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Originally published as JCO Early Release 10.1200/JCO.2004.04.070 on October 25 2004 © 2004 American Society of Clinical Oncology. Gene Expression Signature With Independent Prognostic Significance in Epithelial Ovarian CancerFrom the Program of Gynecologic Medical Oncology and the Genomics Center and Bioinformatics Core, Beth Israel Deaconess Medical Center; Harvard Medical School, Children’s Hospital Informatics Program and Harvard Partners Center for Genetics and Genomics, Boston, MA, and the Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY. Address reprint requests to Stephen A. Cannistra, MD, Program of Gynecologic Medical Oncology, Beth Israel Deaconess Medical Center, 330 Brookline Ave, Boston, MA 02215; e-mail: scannist{at}bidmc.harvard.edu
PURPOSE: Currently available clinical and molecular prognostic factors provide an imperfect assessment of prognosis for patients with epithelial ovarian cancer (EOC). In this study, we investigated whether tumor transcription profiling could be used as a prognostic tool in this disease. METHODS: Tumor tissue from 68 patients was profiled with oligonucleotide microarrays. Samples were randomly split into training and validation sets. A three-step training procedure was used to discover a statistically significant Kaplan-Meier split in the training set. The resultant prognostic signature was then tested on an independent validation set for confirmation. RESULTS: In the training set, a 115-gene signature referred to as the Ovarian Cancer Prognostic Profile (OCPP) was identified. When applied to the validation set, the OCPP distinguished between patients with unfavorable and favorable overall survival (median, 30 months v not yet reached, respectively; log-rank P = .004). The signature maintained independent prognostic value in multivariate analysis, controlling for other known prognostic factors such as age, stage, grade, and debulking status. The hazard ratio for death in the unfavorable OCPP group was 4.8 (P = .021 by Cox proportional hazards analysis). CONCLUSION: The OCPP is an independent prognostic determinant of outcome in EOC. The use of gene profiling may ultimately permit identification of EOC patients appropriate for investigational treatment approaches, based on a low likelihood of achieving prolonged survival with standard first-line platinum-based therapy.
The majority of patients with epithelial ovarian cancer (EOC) are diagnosed with advanced disease involving sites such as the upper abdomen, pleural space, and para-aortic lymph nodes.1 In an attempt to eradicate residual tumor that remains after initial surgery, postoperative chemotherapy is almost always required. Standard chemotherapy with carboplatin in combination with a taxane results in an initial response rate of more than 70%, though subsequent relapse frequently occurs and eventually becomes resistant to a wide variety of agents.2 Consequently, the long-term survival of patients with upper-abdominal involvement (stage III) or those with disease beyond the abdomen (stage IV) ranges from 30% to less than 10%.1 Despite the highly lethal nature of EOC, the clinical course of advanced disease can be difficult to predict in an individual patient. A small fraction of patients will be cured with surgery followed by chemotherapy, another group will experience relapse after a relatively long time interval (eg, > 1 to 2 years), others will relapse and succumb to this disease within months of completing first-line therapy, and some will exhibit primary resistance to first-line chemotherapy. For patients with advanced disease, features associated with a more favorable prognosis include ability to perform an optimal surgical debulking, low-grade disease, nonclear cell histology, age younger than 65 years, a rapid serologic (CA-125) response to chemotherapy, the presence of BRCA-1 germline mutations, and overexpression of proapoptotic proteins such as BAX.1,3-7 Nonetheless, these prognostic factors are imperfect predictors of outcome, and for the most part, they do not provide insight into the biologic mechanisms responsible for clinical behavior. The heterogeneity of clinical outcomes in patients with ovarian cancer suggests that reliable prognostic and/or predictive factors would be of potential clinical value. Accurate predictive markers might identify patients who are appropriate candidates for novel first-line experimental approaches, based on a high chance of exhibiting resistance to standard first-line chemotherapy. Alternatively, accurate prognostic factors may permit identification of patients who are likely to relapse and die of disease, despite achievement of a complete response. Such patients may be appropriate candidates for experimental approaches designed to determine, for instance, the value of maintenance or consolidation strategies. Finally, reliable prognostic and/or predictive factors might provide important insights into the biology of drug resistance and tumor aggressiveness, yielding potentially new molecular targets for drug development. Previous studies investigating the mechanisms of drug resistance, tumor growth, and metastatic potential have revealed that these processes are multifactorial in nature and are associated with genetic abnormalities in multiple gene families. Thus, more recent attempts to develop accurate predictors of clinical outcome in other malignancies have focused on techniques that are capable of assessing global gene expression. This task has become feasible through the development of genome-wide expression arrays (cDNA and oligonucleotide microarrays), which have been capable of distinguishing between specific tumor types (eg, myeloid v lymphoid leukemia), between specific histologic subtypes (eg, follicular v large-cell lymphoma), and between different clinical outcomes.8-11 For example, microarray expression profiles in patients with non-Hodgkin’s lymphoma have recently been shown to provide prognostic information that was independent of standard clinical metrics such as the International Prognostic Index, attesting to the potential clinical utility of this technique.10,11 In this study, we used oligonucleotide microarrays to globally analyze gene expression of primary ovarian cancer samples in order to define profiles that have prognostic relevance. We demonstrate that it is possible to accurately prognosticate clinical outcome in patients with EOC using this technique, and we discuss the potential relevance of these findings for clinical management.
