Identification of Clonal Hematopoiesis Driver Mutations through In Silico Saturation Mutagenesis

Abstract

Abstract Clonal hematopoiesis (CH) is a phenomenon of clonal expansion of hematopoietic stem cells driven by somatic mutations affecting certain genes. Recently, CH has been linked to the development of hematologic malignancies, cardiovascular diseases, and other conditions. Although the most frequently mutated CH driver genes have been identified, a systematic landscape of the mutations capable of initiating this phenomenon is still lacking. In this study, we trained machine learning models for 12 of the most recurrent CH genes to identify their driver mutations. These models outperform expert-curated rules based on prior knowledge of the function of these genes. Moreover, their application to identify CH driver mutations across almost half a million donors of the UK Biobank reproduces known associations between CH driver mutations and age, and the prevalence of several diseases and conditions. We thus propose that these models support the accurate identification of CH across healthy individuals. Significance: We developed and validated gene-specific machine learning models to identify CH driver mutations, showing their advantage with respect to expert-curated rules. These models can support the identification and clinical interpretation of CH mutations in newly sequenced individuals. See related commentary by Arends and Jaiswal, p. 1581