Prediction of tumour response induced by chemotherapy using modelling of CA-125 kinetics in recurrent ovarian cancer patients.

BACKGROUND: The main objective of the present study was to establish the relationships between CA-125 kinetics and tumour size changes during treatment. METHODS: The data from the CALYPSO-randomised phase III trial, comparing two platinum-based regimens in recurrent ovarian cancer (ROC) patients, was randomly split into a 'learning data set' to estimate model parameters and a 'validation data set' to validate model performances. A kinetic-pharmacodynamic semi-mechanistic model was built to describe tumour size and CA-125 kinetics during chemotherapy. The ability of the model to predict tumour response induced by chemotherapy, based on CA-125 values, was assessed. RESULTS: Data from 535 ROC patients were used to model CA-125 kinetics and tumour size changes during the first 513 days after treatment initiation. Using the validated model, we could predict with accuracy the tumour size changes induced by chemotherapy based on the baseline imaging assessment and longitudinal CA-125 values (mean prediction error: 0.3%, mean absolute prediction error: 10.6%). CONCLUSIONS: Using a semi-mechanistic model, the dynamic relationships between tumour size changes and CA-125 kinetics induced by chemotherapy were established in ROC patients. A modelling approach allowed CA-125 to be assessed as a biomarker for tumour size dynamics, to predict treatment efficacy for research and clinical purposes.

A critical review of the analytical approaches for circulating tumor biomarker kinetics during treatment.

Changes in serum tumor biomarkers may indicate treatment efficacy. Traditional tumor markers may soon be replaced by novel serum biomarkers, such as circulating tumor cells (CTCs) or circulating tumor nucleic acids. Given their promising predictive values, studies of their kinetics are warranted. Many methodologies meant to assess kinetics of traditional marker kinetics during anticancer treatment have been reported. Here, we review the methodologies, the advantages and the limitations of the analytical approaches reported in the literature. Strategies based on a single time point were first used (baseline value, normalization, nadir, threshold at a time t), followed by approaches based on two or more time points [half-life (HL), percentage decrease, time-to-events…]. Heterogeneities in methodologies and lack of consideration of inter- and intra-individual variability may account for the inconsistencies and the poor utility in routine. More recently, strategies based on a population kinetics approach and mathematical modeling have been reported. The identification of equations describing individual kinetic profiles of biomarkers may be an alternative strategy despite its complexity and higher number of necessary measurements. Validation studies are required. Efforts should be made to standardize biomarker kinetic analysis methodologies to ensure the optimized development of novel serum biomarkers and avoid the pitfalls of traditional markers.

Predictive values of hCG clearance for risk of methotrexate resistance in low-risk gestational trophoblastic neoplasias.

BACKGROUND: Early identification of patients at high risk for chemoresistance among those treated with methotrexate (MTX) for low-risk gestational trophoblastic neoplasia (GTN) is needed. We modeled human chorionic gonadotropin (hCG) decline during MTX therapy using a kinetic population approach to calculate individual hCG clearance (CL(hCG)) and assessed the predictive value of CL(hCG) for MTX resistance. PATIENTS AND METHODS: A total of 154 patients with low-risk GTN treated with 8-day MTX regimen were retrospectively studied. NONMEM was used to model hCG decrease equations between day 0 and day 40 of chemotherapy. Receiver operating characteristic curve analysis defined the best CL(hCG) threshold. Univariate/multivariate survival analyses determined the predictive value of CL(hCG) and compared it with published predictive factors. RESULTS: A monoexponential equation best modeled hCG decrease: hCG(t) = 3900 x e(-0.149 x t). Median CL(hCG) was 0.57 l/day (quartiles: 0.37-0.74). Only choriocarcinoma pathology [yes versus no: hazard ratio (HR) = 6.01; 95% confidence interval (CI) 2.2-16.6; P < 0.001] and unfavorable CL(hCG) quartile (< or =0.37 versus >0.37 l/day: HR = 6.75; 95% CI 2.7-16.8; P < 0.001) were significant independent predictive factors of MTX resistance risk. CONCLUSION: In the second largest cohort of low-risk GTN patients reported to date, choriocarcinoma pathology and CL(hCG) < or =0.37 l/day were major independent predictive factors for MTX resistance risk.