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Exploring the patient-microbiome interaction patterns for pan-cancer.

ABSTRACT: 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.

SUBMITTER: Zhao L

PROVIDER: S-EPMC9233187 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers.MethodsIn this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM.ResultsPanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect.ConclusionsPanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html .

Project description:The gut microbiome is dynamic and shaped by diet, age, geography, and environment. The disruption of normal gut microbiota (dysbiosis) is closely related to colorectal cancer (CRC) risk and progression. To better identify and characterize CRC-associated dysbiosis, we collected six independent cohorts with matched normal pairs (when available) for comparison and exploration of the microbiota and their interactions with the host. Comparing the microbial community compositions between cancerous and adjacent noncancerous tissues, we found that more microbes were depleted than enriched in tumors. Despite taxonomic variations among cohorts, consistent depletion of normal microbiota (members of Clostridia and Bacteroidia) and significant enrichment of oral-originated pathogens (such as Fusobacterium nucleatum and Parvimonas micra) were observed in CRC compared to normal tissues. Sets of hub and hub-connecting microbes were subsequently identified to infer microbe-microbe interaction networks in CRC. Furthermore, biclustering was used for identifying coherent patterns between patients and microbes. Two patient-microbe interaction patterns, named P0 and P1, can be consistently identified among the investigated six CRC cohorts. Characterization of the microbial community composition of the two patterns revealed that patients in P0 and P1 differed significantly in microbial alpha and beta diversity, and CRC-associated microbiota changes consist of continuous populations of widespread taxa rather than discrete enterotypes. In contrast to the P0, the patients in P1 have reduced microbial alpha diversity compared to the adjacent normal tissues, and P1 possesses more oral-related pathogens than P0 and controls. Collectively, our study investigated the CRC-associated microbiome changes, and identified reproducible microbial signatures across multiple independent cohorts. More importantly, we revealed that the CRC heterogeneity can be partially attributed to the variety and compositional differences of microbes and their interactions to humans.

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Exploring the patient-microbiome interaction patterns for pan-cancer.

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