Weber and Ibn-Salem et al. created a machine learning computational pipeline, EasyFuse, to predict cancer-specific gene fusions (GFs), in particular, trans-like GFs, from clinically relevant fresh frozen (FF) or FFPE samples. Ten of 21 and 1 of 30 predicted fusion neoantigens elicited a CD4+ or CD8+ T cell response, respectively, after in vitro stimulation of patient-derived PBMCs. Fusion peptide-specific CD4+ or CD8+ T cell responses to 3 neoepitopes were detected in healthy donors, likely due to cross-reactive memory T cells. Across 57 FF breast cancer samples, a median of 12 fusion neoantigens per sample could be predicted, with 95% of these being non-recurrent.
Contributed by Ute Burkhardt
ABSTRACT: 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.
Author Info: (1) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (2) TRON - Translational Oncology at the University
Author Info: (1) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (2) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (3) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (4) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (5) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (6) BioNTech SE, Mainz, Germany. (7) BioNTech SE, Mainz, Germany. (8) BioNTech SE, Mainz, Germany. (9) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (10) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (11) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. (12) TRON - Translational Oncology at the University Medical Center of Johannes Gutenberg University Mainz gGmbH, Mainz, Germany. sahin@uni-mainz.de. BioNTech SE, Mainz, Germany. sahin@uni-mainz.de. Johannes Gutenberg University Mainz, Mainz, Germany. sahin@uni-mainz.de.
