GEMINI-NSCLC: Multiomics and Single-Cell Spatial Profiling To Benchmark, Back-Translate, And Build Digital Twins of IO Response

Background
Response to first-line standard-of-care (SoC) chemo-immunotherapy (IO) for patients with NSCLC without targetable mutations is heterogeneous, highlighting the need for predictive biomarkers. GEMINI (NCT05236114) integrates real-world outcomes, whole exome sequencing (WES), single-cell spatial transcriptomics (SpTx), and AI-pathology to establish a benchmarking resource and patient-level digital twins, enabling back-translation into testable hypotheses. With > 4 million cells from 53 biopsies, GEMINI is one of the largest single-cell spatial datasets linked to IO outcomes.

Methods
Patients with metastatic NSCLC were analyzed for outcome associations. Progression-free survival (PFS) was defined from IO start to progression, next regimen, last follow-up, or 2 years. Patients were classified as fast progressors ( < 3 months PFS) or slow progressors ( > 3 months PFS). Baseline biopsies (n = 53) underwent WES and SpTx. Neural networks traced single-cell boundaries on H&E to quantify gene expression; cells were annotated via clustering and LLM-assisted labeling. AI-, manual-, and digital-pathology (DSP) defined tumor, immune, and stroma regions. Cohort-level benchmarking was integrated into patient-level digital twins to back-translate spatial-genomic features into individualized risk and mechanism hypotheses.

Results
WES revealed expected mutation frequencies: STK11 15%, TP53 73%, KEAP1 21%, KRAS 46%, supporting cohort representativeness. Stroma-associated TIL counts were higher in slow versus fast progressors by AI-path (p = 0.034) and manual-path (p = 0.014). DSP showed immune aggregates in slow progressors were lymphocyte-diverse, whereas fast progressors were enriched for five macrophage subtypes consistent with immunosuppressive niches. Spatial proximity of lymphocytes and stroma to a tumor subcluster (C2) predicted progression (p < 0.01). Immunoglobulin light-chain expression localized to the tumor core in slow progressors, suggesting tumor–B cell interactions with disease arrest. Differential expression identified 14 EMT/ECM genes overexpressed in fast-progressor stroma, implicating stromal barrier/ECM remodeling in IO resistance. Digital twins captured these spatial-omic signatures to forecast risk and generate patient-specific, testable hypotheses.

Conclusions
GEMINI provides a large single-cell spatial transcriptomic benchmark linked to IO outcomes for patients with NSCLC and enables AI-driven digital twins for clinical decision support. Fast progressors show stromal EMT/ECM programs and immunosuppressive myeloid niches; slow progressors exhibit lymphocyte diversity and tumor–B cell interactions near subcluster C2. Findings support multimodal risk stratification and nominate stromal EMT/ECM targeting to overcome IO resistance, with prospective validation via digital-twin biomarkers.