Despite initial response in adjuvant chemotherapy, ovarian cancer patients treated with the combination of paclitaxel and carboplatin frequently suffer from recurrence after few cycles of treatment, and the underlying mechanisms causing the chemoresistance remain unclear. Recently, The Cancer Genome Atlas (TCGA) research network concluded an ovarian cancer study and released the dataset to the public. The TCGA dataset possesses large sample size, comprehensive molecular profiles, and clinical outcome information; however, because of the unknown molecular subtypes in ovarian cancer and the great diversity of adjuvant treatments TCGA patients went through, studying chemotherapeutic response using the TCGA data is difficult. Additionally, factors such as sample batches, patient ages, and tumor stages further confound or suppress the identification of relevant genes, and thus the biological functions and disease mechanisms.
To address these issues, herein we propose an analysis procedure designed to reduce suppression effect by focusing on a specific chemotherapeutic treatment, and to remove confounding effects such as batch effect, patient's age, and tumor stages. The proposed procedure starts with a batch effect adjustment, followed by a rigorous sample selection process. Then, the gene expression, copy number, and methylation profiles from the TCGA ovarian cancer dataset are analyzed using a semi-supervised clustering method combined with a novel scoring function. As a result, two molecular classifications, one with poor copy number profiles and one with poor methylation profiles, enriched with unfavorable scores are identified. Compared with the samples enriched with favorable scores, these two classifications exhibit poor progression-free survival (PFS) and might be associated with poor chemotherapy response specifically to the combination of paclitaxel and carboplatin. Significant genes and biological processes are detected subsequently using classical statistical approaches and enrichment analysis.
The proposed procedure for the reduction of confounding and suppression effects and the semi-supervised clustering method are essential steps to identify genes associated with the chemotherapeutic response.
Despite improved outcomes in the past 30 years, less than half of all women diagnosed with epithelial ovarian cancer live five years beyond their diagnosis. Although typically treated as a single disease, epithelial ovarian cancer includes several distinct histological subtypes, such as papillary serous and endometrioid carcinomas. To address whether the morphological differences seen in these carcinomas represent distinct characteristics at the molecular level we analyzed DNA methylation patterns in 11 papillary serous tumors, 9 endometrioid ovarian tumors, 4 normal fallopian tube samples and 6 normal endometrial tissues, plus 8 normal fallopian tube and 4 serous samples from TCGA. For comparison within the endometrioid subtype we added 6 primary uterine endometrioid tumors and 5 endometrioid metastases from uterus to ovary. Data was obtained from 27,578 CpG dinucleotides occurring in or near promoter regions of 14,495 genes. We identified 36 locations with significant increases or decreases in methylation in comparisons of serous tumors and normal fallopian tube samples. Moreover, unsupervised clustering techniques applied to all samples showed three major profiles comprising mostly normal samples, serous tumors, and endometrioid tumors including ovarian, uterine and metastatic origins. The clustering analysis identified 60 differentially methylated sites between the serous group and the normal group. An unrelated set of 25 serous tumors validated the reproducibility of the methylation patterns. In contrast, >1,000 genes were differentially methylated between endometrioid tumors and normal samples. This finding is consistent with a generalized regulatory disruption caused by a methylator phenotype. Through DNA methylation analyses we have identified genes with known roles in ovarian carcinoma etiology, whereas pathway analyses provided biological insight to the role of novel genes. Our finding of differences between serous and endometrioid ovarian tumors indicates that intervention strategies could be developed to specifically address subtypes of epithelial ovarian cancer.
Ovarian cancer is the most lethal gynecologic cancer, and this is largely related to its late diagnosis. High grade serous cancers often initially respond to chemotherapy, resulting in a better survival rate, compared to other ovarian carcinoma subtypes. We review recent work identifying a survival-associated gene expression profile for advanced serous ovarian cancer. Within this signature, the authors identified MAGP2, also known as microfibrillar associated protein 5 (MFAP5), as a highly significant indicator of survival and chemosensitivity. MAGP2 is a multifunctional secreted protein—important for elastic microfibril assembly and modulating endothelial cell behavior—with a newly identified role in cell survival. Through αVβ3 integrin-mediated signaling, MAGP2 promotes tumor and endothelial cell survival and endothelial cell motility, providing a potential mechanistic link between MAGP2 and angiogenesis as well as patient survival.
