A Trinh, K Trumpi, F De Sousa E Melo, X Wang, JH de Jong, E Fessler, PJK Kuppen, MS Reimers, M Swets, M Koopman, ID Nagtegaal, M Jansen, GKJ Hooijer, GJA Offerhaus, O Kranenburg, CJ Punt, JP Medema, F Markowetz, L Vermeulen
Journal name: 
Clin Cancer Res
Citation info: 
PURPOSE: Recent transcriptomic analyses have identified four distinct molecular subtypes of colorectal cancer with evident clinical relevance. However, the requirement for sufficient quantities of bulk tumor and difficulties in obtaining high-quality genome-wide transcriptome data from formalin-fixed paraffin-embedded tissue are obstacles toward widespread adoption of this taxonomy. Here, we develop an immunohistochemistry-based classifier to validate the prognostic and predictive value of molecular colorectal cancer subtyping in a multicenter study. EXPERIMENTAL DESIGN: Tissue microarrays from 1,076 patients with colorectal cancer from four different cohorts were stained for five markers (CDX2, FRMD6, HTR2B, ZEB1, and KER) by immunohistochemistry and assessed for microsatellite instability. An automated classification system was trained on one cohort using quantitative image analysis or semiquantitative pathologist scoring of the cores as input and applied to three independent clinical cohorts. RESULTS: This classifier demonstrated 87% concordance with the gold-standard transcriptome-based classification. Application to three validation datasets confirmed the poor prognosis of the mesenchymal-like molecular colorectal cancer subtype. In addition, retrospective analysis demonstrated the benefit of adding cetuximab to bevacizumab and chemotherapy in patients with RAS wild-type metastatic cancers of the canonical epithelial-like subtypes. CONCLUSIONS: This study shows that a practical and robust immunohistochemical assay can be employed to identify molecular colorectal cancer subtypes and uncover subtype-specific therapeutic benefit. Finally, the described tool is available online for rapid classification of colorectal cancer samples, both in the format of an automated image analysis pipeline to score tumor core staining, and as a classifier based on semiquantitative pathology scoring. Clin Cancer Res; 23(2); 387-98. ©2016 AACR.
Research group: 
Markowetz Group
E-pub date: 
15 Jan 2017
Users with this publication listed: 
Florian Markowetz