Non-invasive cfDNA methylation profiling for prediction of PD-L1 tumor proportion score status in NSCLC

Background: Programmed death-ligand 1 (PD-L1) expression, measured by tumor proportion score (TPS), guides immunotherapy (IO) selection in NSCLC. However, tissue-based PD-L1 immunohistochemistry (IHC) is often limited by insufficient tissue, sampling bias and intratumoral heterogeneity. cfDNA methylation signatures enable non-invasive measurement of tumor-derived epigenetic signals and may be able to capture PD-L1-associated biology from a simple blood draw. We developed a cfDNA methylation-based predictor to identify patients with low PD-L1 TPS (<50%), a group more likely to benefit from IO combination regimens rather than IO monotherapy. Methods: cfDNA methylation profiles of >500 plasma clinical patient samples, each with paired tumor PD-L1 IHC data, were analyzed across thousands of regulatory regions. A regularized logistic regression model was trained to predict samples with PD-L1 TPS <50%. Model performance was evaluated on an independent test cohort (N=90) by comparing predicted calls with IHC-based PD-L1 TPS measurements. Results: The cfDNA methylation predictor for identifying PD-L1 TPS <50% cases achieved >50% sensitivity, 87% specificity, and >90% positive prediction value (PPV). Model performance was consistent across NSCLC histologies (LUAD and LUSC) and remained robust at tumor fractions as low as 0.05%. Approximately 70% of liquid biopsy samples were evaluable, supporting the feasibility of cfDNA methylation analysis for the majority of clinical samples. Conclusions: Our methylation-based PD-L1 low predictor enables non-invasive detection of NSCLC cases with tissue PD-L1 TPS <50%, offering a potential alternative when tissue is limited or not available. Further investigation is warranted to determine whether an epigenetic PDL1 IHC trained classifier can support treatment decisions by identifying NSCLC patients more likely to require IO chemotherapy combination therapy.