Mathematical Prognostic Biomarker Models for Treatment Response and Survival in Epithelial Ovarian Cancer
| Autor: | Walter C. Low, Jason B. Nikas, Kristin L.M. Boylan, Amy P.N. Skubitz |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2011 |
| Předmět: |
Oncology
Cancer Research medicine.medical_specialty Microarray global gene expression analysis medicine.medical_treatment Cell Gene regulatory network Disease Bioinformatics survival lcsh:RC254-282 03 medical and health sciences 0302 clinical medicine Internal medicine medicine Original Research 030304 developmental biology 0303 health sciences Chemotherapy business.industry prognostic biomarker models biomarkers treatment response Cancer medicine.disease lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens 3. Good health ovarian cancer medicine.anatomical_structure 030220 oncology & carcinogenesis Biomarker (medicine) Ovarian cancer business mathematical models |
| Zdroj: | Cancer Informatics, Vol 2011, Iss 10, Pp 233-247 (2011) Cancer Informatics Cancer Informatics, Vol 10 (2011) |
| ISSN: | 1176-9351 |
| Popis: | 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 ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
http://www.la-press.com/mathematical-prognostic-biomarker-models-for-treatment-response-and-su-a2863
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