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

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DOI

  • Trine B. Mattesen
  • Mads H. Rasmussen
  • Juan Sandoval, Health Research Institute La Fe (ISSLaFe)
  • ,
  • Halit Ongen, University of Geneva
  • ,
  • Sigrid S. Árnadóttir
  • ,
  • Josephine Gladov
  • ,
  • Anna Martinez-Cardus, Germans Trias i Pujol Research Institute, Clinical and Provincial Hospital of Barcelona
  • ,
  • Manuel Castro de Moura, Josep Carreras Leukaemia Research Institute 
  • ,
  • Anders H. Madsen
  • Søren Laurberg
  • Emmanouil T. Dermitzakis, University of Geneva
  • ,
  • Manel Esteller, Josep Carreras Leukaemia Research Institute , Centro de Investigacion Biomedica en Red Cancer (CIBERONC), ICREA, Universitat Autònoma Barcelona
  • ,
  • Claus L. Andersen
  • Jesper B. Bramsen

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 < 0.001 (log-rank test)) than UICC tumor, node, metastasis (TNM) staging and microsatellite instability status are identified and validated using DNA methylation-specific PCR. The MethCORR approach is general, and may be similarly successful for other cancer types.

OriginalsprogEngelsk
Artikelnummer2025
TidsskriftNature Communications
Vol/bind11
ISSN2041-1723
DOI
StatusUdgivet - dec. 2020

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