Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

TLDR

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour, yet linking mutation profiles to drug efficacy remains a challenge. The study introduces CDRscan, a deep learning model that predicts anticancer drug responsiveness from genomic signatures of 787 cancer cell lines and structural profiles of 244 drugs. CDRscan uses a two‑step convolutional architecture that processes genomic mutational fingerprints and drug molecular fingerprints separately before merging them via a virtual‑docking in‑silico model. Analysis of goodness‑of‑fit between observed and predicted drug responses shows that CDRscan achieves high prediction accuracy.

Abstract

In the era of precision medicine, cancer therapy can be tailored to an individual patient based on the genomic profile of a tumour. Despite the ever-increasing abundance of cancer genomic data, linking mutation profiles to drug efficacy remains a challenge. Herein, we report Cancer Drug Response profile scan (CDRscan) a novel deep learning model that predicts anticancer drug responsiveness based on a large-scale drug screening assay data encompassing genomic profiles of 787 human cancer cell lines and structural profiles of 244 drugs. CDRscan employs a two-step convolution architecture, where the genomic mutational fingerprints of cell lines and the molecular fingerprints of drugs are processed individually, then merged by 'virtual docking', an in silico modelling of drug treatment. Analysis of the goodness-of-fit between observed and predicted drug response revealed a high prediction accuracy of CDRscan (R