To evaluate how the introduction of robotic-assisted surgery affects treatment-related morbidity and cost of endometrial cancer (EC) staging.

Assessing lymph node metastasis is crucial in determining the optimal therapeutic approach for endometrial cancer (EC). Considering the impact of lymphadenectomy, there is an urgent need for a cost-effective and easily applicable method to evaluate the risk of lymph node metastasis in cases of sentinel lymph node (SLN) biopsy failure. This retrospective monocentric study enrolled EC patients, who underwent surgical staging with nodal assessment. Data concerning demographic, clinicopathological, ultrasound, and surgical characteristics were collected from medical records. Ultrasound examinations were conducted in accordance with the IETA statement. We identified 425 patients, and, after applying exclusion criteria, the analysis included 313 women. Parameters incorporated into the nomogram were selected via univariate and multivariable analyses, including platelet count, myometrial infiltration, minimal tumor-free margin, and CA 125. The nomogram exhibited good accuracy in predicting lymph node involvement, with an AUC of 0.88. Using a cutoff of 10% likelihood of nodal involvement, the nomogram displayed a low false-negative rate of 0.04 (95% CI 0.00-0.19) in the training set. The adaptability of this straightforward model renders it suitable for implementation across diverse clinical settings, aiding gynecological oncologists in preoperative patient evaluations and facilitating the design of personalized treatments. However, external validation is mandatory for confirming diagnostic accuracy.

To assess the incidence, treatment, and outcomes in patients with invasive vulvar extramammary Paget's disease (EMPD) in a national cohort of patients.

To examine the effect of robotic-assisted surgery implementation for treatment of endometrial cancer in the United States on 30-day clinical outcomes and costs.

Recent evidence suggests an association between endometrial cancer and the understudied bacterial species Porphyromonas somerae. This association was demonstrated in previous work that indicated a significantly enriched abundance of P. somerae in the uterine microbiome of endometrial cancer patients. Given the known associations of the Porphyromonas genus and oral cancer, we hypothesized that P. somerae may play a similar pathogenic role in endometrial cancer via intracellular activity. Before testing our hypothesis, we first characterized P. somerae biology, as current background data is limited. These novel characterizations include growth curves in liquid medium and susceptibility tests to antibiotics. We tested our hypothesis by examining growth changes in response to 17β-estradiol, a known risk factor for endometrial cancer, followed by metabolomic profiling in the presence and absence of 17β-estradiol. We found that P. somerae exhibits increased growth in the presence of 17β-estradiol of various concentrations. However, we did not find significant changes in metabolite levels in response to 17β-estradiol. To study direct host-microbe interactions, we used in vitro invasion assays under hypoxic conditions and found evidence for intracellular invasion of P. somerae in endometrial adenocarcinoma cells. We also examined these interactions in the presence of 17β-estradiol but did not observe changes in invasion frequency. Invasion was shown using three lines of evidence including visualization via differential staining and brightfield microscopy, increased frequency of bacterial recovery after co-culturing, and in silico methods to detail relevant genomic and transcriptomic components. These results underscore potential intracellular phenotypes of P. somerae within the uterine microbiome. Furthermore, these results raise new questions pertaining to the role of P. somerae in the progression of endometrial cancer.

Endometrial cancer studies have led to a number of well-defined but mechanistically unconnected genetic and environmental risk factors. One of the emerging modulators between environmental triggers and genetic expression is the microbiome. We set out to inquire about the composition of the uterine microbiome and its putative role in endometrial cancer.

It is unclear whether the transcriptional subtypes of high grade serous ovarian cancer (HGSOC) apply to high grade clear cell (HGCCOC) or high grade endometrioid ovarian cancer (HGEOC). We aim to delineate transcriptional profiles of HGCCOCs and HGEOCs.

Most pelvic high-grade serous (HGS) carcinomas have been proposed to arise from tubal primaries that progress rapidly to advanced disease. However, the temporal sequence of ovarian and peritoneal metastases is not well characterized.