Study Subjects Sixty-eight patients with EOC diagnosed between January 1995 and October 2000 form the basis of this study (38 patients from Beth Israel Deaconess Medical Center [BIDMC] and 30 patients from Memorial Sloan-Kettering Cancer Center [MSKCC]). All patients underwent exploratory laparotomy for diagnosis, staging, and debulking, followed by first-line platinum/taxane–based chemotherapy. Standard postchemotherapy surveillance included serial physical examination, serum CA-125 level, and computed tomography scanning based on clinical suspicion of relapse. At one of the two institutions (MSKCC), patients who were in complete clinical remission after standard chemotherapy were considered for a second-look laparoscopy, though findings from this procedure were not taken into account in the definition of complete clinical remission (see Clinical Definitions). Follow-up data for this study were extracted from the Ovarian Cancer Relational Database at BIDMC and the Ovarian Cancer Clinical Database at MSKCC. The study protocol for collection of tissue and clinical information was approved by the institutional review boards at both institutions, and patients provided written informed consent authorizing the collection and use of the tissue for study purposes.
Clinical Definitions
RNA Isolation
cDNA Synthesis, Microarray Probe Preparation, and Affymetrix GeneChip Hybridization
Training Set Data Analysis
Gene Expression Pattern Analysis and Class Prediction Details on the pattern recognition algorithm are provided in previously published articles.16-23 Briefly, this is a supervised method that is designed to discover patterns of gene expression associated with binary phenotypes. A pattern is defined as a subset of genes whose expression levels are tightly clustered (usually at a high or low expression level) in a subset of samples within a given phenotype. A computer algorithm (SPLASH)17 was used to discover all patterns characteristic of the two phenotypes at a given level of statistical significance, as previously described.16-22 The degree of differential gene expression was assessed by a signal to noise ratio and a permutation test as described previously.8 Class predictions at all steps (training and validation) were carried out using the weighted voting8,11,23-25 and k nearest neighbor (k-nn)9,26,27 algorithms. Predictive accuracy in the training set was assessed by leave-one-out cross-validation.8 The P value for predictor accuracy was calculated using the Fisher’s exact test on the prediction contingency table and by a permutation test as described previously.23,25
Statistical Tests and Survival Analysis
Patient Characteristics The clinical and pathologic characteristics of the 68 patients with epithelial ovarian cancer are shown in Table 1. The median age at diagnosis was 55 years (range, 36 to 80 years), and the majority (96%) had advanced-stage (FIGO stages III/IV) grade 3 tumors (80%), with serous histology (97%). Sixty-five percent of patients were optimally cytoreduced after initial surgery (  1 cm residual diameter disease), and all received postoperative taxane/platinum–based combination chemotherapy. The median follow-up was 40+ months (range, 1 to 74+ months), with a median OS of 49 months for the entire group, and a median DFS of 15 months. Thus, the survival characteristics of this group are typical for patients with advanced epithelial ovarian cancer.
