Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia.

ABSTRACT: Acute myeloid leukemia is a poor-prognosis cancer commonly stratified by genetic aberrations, but these mutations are often heterogeneous and fail to consistently predict therapeutic response. Here, we combine transcriptomic, proteomic, and phosphoproteomic datasets with ex vivo drug sensitivity data to help understand the underlying pathophysiology of AML beyond mutations. We measure the proteome and phosphoproteome of 210 patients and combine them with genomic and transcriptomic measurements to identify four proteogenomic subtypes that complement existing genetic subtypes. We build a predictor to classify samples into subtypes and map them to a "landscape" that identifies specific drug response patterns. We then build a drug response prediction model to identify drugs that target distinc

SUBMITTER: Pino JC 

PROVIDER: S-EPMC10829797 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature