Particles and Prejudice: Nanomedicine Approaches to Reducing Health Disparities in Endometrial Cancer.

Endometrial cancer is the most common gynecological malignancy worldwide and unfortunately has a much higher mortality rate in Black women compared with White women. Many potential factors contribute to these mortality rates, including the underlying effects of systemic and interpersonal racism. Furthermore, other trends in medicine have potential links to these rates including participation in clinical trials, hormone therapy, and pre-existing health conditions. Addressing the high incidence and disparate mortality rates in endometrial cancer requires novel methods, such as nanoparticle-based therapeutics. These therapeutics have been growing in increasing prevalence in pre-clinical development and have far-reaching implications in cancer therapy. The rigor of pre-clinical studies is enhanced by the likeness of the model to the human body. In systems for 3D cell culture, for example, the extracellular matrix mimics the tumor more closely. The increasing emphasis on precision medicine can be applied to cancer using nanoparticle-based methods and applied to pre-clinical models by using patient-derived model data. This review highlights the intersections of nanomedicine, precision medicine, and racial disparities within endometrial cancer and provides insights into reducing health disparities using recent scientific advances on the nanoscale.

Association between plasma leptin and cesarean section after induction of labor: a case control study.

BACKGROUND: Obesity in pregnancy is common, with more than 50% of pregnant women being overweight or obese. Obesity has been identified as an independent predictor of dysfunctional labor and is associated with increased risk of failed induction of labor resulting in cesarean section. Leptin, an adipokine, is secreted from adipose tissue under the control of the obesity gene. Concentrations of leptin increase with increasing percent body fat due to elevated leptin production from the adipose tissue of obese individuals. Interestingly, the placenta is also a major source of leptin production during pregnancy. Leptin has regulatory effects on neuronal tissue, vascular smooth muscle, and nonvascular smooth muscle systems. It has also been demonstrated that leptin has an inhibitory effect on myometrial contractility with both intensity and frequency of contractions decreased. These findings suggest that leptin may play an important role in dysfunctional labor and be associated with the outcome of induction of labor at term. Our aim is to determine whether maternal plasma leptin concentration is indicative of the outcome of induction of labor at term. We hypothesize that elevated maternal plasma leptin levels are associated with a failed term induction of labor resulting in a cesarean delivery. METHODS: In this case-control study, leptin was measured in 3rd trimester plasma samples. To analyze labor outcomes, 174 women were selected based on having undergone an induction of labor (IOL), (115 women with successful IOL and 59 women with a failed IOL). Plasma samples and clinical information were obtained from the UI Maternal Fetal Tissue Bank (IRB# 200910784). Maternal plasma leptin and total protein concentrations were measured using commercially available assays. Bivariate analyses and logistic regression models were constructed using regression identified clinically significant confounding variables. All variables were tested at significance level of 0.05. RESULTS: Women with failed IOL had higher maternal plasma leptin values (0.5 vs 0.3 pg, P = 0.01). These women were more likely to have obesity (mean BMI 32 vs 27 kg/m2, P = 0.0002) as well as require multiple induction methods (93% vs 73%, p = 0.008). Logistic regression showed Bishop score (OR 1.5, p < 0.001), BMI (OR 0.92, P < 0.001), preeclampsia (OR 0.12, P = 0.010), use of multiple methods of induction (OR 0.22, P = 0.008) and leptin (OR 0.42, P = 0.017) were significantly associated with IOL outcome. Specifically, after controlling for BMI, Bishop Score, and preeclampsia, leptin was still predictive of a failed IOL with an odds ratio of 0.47 (P = 0.046). Finally, using leptin as a predictor for fetal outcomes, leptin was also associated with of fetal intolerance of labor, with an odds ratio of 2.3 (P = 0.027). This association remained but failed to meet statistical significance when controlling for successful (IOL) (OR 1.5, P = 0.50). CONCLUSIONS: Maternal plasma leptin may be a useful tool for determining which women are likely to have a failed induction of labor and for counseling women about undertaking an induction of labor versus proceeding with cesarean delivery.

Investigating the effect of optimal cytoreduction in the context of platinum sensitivity in high-grade serous ovarian cancer.

