Raja and Mangalaparthi et al. demonstrated that non-coding cryptic peptides are an important class of immunogenic antigens in ovarian cancer (OC). Proteogenomic analysis identified cryptic HLA class I peptides derived from coding (predominantly 5’ UTRs and typically of lower immunogenicity) and non-coding regions of the genome from five metastatic OC tumors. 311 cryptic peptides were identified from the noncoding regions. Of the candidate cryptic antigens, 70% triggered peptide-specific T cell responses, with increased 4-1BB and IFNγ expression in autologous CD8+ T cells.
Contributed by Shishir Pant
ABSTRACT: Increased infiltration of CD3+ and CD8+ T cells into ovarian cancer (OC) is linked to better prognosis, but the specific antigens involved are unclear. Recent reports suggest that HLA class I can present peptides from noncoding genomic regions, known as noncanonical or cryptic peptides, but their immunogenicity is underexplored. To address this, we used immunopeptidomic analysis and RNA sequencing on five metastatic OC samples, which identified 311 cryptic peptides (40 to 83 per patient). Despite comprising less than 1% of total peptides, cryptic peptides from noncoding transcripts emerged as the predominant antigen class when compared to the other major classes of known tumor-specific and tumor-associated antigens in OC samples. Notably, nearly 70% of the prioritized cryptic peptides elicited T cell activation, as evidenced by increased 4-1BB and IFN-γ expression in autologous CD8+ T cells. This study reveals noncoding cryptic peptides as an important class of immunogenic antigens in OC.
Author Info: (1) Department of Immunology, Mayo Clinic, Phoenix, AZ, USA. (2) Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. (3) Department of Laboratory Medi
Author Info: (1) Department of Immunology, Mayo Clinic, Phoenix, AZ, USA. (2) Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. (3) Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. (4) Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA. (5) Department of Immunology, Mayo Clinic, Phoenix, AZ, USA. (6) Division of Gynecology, Mayo Clinic, Phoenix, AZ, USA. (7) Division of Gynecology, Mayo Clinic, Phoenix, AZ, USA. College of Medicine and Science, Mayo Clinic, Phoenix, AZ, USA. (8) Peter MacCallum Cancer Centre, Melbourne, Australia. Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia. (9) Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA. Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Rochester, MN, USA. Center for Individualized Medicine, Mayo Clinic, 200 First St SW, Rochester, MN, USA. Manipal Academy of Higher Education, Manipal, Karnataka, India. (10) Department of Immunology, Mayo Clinic, Phoenix, AZ, USA. College of Medicine and Science, Mayo Clinic, Phoenix, AZ, USA. Department of Cancer Biology, Mayo Clinic, Phoenix, AZ, USA. Mayo Clinic Comprehensive Cancer Center, Mayo Clinic, Phoenix, AZ, USA.
