Biology subjects

Vensko, S. P.

Publications and source records attributed to Vensko, S. P..

3 recordsLinked to original sources

bioRxiv2026

Immunodominance Hierarchy of Endogenous BBN963 Bladder Cancer Antigens Remains Stable Under Anti-PD1 and Anti-CTLA4 Immunotherapy

Immune checkpoint inhibition (ICI) is clinically active against multiple cancers, including urothelial cancer at the non-muscle invasive, muscle-invasive, and metastatic stages. Despite this, large numbers of patients experience disease progression and relapse after treatment with ICI-containing regimens. Tumor antigen-specific T cells are critical to ICI response, however few studies have evaluated the breadth and magnitude of tumor antigen-specific T cell responses with ICI therapy. In this study, we mapped the tumor antigen immunodominance hierarchy in the BBN963 model of murine basal-like bladder cancer for endogenous tumor neoantigens expressed physiologically. We used a high-throughput matrixed ELISpot assay to detect CD8+ T cell responses to predicted BBN963 tumor antigens derived from multiple mutational genomic sources. We found CD8+ T cell responses were directed against a subset of tumor antigens forming a stable and reproducible immunodominance hierarchy across individual mice. Treatment with anti-PD-1 or anti-CTLA-4 did not substantially reshape this hierarchy or broadly shift dominant responses to previously defined subdominant epitopes. Predicted peptide MHC binding stability and affinity was associated with antigen immunogenicity. Cancer-testis antigens, endogenous retroviral antigens, and SNV-derived tumor antigens that were immunogenic were found across tumor subclones. By diversifying the immunogenic antigen repertoire beyond SNVs, we achieved nearly 100% tumor subclone coverage, suggesting that broader antigen selection could help immunotherapy target more tumor subclones. In conclusion, this study supports the stability of the immunodominance hierarchy under ICI therapy and a role for broadening antigen discovery to multiple expressional sources in immunotherapy design.

cancer biology↗

bioRxiv2022

Shared Graft Versus Leukemia Minor Histocompatibility Antigens in DISCOVeRY-BMT

T cell responses to minor histocompatibility antigens (mHAs) mediate graft versus leukemia (GvL) effects and graft versus host disease (GvHD) in allogeneic hematopoietic cell transplant (alloHCT). Therapies that boost T cell responses improve the efficacy of alloHCT; however, these have been limited by concurrent increases in the incidence and severity of GvHD. mHAs with expression restricted to hematopoietic tissue (GvL mHAs) are attractive targets for driving GvL without causing GvHD. Prior work to identify mHAs has focused on a small set of mHAs or population-level SNP association studies. We report here the discovery of a large set of novel GvL mHAs based on predicted peptide immunogenicity, restriction of expression to hematopoietic tissue or GvHD target organs, and degree of sharing among donor-recipient pairs (DRPs) in the DISCOVeRY-BMT dataset of 3231 alloHCT DRPs. The total number of predicted mHAs and count within each class of predicted mHAs significantly differed by recipient genomic ancestry group, with European American>Hispanic>African American for each. The number of mHAs also differed markedly by HLA allele, even among alleles of the same gene. From the pool of predicted mHAs, we identified the smallest sets of GvL mHAs needed to cover 100% of DRPs with a given HLA allele. We then used mass spectrometry to search for high population frequency mHAs for three common HLA alleles. We validated a total of 24 novel predicted GvL mHAs that cumulatively are found within 98.8%, 60.7%, and 78.9% of DRPs within DISCOVeRY-BMT that express HLA-A*02:01, HLA-B*35:01, and HLA-C*07:02 respectively. We also confirmed in vivo immunogenicity of one example novel mHA via coculture of healthy human CD8 T cells with mHA-pulsed dendritic cells. This work demonstrates that identification of shared mHAs is a feasible and promising technique for expanding mHA-targeting immunotherapeutics to larger numbers of patients.

cancer biology↗

bioRxiv2022

LENS - Landscape of Effective Neoantigens Software

MotivationElimination of cancer cells by T cells is a critical mechanism of anti-tumor immunity and cancer immunotherapy response. T cells recognize cancer cells by engagement of T cell receptors with peptide epitopes presented by major histocompatibility complex (MHC) molecules on the cancer cell surface. Peptide epitopes can be derived from antigen proteins coded for by multiple genomic sources. Bioinformatics tools used to identify tumor-specific epitopes via analysis of DNA and RNA sequencing data have largely focused on epitopes derived from somatic variants, though a smaller number have evaluated potential antigens from other genomic sources. ResultsWe report here an open-source workflow utilizing the Nextflow DSL2 workflow manager, Landscape of Effective Neoantigen Software (LENS), which predicts tumor-specific and tumor-associated antigens from single nucleotide variants, insertions and deletions, fusion events, splice variants, cancer testis antigens, overexpressed self-antigens, viruses, and endogenous retroviruses. The primary advantage of LENS is that it expands the breadth of genomic sources of discoverable tumor antigens using genomics data. Other advantages include modularity, extensibility, ease of use, and harmonization of relative expression level and immunogenicity prediction across multiple genomic sources. We present an analysis of 115 acute myeloid leukemia (AML) samples to demonstrate the utility of LENS. We expect LENS will be a valuable platform and resource for T cell epitope discovery bioinformatics, especially in cancers with few somatic variants where tumor-specific epitopes from alternative genomic sources are an elevated priority. AvailabilityMore information about LENS, including workflow documentation and instructions, can be found at https://gitlab.com/landscape-of-effective-neoantigens-software Contactsteven_vensko@med.unc.edu, benjamin_vincent@med.unc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

cancer biology↗