Tumors acquire numerous mutations during development and progression. When translated into proteins, these mutations give rise to neoantigens that can be recognized by T cells and generate antibodies, representing an exciting direction of cancer immunotherapy. While neoantigens have been reported in many cancer types, the profiling of neoantigens often focused on the class-I subtype that are presented to CD8 + T cells, and the relationship between neoantigen load and clinical outcomes was often inconsistent among cancer types. In this study, we described an informatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. We applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the detected neoantigens. We found recurrent HLA class-I and class-II restricted neoantigens across breast cancer cases, and uncovered associations between neoantigen load and clinical traits. Both class-I and class-II neoantigen loads from SNM and INDEL were found to predict overall survival independent of tumor mutational burden (TMB), breast cancer subtypes, tumor-infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis. Our study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer.
Keywords: Breast cancer; neoantigen; overall survival; tumor mutational burden.
© 2020 The Author(s). Published with license by Taylor & Francis Group, LLC.