Gene Expression Profile for Predicting Survival in Advanced-Stage Serous Ovarian Cancer Across Two Independent Datasets

Background Advanced-stage ovarian cancer patients are generally treated with platinum/taxane-based chemotherapy after primary debulking surgery. However, there is a wide range of outcomes for individual patients. Therefore, the clinicopathological factors alone are insufficient for predicting prognosis. Our aim is to identify a progression-free survival (PFS)-related molecular profile for predicting survival of patients with advanced-stage serous ovarian cancer. Methodology/Principal Findings Advanced-stage serous ovarian cancer tissues from 110 Japanese patients who underwent primary surgery and platinum/taxane-based chemotherapy were profiled using oligonucleotide microarrays. We selected 88 PFS-related genes by a univariate Cox model (p<0.01) and generated the prognostic index based on 88 PFS-related genes after adjustment of regression coefficients of the respective genes by ridge regression Cox model using 10-fold cross-validation. The prognostic index was independently associated with PFS time compared to other clinical factors in multivariate analysis [hazard ratio (HR), 3.72; 95% confidence interval (CI), 2.66–5.43; p<0.0001]. In an external dataset, multivariate analysis revealed that this prognostic index was significantly correlated with PFS time (HR, 1.54; 95% CI, 1.20–1.98; p = 0.0008). Furthermore, the correlation between the prognostic index and overall survival time was confirmed in the two independent external datasets (log rank test, p = 0.0010 and 0.0008). Conclusions/Significance The prognostic ability of our index based on the 88-gene expression profile in ridge regression Cox hazard model was shown to be independent of other clinical factors in predicting cancer prognosis across two distinct datasets. Further study will be necessary to improve predictive accuracy of the prognostic index toward clinical application for evaluation of the risk of recurrence in patients with advanced-stage serous ovarian cancer.

[1]  A. Scarpa,et al.  Pathology and Genetics , 2010 .

[2]  Jeremy J. W. Chen,et al.  A five-gene signature and clinical outcome in non-small-cell lung cancer. , 2007, The New England journal of medicine.

[3]  M. van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.

[4]  L. V. van't Veer,et al.  Cross‐validated Cox regression on microarray gene expression data , 2006, Statistics in medicine.

[5]  David I. Smith,et al.  Gene Expression Profiles Predict Early Relapse in Ovarian Cancer after Platinum-Paclitaxel Chemotherapy , 2005, Clinical Cancer Research.

[6]  H. Hamm,et al.  A Novel Bifunctional Phospholipase C That Is Regulated by Gα12 and Stimulates the Ras/Mitogen-activated Protein Kinase Pathway* , 2001, The Journal of Biological Chemistry.

[7]  U. Brinkmann,et al.  CSE1L/CAS: Its role in proliferation and apoptosis , 2004, Apoptosis.

[8]  R. Tibshirani,et al.  Repeated observation of breast tumor subtypes in independent gene expression data sets , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[9]  C. Prives,et al.  hCAS/CSE1L Associates with Chromatin and Regulates Expression of Select p53 Target Genes , 2007, Cell.

[10]  Roland Eils,et al.  Prediction of clinical outcome and biological characterization of neuroblastoma by expression profiling , 2004, Oncogene.

[11]  P. Harter,et al.  Histological grading in a large series of advanced stage ovarian carcinomas by three widely used grading systems: consistent lack of prognostic significance. A translational research subprotocol of a prospective randomized phase III study (AGO-OVAR 3 protocol) , 2009, Virchows Archiv.

[12]  Christian Marth,et al.  Clinical Relevance of E2F Family Members in Ovarian Cancer—An Evaluation in a Training Set of 77 Patients , 2007, Clinical Cancer Research.

[13]  Jacobus Pfisterer,et al.  Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: A combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials , 2009, Cancer.

[14]  Laurent Ozbun,et al.  A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. , 2008, Cancer research.

[15]  L. Staudt,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[16]  Peter Devilee,et al.  Pathology and Genetics of Tumours of the Breast and Female Genital Organs , 2003 .

[17]  J. Kigawa,et al.  Expression of the c‐myc gene as a predictor of chemotherapy response and a prognostic factor in patients with ovarian cancer , 2004, Cancer science.

[18]  Jung-Hwan Yoon,et al.  Gene Expression–Based Recurrence Prediction of Hepatitis B Virus–Related Human Hepatocellular Carcinoma , 2008, Clinical Cancer Research.

[19]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[20]  R. Ozols,et al.  Prognostic factors for stage III epithelial ovarian cancer: a Gynecologic Oncology Group Study. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[21]  E. Golemis,et al.  Cell cycle-dependent ciliogenesis and cancer. , 2008, Cancer research.

[22]  R. Tothill,et al.  Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome , 2008, Clinical Cancer Research.

