Fig. 6: Drug sensitivity predictions on TCGA samples.
From: Learning and actioning general principles of cancer cell drug sensitivity
A Schema of a drug response prediction workflow leveraging Bulk RNA sequencing data from TCGA patients, as well from new PDAC and GBM cohorts. The process begins with data harmonization using Celligner, followed by drug inhibitory concentration (IC50) predictions through CellHit. Patients are then ranked by their predicted predIC50 values and quantile score to assess drug efficacy. Validation involves comparing TCGA predictions with NCI cancer drug metadata and refining tumor-specific predictions by clustering patient responses within cancer subtypes for experimental validation.; B recall of the recovered drug indications, from NCI cancer drugs, for the TCGA best ranked samples (top 600), according to either predicted IC50 (predIC50) or quantile score metrics; C stacked barplot of the GDSC drugs scoring among the top 600 samples patients with cancer types matching the prescription according to NCI drugs. The height of the barplot’s stacks corresponds to the number of unique samples and the color of the specific cancer type; D circle plot showing drugs predicted for the same pool of patients, i.e., suggesting combination therapies. Circle diameter is proportional to the number of unique samples, among the top 600, best scoring for both drug models. Colors indicate the level of support for that combination, i.e., approved (red) or sharing indication for the same cancer type (dark green); E Inference on TCGA data for the 20 best performing non-oncological drug models in the PRISM dataset. The height of the barplot’s stacks corresponds to the number of unique samples and the color of the specific cancer type (highlighting potential drug repurposing opportunities). Colors are shared with panel C. Source data are provided as a Source Data file.
