MmCMS: mouse models’ consensus molecular subtypes of colorectal cancer

• Published in: British journal of cancer Vol. 128; no. 7; pp. 1333 - 1343
• Main Authors: Amirkhah, Raheleh, Gilroy, Kathryn, Malla, Sudhir B., Lannagan, Tamsin R. M., Byrne, Ryan M., Fisher, Natalie C., Corry, Shania M., Mohamed, Noha-Ehssan, Naderi-Meshkin, Hojjat, Mills, Megan L., Campbell, Andrew D., Ridgway, Rachel A., Ahmaderaghi, Baharak, Murray, Richard, Llergo, Antoni Berenguer, Sanz-Pamplona, Rebeca, Villanueva, Alberto, Batlle, Eduard, Salazar, Ramon, Lawler, Mark, Sansom, Owen J., Dunne, Philip D.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 30.03.2023; Nature Publishing Group
• Subjects: 631/114/1386; 631/67/70; Animal models; Animals; Biomedical and Life Sciences; Biomedicine; Cancer Research; Colorectal cancer; Colorectal carcinoma; Colorectal Neoplasms - pathology; Disease Models, Animal; Drug Resistance; Epidemiology; Genetic engineering; Genotypes; Humans; Mice; Molecular Medicine; Molecular modelling; Oncology; Ontology; Signal Transduction; Tumors
• ISSN: 0007-0920; 1532-1827
• DOI: 10.1038/s41416-023-02157-6

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.