS PKHD1 ENSG00000170927 6 51947297 144 53 1678.62 PASS ttG/ttT L58F Yes Skin

RC-10-0089 MDS RARS RAPGEF3 ENSG00000079337 12 48151789 8 4 81.45 PASS Cgg/Tgg R27W Yes CD3+ T-cells

RC-10-0089 MDS RARS DNMT3A ENSG00000119772 2 25457242 39 21 1678.95 PASS cGc/cAc R882H Yes CD3+ T-cells

RC-10-0089 MDS RARS SF3B1 ENSG00000115524 2 198266834 56 22 2355.32 PASS Aaa/Gaa K700E Yes CD3+ T-cells

RC-10-0089 MDS RARS SLC7A2 ENSG00000003989 8 17417899 75 31 957.57 PASS tCg/tTg S493L Yes CD3+ T-cells

RC-08-0010 MDS RARS MICALL2 ENSG00000164877 7 1484556 7 6 170.48 PASS Gtg/Atg V384M NO† CD3+ T-cells

RC-08-0010 MDS RARS B4GALNT4 ENSG00000182272 11 376345 13 6 302.17 PASS Ccg/Tcg P431S Yes CD3+ T-cells

RC-08-0010 MDS RARS DONSON ENSG00000159147 21 34960634 15 5 404.27 PASS cCg/cGg P105R Yes CD3+ T-cells

RC-08-0010 MDS RARS ENSG00000180044 ENST00000326474 3 159943822 22 10 236.76 PASS Ccg/Gcg P134A Yes CD3+ T-cells

RC-08-0010 MDS RARS SYCP2 ENSG00000196074 20 58461000 40 24 1202.94 PASS Gtt/Ctt V838L Yes CD3+ T-cells

RC-08-0010 MDS RARS VAV3 ENSG00000134215 1 108507419 46 26 1020.97 PASS Tgg/Cgg W25R yes CD3+ T-cells

RC-08-0010 MDS RARS PKHD1L1 ENSG00000205038 8 110465039 116 57 2381.04 PASS atT/atG I2200M Yes CD3+ T-cells

RC-08-0010 MDS RARS COL6A6 ENSG00000206384 3 130287289 193 92 5156.23 PASS Gaa/Aaa E748K Yes CD3+ T-cells

RC-08-0182 MDS RARS GPR112 ENSG00000156920 X 135455180 107 27 585.01 PASS aTg/aCg M2578T Yes CD3+ T-cells

RC-08-0182 MDS RARS SF3B1 ENSG00000115524 2 198267371 97 40 2937.78 PASS caC/caG H662Q Yes CD3+ T-cells

RC-08-0182 MDS RARS WDR62 ENSG00000075702 19 36545988 13 9 284.2 PASS Cca/Aca P39T No† CD3+ T-cells

DOI: 10.3324/haematol.2012.075325

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Supplementary table 6: A complete list of somatic missense and nonsense mutations in 117 MDS patients

detected by 454-Roche NGS sequencing. † denotes confirmed novel somatic mutations found in our cohort of

patients.*denotes novel nonsense/splicesite mutations found in samples where paired constitutional source of

DNA was not available. ‡ denotes previously confirmed somatic mutations in TP53 gene according to

International agency for research on cancer (IARC) TP53 database.

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040015 MDS RARS Low SF3B1 K700E2 51% DNMT3A Q816X3 36%

