No abstract

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman’s rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Highlights d Low mural-b3-integrin in tumor BVs is associated with poor prognosis in human cancers d Mural-b3-integrin loss enhances tumor growth in tumor models without vascular changes d Mural-b3-integrin loss upregulates FAK-p-HGFR-p-Akt-p-p65-driven cytokine production d Mural cell-derived CCL2 activates MEK1-ROCK2-dependent tumor growth

Highlights d Combined diverse evidence for protein kinase-kinase regulatory relationships d Used machine learning to predict (activating/inhibiting) regulatory relationships d Recovered known signaling pathways and validated new relationships independently d Analysis suggests that inter-kinase regulation is far denser than normally considered