Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences

Abstract

Recent advances in high-throughput sequencing have accelerated the accumulation of omics data on the same tumor tissue from multiple sources. Intensive study of multi-omics integration on tumor samples can stimulate progress in precision medicine and is promising in detecting potential biomarkers. However, current methods are restricted owing to highly unbalanced dimensions of omics data or difficulty in assigning weights between different data sources. Therefore, the appropriate approximation and constraints of integrated targets remain a major challenge. In this paper, we proposed an omics data integration method, named high-order path elucidated similarity (HOPES). HOPES fuses the similarities derived from various omics data sources to solve the dimensional discrepancy, and progressively elucidate the similarities from each type of omics data into an integrated similarity with various high-order connected paths. Through a series of incremental constraints for commonality, HOPES can take both specificity of single data and consistency between different data types into consideration. The fused similarity matrix gives global insight into patients' correlation and efficiently distinguishes subgroups. We tested the performance of HOPES on both a simulated dataset and several empirical tumor datasets. The test datasets contain three omics types including gene expression, DNA methylation, and microRNA data for five different TCGA cancer projects. Our method was shown to achieve superior accuracy and high robustness compared with several benchmark methods on simulated data. Further experiments on five cancer datasets demonstrated that HOPES achieved superior performances in cancer classification. The stratified subgroups were shown to have statistically significant differences in survival. We further located and identified the key genes, methylation sites, and microRNAs within each subgroup. They were shown to achieve high potential prognostic value and were enriched in many cancer-related biological processes or pathways.

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

“…In particular, Koh et al (44) predicted breast cancer subtypes by applying a modified shrunken centroid method in the development of their networkbased tool, iOmicsPASS. Further, breast cancer datasets in TGCA represent a benchmark for integrative models (92)(93)(94), as well as AML (95). More recently, the success of deep learning algorithms in various bioinformatics fields (96) prompted the adoption of deep neural networks for omics-integration in precision oncology.…”

Integrative Network Fusion: a multi-omics approach in molecular profiling

“…In particular, Koh and colleagues (41) predicted breast bioRxiv INF cancer subtypes by applying a modified shrunken centroid method in the development of their networkbased tool, iOmicsPASS. Further, breast cancer datasets in TGCA represent a benchmark for integrative models (87,88,89), as well as AML (90). More recently, the success of deep learning algorithms in various bioinformatics fields (91) prompted the adoption of deep neural network for omics-integration in precision oncology.…”

Prediction of miRNA targets by learning from interaction sequences

“…Besides the deep learning methods to predict the mirna-targets, there are other method to be used for the similar task [21]. Also, it has been shown that the finding such regulatory relationship can be used for cancer subtyping [24]. Because of not fully understood rules that govern miRNAs targeting process and different training datasets for different algorithms, there is limited overlap between the targets that are predicted by various programs.…”