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Amirkhah R, Gilroy K, Malla SB, Lannagan TRM, Byrne RM, Fisher NC, Corry SM, Mohamed NE, 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. MmCMS: mouse models' consensus molecular subtypes of colorectal cancer. Br J Cancer 2023; 128:1333-1343. [PMID: 36717674 PMCID: PMC10050155 DOI: 10.1038/s41416-023-02157-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 01/10/2023] [Accepted: 01/12/2023] [Indexed: 02/01/2023] Open
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.
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Affiliation(s)
- Raheleh Amirkhah
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | - Sudhir B Malla
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | - Ryan M Byrne
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Natalie C Fisher
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Shania M Corry
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | | | - Hojjat Naderi-Meshkin
- Wellcome-Wolfson Institute for Experimental Medicine, Queen's University Belfast, Belfast, UK
| | | | | | | | - Baharak Ahmaderaghi
- School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK
| | - Richard Murray
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Antoni Berenguer Llergo
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Rebeca Sanz-Pamplona
- Unit 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 Villanueva
- Chemoresistance 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 Batlle
- Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Ramon Salazar
- Department 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 Lawler
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Glasgow, UK
- School of Cancer Sciences, University of Glasgow, Glasgow, UK
| | - Philip D Dunne
- The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
- Cancer Research UK Beatson Institute, Glasgow, UK.
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