Machine learning prediction of cancer cell sensitivity to drugs based on genomic and chemical properties

Abstract

Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measure them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.

26 pages, 7 figures, including supplemental information, presented by Michael Menden at the 5th annual RECOMB Conference on Regulatory and Systems Genomics with DREAM Challenges; accepted in PLOS ONE

Cited by in corpus (16)

• Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders
• Machine learning algorithms to infer trait-matching and predict species interactions in ecological networks
• IBM Quantum Computers: Evolution, Performance, and Future Directions
• Hybrid quantum neural network for drug response prediction
• When will the mist clear? On the Interpretability of Machine Learning for Medical Applications: a survey
• KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images
• PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
• Prediction of kinase inhibitor response using activity profiling, in-vitro screening, and elastic net regression
• Drug Selection via Joint Push and Learning to Rank
• DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response
• Regression Enrichment Surfaces: a Simple Analysis Technique for Virtual Drug Screening Models
• A Systematic Approach to Featurization for Cancer Drug Sensitivity Predictions with Deep Learning
• DHLP 1&2: Giraph based distributed label propagation algorithms on heterogeneous drug-related networks
• Identification of structural features in chemicals associated with cancer drug response: A systematic data-driven analysis
• Drug cell line interaction prediction