Identifying Actionable Microbial Biomarkers for Cancer Therapy Using Big Data Approaches
Nearly 100 trillion of microbes cohabit the human body. They modulate digestive, nerve and immune systems to help maintain health. However, their dysbiosis were also linked to many diseases, e.g., obesity, autism, allergies, and cancers. Recently, gut microbiota was found predictive of immune checkpoint inhibitor’s efficacy, which achieved durable response in many advanced cancers, however, with a low response rate. It is thus significant if one can modulate the gut flora to improve ICI response, which was successful in pre-clinical models. To truly realize this potential, we are working on integrating big multiomics data sources and developing machine learning approaches for discovering and validating actionable microbial biomarkers for cancer therapies. Our approaches combine state-of-art genome technologies and data sciences and include: large-scale data mining of microbial and immune features in public data space; building a cancer-microbiome data hub; and developing machine learning models to identify markers predictive of immune infiltration and patient outcomes, among others. We expect our effort to provide a comprehensive set of cross-platform and open-source bioinformatics and statistical tools for cancer metagenomics and cancer genomics data analysis and to initiate a new paradigm for future cancer genomics by the integrative analysis of human and microbiome data.