Cancer drug sensitivity prediction from routine histology images

Journal article

Drug sensitivity prediction models can aid in personalising cancer therapy, biomarker discovery, and drug design. Such models require survival data from randomised controlled trials which can be time consuming and expensive. In this proof-of-concept study, we demonstrate for the first time that deep learning can link histological patterns in whole slide images (WSIs) of Haematoxylin & Eosin (H&E) stained breast cancer sections with drug sensitivities inferred from cell lines. We employ patient-wise drug sensitivities imputed from gene expression-based mapping of drug effects on cancer cell lines to train a deep learning model that predicts patients’ sensitivity to multiple drugs from WSIs. We show that it is possible to use routine WSIs to predict the drug sensitivity profile of a cancer patient for a number of approved and experimental drugs. We also show that the proposed approach can identify cellular and histological patterns associated with drug sensitivity profiles of cancer patients

Authors: Dawood, M; Vu, QD; Young, LS; Branson, K; Jones, L

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: npj Precision Oncology
• Volume: 8
• Issue: 1
• Pages: 5-5
• Publication date: 2024-01-06
• DOI: 10.1038/s41698-023-00491-9
• EISSN: 2397-768X
• ISSN: 2397-768X
• Language: English
• Keywords: Biology; Biomarker; Computer science; Sensitivity (control systems); Pathology; Breast cancer; Machine learning; Drug response; Clinical trial; Medicine; Oncology; Drug trial; Cancer drugs; Pharmacology; Artificial intelligence; Internal medicine; Cancer; Drug