Colorectal cancer (CRC) is the third leading cause of cancer-related deaths. Disease recurrence is common, and more than fifty percent of patients pass away from metastatic disease. The human colon is an exceptional environment where dietary nutrients, host cells, microbiota and their products create a complex ecosystem. Hosting thousands of prokaryotic species, the gut microbiome is a treasury of metabolites that can influence human physiology. While most microbiota are believed to be commensal or symbiotic (e.g., producing serotonin or SCFAs), some produce harmful metabolites such as trimethylamine N-oxide, associated with cardiovascular disease risk. Microbiome dysbiosis underlies obesity, inflammatory bowel disease and other disorders in humans. However, the role of microbiome in CRC is understudied. We hypothesize that the naturally occurring microbial entities in the colon microenvironment can modulate cancer cell growth through their metabolites and metabolic reprogramming of tumor cells. Evidence supporting this hypothesis is that i) a high density of bacteria in the colon is associated with a higher incidence of CRC, ii) the biofilm-positive mucosal homogenates of CRC patients are carcinogenic in mice, and iii) the specific gut microbial metabolite pattern can exacerbate CRC progression. Past research has shown associations between the abundance of bacteria such as Fusobacterium nucleatum and Bacteroides fragilis or metabolites such as formate with CRC. Identification of causative microbiota and metabolites would allow us to decipher their mechanism of action in CRC development and reveal preventive strategies or drug targets for therapeutic intervention. Under the hypothesis that “microbiome and its metabolites can modulate colon cancer cell growth”, we aim to employ a data-driven multi-omics approach based on machine learning to study the complex interactions between the gut microbiota and their metabolites with colon cancer and identify causative entities. Applying multidimensional proteomics on host cells treated with causative bacteria and/or metabolites that can modulate cancer cell growth, we will identify vulnerabilities and druggable targets in the host.