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Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

ABSTRACT: Morphologic interpretation is the standard in diagnosing myelodysplastic syndrome (MDS), but it has limitations, such as varying reliability in pathologic evaluation and lack of integration with genetic data. Somatic events shape morphologic features, but the complexity of morphologic and genetic changes makes clear associations challenging. This article interrogates novel clinical subtypes of MDS using a machine-learning technique devised to identify patterns of cooccurrence among morphologic features and genomic events. We sequenced 1079 MDS patients and analyzed bone marrow morphologic alterations and other clinical features. A total of 1929 somatic mutations were identified. Five distinct morphologic profiles with unique clinical characteristics were defined. Seventy-seven percent of higher-risk patients clustered in profile 1. All lower-risk (LR) patients clustered into the remaining 4 profiles: profile 2 was characterized by pancytopenia, profile 3 by monocytosis, profile 4 by elevated megakaryocytes, and profile 5 by erythroid dysplasia. These profiles could also separate patients with different prognoses. LR MDS patients were classified into 8 genetic signatures (eg, signature A had TET2 mutations, signature B had both TET2 and SRSF2 mutations, and signature G had SF3B1 mutations), demonstrating association with specific morphologic profiles. Six morphologic profiles/genetic signature associations were confirmed in a separate analysis of an independent cohort. Our study demonstrates that nonrandom or even pathognomonic relationships between morphology and genotype to define clinical features can be identified. This is the first comprehensive implementation of machine-learning algorithms to elucidate potential intrinsic interdependencies among genetic lesions, morphologies, and clinical prognostic in attributes of MDS.

SUBMITTER: Nagata Y

PROVIDER: S-EPMC7702479 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Myelodysplastic syndromes are clinically heterogeneous disorders characterized by clonal hematopoiesis, impaired differentiation, peripheral-blood cytopenias, and a risk of progression to acute myeloid leukemia. Somatic mutations may influence the clinical phenotype but are not included in current prognostic scoring systems.We used a combination of genomic approaches, including next-generation sequencing and mass spectrometry-based genotyping, to identify mutations in samples of bone marrow aspirate from 439 patients with myelodysplastic syndromes. We then examined whether the mutation status for each gene was associated with clinical variables, including specific cytopenias, the proportion of blasts, and overall survival.We identified somatic mutations in 18 genes, including two, ETV6 and GNAS, that have not been reported to be mutated in patients with myelodysplastic syndromes. A total of 51% of all patients had at least one point mutation, including 52% of the patients with normal cytogenetics. Mutations in RUNX1, TP53, and NRAS were most strongly associated with severe thrombocytopenia (P<0.001 for all comparisons) and an increased proportion of bone marrow blasts (P<0.006 for all comparisons). In a multivariable Cox regression model, the presence of mutations in five genes retained independent prognostic significance: TP53 (hazard ratio for death from any cause, 2.48; 95% confidence interval [CI], 1.60 to 3.84), EZH2 (hazard ratio, 2.13; 95% CI, 1.36 to 3.33), ETV6 (hazard ratio, 2.04; 95% CI, 1.08 to 3.86), RUNX1 (hazard ratio, 1.47; 95% CI, 1.01 to 2.15), and ASXL1 (hazard ratio, 1.38; 95% CI, 1.00 to 1.89).Somatic point mutations are common in myelodysplastic syndromes and are associated with specific clinical features. Mutations in TP53, EZH2, ETV6, RUNX1, and ASXL1 are predictors of poor overall survival in patients with myelodysplastic syndromes, independently of established risk factors. (Funded by the National Institutes of Health and others.).

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Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes.

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