Deep Learning-Driven TCRβ Repertoire Analysis Enhances Diagnosis and Enables Mining of Immunological Biomarkers in Systemic Lupus Erythematosus

Background Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder characterized by abnormal T-cell responses, which significantly contribute to the disease’s immune pathology. The Complementarity Determining Region 3 (CDR3) of the TCRβ chain is pivotal for T-cell specificity, thereby positioning it as a promising target for enhancing diagnostic accuracy and gaining deeper mechanistic insights into SLE. To address these diagnostic limitations in SLE, our team developed DeepTAPE, a deep learning-based diagnostic framework that utilizes CDR3 sequences to achieve robust diagnostic performance for SLE. Results Building upon the foundation established by DeepTAPE, we devised a novel diagnostic approach that effectively integrates a TCR classifier to quantify SLE disease activity. Furthermore, this methodology employs advanced deep learning models for the bio-mining of valuable and efficient preliminary diagnostic biomarkers. As a result, this approach generates an autoimmune risk score (ARS) indicative of SLE probability. Notably, this ARS metric exhibited a strong correlation with disease activity, functioning as a quantitative clinical marker that complements traditional indices such as the SLE Disease Activity Index (SLEDAI). In addition, through a comprehensive analysis of immune repertoire data, we identified SLE-specific amino acid motifs within the CDR3 sequences, including critical 3-mer and gapped-mer oligopeptides. These motifs facilitated rapid and accurate preliminary screening for SLE, achieving an area under the curve (AUC) of 0.908, thereby significantly outperforming other candidate biomarkers. Moreover, our model revealed potential SLE-associated antigens and genes, such as CD109 and INS, which provide new insights into the immunological mechanisms underlying the disease. Conclusion This study highlights the potential of DeepTAPE not only as a diagnostic tool but also as a platform for biomarker discovery in SLE. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a solid foundation for advancing targeted therapies and personalized medicine in autoimmune diseases. Consequently, our findings may pave the way for improved patient outcomes and more effective treatment strategies in the management of SLE.