This project is concerned with improving immune system capabilities in the context of immunotherapy for colorectal cancer treatments. Immunotherapies called checkpoint inhibitors are monoclonal antibody treatments that have been proven to be effective in small percentages of sample populations, especially upon a combination of treatments (e.g. PD-1 with CTLA-4). However, the effectiveness of the treatment response often accompanied by contraindications of autoimmunity.
Allocation of our efforts towards computational modeling of communication networks specific to TLR signaling affords a more intimate study of innate immunity, and the corresponding responses generated, as they pertain to the Tumor Microenvironment. Targeting the mechanisms surrounding TLR signal-transduction affords prospective opportunity for data analytics across various critical elements of colorectal cancer patient data profiles.
This research applies basic computational methodology in the biological context-appropriate manner to further our understanding of network signaling in colorectal cancer. Through the use of the computational methods of (a.) Pearson correlation analysis; (b.) network modeling; and (c.) natural language processing, the objective of this research is to identify signal transduction pathways as therapeutic opportunities to accentuate immunotherapy and chemotherapy protocols in colorectal cancer.
Data analytics of TCGA RNAseq expression values were used in the quantitative assessments of immunological function for each patient within the total patient cohort. These abundance scores were then combined with the corresponding the RNAseq profile for statistical analysis. Pearson correlation analysis was performed for each immune-cell-type (e.g., Neutrophils, Natural Killer Cells, Fibroblasts, etc.) against the curated gene panel selected specifically for associations with Toll-Like-Receptor signal-transduction-pathway communication.
Framing the physiological implications of colorectal cancer in the context of these molecular interactions yields an advantageous perspective to investigate the clinical applications of computational network analysis by using text mining methods to supplement the infrastructure of our network analysis. Our efforts here seek to present computational analysis supporting the claim of additional targeting mechanisms to improve effectiveness in current immunotherapies.