A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues

Author:

de Back Tim R123ORCID,Wu Tan45,Schafrat Pascale JM1236,ten Hoorn Sanne123,Tan Miaomiao57,He Lingli5,van Hooff Sander R123ORCID,Koster Jan12,Nijman Lisanne E123,Vink Geraldine R89ORCID,Beumer Inès J10,Elbers Clara C123,Lenos Kristiaan J123,Sommeijer Dirkje W111,Wang Xin51213,Vermeulen Louis123ORCID

Affiliation:

1. Cancer Center Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam, Netherlands

2. Amsterdam Gastroenterology Endocrinology Metabolism, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Amsterdam, Netherlands

3. Oncode Institute

4. Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China

5. Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China

6. Amsterdam UMC Location Vrije Universiteit Amsterdam, Department of Medical Oncology, Amsterdam, Netherlands

7. Institute of Translational Medicine, Zhejiang Shuren University, Hangzhou, China

8. Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

9. Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, Netherlands

10. GenomeScan B.V., Leiden, Netherlands

11. Flevohospital, Department of Internal Medicine, Almere, Netherlands

12. Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China

13. Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China

Abstract

Consensus Molecular Subtype (CMS) classification of colorectal cancer (CRC) tissues is complicated by RNA degradation upon formalin-fixed paraffin-embedded (FFPE) preservation. Here, we present an FFPE-curated CMS classifier. The CMSFFPE classifier was developed using genes with a high transcript integrity in FFPE-derived RNA. We evaluated the classification accuracy in two FFPE-RNA datasets with matched fresh-frozen (FF) RNA data, and an FF-derived RNA set. An FFPE-RNA application cohort of metastatic CRC patients was established, partly treated with anti-EGFR therapy. Key characteristics per CMS were assessed. Cross-referenced with matched benchmark FF CMS calls, the CMSFFPE classifier strongly improved classification accuracy in two FFPE datasets compared with the original CMSClassifier (63.6% versus 40.9% and 83.3% versus 66.7%, respectively). We recovered CMS-specific recurrence-free survival patterns (CMS4 versus CMS2: hazard ratio 1.75, 95% CI 1.24–2.46). Key molecular and clinical associations of the CMSs were confirmed. In particular, we demonstrated the predictive value of CMS2 and CMS3 for anti-EGFR therapy response (CMS2&3: odds ratio 5.48, 95% CI 1.10–27.27). The CMSFFPE classifier is an optimized FFPE-curated research tool for CMS classification of clinical CRC samples.

Funder

New York Stem Cell Foundation

EC | European Research Council

ZonMw

KWF Kankerbestrijding

Oncode Institute

Research Grants Council, University Grants Committee

Shenzhen Bay Scholars Program

Publisher

Life Science Alliance, LLC

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