Disparity of ovarian cancer survival between urban and rural settings.

OBJECTIVE: To determine if there is a difference in overall survival of patients with epithelial ovarian cancer in rural, urban, and metropolitan settings in the United States. METHODS: We performed a retrospective cohort study using 2004-2016 National Cancer Database (NCDB) data including high and low grade, stage I-IV disease. Bivariate analyses used Student's t-test for continuous variables and χ2 test for dichotomous variables. Kaplan-Meier curves estimated survival of patients based on location of residence, and univariate analyses using Cox proportional HR assessed survival based on baseline characteristics. Multivariate analysis was performed to account for significant covariates. Propensity score matching was used to validate the multivariate survival model. For all tests, p<0.05 was considered statistically significant. RESULTS: A total of 111 627 patients were included with a mean age of 62.5 years for metroolitan (range 18-90), 64.0 years for rural (range 19-90) and 63.2 years for urban areas (range 18-90). Of all patients included, 94 290 were in a metropolitan area (counties >1 million population or 50 000-999 999), 15 386 were in an urban area (population of 10 000-49 999), and 1951 were in a rural area (non-metropolitan/non-core population). Univariate Cox proportional hazards models showed clinically significant differences in survival in patients from metropolitan, urban, and rural areas. Multivariate Cox proportional hazards models showed a clinically significant increase in HRs for patients in rural settings (HR 1.17; 95% CI 1.06 to 1.29). Increasing age and stage, non-insured status, non-white race, and comorbidity were also significant for poorer survival. CONCLUSION: Patients with ovarian cancer who live in rural settings with small populations and greater distance to tertiary care centers have poorer survival. These differences hold after controlling for stage, age, and other significant risk factors related to poorer outcomes. To improve clinical outcomes, we need further studies to identify which of these factors are actionable.

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.

PP2A and E3 ubiquitin ligase deficiencies: Seminal biological drivers in endometrial cancer.

OBJECTIVE: PI3K-AKT pathway mutations initiate a kinase cascade that characterizes endometrial cancer (EC). As kinases seldom cause oncogenic transformation without dysregulation of antagonistic phosphatases, pivotal interactions governing this pathway were explored and correlated with clinical outcomes. METHODS: After exclusion of patients with POLE mutations from The Cancer Genome Atlas EC cohort with endometrioid or serous EC, the study population was 209 patients with DNA sequencing, quantitative gene-specific RNA expression, copy number variation (CNV), and surveillance data available. Extracted data were annotated and integrated. RESULTS: A PIK3CA, PTEN, or PIK3R1 mutant (-mu) was present in 83% of patients; 57% harbored more than 1 mutation without adversely impacting progression-free survival (PFS) (P = .10). PIK3CA CNV of at least 1.1 (CNV high [-H]) was detected in 26% and linked to TP53-mu and CIP2A expression (P < .001) but was not associated with PFS (P = .24). PIK3CA expression was significantly different between those with CIP2A-H and CIP2A low (-L) expression (the endogenous inhibitor of protein phosphatase 2A [PP2A]), when stratified by PIK3CA mutational status or by PIK3CA CNV-H and CNV-L (all P < .01). CIP2A-H or PPP2R1A-mu mitigates PP2A kinase dephosphorylation, and FBXW7-mu nullifies E3 ubiquitin ligase (E3UL) oncoprotein degradation. CIP2A-H and PPP2R1A-mu (PP2A impairment) and FBXW7-mu (E3UL impairment) were associated with compromised PFS (P < .001) and were prognostically discriminatory for PIK3CA-mu and PIK3CA CNV-H tumors (P < .001). Among documented recurrences, 84% were associated with impaired PP2A (75%) and/or E3UL (20%). CONCLUSION: PP2A and E3UL deficiencies are seminal biological drivers in EC independent of PIK3CA-mu, PTEN-mu, and PIK3R1-mu and PIK3CA CNV.

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.

Gynecologic Oncology: Challenges of Minimally Invasive Surgery In a Field of Maximal Complexities.

Minimally invasive surgery has become a standard in the surgical treatment for many women with gynecologic cancers. Within the scope of minimally invasive surgery, several techniques exist. Here, we provide an overview of the challenging and controversial aspects of minimally invasive surgery in the field of gynecologic oncology, including single-site surgery, operating on morbidly obese patients, sentinel lymph node mapping, and recent trials and controversy of treating gynecologic cancer patients with a minimally invasive surgical approach.

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.

Gene Expression Signature-Based Prediction of Lymph Node Metastasis in Patients With Endometrioid Endometrial Cancer.

