Discovery of putative breast cancer antigens using an integrative platform of genomics‐driven immunoproteomics. Issue 15 (August 2017)

• Record Type: Journal Article
• Authors: Qendro, Veneta; Lundgren, Deborah H.; Palczewski, Samuel; Hegde, Poornima; Stevenson, Christina; Perpetua, Laurie; Latifi, Ardian; Merriman, Jesse; Bugos, Grace; Han, David K.
• Is Part Of: Proteomics. Volume 17:Issue 15/16(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-08
• Subjects: Cancer immunotherapy -- Cancer vaccines -- Companion diagnostic test -- Genomics -- Immunoproteomics -- Peptide microarrays -- Personalized medicine -- Precision medicine -- Putative cancer antigens breast cancer -- Tumor mutational burden (TMB); Proteins -- Separation -- Periodicals; Bioinformatics -- Periodicals; Proteomics -- Periodicals; Genomes -- Periodicals; Molecular genetics -- Periodicals; 572.605
• DOI: 10.1002/pmic.201600318
• Languages: English
• ISSNs: 1615-9853

Abstract

Abstract : Recent advances in cancer immuno‐therapeutics such as checkpoint inhibitors, chimeric antigen‐receptor T cells, and tumor infiltrating T cells (TIL) are now significantly impacting cancer patients in a positive manner. Although very promising, reports indicate no more than 25% of cases result in complete remission. One of the limitations of these treatments is the identity of putative cancer antigens in each patient, as it is technically challenging to identify cancer antigens in a rapid fashion. Thus, identification of cancer antigens followed by targeted treatment will increase the efficacy of cancer immunotherapies. To achieve this goal, a combined technologies platform of deep genomic sequencing and personalized immune assessment was devised, termedG enomicsD rivenI mmunoproteomics (GDI). Using this technological platform, we report the discovery of 149 tumor antigens from human breast cancer patients. Significant number of these putative cancer antigens arise from single nucleotide variants (SNVs), as well as insertions and deletions that results into frame‐shift mutations. We propose a general model of anti‐cancer immunity and suggest that the GDI platform may help identify patient‐specific tumor antigens in a timely fashion for precision immunotherapies.