title: Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer.
abstract: Following initial standard chemotherapy (platinum/taxol), more than 75% of those patients with advanced stage epithelial ovarian cancer (EOC) experience a recurrence. There are currently no accurate prognostic tests that, at the time of the diagnosis/surgery, can identify those patients with advanced stage EOC who will respond to chemotherapy. Using a novel mathematical theory, we have developed three prognostic biomarker models (complex mathematical functions) that-based on a global gene expression analysis of tumor tissue collected during surgery and prior to the commencement of chemotherapy-can identify with a high accuracy those patients with advanced stage EOC who will respond to the standard chemotherapy [long-term survivors (>7 yrs)] and those who will not do so [short-term survivors (<3 yrs)]. Our three prognostic biomarker models were developed with 34 subjects and validated with 20 unknown (new and different) subjects. Both the overall biomarker model sensitivity and specificity ranged from 95.83% to 100.00%. The 12 most significant genes identified, which are also the input variables to the three mathematical functions, constitute three distinct gene networks with the following functions: 1) production of cytoskeletal components, 2) cell proliferation, and 3) cell energy production. The first gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a significant impact in the discovery of new, more effective pharmacological treatments that may significantly extend the survival of patients with advanced stage EOC.
authors.affiliation: Department of Neurosurgery.
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authors.initials: JB
authors.lastname: Nikas
authors.forename: Kristin L M
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authors.lastname: Boylan
authors.forename: Amy P N
authors.initials: AP
authors.lastname: Skubitz
authors.forename: Walter C
authors.initials: WC
authors.lastname: Low
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