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Isolation of Circulating Tumor Cells in an Orthotopic Mouse Model of Colorectal Cancer
Published on: July 18, 2017
MmCMS: mouse models' consensus molecular subtypes of colorectal cancer
Raheleh Amirkhah1, Kathryn Gilroy2, Sudhir B Malla1
1The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast, UK.
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.
Insights
Researchers developed new methods to classify mouse colorectal cancer (CRC) models according to human subtypes. A pathway-level approach is best for aligning mouse models with human CRC consensus molecular subtypes (CMS) for drug testing.
Area of Science:
- Oncology
- Bioinformatics
- Translational Research
Background:
- Colorectal cancer (CRC) is classified into four consensus molecular subtypes (CMS1-4).
- Genetically engineered mouse models are crucial for pre-clinical cancer research.
- A reliable method for dual-species (human-to-mouse) CRC classification is lacking.
Purpose of the Study:
- To develop and evaluate methods for classifying mouse colorectal cancer (CRC) tissues according to human consensus molecular subtypes (CMS).
- To identify the optimal classification approach for aligning mouse models with human CRC subtypes for pre-clinical drug testing.
Main Methods:
- Utilized transcriptional data from human (TCGA cohort) and mouse CRC tumors.
- Employed random forest and nearest template prediction algorithms with gene ontology collections.
- Developed and assessed three dual-species classifiers: MmCMS-A (gene-level), MmCMS-B (ontology-level), and MmCMS-C (pathway-level).
Main Results:
- All developed classifiers could identify CMS4-like (stromal-rich) tumors in mice.
- The gene-level classifier (MmCMS-A) failed to accurately classify epithelial-like subtypes (CMS2/3) in mouse tumors.
- Pathway-level classification approaches proved superior for mouse CRC subtyping.
Conclusions:
- Pathway-level classifiers are optimal for applying human-based transcriptional classification to mouse tumor data.
- The study provides an R package to aid researchers in selecting appropriate mouse models for human CRC subtype research.
- Accurate alignment of mouse models to human CRC subtypes enhances the reliability of pre-clinical drug testing.