ovarian cancer; cell survival; MAGP2; αVβ3; endothelial cell
The presence of tumor cells in effusions within serosal cavities is a clinical manifestation of advanced-stage cancer and is generally associated with poor survival. Identifying molecular targets may help to design efficient treatments to eradicate these aggressive cancer cells and improve patient survival. Using a state-of-the-art Taqman-based qRT-PCR assay, we investigated the multidrug resistance (MDR) transcriptome of 32 unpaired ovarian serous carcinoma effusion samples obtained at diagnosis or at disease recurrence following chemotherapy. MDR genes were selected a priori based on an extensive curation of the literature published during the last three decades. We found three gene signatures with a statistically significant correlation with overall survival (OS), response to treatment (complete response - CR vs. other), and progression free survival (PFS). The median log-rank p-values for the signatures were 0.023, 0.034, and 0.008, respectively. No correlation was found with residual tumor status after cytoreductive surgery, treatment (with or without chemotherapy) and stage defined according to the International Federation of Gynecology and Obstetrics. Further analyses demonstrated that gene expression alone can effectively predict the survival outcome of women with ovarian serous carcinoma (OS: log-rank p=0.0000 and PFS: log-rank p=0.002). Interestingly, the signature for overall survival is the same in patients at first presentation and those who had chemotherapy and relapsed. This pilot study highlights two new gene signatures that may help in optimizing the treatment for ovarian carcinoma patients with effusions.
ovarian serous carcinoma; effusion; multidrug resistance; gene signature
Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS).
We implemented a multivariate Cox Lasso model and median time-to-event prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).
We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients.
Epidemiological studies indicate an increased risk of subsequent primary ovarian cancer from women with breast cancer. We have recently identified a 28-gene expression signature that predicts, with high accuracy, the clinical course in a large population of breast cancer patients. This prognostic gene signature also accurately predicts response to chemotherapy commonly used for treating breast cancer, including CMF, Tamoxifen, Paclitaxel, Docetaxel, and Doxorubicin (Adriamycin), in a panel of 60 cancer cell lines of nine different tissue origins. This prompted us to investigate whether this prognostic gene signature could also predict clinical outcome in other cancer types of epithelial origins, including ovarian cancer (n = 124), colon tumors (n = 74), and lung adenocarcinomas (n = 442). The results show that the gene expression signature contributes significantly more accurate (P < 0.05; compared with random prediction) prognostic information in multiple cancer types independent of established clinical parameters. Furthermore, the functional pathway analysis with curated database delineated a biological network with tight connections between the signature genes and numerous well established cancer hallmarks, indicating important roles of this prognostic gene signature in tumor genesis and progression.
prognostic gene signature; breast cancer; ovarian cancer; colon cancer; lung adenocarcinoma
Large multimodal datasets such as The Cancer Genome Atlas present an opportunity to perform correlative studies of tissue morphology and genomics to explore the morphological phenotypes associated with gene expression and genetic alterations. In this paper we present an investigation of Cancer Genome Atlas data that correlates morphology with recently discovered molecular subtypes of glioblastoma. Using image analysis to segment and extract features from millions of cells, we calculate high-dimensional morphological signatures to describe trends of nuclear morphology and cytoplasmic staining in whole-slide images. We illustrate the similarities between the analysis of these signatures and predictive studies of gene expression, both in terms of limited sample size and high-dimensionality. Our top-down analysis demonstrates the power of morphological signatures to predict clinically-relevant molecular tumor subtypes, with 85.4% recognition of the proneural subtype. A complementary bottom-up analysis shows that self-aggregating clusters have statistically significant associations with tumor subtype and reveals the existence of remarkable structure in the morphological signature space of glioblastomas.
bioinformatics; in silico; digital pathology; image analysis; microscopy
New tools are needed to predict outcomes of ovarian cancer patients treated with platinum-based chemotherapy. We hypothesized that a molecular score based on expression of genes that are involved in platinum-induced DNA damage repair could provide such prognostic information.
