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Molecular subtyping combined with biological pathway analyses to study regorafenib response in clinically relevant mouse models of colorectal cancer.

journal contribution
posted on 24.09.2021, 16:22 authored by Adam Lafferty, Alice O'Farrell, Giorgia Migliardi, Niraj Khemka, Andreas Ulrich LindnerAndreas Ulrich Lindner, Francesco Sassi, Eugenia R Zanella, Manuela Salvucci, Evy Vanderheyden, Elodie Modave, Bram Boeckx, Luise HalangLuise Halang, Johannes Betge, Matthias P.A. Ebert, Patrick DickerPatrick Dicker, Guillem Argiles, Josep Tabernero, Rodrigo Dienstmann, Enzo Medico, Diether Lambrechts, Andrea Bertotti, Claudio Isella, Livio Trusolino, Jochen PrehnJochen Prehn, Annette ByrneAnnette Byrne


Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers.

Experimental design:

Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment.


Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance.


Subtype classification systems represent canonical "termini a quo" (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors.


EDIReX: EurOPDX Distributed Infrastructure for Research on patient-derived cancer Xenografts | Funder: EU Horizon 2020 | Grant ID: 731105

ColoForetell: A Xenopatient Discovery Platform for the integrated Systems based Identification of Predictive Biomarkers for Targeted Therapies in Metastatic Colorectal Cancer | Funder: Science Foundation Ireland (SFI) | Grant ID: 13/CDA/2183

COLOSSUS: Advancing a Precision Medicine Paradigm in metastatic Colorectal Cancer: Systems based patient stratification solutions. | Funder: EU Horizon 2020 | Grant ID: 754923

AIRC, Associazione Italiana 752 per la Ricerca sul Cancro, Investigator Grants 20697. 12944 and 22802

AIRC 5x1000 grant 21091

AIRC/CRUK/FC AECC Accelerator Award 22795

My First AIRC Grant 19047

European Research Council Consolidator Grant 724748 – BEAT

Fondazione Piemontese per la Ricerca sul Cancro-ONLUS, 5x1000 Ministero della Salute 2015. 2014 and 2016

Science Foundation Ireland and the Health Research Board (14/IA/2582, 16/US/3301)



The original article is available at

Published Citation

Lafferty A. et al. Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer. Clin Cancer Res. 2021;27(21):5979-5992

Publication Date

23 August 2021

PubMed ID



  • Centre for Systems Medicine
  • Physiology and Medical Physics
  • Public Health and Epidemiology

Research Area

  • Cancer


American Association for Cancer Research (AACR)


  • Accepted Version (Postprint)