Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer
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 advance...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
SAGE Publishing
2011-01-01
|
| Series: | Cancer Informatics |
| Online Access: | https://doi.org/10.4137/CIN.S8104 |
| _version_ | 1818245337459982336 |
|---|---|
| author | Jason B. Nikas Kristin L.M. Boylan Amy P.N. Skubitz Walter C. Low |
| author_facet | Jason B. Nikas Kristin L.M. Boylan Amy P.N. Skubitz Walter C. Low |
| author_sort | Jason B. Nikas |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T14:31:19Z |
| format | Article |
| id | doaj.art-c1616f8350f6420b8551f3c0927a9f94 |
| institution | Directory Open Access Journal |
| issn | 1176-9351 |
| language | English |
| last_indexed | 2024-12-12T14:31:19Z |
| publishDate | 2011-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Cancer Informatics |
| spelling | doaj.art-c1616f8350f6420b8551f3c0927a9f942022-12-22T00:21:30ZengSAGE PublishingCancer Informatics1176-93512011-01-011010.4137/CIN.S8104Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian CancerJason B. Nikas0Kristin L.M. Boylan1Amy P.N. Skubitz2Walter C. Low3Masonic Cancer Center, Medical School, University of Minnesota, Minneapolis, MN, USA.Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN, USA.Department of Laboratory Medicine and Pathology, Medical School, University of Minnesota, Minneapolis, MN, USA.Department of Integrative Biology and Physiology, Medical School, University of Minnesota, Minneapolis, MN, USA.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.https://doi.org/10.4137/CIN.S8104 |
| spellingShingle | Jason B. Nikas Kristin L.M. Boylan Amy P.N. Skubitz Walter C. Low Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer Cancer Informatics |
| title | Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer |
| title_full | Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer |
| title_fullStr | Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer |
| title_full_unstemmed | Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer |
| title_short | Mathematical Prognostic Biomarker Models for Treatment Response and survival in Epithelial Ovarian Cancer |
| title_sort | mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer |
| url | https://doi.org/10.4137/CIN.S8104 |
| work_keys_str_mv | AT jasonbnikas mathematicalprognosticbiomarkermodelsfortreatmentresponseandsurvivalinepithelialovariancancer AT kristinlmboylan mathematicalprognosticbiomarkermodelsfortreatmentresponseandsurvivalinepithelialovariancancer AT amypnskubitz mathematicalprognosticbiomarkermodelsfortreatmentresponseandsurvivalinepithelialovariancancer AT walterclow mathematicalprognosticbiomarkermodelsfortreatmentresponseandsurvivalinepithelialovariancancer |