NeoTCRseek: an integrated platform for high-sensitivity identification of neoantigen-specific TCR clonotypes to track
Bin Song1,2, Geng Liu2, Bo Li2
1College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
Introduction:
Identifying neoantigen-specific T-cell receptor (neoTCR) clonotypes is crucial for tracking clinical T-cell dynamics in personalized cancer immunotherapy. Despite advances in experimental and computational approaches for identifying antigen-specific TCR (asTCR) clonotypes, the identification of neoTCR clonotypes remains challenging due to their low frequencies and the limited sensitivity of current methods.
Methods:
We introduce NeoTCRseek, an integrated platform that combines extended T-cell culture, deep TCR sequencing, and advanced TCR-clustering tools to enhance neoTCR clonotype identification. NeoTCRseek was developed using a model cytomegalovirus (CMV) antigen and subsequently validated under two neoantigen setups: a single neoantigen and a neoantigen pool representing a multi-antigen context. For each antigen setup, we used three cell sorting-based methods to detect enriched clonotypes and built a validation dataset for asTCR clonotype characterization and NeoTCRseek performance evaluation.
Results:
NeoTCR clonotypes exhibited a significantly higher proportion of low-frequency clonotypes (0.01%-0.1%) than CMV-specific ones (70.3% vs. 33.3%). Nonetheless, NeoTCRseek achieved a detection limit of 0.01% and high accuracy in both single- and multi-antigen contexts by integrating expanded clonotype detection with co-clustering-based TCR prediction. Compared with the benchmark, NeoTCRseek improved the mean F1 score across the two neoantigen setups from 0.21 to 0.41.
Conclusion:
NeoTCRseek achieves high analytical sensitivity and accuracy in neoTCR clonotype identification and supports multi-antigen analysis, providing an integrated platform for neoTCR clonotype characterization and for tracking clinical T-cell dynamics in personalized cancer immunotherapy.
Insights
NeoTCRseek enhances the identification of low-frequency neoantigen-specific T-cell receptor (neoTCR) clonotypes, crucial for personalized cancer immunotherapy. This platform improves tracking of clinical T-cell dynamics with high sensitivity and accuracy.
Area of Science:
- Immunology
- Bioinformatics
- Computational Biology
Background:
- Identifying neoantigen-specific T-cell receptor (neoTCR) clonotypes is vital for monitoring T-cell responses in cancer immunotherapy.
- Current methods struggle with the low frequencies and limited sensitivity required for accurate neoTCR identification.
Purpose of the Study:
- To introduce NeoTCRseek, an integrated platform designed to improve the identification of neoTCR clonotypes.
- To evaluate NeoTCRseek's performance in detecting low-frequency neoTCR clonotypes in single- and multi-antigen contexts.
Main Methods:
- Developed NeoTCRseek by combining extended T-cell culture, deep TCR sequencing, and advanced TCR-clustering tools.
- Validated the platform using cytomegalovirus (CMV) antigen and two neoantigen setups (single and pooled).
- Employed cell sorting and a validation dataset for performance evaluation.
Main Results:
- NeoTCR clonotypes showed a higher proportion of low-frequency clonotypes (0.01%-0.1%) compared to CMV-specific ones.
- NeoTCRseek achieved a detection limit of 0.01% with high accuracy in both single- and multi-antigen scenarios.
- The platform improved the mean F1 score from 0.21 to 0.41 compared to benchmarks.
Conclusions:
- NeoTCRseek offers high analytical sensitivity and accuracy for neoTCR clonotype identification.
- The platform supports multi-antigen analysis, aiding in tracking clinical T-cell dynamics for personalized cancer immunotherapy.
