Alexandre Harari

The paper applies T-cell receptor sequencing and epitope annotation tools to quality control and immune monitoring of TIL products by identifying tumor-specific clones and screening for off-cancer reactivity.

This study shows that unpaired TCRα + TCRβ sequencing can train accurate TCR-epitope recognition predictors at lower cost than paired sequencing.

The paper introduces TCR specificity profiles to identify key determinants of TCR-epitope recognition and shows their ability to predict cross-reactivity and interpret machine learning models.

The paper identifies clonally expanded neuron-specific CD8+ T cells in anti-Ri AIE patients using an unbiased approach and shows they express KIR3DL1 and TOX.

The paper uses phage display and machine learning to discover TCRs specific to cancer antigens, identifying functional TCRs from large repertoires.

This study tracks T cell dynamics in a pediatric cancer patient post-ICI therapy to identify tumor-reactive TCRs for personalized TCR-T cell therapy.

This paper introduces MixTCRpred, a deep learning model that predicts TCR-epitope interactions and identifies epitope-specific T cells, showing enrichment in COVID-19 patients.

The paper improves epitope prediction by integrating HLA-I ligand data and TCR recognition models, identifying potent SARS-CoV-2 CD8+ T-cell epitopes with experimental validation.