COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data.

Song He,Jiannan Feng,Xiaochen Bo,Yuqi Wen,Xiaoxi Yang,Xinyu Song,Bowei Yan,Jijun Yu,Lianlian Wu,Manja Marz

doi:10.1371/journal.pcbi.1008769

Abstract

Extensive amounts of multi-omics data and multiple cancer subtyping methods have been developed rapidly, and generate discrepant clustering results, which poses challenges for cancer molecular subtype research. Thus, the development of methods for the identification of cancer consensus molecular subtypes is essential. The lack of intuitive and easy-to-use analytical tools has posed a barrier. Here, we report on the development of the COnsensus Molecular SUbtype of Cancer (COMSUC) web server. With COMSUC, users can explore consensus molecular subtypes of more than 30 cancers based on eight clustering methods, five types of omics data from public reference datasets or users' private data, and three consensus clustering methods. The web server provides interactive and modifiable visualization, and publishable output of analysis results. Researchers can also exchange consensus subtype results with collaborators via project IDs. COMSUC is now publicly and freely available with no login requirement at http://comsuc.bioinforai.tech/ (IP address: http://59.110.25.27/). For a video summary of this web server, see S1 Video and S1 File.

Highlights

With rapid progress in high-throughput technologies, parallel acquisition of multi-omics data for cancer is becoming less expensive, resulting in the accumulation of large-scale multidimensional cancer databases [e.g., Therapeutically Applicable Research To Generate Effective Treatments (TARGET), https://ocg.cancer.gov/programs/target] [1,2,3,4]
A number of methods have been developed for omics data-based subtyping, which has been widely accepted as a relevant source of cancer classification
We have developed the COnsensus Molecular SUbtype of Cancer (COMSUC) web server to provide a user-friendly tool for integrating discrepant clustering results based on multiple platform, multiple omics data and multiple methods

Summary

Introduction

With rapid progress in high-throughput technologies, parallel acquisition of multi-omics data for cancer is becoming less expensive, resulting in the accumulation of large-scale multidimensional cancer databases [e.g., Therapeutically Applicable Research To Generate Effective Treatments (TARGET), https://ocg.cancer.gov/programs/target] [1,2,3,4]. Discrepant results compromise the translational and clinical utility of these methods. Guinney J, et al refers that different colorectal cancer classification methods can only identify subtype based on microsatellite instability and highly expressed mesenchymal genes, but failed to achieve consistency for other subtypes [8]. They applied a network-based algorithm to examine consistency among six independent colorectal cancer classification systems, and to merge them into four consensus molecular subtypes (CMSs). Methods and tools for the identification of cancer CMSs through the integration of discrepant clustering results from multimethods and multi-omics data are valuable resources for researchers

Methods

Results

Conclusion