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GATTCR: A Graph Attention Network With Multi-Feature Fusion for Peripheral Blood TCR Repertoire Classification
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DOI：10.1109/tcbbio.2026.3677212.png)
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Abstract
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T cell receptor (TCR) repertoire profiling provides a promising avenue for noninvasive disease diagnostics by capturing immune signatures directly from peripheral blood. However, the high diversity and sparsity of TCR sequences pose significant challenges for robust immune state classification. In this work, we propose GATTCR, a novel framework that integrates Graph Attention Networks (GATs) with multi-feature fusion to model complex dependencies within TCR repertoires. By representing TCRs as graph nodes and incorporating biological priors—such as sequence embeddings, structural similarity, V gene usage, and clonal frequency—GATTCR enables context-aware, structure-informed representation learning. We evaluate GATTCR across a comprehensive panel of cancer and infectious disease datasets, demonstrating consistent improvements over existing methods. Notably, GATTCR achieves AUROC gains of up to +47.9% under few-shot learning scenarios, highlighting its ability to generalize from limited labeled data. Ablation studies further confirm the critical role of graph-based modeling and immunological features in driving performance gains. Overall, GATTCR offers a scalable approach for TCR repertoire analysis and paves the way for routine, noninvasive immune monitoring in precision medicine.
Keywords:
TCR repertoire
graph attention network
immune state classification
multi-feature fusion
few-shot learning
noninvasive diagnosis
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