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; Andrew D. Campbell; Rachel A. Ridgway; Baharak Ahmaderaghi; Richard Murray; Antoni Berenguer Llergo; Rebeca Sanz-Pamplona; Alberto Villanueva; Eduard Batlle; Ramon Salazar; Mark Lawler; Owen J. Sansom; Philip D. Dunne

doi:10.1038/s41416-023-02157-6

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, Andrew D. Campbell, Rachel A. Ridgway, Baharak Ahmaderaghi, Richard Murray, Antoni Berenguer Llergo, Rebeca Sanz-Pamplona, Alberto Villanueva, Eduard Batlle, Ramon Salazar, Mark LawlerOwen J. Sansom, Philip D. Dunne^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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Abstract

Background
Colorectal 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.

Methods
Using 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.

Results
We 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.

Conclusions
When 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.

Original language	English
Number of pages	11
Journal	British Journal of Cancer
DOIs	https://doi.org/10.1038/s41416-023-02157-6
Publication status	Published - 30 Jan 2023

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10.1038/s41416-023-02157-6Licence: CC BY

MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer
Copyright 2023 the authors. This is an open access article published under a Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
Final published version, 3.02 MBLicence: CC BY

Molecular evolution of aggressive early stage colorectal cancer
Corry, S. M. (Author), Lawler, M. (Supervisor) & Dunne, P. (Supervisor), Jul 2024
Student thesis: Doctoral Thesis › Doctor of Philosophy

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Amirkhah, R., Gilroy, K., Malla, S. B., Lannagan, T. R. M., Byrne, R. M., Fisher, N. C., Corry, S. M., Mohamed, N.-E., Naderi-Meshkin, H., Mills, M. L., Campbell, A. D., Ridgway, R. A., Ahmaderaghi, B., Murray, R., Llergo, A. B., Sanz-Pamplona, R., Villanueva, A., Batlle, E., Salazar, R., ... Dunne, P. D. (2023). MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer. British Journal of Cancer. https://doi.org/10.1038/s41416-023-02157-6

@article{2e51b023e9a54ddb9012f669f91865f3,

title = "MmCMS: mouse models{\textquoteright} consensus molecular subtypes of colorectal cancer",

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.",

author = "Raheleh Amirkhah and Kathryn Gilroy and Malla, {Sudhir B} and Lannagan, {Tamsin R. M.} and Byrne, {Ryan M.} and Fisher, {Natalie C.} and Corry, {Shania M.} and Noha-Ehssan Mohamed and Hojjat Naderi-Meshkin and Mills, {Megan L.} and Campbell, {Andrew D.} and Ridgway, {Rachel A.} and Baharak Ahmaderaghi and Richard Murray and Llergo, {Antoni Berenguer} and Rebeca Sanz-Pamplona and Alberto Villanueva and Eduard Batlle and Ramon Salazar and Mark Lawler and Sansom, {Owen J.} and Dunne, {Philip D.}",

year = "2023",

month = jan,

day = "30",

doi = "10.1038/s41416-023-02157-6",

language = "English",

journal = "British Journal of Cancer",

issn = "0007-0920",

publisher = "Springer Nature",

}

Amirkhah, R, Gilroy, K, Malla, SB, Lannagan, TRM, Byrne, RM , Fisher, NC, Corry, SM, Mohamed, N-E, Naderi-Meshkin, H, Mills, ML, Campbell, AD, Ridgway, RA, Ahmaderaghi, B, Murray, R, Llergo, AB, Sanz-Pamplona, R, Villanueva, A, Batlle, E, Salazar, R, Lawler, M, Sansom, OJ & Dunne, PD 2023, 'MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer', British Journal of Cancer. https://doi.org/10.1038/s41416-023-02157-6

TY - JOUR

T1 - MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer

AU - Amirkhah, Raheleh

AU - Gilroy, Kathryn

AU - Malla, Sudhir B

AU - Lannagan, Tamsin R. M.

AU - Byrne, Ryan M.

AU - Fisher, Natalie C.

AU - Corry, Shania M.

AU - Mohamed, Noha-Ehssan

AU - Naderi-Meshkin, Hojjat

AU - Mills, Megan L.

AU - Campbell, Andrew D.

AU - Ridgway, Rachel A.

AU - Ahmaderaghi, Baharak

AU - Murray, Richard

AU - Llergo, Antoni Berenguer

AU - Sanz-Pamplona, Rebeca

AU - Villanueva, Alberto

AU - Batlle, Eduard

AU - Salazar, Ramon

AU - Lawler, Mark

AU - Sansom, Owen J.

AU - Dunne, Philip D.

PY - 2023/1/30

Y1 - 2023/1/30

N2 - 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.

AB - 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.

U2 - 10.1038/s41416-023-02157-6

DO - 10.1038/s41416-023-02157-6

M3 - Article

C2 - 36717674

SN - 0007-0920

JO - British Journal of Cancer

JF - British Journal of Cancer

ER -

MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer

Abstract

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Student theses

Molecular evolution of aggressive early stage colorectal cancer

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