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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.

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.