Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.07.21.214148v1?rss=1 Authors: Hermida, L. C., Gertz, E. M., Ruppin, E. Abstract: Poore et al.1 recently published a computational approach for deriving microbial abundances from human tumor whole genome (WGS) and transcriptome sequencing (RNA-seq) data by leveraging tools commonly used to remove microbial contamination from such data. They have shown that microbial abundances could be used to predict various tumor-related phenotypes across The Cancer Genome Atlas (TCGA) cohort, including distinguishing tumor from adjacent normal tissue samples, cancer type, and tumor stage. Here, we investigated whether the microbial abundances inferred by Poore et al. in the TCGA cohort, to the best of our knowledge the most comprehensive dataset of its kind, are predictive of patient survival and drug response, two fundamentally important and clinically relevant phenotypes. We find that in four cancer types, adrenocortical carcinoma, cervical squamous cell carcinoma, brain lower grade glioma, and subcutaneous skin melanoma, microbial features are better predictors of survival than clinical covariates alone. In addition, we find seven cancer-drug pairs where microbiome features are more predictive of patients response than clinical covariates alone. These seven pairs include chemotherapy treatments for bladder urothelial carcinoma, docetaxel treatment for breast invasive carcinoma and sarcoma, and several treatments for stomach adenocarcinoma. Copy rights belong to original authors. Visit the link for more info