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 h

SUBMITTER: Nagata Y 

PROVIDER: S-EPMC7702479 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature