{
  "abstract": "Background The amplification of the 11q13 amplicon is a well-established event in breast cancer. High-throughput omics and neoantigen prediction highlight the potential of neoantigen-based immunotherapy, particularly by targeting gene fusions. By identifying 11q13 gene fusions, the resulting tumor-specific neoantigenic repertoire can serve as a target for vaccines, stimulating CD8+ T cell-mediated immune responses against cancer cells. This study aims to discover 11q13 fusion-derived neoantigens as vaccine candidates. RNF121-XRRA1 fusion was prioritized as it generated the highest split-read support among detected fusions.Methods Total RNA and whole exome sequencing (WES) data from 97 breast cancer patient-derived xenografts (PDX), supplied by XenoSTART, were analyzed for 11q13 gene fusions. WES data were evaluated for copy number variants using the DRAGEN structural variant caller. Chimeric RNAs were identified from Total RNA using the Arriba 2.7 fusion prediction tool. Fusions were identified by aligning RNA-level chimeric events with structural variant breakpoints. Reverse Transcription PCR and Sanger sequencing validated the RNF121-XRRA1 fusion junction ( figure 1) from 2 PDX derived from 1 patient. Human leukocyte antigen (HLA) profiling of the patient was performed using the OptiType HLA typing pipeline. 34 neoantigenic peptides were extracted from the fusion junction open reading frame (8-, 9-, 10-, and 11-mers). The binding affinity of peptides to patient HLA molecules was predicted using NetMHCpan 4.1. Peptide-HLA structural complexes were modeled using APE-Gen and assessed for binding energy.Results Across 97 breast cancer PDX models, 4 novel 11q13 gene fusions were identified. Both PDXs shared 6 common HLA Class I alleles across 6 loci. Among these, HLA-B*18:01 demonstrated the highest predicted binding affinity using NetMHCpan 4.1. NetMHCpan 4.1 predicted 14/34 peptides as binders (median percentile rank: 1.97%). APE-Gen identified 22/34 neopeptides with structural binding energies below –15 kcal/mol. 7 neopeptides exhibited strong sequence-based (percentile rank < 2%) and structural (binding energy < –15 kcal/mol) affinity ( figure 2). Top-ranked peptide RELDEVSEL had a NetMHCpan 4.1 percentile rank of 0.43% and a predicted binding energy of -17.6 kcal/mol.Conclusions This study identifies novel gene fusions within the 11q13 amplicon and prioritizes RNF121-XRRA1-derived neoantigens for vaccine development in breast cancer. In future studies, IFN-γ ELISpot assays will be performed using HLA-B*18:01-matched PBMCs to validate the immunogenicity of the 7 predicted high-affinity binding peptides. Additionally, single-cell sequencing of IFN-γ secreting cells will be employed to characterize CD8+ T cell clonotypes activated in response to these peptides, providing insight into the diversity and specificity of the neoantigen-reactive CD8+ T cell repertoire.Abstract 125 Figure 1Characterization of RNF121-XRRA1 gene fusion. A model of the RNF121-XRRA1 fusion transcript. The junction site is shown as exon 1 of RNF121 and exon 16 of XRRA1Abstract 125 Figure 2Neoantigenic peptide-HLA ensembles. Ensembles generated from molecular docking of neoantigenic peptides with HLA-B*18:01. Strongest binding pHLA is highlighted in the top row. Weakest binding pHLA is highlighted in the bottom row",
  "authors": [
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Brandon H Than"
    },
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Shiyanth Thevasagayampillai"
    },
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Aaranyah Kandasamy"
    },
    {
      "affiliations": [
        "XenoSTART, San Antonio, TX, USA"
      ],
      "name": "Michael J Wick"
    },
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Dinler Antunes"
    },
    {
      "affiliations": [
        "University of Houston, Houston, TX, USA"
      ],
      "name": "Preethi H Gunaratne"
    }
  ],
  "title": "125 Uncovering novel 11q13 gene fusions in breast cancer as targets for peptide-based immunotherapy",
  "uid": "1715a496-a089-501d-953f-58477eb5db8c"
}