Predicting response to BET inhibitors using computational modeling: A BEAT AML project study.

ABSTRACT: Despite advances in understanding the molecular pathogenesis of acute myeloid leukaemia (AML), overall survival rates remain low. The ability to predict treatment response based on individual cancer genomics using computational modeling will aid in the development of novel therapeutics and personalize care. Here, we used a combination of genomics, computational biology modeling (CBM), ex vivo chemosensitivity assay, and clinical data from 100 randomly selected patients in the Beat AML project to characterize AML sensitivity to a bromodomain (BRD) and extra-terminal (BET) inhibitor. Computational biology modeling was used to generate patient-specific protein network maps of activated and inactivated protein pathways translated from each genomic profile. Digital drug simulations of a BET inh

SUBMITTER: Drusbosky LM 

PROVIDER: S-EPMC6442457 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature