Gut microbiome, big data and machine learning to promote precision medicine for cancer

Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.

This is the first prospective study of the effects of human gut microbiota and metabolites on immune checkpoint inhibitor (ICT) response in metastatic melanoma patients. Whereas many melanoma patients exhibit profound response to ICT, there are fewer options for patients failing ICT—particularly with BRAF-wild-type disease. In preclinical studies, specific gut microbiota promotes regression of melanoma in mice. We therefore conducted a study of the effects of pretreatment gut microbiota and metabolites on ICT Response Evaluation Criteria in Solid Tumors response in 39 metastatic melanoma patients treated with ipilimumab, nivolumab, ipilimumab plus nivolumab (IN), or pembrolizumab (P). IN yielded 67% responses and 8% stable disease; P achieved 23% responses and 23% stable disease. ICT responders for all types of therapies were enriched for Bacteroides caccae. Among IN responders, the gut microbiome was enriched for Faecalibacterium prausnitzii, Bacteroides thetaiotamicron, and Holdemania filiformis. Among P responders, the microbiome was enriched for Dorea formicogenerans. Unbiased shotgun metabolomics revealed high levels of anacardic acid in ICT responders. Based on these pilot studies, both additional confirmatory clinical studies and preclinical testing of these bacterial species and metabolites are warranted to confirm their ICT enhancing activity.