Back to Search
Start Over
A three protein signature fails to externally validate as a biomarker to predict surgical outcome in high-grade epithelial ovarian cancer.
- Source :
- PLoS ONE, Vol 18, Iss 3, p e0281798 (2023)
- Publication Year :
- 2023
- Publisher :
- Public Library of Science (PLoS), 2023.
-
Abstract
- IntroductionFor patients with advanced epithelial ovarian cancer, complete surgical cytoreduction remains the strongest predictor of outcome. However, identifying patients who are likely to benefit from such surgery remains elusive and to date few surgical outcome prediction tools have been validated. Here we attempted to externally validate a promising three protein signature, which had previously shown strong association with suboptimal surgical debulking (AUC 0.89, accuracy 92.8%), (Riester, M., et al., (2014)).Methods238 high-grade epithelial ovarian cancer samples were collected from patients who participated in a large multicentre trial (ICON5). Samples were collected at the time of initial surgery and before randomisation. Surgical outcome data were collated from prospectively collected study records. Immunohistochemical scores were generated by two independent observers for the three proteins in the original signature (POSTN, CXCL14 and pSmad2/3). Predictive values were generated for individual and combination protein signatures.ResultsWhen assessed individually, none of the proteins showed any evidence of predictive affinity for suboptimal surgical outcome in our cohort (AUC POSTN 0.55, pSmad 2/3 0.53, CXCL 14 0.62). The combined signature again showed poor predictive ability with an AUC 0.58.ConclusionsDespite showing original promise, when this protein signature is applied to a large external cohort, it is unable to accurately predict surgical outcomes. This could be attributed to overfitting of the original model, or differences in surgical practice between cohorts.
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 18
- Issue :
- 3
- Database :
- Directory of Open Access Journals
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.646f72f4c2246529a8550dd57df5393
- Document Type :
- article
- Full Text :
- https://doi.org/10.1371/journal.pone.0281798