Abstract

Introduction

Myelodysplastic syndromes (MDS) are heterogeneous regarding clinical characteristics, as well as prognosis and treatment approaches. Therapeutic decision making relies greatly on prognostic scoring systems. Recently, the gold-standard IPSS (International prognostic scoring system [Greenberg Blood 1997]) has been revised. The new IPSS-R still uses cell counts, marrow blast count, and karyotypic abnormalities to stratify patients (pts) into risk groups [Greenberg Blood 2012]. However, more than 50% of all MDS pts present with a normal karyotype and even in pts with identical chromosomal abnormalities outcome may vary. Somatic mutations are more common than cytogenetic abnormalities and can be identified in over 70% of pts including pts with normal karyotype [Bejar JCO 2012]. Therefore, the addition of such mutations to common prognostic markers might help to refine prognostication in MDS pts and improve therapeutic procedures by individualizing MDS treatment.

Patients and Methods

We analyzed 182 pts with different subtypes of MDS: RCUD, RARS, MDS-U, 5q- (n=28), RCMD +/-RS (n=85), RAEB 1 (n=16), RAEB 2 (n=25), MDS/MPD (n=24), AML <30% marrow blasts (n=4). Median age was 68 (16-87) years. Patients belonged to the following cytogenetic risk groups: 73% good, 13% intermediate, and 14% poor risk according to IPSS. In 29% of pts IPSS risk group was low, 43% intermediate 1, 21% intermediate 2, and 7% belonged to the high risk group. IPSS-R risk groups were very good 10%, good 32%, intermediate 29%, poor 13%, and very poor 16%.

Various molecular assays were performed including sensitive next-generation sequencing for mutations in ASXL1, DNMT3A, EZH2, FLT3-ITD, IDH1, KRAS, MLL-PTD, NRAS, RUNX1, SF3B1, SFRS2, TET2, and TP53. Most of the data was collected prospectively within the clinical routine diagnostic procedures. During that time the marker panel was adjusted, when new analyses became available. Thus, not all markers are currently available for all pts (see table).

To assess the impact of the biomarkers, Kaplan-Meier curves were estimated starting at the day of diagnosis.

Results

The most frequent mutation was TET2 (30.2%), followed by ASXL1 (25.4%), SF3B1 (22.9%), SFRS2 (22.2%), RUNX1 (20.4%), DNMT3A (13.7%), TP53 (9.9%), EZH2 (9.0%), NRAS (4.7%), MLL-PTD (3.8), IDH1 (3.5%), FLT3-ITD (3.3%), KRAS (2.8%), IDH2 (2.8%), and CBL (2.2%).

Three pts with normal karyotype, who would have been classified as ICUS (idiopathic cytopenia of undetermined significance) due to only mild dysplasia, were reclassified as MDS-U as they exhibited typical somatic mutations (RUNX1 plus TET2; TET2, and DNMT3A).

Median follow up was 3.8 years (95% confidence interval [CI] 3.1-4.5). During this time 74 pts died and 108 pts were censored at the last date they were known to be alive. Median survival was 4.9 years (95% CI 2.7-7.1).

A significant influence on survival in univariate analysis could be demonstrated for TP53 (p<0.001), EZH2 (p=0.003), SF3B1 (p=0.016), ASXL1 (p=0.016), and RUNX1 (p=0.042) (see table). Other prognostic variables with significant impact regarding survival were age at diagnosis (p<0.001), Hb (p=0.001), platelet count (p=0.002), marrow blast count (p<0.001), FAB subgroup (p<0.001), IPSS (p<0.001), IPSS-R (p<0.001), and cytogenetic risk groups according to IPSS (p<0.001) as well as IPSS-R (p=0.001).

Conclusion

We could confirm mutations in TP53, EZH2, RUNX1, and ASXL1 to be predictors of poor overall survival, while SF3B1 mutations conferred a favorable prognosis in pts with MDS. Integrating mutation assessment into the clinical routine might improve diagnostic procedures as well as prognostication, and individualize treatment approaches. Further analyses are ongoing.

Mutationpositivenegativep
nmedian survival/ years95% CInmedian survival/ years95% CI
SF3B1 22 not reached  74 3.11 1.73-4.49 0.016 
TET2 54 7.32 2.99-11.65 125 4.50 2.18-6.81 0.241 
DNMT3A 17 3.75 2.01-5.50 107 6.46 2.14-10.79 0.224 
NRAS 3.50 1.26-5.74 162 4.88 1.87-7.90 0.334 
KRAS 3.50 0.87-6.14 171 5.81 3.59-8.03 0.237 
RUNX1 34 3.11 2.12-4.10 133 6.46 2.87-10.06 0.042 
SFRS2 20 2.74 2.26-3.21 70 6.07 0.00-12.99 0.399 
ASXL1 43 2.68 2.14-3.21 126 6.07 1.93-10.21 0.016 
FLT3-ITD 2.44 1.46-3.42 145 4.88 2.72-7.04 0.235 
IDH1 2.39 0.69-4.09 139 5.81 3.09-8.52 0.190 
MLL-PTD 2.06 0.00-4.46 151 6.07 2.38-9.76 0.219 
EZH2 12 1.78 1.02-2.54 121 6.07 3.03-9.11 0.003 
TP53 15 1.27 0.57-1.96 136 6.46 2.80-10.13 <0.001 
Mutationpositivenegativep
nmedian survival/ years95% CInmedian survival/ years95% CI
SF3B1 22 not reached  74 3.11 1.73-4.49 0.016 
TET2 54 7.32 2.99-11.65 125 4.50 2.18-6.81 0.241 
DNMT3A 17 3.75 2.01-5.50 107 6.46 2.14-10.79 0.224 
NRAS 3.50 1.26-5.74 162 4.88 1.87-7.90 0.334 
KRAS 3.50 0.87-6.14 171 5.81 3.59-8.03 0.237 
RUNX1 34 3.11 2.12-4.10 133 6.46 2.87-10.06 0.042 
SFRS2 20 2.74 2.26-3.21 70 6.07 0.00-12.99 0.399 
ASXL1 43 2.68 2.14-3.21 126 6.07 1.93-10.21 0.016 
FLT3-ITD 2.44 1.46-3.42 145 4.88 2.72-7.04 0.235 
IDH1 2.39 0.69-4.09 139 5.81 3.09-8.52 0.190 
MLL-PTD 2.06 0.00-4.46 151 6.07 2.38-9.76 0.219 
EZH2 12 1.78 1.02-2.54 121 6.07 3.03-9.11 0.003 
TP53 15 1.27 0.57-1.96 136 6.46 2.80-10.13 <0.001 
Disclosures:

Kuendgen:Celgene: Honoraria, Research Funding. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Schnittger:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Kohlmann:MLL Munich Leukemia Laboratory: Employment. Gattermann:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Götze:Celgene Corp: Honoraria. Germing:Celgene: Honoraria, Research Funding.

Author notes

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Asterisk with author names denotes non-ASH members.