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

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MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer. / Mattesen, Trine B.; Rasmussen, Mads H.; Sandoval, Juan et al.

I: Nature Communications, Bind 11, 2025, 12.2020.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

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@article{6cf195f037da4e46b2a1765b9c1f588d,
title = "MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer",
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 < 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.",
author = "Mattesen, {Trine B.} and Rasmussen, {Mads H.} and Juan Sandoval and Halit Ongen and {\'A}rnad{\'o}ttir, {Sigrid S.} and Josephine Gladov and Anna Martinez-Cardus and {Castro de Moura}, Manuel and Madsen, {Anders H.} and S{\o}ren Laurberg and Dermitzakis, {Emmanouil T.} and Manel Esteller and Andersen, {Claus L.} and Bramsen, {Jesper B.}",
year = "2020",
month = dec,
doi = "10.1038/s41467-020-16000-6",
language = "English",
volume = "11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "Nature Publishing Group",

}

RIS

TY - JOUR

T1 - MethCORR modelling of methylomes from formalin-fixed paraffin-embedded tissue enables characterization and prognostication of colorectal cancer

AU - Mattesen, Trine B.

AU - Rasmussen, Mads H.

AU - Sandoval, Juan

AU - Ongen, Halit

AU - Árnadóttir, Sigrid S.

AU - Gladov, Josephine

AU - Martinez-Cardus, Anna

AU - Castro de Moura, Manuel

AU - Madsen, Anders H.

AU - Laurberg, Søren

AU - Dermitzakis, Emmanouil T.

AU - Esteller, Manel

AU - Andersen, Claus L.

AU - Bramsen, Jesper B.

PY - 2020/12

Y1 - 2020/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85083865215&partnerID=8YFLogxK

U2 - 10.1038/s41467-020-16000-6

DO - 10.1038/s41467-020-16000-6

M3 - Journal article

C2 - 32332866

AN - SCOPUS:85083865215

VL - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

M1 - 2025

ER -