Intrinsic tumor factors and extrinsic environmental and social exposures contribute to endometrial cancer recurrence patterns.

Purpose: In a previous study, we trained, validated and tested models of endometrial cancer (EC) recurrence integrating clinical, genomic and pathological data from the Oncology Research Information Exchange Network (ORIEN). Preliminary studies also have demonstrated that bacterial communities may influence the risk of EC recurrence by altering the local environment within the upper female genital tract. The objective of this study was to evaluate whether extrinsic and environmental factors, including tumor-associated bacterial communities, tumor immune contexture and air pollution alongside clinical, pathologic and genomic features are associated with EC recurrence across clinically relevant risk groups. Patients and Methods: We performed a retrospective, multi-institution, case-control study with data from the ORIEN network EC dataset. Data was stratified into low-risk, FIGO grade 1 and 2, stage I (N = 329), high-risk, or FIGO grade 3 or stages II-IV (N = 324), and non-endometrioid histology (N = 239) groups. RNA and DNA were extracted from tumor specimens and processed to obtain the necessary genomic/metagenomic data. Genus level microbiome data were extracted and curated) from RNA sequencing using Results: The resulting models, analyzed with topic modelling, demonstrated the complexity of factors involved in recurrence of disease for EC. The components of the resulting topic models, and specifically the microbiome, changed when environmental factors, like air pollutants, were introduced in the model. In the low-risk EC group, microbes that were quite abundant in models before introducing environmental factors, were scarcely seen afterwards, like genera Conclusion: Both extrinsic factors (tumor-associated bacterial communities, tumor immune contexture and air pollution) and intrinsic factors predict EC recurrence. The complexity of tumor and host factors influencing cancer relapses underscore the need for more individualized prediction models of disease outcomes.