使用组合算法鉴定用于个性化T细胞疗法的临床相关T细胞受体。
Identification of clinically relevant T cell receptors for personalized T cell therapy using combinatorial algorithms.
作者信息
Pétremand Rémy, Chiffelle Johanna, Bobisse Sara, Perez Marta A S, Schmidt Julien, Arnaud Marion, Barras David, Lozano-Rabella Maria, Genolet Raphael, Sauvage Christophe, Saugy Damien, Michel Alexandra, Huguenin-Bergenat Anne-Laure, Capt Charlotte, Moore Jonathan S, De Vito Claudio, Labidi-Galy S Intidhar, Kandalaft Lana E, Dangaj Laniti Denarda, Bassani-Sternberg Michal, Oliveira Giacomo, Wu Catherine J, Coukos George, Zoete Vincent, Harari Alexandre
机构信息
Ludwig Institute for Cancer Research, Lausanne Branch, Department of Oncology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Agora Cancer Research Center, Lausanne, Switzerland.
Center for Cell Therapy, CHUV-Ludwig Institute, Lausanne, Switzerland.
出版信息
Nat Biotechnol. 2025 Mar;43(3):323-328. doi: 10.1038/s41587-024-02232-0. Epub 2024 May 7.
A central challenge in developing personalized cancer cell immunotherapy is the identification of tumor-reactive T cell receptors (TCRs). By exploiting the distinct transcriptomic profile of tumor-reactive T cells relative to bystander cells, we build and benchmark TRTpred, an antigen-agnostic in silico predictor of tumor-reactive TCRs. We integrate TRTpred with an avidity predictor to derive a combinatorial algorithm of clinically relevant TCRs for personalized T cell therapy and benchmark it in patient-derived xenografts.
开发个性化癌细胞免疫疗法的一个核心挑战是识别肿瘤反应性T细胞受体（TCR）。通过利用肿瘤反应性T细胞相对于旁观者细胞独特的转录组特征，我们构建并评估了TRTpred，这是一种与抗原无关的肿瘤反应性TCR的计算机预测工具。我们将TRTpred与亲和力预测工具相结合，得出一种用于个性化T细胞疗法的临床相关TCR组合算法，并在患者来源的异种移植模型中对其进行评估。
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