Hypothesis

Integrating spatial multi‑omics with circulating tumor DNA (ctDNA) kinetics using a graph neural network (GNN) that models metabolite exchange can forecast the emergence of therapy‑resistant subclones before clinical relapse.

Rationale

It's now possible to profile tumors at single‑cell resolution while simultaneously tracking ctDNA in plasma, giving high AUCs for early detection [1]. Spatial technologies map genomics, transcriptomics, proteomics and metabolomics within tissue architecture [2]. ctDNA dynamics reflect tumor burden and clonal shifts [3]. However, current models treat each modality separately, missing the spatial‑metabolic feedback loops that drive clonal selection. We propose that a GNN that treats each spatially resolved voxel as a node, with edges weighted by inferred metabolite fluxes (e.g., lactate, glutamine) and linked to ctDNA variant frequencies, will capture the selective pressures that generate resistant phenotypes.

Testable Predictions

1. In a longitudinal cohort of patients receiving targeted therapy, the GNN‑derived risk score will rise significantly (≥2‑fold) at least 8 weeks before radiographic progression.
2. Patients whose spatial‑metabolic GNN score remains low despite rising ctDNA will show pseudoprogression rather than true resistance.
3. Experimental blockade of a top‑predicted metabolite pathway (e.g., inhibiting MCT1 lactate transport) in patient‑derived organoids will reduce the GNN score and delay outgrowth of resistant clones.

Experimental Design

• Collect pretreatment, on‑treatment (every 2 weeks), and post‑progression tumor biopsies for spatial multi‑omics (10x Visium or Slide‑seqV2) and matched plasma for ctDNA sequencing.
• Build a heterogeneous graph: nodes = spatial omics features (gene expression, protein abundance, metabolite levels); edges = diffusion‑based metabolite exchange estimated from extracellular fluid measurements.
• Train a GNN to predict ctDNA variant allele frequency changes; validate on held‑out samples.
• Prospective testing: compute risk scores in real time and compare with clinical outcomes.
• Organoid assays: treat with inhibitor of predicted metabolite transporter; measure clonal composition via single‑cell DNA seq over 14 days.

Potential Implications

If validated, this framework would enable pre‑emptive therapy adjustment, reduce overtreatment, and inform trial designs that target microenvironmental metabolites alongside cancer‑cell intrinsics.

References

[1] Multi‑omics integration improves early‑detection AUCs (https://www.pnas.org/doi/10.1073/pnas.1521919112) [2] Spatial multi‑omics platforms (https://pmc.ncbi.nlm.nih.gov/articles/PMC12766144/) [3] ctDNA dynamics in monitoring tumor burden (https://pmc.ncbi.nlm.nih.gov/articles/PMC12226543/)