Ovarian cancer index predicts time to recurrence

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An index created based on protein biomarkers was able to discriminate between women at high risk and low risk for ovarian cancer recurrence, in a recent analysis. Such information can be used to help improve the treatment and, eventually, the prognosis of patients with ovarian cancer, who are at high risk for tumor recurrence.

Roel G.W. Verhaak, PhD, of The University of Texas MD Anderson Cancer Center in Houston, Texas, and colleagues were able to construct a protein-driven index of ovarian cancer, or PROVAR, by generating ovarian carcinoma protein expression profiles for 412 cases. Specifically, PROVAR is a set of nine protein markers identified as being significantly associated with progression-free survival.

PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk for tumor recurrences as well as short-term and long-term survivors. When Verhaak and team applied multivariate analysis to determine whether the capabilities of PROVAR were independent of known predictive variables, such as patient age, tumor stage, tumor grade, and patient-surgery status, PROVAR was the only factor consistently significant for both progression-free survival and overall survival.

The researchers noted in The Journal of Clinical Investigation that comparison with gene expression–based outcome-classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. They concluded that when combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time-to-tumor recurrence.

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