Development of antigen‐prediction algorithm for personalized neoantigen vaccine using human leukocyte antigen transgenic mouse. Issue 4 (2nd March 2022)

• Record Type: Journal Article
• Authors: Charneau, Jimmy; Suzuki, Toshihiro; Shimomura, Manami; Fujinami, Norihiro; Mishima, Yuji; Hiranuka, Kazushi; Watanabe, Noriko; Yamada, Takashi; Nakamura, Norihiro; Nakatsura, Tetsuya
• Is Part Of: Cancer science. Volume 113:Issue 4(2022)
• Page Start: 1113
• Page End: 1124
• Publication Date: 2022-03-02
• Subjects: epitope peptide -- HLA transgenic mice -- HLA‐class I -- neoantigen -- prediction algorithm; Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.15291
• Languages: English
• ISSNs: 1347-9032

Abstract

Abstract: Immunotherapy is currently recognized as the fourth modality in cancer therapy. CTL can detect cancer cells via complexes involving human leukocyte antigen (HLA) class I molecules and peptides derived from tumor antigens, resulting in antigen‐specific cancer rejection. The peptides may be predicted in silico using machine learning‐based algorithms. Neopeptides, derived from neoantigens encoded by somatic mutations in cancer cells, are putative immunotherapy targets, as they have high tumor specificity and immunogenicity. Here, we used our pipeline to select 278 neoepitopes with high predictive "SCORE" from the tumor tissues of 46 patients with hepatocellular carcinoma or metastasis of colorectal carcinoma. We validated peptide immunogenicity and specificity by in vivo vaccination with HLA‐A2, A24, B35, and B07 transgenic mice using ELISpot assay, in vitro and in vivo killing assays. We statistically evaluated the power of our prediction algorithm and demonstrated the capacity of our pipeline to predict neopeptides (area under the curve = 0.687, P < 0.0001). We also analyzed the potential of long peptides containing the predicted neoepitopes to induce CTLs. Our study indicated that the short peptides predicted using our algorithm may be intrinsically present in tumor cells as cleavage products of long peptides. Thus, we empirically demonstrated that the accuracy and specificity of our prediction tools may be potentially improved in vivo using the HLA transgenic mouse model. Our data will help to design feedback algorithms to improve in silico prediction, potentially allowing researchers to predict peptides for personalized immunotherapy. Abstract : Using HLA‐transgenic mice, we assessed in vivo immunogenicity of neoantigen peptides by our prediction pipeline from patient tissues. Our results demonstrated that our prediction pipeline could propose immunogenic candidates; however, the power prediction is still insufficient for clinical application. Assessment of immunogenicity in an in vivo model is essential for clinical efficiency of peptide vaccines.