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Genome Med. 2019 Aug 28;11(1):56. doi: 10.1186/s13073-019-0666-2.

Best practices for bioinformatic characterization of neoantigens for clinical utility.

Author information

1
Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA.
2
McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA.
3
Division of Hematology and Oncology, Medical Plaza Driveway, Department of Medicine, University of California, Los Angeles, Los Angeles, CA, 90024, USA.
4
Department of Surgery, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA.
5
Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA.
6
Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA. obigriffith@wustl.edu.
7
McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA. obigriffith@wustl.edu.
8
Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA. obigriffith@wustl.edu.
9
Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA. obigriffith@wustl.edu.
10
Division of Oncology, Department of Internal Medicine, Washington University School of Medicine, St. Louis, MO, 63110, USA. mgriffit@wustl.edu.
11
McDonnell Genome Institute, Forest Park Avenue, Washington University School of Medicine, St. Louis, MO, 63108, USA. mgriffit@wustl.edu.
12
Siteman Cancer Center, Parkview Place, Washington University School of Medicine, St. Louis, MO, 63110, USA. mgriffit@wustl.edu.
13
Department of Genetics, South Euclid Avenue, Washington University School of Medicine, St. Louis, MO, 63110, USA. mgriffit@wustl.edu.

Abstract

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor-normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

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