Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

By Michael P. Menden, Francesco Iorio, Mathew Garnett, Ultan McDermott, Cyril H. Benes, Pedro J. Ballester, J. Sáez-Rodríguez. 2012

475 citations recorded by the source corpus.

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 measuring 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.

Subfields: Computational Theory and Mathematics, Molecular Biology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “[287] used a shallow neural network to predict sensitivity of cancer cell lines to drug treatment using both cell line and drug features, opening the door to precision medicine and drug repositioning opportunities in cancer.”
Deep learning for computational biology (2016, neutral): “One typical application in biology is to predict the viability of a cancer cell line when exposed to a chosen drug (Menden et al , 2013; Eduati et al , 2015).”
Artificial intelligence in drug development: present status and future prospects. (2019, background): “The other uses of AI in drug development include the prediction of feasible synthetic routes for drug-like molecules [32], pharmacological properties [33], protein characteristics as well as efﬁcacy [34], drug combination and drug–target association [35] and drug repurposing [36].”
Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines (2014, methodology): “, who recently constructed models using the CGP cell line data, with the aim of predicting in vitro drug sensitivity [49].”
A survey of current trends in computational drug repositioning (2016, unknown): “[58] developed machine-learning models to”
eDoctor: machine learning and the future of medicine (2018, background): “The power of ML allows for this individualization at many levels: diagnosis [33], prognosis and treatment [34, 35].”
Toward more realistic drug–target interaction predictions (2014, unknown): “We note that similar issues in the evaluation sensitivities across cancer cell types [43, 44].”
Machine learning approaches to drug response prediction: challenges and recent progress (2020, background): “In fact, despite the recent prevalence of deep neural network (DNN) methods across many areas and industries, including related ﬁ elds, such as computational chemistry 40 – 45 , DNNs have only fairly recently found their way into the drug response prediction.”
Concise Polygenic Models for Cancer-Specific Identification of Drug-Sensitive Tumors from Their Multi-Omics Profiles (2020, methodology): “An overfitted model predicts training data much better than test data, which we have observed when using learning algorithms such as RF [15,19] or XGBoost [20,21].”