Hypothesis\nWe hypothesize that integrating spatial proteomics, metabolomics, and single-cell transcriptomics from serial liquid biopsies can identify pre-resistant niches in the tumor microenvironment that activate bypass signaling pathways (e.g., MET/HER2, VEGF) weeks before radiographic progression. This early detection will allow preemptive therapeutic adaptation, improving progression‑free survival in patients with EGFR‑mutant NSCLC undergoing TKI therapy.\n\n## Rationale\nMulti‑omics reveals functional layers missed by genomics, such as post‑translational activation of bypass receptors and TME-derived metabolites that drive resistance [1][2]. Spatial technologies now map where these active proteins and metabolites coexist with immune and stromal cells, revealing signaling hotspots that bulk assays miss. The TuPro trial showed serial biopsies altered therapy in 50% of cases, proving resistance evolves temporally [3]. By coupling serial liquid biopsies (ctDNA, exosomes) with spatial omics of circulating tumor‑derived microvesicles, we can capture the evolving TME without invasive repeats.\n\n## Predictions\n1. Patients whose baseline liquid‑biopsy spatial atlas shows co-localization of phosphorylated MET/HER2 with lactate-rich stromal zones will develop TKI resistance within 8 weeks, whereas those lacking this signature will remain progression‑free >6 months.\n2. Early intervention (switch to MET/HER2 inhibitor combo) guided by the spatial signature will prolong median PFS by at least 40% compared with standard care.\n3. If the spatial signature fails to predict resistance in >=30% of cases, the hypothesis is falsified.\n\n## Experimental Design\n- Cohort: 60 EGFR‑mutant NSCLC patients starting first-generation TKI.\n- Sampling: Peripheral blood collected at baseline, every 2 weeks for 3 months; plasma exosomes isolated.\n- **Assays":\n - Single‑cell RNA‑seq on exosome‑associated RNA.\n - Spatial proteomics (CODEX or Imaging Mass Cytometry) on exosome‑derived protein arrays.\n - Targeted metabolomics (LC‑MS) for lactate, kynurenine, ATP.\n- Analysis: Build a multimodal graph neural network (GNN) that learns spatial co‑localization patterns; train on first 30 patients, test on remaining 30.\n- Intervention: For predicted‑high‑risk patients, add MET/HER2 inhibitor at first molecular alert; control group continues TKI alone.\n- Endpoints: Radiographic PFS, timing of molecular alert vs clinical progression, overall response rate.\n\n## Potential Outcomes\n- Success: Spatial signature predicts resistance with AUC > 0.85; early combo therapy improves PFS; validates spatial multi‑omics as a leading indicator.\n- Failure: No significant association between spatial signature and PFS; suggests either temporal resolution insufficient or that resistance arises from cell‑intrinsic mechanisms not captured in exosomes; hypothesis falsified.\n\n## Implications\nA validated spatial liquid‑biopsy pipeline would transform resistance monitoring from reactive to proactive, reduce unnecessary biopsies, and enable rational drug pairing grounded in the actual functional state of the tumor niche. It also addresses the translational gaps highlighted by missing biomarker utilization and lack of standardized integration frameworks [4]