Colon cancer subtypes: concordance, effect on survival and selection of the most representative preclinical models.

Zsófia Sztupinszki,Balázs Győrffy

doi:10.1038/srep37169

Abstract

Multiple gene-expression-based subtypes have been proposed for the molecular subdivision of colon cancer in the last decade. We aimed to cross-validate these classifiers to explore their concordance and their power to predict survival. A gene-chip-based database comprising 2,166 samples from 12 independent datasets was set up. A total of 22 different molecular subtypes were re-trained including the CCHS, CIN25, CMS, ColoGuideEx, ColoGuidePro, CRCassigner, MDA114, Meta163, ODXcolon, Oncodefender, TCA19, and V7RHS classifiers as well as subtypes established by Budinska, Chang, DeSousa, Marisa, Merlos, Popovici, Schetter, Yuen, and Watanabe (first authors). Correlation with survival was assessed by Cox proportional hazards regression for each classifier using relapse-free survival data. The highest efficacy at predicting survival in stage 2–3 patients was achieved by Yuen (p = 3.9e-05, HR = 2.9), Marisa (p = 2.6e-05, HR = 2.6) and Chang (p = 9e-09, HR = 2.35). Finally, 61 colon cancer cell lines from four independent studies were assigned to the closest molecular subtype.

Highlights

There are multiple unsolved issues with the proposed subtypes
The largest dataset (GSE39582) with 566 samples accounted for 26% of the entire database
Aggregate clinical parameters for the entire database utilized in the validation analysis are depicted in Fig. 1B, and the clinical properties for each of the datasets including gender, grade, microsatellite instability (MSI), stage, age and location are listed in Supplementary Table 1

Summary

Introduction

The performance of many of these classification schemes has been assessed using an independent set of patients, to date no study has compared them using the same set of patients. The ability to select a clinically relevant subtype using patient-derived material does not ensure the immediate translation of these results into patient therapy. Our goal was to evaluate published molecular subtypes using the same large set of patients. We obtained gene expression signatures of colorectal cancer cell lines and evaluated each cell line to identify the most representative preclinical model for each subtype within each classifier

Objectives

Methods

Results

Conclusion