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Vincent, B. G.

Publications and source records attributed to Vincent, B. G..

8 recordsLinked to original sources

Discovery of frameshift-associated TCRs following personalized neoantigen vaccination in a patient with osteosarcoma

Personalized antigen-directed immunotherapies, including personalized cancer vaccines, are emerging as promising therapeutic modalities for a range of cancer types. Immune monitoring can be used to evaluate the efficacy of a vaccine in stimulating the immune response pre- and post-vaccination; however, such approaches typically stop short of identifying which T cell receptors (TCRs) are functionally driving therapeutic responses. Here, we present a case report of immune monitoring and subsequent TCR discovery experiments following administration of personalized cancer vaccines in a patient with locally recurrent osteosarcoma. Whole PBMCs were stimulated with vaccine peptides (>16 AA) in vitro for 11 days, after which IFN{gamma} ELISPOT identified total T cell responses for 12 of 15 tested peptides post-vaccination. In vitro expanded samples were submitted for 10x single-cell TCR sequencing, from which 26 TCRs were chosen for generation of TCR-T cells based on clonal expansion and culture specificity. Of the generated TCR-T cells, 25 produced functional TCRs, 4 of which were specific to vaccine peptides. However, downstream testing revealed each was MHC class-II restricted. To address this, we repeated the in vitro stimulations with co-cultures of isolated CD8+ T cells with autologous dendritic cells, and cultures of whole PBMCs with anti-MHC class-II blocking antibody. These cultures yielded 10 additional TCRs, 2 of which were responsive to a vaccine peptide containing a MAP2 frameshift mutation. These findings demonstrate an effective approach for leveraging post-vaccination expanded antigen-specific T cell populations for discovery of novel T cell receptors for downstream cellular therapies.

immunology↗

PRAME Epitopes are T-Cell Immunovulnerabilities in BRD4::NUTM1 Initiated NUT Carcinoma

BackgroundNUT carcinoma is a rare but highly lethal solid tumor without an effective standard of care. NUT carcinoma is caused by bromodomain-containing NUTM1 fusion oncogenes, most commonly BRD4::NUTM1. BRD4::NUTM1 recruits p300 to acetylate H3K27 forming expansive stretches of hyperacetylated chromatin called "megadomains" with the overexpression of corresponding oncogenes, including MYC. We hypothesized that transcriptional dysregulation caused by BRD4::NUTM1 would lead to the generation of cancer-specific antigens that could be therapeutically actionable. MethodsWe integrated genomics, computational antigen prediction software, targeted immunopeptidomics using single- and double-labeled peptide standards, and gain/loss-of-function genetic experiments on a panel of cell lines (N=5), a patient derived xenograft, a tissue microarray (N=77), and patient samples from the Tempus AI Sequencing Database harboring evidence of NUTM1 fusions (N=165). We created an PRAME425 T-cell receptor x SP34 CD3 bispecific molecule modeled after brenetafusp, an PRAME425 T-cell receptor bispecific T-cell engager, as well as PRAME425 TCR T-cells based on anzutresgene autoleucel and we applied these products to NUT carcinoma cells in vitro. ResultsWe identified PRAME as the most commonly expressed cancer/testis antigen in patient samples harboring the three canonical NUT carcinoma fusions (BRD4::NUTM1, BRD3::NUTM1, and NSD3::NUTM1). Additionally, 56% (43/77) of NUT carcinoma tissue microarray samples stained positive for PRAME. BRD4::NUTM1 expression in HEK 293T cells enhanced PRAME levels and BRD4::NUTM1 knockout in NUT carcinoma cells reduced PRAME levels. Immunopeptidomics detected more PRAME-derived HLA ligands (N=9) than all other cancer/testis antigens combined (N=5). Targeted mass spectrometry detected the HLA-A*02:01/SLLQHLIGL (PRAME425) epitope in 100% (4/4) of HLA-A*02+, PRAME+ NUT carcinoma samples at higher levels (>0.01 fM) than HLA-A*02:01/RLDQLLRHV (PRAME312) or HLA-A*02:01/YLHARLREL (PRAME462). The PRAME425 T-cell receptor x SP34 CD3 bispecific molecule and PRAME425 TCR T-cells each exhibited potent, T-cell mediated cytotoxicity against PRAME+ NUT carcinoma cells. ConclusionsPRAME is highly and frequently expressed in NUT carcinoma and the most common oncoprotein causing NUT carcinoma, BRD4::NUTM1, contributes to these high PRAME levels. PRAME epitopes presented by HLA Class I are a previously unrecognized therapeutic vulnerability for NUT carcinoma that warrant clinical trials testing PRAME targeted immunotherapies in this neglected patient population. What is already known on this topicNUT carcinoma is a devastating malignancy that is recalcitrant to cytotoxic chemotherapy, T-cell checkpoint blockade, and targeted therapies in the form of bromodomain inhibitors. What this study addsNUT carcinoma tumors are high in the cancer/testis gene PRAME. The oncogene most commonly causing NUT carcinoma, BRD4::NUTM1, contributes to these high levels. NUT carcinoma cells present PRAME epitopes on HLA Class I molecules and are susceptible to PRAME-directed, T-cell mediated cytotoxicity. How this study might affect research, practice or policyOur results argue for phase I/II clinical trials testing PRAME immunotherapies like brenetafusp or anzutresgene autoleucel in PRAME+ NUT carcinoma patients. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=149 SRC="FIGDIR/small/642090v3_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@495654org.highwire.dtl.DTLVardef@c2b975org.highwire.dtl.DTLVardef@1de507org.highwire.dtl.DTLVardef@a76d33_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