[23]  R. Berkowitz,et al.  Increased HLA-DMB Expression in the Tumor Epithelium Is Associated with Increased CTL Infiltration and Improved Prognosis in Advanced-Stage Serous Ovarian Cancer , 2008, Clinical Cancer Research.

[24]  A. Dupuy,et al.  Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.

[25]  U. Brinkmann,et al.  CSE 1 L / CAS : Its role in proliferation and apoptosis , 2022 .

[26]  Classification and Staging of Malignant Tumours in the Female Pelvis , 1971 .

[27]  W. Weichert,et al.  A prognostic gene expression index in ovarian cancer—validation across different independent data sets , 2009, The Journal of pathology.

[28]  R. Agarwal,et al.  Expression profiling and individualisation of treatment for ovarian cancer. , 2006, Current opinion in pharmacology.

[29]  Marie Joseph,et al.  Gene expression signature with independent prognostic significance in epithelial ovarian cancer. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  R. Ozols Treatment goals in ovarian cancer , 2005, International Journal of Gynecologic Cancer.

[31]  M. Piccart,et al.  Randomized intergroup trial of cisplatin-paclitaxel versus cisplatin-cyclophosphamide in women with advanced epithelial ovarian cancer: three-year results. , 2000, Journal of the National Cancer Institute.

[32]  A. Whittemore,et al.  A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2 , 2009, Nature Genetics.

[33]  Shin Ishii,et al.  Prediction of recurrence in advanced gastric cancer patients after curative resection by gene expression profiling , 2005, International journal of cancer.

[34]  Kenichi Tanaka,et al.  Genome-Wide Expression of Azoospermia Testes Demonstrates a Specific Profile and Implicates ART3 in Genetic Susceptibility , 2008, PLoS genetics.

[35]  S. Cannistra,et al.  Gene-expression profiling in epithelial ovarian cancer , 2008, Nature Clinical Practice Oncology.

[36]  M Van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. , 2000, Journal of the National Cancer Institute.

[37]  S. Silverberg Histopathologic grading of ovarian carcinoma: a review and proposal. , 2000, International journal of gynecological pathology : official journal of the International Society of Gynecological Pathologists.

[38]  A. Whittemore,et al.  Survival among U.S. women with invasive epithelial ovarian cancer. , 2002, Gynecologic oncology.

[39]  H. Kottmeier Classification and staging of malignant tumours in the female pelvis. , 1968, Acta obstetricia et gynecologica Scandinavica.

[40]  Michael T Deavers,et al.  Grading Ovarian Serous Carcinoma Using a Two-Tier System , 2004, The American journal of surgical pathology.

[41]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[42]  Anil Potti,et al.  An integrated genomic-based approach to individualized treatment of patients with advanced-stage ovarian cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[43]  Aimo Ojala Studies on Bilirubin in Amniotic Fluid: With Special Reference to Liver Function Tests , 1971, Acta obstetricia et gynecologica Scandinavica. Supplement.

[44]  M. McKay,et al.  Cancer of the ovary. , 1994, The New England journal of medicine.

[45]  Arnoldo Frigessi,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm305 Gene expression Predicting survival from microarray data—a comparative study , 2022 .

[46]  Maurice P H M Jansen,et al.  Molecular profiling of platinum resistant ovarian cancer , 2006, International journal of cancer.

[47]  R. Tibshirani,et al.  Semi-Supervised Methods to Predict Patient Survival from Gene Expression Data , 2004, PLoS biology.

[48]  H. Hollema,et al.  Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer , 2009, PLoS medicine.

[49]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[50]  E. Partridge,et al.  Cyclophosphamide and cisplatin compared with paclitaxel and cisplatin in patients with stage III and stage IV ovarian cancer , 1996, New England Journal of Medicine.

[51]  Ituro Inoue,et al.  Gene expression profiling of advanced‐stage serous ovarian cancers distinguishes novel subclasses and implicates ZEB2 in tumor progression and prognosis , 2009, Cancer science.

[52]  T. Jobo,et al.  Dose-dense paclitaxel once a week in combination with carboplatin every 3 weeks for advanced ovarian cancer: a phase 3, open-label, randomised controlled trial , 2009, The Lancet.

[53]  S. Coughlin,et al.  Incidence of ovarian cancer by race and ethnicity in the United States, 1992–1997 , 2003, Cancer.

[54]  Jingqin Luo,et al.  Microarray Analysis of Early Stage Serous Ovarian Cancers Shows Profiles Predictive of Favorable Outcome , 2009, Clinical Cancer Research.

[55]  Jeffrey T. Chang,et al.  Oncogenic pathway signatures in human cancers as a guide to targeted therapies , 2006, Nature.

[56]  M. Birrer,et al.  Prognostic relevance of c-MYC gene amplification and polysomy for chromosome 8 in suboptimally-resected, advanced stage epithelial ovarian cancers: a Gynecologic Oncology Group study. , 2009, Gynecologic oncology.