Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer

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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 specifcity ranged from 95.83% to 100.00%. The 12 most signifcant genes identifed, 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 frst gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a signifcant impact in the discovery of new, more effective pharmacological treatments that may signifcantly extend the survival of patients with advanced stage EOC.

Original languageEnglish (US)
Pages (from-to)233-247
Number of pages15
JournalCancer Informatics
Volume10
DOIs
StatePublished - Nov 14 2011

Fingerprint

Biomarkers
Drug Therapy
Survival
Gene Regulatory Networks
Survivors
Epothilones
Taxoids
Therapeutics
Tubulin
Paclitaxel
Platinum
Theoretical Models
Cell Proliferation
Pharmacology
Gene Expression
Recurrence
Ovarian epithelial cancer
Genes
Neoplasms

Keywords

  • Biomarkers
  • Global gene expression analysis
  • Mathematical models
  • Ovarian cancer
  • Prognostic biomarker models
  • Survival
  • Treatment response

Cite this

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title = "Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer",
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 specifcity ranged from 95.83{\%} to 100.00{\%}. The 12 most signifcant genes identifed, 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 frst gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a signifcant impact in the discovery of new, more effective pharmacological treatments that may signifcantly extend the survival of patients with advanced stage EOC.",
keywords = "Biomarkers, Global gene expression analysis, Mathematical models, Ovarian cancer, Prognostic biomarker models, Survival, Treatment response",
author = "Nikas, {Jason B.} and Boylan, {Kristin L M} and Skubitz, {Amy P N} and Low, {Walter C.}",
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AU - Low, Walter C.

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AB - 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 specifcity ranged from 95.83% to 100.00%. The 12 most signifcant genes identifed, 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 frst gene network is directly associated with the mechanism of action of anti-tubulin chemotherapeutic agents, such as taxanes and epothilones. This could have a signifcant impact in the discovery of new, more effective pharmacological treatments that may signifcantly extend the survival of patients with advanced stage EOC.

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