The study of tumour-specific antigens (TSAs) as targets for antitumour therapies has accelerated within the past decade. The most commonly studied class of TSAs are those derived from non-synonymous single-nucleotide variants (SNVs), or SNV neoantigens. However, to increase the repertoire of available therapeutic TSA targets, 'alternative TSAs', defined here as high-specificity tumour antigens arising from non-SNV genomic sources, have recently been evaluated. Among these alternative TSAs are antigens derived from mutational frameshifts, splice variants, gene fusions, endogenous retroelements and other processes. Unlike the patient-specific nature of SNV neoantigens, some alternative TSAs may have the advantage of being widely shared by multiple tumours, allowing for universal, off-the-shelf therapies. In this Opinion article, we will outline the biology, available computational tools, preclinical and/or clinical studies and relevant cancers for each alternative TSA class, as well as discuss both current challenges preventing the therapeutic application of alternative TSAs and potential solutions to aid in their clinical translation.
Author Info: (1) Department of Microbiology and Immunology, UNC School of Medicine, Marsico Hall, Chapel Hill, NC, USA. Lineberger Comprehensive Cancer Center, University of North Carolina at C
Author Info: (1) Department of Microbiology and Immunology, UNC School of Medicine, Marsico Hall, Chapel Hill, NC, USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. (2) Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Lineberger Bioinformatics Core, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Marsico Hall, Chapel Hill, NC, USA. (3) Department of Microbiology and Immunology, UNC School of Medicine, Marsico Hall, Chapel Hill, NC, USA. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. (4) Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. (5) Department of Microbiology and Immunology, UNC School of Medicine, Marsico Hall, Chapel Hill, NC, USA. benjamin_vincent@med.unc.edu. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. benjamin_vincent@med.unc.edu. Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. benjamin_vincent@med.unc.edu. Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. benjamin_vincent@med.unc.edu. Program in Computational Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. benjamin_vincent@med.unc.edu. (6) Department of Microbiology and Immunology, UNC School of Medicine, Marsico Hall, Chapel Hill, NC, USA. jonathan_serody@med.unc.edu. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. jonathan_serody@med.unc.edu. Division of Hematology/Oncology, Department of Medicine, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. jonathan_serody@med.unc.edu. Program in Computational Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. jonathan_serody@med.unc.edu.