Development of the Ovarian Cancer Prognostic Profile The strategy for identifying a gene expression profile with prognostic significance is shown in Figure 1. In the first training step, 14 samples out of a randomly chosen training set of 34 samples were initially selected for pattern analysis. This group consisted of seven samples with the shortest OS time (4, 10, 12, 18, 19, 24, 26 months) and seven samples with the longest OS times (58+, 59+, 61+, 63, 65+, 68+, 73+). These samples were selected in an orderly fashion starting from the most extreme sample on each end of the survival spectrum, with the two groups roughly representing less than 2-year, and more than 5-year survival. Pattern analysis comparing the two groups revealed approximately 100-multigene patterns that were associated with the two survival groups (P < .001 for each pattern; additional data may be found in a supplementary Web site). We performed class prediction with the weighted voting and the k-nn algorithm. Predictor sets ranging from 120 to 300 genes using the k-nn algorithm showed high predictive accuracy (100%; P = .0004 by Fisher’s exact test and P < .001 by a permutation test). Similar results were obtained with the weighted voting algorithm (100%; P = .0003) using 180 to 400 genes. In the second training step, we used a weighted voting predictor with 200 genes (with 100% accuracy for the initial 14 samples) to assign labels (favorable and unfavorable) to the remaining 20 samples from the training set and then generated survival curves for the entire group. Kaplan-Meier analysis showed a statistically significant difference in OS between the two groups. The unfavorable group had a median survival of 33 months, whereas the favorable group had a median survival that had not yet been reached (log-rank P = .0008). We also tested a range of the other highly accurate predictors (between 120 and 400 genes both by the k-nn and weighted voting methods) for their performance on the remaining 20 samples and obtained similar results, with P values for the Kaplan-Meier analysis being very similar to those of the 200-gene predictor. In the third training step, we utilized the entire group of 34 training samples to develop a final candidate signature. We carried out pattern recognition and obtained 766 multigene patterns that were associated with the two classes (favorable and unfavorable; P < .001). The highest predictive accuracy was obtained using a 115-gene predictor (85% by weighted voting; 91% by k-nn; P = .00005 by Fisher’s exact test and P < .001 by permutation test). This final gene expression profile is shown in Figure 2 and will be referred to as the Ovarian Cancer Prognostic Profile (OCPP).
Association of the OCPP With Survival The OCPP was used to assign labels (favorable v unfavorable) to a randomly chosen validation set of 34 patient samples, followed by Kaplan-Meier analysis. These samples are distinct from the training set and had not been used at any step in the generation of the OCPP. As shown in Figure 3A, a strong survival split was observed on the basis of the OCPP, with a median OS in the unfavorable and favorable groups of 30 months and not yet reached, respectively, at a median follow-up of 47 months (log-rank P = .004). In addition, there is a suggestion of a plateau in the favorable curve that identifies a subset of patients with a particularly indolent course, having a close to 70% long-term survival at 5 years. After the prognostic value of the signature was validated, we applied the signature to the entire set of 68 patients to arrive at a more stable estimate of the effect size (Fig 3B). The median survival for the unfavorable group was 30 months, while it has not yet been reached for the favorable group at a median follow-up of 49 months (log-rank P = .0001). By a univariate Cox proportional hazards model, the hazard ratio (HR) for death in the unfavorable group was 4.6 (95% CI, 2.0 to 10.7; P = .0001) relative to the favorable group. Patients from MSKCC and BIDMC were similarly represented, with 47% and 60% of samples from each site assigned to the unfavorable group, respectively. The OCPP was similarly prognostic when used to analyze the BIDMC versus MSKCC groups separately (refer to supplementary Web site indicated earlier). The OCPP was also used to assess DFS, as shown in Figure 4. Within the validation set, median DFS was 10 and 33 months for the unfavorable and favorable groups, respectively (log-rank P = .01). When all 68 patients were considered together, the median DFS was 10 and 20 months, respectively (log-rank P = .015).
Figure 5 shows the Kaplan-Meier analysis as a function of gene profile for homogeneous subsets of patients with stage III/IV disease (n = 33), grade 3 disease (n = 29), or optimal debulking status (n = 24) in the validation set. We purposely avoided mixing the training and validation sets for these subset analyses, in order to avoid reanalyzing samples that had already been used to generate the prognostic gene profile. For the subset of patients with stage III/IV disease, the median OS for the unfavorable and the favorable gene profile classes was 30 months versus not yet reached, respectively (Fig 5A; P = .006). For patients with grade 3 disease, the median OS for the unfavorable and the favorable profile was also 30 months versus not yet reached, respectively (Fig 5B; P < .0006). Restricting the analysis to only those patients who were optimally debulked, the median OS for the unfavorable versus the favorable gene profile groups was 41 months versus not yet reached, respectively (Fig 5C, P = .08). Thus, the OCPP provided excellent discrimination of survival curves for these patient subsets. More specifically, these results indicate that the survival predictions shown in Figure 3 were not sensitive to the small number of early-stage and low-grade patients contained within this patient cohort.