050024 MDS RCMD Int-I CD3+ T-Cells

ASXL1 E635fsX154 47%

EZH2 C548X† 73%

050145 MDS RAEB-II Int-II

ASXL1 G635RfsX154 17%

IDH2 R140Q5

050187 MDS RCMD-RS Int-I

IDH2 R140W6 24%

050196 MDS RAEB-II Int-II ZRSR2 E133Gfs11X† 54%

MPL W515K7 41%

050218 AML/MDS N/A U2AF1 Q157P8 41% ASXL1 E635fsX154 36%

IDH2 R140Q5 33% TP53 T253fsX57‡ 80%

050231 MDS RAEB-I Int-I U2AF1 S34F4 10%

TET2 G1275E9 68%

TET2 G1282D9 68%

TET2 D1844G9 68%

050237 MDS RCMD Int-II

TP53 K132Q‡ 42%

TP53 G262V‡ 35%

050276 MDS RAEB-II Int-II SRSF2 P95L10 39%

NRAS G13V11 11%

050278 MDS/MPD N/A CD3+ T-Cells SRSF2 Y93fsX121† 16%

NRAS G12S12 42%

060007 MDS RCMD-RS Low SF3B1 K666R13 49%

CCBL R420Q 1413%

060009 MDS RAEB-II Int-II

IDH2 R140Q5 33% TP53 K164E‡ 7%

RUNX1 S322X* 20%

060017 MDS RARS Low SF3B1 K700E2 51% TET2 S1494X15 40%

060018 MDS RCMD-RS Low SF3B1 K666Q2 43% DNMT3A SA 3' Exon 19* 33% CEBPA G100_G101del* 6%

06-0031 MDS RARS Low SF3B1 E622D2 60%

060039 MDS RCMD Int-I Skin

DNMT3A W313X† 32%

060070 AML/MDS N/A

ASXL1 R774fsX* 10 % RUNX1 R162K16 72%

060075 MDS RCMD Int-I CD3+ T-Cells SRSF2 Y93fsX121† 24%

060084 MDS RCMD Int-I Skin SRSF2 Y93fsX121† 35% IDH2 R140Q5 34%

060103 AML/MDS N/A CD3+ T-Cells

IDH2 R140Q5 12% RUNX1 S369X† 11%

060114 AML/MDS N/A CD3+ T-Cells

FLT3 L601_K602Ins9† 40%

CCBL Y368S† 8%

060129 MDS RARS Low SF3B1 K700E2 41%

JAK2 V617F17 24%

060145 MDS RCMD Int-I SRSF2 P95L10 49% TET2 L371X9 47.5%

TET2 Y1569fsX19 37%

060152 MDS RAEB 1 Int-I SRSF2 P95L10 50% ASXL1 R693X18 26%

060158 MDS RAEB-II High

TP53 R273P‡ 18%

060160 MDS RAEB-II High CD3+ T-Cells

DNMT3A F751V† 25%

DNMT3A P904S† 20%

060187 MDS RCMD Low

TET2 C1193W9 49%

EZH2 R690H19 70%

060188 MDS RARS Low CD3+ T-Cells

ASXL1 G643V† 32%

DOI: 10.3324/haematol.2012.075325

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060199 tMDS Int-II CD3+ T-Cells

