A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

TLDR

Cancers that appear similar often respond differently to the same drugs, creating a need for better patient–drug matching. The study aims to identify robust molecular markers for targeted treatment of acute myeloid leukemia. It uses data from 30 AML patients (gene‑expression profiles and sensitivity to 160 chemotherapies) and a computational method that incorporates multi‑omic prior information to pinpoint reliable gene‑expression markers for drug sensitivity. The method outperforms existing approaches in marker discovery and drug‑sensitivity prediction, and identifies SMARCA4 as a driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, as shown by increased sensitivity in SMARCA4‑overexpressing cell lines.

Abstract

Cancers that appear pathologically similar often respond differently to the same drug regimens. Methods to better match patients to drugs are in high demand. We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia (AML) by introducing: data from 30 AML patients including genome-wide gene expression profiles and in vitro sensitivity to 160 chemotherapy drugs, a computational method to identify reliable gene expression markers for drug sensitivity by incorporating multi-omic prior information relevant to each gene's potential to drive cancer. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone, and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.