INTRODUCTION: The survival benefits of surgical cytoreduction in ovarian cancer are well-established. However, the surgical outcome has never been assessed while controlling for the efficacy of chemotherapy. This leaves the possibility that cytoreduction may not be beneficial for patients whose cancer does not respond well to adjuvant treatment. We sought to answer whether surgical cytoreduction independently improves overall survival when controlling for chemotherapy outcome. MATERIAL AND METHODS: We performed a retrospective case-control study using our institution's ovarian cancer database to evaluate the effect of optimal cytoreduction on advanced stage, high-grade serous ovarian cancer. Patients' characteristics were compared using both univariate and multivariate regression modeling to assess for independent predictors of overall survival. RESULTS: A total of 470 patients were assessed for inclusion; 234 responders to chemotherapy and 98 nonresponders. Significant survival characteristics were identified and included in the multivariate analysis. Independent predictors of survival in the multivariate analysis were age, responder status, optimal cytoreduction, neoadjuvant chemotherapy, and number of chemotherapy cycles. Kaplan-Meier survival curves showed improved survival for both patients who responded to chemotherapy and for those undergoing optimal cytoreduction (p < 0.001). We also demonstrated improved survival for patients receiving optimal cytoreduction among both nonresponders and responders (p < 0.001). CONCLUSIONS: Our analysis shows that patients who undergo optimal cytoreduction have an overall survival benefit regardless of their response to chemotherapy. Therefore, cytoreduction should be considered in all patients, even in those with advanced disease, if an optimal result can be achieved. This study was underpowered to assess patients who received neoadjuvant chemotherapy as a separate subgroup, but the order of treatment was controlled for in the overall analysis.

Integration of Genomic and Clinical Retrospective Data to Predict Endometrioid Endometrial Cancer Recurrence.

Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. However, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case−control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 55 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine-learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone.

Using Genomic Variation to Distinguish Ovarian High-Grade Serous Carcinoma from Benign Fallopian Tubes.

The preoperative diagnosis of pelvic masses has been elusive to date. Methods for characterization such as CA-125 have had limited specificity. We hypothesize that genomic variation can be used to create prediction models which accurately distinguish high grade serous ovarian cancer (HGSC) from benign tissue. METHODS: In this retrospective, pilot study, we extracted DNA and RNA from HGSC specimens and from benign fallopian tubes. Then, we performed whole exome sequencing and RNA sequencing, and identified single nucleotide variants (SNV), copy number variants (CNV) and structural variants (SV). We used these variants to create prediction models to distinguish cancer from benign tissue. The models were then validated in independent datasets and with a machine learning platform. RESULTS: The prediction model with SNV had an AUC of 1.00 (95% CI 1.00-1.00). The models with CNV and SV had AUC of 0.87 and 0.73, respectively. Validated models also had excellent performances. CONCLUSIONS: Genomic variation of HGSC can be used to create prediction models which accurately discriminate cancer from benign tissue. Further refining of these models (early-stage samples, other tumor types) has the potential to lead to detection of ovarian cancer in blood with cell free DNA, even in early stage.

Mutated p53 portends improvement in outcomes when bevacizumab is combined with chemotherapy in advanced/recurrent endometrial cancer: An NRG Oncology study.

BACKGROUND: Successfully combining targeted agents with chemotherapy is an important future goal for cancer therapy. However, an improvement in patient outcomes requires an enhanced understanding of the tumor biomarkers that predict for drug sensitivity. NRG Oncology/Gynecologic Oncology Group (GOG) Study GOG-86P was one of the first attempts to combine targeted agents (bevacizumab or temsirolimus) with chemotherapy in patients with advanced endometrial cancer. Herein we performed exploratory analyses to examine the relationship between mutations in TP53, the most commonly mutated gene in cancer, with outcomes on GOG-86P. METHODS: TP53 mutational status was determined and correlated with progression-free survival (PFS) and overall survival (OS) on GOG-86P. RESULTS: Mutations in TP53 were associated with improved PFS and OS for patients that received bevacizumab as compared to temsirolimus (PFS: HR 0.48, 95% CI 0.31, 0.75; OS: HR: 0.61, 95% CI 0.38, 0.98). By contrast, there was no statistically significant difference in PFS or OS between arms for cases with WT TP53. CONCLUSIONS: This exploratory study suggests that combining chemotherapy with bevacizumab, but not temsirolimus, may enhance PFS and OS for patients whose tumors harbor mutant p53. These data set the stage for larger clinical studies evaluating the potential of TP53 mutational status as a biomarker to guide choice of treatment for endometrial cancer patients. Clintrials.gov: NCT00977574.

Identification of Novel Fusion Transcripts in High Grade Serous Ovarian Cancer.

