Endometriosis is common and poses significant morbidity of lasting impact to young, pre-menopausal women, while ovarian cancer is a lethal gynecologic condition. Both conditions need better treatment. The human omentum is an apron of adipose tissue in the abdominopelvic cavity, the same space in which endometriosis and ovarian cancer manifest. We aim to determine molecular cues emitted by the omentum that aid the trans-coelomic spread of endometriosis and ovarian cancer in the abdomen–pelvic/peritoneal space. Endometriosis and ovarian cancer patients were prospectively recruited. Primary cell cultures of surgically-resected omentum, endometriosis and ovarian cancer were generated, and conditioned media (CM) from the omentum was derived. They were used for in vitro assays to evaluate the effect of the omentum on cell migration, angiogenesis and proliferation in endometriosis and ovarian cancer. Omental CM promoted cell migration in primary cultures of endometriosis and ovarian cancer. Omental CM contained high levels of HGF, SDF-1a, MCP-1, VEGF-A, IL-6 and IL-8. The observed cell migration was blocked by c-MET inhibition, suggesting that HGF/c-MET signaling mediates cell migration in endometriosis and ovarian cancer. Furthermore, PTTG1 was consistently upregulated in the migrated cells in both endometriosis and ovarian cancer. The omentum provides a favorable environment for trans-coelomic spread of endometriosis and ovarian cancer. HGF, c-MET and PTTG1 are potential therapeutic targets for inhibiting the abdomen–pelvic/peritoneal spread of endometriosis and ovarian cancer.

Background: Background breast features are frequently noted in pathology reports alongside invasive breast cancer but rarely factor into prognosis or treatment decisions. Their relationship to tumor characteristics and patient outcomes remains incompletely characterized. Methods: We conducted a retrospective cohort study of 7603 patients with Stage I–III invasive breast cancer (diagnosed 1991–2022, age < 80 years) from the Joint Breast Cancer Registry in Singapore. Natural language processing (NLP) was applied to 9754 free-text pathology reports to extract co-existing background breast features, with accuracy validated by dual-reviewer assessment of 200 reports. Because background features are most reliably assessed on excision specimens, the primary analytic cohort comprised 3988 patients with available excision pathology reports. Unsupervised hierarchical clustering grouped extracted features into three categories. Associations with tumor characteristics were assessed with multinomial logistic regression and ten-year overall survival by Cox proportional hazards models (median follow-up 9.6 years; 620 deaths). Results: Here, we show that NLP-based extraction of background breast features from routine pathology reports achieves an accuracy of over 90% across features. Lobular neoplasia and benign proliferative changes are associated with less aggressive tumor characteristics, whereas early neoplastic and papillary lesions are more prevalent in HER2-enriched and luminal B tumor subtypes. Benign proliferative changes are associated with better survival in age- and year-adjusted models (hazard ratio 0.91, 95% CI 0.86–0.97), but this association is attenuated after adjustment for stage and subtype. Conclusions: NLP-enabled extraction of background breast features from pathology text is feasible at scale. These features reflect tumor biology but do not independently add prognostic information beyond established clinical variables.