Strategies to integrate multi-omics data for patient survival prediction

TL;DR

This paper reviews methods for integrating multi-omics data, especially from TCGA, to improve cancer patient survival prediction using deep learning and biological knowledge, demonstrating robustness despite limited data.

Contribution

It introduces and compares advanced computational methods like Cox-nnet and DeepProg for multi-omics integration in survival prediction, highlighting their effectiveness.

Findings

Methods show significant survival prediction accuracy.

Deep learning approaches outperform traditional models.

Biological insights are gained from model analysis.

Abstract

Genomics, especially multi-omics, has made precision medicine feasible. The completion and publicly accessible multi-omics resource with clinical outcome, such as The Cancer Genome Atlas (TCGA) is a great test bed for developing computational methods that integrate multi-omics data to predict patient cancer phenotypes. We have been utilizing TCGA multi-omics data to predict cancer patient survival, using a variety of approaches, including prior-biological knowledge (such as pathways), and more recently, deep-learning methods. Over time, we have developed methods such as Cox-nnet, DeepProg, and two-stage Cox-nnet, to address the challenges due to multi-omics and multi-modality. Despite the limited sample size (hundreds to thousands) in the training datasets as well as the heterogeneity nature of human populations, these methods have shown significance and robustness at predicting patient survival in independent population cohorts. In the following, we would describe in detail these methodologies, the modeling results, and important biological insights revealed by these methods.