TY - JOUR
T1 - NanoCMSer
T2 - a consensus molecular subtype stratification tool for fresh-frozen and paraffin-embedded colorectal cancer samples
AU - Torang, Arezo
AU - van de Weerd, Simone
AU - Lammers, Veerle
AU - van Hooff, Sander
AU - van den Berg, Inge
AU - van den Bergh, Saskia
AU - Koopman, Miriam
AU - IJzermans, Jan N.
AU - Roodhart, Jeanine M.L.
AU - Koster, Jan
AU - Medema, Jan Paul
N1 - Publisher Copyright:
© 2024 The Author(s). Molecular Oncology published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.
PY - 2025/5
Y1 - 2025/5
N2 - 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).
AB - 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).
KW - colorectal cancer
KW - consensus molecular subtypes
KW - machine learning
KW - NanoString
KW - prognosis biomarker
UR - https://www.scopus.com/pages/publications/85212948430
U2 - 10.1002/1878-0261.13781
DO - 10.1002/1878-0261.13781
M3 - Article
C2 - 39720854
AN - SCOPUS:85212948430
SN - 1574-7891
VL - 19
SP - 1332
EP - 1346
JO - Molecular Oncology
JF - Molecular Oncology
IS - 5
ER -