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Predicting time to ovarian carcinoma recurrence using protein markers.
Ji-Yeon Yang1, Kosuke Yoshihara, Kenichi Tanaka
1Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas 77230-1402, USA.
The Journal of Clinical Investigation
|August 16, 2013
Summary
A new protein expression index, PROVAR, accurately predicts ovarian cancer recurrence risk and survival. This protein-based model outperforms gene expression methods, offering improved insights into tumor biology and clinical predictions.
Area of Science:
- Oncology
- Proteomics
- Biomarker Discovery
Background:
- Ovarian cancer frequently recurs, necessitating improved prediction of treatment outcomes.
- Accurate prediction models are crucial for developing targeted therapeutic strategies and improving patient survival.
Purpose of the Study:
- To develop and validate a novel protein expression-based index for predicting tumor recurrence and survival in ovarian cancer patients.
- To compare the predictive performance of the protein-based index against existing gene expression-based models.
Main Methods:
- Generated ovarian carcinoma protein expression profiles using reverse-phase protein arrays on 412 TCGA cases.
- Constructed a Protein-driven index of OVARian cancer (PROVAR).
- Validated PROVAR in an independent cohort of 226 high-grade serous ovarian carcinomas.
Main Results:
- PROVAR significantly stratified patients into high-risk and low-risk groups for tumor recurrence.
- PROVAR accurately predicted short-term versus long-term survival.
- The protein-based PROVAR demonstrated superior predictive capacity for tumor progression compared to gene expression-based models.
Conclusions:
- The PROVAR index offers a robust, protein-based approach for predicting ovarian cancer recurrence and survival.
- Protein expression profiling provides valuable insights into ovarian cancer biology and recurrence mechanisms.
- PROVAR, potentially combined with clinical factors like BRCA mutation status, may enhance clinical decision-making for ovarian cancer management.
