Hypothesis

Integrating spatially resolved single‑cell proteomics with longitudinal plasma metabolomics via a graph transformer architecture will prospectively identify emerging therapy‑resistant subclones in metastatic colorectal cancer (mCRC) at least 8 weeks before radiographic progression.

Mechanistic Rationale

Multi‑omics fusion has shown that non‑linear models uncover patterns invisible to single‑layer analysis[1][2]. Spatial proteomics reveals zonated signaling activity within tumors, while longitudinal plasma metabolomics captures systemic metabolic rewiring that precedes phenotypic resistance[4][5]. A graph transformer can model (1) cell‑to‑cell interaction networks derived from spatial proteomics, (2) temporal metabolite fluxes, and (3) their cross‑modal dependencies, thereby learning emergent signatures of adaptive bypass signaling (e.g., EGFR‑independent MAPK re‑activation) and metabolic symbiosis between cancer and stromal cells.

Key Mechanistic Insights

• Spatial heterogeneity drives resistance: Subclones at the invasive margin exhibit distinct proteomic states (e.g., high AXL, low E‑cadherin) that are not evident in bulk biopsies[4].
• Metabolic precursors precede phenotypic shifts: Accumulation of specific oncometabolites (e.g., 2‑hydroxyglutarate) and depletion of TCA‑cycle intermediates appear in plasma 4‑6 weeks before proteomic re‑programming[5].
• Graph transformers capture long‑range dependencies: By treating each cell as a node and metabolite concentrations as global context tokens, the model can learn how distal stromal signals reprogram tumor cell proteomics over time[2].

Testable Predictions

1. Early Detection: The model will assign a resistance risk score that exceeds a predefined threshold (AUC ≥ 0.85) at least 8 weeks prior to CT‑defined progression in a prospective cohort.
2. Specificity: Scores will remain low (<0.3) in patients who respond to standard EGFR‑targeted therapy without developing resistance.
3. Mechanistic Validation: High‑risk patients will show enrichment of AXL‑high/MET‑low proteomic signatures and elevated plasma 2‑hydroxyglutarate in matched spatial‑single‑cell and metabolomic samples collected at the time of score elevation.

Experimental Design

• Cohort: Enroll 120 mCRC patients initiating first‑line EGFR inhibitor therapy; collect baseline tumor biopsy, plasma, and follow‑up samples every 2 weeks for up to 6 months.
• Assays: Perform multiplexed ion beam imaging (MIBI) or CODEX for spatial proteomics (≈40 markers) and targeted LC‑MS/MS for plasma metabolomics (≈150 metabolites).
• Model: Train a heterogeneous graph transformer where nodes represent cells (features = proteomic markers), edges represent spatial proximity (<20 µm), and global tokens represent longitudinal metabolite vectors. Use a variational autoencoder pretraining step to handle missing data, then fine‑tune for progression prediction.
• Endpoint: Primary endpoint is time to radiographic progression (RECIST 1.1). Secondary endpoint is concordance between predicted high‑risk scores and emergent resistant clones identified by post‑progression single‑cell DNA sequencing.
• Statistical Plan: Compute time‑dependent AUC and integrated Brier score; compare against a baseline model using only genomics or bulk proteomics (DeLong test). A pre‑specified superiority margin of 0.07 in AUC will deem the hypothesis supported.

Potential Impact

If validated, this approach would shift multi‑omics from retrospective biomarker discovery to real‑time, N‑of‑1 therapeutic optimization, directly addressing the validation gap highlighted in current literature[1][6].