Gene expression data was extracted from The Cancer Genome Atlas (TCGA) database for 151 DNA repair genes from tumors of serous ovarian cystadenocarcinoma patients (n = 511). A molecular score was generated based on the expression of 23 genes involved in platinum-induced DNA damage repair pathways. Patients were divided into low (scores 0–10) and high (scores 11–20) score groups, and overall survival (OS) was analyzed by Kaplan–Meier method. Results were validated in two gene expression microarray datasets. Association of the score with OS was compared with known clinical factors (age, stage, grade, and extent of surgical debulking) using univariate and multivariable Cox proportional hazards models. Score performance was evaluated by receiver operating characteristic (ROC) curve analysis. Correlations between the score and likelihood of complete response, recurrence-free survival, and progression-free survival were assessed. Statistical tests were two-sided.
Improved survival was associated with being in the high-scoring group (high vs low scores: 5-year OS, 40% vs 17%, P < .001), and results were reproduced in the validation datasets (P < .05). The score was the only pretreatment factor that showed a statistically significant association with OS (high vs low scores, hazard ratio of death = 0.40, 95% confidence interval = 0.32 to 0.66, P < .001). ROC curves indicated that the score outperformed the known clinical factors (score in a validation dataset vs clinical factors, area under the curve = 0.65 vs 0.52). The score positively correlated with complete response rate, recurrence-free survival, and progression-free survival (Pearson correlation coefficient [r2] = 0.60, 0.84, and 0.80, respectively; P < .001 for all).
The DNA repair pathway–focused score can be used to predict outcomes and response to platinum therapy in ovarian cancer patients.
This study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).
Patients and Methods
Gene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.
A 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.
We have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.
Objectives. The objectives of the present study are to determine if a metabolomic study by HRMAS-NMR can (i) discriminate between different histological types of epithelial ovarian carcinomas and healthy ovarian tissue, (ii) generate statistical models capable of classifying borderline tumors and (iii) establish a potential relationship with patient's survival or response to chemotherapy. Methods. 36 human epithelial ovarian tumor biopsies and 3 healthy ovarian tissues were studied using 1H HRMAS NMR spectroscopy and multivariate statistical analysis. Results. The results presented in this study demonstrate that the three histological types of epithelial ovarian carcinomas present an effective metabolic pattern difference. Furthermore, a metabolic signature specific of serous (N-acetyl-aspartate) and mucinous (N-acetyl-lysine) carcinomas was found. The statistical models generated in this study are able to predict borderline tumors characterized by an intermediate metabolic pattern similar to the normal ovarian tissue. Finally and importantly, the statistical model of serous carcinomas provided good predictions of both patient's survival rates and the patient's response to chemotherapy. Conclusions. Despite the small number of samples used in this study, the results indicate that metabolomic analysis of intact tissues by HRMAS-NMR is a promising technique which might be applicable to the therapeutic management of patients.
The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets.
Methods and Results
Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3′-untranslated region (with less frequency observed for coding sequence and 5′-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability.
This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers.
The frequency of BRCA1 and BRCA2 germ-line mutations in women with ovarian cancer is unclear; reports vary from 3% to 27%. The impact of germ-line mutation on response requires further investigation to understand its impact on treatment planning and clinical trial design.
Patients and Methods
Women with nonmucinous ovarian carcinoma (n = 1,001) enrolled onto a population-based, case-control study were screened for point mutations and large deletions in both genes. Survival outcomes and responses to multiple lines of chemotherapy were assessed.
Germ-line mutations were found in 14.1% of patients overall, including 16.6% of serous cancer patients (high-grade serous, 22.6%); 44% had no reported family history of breast or ovarian cancer. Patients carrying germ-line mutations had improved rates of progression-free and overall survival. In the relapse setting, patients carrying mutations more frequently responded to both platin- and nonplatin-based regimens than mutation-negative patients, even in patients with early relapse after primary treatment. Mutation-negative patients who responded to multiple cycles of platin-based treatment were more likely to carry somatic BRCA1/2 mutations.
