Deep learning for drug response prediction in cancer

• Published in: Briefings in bioinformatics Vol. 22; no. 1; pp. 360 - 379
• Main Authors: Baptista, Delora, Ferreira, Pedro G, Rocha, Miguel
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.01.2021; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biotechnology; Cancer; Deep learning; Drug screening; High-throughput screening; Learning algorithms; Machine learning; Precision medicine; Prediction models; Predictions; Tumor cell lines; Tumors; drug synergy; deep learning; precision medicine; drug sensitivity; cancer
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbz171

Abstract Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:  mrocha@di.uminho.pt