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

Abstract

Background: 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.

Results: 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.

Conclusion: 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 .

Keywords: Drug response prediction; Multi-omics data; Pharmacogenomics; Precision oncology; Triplet loss; encoder using supervised methods.

Fig. 1

Workflow of Super.FELT. a Reduction of the large dimension of omics data with feature selection using a variance threshold based on the elbow method. b Supervised encoder using triplet loss function (SET) encodes each reduced omics data independently. c After encoding, all encoded omics data are integrated as the input data of the classifier. d A neural network classifier, for which the loss function is binary cross entropy (BCE) function, is trained for predicting drug response

Fig. 2

a The distributions of AUC values of cross validation on 243 drugs in GDSC for Super.FELT, MOLIF, AE, ANNF, MOLI, AutoBorutaRF, SVM, Super.FELT E, and Super.FELT M&C. b The scatter plot of cross validation AUC values, where x- and y-axis represent AUCs of Super.FELT and other methods, respectively

Fig. 3

a The scatter plot of external validation AUC values on CTRP and CCLE. b The scatter plot of external AUC on PDX and TCGA. x- and y-axis represent AUCs of Super.FELT and other methods, respectively

Fig. 4

Comparison between the AUC values in GDSC and the test AUC values of drugs on PDX and TCGA for a Super.FELF, b Super.FELF E, c Super.FELF M&C, d AutoBorutaRF, e SVM, f MOLF, g AE, h ANNF, i MOLI, and j MOLI*

Fig. 5

a The box plots of drug response prediction probability of Cisplatin, Temozolomide, and Docetaxel on TCGA samples, respectively. The red lines represent the top and bottom 1% prediction probabilities on samples. b Heat maps of gene expression, CNA, and mutation values of most discriminative genes between resistant and sensitive samples of Cisplatin, Temozolomide, and Docetaxel