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Schoenberger, S. P.

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

6 recordsLinked to original sources

Identification of pre-existing ubiquitous neoantigen-reactive tumor-infiltrating T-cells in a patient with metastatic pancreatic neuroendocrine tumor

BackgroundMutation-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. MethodsWhole-exome and RNA sequencing were performed on 14 tumor samples (primary and 13 synchronous nodal metastases) from a treatment-naive 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-{gamma} 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 TCR{beta} repertoire sequencing. ResultsAmong 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-{gamma} ELISpot response in peripheral blood, confirming its immunogenicity. Further, we identified the corresponding neoantigen-reactive TCR clonotypes in blood. Comparison with bulk TCR{beta} repertoires from eight metastatic sites showed that these clonotypes were present in every site analyzed, with evidence of local clonal expansion. ConclusionThis 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 topicNeoantigen-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 addsIn 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 policyThese 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.

immunology↗

SUMO2 Deletion Changes Chromatin Accessibility and Enhances Cytotoxic T Cell Activation and Tumor Infiltration

Cytotoxic T cells (CTL) are crucial for adaptive immunity that leads to prolonged survival and potential cures for cancer. Recent clinical data has shown that pharmacological inhibition of SUMOylation (SUMOi) profoundly modifies tumor microenvironment (TME) and activates CTL, although the mechanism is not well described. In this study, we found that T cell specific knock out (KO) of the most dominant SUMO paralog, Sumo2/SUMO2, in both mouse and human CD8+ T cells significantly enhanced CD8+ T cell activation that is independent of the known mechanism - inducing type I IFN (IFN-I) expression by myeloid cells. Sumo2/SUMO2 KO in CD8+ T cells increased chromatin accessibility for transcription factors BATF, JunB, ATF3, FRA1, FRA2, and AP1 that are known to promote T cell activation and proliferation. Using antigen-specific T cell models, OT1 and Chimeric Antigen Receptor (CAR)-T cells, we found that Sumo2 KO CD8+ T cells had significantly higher tumor infiltration as revealed by flow cytometry, immuno-fluorescence (IF) staining, and single nuclei RNA-sequencing (snRNA-seq) and conferred greater tumor growth inhibition than wildtype (WT) control T cells. snRNA-seq also revealed Sumo2 KO CD8+ T cells increased the expression of Tumor Necrosis Factor-Related Apoptosis-inducing Ligand (TRAIL), induced apoptosis genes in tumor cells and activated IFN-I and IFN-{gamma} responsive genes in all cell types in the TME. These findings elucidate a novel mechanism regarding how SUMOylation can directly control CTL activation and tumor infiltration that activate anti-tumor immunity in the TME. SUMO2 KO can also be a potential strategy to enhance adoptive T cell therapies of solid tumors by enhancing their activity, tumor infiltration and their ability to after the TME.

immunology↗

Machine learning of three-dimensional protein structures to predict the functional impacts of genome variation

Research in the human genome sciences generates a substantial amount of genetic data for hundreds of thousands of individuals, which concomitantly increases the number of variants with unknown significance (VUS). Bioinformatic analyses can successfully reveal rare variants and variants with clear associations to disease-related phenotypes. These studies have made a significant impact on how clinical genetic screens are interpreted and how patients are stratified for treatment. There are few, if any, comparable computational methods for variants to biological activity predictions. To address this gap, we developed a machine learning method that uses protein three-dimensional structures from AlphaFold to predict how a variant will influence changes to a genes downstream biological pathways. We trained state-of-the-art machine learning classifiers to predict which protein regions will most likely impact transcriptional activities of two proto-oncogenes, nuclear factor erythroid 2 (NFE2)-related factor 2 (Nrf2) and c-MYC. We have identified classifiers that attain accuracies higher than 80%, which have allowed us to identify a set of key protein regions that lead to significant perturbations in c-MYC or Nrf2 transcriptional pathway activities. SignificanceThe vast majority of mutations are either unspecified and/or their downstream biological implications are poorly understood. We have created a method that utilizes protein structure to cluster mutations from population-scale repositories to predict downstream functional impacts. The broader impacts of this approach include advanced filtering of mutations that are likely to impact genome function.

bioinformatics↗

Linked CD4+/CD8+ T cell neoantigen vaccination overcomes immune checkpoint blockade resistance and enables tumor regression

Therapeutic benefit to immune checkpoint blockade (ICB) is currently limited to the subset of cancers thought to possess a sufficient tumor mutational burden (TMB) to allow for the spontaneous recognition of neoantigens (NeoAg) by autologous T cells. We explored whether the response of an aggressive low TMB squamous cell tumor to ICB could be improved through combination immunotherapy using functionally defined NeoAg as targets for endogenous CD4+ and CD8+ T cells. We found that, whereas vaccination with CD4+ or CD8+ NeoAg alone did not offer prophylactic or therapeutic immunity, vaccines containing NeoAg recognized by both subsets overcame ICB resistance and led to the eradication of large established tumors that contained a subset of PD-L1+ tumor-initiating cancer stem cells (tCSC), provided the relevant epitopes were physically linked. Therapeutic CD4+/CD8+ T cell NeoAg vaccination produced a modified tumor microenvironment (TME) with increased numbers of NeoAg-specific CD8+ T cells existing in progenitor and intermediate exhausted states enabled by combination ICB-mediated intermolecular epitope spreading. The concepts explored herein should be exploited for the development of more potent personalized cancer vaccines that can expand the range of tumors treatable with ICB.

immunology↗

Treatment of pancreatic cancer with irreversible electroporation and intratumoral CD40 antibody stimulates systemic immune responses that inhibit liver metastasis in an orthotopic model.