ACE Configurator for ELISpot (ACE): Optimizing Combinatorial Design of Pooled ELISpot Assays with an Epitope Similarity Model

The ELISpot assay is a powerful in vitro immunoassay that enables cost-effective quantification of antigen-specific T-cell reactivity. It is widely used in the context of cancer and infectious diseases to validate the immunogenicity of epitopes. While technological advances in hardware and software have kept pace with the need for increased throughput, assay design and deconvolution methodology have largely remained stagnant. Current methods for designing multiplexed ELISpot assays are restricted to preset configurations, lack support for high-throughput scenarios, and ignore peptide identity during pool assignment. We introduce the ACE Configurator for ELISpot (ACE) to address these gaps. ACE generates optimized peptide-pool assignments from highly customizable user inputs and handles positive peptide deconvolution using assay readouts. We present a novel sequence-aware pooling strategy, powered by a fine-tuned ESM-2 deep sequence model to identify immunologically similar peptides, reducing the number of false positives and subsequent confirmatory assays. To validate the performance of ACE using real-world datasets, we conducted a comprehensive benchmark study against various design heuristics, deconvolution methods, and experimental conditions, contextualizing design parameter choices with their impact on precision and number of total pools. Our results demonstrate ACEs capacity to further increase precision of identified immunogenic peptides, maximizing experimental efficiency at the bench-side. ACE is freely available as an executable with a graphical user interface and command-line interfaces at https://github.com/pirl-unc/ace.

bioinformatics↗

A Community Challenge to Predict Clinical Outcomes After Immune Checkpoint Blockade in Non-Small Cell Lung Cancer

PurposePredictive biomarkers of immune checkpoint inhibitors (ICIs) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. MethodsParticipants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. ResultsA total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The bestperforming models showed improved predictive power over reference variables, including TMB or PD-L1. ConclusionThis DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICIs clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICIs efficacy. Context summaryO_ST_ABSKey objectiveC_ST_ABSNot all patients with non-small cell lung cancer (NSCLC) eligible for immune checkpoint inhibitor (ICIs) respond to treatment, but accurate predictive biomarkers of ICIs clinical outcomes are currently lacking. This crowdsourced initiative enabled the robust assessment of predictive models using data from two randomized clinical trials of first-line ICI in metastatic NSCLC. Knowledge generatedModels submitted indicate that a combination of programmed death ligand 1 (PD-L1), tumor mutational burden (TMB), and immune gene signatures might be able to identify patients more likely to respond to ICIs. TMB and PD-L1 seemed important to predict progression-free survival and overall survival. Mechanisms including apoptosis, T-cell crosstalk, and adaptive immune resistance appeared essential to predict response. Relevance

cancer biology↗

Associations of Minor Histocompatibility Antigens with Clinical Outcomes Following Allogeneic Hematopoietic Cell Transplantation

The role of minor histocompatibility antigens (mHAs) in mediating graft versus leukemia (GvL) and graft versus host disease (GvHD) following allogeneic hematopoietic cell transplantation (alloHCT) is recognized but not well-characterized. By implementing improved methods for mHA prediction in two large patient cohorts, this study aimed to comprehensively explore the role of mHAs in alloHCT by analyzing whether (1) the number of predicted mHAs, or (2) individual mHAs are associated with clinical outcomes using multi-variate survival models corrected for multiple testing. Cox proportional hazard results showed that patients with a class I mHA count greater than the population median had an increased hazard of GvHD mortality (HR=1.39, 95%CI 1.01, 1.77, P=0.046). Competing risk analyses identified the class I mHAs DLRCKYISL (gene GSTP), WEHGPTSLL (CRISPLD2) and STSPTTNVL (SERPINF2) were associated with increased GVHD death (HR=2.84, 95%CI 1.52, 5.31, P=0.01), decreased leukemia-free survival (LFS) (HR=1.94,95%CI 1.27, 2.95, P=0.044), and increased disease-related mortality (DRM) (HR=2.32, 95%CI 1.5, 3.6, P=0.008), respectively. One class II mHA YQEIAAIPSAGRERQ (TACC2) was associated with increased risk of treatment-related mortality (TRM) (HR=3.05, 95%CI 1.75, 5.31, P=0.02). WEHGPTSLL and STSPTTNVL were present in conjunction within HLA haplotype B*40:01-C*03:04 and showed a positive dose-response relationship with increased all-cause mortality and DRM and decreased LFS, indicating these two mHAs contribute to risk of mortality in an additive manner. Our study reports the first large scale investigation of the associations of predicted class I and class II mHA peptides with clinical outcomes following alloHCT. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/506092v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1516a9aorg.highwire.dtl.DTLVardef@2db79aorg.highwire.dtl.DTLVardef@170e19aorg.highwire.dtl.DTLVardef@1eb7148_HPS_FORMAT_FIGEXP M_FIG C_FIG

