Variant-informed multimodal tissue-state modeling reveals an NRF2 stress niche and dependency axis in...

Cancer genotypes influence transcriptional programs, spatial organization, histopathology, and treatment-response states, yet these molecular and tissue-level features are often analyzed separately in public-data studies. VISTA was developed as a variant-informed, multimodal framework for modeling genotype-linked tissue states and prioritizing validation-ready biological hypotheses.

VISTA integrates somatic variants, protein-region and AlphaMissense-informed priors, RNA-state scoring, whole-slide image embeddings, immunotherapy cohorts, spatial and single-cell references, cell-line dependency maps, and pharmacogenomic screens. We evaluated the framework in KEAP1/NFE2L2-altered lung cancer and melanoma, using the NRF2 stress axis as a biologically defined test case. Analyses incorporated project-adjusted modeling, bootstrap confidence intervals, permutation testing, leave-project sensitivity analyses, independent tumor-level validation cohorts, fixed-effect external meta-analysis, model-system analyses using DepMap/CCLE, tissue-context analyses, immunotherapy cohorts, and exploratory whole-slide image analyses.

In TCGA LUAD, LUSC, and SKCM pilot cohorts, KEAP1 and NFE2L2 alterations were consistently associated with NRF2 stress activation across project-adjusted models and sensitivity analyses. Independent analyses of CPTAC LUAD, CPTAC LSCC, and non-TCGA LUAD cohorts reproduced higher NRF2-state scores in KEAP1/NFE2L2-altered tumors. Fixed-effect external meta-analysis supported associations with both KEAP1 mutation and combined KEAP1-or-NFE2L2 alteration status. DepMap/CCLE analyses provided complementary model-system evidence: KEAP1/NFE2L2-mutant models had higher NRF2 scores, while NRF2-high or KEAP1/NFE2L2-mutant contexts showed stronger NFE2L2 dependency. Tissue-context analyses positioned NRF2 stress near hypoxic, stromal, myeloid, and immune programs. Immunotherapy cohorts supported the relevance of NRF2-associated states to treatment-response biology, while also indicating limited utility for static pan-cancer prediction. Exploratory whole-slide image analyses using pathology foundation-model embeddings identified morphology associations with myeloid, hypoxic, and CAF/TGF-beta barrier programs.

VISTA provides an evidence-layered approach to variant-informed tissue-state modeling. By integrating genomic, transcriptomic, imaging, spatial, cellular, dependency, and pharmacogenomic evidence, it can help prioritize genotype-linked cancer states and validation-ready hypotheses. VISTA is not intended to function as a stand-alone clinical predictor; its claims are bounded by the evidence available in each data layer and by the biological and clinical context of the analyzed cohorts.