Table 3.

Integrated multi-omic network framework linking genomic, epigenomic, proteomic, spatial, and systems-biology layers to decode NSCLC immunotherapy response and resistance

Omic Axis	Mechanistic Focus	Analytical Tools / Approaches	Clinical / Translational Impact	Advantages	Challenges / Limitations	References
Genomic & Transcriptomic Correlates	Genomic lesions+transcriptomic states co-govern ICI response via antigen presentation, IFN-signaling, and immune-metabolic rewiring	BDVAE deep-learning model; ICBatlas; single-cell+bulk RNA integration; CRISPR-validated EGFR.Sig signature	Predicts responders/non-responders; identifies combinatorial vulnerabilities (e.g., m⁶A, CAF–immune crosstalk)	Multi-layer precision prediction; mechanistic interpretability	Requires multi-cohort harmonization and large curated datasets	(98)
Epigenomic Reprogramming (DNA Methylation)	Hypermethylation of immune genes (MHC, STING, IFN-pathway) suppresses antigen presentation → immune escape	Single-cell methylomics+HiChIP; DNMT inhibitor+STING agonist synergy modeling	Enables epigenetic-ICI combination strategies; identifies methylation-based resistance biomarkers	Mechanistically reversible immune silencing; therapeutic synergy	Tumor heterogeneity; off-target demethylation; compensatory histone regulation	(135)
Proteomic & Metabolomic Checkpoints	IDO1 / ARG1 axes deplete tryptophan & arginine → T-cell suppression via AHR, mTOR, GCN2 pathways	Spatial proteo-metabolomics; LC–MS/MS Kyn/Trp ratio; AI-based metabolic-network reconstruction	Guides immunometabolic therapy (IDO/TDO/ARG inhibitors, AHR antagonists)	Quantifiable, pathway-specific biomarkers; directly druggable	Dynamic flux adaptation: metabolic compensation across cell types	(136)
Spatial Single-Cell & Multi-Omic Integration	Spatial transcriptomics+scRNA+CITE-seq → map immune-suppressive niches & micro-domain heterogeneity	TopSpace / SPAC toolkits; Bayesian spatial-topic models; multiplex PhenoCycler	Enables niche-level therapeutic targeting & spatial vulnerability mapping	Resolves tumor–immune micro-architecture; links space → function	High computational cost; batch correction & alignment complexity	(137)
Systems-Biology Frameworks	Causal network integration of genome → epigenome → transcriptome → proteome → metabolome → immune phenotype	Multi-omic Bayesian networks; digital-twin immunogenomics; causal inference AI (SCMs, SEMs)	Predicts immunophenotypes & therapy outcomes; supports in silico perturbation testing	Mechanistic causality; cross-scale explainability; patient-specific modeling	Data interoperability; experimental validation lag; AI overfitting risk	(138)