Fusion genes are structural chromosomal rearrangements resulting in the exchange of DNA sequences between genes. This results in the formation of a new combined gene. They have been implicated in carcinogenesis in a number of different cancers, though they have been understudied in high grade serous ovarian cancer. This study used high throughput tools to compare the transcriptome of high grade serous ovarian cancer and normal fallopian tubes in the interest of identifying unique fusion transcripts within each group. Indeed, we found that there were significantly more fusion transcripts in the cancer samples relative to the normal fallopian tubes. Following this, the role of fusion transcripts in chemo-response and overall survival was investigated. This led to the identification of fusion transcripts significantly associated with overall survival. Validation was performed with different analytical platforms and different algorithms to find fusion transcripts.

Identification of Novel lncRNAs in Ovarian Cancer and Their Impact on Overall Survival.

Long non-coding RNA's (lncRNA) are RNA sequences that do not encode proteins and are greater than 200 nucleotides in length. They regulate complex cellular mechanisms and have been associated with prognosis in various types of cancer. We aimed to identify lncRNA sequences that are associated with high grade serous ovarian cancer (HGSC) and assess their impact on overall survival. RNA was extracted from 112 HGSC patients and 12 normal fallopian tube samples from our Biobank tissue repository. RNA was sequenced and the Ultrafast and Comprehensive lncRNA detection and quantification pipeline (UClncR) was used for the identification of lncRNA sequences. Univariate logistic and multivariate lasso regression analyses identified lncRNA that was associated with HGSC. Univariate and multivariate Cox proportional hazard ratios were used to evaluate independent predictors of survival. 1943 of 16,325 investigated lncRNA's were differentially expressed in HGSC as compared to controls (p < 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA's associated with survival in HGSC. Three out of these four were found to be independently significant after accounting for all clinical covariates. Lastly, seven lncRNAs were independently associated with initial response to chemotherapy; four portended a worse response, while three were associated with improved response. More research is needed, but there is potential for these lncRNAs to be used as biomarkers of HGSC or predictors of treatment outcome in the future.

Interval debulking surgery is not worth the wait: a National Cancer Database study comparing primary cytoreductive surgery versus neoadjuvant chemotherapy.

OBJECTIVE: In previous studies, neoadjuvant chemotherapy followed by interval debulking surgery was not inferior to primary cytoreductive surgery as initial treatment for advanced epithelial ovarian cancer. Our study aimed to compare surgical and survival outcomes between the two treatments in a large national database. METHODS: Data were extracted from the National Cancer Database from January 2004 to December 2015. Patients with FIGO (International Federation of Gynecologists and Obstetricians) stage III-IV epithelial ovarian cancer and known sequence of treatment were included: primary cytoreductive (surgery=26 717 and neoadjuvant chemotherapy=9885). Tubal and primary peritoneal cancer diagnostic codes were not included. Residual disease after treatment was defined based on recorded data: R0 defined as microscopic or no residual disease; R1 defined as macroscopic residual disease. Multivariate Cox proportional HR was used for survival analysis. Multivariate logistic regression analysis was utilized to compare mortality between groups. Outcomes were adjusted for significant covariates. Validation was performed using propensity score matching of significant covariates. RESULTS: A total of 36 602 patients were included in the analysis. Patients who underwent primary cytoreductive surgery had better survival than those treated with neoadjuvant chemotherapy followed by interval surgery, after adjusting for age, co-morbidities, stage, and residual disease (p<0.001). Primary cytoreductive surgery patients with R0 disease had best median survival (62.6 months, 95% CI 60.5-64.5). Neoadjuvant chemotherapy patients with R1 disease had worst median survival (29.5 months, 95% CI 28.4-31.9). There were small survival differences between primary cytoreductive surgery with R1 (38.9 months) and neoadjuvant chemotherapy with R0 (41.8 months) (HR 0.93, 95% CI 0.87 to 1.0), after adjusting for age, co-morbidities, grade, histology, and stage. Neoadjuvant chemotherapy had 3.5 times higher 30-day mortality after surgery than primary cytoreductive surgery (95% CI 2.46 to 5.64). The 90-day mortality was higher for neoadjuvant chemotherapy in multivariate analysis (HR 1.31, 95% CI 1.06 to 1.61) but similar to primary cytoreductive surgery after excluding high-risk patients. CONCLUSIONS: Most patients with advanced epithelial ovarian cancer may benefit from primary cytoreductive surgery. Patients treated with neoadjuvant chemotherapy should be those with co-morbidities unfit for surgery.

