Mathematical Prognostic Biomarker Models for Treatment Response and Survival in Epithelial Ovarian Cancer.
Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy in the U.S.A. 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, authors have developed three prognostic biomarker models (complex mathematical functions) that—based on a global gene expression analysis of tumour 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)].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.
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%.
Authors: Jason B. Nikas, Kristin L.M. Boylan, Amy P.N. Skubitz, and Walter C. Low
Source: http://www.la-press.com. doi: 10.4137/CIN.S8104.
Publication date: 3rd Oct, 2011.
Comment feed for this post
