Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values

Several artificial neural networks (ANNs) have recently been developed as the Cox proportional hazard model for predicting cancer prognosis based on tumor transcriptome. However, they have not demonstrated significantly better performance than the traditional Cox regression with regularization. Training an ANN with high prediction power is challenging in the presence of a limited number of data samples and a high-dimensional feature space. Recent advancements in image classification have shown that contrastive learning can facilitate further learning tasks by learning good feature representation from a limited number of data samples. In this paper, we applied supervised contrastive learning to tumor gene expression and clinical data to learn feature representations in a low-dimensional space. We then used these learned features to train the Cox model for predicting cancer prognosis. Using data from The Cancer Genome Atlas (TCGA), we demonstrated that our contrastive learning-based Cox model (CLCox) significantly outperformed existing methods in predicting the prognosis of 18 types of cancer under consideration. We also developed contrastive learning-based classifiers to classify tumors into different risk groups and showed that contrastive learning can significantly improve classification accuracy.