Association of the OCPP With Other Clinical Parameters Table 2 shows the distribution of several known prognostic factors as a function of gene profile assignment. The two groups (favorable and unfavorable) were well balanced for grade, stage, and histology. However, the favorable-profile group was enriched for patients who were optimally cytoreduced (81% v 51%; P = .02), whereas the unfavorable profile group was characterized by a higher median age (61 v 52 years; P = 0001). Therefore, several prognostic factors were next evaluated by both univariate and multivariate analysis (Table 3). In addition to gene profile, debulking status and age maintained prognostic value for OS in univariate analysis. However, the OCPP maintained independent prognostic significance in multivariate analysis (Table 3) when correcting for debulking status and age. Specifically, the HR for death for the unfavorable versus the favorable group was 4.8 in the validation set (95% CI, 1.3 to 17.9; P = .021), as well as in the entire data set (HR, 3.6; 95% CI, 1.6 to 8.3; P = .002), while controlling for debulking status and age. Debulking status and age were not independently associated with survival in any of the analyses (training set, validation set, or entire data set) while controlling for each other and for the OCPP, though debulking status showed a trend toward statistical significance in the validation set (HR, 2.6; 95% CI, 2.9 to 7.5; P = .069).
Association Between the OCPP and Response to First-Line Chemotherapy As presented in Table 4, the percentage of patients achieving a CCR after first-line therapy in the favorable versus unfavorable groups was 96% and 81% (P = .063). Although this trend did not reach statistical significance by a two-sided Fisher’s test, it suggests that the association between the OCPP and survival (Figs 3 and 4) may be partly related to the likelihood of achieving a CCR with first-line chemotherapy. However, after excluding patients who did not achieve a CCR to chemotherapy, the unfavorable and favorable groups as defined by the OCPP still showed significantly different OS (41 months v not yet reached, respectively; P = .012). Taken together, these observations suggest that the prognostic influence of the OCPP is partly independent of response to first-line treatment.
Second-look laparoscopy was routinely performed at one of the two participating institutions (MSKCC) on patients who had achieved CCR, who had no detectable tumor by computed tomography scan at the end of postoperative chemotherapy, and who met eligibility criteria for various investigational protocols. Twenty-four of the 30 patients from MSKCC had second-look laparoscopy, with 14 patients having evidence of residual disease. There was no statistically significant association between gene profile (favorable/unfavorable) and the second-look laparoscopy findings. Specifically, the percentage of patients with positive second-look laparoscopy in the unfavorable and favorable groups was 55% and 61%, respectively (Fisher’s P = 1.0).
Functional Classification of Genes Contained in the OCPP
Currently available clinical factors provide an imperfect assessment of prognosis for patients with advanced epithelial ovarian cancer. By using gene expression profiling, we now demonstrate the independent prognostic value of this technique when applied to tissue samples obtained at the time of initial diagnostic laparotomy. To define the OCPP, we combined many well-described methods of microarray analysis and phenotypic prediction in a way that allowed us to approach survival as a continuous and censored variable. The training approach that we have developed is based on an initial assessment of samples at the extreme ends of the survival spectrum, but avoids using an arbitrary cutoff for defining "long" and "short" survival durations. In this regard, our analysis is similar to that used in previous studies involving lymphoma and lung cancer,10,28 which also approached survival as a continuous outcome in order to discover relevant prognostic signatures. The gene profile shown in Figure 2 provided independent prognostic information for OS in patients with advanced ovarian cancer. Specifically, we were able to discriminate between two distinct OS groups on the basis of the OCPP (Fig 3)—one with a median OS of 30 months and another with a median OS that had not yet been reached after a median follow-up of 49 months (P = .0001). Importantly, the gene profile was strongly associated with survival in the independent validation set. Beyond the difference in median survival, it is notable that the favorable group demonstrated a possible survival plateau, with a subset of patients having a 70% probability of survival at 5 years. This level of discrimination between high- and low-risk patients is not generally possible using conventional clinical factors, and may provide a powerful way to identify those patients who are at highest risk for an unfavorable outcome with conventional treatment approaches, at the time of diagnosis. In addition to OS, the OCPP was also prognostic for DFS (Fig 4). The prognostic power of our gene expression profile was not dependent on its association with other known characteristics, as it retained independent significance in multivariate analysis (Table 3). This is a particularly important aspect of this study, given the