Liquid biopsy cfDNA methylation predicts lung tumor size and metastatic potential in a single assay

Liquid biopsy enables noninvasive assessment of cancer severity and prognosis, informing clinical decisions across the cancer care spectrum. Estimates of the fraction of tumor-derived DNA shed into circulation (tumor content; TC) reflect disease severity, with higher TC more frequently observed in advanced stages and linked to poorer outcomes. However, TC is a product of a complex pathology and is impacted by many factors including tumor type, size, shedding rate, aggressiveness, vascularization, genotype, and metastatic state. We aim to predict two of them, size and metastatic state, using a targeted cell-free DNA (cfDNA) methylation assay. To assess metastatic potential, we trained models for two binary prediction tasks in lung cancer: distant metastasis (Stage I/II vs IV) and late stage (Stage I/II vs III/IV). Features quantifying region-level methylation patterns were residualized with respect to sample-level TC to capture signals orthogonal to size. TC was evaluated as a single predictor in parallel. We trained on a dataset of 54 true and 324 synthetic lung cancer cfDNA samples to further emphasize size-independent, stage-related features. All models were assessed at 90% target specificity on an independent test set of 30 true cancer samples classified as lung cancer by a multiclass tissue-of-origin model to represent the end-to-end performance of the assay. For size prediction, we developed a lung-specific TC estimator, fit log-linear models linking TC to radiology-derived tumor size metrics, and generated predictions for 57 held-out test samples. All samples were from the CORE-HH clinical study (NCT05435066). The residual methylation models identified distant metastasis with 87.5% sensitivity (14/16 Stage IV) at 83% specificity (5/6 Stage I/II) and late stage with 67% sensitivity (16/24 Stage III/IV) at 83% specificity (5/6 Stage I/II). These results substantially improved on the TC-only model (19% and 17% sensitivity, respectively, at the same specificities), indicating methylation metrics capture tumor progression-associated signal orthogonal to TC. For size prediction, restricting to Stage I-III cases with PET metrics (N = 19) yielded the strongest fits (log 10 (total volume) R² > 0.5, p < 5x10⁻⁴). Test set predictions showed moderate explanatory power (R² = 0.24, p = 1.3x10 -4 ), consistent with factors beyond size (e.g., visceral metastasis) influencing shedding. These findings highlight the potential for stage and size prediction models to deliver clinically actionable insights from a single blood draw. Late-stage prediction could guide workup prioritization, treatment intensity, and surveillance strategies, while size prediction may support prognosis and therapy selection. These capabilities motivate further model development for complementary prediction tasks (e.g., aggressiveness, genotype) toward a suite of tools for precision oncology.