Systems vaccinology approaches have identified factors affecting vaccine responses in multiple studies, but the ability of computational models to generalize these findings to unseen data remains unclear. We established a community resource to create and compare models predicting B. pertussis booster vaccination responses and put such modeling approaches to the test. We compiled multi-modal experimental training data from three independent cohorts (n=117 individuals), and asked investigators to predict vaccine responses in a cohort of 54 newly recruited individuals using only their pre-booster vaccination data. We benchmarked a total of 107 computational models. Top-performing models were characterized by workflows that prioritized rigorous data preprocessing, robust imputation of missing data, and the use of multi-omics integration or non-linear machine learning. We identified pre-existing antigen-specific antibody titers and baseline monocyte frequencies as the most consistent predictors of post-vaccination immunity, highlighting the dominant role of individual immune setpoints. We established the resulting datasets and evaluation framework as a community resource to advance predictive immunology and facilitate personalized vaccination strategies.
Pramod Shinde, Lisa Willemsen, Jiyeun Lee et al.· bioRxiv· 0 citations
Background Mutation-derived neoantigens, typically identified in primary tumors, are emerging therapeutic targets for personalized cancer vaccines and adoptive T-cell therapies. However, clinical efficacy of neoantigen-directed therapies in patients with metastatic disease remains limited, partly due to inter-site genetic heterogeneity. We investigated whether ubiquitous neoantigens–derived from mutations shared across all tumor sites–could provide more effective, durable targets, particularly in patients undergoing resection of metastatic lesions. Methods Whole-exome and RNA sequencing were performed on 14 tumor samples (primary and 13 synchronous nodal metastases) from a treatment-naïve patient with pancreatic neuroendocrine tumor (PNET). Ubiquitous mutations were identified bioinformatically, and their immunogenicity assessed using in-vitro stimulation of autologous peripheral blood mononuclear cells followed by IFN-γ ELISpot assay. Neoantigen-specific T-cell clonotypes were further identified by HLA-tetramer staining and single-cell RNA/TCR sequencing. Neoantigen-reactive clonotypes identified in peripheral blood were tracked across multiple metastatic sites using bulk TCRβ repertoire sequencing. Results Among 1,195 non-synonymous mutations detected, eight were shared across all 14 tumor sites. Of these, one encoded a neoantigen that elicited a reproducible IFN-γ ELISpot response in peripheral blood, confirming its immunogenicity. Further, we identified the corresponding neoantigen-reactive TCR clonotypes in blood. Comparison with bulk TCRβ repertoires from eight metastatic sites showed that these clonotypes were present in every site analyzed, with evidence of local clonal expansion. Conclusion This study provides direct evidence that a single ubiquitous mutation-derived neoantigen can generate systemic T-cell responses and clonotype expansion across multiple metastatic sites in a TMB-low, TIL-low tumor. Our findings support incorporating mutation-sharing status across metastases as a key criterion for neoantigen selection in cancer vaccines and adoptive T-cell therapies. This approach could inform the design of neoantigen-directed immunotherapies in metastatic PNET and potentially other metastatic solid tumors. What is already known on this topic Neoantigen-directed therapies, such as personalized cancer vaccines or adoptive T-cell transfer, can induce anti-tumor responses but have shown limited success in metastatic disease. One major barrier is genetic heterogeneity between tumor sites, suggesting that targeting ubiquitous mutations–those shared across all tumor sites–may improve the efficacy of such therapies. What this study adds In one patient with metastatic pancreatic neuroendocrine tumor involving 13 lymph nodes, we identified eight ubiquitous mutations, one of which generated a detectable neoantigen-specific T-cell response in blood. The corresponding T-cell clonotypes were found across all metastatic sites analyzed and showed evidence of clonal expansion, providing direct evidence of systemic and local recognition of a shared neoantigen in a TMB-low/TIL-low cancer. How this study might affect research, practice or policy These findings support incorporating mutation sharing across metastases as a key criterion in neoantigen selection for cancer vaccines and adoptive T-cell therapies. This strategy could enhance the relevance and durability of neoantigen- directed approaches in patients with metastatic disease.
J. Tanis, Katy J. McCann, F. E. Castañeda-Castro et al.· bioRxiv· 0 citations
Cancer epitopes, the molecular structures recognized by T and B cells at the tumor interface, are central to understanding antitumor immunity and developing immunotherapies. Yet despite the rapid growth of cancer immunology data, a comprehensive, continuously updated, and accessible resource for cancer epitope data has been lacking. The Cancer Epitope Database and Analysis Resource (CEDAR, cedar.iedb.org) was established in 2021 to fill this gap, providing curated experimental epitope data alongside a suite of cancer-specific computational tools for epitope prediction and analysis. Built on the validated infrastructure of the Immune Epitope Database (IEDB), CEDAR integrates cancer epitope data with biological, immunological, and clinical context, enabling researchers to explore immune recognition of tumors, identify candidate targets for immunotherapy, and benchmark prediction methods. Here we describe CEDAR’s current capabilities, report on progress in curation, database development, and tool availability, and outline the opportunities and challenges ahead for expanding its scope and utility to the cancer research community.
Zeynep Koşaloğlu-Yalçın, Ibel Carri, Daniel Marrama et al.· Frontiers in Oncology· 0 citations
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