BRCA mutation status has a major influence on survival in ovarian cancer patients and should be an additional stratification factor in clinical trials. Treatment outcomes in BRCA1/2 carriers challenge conventional definitions of platin resistance, and mutation status may be able to contribute to decision making and systemic therapy selection in the relapse setting. Our data, together with the advent of poly(ADP-ribose) polymerase inhibitor trials, supports the recommendation that germ-line BRCA1/2 testing should be offered to all women diagnosed with nonmucinous, ovarian carcinoma, regardless of family history.
Lysophosphatidic acid (LPA) governs a number of physiologic and pathophysiological processes. Malignant ascites fluid is rich in LPA, and LPA receptors are aberrantly expressed by ovarian cancer cells, implicating LPA in the initiation and progression of ovarian cancer. However, there is an absence of systematic data critically analyzing the transcriptional changes induced by LPA in ovarian cancer.
Methodology and Principal Findings
In this study, gene expression profiling was used to examine LPA-mediated transcription by exogenously adding LPA to human epithelial ovarian cancer cells for 24 h to mimic long-term stimulation in the tumor microenvironment. The resultant transcriptional profile comprised a 39-gene signature that closely correlated to serous epithelial ovarian carcinoma. Hierarchical clustering of ovarian cancer patient specimens demonstrated that the signature is associated with worsened prognosis. Patients with LPA-signature-positive ovarian tumors have reduced disease-specific and progression-free survival times. They have a higher frequency of stage IIIc serous carcinoma and a greater proportion is deceased. Among the 39-gene signature, a group of seven genes associated with cell adhesion recapitulated the results. Out of those seven, claudin-1, an adhesion molecule and phenotypic epithelial marker, is the only independent biomarker of serous epithelial ovarian carcinoma. Knockdown of claudin-1 expression in ovarian cancer cells reduces LPA-mediated cellular adhesion, enhances suspended cells and reduces LPA-mediated migration.
The data suggest that transcriptional events mediated by LPA in the tumor microenvironment influence tumor progression through modulation of cell adhesion molecules like claudin-1 and, for the first time, report an LPA-mediated expression signature in ovarian cancer that predicts a worse prognosis.
The heterogeneity that soft tissue sarcomas (STS) exhibit in their clinical behavior, even within histological subtypes, complicates patient care. Histological appearance is determined by gene expression. Morphologic features are generally good predictors of biologic behavior, however, metastatic propensity, tumor growth, and response to chemotherapy may be determined by gene expression patterns that do not correlate well with morphology. One approach to identify heterogeneity is to search for genetic markers that correlate with differences in tumor behavior. Alternatively, subsets may be identified based on gene expression patterns alone, independent of knowledge of clinical outcome. We have reported gene expression patterns that distinguish two subgroups of clear cell renal carcinoma (ccRCC), and other gene expression patterns that distinguish heterogeneity of serous ovarian carcinoma (OVCA) and aggressive fibromatosis (AF). In this study, gene expression in 53 samples of STS and AF [including 16 malignant fibrous histiocytoma (MFH), 9 leiomyosarcoma, 12 liposarcoma, 4 synovial sarcoma, and 12 samples of AF] was determined at Gene Logic Inc. (Gaithersburg, MD) using Affymetrix GeneChip® U_133 arrays containing approximately 40,000 genes/ESTs. Gene expression analysis was performed with the Gene Logic Genesis Enterprise System® Software and Expressionist software. Hierarchical clustering of the STS using our three previously reported gene sets, each generated subgroups within the STS that for some subtypes correlated with histology, and also suggested the existence of subsets of MFH. All three gene sets also recognized the same two subsets of the fibromatosis samples that we had found in our earlier study of AF. These results suggest that these subgroups may have biological significance, and that these gene sets may be useful for sub-classification of STS. In addition, several genes that are targets of some anti-tumor drugs were found to be differentially expressed in particular subsets of STS.
