Cancer drug sensitivity prediction from routine histology images

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...

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Bibliographic Details
Published in:NPJ precision oncology 2024-01, Vol.8 (1), p.5-5, Article 5
Main Authors: Dawood, Muhammad, Vu, Quoc Dang, Young, Lawrence S., Branson, Kim, Jones, Louise, Rajpoot, Nasir, Minhas, Fayyaz ul Amir Afsar
Format: Article
Language:English
Subjects:
631/67/1347
692/4028/67/1347
Biomarkers
Breast cancer
Cancer Research
Cancer therapies
Datasets
Deep learning
DNA methylation
Drugs
Gene expression
Gene Therapy
Genomics
Histology
Human Genetics
Internal Medicine
Machine learning
Medicine
Medicine & Public Health
Mutation
Oncology
Patients
Tumors
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Summary: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.
ISSN:2397-768X
2397-768X
DOI:10.1038/s41698-023-00491-9