A Hierarchical Spatial Graph Neural Network Resolves Immunogenic and Tolerogenic Tertiary Lymphoid Structures in Renal Cell Carcinoma

Abstract Tertiary lymphoid structures (TLS) in the tumour microenvironment span a functional spectrum from immunogenic — driving germinal centre reactions and anti-tumour immunity — to tolerogenic, harbouring regulatory T cells and suppressive myeloid populations. Distinguishing these states is clinically critical: immunogenic TLS predict ICI response whereas tolerogenic TLS may promote immune evasion. Bulk transcriptomics conflates productive TLS with exhausted immune infiltrates, masking this distinction. We present a hierarchical graph neural network (GNN) that operates directly on 10x Visium spatial transcriptomics graphs to classify TLS functional state at the cluster level. Using a three-scale architecture combining graph attention (GAT) and differentiable pooling (DiffPool), the model hierarchically aggregates spot-level signals into niche- and region-level representations before predicting immunogenic versus tolerogenic state. Trained on 915 TLS clusters from 24 renal cell carcinoma (RCC) Visium samples (GSE175540), the model achieves a validation AUC-ROC of 0.718 and a clinical AUC of 0.908 on IgG-validated samples from the BIONIKK cohort. Zero-shot transfer to an independent multi-cancer Visium cohort (GSE203612; breast, liver, ovarian, pancreatic, uterine) correctly identifies hepatocellular carcinoma as harbouring the most tolerogenic TLS, consistent with the known immunosuppressive liver tumour microenvironment. Spatial decomposition of CXCL13 across TLS and non-TLS compartments reveals that 85% of tissue CXCL13 signal originates from non-TLS parenchyma, where it co-expresses primarily with exhaustion markers (mean Spearman rho = 0.233) rather than Tfh markers (CXCR5 rho = 0.039) — a pattern consistent with the paradoxical association of bulk CXCL13 with worse overall survival in TCGA-KIRC (HR = 1.38, p < 0.001). Code and processed data are deposited at GitHub and Zenodo. Competing Interest Statement The authors have declared no competing interest. Footnotes Remove "Ontario Institute for Cancer Research, Toronto, Ontario, Canada" from affiliated institution