Landscape of genetic lesions in 944 patients with myelodysplastic syndromes

T Haferlach, Y Nagata, V Grossmann, Y Okuno, U Bacher, G Nagae, S Schnittger, M Sanada, A Kon, T Alpermann, K Yoshida, A Roller, N Nadarajah, Y Shiraishi, Y Shiozawa, K Chiba, H Tanaka, H P Koeffler, H-U Klein, M Dugas, H Aburatani, A Kohlmann, S Miyano, C Haferlach, W Kern, S Ogawa, T Haferlach, Y Nagata, V Grossmann, Y Okuno, U Bacher, G Nagae, S Schnittger, M Sanada, A Kon, T Alpermann, K Yoshida, A Roller, N Nadarajah, Y Shiraishi, Y Shiozawa, K Chiba, H Tanaka, H P Koeffler, H-U Klein, M Dugas, H Aburatani, A Kohlmann, S Miyano, C Haferlach, W Kern, S Ogawa

Abstract

High-throughput DNA sequencing significantly contributed to diagnosis and prognostication in patients with myelodysplastic syndromes (MDS). We determined the biological and prognostic significance of genetic aberrations in MDS. In total, 944 patients with various MDS subtypes were screened for known/putative mutations/deletions in 104 genes using targeted deep sequencing and array-based genomic hybridization. In total, 845/944 patients (89.5%) harbored at least one mutation (median, 3 per patient; range, 0-12). Forty-seven genes were significantly mutated with TET2, SF3B1, ASXL1, SRSF2, DNMT3A, and RUNX1 mutated in >10% of cases. Many mutations were associated with higher risk groups and/or blast elevation. Survival was investigated in 875 patients. By univariate analysis, 25/48 genes (resulting from 47 genes tested significantly plus PRPF8) affected survival (P<0.05). The status of 14 genes combined with conventional factors revealed a novel prognostic model ('Model-1') separating patients into four risk groups ('low', 'intermediate', 'high', 'very high risk') with 3-year survival of 95.2, 69.3, 32.8, and 5.3% (P<0.001). Subsequently, a 'gene-only model' ('Model-2') was constructed based on 14 genes also yielding four significant risk groups (P<0.001). Both models were reproducible in the validation cohort (n=175 patients; P<0.001 each). Thus, large-scale genetic and molecular profiling of multiple target genes is invaluable for subclassification and prognostication in MDS patients.

Figures

Figure 1
Figure 1
Significantly mutated genes in MDS. (a) Frequency of mutations in 47 significantly mutated genes in 944 cases with different WHO subtypes, which are shown in indicated colors. (b) Frequency of gene mutations involved in common functional pathways, which are defined in Supplementary Table S3. (c) Number of gene mutations detected in different MDS subtypes. (d) Distribution of mutations/deletions of significantly mutated genes in 944 MDS cases.
Figure 2
Figure 2
Comparison of mutation loads between major gene targets in MDS. (a) Correlations between major genetic lesions, where the correlation coefficients are indicated by a color gradient and show diagonal plots of variant allele frequencies (VAFs) between ASXL1 and U2AF1 mutations (b) and between mutations in RAS pathway genes (CBL, KRAS, NF1, NRAS and PTPN11) and DNA methylation-related genes (TET2, IDH1/2 and DNMT3A) (c), in which copy number-adjusted VAF values between indicated mutations or mutations of indicated functional pathways were compared using paired T-tests. Comparison was made exhaustively between all possible combinations of commonly mutated genes (>2.5% of mutation rates) (d) or gene pathways (e) with adjustment for multiple testing. Significance (q-values) and difference (relative difference in VAFs) is indicated by the size of circles and color gradient, as indicated, respectively.
Figure 3
Figure 3
Illustration of hazard ratios for Model-1 and Model-2. Hazard ratios (HRs, given in numbers) as well as logHR and their 95% confidential intervals (given as chart) for parameters used for Model-1 including clinical and genetic variables (a) and for Model-2 including only genetic variables (referring to the training cohort) (b) are plotted. For a, baseline level for the analysis was the respective IPSS-R risk category with the least risk score (hemoglobin: ⩾10 g/dl, platelets score: ⩾100 × 109/l, blast score: ⩽2%, cytogenetic score very good).
Figure 4
Figure 4
Development of a novel prognostic risk classification. (a) Kaplan–Meier estimates of OS in months (m) for four groups according to Model-1 in the training cohort. Three-year OS for low, intermediate, high and very high-risk groups amounts to 95.2, 69.3, 32.8 and 5.3%, respectively. (b) Kaplan–Meier estimates of OS for four groups according to Model-2 in the training cohort. Three-year OS for low, intermediate, high and very high-risk groups amounts to 83.3, 66.4, 39.7 and 9.5%, respectively. (c) Kaplan–Meier estimates of OS for five groups according to IPSS-R in the training cohort. Three-year OS for very low, low, intermediate, high and very high-risk groups amounts to 88.2, 73.9, 51.9, 45.3 and 19.6%, respectively. (d) Kaplan–Meier estimates of OS for four groups according to Model-1 in the validation cohort. Three-year OS for low, intermediate, high and very high-risk groups amounts to 88.3, 84.4, 55.7 and 22.8%, respectively. (e) Kaplan–Meier estimates of OS for four groups according to Model-2 in the validation cohort. Three-year OS for low, intermediate, high and very high-risk groups amounts to 83.3, 77.0, 64.1 and 33.3%, respectively. (f) Kaplan–Meier estimates of OS for five groups according to IPSS-R in the validation cohort. Three-year OS was 83.3, 93.7, 59.3 and 57.1% for the very low, low, intermediate and high-risk groups. For the very high-risk group, the median OS was 9.2 months, as 3-year OS was not applicable.

