Biology subjects

Van Houcke, M.

Publications and source records attributed to Van Houcke, M..

3 recordsLinked to original sources

bioRxiv2026

IMMREP25: Unseen Peptides

T cell receptors (TCRs) can bind to peptides presented by MHC molecules (pMHC) as a first step to trigger a T cell response. Reliable approaches to predict TCR:pMHC binding would have broad applications in clinical diagnostics, therapeutics, and the fundamental understanding of molecular interactions. IMMREP is a community organized series of prediction contests that asks participants to predict TCR:pMHC binding on unpublished datasets. Previous iterations in 2022 and 2023 showed multiple approaches can predict TCR-pMHC binding with significant accuracy (median AUC_0.1[≥]0.7) for peptides where experimental data is available ("seen" peptides). In contrast, models did not outperform random guessing for peptides that have no such data available ("unseen" peptides). Here we report on the results of IMMREP25, which focused solely on unseen peptides in order to evaluate the cutting edge of the field. We received 126 named submissions predicting the specificity of 1,000 TCRs against twenty unseen peptides restricted by one of two MHC molecules (HLA-A*02:01 and HLA-B*40:01). The best performing methods showed a macro-AUC_0.1 of 0.60, significantly better than random, demonstrating significant advances in the field. The top performing methods incorporated structural modeling into their approach, indicating that especially for unseen peptides, a structural understanding aids in the prediction of TCR:pMHC interactions. The results from this benchmark highlight the significant challenges remaining for TCR:pMHC predictions and will inform future method development.

bioinformatics↗

bioRxiv2026

Applications of T-cell receptor specificity annotation models for quality control and immunomonitoring in adoptive T-cell therapies

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TIL) is a powerful candidate immunotherapy. However, the personalized production process that underlies its potential also introduces an element of stochasticity, where the actual target of the TILs is variable across patients and protocols, and remains largely unknown. In this letter, we describe the application of T-cell receptor (TCR) sequencing in combination with computational tools for annotating epitope-specificity of the TCR repertoire of TIL products as a potent quality control. We highlight the potential of this approach by demonstrating the tumor antigen-specificity of TCR clusters within the TIL product in silico, and validating these responses in vitro. We also demonstrate screening for off-cancer reactivity against viral antigens, and immune monitoring of identified TCR clones. HighlightsO_LIT-cell receptor sequencing combined with epitope annotation tools are viable for quality control of TIL products by identifying tumor-specific clones. C_LIO_LIWe showed the additional utility of screening for off-cancer reactivity, such as viral antigens. C_LIO_LIThe identified tumor-specific TCR clones can be tracked in vivo providing a means of immune monitoring. C_LI

bioinformatics↗

bioRxiv2021

clusTCR: a Python interface for rapid clustering of large sets of CDR3 sequences

The T-cell receptor (TCR) determines the specificity of a T-cell towards an epitope. As of yet, the rules for antigen recognition remain largely undetermined. Current methods for grouping TCRs according to their epitope specificity remain limited in performance and scalability. Multiple methodologies have been developed, but all of them fail to efficiently cluster large data sets exceeding 1 million sequences. To account for this limitation, we developed clusTCR, a rapid TCR clustering alternative that efficiently scales up to millions of CDR3 amino acid sequences. Benchmarking comparisons revealed similar accuracy of clusTCR with other TCR clustering methods. clusTCR offers a drastic improvement in clustering speed, which allows clustering of millions of TCR sequences in just a few minutes through efficient similarity searching and sequence hashing. clusTCR was written in Python 3. It is available as an anaconda package (https://anaconda.org/svalkiers/clustcr) and on github (https://github.com/svalkiers/clusTCR).

bioinformatics↗