Evidence map›Paper›PMID 36717674›Full record

ArticleBritish journal of cancer2023

MmCMS: mouse models' consensus molecular subtypes of colorectal cancer.

Raheleh Amirkhah, Kathryn Gilroy, Sudhir B Malla, Tamsin R M Lannagan, Ryan M Byrne, Natalie C Fisher, Shania M Corry, Noha-Ehssan Mohamed, Hojjat Naderi-Meshkin, Megan L Mills and 12 more

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Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Mechanisms of metastatic colorectal cancer.Nature reviews. Gastroenterology & hepatology · 2024
    Review
  11. Clinical Challenges of Consensus Molecular Subtype CMS4 Colon Cancer in the Era of Precision Medicine.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024
    Review
  12. Article
  13. Noninvasive Stratification of Colon Cancer by Multiplex PET Imaging.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. BRAFCommunications biology · 2023
    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

22 authors.

Raheleh AmirkhahThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Kathryn GilroyCancer Research UK Beatson Institute, Glasgow, UK.
Sudhir B MallaThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.ORCID 0000-0001-5862-3815
Tamsin R M LannaganCancer Research UK Beatson Institute, Glasgow, UK.
Ryan M ByrneThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.ORCID 0000-0001-6505-6345
Natalie C FisherThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.ORCID 0000-0003-1769-7468
Shania M CorryThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Noha-Ehssan MohamedCancer Research UK Beatson Institute, Glasgow, UK.
Hojjat Naderi-MeshkinWellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK.
Megan L MillsCancer Research UK Beatson Institute, Glasgow, UK.
Andrew D CampbellCancer Research UK Beatson Institute, Glasgow, UK.ORCID 0000-0003-3930-1276
Rachel A RidgwayCancer Research UK Beatson Institute, Glasgow, UK.
Baharak AhmaderaghiSchool of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK.ORCID 0000-0003-4166-6290
Richard MurrayThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Antoni Berenguer LlergoInstitute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain.ORCID 0000-0002-3742-8161
Rebeca Sanz-PamplonaUnit of Biomarkers and Susceptibility, Oncology Data Analytics Program (ODAP), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL) and CIBERESP, L'Hospitalet de Llobregat, Barcelona, Spain.
Alberto VillanuevaChemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet del Llobregat, Barcelona, Spain.
Eduard BatlleInstitute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain.ORCID 0000-0003-2422-0326
Ramon SalazarDepartment of Medical Oncology, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), CIBERONC and Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain.
Mark LawlerThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
Owen J SansomCancer Research UK Beatson Institute, Glasgow, UK.
Philip D DunneThe Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK. p.dunne@qub.ac.uk.ORCID 0000-0001-9160-283X

Funding

Cancer Research UK 29834Cancer Research UK A21139Cancer Research UK A25142Medical Research Council MC_PC_21042
6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) primary tumours are molecularly classified into four consensus molecular subtypes (CMS1-4). Genetically engineered mouse models aim to faithfully mimic the complexity of human cancers and, when appropriately aligned, represent ideal pre-clinical systems to test new drug treatments. Despite its importance, dual-species classification has been limited by the lack of a reliable approach. Here we utilise, develop and test a set of options for human-to-mouse CMS classifications of CRC tissue.

methodsUsing transcriptional data from established collections of CRC tumours, including human (TCGA cohort; n = 577) and mouse (n = 57 across n = 8 genotypes) tumours with combinations of random forest and nearest template prediction algorithms, alongside gene ontology collections, we comprehensively assess the performance of a suite of new dual-species classifiers.

resultsWe developed three approaches: MmCMS-A; a gene-level classifier, MmCMS-B; an ontology-level approach and MmCMS-C; a combined pathway system encompassing multiple biological and histological signalling cascades. Although all options could identify tumours associated with stromal-rich CMS4-like biology, MmCMS-A was unable to accurately classify the biology underpinning epithelial-like subtypes (CMS2/3) in mouse tumours.

conclusionsWhen applying human-based transcriptional classifiers to mouse tumour data, a pathway-level classifier, rather than an individual gene-level system, is optimal. Our R package enables researchers to select suitable mouse models of human CRC subtype for their experimental testing.

Indexed as

Colorectal NeoplasmsAnimalsDisease Models, AnimalHumansMiceSignal Transduction

Identifiers

PMID36717674
PMCPMC10050155

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LicenceCC BY
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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.