Integration of Clinical and Molecular Features into Prediction Models for Outcomes in Endometrial Cancer.

Endometrial cancer recurrence carries a poor prognosis. The rising incidence of endometrial cancer calls for improvements in treatment of advanced and recurrent diseases. Efforts have been made to molecularly characterize endometrial cancer with the goal of improving therapies. The study presented here describes the utilization of molecular features of endometrial cancer tumors that are likely to recur, along with clinical characteristics utilized together to predict recurrence. This work further studies recurrent endometrial cancers to group them into "clusters" based on the tumor's molecular makeups with the ultimate aim to focus therapy on the molecular pathways potentially leading to recurrence.

Prediction of Epithelial Ovarian Cancer Outcomes With Integration of Genomic Data.

Some of the patients with epithelial ovarian cancer will not respond to initial therapy. These patients have a poor prognosis. Our aim was to identify patients with a worse prognosis by integrating clinical, pathologic, and genomic data. Using publicly available genomic data and integrating it with clinical data, we significantly improved the prediction of patients with worse surgical outcomes and those who do not respond to initial chemotherapy. We further improved these models with more precise data collection and better understanding of the genetic background of the studied population. Better prediction will lead to better patient classification and opportunities for individualized treatment.

Characterization of a TP53 Somatic Variant of Unknown Function From an Ovarian Cancer Patient Using Organoid Culture and Computational Modeling.

In our proof-of-concept study of 1 patient with stage IIIC carcinosarcoma of the ovary, we discovered a rare mutation in the tumor suppressor, TP53, that results in the deletion of N131. Immunofluorescence imaging of the organoid culture revealed hyperstaining of p53 protein. Computational modeling suggests this residue is important for maintaining protein conformation. Drug screening identified the combination of a proteasome inhibitor with a histone deacetylase inhibitor as the most effective treatment. These data provide evidence for the successful culture of a patient tumor and analysis of drug response ex vivo.

Population Substructure Has Implications in Validating Next-Generation Cancer Genomics Studies with TCGA.

In the era of large genetic and genomic datasets, it has become crucially important to validate results of individual studies using data from publicly available sources, such as The Cancer Genome Atlas (TCGA). However, how generalizable are results from either an independent or a large public dataset to the remainder of the population? The study presented here aims to answer that question. Utilizing next generation sequencing data from endometrial and ovarian cancer patients from both the University of Iowa and TCGA, genomic admixture of each population was analyzed using STRUCTURE and ADMIXTURE software. In our independent data set, one subpopulation was identified, whereas in TCGA 4⁻6 subpopulations were identified. Data presented here demonstrate how different the genetic substructures of the TCGA and University of Iowa populations are. Validation of genomic studies between two different population samples must be aware of, account for and be corrected for background genetic substructure.

Molecular Characterization of Non-responders to Chemotherapy in Serous Ovarian Cancer.

Nearly one-third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial treatment with platinum-based therapy. Genomic and clinical characterization of these patients may lead to potential alternative therapies. Here, the objective is to classify non-responders into subsets using clinical and molecular features. Using patients from The Cancer Genome Atlas (TCGA) dataset with platinum-resistant or platinum-refractory HGSC, we performed a genome-wide unsupervised cluster analysis that integrated clinical data, gene copy number variations, gene somatic mutations, and DNA promoter methylation. Pathway enrichment analysis was performed for each cluster to identify the targetable processes. Following the unsupervised cluster analysis, three distinct clusters of non-responders emerged. Cluster 1 had overrepresentation of the stage IV disease and suboptimal debulking, under-expression of miRNAs and mRNAs, hypomethylated DNA, "loss of function" TP53 mutations, and the overexpression of genes in the PDGFR pathway. Cluster 2 had low miRNA expression, generalized hypermethylation, MUC17 mutations, and significant activation of the HIF-1 signaling pathway. Cluster 3 had more optimally cytoreduced stage III patients, overexpression of miRNAs, mixed methylation patterns, and "gain of function" TP53 mutations. However, the survival for all clusters was similar. Integration of genomic and clinical data from patients that do not respond to chemotherapy has identified different subgroups or clusters. Pathway analysis further identified the potential alternative therapeutic targets for each cluster.

A Prediction Model for Preoperative Risk Assessment in Endometrial Cancer Utilizing Clinical and Molecular Variables.

