Table 2.

Multi-axis, AI-integrated biomarker framework that unifies genomic, immune, and liquid profiles for precision adaptive immunotherapy

Biomarker Axis	Mechanistic Basis	Key 2025 Technologies	Clinical Impact	Advantages	Challenges / Limitations	References
Tumor Mutational Burden (TMB)	High mutation load → more neoantigens and T-cell visibility	AI-calibrated TMB models (DeepHLApan 2.0, NetMHCpan-2025)	Refines ICI stratification beyond PD-L1	Quantitative, broadly applicable	Variable assays, poor specificity	(90)
MSI / DDR Mutations	dMMR or DDR defects → frameshift indels+STING activation	DDR-gene panels, mutational-signature AI deconvolution (DRIA index)	Expands ICI eligibility beyond MSI-H	Mechanistic clarity; multi-tumor utility	Overlapping signatures, unclear cut-offs	(62, 91)
Peripheral & Tissue Immune Signatures	CD8⁺ T-cell phenotypes+IFN-γ / cytokine circuits	scRNA/TCR-seq, spatial cytokine AI maps	Predicts ICI response via immune functional states	Dynamic and context-aware	Costly multi-omic profiling	(92, 93)
Liquid Biopsies (ctDNA, exoPD-L1, TCR)	ctDNA=tumor burden; exoPD-L1=systemic suppression; TCR=adaptive response	Ultra-sensitive fragmentomics, AI ExoFlow-ML, DeepCaTCR models	Enables real-time therapy monitoring	Minimally invasive, multi-dimensional	Isolation biases, data integration issues	(94)
Composite Response Indices	Weighted fusion of multi-modal biomarkers → latent response score	Elastic-net / Bayesian fusion, digital-twin simulation	Mechanistic and predictive surrogate endpoint	Integrates orthogonal signals; regulatory potential	Needs standardized cut-offs & external validation	(95)