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Unique gene expression profile based upon pathologic response in epithelial ovarian cancer

ABSTRACT: PURPOSE: We investigated whether tumor tissue obtained at diagnosis expresses a specific gene profile that is predictive of findings at second-look surgery in patients with epithelial ovarian cancer (EOC). PATIENTS AND METHODS: Tumor tissue obtained at the time of diagnosis was profiled with oligonucleotide microarrays. Class prediction analysis was performed in a training set of 24 patients who had undergone a second-look procedure. The resultant predictive signature was then tested on an independent validation set comprised of 36 patients. RESULTS: A 93-gene signature referred to as the Chemotherapy Response Profile (CRP) was identified through its association with pathologic complete response. When applied to a separate validation set, the CRP distinguished between patients with unfavorable versus favorable overall survival (median 41 months v not yet reached, respectively, log-rank P = .007), with a median follow-up of 52 months. The signature maintained independent prognostic value in multivariate analysis, controlling for other known prognostic factors such as age, stage, grade, and debulking status. There was no genetic overlap between the CRP and our previously described Ovarian Cancer Prognostic Profile (OCPP), which demonstrated similar prognostic value. The combination of the CRP and OCPP yielded better prognostic discrimination then either profile alone. Genes present in the CRP include BAX, a proapoptotic protein previously associated with chemotherapy response in ovarian cancer. CONCLUSION: Identification of a gene expression profile based on pathologic response in EOC provides independent prognostic information and offers potential insights into the mechanism of drug resistance. Efforts to identify a more tailored profile using selected genes from both the CRP and OCPP are underway. keyword(s): Correlation with outcome

ORGANISM(S): Homo sapiens

PROVIDER: GSE31245 | GEO | 2011/08/12

SECONDARY ACCESSION(S): PRJNA146053

REPOSITORIES: GEO

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Project description:PURPOSE: We investigated whether tumor tissue obtained at diagnosis expresses a specific gene profile that is predictive of findings at second-look surgery in patients with epithelial ovarian cancer (EOC). PATIENTS AND METHODS: Tumor tissue obtained at the time of diagnosis was profiled with oligonucleotide microarrays. Class prediction analysis was performed in a training set of 24 patients who had undergone a second-look procedure. The resultant predictive signature was then tested on an independent validation set comprised of 36 patients. RESULTS: A 93-gene signature referred to as the Chemotherapy Response Profile (CRP) was identified through its association with pathologic complete response. When applied to a separate validation set, the CRP distinguished between patients with unfavorable versus favorable overall survival (median 41 months v not yet reached, respectively, log-rank P = .007), with a median follow-up of 52 months. The signature maintained independent prognostic value in multivariate analysis, controlling for other known prognostic factors such as age, stage, grade, and debulking status. There was no genetic overlap between the CRP and our previously described Ovarian Cancer Prognostic Profile (OCPP), which demonstrated similar prognostic value. The combination of the CRP and OCPP yielded better prognostic discrimination then either profile alone. Genes present in the CRP include BAX, a proapoptotic protein previously associated with chemotherapy response in ovarian cancer. CONCLUSION: Identification of a gene expression profile based on pathologic response in EOC provides independent prognostic information and offers potential insights into the mechanism of drug resistance. Efforts to identify a more tailored profile using selected genes from both the CRP and OCPP are underway. keyword(s): Correlation with outcome All microarray experiments were performed at the BIDMC Genomics Center. Ovarian cancer samples were collected at the time of primary debulking surgery and frozen at 80°C. Tumor samples were pulverized in liquid nitrogen and homogenized in Trizol solution, followed by RNA isolation, probe labeling, and Affymetrix GeneChip hybridization using standard manufacturer protocols. The dChip algorithm was used for data normalization and for generating gene expression signal values. Two outlier arrays as identified by the dChip software were excluded from further analysis.

Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both. The expression values of 204 epithelial ovarian cancer tissues are determined and used for the validation of a subclassification approach (Cancer Sci. 2009 Aug;100(8):1421-8.)

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Unique gene expression profile based upon pathologic response in epithelial ovarian cancer

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