Analysis of the molecular nature associated with microsatellite status in colon cancer identifies clinical implications for immunotherapy. Issue 2 (7th October 2020) - Bao, Xuanwen; Zhang, Hangyu; Wu, Wei; Cheng, Shaobing; Dai, Xiaomeng; Zhu, Xudong; Fu, Qihan; Tong, Zhou; Liu, Lulu; Zheng, Yi; Zhao, Peng; Fang, Weijia; et al.
Abstract : Background: Microsatellite instability in colon cancer implies favorable therapeutic outcomes after checkpoint blockade immunotherapy. However, the molecular nature of microsatellite instability is not well elucidated. Methods: We examined the immune microenvironment of colon cancer using assessments of the bulk transcriptome and the single-cell transcriptome focusing on molecular nature of microsatellite stability (MSS) and microsatellite instability (MSI) in colorectal cancer from a public database. The association of the mutation pattern and microsatellite status was analyzed by a random forest algorithm in The Cancer Genome Atlas (TCGA) and validated by our in-house dataset (39 tumor mutational burden (TMB)-low MSS colon cancer, 10 TMB-high MSS colon cancer, 15 MSI colon cancer). A prognostic model was constructed to predict the survival potential and stratify microsatellite status by a neural network. Results: Despite the hostile CD8 + cytotoxic T lymphocyte (CTL)/Th1 microenvironment in MSI colon cancer, a high percentage of exhausted CD8 + T cells and upregulated expression of immune checkpoints were identified in MSI colon cancer at the single-cell level, indicating the potential neutralizing effect of cytotoxic T-cell activity by exhausted T-cell status. A more homogeneous highly expressed pattern of PD1 was observed in CD8 + T cells from MSI colon cancer; however, a small subgroup of CD8 + T cells with high expression of checkpoint molecules was identified in MSS patients. A random forest algorithm predicted important mutations that were associated with MSI status in the TCGA colon cancer cohort, and our in-house cohort validated higher frequencies of BRAF, ARID1A, RNF43, and KM2B mutations in MSI colon cancer. A robust microsatellite status–related gene signature was built to predict the prognosis and differentiate between MSI and MSS tumors. A neural network using the expression profile of the microsatellite status–related gene signature was constructed. A receiver operating characteristic curve was used to evaluate the accuracy rate of neural network, reaching 100%. Conclusion: Our analysis unraveled the difference in the molecular nature and genomic variance in MSI and MSS colon cancer. The microsatellite status–related gene signature is better at predicting the prognosis of patients with colon cancer and response to the combination of immune checkpoint inhibitor–based immunotherapy and anti-VEGF therapy.