title: Prediction and prioritization of neoantigens: integration of RNA sequencing data with whole-exome sequencing.
abstract: The importance of neoantigens for cancer immunity is now well-acknowledged. However, there are diverse strategies for predicting and prioritizing candidate neoantigens, and thus reported neoantigen loads vary a great deal. To clarify this issue, we compared the numbers of neoantigen candidates predicted by four currently utilized strategies. Whole-exome sequencing and RNA sequencing (RNA-Seq) of four non-small-cell lung cancer patients was carried out. We identified 361 somatic missense mutations from which 224 candidate neoantigens were predicted using MHC class I binding affinity prediction software (strategy I). Of these, 207 exceeded the set threshold of gene expression (fragments per kilobase of transcript per million fragments mapped ≥1), resulting in 124 candidate neoantigens (strategy II). To verify mutant mRNA expression, sequencing of amplicons from tumor cDNA including each mutation was undertaken; 204 of the 207 mutations were successfully sequenced, yielding 121 mutant mRNA sequences, resulting in 75 candidate neoantigens (strategy III). Sequence information was extracted from RNA-Seq to confirm the presence of mutated mRNA. Variant allele frequencies ≥0.04 in RNA-Seq were found for 117 of the 207 mutations and regarded as expressed in the tumor, and finally, 72 candidate neoantigens were predicted (strategy IV). Without additional amplicon sequencing of cDNA, strategy IV was comparable to strategy III. We therefore propose strategy IV as a practical and appropriate strategy to predict candidate neoantigens fully utilizing currently available information. It is of note that different neoantigen loads were deduced from the same tumors depending on the strategies applied.
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Takahiro
authors.initials: T
authors.lastname: Karasaki
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Nagayama
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Hideki
authors.initials: H
authors.lastname: Kuwano
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Jun-Ichi
authors.initials: JI
authors.lastname: Nitadori
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Masaaki
authors.initials: M
authors.lastname: Sato
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Masaki
authors.initials: M
authors.lastname: Anraku
authors.affiliation: Department of Immunotherapeutics, The University of Tokyo Hospital, Tokyo, Japan.|Medinet Co. Ltd, Yokohama, Japan.
authors.forename: Akihiro
authors.initials: A
authors.lastname: Hosoi
authors.affiliation: Department of Immunotherapeutics, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Hirokazu
authors.initials: H
authors.lastname: Matsushita
authors.affiliation: Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Japan.
authors.forename: Masaki
authors.initials: M
authors.lastname: Takazawa
authors.affiliation: Department of Technology Development, Kazusa DNA Research Institute, Kisarazu, Japan.
authors.forename: Osamu
authors.initials: O
authors.lastname: Ohara
authors.affiliation: Department of Thoracic Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
authors.forename: Jun
authors.initials: J
authors.lastname: Nakajima
authors.affiliation: Department of Immunotherapeutics, The University of Tokyo Hospital, Tokyo, Japan.
authors.forename: Kazuhiro
authors.initials: K
authors.lastname: Kakimi
mesh_terms: D000230:Adenocarcinoma; D000328:Adult; D000368:Aged; D000465:Algorithms; D000951:Antigens, Neoplasm / Q000032:analysis / Q000235:genetics*; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics* / Q000276:immunology; D018076:DNA, Complementary; D059472:Exome; D015870:Gene Expression; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D015395:Histocompatibility Antigens Class I / Q000235:genetics*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000276:immunology; D008297:Male; D046228:Microarray Analysis / Q000379:methods; D020125:Mutation, Missense; D012333:RNA, Messenger / Q000235:genetics; D017423:Sequence Analysis, RNA / Q000379:methods*; D013602:T-Lymphocytes, Cytotoxic / Q000276:immunology
chemical_list: D000951:Antigens, Neoplasm; D018076:DNA, Complementary; D015395:Histocompatibility Antigens Class I; D012333:RNA, Messenger
keywords: Lung cancer; RNA-Seq; neoantigen; next-generation sequencing; whole-exome sequencing