Accurate detection of tumor-specific gene fusions reveals strongly immunogenic personal neo-antigens
Cet article présente un algorithme d'apprentissage automatique permettant, à partir de données de séquençage d'ARNs extraits d'échantillons tumoraux humains, de détecter des fusions de gènes spécifiques de tumeurs pour découvrir des néo-antigènes fortement immunogènes et personnaliser les immunothérapies
Cancer-associated gene fusions are a potential source for highly immunogenic neoantigens, but the lack of computational tools for accurate, sensitive identification of personal gene fusions has limited their targeting in personalized cancer immunotherapy. Here we present EasyFuse, a machine learning computational pipeline for detecting cancer-specific gene fusions in transcriptome data obtained from human cancer samples. EasyFuse predicts personal gene fusions with high precision and sensitivity, outperforming previously described tools. By testing immunogenicity with autologous blood lymphocytes from patients with cancer, we detected pre-established CD4+ and CD8+ T cell responses for 10 of 21 (48%) and for 1 of 30 (3%) identified gene fusions, respectively. The high frequency of T cell responses detected in patients with cancer supports the relevance of individual gene fusions as neoantigens that might be targeted in personalized immunotherapies, especially for tumors with low mutation burden.
Nature Biotechnology , résumé, 2022