Molecular patterns identify distinct subclasses of myeloid neoplasia

Research Square (Research Square)(2022)

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Abstract Background Genomic mutations drive the pathogenesis of myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). While morphological and clinical features, complemented by cytogenetics, have dominated the classical criteria for diagnosis and classification, incorporation of molecular mutational data can illuminate functional pathobiology. Methods We combined cytogenetic and molecular features from a multicenter cohort of 3588 MDS, MDS/ myeloproliferative neoplasm (including chronic myelomonocytic leukemia [CMML]), and secondary AML patients to generate a molecular-based scheme using machine learning methods and then externally validated the model on 412 patients. Molecular signatures driving each cluster were identified and used for genomic subclassification. Findings Unsupervised analyses identified 14 distinctive and clinically heterogenous molecular clusters (MCs) with unique pathobiological associations, treatment responses, and prognosis. Normal karyotype (NK) was enriched in MC2, MC4, MC6, MC9, MC10, and MC12 with different distributions of TET2, SF3B1, ASXL1, DNMT3A, and RAS mutations. Complex karyotype and trisomy 8 were enriched in MC13 and MC1, respectively. We then identified five risk groups to reflect the biological differences between clusters. Our clustering model was able to highlight the significant survival differences among patients assigned to the similar IPSS-R risk group but with heterogenous molecular configurations. Different response rates to hypomethylating agents (e.g., MC9 and MC13 [OR: 2.2 and 0.6, respectively]) reflected the biological differences between the clusters. Interestingly, our clusters continued to show survival differences regardless of the bone marrow blast percentage. Interpretation Despite the complexity of the molecular alterations in myeloid neoplasia, our model recognized functional objective clusters, irrespective of anamnestic clinico-morphological features, that reflected disease evolution and informed classification, prognostication, and molecular interactions. Our subclassification model is available via a web-based open-access resource as well (https://drmz.shinyapps.io/mds_latent).
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myeloid neoplasia,molecular patterns
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