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Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer
| Content Provider | SAGE Publishing |
|---|---|
| Author | Nikas, Jason B. Kristin L.M. Boylan Amy P.N. Skubitz Low, Walter C. |
| Copyright Year | 2011 |
| 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. |
| Related Links | https://journals.sagepub.com/doi/pdf/10.4137/CIN.S8104?download=true |
| ISSN | 11769351 |
| Volume Number | 10 |
| Journal | Cancer Informatics (CIX) |
| e-ISSN | 11769351 |
| DOI | 10.4137/CIN.S8104 |
| Language | English |
| Publisher | Sage Publications UK |
| Publisher Date | 2011-10-03 |
| Publisher Place | London |
| Access Restriction | Open |
| Rights Holder | © 2011 SAGE Publications. |
| Subject Keyword | treatment response ovarian cancer survival global gene expression analysis biomarkers mathematical models prognostic biomarker models |
| Content Type | Text |
| Resource Type | Article |
| Subject | Cancer Research Oncology |
