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NanoCMSer: a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples

  • Arezo Torang
  • , Simone van de Weerd
  • , Veerle Lammers
  • , Sander van Hooff
  • , Inge van den Berg
  • , Saskia van den Bergh
  • , Miriam Koopman
  • , Jan N. IJzermans
  • , Jeanine M.L. Roodhart
  • , Jan Koster
  • , Jan Paul Medema

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Colorectal cancer (CRC) is a significant contributor to cancer-related mortality, emphasizing the need for advanced biomarkers to guide treatment. As part of an international consortium, we previously categorized CRCs into four consensus molecular subtypes (CMS1-CMS4), showing promise for outcome prediction. To facilitate clinical integration of CMS classification in settings where formalin-fixed paraffin-embedded (FFPE) samples are routinely used, we developed NanoCMSer, a NanoString-based CMS classifier using 55 genes. NanoCMSer achieved high accuracy rates, with 95% for fresh-frozen samples from the MATCH cohort and 92% for FFPE samples from the CODE cohort, marking the highest reported accuracy for FFPE tissues to date. Additionally, it demonstrated 96% accuracy across a comprehensive collection of 23 RNAseq-based datasets, compiled in this study, surpassing the performance of existing models. Classifying with only 55 genes, the CMS predictions were still biologically relevant, recognizing CMS-specific biology upon enrichment analysis. Additionally, we observed substantial differences in recurrence-free survival curves when comparing CMS2/3 patients in stage III versus II. Probability of recurrence after 5 years increased by 21% in CMS2 and 31% in CMS3 for patients in stage III, whereas this difference was less pronounced for CMS1 and CMS4, with 11% and 10%, respectively. We posit NanoCMSer as a robust tool for subtyping CRCs for both tumor biology and clinical practice, accessible via nanocmser r package (https://github.com/LEXORlab/NanoCMSer) and Shinyapp (https://atorang.shinyapps.io/NanoCMSer).

Original languageEnglish
Pages (from-to)1332-1346
Number of pages15
JournalMolecular oncology
Volume19
Issue number5
DOIs
Publication statusPublished - May 2025
Externally publishedYes

Keywords

  • NanoString
  • colorectal cancer
  • consensus molecular subtypes
  • machine learning
  • prognosis biomarker
  • Paraffin Embedding
  • Humans
  • Colorectal Neoplasms/genetics
  • Consensus
  • Biomarkers, Tumor/genetics

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