High expression of vascular cell adhesion molecule 1 (VCAM1) has been shown to be associated with several cancers although its role in ovarian cancer development is largely undefined. The purpose of this study is to investigate its role in ovarian cancer using the epithelial cells and ovarian cancer cell lines and correlate its expression with clinicopathologic parameters in ovarian cancer patients. VCAM1 expression was examined via immunohistochemical staining of 251 high grade serous carcinoma samples using tissue microarray. The expression of VCAM1 was silenced in RAS-transformed ovarian epithelial cell lines and two high grade ovarian cancer cell lines. Cell migration was analyzed in vitro and effect on tumor growth was analyzed in nude mice. High VCAM1 expression was found to be was related with response to surgery and chemotherapy drugs (P = 0.025) and elder age at diagnosis (P = 0.008). Cox regression multivariable analysis showed that VCAM1 expression in tumor cells was an independent prognostic factor. Ovarian cancer cells with VCAM1 overexpression, compared with corresponding control cells, had increased cell migration and enhanced growth of xenograft tumors in mice. Our data provide strong evidence that VCAM1 plays an important role in ovarian tumor growth, and it may be used as a prognostic factor and novel therapeutic target for ovarian cancer.
Ovarian cancer; VCAM1; overall survival; tumor growth
Folate receptor alpha (FR-α) has been identified as a potential target in ovarian cancer for diagnostic and therapeutic purposes, based on its overexpression in serous epithelial ovarian carcinoma. The effect of chemotherapy on FR-α expression may be important in the applicability of FR-α directed agents in the case of residual tumor tissue. The objective of this study was to assess FR-α expression in ovarian carcinoma and to evaluate whether FR-α expression is altered by chemotherapy.
Materials & methods
FR-α expression was analyzed by semi-quantitative scoring of immunohistochemical staining on tissue microarrays (TMAs) from a database containing 361 ovarian cancer tissue samples, of which 210 serous and 116 non-serous carcinoma (35 missing). Serous carcinoma samples included 28 matched samples with tissue from both primary surgery and interval debulking surgery, and 12 matched samples with tissue from both primary surgery and surgery for recurrent disease.
FR-α expression was seen in 81.8% of serous ovarian cancers versus 39.9% of non-serous carcinomas (p < 0.001). In matched serous carcinoma samples, no significant change in FR-α expression in vital tumor tissue after chemotherapy was observed (p = 0.1). FR-α expression was not a prognostic marker of progression free survival (p = 0.8) or overall survival (p = 0.7).
FR-α was expressed in the majority of serous ovarian tumors, although >50% of cases showed only weak expression. Chemotherapy did not alter expression rates in remaining vital tumor tissue, indicating that folate-targeted agents may have a place in the treatment for ovarian cancer, before as well as after chemotherapy. Furthermore, FR-α status did not influence survival.
Folate receptor-alpha; Ovarian cancer; Immunohistochemistry
WT1 is a tumor suppressor gene responsible for Wilms' tumor. WT1 reactivity is limited to ovarian serous carcinomas. Recent studies have shown that WT1 plays an important role in the progression of disease and indicates a poorer prognosis of human malignancies such as acute myeloid leukemia and breast cancer. The aims of this study were to determine the survival and recurrence-free survival of women with advanced serous epithelial ovarian carcinoma in relation to WT1 gene expression.
The study accrued women over an 18-year period, from 1987–2004. During the study period, 163 patients were diagnosed with advanced serous epithelial ovarian carcinoma and had undergone complete post-operative chemotherapy, but the final study group comprised 99 patients. The records of these women were reviewed and the paraffin-embedded tissue of these women stained with WT1 immunostaining. Survival analysis was performed using Kaplan-Meier and Cox regression methods.
Fifty patients showed WT1 staining and forty-nine did not. Five-year survival of non-staining and staining groups were 39.4% and 10.7% (p < 0.00005); five-year recurrence-free survival of these groups were 29.8% and ≤ 7.5% (p < 0.00005), respectively. For survival the HR of WT1 staining, adjusted for residual tumor and chemotherapy response, was 1.98 (95% CI 1.28–3.79), and for recurrence-free survival the HR was 3.36 (95% CI 1.60–7.03). The HR for recurrence-free survival was not confounded by any other variables.
This study suggests that expression of WT1 gene may be indicative of an unfavorable prognosis in patients with advanced serous epithelial ovarian carcinoma.
