Table 5.

AI-driven multi-omic integration transforms non-small cell lung cancer (NSCLC) immunotherapy into dynamic, continuously learning precision medicine

Subsection	Core Mechanistic Concept	Analytical /Computational Approaches	Clinical / Translational Significance	Advantages	Challenges / Limitations	References
Biomarker-Guided Checkpoint Selection	Composite biomarker axes (PD-L1 gradient, IFN-γ genomics, exhaustion-receptor maps, chromatin accessibility) determine PD-1, CTLA-4, TIGIT, LAG-3, and TIM-3 blockade choice	Single-cell transcriptomics, multiplex immunofluorescence, receptor co-expression profiling	Matches checkpoint to inhibitory topology; reduces toxicity; enhances durable benefit	Adaptive receptor-network logic; precision patient stratification	Real-time biomarker measurement; incomplete inhibitory-axis coverage	(182, 183)
Rational Immunotherapy Combinations	Integration of chemo-/radio-/targeted therapy, vaccines, and ACT to exploit immunogenic cell death and vascular remodeling	Radiomic–immunomic fusion, STING-pathway modeling, temporal dosing synchronization	Converts immune-excluded tumors to an inflamed state; achieves durable synergistic responses	Spatiotemporal synergy; improved infiltration and antigen presentation	Scheduling complexity, toxicity, and pharmacokinetic variability	(168)
Temporal Sequencing & Adaptive Algorithms	Dynamic biomarkers (cfDNA, TCR clonality, cytokine shifts) steer treatment adaptation in real time	CloneSeq-SV, ABF-CatBoost, ΔAT & eTTP indices, PK/PD fusion models	Enables closed-loop control; anticipates resistance; optimizes timing	Evolution-aware precision; rapid feedback	Costly longitudinal data; regulatory approval lag	(184)
Case-Based Personalization Frameworks	Multi-omic patient profiling combined with trial-specific AI models for individualized therapy design	PBMF contrastive AI, federated learning on KEYNOTE/CheckMate /IMpower datasets	N=1 trial allocation; dynamic switching of IO+VEGF/TKI regimens	Trial-level adaptivity; mechanistic enrichment of enrollment	Data-privacy barriers; assay standardization needs	(178, 185)