Mapping the proteogenomic landscape enables prediction of drug response in acute myeloid leukemia.
Cell Rep Med
; 5(1): 101359, 2024 01 16.
Article
de En
| MEDLINE
| ID: mdl-38232702
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 distinct subtypes and validate our findings on cell lines representing various stages of quizartinib resistance. Our results show how multiomics data together with drug sensitivity data can inform therapy stratification and drug combinations in AML.
Mots clés
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Sujet principal:
Leucémie aigüe myéloïde
/
Protéogénomique
Type d'étude:
Prognostic_studies
/
Risk_factors_studies
Limites:
Humans
Langue:
En
Journal:
Cell Rep Med
Année:
2024
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique