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There are strong correlations between the microbiome and human disease, including cancer. However, very little is known about potential mechanisms associated with malignant transformation in microbiome-associated gynecological cancer, except for HPV-induced cervical cancer. Our hypothesis is that differences in bacterial communities in upper genital tract epithelium may lead to selection of specific genomic variation at the cellular level of these tissues that may predispose to their malignant transformation. We first assessed differences in the taxonomic composition of microbial communities and genomic variation between gynecologic cancers and normal samples. Then, we performed a correlation analysis to assess whether differences in microbial communities selected for specific single nucleotide variation (SNV) between normal and gynecological cancers. We validated these results in independent datasets. This is a retrospective nested case-control study that used clinical and genomic information to perform all analyses. Our present study confirms a changing landscape in microbial communities as we progress into the upper genital tract, with more diversity in lower levels of the tract. Some of the different genomic variations between cancer and controls strongly correlated with the changing microbial communities. Pathway analyses including these correlated genes may help understand the basis for how changing bacterial landscapes may lead to these cancers. However, one of the most important implications of our findings is the possibility of cancer prevention in women at risk by detecting altered bacterial communities in the upper genital tract epithelium.

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.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.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).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.

ObjectiveTo identify high-risk disease in clinicopathologic low-risk endometrial cancer (EC) with high microsatellite instability (MSI-H) or no specific molecular profile (NSMP) and therapeutic insensitivity in clinicopathologic high-risk MSI-H/NSMP EC.MethodsWe searched The Cancer Genome Atlas for DNA sequencing, RNA expression, and surveillance data regarding MSI-H/NSMP EC. We used a molecular classification system of E2F1 and CCNA2 expression and sequence variations in POLE, PPP2R1A, or FBXW7 (ECPPF) to prognostically stratify MSI-H/NSMP ECs. Clinical outcomes were annotated after integrating ECPPF and sequence variations in homologous recombination (HR) genes.ResultsData were available for 239 patients with EC, which included 58 MSI-H and 89 NSMP cases. ECPPF effectively stratified MSI-H/NSMP EC into distinct molecular groups with prognostic implications: molecular low risk (MLR), with low CCNA2 and E2F1 expression, and molecular high risk (MHR), with high CCNA2 and E2F1 expression and/or PPP2R1A and/or FBXW7 variants. The 3-year disease-free survival (DFS) rate was 43.8% in the MHR group with clinicopathologic low-risk indicators and 93.9% in the MLR group (PPConclusionECPPF may resolve prognostic challenges for MSI-H/NSMP EC by identifying occult high-risk disease in EC with clinicopathologic low-risk indicators and therapeutic insensitivity in EC with clinicopathologic high-risk indicators.

Supplementary Figure 4: Correlations between Methylation at CpG Sites and TAP1 mRNA Expression

Supplementary Figure 1: Methylation Signature Approximation

Supplementary Figure 2: No Association between Methylation Signature and Baseline Clinical Factors

Supplementary Table 6: Multivariate Modeling of Time to Recurrence/Death Among Studies with Gene Expression Data

Supplementary Figure 3: Immune Analysis

Supplementary Methods 1: The original 60 CpG loci defining methylation-based subtypes, and the approximation method for methylation signature

Supplementary Table 5: Modeling of Time to Recurrence/Death Among Studies with Gene Expression Data, excluding data from Previously Published Participants

Supplementary Table 4: Multivariate Modeling of Methylation Signature Among Studies with Multiple Datatypes

Background:Better understanding of prognostic factors in tubo-ovarian high-grade serous carcinoma (HGSC) is critical, as diagnosis confers an aggressive disease course. Variation in tumor DNA methylation shows promise predicting outcome, yet prior studies were largely platform-specific and unable to evaluate multiple molecular features.Methods:We analyzed genome-wide DNA methylation in 1,040 frozen HGSC, including 325 previously reported upon, seeking a multi-platform quantitative methylation signature that we evaluated in relation to clinical features, tumor characteristics, time to recurrence/death, extent of CD8+ tumor-infiltrating lymphocytes (TIL), gene expression molecular subtypes, and gene expression of the ATP-binding cassette transporter TAP1.Results:Methylation signature was associated with shorter time to recurrence, independent of clinical factors (N = 715 new set, hazard ratio (HR), 1.65; 95% confidence interval (CI), 1.10–2.46; P = 0.015; N = 325 published set HR, 2.87; 95% CI, 2.17–3.81; P = 2.2 × 10−13) and remained prognostic after adjustment for gene expression molecular subtype and TAP1 expression (N = 599; HR, 2.22; 95% CI, 1.66–2.95; P = 4.1 × 10−8). Methylation signature was inversely related to CD8+ TIL levels (P = 2.4 × 10−7) and TAP1 expression (P = 0.0011) and was associated with gene expression molecular subtype (P = 5.9 × 10−4) in covariate-adjusted analysis.Conclusions:Multi-center analysis identified a novel quantitative tumor methylation signature of HGSC applicable to numerous commercially available platforms indicative of shorter time to recurrence/death, adjusting for other factors. Along with immune cell composition analysis, these results suggest a role for DNA methylation in the immunosuppressive microenvironment.Impact:This work aids in identification of targetable epigenome processes and stratification of patients for whom tailored treatment may be most beneficial.

Supplementary Table 2: CpGs That Defined Previously Reported Methylation Subtypes Using a Semi-Supervised Clustering Approach

Supplementary Table 1: Characteristics of Study Participants with High Grade Serous Tubo-Ovarian Carcinoma by Contributing Study

PDF file - 3931KB, Genes/probe sets.

PDF file - 103KB, SUPPLEMENTARY EXPERIMENTAL PROCEDURES AND SUPPLEMENTAL REFERENCES.

PDF file - 450KB, Genes/probe sets in patient samples.

PDF file - 65KB, BAD pathway signature genes: probe sets representing 47 unique genes comprising a BAD-pathway signature.

PDF file - 76KB, Diagram of dosing schedules.

Purpose: Despite initial sensitivity to chemotherapy, ovarian cancers (OVCA) often develop drug resistance, which limits patient survival. Using specimens and/or genomic data from 289 patients and a panel of cancer cell lines, we explored genome-wide expression changes that underlie the evolution of OVCA chemoresistance and characterized the BCL2 antagonist of cell death (BAD) apoptosis pathway as a determinant of chemosensitivity and patient survival.Experimental Design: Serial OVCA cell cisplatin treatments were performed in parallel with measurements of genome-wide expression changes. Pathway analysis was carried out on genes associated with increasing cisplatin resistance (EC50). BAD-pathway expression and BAD protein phosphorylation were evaluated in patient samples and cell lines as determinants of chemosensitivity and/or clinical outcome and as therapeutic targets.Results: Induced in vitro OVCA cisplatin resistance was associated with BAD-pathway expression (P < 0.001). In OVCA cell lines and primary specimens, BAD protein phosphorylation was associated with platinum resistance (n = 147, P < 0.0001) and also with overall patient survival (n = 134, P = 0.0007). Targeted modulation of BAD-phosphorylation levels influenced cisplatin sensitivity. A 47-gene BAD-pathway score was associated with in vitro phosphorylated BAD levels and with survival in 142 patients with advanced-stage (III/IV) serous OVCA. Integration of BAD-phosphorylation or BAD-pathway score with OVCA surgical cytoreductive status was significantly associated with overall survival by log-rank test (P = 0.004 and P < 0.0001, respectively).Conclusion: The BAD apoptosis pathway influences OVCA chemosensitivity and overall survival, likely via modulation of BAD phosphorylation. The pathway has clinical relevance as a biomarker of therapeutic response, patient survival, and as a promising therapeutic target. Clin Cancer Res; 17(19); 6356–66. ©2011 AACR.

Supplementary Figure S1 from LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer

Supplementary Table S4 from LIN28B Polymorphisms Influence Susceptibility to Epithelial Ovarian Cancer

Whole genome regions with copy number gain/loss were included ; All genes with CNA affecting candidate pathway genes and statistically significant positive correlation (p-value ; Gene expression signature predictive of complete response (CR) versus incomplete response (IR) using the CNA-Correlated-Pathway (CCP) gene subset ; Differentially methylated genes between the CR and IR groups ; Differentially expressed miRNA between the CR and IR groups ; Differentially expressed genes (CR versus IR) from the 1,772 genes identified to have TF binding sites within the CNA-Correlated-Pathway (CCP) gene subset ; 422 significant genes included in the final model or signature ; Pathways and biological processes identified with the software for enrichment analysis GeneGo. These pathways were overrepresented in cluster #1 of the NMF consensus clustering ; Pathways and biological processes identified with the software for enrichment analysis GeneGo. These pathways overrepresented in cluster #2 of the NMF consensus clustering .

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.

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.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.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%).PP2A and E3UL deficiencies are seminal biological drivers in EC independent of PIK3CA-mu, PTEN-mu, and PIK3R1-mu and PIK3CA CNV.

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.

ObjectiveTo 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.MethodsWe 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, pResultsA 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.ConclusionPatients 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.

In endometrial cancer, occult high-risk subtypes (rooted in histomorphologically low-risk disease) with insensitivity to adjuvant therapies impede improvements in therapeutic efficacy. Therefore, we aimed to assess the ability of molecular high-risk (MHR) and low-risk (MLR) ECPPF (E2F1, CCNA2, POLE, PPP2R1A, FBXW7) stratification to profile recurrence in early, low-risk endometrioid endometrial cancer (EEC) and insensitivity to platinum-based chemotherapy or radiotherapy (or both) in high-risk EEC. Using The Cancer Genome Atlas endometrial cancer database, we identified 192 EEC cases with available DNA sequencing and RNA expression data. Molecular parameters were integrated with clinicopathologic risk factors and adverse surveillance events. MHR was defined as high (-H) CCNA2 or E2F1 log2 expression (≥2.75), PPP2R1A mutations (-mu), or FBXW7mu; MLR was defined as low (-L) CCNA2 and E2F1 log2 expression (POLEmu for favorable-outcomes comparisons. MHR and MLR had significantly different progression-free survival (PFS) rates (P < .001), independent of traditional risk factors (eg, TP53mu), except for stage IV disease. PFS of CCNA2-L/E2F1-L paralleled that of POLEmu. ECPPF status stratified responses to adjuvant therapy in stage III-IV EEC (P < .01) and profiled stage I, grade 1–2 cases with risk of recurrence (P < .001). MHR was associated with CTNNB1mu-linked treatment failures (P < .001). Expression of homologous recombination repair (HR) and cell cycle genes was significantly elevated in CCNA2-H/E2F1-H compared with CCNA2-L/E2F1-L (PP = .02). Favorable PFS in clinically high-risk EEC was associated with HRmu and MLR ECPPF (P < .001). In summary, MLR ECPPF and HRmu were associated with therapeutic efficacy in EEC. MHR ECPPF was associated with low-risk, early-stage recurrences and insensitivity to adjuvant therapies.

Background: Advanced high grade serous (HGSC) ovarian cancer is treated with either primary surgery followed by chemotherapy or neoadjuvant chemotherapy followed by interval surgery. The decision to proceed with surgery either primarily or after chemotherapy is based on a surgeon’s clinical assessment and prediction of an optimal outcome. Optimal surgery is correlated with improved overall survival. This clinical assessment results in an optimal surgery approximately 70% of the time. We hypothesize that this prediction can be improved by using biological tumor data to predict optimal cytoreduction.Methods: With access to a large biobank of ovarian cancer tumors, we obtained genomic data on 83 patients encompassing gene expression, exon expression, long non-coding RNA, micro RNA, single nucleotide variants, copy number variation, DNA methylation, and fusion transcripts. We then used machine learning to incorporate this data with pre-operative clinical information to create predictive models which successfully predicted whether or not a patient’s cytoreductive surgery would have an optimal outcome. These models were then validated within The Cancer Genome Atlas (TCGA) HGSC database. Results: Of the 124 models created and validated, 21 performed at least equal if not better than our historical clinical rate of optimal debulking in advanced-stage HGSC as a control, 78%. Conclusions: This is the first time tumor genomic data has been used to predict surgical outcome in ovarian cancer. Prospective validation of these models could result in improving our ability to objectively predict which patients will undergo optimal cytoreduction and, therefore, improve our ovarian cancer outcomes.

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.

The 8q24 chromosomal region is strongly associated with an increased risk of ovarian cancer. One single nucleotide polymorphism that is associated with ovarian cancer in this region is rs6983267, located within the long non-coding RNA colon cancer associated transcript 2 (CCAT2). The aim of the present study was to assess the association between rs6983267 and clinical outcomes in patients with high-grade serous ovarian cancer (HGSOC). The present retrospective genetic association study utilized Sanger sequencing to determine the genotype at the rs6983267 locus (GG, GT, TT) in 98 patients with HGSOC. Survival time and chemotherapy responses between patients were compared with the TT genotype and patients with a genotype containing a G allele (GT, GG). Survival analyses were performed using Cox proportional hazard ratio analysis. Association with chemo-response was performed using a logistic regression. The results revealed that patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with patients with genotypes containing a G allele [hazard ratio=0.59; 95% confidence interval (CI), 0.36-0.97; P=0.039] and were significantly associated with International Federation of Gynecology and Obstetrics stage [odds ratio (OR)=5.34; 95% CI, 1.50-22.62; P=0.014] and positive chemo-response (OR=4.51; 95% CI, 1.40-18.00; P=0.018). In summary, patients with HGSOC and the TT genotype at the rs6983267 locus had improved survival time compared with those with a G allele, despite being associated with more advanced disease; this was possibly due to an improved response to chemotherapy.

The epigenome offers an additional facet of cancer that can help categorize patients into those at risk of disease, recurrence, or treatment failure. We conducted a retrospective, nested, case-control study of advanced and recurrent high-grade serous ovarian cancer (HGSOC) patients in which we assessed epigenome-wide association using Illumina methylationEPIC arrays to characterize DNA methylation status and RNAseq to evaluate gene expression. Comparing HGSOC tumors with normal fallopian tube tissues we observe global hypomethylation but with skewing towards hypermethylation when interrogating gene promoters. In total, 5,852 gene interrogating probes revealed significantly different methylation. Within HGSOC, 57 probes highlighting 17 genes displayed significant differential DNA methylation between primary and recurrent disease. Between optimal vs suboptimal surgical outcomes 99 probes displayed significantly different methylation but only 29 genes showed an inverse correlation between methylation status and gene expression. Overall, differentially methylated genes point to several pathways including RAS as well as hippo signaling in normal vs primary HGSOC; valine, leucine, and isoleucine degradation and endocytosis in primary vs recurrent HGSOC; and pathways containing immune driver genes in optimal vs suboptimal surgical outcomes. Thus, differential DNA methylation identified numerous genes that could serve as potential biomarkers and/or therapeutic targets in HGSOC.

Objectives: Endometrial cancer incidence and mortality are rising in the US. Disease recurrence has been shown to have a significant impact on mortality. However, to date, there are no accurate and validated prediction models that would discriminate which individual patients are likely to recur. Reliably predicting recurrence would be of benefit for treatment decisions following surgery. We present an integrated model constructed with comprehensive clinical, pathological and molecular features designed to discriminate risk of recurrence for patients with endometrioid endometrial adenocarcinoma. Subjects and methods: A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation. Results: Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence. Conclusion: By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.

Long non-coding RNA&rsquo, 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&rsquo, s were differentially expressed in HGSC as compared to controls (p &lt, 0.001). Nine of these demonstrated association with cancer after multivariate lasso regression. Our multivariate analysis of survival identified four lncRNA&rsquo, 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.

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.

Objectives: There is a continued effort to diagnose ovarian cancer in early stages. Alternative splicing analysis may provide a unique signature to discriminate between malignant and benign tissues. RNA-sequencing has enabled genome-wide assessment of alternative splicing. Furthermore, deep-learning prediction models of alternative splicing based on exon-specific sequences features enhances the value of this analysis. The aim of this study is to create a model that would discriminate high grade serous ovarian cancer (HGSC) from normal tube using deep-learning (or artificial intelligence, AI) and alternative splicing analysis. Methods: This is a case-control study of patients with confirmed HGSC and patients without personal history of HGSC undergoing salpingectomy for benign conditions. RNA-sequencing was performed on all tissue samples. Resulting RNA sequences were introduced in DARTS (Deep-learning Augmented RNA-Seq) software suite. DARTS created a model of differential alternative splicing aimed to discriminate between HGSC and normal fallopian tube. Performance of prediction models were measured by area under the curve (AUC). Results: 112 HGSC and 12 benign samples were successfully sequenced. Prior to deep machine-learning, we found 1,726 differentially spliced single exon transcripts between HGSC and benign samples (p Conclusions: Individual exon expression used to assess alternative splicing was analyzed with AI methods and created a model that identified HGSC with an AUC of 91%. This method could be applied to single-cell RNA-seq technologies to create diagnostic tools for detecting early HGSC.

