Bioinformatics Approaches to Predict Drug Responses from Genomic Sequencing.

TitleBioinformatics Approaches to Predict Drug Responses from Genomic Sequencing.
Publication TypeJournal Article
Year of Publication2018
AuthorsMadhukar NS, Elemento O
JournalMethods Mol Biol
Date Published2018
KeywordsAnimals, Biomarkers, Computational Biology, Drug Discovery, Genomics, Humans, Machine Learning, Pharmacogenetics, Pharmacology, Clinical, Polymorphism, Genetic, Precision Medicine, Transcriptome

Fulfilling the promises of precision medicine will depend on our ability to create patient-specific treatment regimens. Therefore, being able to translate genomic sequencing into predicting how a patient will respond to a given drug is critical. In this chapter, we review common bioinformatics approaches that aim to use sequencing data to predict sample-specific drug susceptibility. First, we explain the importance of customized drug regimens to the future of medical care. Second, we discuss the different public databases and community efforts that can be leveraged to develop new methods for identifying new predictive biomarkers. Third, we cover the basic methods that are currently used to identify markers or signatures of drug response, without any prior knowledge of the drug's mechanism of action. We further discuss how one can integrate knowledge about drug targets, mechanisms, and predictive markers to better estimate drug response in a diverse set of samples. We begin this section with a primer on popular methods to identify targets and mechanism of action for new small molecules. This discussion also includes a set of computational methods that incorporate other drug features, which do not relate to drug-induced genetic changes or sequencing data such as drug structures, side-effects, and efficacy profiles. Those additional drug properties can aid in gaining higher accuracy for the identification of drug target and mechanism of action. We then progress to discuss using these targets in combination with disease-specific expression patterns, known pathways, and genetic interaction networks to aid drug choice. Finally, we conclude this chapter with a general overview of machine learning methods that can integrate multiple pieces of sequencing data along with prior drug or biological knowledge to drastically improve response prediction.

Alternate JournalMethods Mol. Biol.
PubMed ID29344895
Grant ListR01 CA194547 / CA / NCI NIH HHS / United States