TCR
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Related Articles from SNS
High-avidity TCR signaling induces a distinct KLR-positive exhaustion state in human tumor-infiltrating CD8 T cells associated with immunotherapy response
Tumor-specific exhausted CD8 T cells (Tex) adopt diverse phenotypes across human cancers, but the drivers of this heterogeneity remain poorly understood. Using flow cytometry and single-cell RNA and T cell receptor (TCR) sequencing of 106,667 tumor-infiltrating CD8 T cells from head and neck squamous cell carcinoma (HNSCC) tumors, we identified and validated three Tex subsets, each with distinct clonotypes: (1) Tex-Conv, expressing conventional exhaustion genes; (2) Tex-CCR6, distinguished...
New Benchmarking Shows Limited Generalization Power of TCR Antigenic Epitope Prediction Models
arXiv:2606.04994v1 Announce Type: new Abstract: Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications. A major limitation is the absence of rigorously defined, unseen benchmark datasets that allow unbiased evaluation of model performance and generalizability. Here, we describe two complementary...
Unbiased identification of responding T cell clones from longitudinal repertoire sequencing with CloneSearch
T cells activate and expand upon interaction with cognate antigen, derived from pathogens or mutated proteins. T cell clones can be identified by their T cell receptor (TCR) which can act as a unique barcode to track their expansion. Longitudinal TCR sequencing can be used to track T cell responses to a large array of stimuli.
CaliPPer: quantifying, predicting and improving AI model performance for binding prediction
arXiv:2606.07258v1 Announce Type: new Abstract: Binding prediction models accelerate therapeutic antibody and TCR discovery, but their performance on new datasets is unpredictable, often leading to low discovery rates. Density-ratio methods (PAPE, M-CBPE) provide label-free performance estimation for binary classification, but their assumptions and aggregate-only outputs limit binding prediction on neoepitopes, antigen variants and chemical scaffolds. Here we present CaliPPer (Calibration...