Objectives: To determine if there is a difference in overall survival of patients with epithelial ovarian cancer between rural, urban and metropolitan settings in the United States. Methods: We performed a NCDB retrospective cohort study from 2004-2016 which included high and low grade, Stage I-IV disease. Bivariate analyses using Student's t-test for continuous variables and Chi-square test for dichotomous variables was conducted. Kaplan-Meier curves were used to estimate survival of patients based on location of residence. Univariate analyses using Cox proportional Hazard ratio were used to assess survival based on their baseline characteristics followed by multivariate analysis using Cox proportional Hazard ratio to account for variables significant (p-value Download : Download high-res image (145KB) Download : Download full-size image Results: A total of 111,627 patients were included with mean age of 62-64 years of age. 94,290 patients were in a metropolitan area (>1 million population or a 50,000-999,999). There were 15,386 urban patients (10,000-49,999 persons). There were 1,951 rural patients (nonmetropolitan/noncore population). Univariate Cox proportional ratio was performed with clinically significant differences in hazard ratios between the metropolitan, urban, and rural patients. Multivariate Cox proportional Hazard ratio showed clinically significant increase in hazard ratios for patients in rural settings (HR 1.117). Conclusions: Our analysis shows that patients who live in rural settings with small populations and greater distance to tertiary care centers have poorer survival in ovarian cancer. These differences hold after controlling for stage, age and other significant risk factors related to poorer outcomes. Possible reasons for rural disparity include lack of specialty care/diagnosing physicians, increased comorbidities of rural patients, later stage at diagnosis, distance to health care centers/diagnosing physicians, lack of insurance, and lower socioeconomic status. This study shows that this population is at significant risk of poorer overall outcomes and this should be considered when caring for patients in this demographic. Download : Download high-res image (299KB) Download : Download full-size image

Objectives: We aimed to assess whether patients who underwent primary optimal cytoreduction experienced improved survival regardless of platinum sensitivity. 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 logistic regression and both univariate and multivariate Cox-proportional hazard regression. Validation of the model was then performed within The Cancer Genome Atlas (TCGA) database. Download : Download high-res image (364KB) Download : Download full-size image Results: A total of 470 patients were assessed for inclusion. A total of 234 patients were included as responders to chemotherapy and 98 were included as non-responders. Significant survival characteristics were identified and included in the multivariate analysis. Figure 2 demonstrates independent predictors of survival. Kaplan-Meier survival curves showed improved survival both for patients who were responders to chemotherapy as well as optimal cytoreduction (Figure 3; p Conclusions: Our analysis shows that patients who undergo primary optimal cytoreduction have a survival benefit regardless of their response to chemotherapy. Our model is well-validated within the TCGA database. Therefore, optimal primary cytoreduction should be considered, even in patients with advanced disease. If we are able to someday predict whether or not patients will respond to chemotherapy, cytoreductive surgery should still be considered a part of the treatment of non-responders.

Nearly a third of patients with high-grade serous ovarian cancer (HGSC) do not respond to initial therapy and have an overall poor prognosis. However, there are no validated tools that accurately predict which patients will not respond. Our objective is to create and validate accurate models of prediction for treatment response in HGSC. This is a retrospective case–control study that integrates comprehensive clinical and genomic data from 88 patients with HGSC from a single institution. Responders were those patients with a progression-free survival of at least 6 months after treatment. Only patients with complete clinical information and frozen specimen at surgery were included. Gene, miRNA, exon, and long non-coding RNA (lncRNA) expression, gene copy number, genomic variation, and fusion-gene determination were extracted from RNA-sequencing data. DNA methylation analysis was performed. Initial selection of informative variables was performed with univariate ANOVA with cross-validation. Significant variables (p

Recently, the compilation of massive amounts of genetic and genomic information on a wide variety of human cancer types, collectively known as The Cancer Genome Atlas (TCGA), has revealed a wealth of descriptive classification schemes both within and between different types and sources of cancer. In endometrial cancer, TCGA analyses have produced a post hoc scheme composed of four clusters: DNA polymerase ε catalytic subunit A (POLE) ultra-mutated (cluster 1), microsatellite instability (MSI) hypermutated (cluster 2), copy-number low (endometrioid, cluster 3) and copy-number high (serous-like, cluster 4). Given that cultured cells are the pre-clinical platform of cancer research, it was questioned how representative endometrial cancer cultured cell lines are in the context of TCGA-driven classification scheme. To address this issue in endometrial cancer cell lines, the present study investigated five commonly used cell lines: Ishikawa, ECC-1, Hec50co, KLE And RL95-2. The histology, mutation profile, MutL homolog 1 promoter methylation, copy-number variation, homologous recombination repair and microsatel-lite instability in each of these cell lines was assessed. The result of this characterization was that none of the cell lines fits neatly into any one of TCGA classes but are still useful models for groups of endometrial tumors. Furthermore, the contention that the ECC-1 cell line is actually Ishikawa was addressed using additional data. It was confirmed that ECC-1 cells likely no longer exist as ECC-1 but that they are not exactly Ishikawa either. For this reason, ECC-1 cells are suggested to be used in vitro but with this caveat in mind. Finally, we compiled a database of 127 endometrial cancer cell lines, including the five reported on here. The wide range of variation found in these cell lines highlights the need to further characterize these cells to select models that are more representative of the various histological and genomic aspects of endometrial cancer.

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.