GSE108003
A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [RNA-Seq]
We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. 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. Overall design: We measured the gene expression of samples from 30 different AML patients with acute myeloid leukemia in order to identify reliable gene expression markers for drug sensitivity. We used this dataset for validation. This Series represents 12 patient samples.