Longitudinal Circulating Tumor DNA Modeling to Predict Disease Progression in First‐Line Mutant Epidermal Growth Factor Receptor Non‐Small Cell Lung Cancer

Abstract

This exploratory, post hoc analysis aimed to model circulating tumor DNA (ctDNA) dynamics and predict disease progression in patients with treatment‐naïve locally advanced/metastatic epidermal growth factor receptor mutation (EGFRm)‐positive non‐small cell lung cancer, from the FLAURA trial (NCT02296125). Patients were randomized 1:1 and received osimertinib 80 mg once daily (q.d.) or comparator EGFR‐TKIs (gefitinib 250 mg q.d. or erlotinib 150 mg q.d.). Plasma was collected at baseline and multiple timepoints until treatment discontinuation. Patients with Response Evaluation Criteria in Solid Tumors (RECIST) imaging data and detectable EGFR mutations (Ex19del/L858R) at baseline and ≥ 3 additional timepoints were evaluable. Joint modeling was conducted to characterize the relationship between longitudinal changes in ctDNA and probability of progression‐free survival (PFS). A Bayesian joint model of ctDNA and PFS was developed solving differential equations with the ctDNA dynamics and the PFS time‐to‐event probability. Of 556 patients, 353 had detectable ctDNA at baseline. Evaluable patients (with available imaging and ≥ 3 additional timepoints, n = 320; ctDNA set) were divided into training (n = 259) and validation (n = 61) sets. In the validation set, the model predicted a median PFS of 17.7 months (95% confidence interval (CI): 11.9–28.3) for osimertinib (n = 23) and 9.1 months (95% CI: 6.3–14.8) for comparator (n = 38), consistent with observed RECIST PFS (16.4 months and 9.7, respectively). The model demonstrates that EGFRm ctDNA dynamics can predict the risk of disease progression in this patient population and could be used to predict RECIST‐defined disease progression.