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Cited by 1,532Acute Myeloid Leukemia ResearchCancer Genomics and DiagnosticsChromatin Remodeling and Cancer

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Landscape of genetic lesions in 944 patients with myelodysplastic syndromes

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Haferlach, T, Nagata, Y, Grossmann, V, Okuno, Y, Bacher, U, Nagae, G, Schnittger, S, Sanada, M, Kon, A, Alpermann, T, Yoshida, K, Roller, A, Nadarajah, N, Shiraishi, Y, Shiozawa, Y, Chiba, K, Tanaka, H, Koeffler, H P, Klein, H-U, Dugas, M, Aburatani, H, Kohlmann, A, Miyano, S, Haferlach, C, Kern, W, Ogawa, S·2014·Leukemia
DOI 10.1038/leu.2013.336PMC PMC3918868
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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.

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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 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.CC BY-NC-SA · PubMed Central
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 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.CC BY-NC-SA · PubMed Central
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 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).CC BY-NC-SA · PubMed Central
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.
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.CC BY-NC-SA · PubMed Central

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Research works printed but not linked: 1

  1. Brunning RD, Bennett JM, Flandrin G, Matutes E, Head D, Vardiman J et al. Myelodysplastic syndromes. In: Jaffe ES, Harris N, Stein H, Vardiman J, eds World Health Organization of Tumors, Pathology & Genetics, Tumors of haematopoietic and lymphoid tissues. IARC Press: Lyon, France, 2001; pp 61–73.

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Metadata

Authors
Haferlach, T., Nagata, Y., Grossmann, V, Okuno, Y., Bacher, U., Nagae, G., Schnittger, S., Sanada, M., Kon, A., Alpermann, T., Yoshida, K., Roller, A., Nadarajah, N., Shiraishi, Y., Shiozawa, Y., Chiba, K., Tanaka, H., Koeffler, H. P., Klein, H.-U., Dugas, M., Aburatani, H., Kohlmann, A., Miyano, S., Haferlach, C., Kern, W., & Ogawa, S.
Affiliations
  • Munich Leukemia Laboratory (Germany) (DE)
  • Kyoto University (JP)
  • The University of Tokyo (JP)
  • Research Center for Advanced Science and Technology, The University of Tokyo (JP)
  • Institute of Medical Science, The University of Tokyo (JP)
  • Cedars-Sinai Medical Center (US)
  • University of Münster (DE)
Corresponding author
  • Haferlach, T
  • Ogawa, S
ORCID
Venue
Leukemia
Publisher
Springer Science and Business Media LLC
Published
2013-11-13
Volume
28
Issue
2
Pages
241-247
ISSN
  • 0887-6924 (ISSN-L)
  • 1476-5551
DOI
10.1038/leu.2013.336
PMC
PMC3918868
OpenAlex
W2026134786
Cited by
1,532
References
32
Citations by year
2026: 56 · 2025: 85 · 2024: 102 · 2023: 132 · 2022: 133 · 2021: 177 · 2020: 167 · 2019: 134 · 2018: 117 · 2017: 136 · 2016: 126 · 2015: 105 · 2014: 56 · 2013: 3
FWCI
69.37
Type
journal article
Language
en
Open access
Licence
Locations
  • Leukemia (publishedVersion, cc-by-nc-sa)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-sa)
  • PubMed Central (publishedVersion, cc-by-nc-sa)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by-nc-sa)
Funders
  • U.S. Public Health Service (08C46598A)
  • Japan Society for the Promotion of Science (24390242, 24659458, 21790907, 25670446, 24221011, 23249052)
Topics
  • Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Keywords
myelodysplastic syndrome, somatic mutations, prognostic model, risk stratification, TET2, SF3B1, ASXL1, SRSF2, DNMT3A, RUNX1, PRPF8, survival
MeSH terms
Adult, Aged, Aged, 80 and over, Female, Gene Frequency, Genetic Markers, Humans, Male, Middle Aged, Mutation, Myelodysplastic Syndromes, Prognosis, Proportional Hazards Models, Polymorphism, Single Nucleotide, Young Adult, Genetic Association Studies, High-Throughput Nucleotide Sequencing, Mutation Rate

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