Gene expression profiles predict early relapse in ovarian cancer after platinum-paclitaxel chemotherapy

Lynn C. Hartmann, Karen H. Lu, Gerald P. Linette, William A. Cliby, Kimberly R. Kalli, David Gershenson, Robert C. Bast, James Stec, Natalia Iartcliouk, David I. Smith, Jeffrey S. Ross, Sebastian Hoersch, Viji Shridhar, James Lillie, Scott H. Kaufmann, Edwin A. Clark, Andrew I. Damokosli

Research output: Contribution to journalArticlepeer-review

117 Scopus citations


Purpose: Women with advanced epithelial ovarian cancer are routinely treated with platinum-paclitaxel chemotherapy following cytoreductive surgery, yet only ∼20% achieve long-term disease-free survival. We hypothesized that differences in gene expression before treatment could distinguish patients with short versus long time to recurrence after administration of platinum-paclitaxel combination chemotherapy. Experimental Design: To test this hypothesis, gene expression profiling of 79 primary surgically resected tumors from women with advanced-stage, high-grade epithelial ovarian cancer was done using cDNA microarrays containing 30,721 genes. Supervised learning algorithms were applied in an effort to develop a binary classifier that could discriminate women at risk for early (≤21 months) versus late (>21 months) relapse after initial chemotherapy. Results: A 14-gene predictive model was developed using a set of training samples (n = 51) and subsequently tested using an independent set of test samples (n = 28). This model correctly predicted the outcome of 24 of the 28 test samples (86% accuracy) with 95% positive predictive value for early relapse. Conclusions: Predictive markers for early recurrence can be identified for platinum-paclitaxel combination chemotherapy in primary ovarian carcinoma. The proposed 14-gene model requires further validation.

Original languageEnglish (US)
Pages (from-to)2149-2155
Number of pages7
JournalClinical Cancer Research
Issue number6
StatePublished - Mar 15 2005

ASJC Scopus subject areas

  • Oncology
  • Cancer Research


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