# T-cell clonal niches

CloneTrast uses clonal identities from paired single-cell RNA and TCR sequencing as training labels, so the model learns T-cell clonal-functional states from gene expression alone and needs no TCR data at inference. Training used 26 datasets across 14 cancer types with more than 1.5 million T cells, and external testing used 11 datasets with more than 1 million T cells, including two cancer types unseen in training. On both training and test sets, patient clonal purity improved significantly over a conventional expression embedding, and one NK-like niche enriched for tumor-blood shared TCRs was associated with poor immunotherapy response across studies.

CloneTrast uses clonal identities from paired single-cell RNA and TCR sequencing as training labels, so the model learns T-cell clonal-functional states from gene expression alone and needs no TCR data at inference. Training used 26 datasets across 14 cancer types with more than 1.5 million T cells, and external testing used 11 datasets with more than 1 million T cells, including two cancer types unseen in training. On both training and test sets, patient clonal purity improved significantly over a conventional expression embedding, and one NK-like niche enriched for tumor-blood shared TCRs was associated with poor immunotherapy response across studies.