Table 6.

Evidence levels supporting PD-L1-associated biomarkers and computational models in non-small cell lung cancer (NSCLC) immunotherapy

Biomarker / Approach	Strengths (Clinical Value)	Limitations	Current Evidence Type & Validation Status	Translational Readiness	References
PD-L1 Expression (tissue-based IHC)	Widely available; approved companion diagnostic; correlates with ICI response in subsets	High assay variability (22C3 vs SP142); temporal and spatial heterogeneity; threshold uncertainty	Clinical evidence; validated in multiple trials	High (established)	(210)
Tumor Mutational Burden (TMB)	Reflects neoantigen potential; improves predictive ability when combined with PD-L1	Not predictive alone; inconsistent cutoffs across platforms; assay cost	Clinical + computational evidence; partial validation	Moderate–High (conditional)	(189)
Exosomal / Soluble PD-L1	Non-invasive; reflects systemic immunosuppression and dynamic immune response	No standardized isolation or quantification method; high variability	Emerging translational evidence; early cohorts only	Moderate (pending validation)	(211)
ctDNA Dynamics	Enables real-time monitoring of treatment response and resistance evolution	Dependent on tumor shedding, may miss low-volume disease	Clinical evidence (increasing trial validation)	High (trial-ready)	(211)
TCR Repertoire Diversity / Clonality	Captures immune activation and ICI-associated clonal expansion	Lack of universal scoring metrics; complex interpretation	Translational + computational evidence	Moderate	(12)
Spatial Multi-omics / Multiplex Profiling	Captures immune micro-architecture; identifies immunosuppressive niches	Expensive; limited standardization; restricted clinical availability	Hybrid evidence (preclinical + emerging clinical)	Moderate (research-phase)	(2)
Multi-omic Composite Signatures	Higher accuracy than single biomarkers; integrates immune, genomic, spatial, and metabolic layers	Requires harmonized pipelines and large datasets; reproducibility challenges	Emerging clinical + computational evidence	Moderate–High (pending large validation)	(189)
AI-Driven Predictive Modeling (radiomics, digital twins, deep learning)	Enables adaptive prediction, response modeling, and personalization	Requires large datasets, explainability, regulatory approval, and dataset bias	Computational / simulation evidence; limited prospective validation	Low → Future-High	(12)