This study assesses the ability of multidrug resistance (MDR)-associated gene expression patterns to predict survival in patients with newly diagnosed carcinoma of the ovary. The scope of this research differs substantially from that of previous reports, as a very large set of genes was evaluated whose expression has been shown to affect response to chemotherapy.
We applied a customized TaqMan Low Density Array, a highly sensitive and specific assay, to study the expression profiles of 380 MDR-linked genes in 80 tumor specimens collected at initial surgery to debulk primary serous carcinoma. The RNA expression profiles of these drug resistance genes were correlated with clinical outcomes.
Leave-one-out cross-validation was used to estimate the ability of MDR gene expression to predict survival. Although gene expression alone does not predict overall survival (P=0.06), four covariates (age, stage, CA125 level and surgical debulking) do (P=0.03). When gene expression was added to the covariates, we found an 11-gene signature that provides a major improvement in overall survival prediction (log-rank statistic P<0.003). The predictive power of this 11-gene signature was confirmed by dividing high and low risk patient groups, as defined by their clinical covariates, into four specific risk groups based on expression levels.
This study reveals an 11-gene signature that allows a more precise prognosis for patients with serous cancer of the ovary treated with carboplatin- and paclitaxel-based therapy. These 11 new targets offer opportunities for new therapies to improve clinical outcome in ovarian cancer.
chemotherapy; gene expression profiling; multidrug resistance; ovarian cancer; risk prediction
Because of the high risk of recurrence in high-grade serous ovarian carcinoma (HGS-OvCa), the development of outcome predictors could be valuable for patient stratification. Using the catalog of The Cancer Genome Atlas (TCGA), we developed subtype and survival gene expression signatures, which, when combined, provide a prognostic model of HGS-OvCa classification, named “Classification of Ovarian Cancer” (CLOVAR). We validated CLOVAR on an independent dataset consisting of 879 HGS-OvCa expression profiles. The worst outcome group, accounting for 23% of all cases, was associated with a median survival of 23 months and a platinum resistance rate of 63%, versus a median survival of 46 months and platinum resistance rate of 23% in other cases. Associating the outcome prediction model with BRCA1/BRCA2 mutation status, residual disease after surgery, and disease stage further optimized outcome classification. Ovarian cancer is a disease in urgent need of more effective therapies. The spectrum of outcomes observed here and their association with CLOVAR signatures suggests variations in underlying tumor biology. Prospective validation of the CLOVAR model in the context of additional prognostic variables may provide a rationale for optimal combination of patient and treatment regimens.
Despite a high initial response rate to first-line platinum/paclitaxel chemotherapy, most women with epithelial ovarian cancer relapse with recurrent disease that becomes refractory to further cytotoxic treatment. We have previously shown that the E3 ubiquitin ligase, EDD, a regulator of DNA damage responses, is amplified and overexpressed in serous ovarian carcinoma. Given that DNA damage pathways are linked to platinum resistance, the aim of this study was to determine if EDD expression was associated with disease recurrence and platinum sensitivity in serous ovarian cancer. High nuclear EDD expression, as determined by immunohistochemistry in a cohort of 151 women with serous ovarian carcinoma, was associated with an approximately two-fold increased risk of disease recurrence and death in patients who initially responded to first-line chemotherapy, independently of disease stage and suboptimal debulking. Although EDD expression was not directly correlated with relative cisplatin sensitivity of ovarian cancer cell lines, sensitivity to cisplatin was partially restored in platinum-resistant A2780-cp70 ovarian cancer cells following siRNA-mediated knockdown of EDD expression. These results identify EDD as a new independent prognostic marker for outcome in serous ovarian cancer, and suggest that pathways involving EDD, including DNA damage responses, may represent new therapeutic targets for chemoresistant ovarian cancer.
ovarian cancer; serous; EDD; recurrence; chemoresistance; cisplatin
Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression–based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.
