bioRxivCancer BiologyPreprint
Comparative Molecular Genomic Analyses of a Spontaneous Rhesus Macaque Model of Mismatch Repair-Deficient Colorectal Cancer
N. Ozirmak Lermi, S. B. Gray, C. M. Bowen, L. Reyes-Uribe, B. K. Dray, N. Deng, R. A. Harris, M. Raveendran, F. Benavides, C. L. Hodo, M. W. Taggart, K. Colbert Maresso, K. M. Sinha, J. Rogers, E. Vilar
Abstract
Colorectal cancer (CRC) remains the third most common cancer in the US with 15% of cases displaying Microsatellite Instability (MSI) secondary to Lynch Syndrome (LS) or somatic hypermethylation of the MLH1 promoter. A cohort of rhesus macaques from our institution developed spontaneous mismatch repair deficient (MMRd) CRC with a notable fraction harboring a pathogenic germline mutation in MLH1 (c.1029C<G, p.Tyr343Ter). Our study incorporated a detailed molecular characterization of rhesus CRC for cross-comparison with human MMRd CRC. We performed PCR-based MSI testing, transcriptomic analysis, and reduced-representation bisulfite sequencing (RRBS) of rhesus CRC (n=41 samples) using next-generation sequencing (NGS). Systems biology pipelines were used for gene set enrichment analysis (GSEA) for pathway discovery, consensus molecular subtyping (CMS), and somatic mutation profiling. Overall, the majority of rhesus tumors displayed high levels of MSI (MSI-high) and differential gene expression profiles that were consistent with known deregulated pathways in human CRC. DNA methylation analysis exposed differentially methylated patterns among MSI-H, MSI-L (MSI-low)/MSS (MS-stable) and LS tumors with MLH1 predominantly inactivated among sporadic MSI-H CRCs. The findings from this study support the use of rhesus macaques as the preferred animal model to study carcinogenesis, develop immunotherapies and vaccines, and implement chemoprevention approaches pertinent to sporadic MSI-H and LS CRC in humans.
- organism: rat
- assay: bulk RNA-seq
- assay: DNA methylation
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- approach: machine learning (classical)
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