Microbes play important roles in human health and disease. Immunocompromised cancer patients are more vulnerable to getting microbial infections. Regions of hypoxia and acidic tumor microenvironment shape the microbial community diversity and abundance. Each cancer has its own microbiome, making cancer-specific sets of microbiomes. High-throughput profiling technologies provide a culture-free approach for microbial profiling in tumor samples. Microbial compositional data was extracted and examined from the TCGA unmapped transcriptome data. Biclustering, correlation, and statistical analyses were performed to determine the seven patient-microbe interaction patterns. These two-dimensional patterns consist of a group of microbial species that show significant over-representation over the 7 pan-cancer subtypes (S1-S7), respectively. Approximately 60% of the untreated cancer patients have experienced tissue microbial composition and functional changes between subtypes and normal controls. Among these changes, subtype S5 had loss of microbial diversity as well as impaired immune functions. S1, S2, and S3 had been enriched with microbial signatures derived from the Gammaproteobacteria, Actinobacteria and Betaproteobacteria, respectively. Colorectal cancer (CRC) was largely composed of two subtypes, namely S4 and S6, driven by different microbial profiles. S4 patients had increased microbial load, and were enriched with CRC-related oncogenic pathways. S6 CRC together with other cancer patients, making up almost 40% of all cases were classified into the S6 subtype, which not only resembled the normal control's microbiota but also retained their original "normal-like" functions. Lastly, the S7 was a rare and understudied subtype. Our study investigated the pan-cancer heterogeneity at the microbial level. The identified seven pan-cancer subtypes with 424 subtype-specific microbial signatures will help us find new therapeutic targets and better treatment strategies for cancer patients.
Keywords: Biclustering; Cancer microbiome; Heterogeneity; Microbial signature; Patient-microbe interaction.
© 2022 The Authors.