Abstract 986: Mathematical modeling of ctDNA dynamics to predict utility of ultrasensitive MRD assays in early stage non-small cell lung cancer (NSCLC)

Abstract Background: ctDNA assay use for minimal residual disease (MRD) detection in early stage NSCLC has been limited by suboptimal sensitivity of first generation assays. It is unclear how improving assay limit of detection (LOD) would translate to improvements in clinical sensitivity. Here, we performed mathematical modeling of ctDNA dynamics after curative-intent treatment in early stage NSCLC to predict the clinical impact of MRD assays with improved analytical sensitivity. Methods: We analyzed a recently reported dataset of post-surgical ctDNA MRD surveillance in early stage NSCLC (Abbosh et al. , Nature 2023) to investigate ctDNA growth dynamics. Of 70 relapsed patients in this cohort, 23 had 3 consecutive ctDNA-positive samples with no intervening therapy. We used these to generate patient-specific mathematical models for ctDNA variant allele fractions (VAF) over time, initially assuming exponential growth. We assessed this assumption by creating log-linear models for ctDNA increase in these cases. Using slopes of the log-linear models, we calculated patient-specific doubling times and assessed their relation to stage, histology, and outcome. We used the log-linear models to simulate a VAF distribution each day after surgery, enabling projection of clinical sensitivities and estimation of lead times for recurrence detection at varying LODs. Results: For the 23 cases with 3+ consecutive ctDNA positive samples, we developed log-linear models for ctDNA VAF growth, which were strongly correlated (r 0. 5) in 18/23 (78%) patients (median r = 0. 89), supporting that ctDNA can be modeled as exponential growth. We calculated ctDNA doubling times for the 18 patients with exponential ctDNA kinetics and 10 patients with 2 consecutive ctDNA positive samples. Doubling times were remarkably consistent with median 51 days (IQR 40-79). Doubling times were longer for stage I tumors compared to stage II/III (p = 0. 04) but were not different between NSCLC subtypes. Doubling time correlated with time to clinical relapse, with patients having more rapid ctDNA growth relapsing earlier (r = 0. 52, p=0. 03). Finally, we used patient-specific log-linear models to project ctDNA VAFs at a landmark 30 days after surgery. By improving the LOD for ctDNA detection from 10−4 to 10−6, the projected clinical sensitivity doubled (from 43% to 86%). Simulations predicted that this improvement in sensitivity would increase median lead time from 183 days for LOD=10−4 to 349 days for LOD=10−6 (p0. 0001). Conclusion: For most relapsed NSCLC patients, the natural history of ctDNA MRD growth can be reliably modeled with exponential growth kinetics. ctDNA doubling time is associated with stage and clinical outcome. Ultrasensitive MRD assays with LOD of 10−6 are predicted to meaningfully improve clinical sensitivity at the post-surgical landmark and to increase the lead time for MRD detection before relapse. Citation Format: Jordan S. Goldstein, Christopher Abbosh, Ash A. Alizadeh, Maximilian Diehn, David M. Kurtz. Mathematical modeling of ctDNA dynamics to predict utility of ultrasensitive MRD assays in early stage non-small cell lung cancer (NSCLC) abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 986.