Matthew Alderdice, Dale Vimalachandran, Chris Hurt, Aideen Roddy, Mark Lawler, Enzo Medico, Darragh G McArt, Richard Adams, Tim Maughan, Philip D Dunne, Simon Gollins, Amy McCorry, Peter Stewart, Susan D Richman, Claudio Isella
Colorectal cancer (CRC) biopsies underpin accurate diagnosis, but are also relevant for patient stratification in molecularly-guided clinical trials. The consensus molecular subtypes (CMS) and colorectal cancer intrinsic subtypes (CRIS) transcriptional signatures have potential clinical utility for improving prognostic/predictive patient assignment. However, their ability to provide robust classification, particularly in pre-treatment biopsies from multiple regions or at different time points remains untested.
In this study, we undertook a comprehensive assessment of the robustness of CRC transcriptional signatures, including CRIS and CMS, using a range of tumour sampling methodologies currently employed in clinical and translational research. These include analyses using (i) laser-capture microdissected CRC tissue, (ii) eight publically available rectal cancer biopsy data sets (n=543), (iii) serial biopsies (from AXEBeam trial, NCT00828672; n=10), (iv) multi-regional biopsies from colon tumours (n=29 biopsies, n=7 tumours) and (v) pre-treatment biopsies from the phase II rectal cancer trial COPERNCIUS (NCT01263171; n=44). Compared to previous results obtained using CRC resection material, we demonstrate that CMS classification in biopsy tissue is significantly less capable of reliably classifying patient subtype (43% unknown in biopsy versus 13% unknown in resections, p=0.0001). In contrast, there was no significant difference in classification rate between biopsies and resections when using the CRIS classifier. Additionally, we demonstrated that CRIS provides significantly better spatially- and temporally- robust classification of molecular subtypes in CRC primary tumour tissue compared to CMS (p= 0.003 and p=0.02, respectively).
These findings have potential to inform ongoing biopsy-based patient stratification in CRC, enabling robust and stable assignment of patients into clinically-informative arms of prospective multi-arm, multi-stage clinical trials.
