Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

• Published in: BMC bioinformatics Vol. 22; no. 1; pp. 269 - 23
• Main Authors: Park, Sejin, Soh, Jihee, Lee, Hyunju
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 25-05-2021; BioMed Central; BMC
• Subjects: Ablation; Analysis; Biological Phenomena; Cancer; Coders; Copy number; Datasets; Deep learning; Drug response prediction; Drugs; encoder using supervised methods; Encyclopedias; Extraction (Chemistry); Feature extraction; Feature selection; Gene expression; Genomes; Genomics; Humans; Machine learning; Methods; Multi-omics data; Mutation; Neoplasms; Neural networks; Patients; Pharmaceutical Preparations; Pharmacogenomics; Precision Medicine; Precision oncology; Predictions; Support vector machines; Triplet loss; Xenografts; Xenotransplantation; South Korea; Triplet loss; encoder using supervised methods; Multi-omics data; Precision oncology; Drug response prediction; Pharmacogenomics
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04146-z

Predicting the drug response of a patient is important for precision oncology. In recent studies, multi-omics data have been used to improve the prediction accuracy of drug response. Although multi-omics data are good resources for drug response prediction, the large dimension of data tends to hinder performance improvement. In this study, we aimed to develop a new method, which can effectively reduce the large dimension of data, based on the supervised deep learning model for predicting drug response.
We proposed a novel method called Supervised Feature Extraction Learning using Triplet loss (Super.FELT) for drug response prediction. Super.FELT consists of three stages, namely, feature selection, feature encoding using a supervised method, and binary classification of drug response (sensitive or resistant). We used multi-omics data including mutation, copy number aberration, and gene expression, and these were obtained from cell lines [Genomics of Drug Sensitivity in Cancer (GDSC), Cancer Cell Line Encyclopedia (CCLE), and Cancer Therapeutics Response Portal (CTRP)], patient-derived tumor xenografts (PDX), and The Cancer Genome Atlas (TCGA). GDSC was used for training and cross-validation tests, and CCLE, CTRP, PDX, and TCGA were used for external validation. We performed ablation studies for the three stages and verified that the use of multi-omics data guarantees better performance of drug response prediction. Our results verified that Super.FELT outperformed the other methods at external validation on PDX and TCGA and was good at cross-validation on GDSC and external validation on CCLE and CTRP. In addition, through our experiments, we confirmed that using multi-omics data is useful for external non-cell line data.
By separating the three stages, Super.FELT achieved better performance than the other methods. Through our results, we found that it is important to train encoders and a classifier independently, especially for external test on PDX and TCGA. Moreover, although gene expression is the most powerful data on cell line data, multi-omics promises better performance for external validation on non-cell line data than gene expression data. Source codes of Super.FELT are available at  https://github.com/DMCB-GIST/Super.FELT .