What question did this study set out to answer?

The aim is to quantify the timing of tumor-associated autoantibodies emergence relative to tumor growth and antigen levels in lung adenocarcinoma.

May 20, 2026

B111-14 Mathematical Modeling of Tumor-Associated Autoantibody Emergence and Persistence in Lung Adenocarcinoma: Insights Into Diagnostic Lead Time

Key Points

The aim is to quantify the timing of tumor-associated autoantibodies emergence relative to tumor growth and antigen levels in lung adenocarcinoma.
Developed a mechanistic model based on tumor kinetics using a Gompertz growth model.
Simulated the emergence of autoantibodies as tumor burden surpasses specific thresholds.
Analyzed biomarker detectability over a 12-year period.
Autoantibodies became detectable 0.7 to 6.5 years before traditional imaging methods.
Antigen biomarkers appeared 0.7 to 7.5 years after autoantibody detectability.
Model predictions indicate the potential for immune-based biomarkers to enhance early detection.

Abstract

Abstract Rationale Early diagnosis of lung cancer remains crucial to improving outcomes, yet early-stage tumors release minimal tumor-associated antigens (TAAs) and remain radiographically occult. Tumor-associated autoantibodies (AABs), generated by the host immune response, may arise earlier in tumor evolution, but the temporal relationship between tumor growth, antigen shedding, and antibody detectability has not been quantitatively characterized. We developed a mechanistic model to simulate the emergence of circulating AABs relative to tumor burden and antigen levels, estimating the theoretical diagnostic lead time compared with standard antigen assays and radiographic detectability. Methods Tumor kinetics were represented by a Gompertz growth model parameterized for lung adenocarcinoma (200-day doubling time at 1 cm, K = 1012 cells). Immune activation occurred once tumor burden exceeded 102 cells, after a 60-day germinal-center lag. Plasma-cell activation scaled with tumor size via a Hill function with antibody-mediated feedback inhibition. Short-lived and long-lived plasma-cell compartments (half-lives = 60 days and 1 year, respectively) secreted IgG with a 30-day half-life. The standard TAA model followed first-order Hori-type kinetics, incorporating tumor vascularity-dependent permeability and 0.5-day plasma clearance. ELISA-like thresholds (3 ng/mL for AABs, 2 ng/mL for antigen) defined detectability, and a 0.5 cm diameter tumor (∼6 × 107 cells) approximated CT sensitivity. The coupled nonlinear system was solved over 12 years to characterize the sequence of immune recognition, biomarker emergence, and radiographic visibility. Results Simulations revealed a consistent temporal hierarchy: immune recognition occurred while tumors were 0.1 cm, triggering a rapid exponential rise in AABs that plateaued years before imaging detectability. The TAA concentration remained near baseline until late exponential growth, when shedding outpaced clearance. Across plausible biological ranges, AABs reached detectability 0.7 - 6.5 years before radiographic visibility, whereas antigen biomarkers became positive 0.7 - 7.5 years after AAB appearance. The interval was modulated by plasma-cell longevity and feedback strength, with slower turnover or weaker inhibition extending the lead time. Conclusions This systems model quantitatively explains how tumor-associated autoantibodies can precede both antigen biomarkers and imaging by several years, establishing an upper bound for diagnostic lead time in lung adenocarcinoma. The results highlight the potential of immune-based biomarkers to close the detection gap between tumor emergence and radiographic visibility, informing the design of prospective validation studies and precision early-detection strategies. This abstract is funded by: Biodesix

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