emphasis recently placed on appropriate multivariate assessment of genomic signatures used for clinical prediction.29 Although there were three patients with early-stage disease in our initial cohort (Table 2), excluding these patients from the analysis did not diminish the prognostic significance of the gene profile when applied only to patients with advanced-stage disease (Fig 5A). Similarly, the prognostic value of the gene profile was not sensitive to the small number of low-grade tumors that were present in our study (Fig 5B). Furthermore, the profile showed prognostic value even within the subset of optimally debulked patients, though this did not reach statistical significance (P = .08; Fig 5C). Insight into potential mechanisms underlying the prognostic value of the OCPP was obtained by analyzing its association with response to first-line chemotherapy. Surprisingly, although the favorable OCPP was associated with a trend toward higher chemotherapy response (P = .063; Table 4), the profile maintained strong prognostic significance even when applied to the homogeneous group of patients with chemosensitive disease. This observation suggests that achievement of clinical response is an imperfect surrogate for survival, and it raises the possibility that the OCPP may be identifying other factors, such as proliferative rate or metastatic potential, that could alter the natural history of this disease. In this regard, several genes with potential functional relevance were overexpressed in the unfavorable group (Table 5; Fig 2), including the platelet-derived growth factor receptor30,31 and mesenchymal markers such as fibronectin32 and connective tissue growth factor.33 The coordinated expression of these and other mesenchymal genes (such as fibromodulin and vimentin; Table 5) observed in the OCPP may reflect a contribution from tumor stroma, and/or might represent a process known as epithelial-mesenchymal transition, which has been correlated with aggressive tumor behavior in preclinical model systems.34-39 Additionally, the overexpression of estrogen pathway–related genes (such as the estrogen receptor binding site–associated antigen 9) in the favorable group could imply that estrogen responsiveness may contribute to an overall improved outcome, reminiscent of the well-described prognostic association in breast cancer. It is particularly interesting that certain genes that were upregulated in the unfavorable OCPP signature (Table 5) have been previously associated with poor prognosis in EOC. For example, expression of plasminogen activator inhibitor type 1 (PAI-1), a potentially important mediator of tumor invasion, has correlated with tumor aggressiveness and poor patient outcome in EOC,40-43 as well as in other tumor types.41 Likewise, thrombospondin-2 expression has been associated with poor prognosis in endometrial cancer44 and in EOC.45 Finally, VEGF-C expression has been previously associated with inferior survival and lymphatic spread in EOC.46-48 These interesting observations notwithstanding, it is important to point out that the functional role of these genes in ovarian cancer remains to be established and cannot be conclusively derived from this descriptive study. This study demonstrates that it is feasible to define a gene profile that independently correlates with survival in epithelial ovarian cancer. The use of a powerful prognostic tool such as the OCPP may enable clinicians to identify those patients most appropriate for investigational approaches, such as novel first-line or maintenance strategies. Additionally, the availability of molecularly defined profiles may eventually permit a more rational choice of targeted therapy using agents that inhibit the vascular endothelial growth factor or platelet-derived growth factor pathways, for instance. Finally, although not directly tested in our study, it may be possible to use gene profiling to identify patients with early-stage disease who are at high risk for relapse, and who are therefore most appropriate for adjuvant platinum-based chemotherapy. Although our data suggest the potential utility of this approach, it is recognized that the prognostic value of gene profiling in ovarian cancer must be further evaluated in additional prospective studies of patients with both advanced, as well as early-stage disease.
The authors indicated no potential conflicts of interest.
We thank Todd Golub, MD, for his helpful comments during manuscript preparation. We also thank Arthur Sytkowski, MD, and the Clinical Investigator Training Program (CITP) for providing Dimitrios Spentzos, MD, with prior research experience during fellowship training. Finally, we wish to acknowledge the efforts of gynecologic oncologists at BIDMC and MSKCC in providing tissue samples used in this analysis.
Supported in part through grants from the Patricia Cronin Foundation, the Director’s Challenge Grant (U01 CA88175), RO1 CA85467, U24 DK58739, and through donations made to the Ovarian Cancer Research Fund at the Beth Israel Deaconess Medical Center in memory of Amy Sachs Simon. Presented in part at the 39th Annual Meeting of the American Society of Clinical Oncology, May 31-June 3, 2003, Chicago, IL. Authors’ disclosures of potential conflicts of interest are found at the end of this article.
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INTRODUCTION
