Background
Until recently, the properties of microbiome and mycobiome in humans and its relevance to disease have largely been unexplored. While the interest of microbiome and malignancy over the past few years have burgeoned with advent of new technologies, no research describing the composition of mycobiome in bladder cancer has been done. Deciphering of the metagenome and its aggregate genetic information can be used to understand the functional properties and relationships between the bacteria, fungi, and cancer.
Conclusions
Our study is the first to characterize the enteric mycobiome in patients with bladder cancer and describe complex ecological network alterations, indicating complex bacteria-fungi interactions, particularly highlighted among patients with complete neoadjuvant chemotherapy response. Patient summary: Our study has demonstrated that the composition of stool mycobiome (fungal inhabitants of the gastrointestinal tract) in patients with bladder cancer is different from that in noncancer individuals. Furthermore, when evaluating how patients respond to chemotherapy given prior to their surgery, our study noted significant differences between patients who responded and those who did not.
Objective
The aim of this project is to characterize the compositional range of the normal versus bladder cancer mycobiome of the gut. Design setting and participants: An internal transcribed spacer (ITS) survey of 52 fecal samples was performed to evaluate the gut mycobiome differences between noncancer controls and bladder cancer patients. Outcome measurements and statistical analysis: Our study evaluated the differences in mycobiome among patients with bladder cancer, versus matched controls. Our secondary analysis evaluated compositional differences in the gut as a function of response status with neoadjuvant chemotherapy. Data demultiplexing and classification were performed using the QIIME v.1.1.1.1 platform. The Ion Torrent-generated fungal ITS sequence data were processed using QIIME (v.1.9.1), and the reads were demultiplexed, quality filtered, and clustered into operation taxonomic units using default parameters. Alpha and beta diversity were computed and plotted in Phyloseq, principal coordinate analysis was performed on Bray-Curtis dissimilarity indices, and a one-way permutational multivariate analysis of variance was used to test for significant differences between cohorts. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was applied to infer functional categories associated with taxonomic composition.