TP53 Y220C‡ 42%

TP53 Q331H‡ 41%

CEBPA P183_P185del† 7%

060219 MDS RAEB 1 Int-I CD3+ T-Cells SRSF2 P95L10 41% ASXL1 Q760X20 37% RUNX1 SA 3' Exon4† 32%

060237 CMML N/A SRSF2 P95H4 34% TET2 G1288V9 43%

TET2 Q1699X9 49% CKIT D816V21 35%

060247 MDS RAEB-II High SRSF2 P95H4 45% ASXL1 G646WfsX1222 22% FLT3 R595_612dup† 5%

NRAS Q61R23 11%

060256 MDS RARS Low Skin SF3B1 D781G10 45%

060261 MDS RAEB-II High

JAK2 V617F17 58%

060262 MDS/MPD Low Skin U2AF1 Q157P8 45%

DNMT3A N551S† 12%

ASXL1 W583X† 31%

TET2 R1216X9 41%

060264 MDS RAEB 1 Int-II

TET2 R1878H9 6% TP53 L43X‡ 40%

TP53 C238Y‡ 41 %

060265 MDS RAEB 1 Int-II Skin

DNMT3A R326H† 22%

ASXL1 G646WfsX1222 18%

EZH2 R583X24 38%

060266 MDS RCMD Low U2AF1 Q157P8 27%

060270 AML/MDS N/A CD3+ T-Cells SF3B1 K666N10 34%

060272 MDS RAEB 1 Int-I SRSF2 P95L10 56% TET2 Q888X9 45%

TET2 R1465X9 35%

060273 MDS RCMD Int-I SRSF2 P95L10 63% TET2 S716X9 45%

060275 MDS RCMD-RS Int-I Skin SF3B1 E622D10 40%

060278 MDS RARS Int-I Skin SF3B1 H662Q10 37% TET2 L1245delCTfsX2225 45%

060282 MDS RCMD Int-II

DNMT3A A741V26 44%

ASXL1 G646WfsX1222 17%

060285 MDS RCMD Int-I

TET2 I1195V9 47%

060286 CMML N/A

ASXL1 Q1234X * 10%

TET2 N1743fsX9 17 46% NRAS G12S12 41%

060290 MDS RAEB-II N/A U2AF1 Q157P8 28%

060297 MDS RCMD Low

TET2 Q1529X9 38%

060309 MDS RAEB 1 Int-II CD3+ T-Cells

TET2 L1322fsX† 22%

060329 CMML N/A SF3B1 K666R10 50% ASXL1 E635fsX154 27%

060331 AML/MDS Int-II U2AF35 Q157P4 34% IDH2 R140Q5 37%

060335 MDS RAEB 1 Int-II

TP53 V157F‡ 24%

060336 MDS RAEB 1 Int-II

TET2 Y1148C27 25% TP53 G266E‡ 28%

TP53 G325fsX12‡ 28%

060337*¹ MDS RCMD-RS Low Skin SF3B1 R625C28 39% TET2 S835X† 60%

060337*² MDS RCMD-RS

Skin SF3B1 R625C28 42% TET2 S835X† FLT3 F590_W603dupInsP†

RUNX1 F163Y29 30%

060337*³ MDS RCMD-RS

Skin SF3B1 R625C28 46% TET2 S5835X† 45% FLT 3 F590_W603dupInsP†

RUNX1 F163Y29 40%

060338 MDS RCMD-RS Int-I Skin

IDH2 R140Q5 CEBPA P183_P185del† 8%

060339 MDS RCMD Int-I Skin U2AF1 R156H4 36% DNMT3A R882P3,26,30 35%

060342 MDS RA Low

TET2 V1862fsX249 98%

060361 MDS RCMD Low

TET2 Q644X9 36%

TET2 L1360R9 36%

TET2 P1962L9 40%

060365 MDS RCMD Int-II CD3+ T-Cells

ASXL1 R860X† 34% RUNX1 R201Q31 31%

070066 MDS RAEB-II Int-II

TET2 A1355P9 26%

TET2 V160fsX229 19%

DOI: 10.3324/haematol.2012.075325

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070071 CMML N/A