genetics↗

Evaluation of tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with cyclophosphamide and pembrolizumab

BackgroundThe role of B cells in antitumor immunity is becoming increasingly appreciated, as B cell populations have been associated with response to immune checkpoint blockade (ICB) in breast cancer patients and murine models of breast cancer. Deeper understanding of antibody responses to tumor antigens is needed to clarify the function of B cells in determining response to immunotherapy. MethodsWe evaluated tumor antigen-specific antibody responses in patients with metastatic triple negative breast cancer treated with pembrolizumab following low dose cyclophosphamide therapy using computational linear epitope prediction and custom peptide microarrays. ResultsWe found that a minority of predicted linear epitopes were associated with antibody signal, and signal was associated with both neoepitopes and self-peptides. No association was observed between signal presence and subcellular localization or RNA expression of parent proteins. Patient-specific patterns of antibody signal boostability were observed that were independent of clinical response. Intriguingly, measures of cumulative antibody signal intensity relative to immunotherapy treatment showed that the one complete responder in the trial had the greatest increase in total antibody signal, which supports a potential association between ICB-dependent antibody boosting and clinical response. The antibody boost in the complete responder was largely driven by increased levels of IgG specific to a sequence of N-terminal residues in native Epidermal Growth Factor Receptor Pathway Substrate 8 (EPS8) protein, a known oncogene in several cancer types including breast cancer. Structural protein prediction showed that the targeted epitope of EPS8 was in a region of the protein with mixed linear/helical structure, and that this region was solvent-exposed and not predicted to bind to interacting macromolecules. ConclusionsThis study highlights the potential importance of the humoral immune response targeting neoepitopes as well as self epitopes in shaping clinical response to immunotherapy.

cancer biology↗

SARS-CoV-2 peptide vaccine elicits T-cell responses in mice but does not protect against infection or disease

We vaccinated BALB/c mice with peptides derived from the SARS-CoV-2 proteome selected in silico to elicit T-cell responses and/or B-cell responses against linear epitopes. These peptides were administered in combination with either of two adjuvants, poly(I:C) and the STING agonist BI-1387466. Antibody responses against predicted linear epitopes were not observed but both adjuvants consistently elicited T-cell responses to the same peptides, which were primarily from the set chosen for predicted T-cell immunogenicity. The magnitude of T-cell responses was significantly higher with BI-1387466 compared with poly(I:C). Neither adjuvant group, however, provided any protection against infection with the murine adapted virus SARS-CoV-2-MA10 or from disease following infection. In light of more recent evidence for protection from severe disease mediated by CD8+ T-cells, we suspect that the epitopes selected for vaccination were not presented by infected murine cells.

immunology↗

Landscape and Selection of Vaccine Epitopes in SARS-CoV-2

There is an urgent need for a vaccine with efficacy against SARS-CoV-2. We hypothesize that peptide vaccines containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation would drive both humoral and cellular immunity with high specificity, potentially avoiding undesired effects such as antibody-dependent enhancement (ADE). Additionally, such vaccines can be rapidly manufactured in a distributed manner. In this study, we combine computational prediction of T cell epitopes, recently published B cell epitope mapping studies, and epitope accessibility to select candidate peptide vaccines for SARS-CoV-2. We begin with an exploration of the space of possible T cell epitopes in SARS-CoV-2 with interrogation of predicted HLA-I and HLA-II ligands, overlap between predicted ligands, protein source, as well as concurrent human/murine coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, viral source protein abundance, sequence conservation, coverage of high frequency HLA alleles and co-localization of CD4+ and CD8+ T cell epitopes. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering to select regions with surface accessibility, high sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we propose a set of SARS-CoV-2 vaccine peptides for use in subsequent murine studies and clinical trials. O_FIG O_LINKSMALLFIG WIDTH=180 HEIGHT=200 SRC="FIGDIR/small/135004v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@1fc6599org.highwire.dtl.DTLVardef@1725a45org.highwire.dtl.DTLVardef@84a233org.highwire.dtl.DTLVardef@1b4c7aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