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Войти в исследования Genome medicine · 2026
Cross-species molecular stratification enables T cell receptor diagnosis of Parkinson's disease.
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Chen Jiayu, Xia Xian, Zhang Yuruo, Tong Xiaoge, Liang Yue, Fan Xiangding, Meng Qinghao, Wei Yihong, Hu Gang
Аннотация
BACKGROUND: Developing peripheral blood-based diagnostic models for idiopathic Parkinson's disease (iPD), particularly those leveraging the T-cell receptor (TCR) repertoire, has long been considered infeasible because patient-derived TCRs appear to lack convergent sequence motifs. We reasoned that this apparent absence of shared TCR features likely reflects both insufficient sample sizes and unaccounted immune heterogeneity within the iPD population.
METHODS: We reconstructed TCR repertoires from the two largest iPD cohorts currently available, anchoring them with three mechanistically distinct mouse models to stratify patients into model-anchored informed subtypes. Within these data-driven subtypes, we trained subtype-specific, multimodal multi-instance classifiers.
RESULTS: We demonstrated that iPD patients can be successfully stratified into model-anchored informed subtypes. Furthermore, the trained subtype-specific, multimodal multi-instance classifiers achieved an Area Under Curve (AUC) exceeding 0.8 for both subtypes.
CONCLUSIONS: Our findings underscore the critical role of disease stratification in enabling TCR repertoire-based modeling in neurodegenerative diseases.
METHODS: We reconstructed TCR repertoires from the two largest iPD cohorts currently available, anchoring them with three mechanistically distinct mouse models to stratify patients into model-anchored informed subtypes. Within these data-driven subtypes, we trained subtype-specific, multimodal multi-instance classifiers.
RESULTS: We demonstrated that iPD patients can be successfully stratified into model-anchored informed subtypes. Furthermore, the trained subtype-specific, multimodal multi-instance classifiers achieved an Area Under Curve (AUC) exceeding 0.8 for both subtypes.
CONCLUSIONS: Our findings underscore the critical role of disease stratification in enabling TCR repertoire-based modeling in neurodegenerative diseases.