Mathematical modeling can be used to analyze longitudinal CA125 kinetics, and predict treatment efficacy. The modeled CA-125 elimination parameter KELIM was a predictive factor of efficacy in CALYPSO trial (You et al. Gynecol Oncol 2013). The present study aims at validating the independent predictive value of KELIM in phase III trial datasets with different 1stline treatments.
Data from AGO-OVAR 7 (carboplatin-paclitaxel (CP) +/- topotecan; n = 1308); AGO-OVAR 9 (CP +/- gemcitabine; n = 1742) and ICON-7 trials (CP +/- bevacizumab; French dataset only, n= 196) were analyzed. The biggest AGO 9 dataset was used as a training set, while AGO 7 & ICON7 were used as validation sets. CA125 concentration-time profiles was fit with following parameters: tumor growth rate (BETA); CA 125 tumor production (KPROD); CA 125 elimination rate (KELIM) & treatment indirect effect (Emax relationships) “d[CA125]/dt = (KPROD* exp (BETA*t)) * (1 - (A/(A + A50))) – KELIM * [CA125]” where t is time. The predictive value of KELIM dichotomized by the median was tested regarding progression free survival (PFS) against other reported prognostic factors (stage; pathology; surgery/completeness if any; grade; arms; Rustin) using Cox-models.
Individual CA125 profiles were well described by the model in training and validation datasets, as validated by visual predictive checks. KELIM ( 0.0598) exhibited strong independent predictive value regarding PFS in training dataset (univariate: 24.0 vs 11.3 months, P
The independent predictive value of KELIM was reproducible in large 3 datasets of ovarian cancer patients treated with different regimens. This may be a novel predictive factor, helpful for early selection of the best candidates during drug development.
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All authors have declared no conflicts of interest.