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
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients
Drug Ranking Using ML (DRUML) consists of an ensemble of ML models trained on the responses of cells to >400 drugs, which allows these agents to be ranked based on their predicted efficacy within a sample (Fig. 1a)
We used phosphoproteomics and proteomics datasets obtained from 48 acute myeloid leukemia (AML) (n = 26), esophagus (n = 10) and hepatocellular (n = 12) cancer cell lines as the input for DRUML to build models that may be applied to leukemia and solid tumors (Fig. 1b)
Summary
Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Cancers derived from the same tissue of origin and pathological classification exhibit high degrees of genetic and phenotypic variability within individuals[1,2,3] This heterogeneity translates to patients having differential responses to therapy. The application of machine learning (ML) to biomedicine promises to revolutionize how cancers are diagnosed and treated in the future[15,16] Projects such as the Cancer Target Discovery and Development and Genomics of Drug Sensitivity in Cancer have evaluated ML as a means to predict drug responses by associating genomic features, gene expression patterns and copy number alterations to drug sensitivity[17,18,19,20,21]. A limitation has been the low sample throughput of proteomics and phosphoproteomics by liquid chromatography coupled to tandem mass spectrometry
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