A two-tier grading system based on nuclear grade divides ovarian serous carcinomas into low- (nuclear grade 1) and high-grade (nuclear grade 3). In most instances the separation is straightforward but at times, the morphologic distinction between them can be difficult. We studied eleven ovarian serous carcinomas with features that were “intermediate” (nuclear grade 2) between low and high grade. All the cases were high stage and had a poor clinical outcome. None of the tumors showed mutations in KRAS, BRAF and ERBB2 genes which characterize most low-grade serous carcinomas. In contrast, 10 (90.9%) of 11 cases contained non-synonymous TP53 mutations characteristic of high-grade serous carcinomas. In summary, the molecular genetic profile and behavior of serous carcinomas with grade 2 nuclei are virtually the same as those of serous carcinomas with grade 3 nuclei, supporting the use of the two-tier grading system for classifying ovarian serous carcinomas.
Ovarian cancer; serous carcinoma; grade; TP53; two-tier grading system
Using Reverse Phase Protein Array (RPPA) we measured protein expression associated with response to primary chemotherapy in patients with advanced-stage high-grade serous ovarian cancer.
Tumor samples were obtained from forty-five patients with advanced high-grade serous cancers from the Gynecology Tumor Bank at the British Columbia Cancer Agency. Treatment consisted of platinum-based chemotherapy following debulking surgery. Protein lysates were prepared from fresh frozen tumor samples and 80 validated proteins from signaling pathways implicated in ovarian carcinogenesis were measured by RPPA. Normalization of Ca-125 by the 3rd cycle of chemotherapy was chosen as the primary outcome measure of chemotherapy response. Logistic regression was used for multivariate analysis to identify protein predictors of Ca-125 normalization, and Cox regression to test for the association between protein expression and PFS. A significance level of p ≤ 0.05 was used.
The mean age at diagnosis was 56.8 years. EGFR, YKL-40 and several TGFβ pathway proteins (c-Jun N-terminal kinase JNK, JNK phosphorylated at residues 183 and 185, PAI-1, Smad3, TAZ) showed significant associations with Ca-125 normalization on univariate testing. On multivariate analysis, EGFR (p < 0.02), JNK (p < 0.01), and Smad3 (p < 0.04) were significantly associated with normalization of Ca-125. Contingency table analysis of pathway-classified proteins revealed that the selection of TGFβ pathway proteins was unlikely due to false discovery (p < 0.007, Bonferroni-adjusted).
TGFβ pathway signaling likely plays an important role as a marker or mediator of chemoresistance in advanced serous ovarian cancer. On this basis, future studies to develop and validate a useful predictor of treatment failure are warranted.
ovarian cancer; functional proteomics; chemotherapy response; reverse phase protein array; Ca-125
Few overlap between independently developed gene signatures and poor inter-study applicability of gene signatures are two of major concerns raised in the development of microarray-based prognostic gene signatures. One recent study suggested that thousands of samples are needed to generate a robust prognostic gene signature.
A data set of 1,372 samples was generated by combining eight breast cancer gene expression data sets produced using the same microarray platform and, using the data set, effects of varying samples sizes on a few performances of a prognostic gene signature were investigated. The overlap between independently developed gene signatures was increased linearly with more samples, attaining an average overlap of 16.56% with 600 samples. The concordance between predicted outcomes by different gene signatures also was increased with more samples up to 94.61% with 300 samples. The accuracy of outcome prediction also increased with more samples. Finally, analysis using only Estrogen Receptor-positive (ER+) patients attained higher prediction accuracy than using both patients, suggesting that sub-type specific analysis can lead to the development of better prognostic gene signatures
Increasing sample sizes generated a gene signature with better stability, better concordance in outcome prediction, and better prediction accuracy. However, the degree of performance improvement by the increased sample size was different between the degree of overlap and the degree of concordance in outcome prediction, suggesting that the sample size required for a study should be determined according to the specific aims of the study.
Serous carcinoma is the most common type of epithelial ovarian cancer. In this review, we provide a comprehensive picture of ovarian serous cancers from multiple aspects: the first part of this review summarizes the morphological, histological, and immunological signatures of ovarian serous carcinoma; subsequently, we review the history of the evolvement of different grading systems used in ovarian serous cancer; in the end, we focus on characterizing the genetics that underlie the 2-tiered pathways through which ovarian serous cancers are believed to arise: the low-grade and the high-grade pathways.
ovarian carcinoma; grading; morphology; molecular genetics; tumorigenesis