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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.

Authors :
Janssen JM
Verheijen RB
van Duijl TT
Lin L
van den Heuvel MM
Beijnen JH
Steeghs N
van den Broek D
Huitema ADR
Dorlo TPC
Source :
Clinical and translational science [Clin Transl Sci] 2022 Aug; Vol. 15 (8), pp. 1916-1925. Date of Electronic Publication: 2022 Jun 30.
Publication Year :
2022

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 <superscript>-4</superscript> , 95% CI 2.0 × 10 <superscript>-4</superscript> -7.0 × 10 <superscript>-4</superscript> /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.<br /> (© 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Details

Language :
English
ISSN :
1752-8062
Volume :
15
Issue :
8
Database :
MEDLINE
Journal :
Clinical and translational science
Publication Type :
Academic Journal
Accession number :
35775126
Full Text :
https://doi.org/10.1111/cts.13300