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.
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.
Authors: Manuel Aivado; Dimitrios Spentzos; Ulrich Germing; Gil Alterovitz; Xiao-Ying Meng; Franck Grall; Aristoteles A N Giagounidis; Giannoula Klement; Ulrich Steidl; Hasan H Otu; Akos Czibere; Wolf C Prall; Christof Iking-Konert; Michelle Shayne; Marco F Ramoni; Norbert Gattermann; Rainer Haas; Constantine S Mitsiades; Eric T Fung; Towia A Libermann Journal: Proc Natl Acad Sci U S A Date: 2007-01-12 Impact factor: 11.205
Authors: Roel G W Verhaak; Pablo Tamayo; Ji-Yeon Yang; Diana Hubbard; Hailei Zhang; Chad J Creighton; Sian Fereday; Michael Lawrence; Scott L Carter; Craig H Mermel; Aleksandar D Kostic; Dariush Etemadmoghadam; Gordon Saksena; Kristian Cibulskis; Sekhar Duraisamy; Keren Levanon; Carrie Sougnez; Aviad Tsherniak; Sebastian Gomez; Robert Onofrio; Stacey Gabriel; Lynda Chin; Nianxiang Zhang; Paul T Spellman; Yiqun Zhang; Rehan Akbani; Katherine A Hoadley; Ari Kahn; Martin Köbel; David Huntsman; Robert A Soslow; Anna Defazio; Michael J Birrer; Joe W Gray; John N Weinstein; David D Bowtell; Ronny Drapkin; Jill P Mesirov; Gad Getz; Douglas A Levine; Matthew Meyerson Journal: J Clin Invest Date: 2012-12-21 Impact factor: 14.808
Authors: Samuel C Mok; Tomas Bonome; Vinod Vathipadiekal; Aaron Bell; Michael E Johnson; Kwong-kwok Wong; Dong-Choon Park; Ke Hao; Daniel K P Yip; Howard Donninger; Laurent Ozbun; Goli Samimi; John Brady; Mike Randonovich; Cindy A Pise-Masison; J Carl Barrett; Wing H Wong; William R Welch; Ross S Berkowitz; Michael J Birrer Journal: Cancer Cell Date: 2009-12-08 Impact factor: 31.743
Authors: Michael C J Quinn; Daniel J Wilson; Fiona Young; Adam A Dempsey; Suzanna L Arcand; Ashley H Birch; Paulina M Wojnarowicz; Diane Provencher; Anne-Marie Mes-Masson; David Englert; Patricia N Tonin Journal: J Transl Med Date: 2009-07-06 Impact factor: 5.531
Authors: Melissa A Merritt; Peter G Parsons; Tanya R Newton; Adam C Martyn; Penelope M Webb; Adèle C Green; David J Papadimos; Glen M Boyle Journal: BMC Cancer Date: 2009-10-23 Impact factor: 4.430