BackgroundPancreatic cancer (PC) has a poor prognosis, and most patients present with either locally advanced or distant metastatic disease. Irreversible Electroporation (IRE) is a non-thermal method of ablation used clinically in locally advanced PC, but most patients eventually develop distant recurrence. We have previously shown that IRE alone is capable of generating protective, neoantigen-specific immunity. Here we aim to generate meaningful therapeutic immune effects by combining IRE with local (intratumoral) delivery of a CD40 agonistic antibody (CD40Ab). MethodsKPC46 organoids were generated from a tumor-bearing male KrasLSL-G12D-p53LSL-R172H-Pdx-1-Cre (KPC) mouse. Orthotopic tumors were established in the pancreatic tail of B6/129 F1J mice via laparotomy. Mice were randomized to treatment with either sham laparotomy, IRE alone, CD40Ab alone, or IRE followed immediately by CD40Ab injection. Metastatic disease and immune infiltration in the liver were analyzed 14 days post-procedure using flow cytometry and multiplex immunofluorescence imaging with spatial analysis. Candidate neoantigens were identified by mutanome profiling of tumor tissue for ex vivo functional analyses. ResultsThe combination of IRE+CD40Ab improved median survival to greater than 35 days, significantly longer than IRE (21 days) or CD40Ab (24 days) alone (p<0.01). CD40Ab decreased metastatic disease burden, with less disease in the combination group than in the sham group or IRE alone. Immunohistochemistry of liver metastases revealed a more than two-fold higher infiltration of CD8+ T-cells in the IRE+CD40Ab group than in any other group (p<0.01). Multiplex immunofluorescence imaging revealed a 4-6-fold increase in the density of CD80+CD11c+ activated dendritic cells (p<0.05), which were spatially distributed throughout the tumor unlike the sham group, where they were restricted to the periphery. In contrast, CD4+FoxP3+ T-regulatory cells (p<0.05) and Ly6G+ MDSCs (P<0.01) were reduced and restricted to the tumor periphery in the IRE+CD40Ab group. T-cells from the IRE+CD40Ab group recognized significantly more peptides representing candidate neoantigens than did T-cells from the IRE or untreated control groups. ConclusionsIRE can induce local tumor regression and neoantigen-specific immune responses. Addition of CD40Ab to IRE improved dendritic cell activation and neoantigen recognition, while generating a strong systemic anti-tumor T-cell response that inhibited metastatic disease progression. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/510847v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1a42713org.highwire.dtl.DTLVardef@16be8org.highwire.dtl.DTLVardef@15362eaorg.highwire.dtl.DTLVardef@182f988_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

Combined assessment of MHC binding and antigen expression improves T cell epitope predictions

MHC class I antigen processing consists of multiple steps that result in the presentation of MHC bound peptides that can be recognized as T cell epitopes. Many of the pathway steps can be predicted using computational methods, but one is often neglected: mRNA expression of the epitope source proteins. In this study, we improve epitope prediction by taking into account both peptide-MHC binding affinities and expression levels of the peptides source protein. Specifically, we utilized biophysical principles and existing MHC binding prediction tools in concert with RNA expression to derive a function that estimates the likelihood of a peptide being presented on a given MHC class I molecule. Our combined model of Antigen eXpression based Epitope Likelihood-Function (AXEL-F) outperformed predictions based only on binding or based only on antigen expression for discriminating eluted ligands from random background peptides as well as in predicting neoantigens that are recognized by T cells. We also showed that in cases where cancer patient-specific RNA-Seq data is not available, cancer-type matched expression data from TCGA can be used to accurately estimate patient-specific gene expression. Using AXEL-F together with TGCA expression data we were able to more accurately predict neoantigens that are recognized by T cells. The method is available in the IEDB Analysis Resource and free to use for the academic community. Significance statementEpitope prediction tools have been used to call epitopes in viruses and other pathogens for almost 30 years, and more recently, to call cancer neoantigens. Several such tools have been developed, however most of them ignore the mRNA expression of the epitope source proteins. In the present study, we have, to our knowledge for the first time, developed a biophysically motivated model to combine peptide-MHC binding and abundance of the peptides source protein to improve epitope predictions. Our novel tool AXEL-F is freely available on the IEDB and presents a clear opportunity for predicting and selecting epitopes more efficiently.

bioinformatics↗