Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning
- 1Technion - Israel Institute of Technology
Abstract
T-cell clonal organization is central to anti-tumor immunity, yet its functional characterization typically requires paired single-cell RNA and TCR sequencing (scRNA/TCR-seq). Existing multimodal methods integrate transcriptome and TCR information but are limited when TCR data are unavailable. Here we present CloneTrast, a supervised contrastive-learning framework that uses paired scRNA/TCR-seq during training to learn clonal-functional states from gene expression, while requiring only scRNA-seq at inference. Using paired scRNA/TCR-seq profiles from 2.6 million human T cells across 16 cancer types, CloneTrast identified distinct T-cell clonal niches not explained solely by TCR sequence similarity or conventional cell-state annotations. Among these, an NK-like T-cell niche enriched for tumor-blood shared TCRs was consistently associated with poor immunotherapy response at the pan-cancer level. Analysis of antigen-specific T-cell clones revealed that tumor-resident and blood-borne antigen-specific T cells were preferentially associated with distinct clonal niches. Overall, CloneTrast enables scalable and interpretable inference of clonal organization from single-cell transcriptomes.
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BibTeX
@misc{shorer2026revealing,
title = {{Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning}},
author = {O. Shorer and A. Pinhasi and R. Amit and K. Yizhak},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.06.757206},
url = {https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Revealing pan-cancer clonal niches of T cells from single-cell RNA sequencing using contrastive learning
AU - O. Shorer
AU - A. Pinhasi
AU - R. Amit
AU - K. Yizhak
PY - 2026
DA - 2026/10/06
PB - bioRxiv
DO - 10.64898/2026.10.06.757206
UR - https://www.biorxiv.org/content/10.64898/2026.10.06.757206v1
AB - T-cell clonal organization is central to anti-tumor immunity, yet its functional characterization typically requires paired single-cell RNA and TCR sequencing (scRNA/TCR-seq). Existing multimodal methods integrate transcriptome and TCR information but are limited when TCR data are unavailable. Here we present CloneTrast, a supervised contrastive-learning framework that uses paired scRNA/TCR-seq during training to learn clonal-functional states from gene expression, while requiring only scRNA-seq at inference. Using paired scRNA/TCR-seq profiles from 2.6 million human T cells across 16 cancer types, CloneTrast identified distinct T-cell clonal niches not explained solely by TCR sequence similarity or conventional cell-state annotations. Among these, an NK-like T-cell niche enriched for tumor-blood shared TCRs was consistently associated with poor immunotherapy response at the pan-cancer level. Analysis of antigen-specific T-cell clones revealed that tumor-resident and blood-borne antigen-specific T cells were preferentially associated with distinct clonal niches. Overall, CloneTrast enables scalable and interpretable inference of clonal organization from single-cell transcriptomes.
ER -