# Deep learning-driven TCR[image] repertoire analysis enhances diagnosis and enables mining of immunological biomarkers in systemic lupus erythematosus

## TL;DR

A deep learning model called DeepTAPE improves SLE diagnosis and identifies immune markers by analyzing T-cell receptor sequences.

## Contribution

DeepTAPE introduces a novel deep learning framework for SLE diagnosis and biomarker discovery using TCR CDR3 sequences.

## Key findings

- DeepTAPE achieved an AUC of 0.908 in SLE classification using CDR3 motifs.
- The autoimmune risk score (ARS) strongly correlates with SLE disease activity.
- SLE-specific motifs and antigens like CD109 and INS were identified as potential biomarkers.

## Abstract

Systemic Lupus Erythematosus (SLE) is a complex autoimmune disorder involving dysregulation of multiple immune components, including T cells. Aberrant T-cell activity contributes significantly to the immune pathology of SLE, for instance, by facilitating autoantibody production. The Complementarity Determining Region 3 (CDR3) of the TCR\documentclass[12pt]{minimal}
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\begin{document}$$\beta$$\end{document} 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 classification performance for SLE.

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 disease-associated motifs that serve as potential 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 demonstrated high efficacy in SLE classification, 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.

This study highlights the potential of DeepTAPE as a supportive tool for biomarker discovery and assessing SLE disease activity, which complements traditional diagnostic approaches. By deepening our understanding of the immunological characteristics and mechanisms associated with SLE, this work lays a 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.

The online version contains supplementary material available at 10.1186/s13040-025-00490-5.