Close
Help




JOURNAL

Cancer Informatics

Mathematical Prognostic Biomarker Models for Treatment Response and Survival in Epithelial Ovarian Cancer

Submit a Paper


Cancer Informatics 2011:10 233-247

Original Research

Published on 03 Oct 2011

DOI: 10.4137/CIN.S8104


Further metadata provided in PDF



Sign up for email alerts to receive notifications of new articles published in Cancer Informatics

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.



Downloads

PDF  (716.24 KB PDF FORMAT)

RIS citation   (ENDNOTE, REFERENCE MANAGER, PROCITE, REFWORKS)

BibTex citation   (BIBDESK, LATEX)

XML




What Your Colleagues Say About Cancer Informatics
I would like to extend my gratitude for creating the next generation of a scientific journal -- the science journal of tomorrow. The entire process bespoke of exceptional efficiency, celerity, professionalism, competency, and service.
Dr Jason B. Nikas (Medical School University of Minnesota, Minneapolis, MN, USA)
More Testimonials

Quick Links


New article and journal news notification services