The utility of comprehensive surgical staging in patients with low risk disease has been questioned. Thus, a reliable means of determining risk would be quite useful. The aim of our study was to create the best performing prediction model to classify endometrioid endometrial cancer (EEC) patients into low or high risk using a combination of molecular and clinical-pathological variables. We then validated these models with publicly available datasets. Analyses between low and high risk EEC were performed using clinical and pathological data, gene and miRNA expression data, gene copy number variation and somatic mutation data. Variables were selected to be included in the prediction model of risk using cross-validation analysis; prediction models were then constructed using these variables. Model performance was assessed by area under the curve (AUC). Prediction models were validated using appropriate datasets in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A prediction model with only clinical variables performed at 88%. Integrating clinical and molecular data improved prediction performance up to 97%. The best prediction models included clinical, miRNA expression and/or somatic mutation data, and stratified pre-operative risk in EEC patients. Integrating molecular and clinical data improved the performance of prediction models to over 95%, resulting in potentially useful clinical tests.

Elevated vasopressin in pregnant mice induces T-helper subset alterations consistent with human preeclampsia.

The pathogenesis of preeclampsia (PreE), a hypertensive disorder of pregnancy, involves imbalanced T helper (TH) cell populations and resultant changes in pro- and anti-inflammatory cytokine release. Elevated copeptin (an inert biomarker of arginine vasopressin (AVP)), secretion precedes the development of symptoms in PreE in humans, and infusion of AVP proximal to and throughout gestation is sufficient to initiate cardiovascular and renal phenotypes of PreE in wild-type C57BL/6J mice. We hypothesize that AVP infusion in wild-type mice is sufficient to induce the immune changes observed in human PreE. AVP infusion throughout gestation in mice resulted in increased pro-inflammatory interferon γ (IFNg) (TH1) in the maternal plasma. The TH17-associated cytokine interleukin (IL)-17 was elevated in the maternal plasma, amniotic fluid, and placenta following AVP infusion. Conversely, the TH2-associated anti-inflammatory cytokine IL-4 was decreased in the maternal and fetal kidneys from AVP-infused dams, while IL-10 was decreased in the maternal kidney and all fetal tissues. Collectively, these results demonstrate the sufficiency of AVP to induce the immune changes typical of PreE. We investigated if T cells can respond directly to AVP by evaluating the expression of AVP receptors (AVPRs) on mouse and human CD4+ T cells. Mouse and human T cells expressed AVPR1a, AVPR1b, and AVPR2. The expression of AVPR1a was decreased in CD4+ T cells obtained from PreE-affected women. In total, our data are consistent with a potential initiating role for AVP in the immune dysfunction typical of PreE and identifies putative signaling mechanism(s) for future investigation.

Current Landscape and the Potential Role of Hypoxia-Inducible Factors and Selenium in Clear Cell Renal Cell Carcinoma Treatment.

In the last two decades, the discovery of various pathways involved in renal cell carcinoma (RCC) has led to the development of biologically-driven targeted therapies. Hypoxia-inducible factors (HIFs), angiogenic growth factors, von Hippel⁻Lindau (VHL) gene mutations, and oncogenic microRNAs (miRNAs) play essential roles in the pathogenesis and drug resistance of clear cell renal cell carcinoma. These insights have led to the development of vascular endothelial growth factor (VEGF) inhibitors, Mechanistic target of rapamycin (mTOR) inhibitors, and immunotherapeutic agents, which have significantly improved the outcomes of patients with advanced RCC. HIF inhibitors will be a valuable asset in the growing therapeutic armamentarium of RCC. Various histone deacetylase (HDAC) inhibitors, selenium, and agents like PT2385 and PT2977 are being explored in various clinical trials as potential HIF inhibitors, to ameliorate the outcomes of RCC patients. In this article, we will review the current treatment options and highlight the potential role of selenium in the modulation of drug resistance biomarkers expressed in clear cell RCC (ccRCC) tumors.

High stathmin expression is a marker for poor clinical outcome in endometrial cancer: An NRG oncology group/gynecologic oncology group study.