TET2 SD 3 exon 109 42%

TET2 Y1631fsX289 51% NRAS G12D12 35%

070076 MDS RAEB-I Int-II U2AF1 S34F4 44%

NRAS G12D12 44%

070077 MDS RARS Low Skin SF3B1 K700E2 38%

CEBPA P183_P185del† 12%

070088 MDS RCMD Int-I

TET2 T1393P9 39%

070093 MDS/MPD N/A Skin

EZH2 G686D† 22%

070095 MDS RCMD-RS Int-II

TP53 V173M‡ 24%

TP53 H214R‡ 13%

070104 MDS RCMD Low

TET2 P1962L9 50%

070141 MDS RAEB-I Int-I SF3B1 K666R2 28%

U2AF1 Q157R4 28% IDH2 R140Q5 16%

070148 MDS/MPD N/A SRSF2 P95R8 30%

070180 AML/MDS High Buccal U2AF1 Q157P4 27%

TP53 R248Q‡ 57%

RUNX1 G165R†32 28%

070186 MDS/MPD N/A Skin

ASXL1 E635fsX154 23%

EZH2 N693H† 50% CCBL Y368S† 37%

070192 MDS/MPD N/A Buccal

TET2 C1273F9 71%

TET2 R1452X9 20%

EZH2 SD 5' Exon 19† 20%

070203 MDS RAEB-II High

TP53 SD Exon 3'‡ 35%

TP53 H178P‡ 40%

070207 MDS/MPD N/A SRSF2 P95H4 44%

JAK2 V617F17 32%

070217 MDS RAEB-II Int-II

ASXL1 G646WfsX1222 22 %

070221 AML/MDS N/A U2AF1 Q157P4 27% IDH2 R172K33 11%

070222 MDS RCMD Int-I Skin U2AF1 Q157P4 16% ASXL1 W898X† 13%

EZH2 G743V† 16%

070226 MDS RCMD Int-I SRSF2 P95L10 48% TET2 Q1687X9 41%

TET2 I1873N9 39%

070259 CMML N/A SRSF2 P95R8 42% TET2 H1380Y9 50% FLT3 F593_D600dupInsF† 44%

070280 MDS RCMD Int-I Skin

DNMT3A A376T† 34%

EZH2 V626M34 40%

070297 MDS RCMD Int-I U2AF1 S34F4 10%

070301 MDS RCMD Int-II Skin

TET2 C1135S† 12%

070311 MDS RCMD Int-I Skin SRSF2 Y93fsX121† 13%

070317 MDS RAEB-I Int-II

TP53 L257Q‡ 60%

070319 MDS RAEB-II Int-II

IDH2 R140Q5

070329 MDS RAEB-II High Skin SRSF2 P95L10 38% TET2 Q255R† 35%

TET2 R1452X†9 35%

080010 MDS RARS Int-I CD3+ T-Cells SF3B1 K700E2 43%

080027 CMML High CD3+ T-Cells SRSF2 P95H4 32%

ASXL1 S666SfsX22†

EZH2 G564fsX107† 40%

EZH2 V679L† 43%

FLT3 E596_Y597Ins14† 15%

CCBL R348X† 10%

CCBL K396Y† 40%

RUNX1 R166X35 38%

080040 tMDS Int-II

TP53 V173L‡ 93%

080046 MDS RAEB-I Int-II Skin

DNMT3A T835A 22% TP53 Y163C‡ 2.5%

TP53 C275Y‡ 47%

080077 AML/MDS High

TP53 P152fsX28‡ 70%

080117 MDS RAEB-II Int-II CD3+ T-Cells

ASXL1 G646WfsX1222 10%

EZH2 N130I 9%

080129 MDS RAEB-I Int-II

TP53 S149fsX20‡ 90%

080161 MDS RCMD-RS Int-I Skin

TET2 Q654X† 34%

TET2 Q916X† 42%

080169 MDS RAEB-II Int-II Skin

TET2 S1369P† 23% CCBL SD del Exon-8† 5%

RUNX1 R166Q 36 32%

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080171 MDS RARS Low Skin SF3B1 K700E2 42%