Transforming growth factor beta (TGF-β) signaling has pleiotropic functions regulating cancer initiation, development, and metastasis, and also plays important roles in the interaction between stromal and cancer cells, making the pathway a potential therapeutic target. LY2157299 monohydrate (LY), an inhibitor of TGF-β receptor I (TGFBRI), was examined for its ability to inhibit ovarian cancer (OC) growth both in high-grade serous ovarian cancer (HGSOC) cell lines and xenograft models. Immunohistochemistry, qRT-PCR, and Western blot were performed to study the effect of LY treatment on expression of cancer- and fibroblast-derived genes. Results showed that exposure to TGF-β1 induced phosphorylation of SMAD2 and SMAD3 in all tested OC cell lines, but this induction was suppressed by pretreatment with LY. LY alone inhibited the proliferation, migration, and invasion of HGSOC cells in vitro. TGF-β1-induced fibroblast activation was blocked by LY. LY also delayed tumor growth and suppressed ascites formation in vivo. In addition, independent of tumor inhibition, LY reduces ascites formation in vivo. Using OVCAR8 xenograft specimens we confirmed the inhibitory effect of LY on TGF-β signaling and tumor stromal expression of collagen type XI chain 1 (COL11A1) and versican (VCAN). These observations suggest a role for anti-TGF-β signaling-directed therapy in ovarian cancer.

Variability in representation of microbial communities can be caused by differences in microbial composition or artifacts introduced at sample collection or processing. Alterations in community representation introduced by variations in starting DNA concentrations have not been systematically investigated in stool samples. The goal of this study was to evaluate the effect of the genomic DNA (gDNA) concentration in the resulting 16S rRNA gene library composition and compare its effect to other sample processing variables in homogenized human fecal material. Compared to a gDNA input of 1 ng/μl, inputs of ≤1.6 × 10-3 ng/μl resulted in a marked decrease in the concentration of the 16S rRNA gene amplicon (P < 0.001). Low gDNA concentrations (≤1.6 × 10-3 ng/μl) were also associated with a decrease (P < 0.001) in the number of operational taxonomic units and significant divergence in β-diversity profiles (unweighted UniFrac distance, P < 0.001), as characterized by an overestimation of Proteobacteria and underestimation of Firmicutes. Even a gDNA concentration of 4 × 10-2 ng/μl showed a significant impact on the β-diversity profile (unweighted UniFrac distance, P = 0.03). Overall, the gDNA concentration explained 22.4% to 38.1% of the microbiota variation based on various β-diversity measures (P < 0.001). By comparison, the DNA extraction methods and PCR volumes tested did not significantly affect the microbial composition profile, and the PCR cycling method explained less than 3.7% of the microbiota variation (weighted UniFrac distance, P = 0.03). The 16S rRNA gene yield and the microbial community representation of human homogenized stool samples are significantly altered by gDNA template concentrations of ≤1.6 × 10-3 ng/μl. In addition, data from studies with a gDNA input of ≤4 × 10-2 ng/μl should be interpreted with caution. IMPORTANCE The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.

