Evidence map›Paper›PMID 39720854›Full record

ArticleMolecular oncology2025

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 and 1 more

Abstract read
In one paragraph

Article in Molecular oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Embryonic Signaling Pathways Shape Colorectal Cancer Subtypes: Linking Gut Development to Tumor Biology.Pathophysiology : the official journal of the International Society for Pathophysiology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Arezo TorangAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0002-7912-3964
Simone van de WeerdAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Veerle LammersAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Sander van HooffAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Inge van den BergDepartment of Surgery, Erasmus MC, University Medical Center Rotterdam, The Netherlands.ORCID https://orcid.org/0000-0002-7529-4686
Saskia van den BerghAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Miriam KoopmanDepartment of Medical Oncology, University Medical Center Utrecht, Utrecht University, The Netherlands.
Jan N IJzermansDepartment of Surgery, Erasmus MC, University Medical Center Rotterdam, The Netherlands.
Jeanine M L RoodhartDepartment of Medical Oncology, University Medical Center Utrecht, Utrecht University, The Netherlands.
Jan KosterAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.
Jan Paul MedemaAmsterdam UMC, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam, University of Amsterdam, The Netherlands.ORCID https://orcid.org/0000-0003-3045-2924

Funding

KWFOncode Institute
6 · The paper itself

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).

Indexed as

Colorectal NeoplasmsParaffin EmbeddingBiomarkers, TumorConsensusHumansBiomarkers, Tumorcolorectal cancerconsensus molecular subtypesmachine learningNanoStringprognosis biomarker

Identifiers

PMID39720854
PMCPMC12077266

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.