BenchRep-T: Systematic Benchmark for T-Cell Repertoire Disease Diagnostics
Researchers introduced BenchRep-T, a unified benchmark that systematically evaluates nine computational methods for diagnosing diseases using T-cell receptor (TCR) sequence data from blood samples. The study found that simple statistical baselines using gene-usage features were often competitive with more complex deep learning approaches. The findings highlight that the field lacks a clear leading method, underscoring the need for standardized evaluation before TCR diagnostics can be reliably deployed clinically.
BenchRep-T is a newly proposed benchmark framework designed to address a key reproducibility problem in T-cell receptor (TCR) repertoire-based disease diagnostics: existing computational methods have been developed and tested on different datasets, preprocessing pipelines, and metrics, making fair comparison nearly impossible. The benchmark standardizes multiple publicly available TCR repertoire datasets and evaluates nine approaches spanning statistical enrichment, feature-engineered ensembles, deep learning, and sequence clustering. Evaluation covered four distinct tasks: disease classification, performance under reduced sequencing depth, recovery of known antigen-specific sequences, and sensitivity to demographic confounding factors. A notable finding is that tree-based models trained on relatively simple features — V- and J-gene usage and short sequence motifs — approached the classification performance of far more complex methods. No single method consistently outperformed others across all four tasks, suggesting the field has not yet converged on a robust solution. The authors argue that BenchRep-T provides the infrastructure for rigorous, reproducible comparisons that could accelerate progress toward clinically viable immune repertoire diagnostics.
What's missing
The study does not address the clinical translation pathway, including what classification performance thresholds would be required for regulatory approval or real-world diagnostic use. Open questions include whether BenchRep-T's tasks adequately capture the complexity of multi-disease or longitudinal diagnostic scenarios, and how methods would perform on entirely held-out external cohorts.
What different sources said
- bioRxivCenter
BenchRep-T: A Systematic Evaluation of T-Cell Repertoire-Based Disease Diagnostics
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