CEBPA G100del†13%

CEBPA P183_P183del† 8%

080182 MDS RARS Low CD3+ T-Cells SF3B1 H662Q37 45%

080186 MDS RAEB-II High CD3+ T-Cells

DNMT3A R366H† 22%

ASXL1 R693X38

080194 MDS RAEB-II High

DNMT3A R882C26 34%

EZH2 V626M34 78%

080215 MDS RAEB-I Int-II

ASXL1 G646WfsX1222 15% NRAS G12V39 23%

080223 MDS RAEB-II High

TP53 SD Exon 5'‡ 28%

TP53 R348W‡ 38%

NRAS G12D12 35%

080236 MDS RAEB-II High Skin

ASXL1 Q708X†

080260 MDS RCMD-RS Int-I Skin SF3B1 K700E2 40% IDH2 R140Q5 35% CEBPA G100del† 6%

090006*¹ MDS RARS Low SF3B1 H662Q40 42%

RUNX1 Y281X41 50%

090006*² MDS RARS

SF3B1 H662Q40 42%

RUNX1 Y281X 4150%

090035 MDS RCMD Int-II

ASXL1 G646WfsX1222 9%

090045 MDS RAEB-II High U2AF1 S34F4 29%

TP53 F134L‡ 14%

090083 tMDS Int-II

TP53 Y163C‡ 45%

TP53 Y234C‡ 35%

090152 CMML Int-II SRSF2 P95R8 42% TET2 R1465X9 50% CCBL C404Y42 80%

090182 MDS RCMD-RS Int-I CD3+ T-Cells SF3B1 H662Q40 30% DNMT3A W581S† 22%

090214 MDS RAEB-II High Skin U2AF1 Q157P4 23% TET2 R1878H† 27%

IDH1 R132H43

090251 MDS RAEB-II High

ASXL1 E635fsX154 40%

090296 MDS RAEB-II High SRSF2 P95R8 25% ASXL1 G647X* 30% NRAS G12D12 34%

090387 MDS RAEB-I Int-II

DNMT3A SA 3' Exon 18* 43%

IDH1 R132L44

100089 MDS RARS Low SF3B1 K700E2 40% DNMT3A R882H45 50%

100101 AML/MDS N/A Skin SF3B1 K700E2 35%

ZRSR2 S439_R440del† 64% IDH2 R140Q5 21%

100135 MDS RARS Low Buccal SF3B1 K700E2 45% TET2 R550X46 84% CEBPA P183del† 9%

100162 MDS RARS Int-I Skin SF3B1 K700E2 30% TET2 Q652X† 34%

Supplementary Table 7: Multivariable analysis of overall survival. The variables included are age, WHO category,

medullary blasts, IPSS cytogenetic risk groups, transfusion dependency, SF3B1 mutations, NRAS mutations and TP53

mutations.

OS

Variable Hazard Ratio 95% CI p value

Age 1.0 1-1.1 0.05

WHO category 1.1 0.9-1.3 0.12

IPSS Cytogenetic 1.9 1.3-2.6 0.001

Medullary blast count 1 0.9-1 0.2

Transfusion dependency 1.7 0.9-3 0.08

SF3B1 0.2 0.1-0.8 0.03

TP53 2.1 1.1-4.4 0.04

DOI: 10.3324/haematol.2012.075325

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Supplementary Figure 1A and 1B: Frequency and distribution of various gene mutations in MDS patients as depicted by circos

diagram9. The arc length corresponds to the frequency of mutations in relevant gene and the ribbon width corresponds to the percentage of

the patients that have other coexisting mutations. Diagram showing the coexistent mutations between 22 individual genes (1A). Diagram

showing the coexistent mutations between splicing factor genes (SF3B1, SRSF2, U2AF1, ZRSR2), epigenetic modifiers, Cell

Signalling/transcription regulators, TP53 and CEBPA (1B).

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Supplementary Figure 2: Clonal evolution from MDS to AML with sequential acquisition of mutations. A normal haematopoietic

stem cell (green cell) acquires mutations in SF3B1 and TET2 genes during early MDS disease phase. Subsequently, one sub-clone

harbouring SF3B1 and TET2 mutations acquires additional mutations in FLT3 and RUNX1 genes and evolves to become the dominant

clone at transformation to AML. This is followed by further clonal expansion during subsequent progression with accompanying rise in

blast count. There was a period of a transient morphological remission between day 125 and 198 due to chemotherapy, although

samples were not available at the time of transient remission phase. The internal areas are representative of disease sub-clone size.

Sample time points are indicated at by vertical white lines representing time of diagnosis (day 0), transformation to AML (day 125)

and further disease progression after a short duration of morphological remission (day 198). The order of which SF3B1 and TET2

mutations occur is unknown and only exampled here.

DOI: 10.3324/haematol.2012.075325

43

Supplementary Figure 3A-3D: Overall survival (A) and progression free survival (C) for patients with TP53 mutations (n=19) compared

with wild type TP53 (n=135). Overall survival (B) and progression free survival (D) for patients with NRAS (n=9) compared with patients

without NRAS mutations (n=145)

DOI: 10.3324/haematol.2012.075325

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Supplementary 4A and 4B: Overall survival (A) and progression free survival (B) for patients with epigenetic modifier mutations stratified

according to coexistence of splicing factor mutations with epigenetic modifier mutations (n=36) or epigenetic modifier mutations alone

(n=41).

DOI: 10.3324/haematol.2012.075325