Cox-sMBPLS: An Algorithm for Disease Survival Prediction and Multi-Omics Module Discovery Incorporating Cis-Regulatory Quantitative Effects

Abstract

BackgroundThe development of high-throughput techniques has enabled profiling a large number of biomolecules across a number of molecular compartments. The challenge then becomes to integrate such multimodal Omics data to gain insights into biological processes and disease onset and progression mechanisms. Further, given the high dimensionality of such data, incorporating prior biological information on interactions between molecular compartments when developing statistical models for data integration is beneficial, especially in settings involving a small number of samples.ResultsWe develop a supervised model for time to event data (e.g., death, biochemical recurrence) that simultaneously accounts for redundant information within Omics profiles and leverages prior biological associations between them through a multi-block PLS framework. The interactions between data from different molecular compartments (e.g., epigenome, transcriptome, methylome, etc.) were captured by using cis-regulatory quantitative effects in the proposed model. The model, coined Cox-sMBPLS, exhibits superior prediction performance and improved feature selection based on both simulation studies and analysis of data from heart failure patients.ConclusionThe proposed supervised Cox-sMBPLS model can effectively incorporate prior biological information in the survival prediction system, leading to improved prediction performance and feature selection. It also enables the identification of multi-Omics modules of biomolecules that impact the patients’ survival probability and also provides insights into potential relevant risk factors that merit further investigation.

Survival Prediction Landscape: An In-Depth Systematic Literature Review on Activities, Methods, Tools, Diseases, and Databases

“…A deeper analysis of existing survival predictors reveals that among the 74 studies 54 utilized publicly accessible data from three key databases: the Cancer Genome Atlas Program (TCGA) 17 , NCI Genomic Data Commons (GDC) 18 , and the Gene Expression Omnibus (GEO) 31, 32, 72, 73, 80, 82, 87, 90, 91, 130, 131 . Apart from public databases, there also exist private databases that have been utilized in existing survival prediction studies 66,75,81,112,113,117,118 . However, these private databases often restrict data access and may require extensive research proposals for data retrieval.…”

“…Among 10 data modalities, 3 modalities namely, proteomic, lncRNA and WES have been utilized the least having limited applicability to clear renal cell cancer 31 , pancreatic cancer 26 , breast cancer 23 , localized prostate cancer 22 , and pancancer 107 . In terms of other diseases i.e., COVID-19 and heart diseases, proteomics, methylation, mRNA, metabolic, and methylation have been the only omics types utilized for survival prediction 113, 115, 117 .…”

“…Table 8 provides information about 44 diseases and the corresponding survival prediction algorithms utilized in these diseases. A deeper analysis of Table 8 shows that Cox-PH and lasso Cox-PH models have been extensively utilized for disease specific survival prediction i.e., ASCVD 29,111 , bladder cancer 40,82 , colorectal cancer 74–77 , hepatocellular carcinoma 43,86,87 , ovarian cancer 88–90,103 , lung adenocarcinoma 101 , heart failure 118 , HER2-negative metastatic breast cancer 67 , pancreatic cancer 26,71 , trauma 120 , nasopharyngeal carcinoma 66 , triple-negative breast cancer 68 , lymphoma 85 , breast cancer 40,81,82 , ovarian cancer 88–90,103 , and lower-grade glioma 80 , cardiovascular disease 112,114–117 , invasive ductal carcinoma 70 , liver transplantation 119 , gastric cancer 42 , lung cancer 27 , esophageal squamous cell carcinoma 79 , glioma 69 , and liver cancer 41 . RSF has been employed in 13 studies for 6 diseases namely, ASCVD 29 , bladder cancer 82 , gastrointestinal cancer 30 , cervical cancer 73 , liver transplantation 119 , and heart failure 118 .…”

“…Two studies have incorporated OS, DFS, and PFS 40,108 and 2 studies have OS, and PFS as the survival end- points 31,81 . A single study has focused on DFS only 95 , and 2 only on BC 94,117 . The rest of studies either did not explicitly specify their endpoints for survival prediction or predominantly concentrated on predicting patients' survival outcomes without a specific focus on distinct survival endpoints.…”

asmbPLS: Adaptive Sparse Multi-block Partial Least Square for Survival Prediction using Multi-Omics Data

“…For predicting the phenotypic features such as the survival outcome of patients or time till a severe disease development, many methods have been developed using the Cox model [22][23][24],…”