DataSheet3_Molecular Characterization of the Highest Risk Adult Patients With Acute Myeloid Leukemia (AML) Through Multi-Omics Clustering.CSV (7.41 kB)
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DataSheet3_Molecular Characterization of the Highest Risk Adult Patients With Acute Myeloid Leukemia (AML) Through Multi-Omics Clustering.CSV

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posted on 29.10.2021, 04:13 authored by Trinh Nguyen, John W Pepper, Cu Nguyen, Yu Fan, Ying Hu, Qingrong Chen, Chunhua Yan, Daoud Meerzaman

Background: Acute myeloid leukemia (AML) is a clinically heterogeneous group of cancers. While some patients respond well to chemotherapy, we describe here a subgroup with distinct molecular features that has very poor prognosis under chemotherapy. The classification of AML relies substantially on cytogenetics, but most cytogenetic abnormalities do not offer targets for development of targeted therapeutics. Therefore, it is important to create a detailed molecular characterization of the subgroup most in need of new targeted therapeutics.

Methods: We used a multi-omics approach to identify a molecular subgroup with the worst response to chemotherapy, and to identify promising drug targets specifically for this AML subgroup.

Results: Multi-omics clustering analysis resulted in three primary clusters among 166 AML adult cancer cases in TCGA data. One of these clusters, which we label as the high-risk molecular subgroup (HRMS), consisted of cases that responded very poorly to standard chemotherapy, with only about 10% survival to 2 years. The gene TP53 was mutated in most cases in this subgroup but not in all of them. The top six genes over-expressed in the HRMS subgroup included E2F4, CD34, CD109, MN1, MMLT3, and CD200. Multi-omics pathway analysis using RNA and CNA expression data identified in the HRMS subgroup over-activated pathways related to immune function, cell proliferation, and DNA damage.

Conclusion: A distinct subgroup of AML patients are not successfully treated with chemotherapy, and urgently need targeted therapeutics based on the molecular features of this subgroup. Potential drug targets include over-expressed genes E2F4, and MN1, as well as mutations in TP53, and several over-activated molecular pathways.

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