References

    1. Brunning R, Orazi A, Germing U, Le Beau MM, Porwit A, Baumann I, et al. Myelodysplastic syndromesIn: Swerdlow S, Campo E, Lee Harris N, Jaffe ES, Pileri SA, Stein H, et al(eds). 4 edn,WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues 200887–93.
    1. Cazzola M, la Porta MG, Travaglino E, Malcovati L. Classification and prognostic evaluation of myelodysplastic syndromes. Semin Oncol. 2011;38:627–634.
    1. Greenberg P, Cox C, Le Beau MM, Fenaux P, Morel P, Sanz G, et al. International Scoring System for evaluating prognosis in myelodysplastic syndromes. Blood. 1997;89:2079–2088.
    1. Malcovati L, Germing U, Kuendgen A, Della Porta MG, Pascutto C, Invernizzi R, et al. Time-dependent prognostic scoring system for predicting survival and leukemic evolution in myelodysplastic syndromes. J Clin Oncol. 2007;25:3503–3510.
    1. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised International Prognostic Scoring System (IPSS-R) for myelodysplastic syndromes. Blood. 2012;120:2454–2465.
    1. Bejar R, Levine R, Ebert BL. Unraveling the molecular pathophysiology of myelodysplastic syndromes. J Clin Oncol. 2011;29:504–515.
    1. Delhommeau F, Dupont S, Della Valle V, James C, Trannoy S, Masse A, et al. Mutation in TET2 in myeloid cancers. N Engl J Med. 2009;360:2289–2301.
    1. Mardis ER. New strategies and emerging technologies for massively parallel sequencing: applications in medical research. Genome Med. 2009;1:40.
    1. Yoshida K, Sanada M, Kato M, Kawahata R, Matsubara A, Takita J, et al. A nonsense mutation of IDH1 in myelodysplastic syndromes and related disorders. Leukemia. 2011;25:184–186.
    1. Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, et al. DNMT3A mutations in acute myeloid leukemia. N Engl J Med. 2010;363:2424–2433.
    1. Gelsi-Boyer V, Trouplin V, Roquain J, Adelaide J, Carbuccia N, Esterni B, et al. ASXL1 mutation is associated with poor prognosis and acute transformation in chronic myelomonocytic leukaemia. Br J Haematol. 2010;151:365–375.
    1. Ernst T, Chase AJ, Score J, Hidalgo-Curtis CE, Bryant C, Jones AV, et al. Inactivating mutations of the histone methyltransferase gene EZH2 in myeloid disorders. Nat Genet. 2010;42:722–726.
    1. Nikoloski G, Langemeijer SM, Kuiper RP, Knops R, Massop M, Tonnissen ER, et al. Somatic mutations of the histone methyltransferase gene EZH2 in myelodysplastic syndromes. Nat Genet. 2010;42:665–667.
    1. Yoshida K, Sanada M, Shiraishi Y, Nowak D, Nagata Y, Yamamoto R, et al. Frequent pathway mutations of splicing machinery in myelodysplasia. Nature. 2011;478:64–69.
    1. Papaemmanuil E, Cazzola M, Boultwood J, Malcovati L, Vyas P, Bowen D, et al. Somatic SF3B1 mutation in myelodysplasia with ring sideroblasts. N Engl J Med. 2011;365:1384–1395.
    1. Graubert TA, Shen D, Ding L, Okeyo-Owuor T, Lunn CL, Shao J, et al. Recurrent mutations in the U2AF1 splicing factor in myelodysplastic syndromes. Nat Genet. 2012;44:53–57.
