Deep learning methods for drug response prediction in cancer: Predominant and emerging trends

Abstract

Cancer claims millions of lives yearly worldwide. While many therapies have been made available in recent years, by in large cancer remains unsolved. Exploiting computational predictive models to study and treat cancer holds great promise in improving drug development and personalized design of treatment plans, ultimately suppressing tumors, alleviating suffering, and prolonging lives of patients. A wave of recent papers demonstrates promising results in predicting cancer response to drug treatments while utilizing deep learning methods. These papers investigate diverse data representations, neural network architectures, learning methodologies, and evaluations schemes. However, deciphering promising predominant and emerging trends is difficult due to the variety of explored methods and lack of standardized framework for comparing drug response prediction models. To obtain a comprehensive landscape of deep learning methods, we conducted an extensive search and analysis of deep learning models that predict the response to single drug treatments. A total of 61 deep learning-based models have been curated, and summary plots were generated. Based on the analysis, observable patterns and prevalence of methods have been revealed. This review allows to better understand the current state of the field and identify major challenges and promising solution paths.

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

“…Gene expressions . Gene expressions have been considered to provide more predictive power than other omics data types for drug response prediction (DRP) ( 27 ), and therefore, are often used to represent cancer in DRP models, either standalone or in a combination with other multiomics ( 28 ). However, the high dimensionality of gene expressions and the relatively small sample size can lead to overfitting ( 2 ).…”

Identification of biological markers in cancer disease using explainable artificial intelligence

“…The utilization of deep learning models has the potential to revolutionize precision medicine by enabling personalized treatment options for cancer patients based on their individual genomic and drug sensitivity profiles. 10 Only a few previous studies use omics and chemical compound features to predict "cell line-drug sensitivity". 11 These studies implement methods, including autoencoders combined with a neural network, 6 SMILES encoding and bidirectional Recurrent Neural Network (bRNN), 11 and different deep learning models.…”

“…DNNs are used for various tasks, including feature selection, drug combination prediction, and drug resistance prediction. The utilization of deep learning models has the potential to revolutionize precision medicine by enabling personalized treatment options for cancer patients based on their individual genomic and drug sensitivity profiles 10 …”

From cancer big data to treatment: Artificial intelligence in cancer research

“…Despite these advances, cancer remains a serious global public health concern 3,9–11 . Artificial intelligence (AI) methods are becoming increasingly popular in the field of cancer research with respect to finding novel therapeutic targets and predicting the efficacy of drugs 12–14 . These techniques help to explore the possible disease mechanism and can assist in the best possible treatment for a patient even before they begin treatment.…”