Detecting microbiome species unique or enriched in 20+ cancer types and building cancer microbiome heterogeneity networks

It is postulated that tumor tissue microbiome is one of the enabling characteristics that either promote or suppress cancer cells and tumors to acquire certain hallmarks (functional traits) of cancers, which highlights their critical importance to carcinogenesis, cancer progression and therapy responses. However, characterizing the tumor microbiomes is extremely challenging because of their low biomass and severe difficulties in controlling laboratory-borne contaminants, which is further aggravated by lack of comprehensively effective computational approaches to identify unique or enriched microbial species associated with cancers. Here we take advantages of two recent computational advances, one by Poore et al (2020, Nature ) that computationally generated the microbiome datasets of 33 cancer types [of 10481 patients, including primary tumor (PT), solid normal tissue (NT), and blood samples] from whole-genome and whole-transcriptome data deposited in “The Cancer Genome Atlas” (TCGA), another termed “specificity diversity framework” (SDF) developed recently by Ma (2023). By reanalyzing Poore’s datasets with the SDF framework, further augmented with complex network analysis, we produced the following catalogues of microbial species (archaea, bacteria and viruses) with statistical rigor including unique species (USs) and enriched species (ESs) in PT, NT, or blood tissues. We further reconstructed species specificity network (SSN) and cancer microbiome heterogeneity network (CHN) to identify core/periphery network structures, from which we gain insights on the codependency of microbial species distribution on landscape of cancer types, which seems to suggest that the codependency appears to be universal across all cancer types.