During the past decade, the age-adjusted mortality rate for endometrial cancer (EC) increased 1.9% annually with TP53 mutant (TP53-mu) EC disproportionally represented in advanced disease and deaths. Therefore, we aimed to assess pivotal molecular parameters that differentiate clinical outcomes in high- and low-risk EC. Using the Cancer Genome Atlas, we analyzed EC specimens with available DNA sequences and quantitative gene-specific RNA expression data. After polymerase ɛ (POLE)-mutant specimens were excluded, differential gene-specific mutations and mRNA expressions were annotated and integrated. Consequent to TP53-mu failure to induce p21, derepression of multiple oncogenes harboring promoter p21 repressive sites was observed, including CCNA2 and FOXM1 (P < .001 compared with TP53 wild type [TP53-wt]). TP53-wt EC with high CCNA2 expression (CCNA2-H) had a targeted transcriptomic profile similar to that of TP53-mu EC, suggesting CCNA2 is a seminal determinant for both TP53-wt and TP53-mu EC. CCNA2 enhances E2F1 function, upregulating FOXM1 and CIP2A, as observed in TP53-mu and CCNA2-H TP53-wt EC (P < .001). CIP2A inhibits protein phosphatase 2A, leading to AKT inactivation of GSK3β and restricted oncoprotein degradation; PPP2R1A and FBXW7 mutations yield similar results. Upregulation of FOXM1 and failed degradation of FOXM1 is evidenced by marked upregulation of multiple homologous recombination genes (P < .001). Integrating these molecular aberrations generated a molecular biomarker panel with significant prognostic discrimination (P = 5.8×10-7); adjusting for age, histology, grade, myometrial invasion, TP53 status, and stage, only CCNA2-H/E2F1-H (P = .0003), FBXW7-mu/PPP2R1A-mu (P = .0002), and stage (P = .017) were significant. The generated prognostic molecular classification system identifies dissimilar signaling aberrations potentially amenable to targetable therapeutic options.

To examine the trends and survival for women with early-stage epithelial ovarian cancer who underwent adequate lymphadenectomy during surgical treatment.

Mesenchymal (MES) subtype of high-grade serous ovarian cancer (HGSOC) is associated with worse outcomes including survival and resectability compared with other molecular subtypes. Molecular subtypes have historically been derived from 'tumor', consisting of both cancer and stromal cells. We sought to determine the origins of multiple MES subtype gene signatures in HGSOC.

MISIIR is a potential target for ovarian cancer (OC) therapy due to its tissue-specific pattern of expression. 3C23K is a novel therapeutic monoclonal anti-MISIIR antibody designed to recruit effector cells and promote cell death through ADCC (antibody dependent cell-mediated cytotoxicity). Our objective was to determine the tolerability and efficacy of 3C23K in OC patient-derived xenografts (PDX) and to identify factors affecting efficacy. Quantitative RT-PCR, immunohistochemistry (IHC), and flow cytometry were used to categorize MISIIR expression in established PDX models derived from primary OC patients. We selected two high expressing models and two low expressing models for in vivo testing. One xenograft model using an MISIIR over-expressing SKOV3ip cell line (Z3) was a positive control. The primary endpoint was change in tumor size. The secondary endpoint was final tumor mass. We observed no statistically significant differences between control and treated animals. The lack of response could be secondary to a number of variables including the lack of known biomarkers of response, the low membrane expression of MISIIR, and a limited ability of 3C23K to induce ADCC in PDX models. Further study is needed to determine the magnitude of ovarian cancer response to 3C23K and also if there is a threshold surface expression to predict response.

This study is designed to identify genes and pathways that could promote metastasis to the bowel in high-grade serous ovarian cancer (OC) and evaluate their associations with clinical outcomes.

Ovarian cancer (OC) is the second most common gynecological malignancy and the fifth leading cause of death due to cancer in women in the United States mainly due to the late-stage diagnosis of this cancer. It is, therefore, critical to identify potential indicators to aid in early detection and diagnosis of this disease. We investigated the microbiome associated with OC and its potential role in detection, progression as well as prognosis of the disease. We identified a distinct OC microbiome with general enrichment of several microbial taxa, including Dialister, Corynebacterium, Prevotella, and Peptoniphilus in the OC cohort in all body sites excluding stool and omentum which were not sampled from the benign cohort. These taxa were, however, depleted in the advanced-stage and high-grade OC patients compared to early-stage and low-grade OC patients suggestive of decrease accumulation in advanced disease and could serve as potential indicators for early detection of OC. Similarly, we also observed the accumulation of these mainly pathogenic taxa in OC patients with adverse treatment outcomes compared to those without events and could also serve as potential indicators for predicting patients' responses to treatment. These findings provide important insights into the potential use of the microbiome as indicators in (1) early detection of and screening for OC and (2) predicting patients' response to treatment. Given the limited number of patients enrolled in the study, these results would need to be further investigated and confirmed in a larger study.