Longitudinal nonlinear mixed effects modeling of EGFR mutations in ctDNA as predictor of disease progression in treatment of EGFR‐mutant non‐small cell lung cancer

Abstract

Correlations between increasing concentrations of circulating tumor DNA (ctDNA) in plasma and disease progression have been shown. A nonlinear mixed effects model to describe the dynamics of epidermal growth factor receptor (EGFR) ctDNA data from patients with non‐small cell lung cancer (NSCLC) combined with a parametric survival model were developed to evaluate the ability of these modeling techniques to describe ctDNA data. Repeated ctDNA measurements on L858R, exon19del, and T790M mutants were available from 54 patients with EGFR mutated NSCLC treated with erlotinib or gefitinib. Different dynamic models were tested to describe the longitudinal ctDNA concentrations of the driver and resistance mutations. Subsequently, a parametric time‐to‐event model for progression‐free survival (PFS) was developed. Predicted L858R, exon19del, and T790M concentrations were used to evaluate their value as predictor for disease progression. The ctDNA dynamics were best described by a model consisting of a zero‐order increase and first‐order elimination (19.7/day, 95% confidence interval [CI] 14.9–23.6/day) of ctDNA concentrations. In addition, time‐dependent development of resistance (5.0 × 10 −4 , 95% CI 2.0 × 10 −4 –7.0 × 10 −4 /day) was included in the final model. Relative change in L858R and exon19del concentrations from baseline was identified as most significant predictor of disease progression ( p = 0.001). The dynamic model for L858R, exon19del, and T790M concentrations in ctDNA and time‐to‐event model adequately described the observed concentrations and PFS data in our clinical cohort. In addition, it was shown that nonlinear mixed effects modeling is a valuable method for the analysis of longitudinal and heterogeneous biomarker datasets obtained from clinical practice.

Machine Learning in Modeling Disease Trajectory and Treatment Outcomes: An Emerging Enabler for Model‐Informed Precision Medicine

“…with integration of and radiomics-based covariates, 17,18 disease progression modeling of longitudinal high-dimensional ctDNA or radiomics profiles for elucidating POC and for identifying patient subgroups with differential treatment response remain largely untapped opportunities in drug development settings. In a study involving 466 patients with non-small cell lung cancer from a clinical trial that evaluated atezolizumab-based treatments, ML was applied to jointly model the dynamics of multiple ctDNA features as predictors of overall survival.…”

“…Advances in liquid biopsy are enabling deep molecular characterization of cancer evolution and molecular response dynamics via circulating tumor DNA (ctDNA) measurements in clinical trials, whereas advances in radiomics have exponentially increased the information content in radiographic imaging data. Although linkage between changes in ctDNA or radiomic signatures and survival outcomes have been described 16 and progress has been made in applying pharmacometric methodologies to modeling tumor size with integration of ctDNA and radiomics‐based covariates, 17,18 disease progression modeling of longitudinal high‐dimensional ctDNA or radiomics profiles for elucidating POC and for identifying patient subgroups with differential treatment response remain largely untapped opportunities in drug development settings. In a study involving 466 patients with non‐small cell lung cancer from a clinical trial that evaluated atezolizumab‐based treatments, ML was applied to jointly model the dynamics of multiple ctDNA features as predictors of overall survival 19 .…”

Moving the Needle for Oncology Dose Optimization: A Call for Action

“…Recent examples indicate the emerging value of circulating tumor DNA (ctDNA). 20,21 The translational utility of ctDNA, cancer cell DNA found in the bloodstream, is manifold, including detecting and diagnosing cancer, guiding tumor-specific treatment, monitoring treatment, and remission. In the context of dose optimization, characterizing the underlying exposure-response relationship for on-treatment ctDNA dynamics to inform definition of a clinically active dose range represents an untapped opportunity.…”

Toward Project Optimus for Oncology Precision Medicine: Multi‐Dimensional Dose Optimization Enabled by Quantitative Clinical Pharmacology

“…With advances in biomarker technologies for molecular monitoring of tumor burden using circulating cell‐free plasma‐derived tumor DNA (ctDNA) measurements, it should be possible to leverage such longitudinal measurements for PK/PD modeling using semimechanistic tumor kinetic modeling frameworks to inform dose and schedule selection. Tumor kinetic modeling of longitudinal ctDNA concentrations using population methods to describe the dynamics of driver and resistance mutational profiles has been described 31 . However, application for characterizing exposure‐response relationships for antitumor activity to inform dose selection in drug development remains a largely untapped opportunity.…”

“…Tumor kinetic modeling of longitudinal ctDNA concentrations using population methods to describe the dynamics of driver and resistance mutational profiles has been described. 31 However, application for characterizing exposure-response relationships for antitumor activity to inform dose selection in drug development remains a largely untapped opportunity. Progress will require collaboration across the disciplines of quantitative clinical pharmacology and molecular biomarker sciences for early and efficient learning of dose/exposureresponse relationships in phase I trials via population PK/PD modeling of ctDNA dynamics.…”