MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer

Trine B. Mattesen, Mads H. Rasmussen, Juan Sandoval, Halit Ongen, Sigrid S. Árnadóttir, Josephine Gladov, Anna Martinez-Cardus, Manuel Castro de Moura, Anders H. Madsen, Søren Laurberg, Emmanouil T. Dermitzakis, Manel Esteller, Claus L. Andersen () and Jesper B. Bramsen ()
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Trine B. Mattesen: Aarhus University Hospital
Mads H. Rasmussen: Aarhus University Hospital
Juan Sandoval: Health Research Institute La Fe (ISSLaFe)
Halit Ongen: University of Geneva Medical School-CMU
Sigrid S. Árnadóttir: Aarhus University Hospital
Josephine Gladov: Aarhus University Hospital
Anna Martinez-Cardus: Germans Trias i Pujol Research Institute (IGTP)
Manuel Castro de Moura: Josep Carreras Leukaemia Research Institute (IJC)
Anders H. Madsen: Hospitalsenheden Vest
Søren Laurberg: Aarhus University Hospital
Emmanouil T. Dermitzakis: University of Geneva Medical School-CMU
Manel Esteller: Josep Carreras Leukaemia Research Institute (IJC)
Claus L. Andersen: Aarhus University Hospital
Jesper B. Bramsen: Aarhus University Hospital

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Transcriptional characterization and classification has potential to resolve the inter-tumor heterogeneity of colorectal cancer and improve patient management. Yet, robust transcriptional profiling is difficult using formalin-fixed, paraffin-embedded (FFPE) samples, which complicates testing in clinical and archival material. We present MethCORR, an approach that allows uniform molecular characterization and classification of fresh-frozen and FFPE samples. MethCORR identifies genome-wide correlations between RNA expression and DNA methylation in fresh-frozen samples. This information is used to infer gene expression information in FFPE samples from their methylation profiles. MethCORR is here applied to methylation profiles from 877 fresh-frozen/FFPE samples and comparative analysis identifies the same two subtypes in four independent cohorts. Furthermore, subtype-specific prognostic biomarkers that better predicts relapse-free survival (HR = 2.66, 95%CI [1.67–4.22], P value

Date: 2020
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DOI: 10.1038/s41467-020-16000-6

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