C-Reactive Protein as an Early Predictor of Efficacy in Advanced Non-Small-Cell Lung Cancer Patients: A Tumor Dynamics-Biomarker Modeling Framework

SIMPLE SUMMARY: In oncology, the identification of early predictors of response/survival is of particular interest. C-reactive protein (CRP) concentrations have been associated with advanced non-small-cell lung cancer and poor prognosis. We characterized the association between anticancer drug exposure, tumor size as a marker of tumor dynamics, and CRP as a marker of inflammation and derived different predictors. CRP at the beginning of treatment cycle 3 (day 42) was identified as the strongest predictor of both progression-free survival and overall survival, and the inflammatory status, monitored by CRP concentration, emerged as a promising prognostic marker. The high significance of longitudinal CRP concentrations compared to baseline concentrations provided a true reflection of the patient status. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies, allowing the identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and offer alternative treatment decisions. ABSTRACT: In oncology, longitudinal biomarkers reflecting the patient’s status and disease evolution can offer reliable predictions of the patient’s response to treatment and prognosis. By leveraging clinical data in patients with advanced non-small-cell lung cancer receiving first-line chemotherapy, we aimed to develop a framework combining anticancer drug exposure, tumor dynamics (RECIST criteria), and C-reactive protein (CRP) concentrations, using nonlinear mixed-effects models, to evaluate and quantify by means of parametric time-to-event models the significance of early longitudinal predictors of progression-free survival (PFS) and overall survival (OS). Tumor dynamics was characterized by a tumor size (TS) model accounting for anticancer drug exposure and development of drug resistance. CRP concentrations over time were characterized by a turnover model. An x-fold change in TS from baseline linearly affected CRP production. CRP concentration at treatment cycle 3 (day 42) and the difference between CRP concentration at treatment cycles 3 and 2 were the strongest predictors of PFS and OS. Measuring longitudinal CRP allows for the monitoring of inflammatory levels and, along with its reduction across treatment cycles, presents a promising prognostic marker. This framework could be applied to other treatment modalities such as immunotherapies or targeted therapies allowing the timely identification of patients at risk of early progression and/or short survival to spare them unnecessary toxicities and provide alternative treatment decisions.