Journal
CELL
Volume 183, Issue 3, Pages 818-+Publisher
CELL PRESS
DOI: 10.1016/j.cell.2020.09.015
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Funding
- PICI
- NIH [R21 AI34127, GM08042]
- UCLA Tumor Immunology training grant [NIH T32CA009120]
- CRI Irvington Postdoctoral Fellowship Program
- Queen Wilhelmina Cancer Research Award
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Many approaches to identify therapeutically relevant neoantigens couple tumor sequencing with bioinformatic algorithms and inferred rules of tumor epitope immunogenicity. However, there are no reference data to compare these approaches, and the parameters governing tumor epitope immunogenicity remain unclear Here, we assembled a global consortium wherein each participant predicted immunogenic epitopes from shared tumor sequencing data. 608 epitopes were subsequently assessed for T cell binding in patient-matched samples. By integrating peptide features associated with presentation and recognition, we developed a model of tumor epitope immunogenicity that filtered out 98% of non-immunogenic peptides with a precision above 0.70. Pipelines prioritizing model features had superior performance, and pipeline alterations leveraging them improved prediction performance. These findings were validated in an independent cohort of 310 epitopes prioritized from tumor sequencing data and assessed for T cell binding. This data resource enables identification of parameters underlying effective anti-tumor immunity and is available to the research community.
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