    1. Walter MJ, Shen D, Shao J, Ding L, White B, Kandoth C, et al. Clonal diversity of recurrently mutated genes in myelodysplastic syndromes. Leukemia. 2013;27:1275–1282.
    1. Walter MJ, Shen D, Ding L, Shao J, Koboldt DC, Chen K, et al. Clonal architecture of secondary acute myeloid leukemia. N Engl J Med. 2012;366:1090–1098.
    1. Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, Van Loo P, et al. Clinical and biological implications of driver mutations in myelodysplastic syndromes Blood 2013. e-pub ahead of print 12 September 2013;doi:10.1182/blood-2013-08-518886
    1. Bejar R, Stevenson K, Abdel-Wahab O, Galili N, Nilsson B, Garcia-Manero G, et al. Clinical effect of point mutations in myelodysplastic syndromes. N Engl J Med. 2011;364:2496–2506.
    1. Bejar R, Stevenson KE, Caughey BA, Abdel-Wahab O, Steensma DP, Galili N, et al. Validation of a prognostic model and the impact of mutations in patients with lower-risk myelodysplastic syndromes. J Clin Oncol. 2012;30:3376–3382.
    1. Malcovati L, Hellstrom-Lindberg E, Bowen D, Ades L, Cermak J, del Canizo C, et al. Diagnosis and treatment of primary myelodysplastic syndromes in adults: recommendations from the European LeukemiaNet. Blood. 2013;122:2943–2964.
    1. Metzker ML. Sequencing technologies—the next generation. Nat Rev Genet. 2010;11:31–46.
    1. Meyerson M, Gabriel S, Getz G. Advances in understanding cancer genomes through second-generation sequencing. Nat Rev Genet. 2010;11:685–696.
    1. Tibshirani R. Regression shrinkage and selection via the Lasso. J Royal Statist Soc B. 1996;58:267–288.
    1. Goeman JJ. L1 penalized estimation in the Cox proportional hazards model. Biom J. 2010;52:70–84.
    1. Davidson R, Mackinnon JG. Several tests for model specification in the presence of alternative hypotheses. Econometrica. 1981;49:781–793.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57:289–300.
    1. Makishima H, Visconte V, Sakaguchi H, Jankowska AM, Abu KS, Jerez A, et al. Mutations in the spliceosome machinery, a novel and ubiquitous pathway in leukemogenesis. Blood. 2012;119:3203–3210.
    1. Welch JS, Ley TJ, Link DC, Miller CA, Larson DE, Koboldt DC, et al. The origin and evolution of mutations in acute myeloid leukemia. Cell. 2012;150:264–278.
    1. Willman CL, Sever CE, Pallavicini MG, Harada H, Tanaka N, Slovak ML, et al. Deletion of IRF-1, mapping to chromosome 5q31.1, in human leukemia and preleukemic myelodysplasia. Science. 1993;259:968–971.
    1. Boultwood J, Fidler C, Lewis S, MacCarthy A, Sheridan H, Kelly S, et al. Allelic loss of IRF1 in myelodysplasia and acute myeloid leukemia: retention of IRF1 on the 5q- chromosome in some patients with the 5q- syndrome. Blood. 1993;82:2611–2616.
    1. Brunning RD, Bennett JM, Flandrin G, Matutes E, Head D, Vardiman J, et al. Myelodysplastic syndromesIn: Jaffe ES, Harris N, Stein H, Vardiman J, edsWorld Health Organization of Tumors, Pathology & Genetics, Tumors of haematopoietic and lymphoid tissues IARC Press: Lyon, France; 200161–73.

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