OBJECTIVE: Gynecologic Oncology Group (GOG) 177 demonstrated that addition of paclitaxel to a backbone of adriamycin/cisplatin improves overall survival (OS) and progression-free survival (PFS) for patients with advanced or recurrent endometrial cancer. Using patient specimens from GOG-177, our objective was to identify potential mechanisms underlying the improved clinical response to taxanes. Stathmin (STMN1) is a recognized poor prognostic marker in endometrial cancer that functions as a microtubule depolymerizing protein, allowing cells to transit rapidly through mitosis. Therefore, we hypothesized that one possible mechanism underlying the beneficial effects of paclitaxel could be to counter the impact of stathmin. METHODS: We analyzed the expression of stathmin by immunohistochemistry (IHC) in 69 specimens from patients enrolled on GOG-177. We also determined the correlation between stathmin mRNA expression and clinical outcomes in The Cancer Genome Atlas (TCGA) dataset for endometrial cancer. RESULTS: We first established that stathmin expression was significantly associated with shorter PFS and OS for all analyzed cases in both GOG-177 and TCGA. However, subgroup analysis from GOG-177 revealed that high stathmin correlated with poor PFS and OS particularly in patients who received adriamycin/cisplatin only. In contrast, there was no statistically significant association between stathmin expression and OS or PFS in patients treated with paclitaxel/adriamycin/cisplatin. CONCLUSIONS: Our findings demonstrate that high stathmin expression is a poor prognostic marker in endometrial cancer. Paclitaxel may help to negate the impact of stathmin overexpression when treating high risk endometrial cancer cases.

Dysregulation of miR-181c expression influences recurrence of endometrial endometrioid adenocarcinoma by modulating NOTCH2 expression: An NRG Oncology/Gynecologic Oncology Group study.

OBJECTIVE: Endometrial cancer can be diagnosed early and cured, yet cases that recur portend a very poor prognosis with over 10,000 women succumbing to the disease every year. In this study we addressed the question of how to recognize cases likely to recur early in the course of therapy using dysregulation of tumor microRNAs (miRNAs) as predictors. METHODS: Using the tissue collection from Gynecologic Oncology Group Study-210, we selected and analyzed expression of miRNAs in 54 recurrent and non-recurrent cases. The three most common histologic types, endometrioid adenocarcinoma (EEA), serous adenocarcinoma (ESA) and carcinosarcoma (UCS), were analyzed as three independent sets and their miRNA expression profiles compared. RESULTS: Only one miRNA was statistically different between recurrent and non-recurrent cases, and in only one histologic type: significant down-regulation of miR-181c was observed in EEA recurrence. Using several well-known databases to assess miR-181c targets, one target of particular relevance to cancer, NOTCH2, was well supported. Using The Cancer Genome Atlas and our validation tumor panel from the GOG-210 cohort, we confirmed that NOTCH2 is significantly over-expressed in EEA. In the most relevant endometrial adenocarcinoma cell model, Ishikawa H, altering miR-181c expression produces significant changes in NOTCH2 expression, consistent with direct targeting. CONCLUSIONS: Our findings suggest that increased NOTCH2 via loss of miR-181c is a significant component of EEA recurrence. This presents an opportunity to develop miR-181c and NOTCH2 as markers for early identification of high risk cases and the use of NOTCH inhibitors in the prevention or treatment of recurrent disease.

Placenta-Specific Protein 1 Expression in Human Papillomavirus 16/18-Positive Cervical Cancers Is Associated With Tumor Histology.

OBJECTIVE: Expression of the trophoblast-specific gene placenta-specific protein 1 (PLAC1) has been detected in a wide variety of cancers. However, to date, PLAC1 expression has not been shown in cervical cancer. We have carried out a preliminary study that shows for the first time that PLAC1 is expressed in cervical cancers. METHODS: A total of 16 primary cervical tumors were obtained from patients shown to be human papillomavirus (HPV) 16/18 positive. Total cellular RNA, genomic DNA, and total protein were purified from each tumor. These materials were then used to determine PLAC1 expression, TP53 mutation status, and p53 expression. RESULTS: The PLAC1 expression was demonstrated in all 16 primary cervical tumors. The highest levels of expression were found in the more aggressive squamous and adenosquamous histologic types compared with adenocarcinomas. Moreover, the proportion of total PLAC1 message coming from the P1 promoter, also termed the distal or cancer promoter, was significantly greater in the more aggressive squamous and adenosquamous histologic types compared with adenocarcinomas. Finally, in spite of all 16 tumors being HPV-16/18 positive, 3 of 8 squamous cell cancers and 2 of 5 adenocarcinomas expressed wild-type p53 protein. Consistent with the recently shown suppression of the PLAC1P1 promoter by wild-type p53, these p53 positive tumors displayed among the lowest P1-specific PLAC1 expression levels. CONCLUSIONS: The PLAC1 expression has been demonstrated for the first time in cervical cancers. This preliminary study has further revealed a complex relationship between PLAC1 expression, cervical cancer histologic type, p53, and HPV type that requires further investigation.