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

Abstract

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.

Preclinical Modeling of Tumor Growth and Angiogenesis Inhibition to Describe Pazopanib Clinical Effects in Renal Cell Carcinoma

“…The use of data on circulating biomarkers can also supplement the information encompassed in tumor size measurements to better predict patients' outcomes in response to treatment (see refs. 12, 13, 14 as examples). Challenges in translating data from animal models in oncology are frequently cited as a critical impediment to drug development efforts,15 yet herein we propose that preclinical tumor size data can provide sufficient insights to facilitate the development of a more detailed mechanistic model of clinical tumor size response.…”

Modeling CA-125 During Neoadjuvant Chemotherapy for Predicting Optimal Cytoreduction and Relapse Risk in Ovarian Cancer

“…Indeed, this modern approach presents several advantages, such as low dependence on selected timepoints, longitudinal analysis of tumor marker kinetics, along with consideration of inter-and intra-individual variability (12). The reproducible predictive values of modeled kinetic parameters of other serum tumor markers, such as CA-125, prostatespecific antigen (PSA), human chorionic gonadotropin (hCG), alpha-fetoprotein (AFP) and circulating tumor cells (CTCs) have been reported in previous works (13)(14)(15)(16)(17). A modeled kinetic parameter related to CA-125 elimination, KELIM (modeled CA-125 elimination rate constant), was reported with a strong prognostic value regarding progression free survival in patients with recurrent ovarian cancer treated with carboplatin-based chemotherapy regimens in CALYPSO phase III trial (17).…”

Prediction of Response to Temozolomide in Low‐Grade Glioma Patients Based on Tumor Size Dynamics and Genetic Characteristics

“…Molecular profiling of tumors is a well‐known strategy to personalize anticancer treatments. Another approach is mathematical modeling . Mathematical models of tumor growth and response to treatment allow characterizing quantitatively the efficacy and toxicity of anticancer agents and can be used to predict clinical response .…”

“…Another approach is mathematical modeling. 28,29 Mathematical models of tumor growth and response to treatment allow characterizing quantitatively the efficacy and toxicity of anticancer agents and can be used to predict clinical response. 7,30 In the present study, we show that combining longitudinal tumor size measurements through the use of quantitative modeling with a tumor's genetic characterization is a promising strategy to personalize treatments in patients with lowgrade gliomas.…”