OBJECTIVE: This study aimed to develop a prediction model for lymph node metastasis using a gene expression signature in patients with endometrioid-type endometrial cancer. METHODS: Newly diagnosed endometrioid-type endometrial cancer cases in which the patients had undergone lymphadenectomy during a surgical staging procedure were identified from a national dataset (N = 330). Clinical and pathologic data were extracted from patient medical records, and gene expression datasets of their tumors were used to create a 12-gene predictive model for lymph node metastasis. We used principal components analysis on a training set (n = 110) to develop multivariate logistic models to predict low-risk patients having a probability of lymph node metastasis of less than 4%. The model with the highest prediction performance was selected for an evaluation set (n = 112), which, in turn, was validated in an independent validation set (n = 108). RESULTS: The model applied to the evaluation set showed 100% sensitivity (90% confidence interval [CI], 74%-100%) and 42% specificity (90% CI, 34%-51%), which resulted in 100% negative predictive value (90% CI, 89%-100%). In the validation set, we confirmed that the model consistently showed 100% sensitivity (90% CI, 88%-100%), 42% specificity (90% CI, 32%-50%), and 100% negative predictive value (90% CI, 88%-100%). CONCLUSIONS: Our 12-gene signature model is a useful tool for the identification of patients with endometrioid-type endometrial cancer at low risk of lymph node metastasis, particularly given that it can be used to analyze histologic tissue before surgery and used to tailor surgical options.

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.

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.

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.

Prediction of chemo-response in serous ovarian cancer.

BACKGROUND: Nearly one-third of serous ovarian cancer (OVCA) patients will not respond to initial treatment with surgery and chemotherapy and die within one year of diagnosis. If patients who are unlikely to respond to current standard therapy can be identified up front, enhanced tumor analyses and treatment regimens could potentially be offered. Using the Cancer Genome Atlas (TCGA) serous OVCA database, we previously identified a robust molecular signature of 422-genes associated with chemo-response. Our objective was to test whether this signature is an accurate and sensitive predictor of chemo-response in serous OVCA. METHODS: We first constructed prediction models to predict chemo-response using our previously described 422-gene signature that was associated with response to treatment in serous OVCA. Performance of all prediction models were measured with area under the curves (AUCs, a measure of the model's accuracy) and their respective confidence intervals (CIs). To optimize the prediction process, we determined which elements of the signature most contributed to chemo-response prediction. All prediction models were replicated and validated using six publicly available independent gene expression datasets. RESULTS: The 422-gene signature prediction models predicted chemo-response with AUCs of ~70 %. Optimization of prediction models identified the 34 most important genes in chemo-response prediction. These 34-gene models had improved performance, with AUCs approaching 80 %. Both 422-gene and 34-gene prediction models were replicated and validated in six independent datasets. CONCLUSIONS: These prediction models serve as the foundation for the future development and implementation of a diagnostic tool to predict response to chemotherapy for serous OVCA patients.

Stratification of endometrioid endometrial cancer patients into risk levels using somatic mutations.

OBJECTIVE: Patients with endometrioid endometrial cancer are stratified as high risk and low risk for extrauterine disease by surgical staging. Since patients with low-grade, minimally invasive disease do not benefit from comprehensive staging, pre-surgery stratification into a risk category may prevent unnecessary surgical staging in low risk patients. Our objective was to develop a predictive model to identify risk levels using somatic mutations that could be used preoperatively. METHODS: We classified endometrioid endometrial cancer patients in The Cancer Genome Atlas (TCGA) dataset into high risk and low risk categories: high risk patients presented with stage II, III or IV disease or stage I with high-intermediate risk features, whereas low risk patients consisted of the remaining stage I patients with either no myometrial invasion or low-intermediate risk features. Three strategies were used to build the prediction model: 1) mutational status for each gene; 2) number of somatic mutations for each gene; and 3) variant allele frequencies for each somatic mutation for each gene. RESULTS: Each prediction strategy had a good performance, with an area under the curve (or AUC) between 61% and 80%. Analysis of variant allele frequency produced a superior prediction model for risk levels of endometrial cancer as compared to the other two strategies, with an AUC=91%. Lasso and Ridge methods identified 53 mutations that together had the highest predictability for high risk endometrioid endometrial cancer. CONCLUSIONS: This prediction model will assist future retrospective and prospective studies to categorize endometrial cancer patients into high risk and low risk in the preoperative setting.

Differentially expressed genes in preimplantation human embryos: potential candidate genes for blastocyst formation and implantation.

PURPOSE: The aim of this study was to determine which genes and gene pathways are differentially expressed when comparing human blastocysts with cleavage-stage embryos. METHODS: We individually assessed gene expression in preimplantation human embryos at cleavage (n = 3) and blastocyst (n = 3) stages. Gene expression patterns were then validated in publically available datasets and then independently validated in vitro with additional human embryos using TaqMan gene expression assays. Immunolocalization studies were conducted to identify protein expression in intact blastocyst-stage embryos. RESULTS: Compared to cleavage-stage embryos, blastocyst-stage embryos differentially expressed 51 genes (p < 0.001), with overrepresentation in amoebiasis pathways and pathways in cancer. Of these 51 genes, 21 were found to be independently validated in a separate, publically available dataset, with a substantial agreement with our initial findings (κ = 0.8). In an independent set of cleavage- and blastocyst-stage embryos, we validated that six of eight tested genes were differentially expressed (p < 0.05) by RT-qPCR. Immunofluorescence studies documented the presence of two studied proteins in the trophectoderm of blastocyst-stage embryos. CONCLUSIONS: Differentially expressed genes may be implicated in the invasion and proliferation of the early embryo. Our research highlights specific genes that may be further studied for their role in the implantation process and additionally raises questions about localized gene and/or protein expression in the trophectoderm, which could affect protocols for, and interpretation of, trophectoderm biopsies performed in in vitro fertilization cycles.