Candidate tumor-specific CD8+ T cell subsets identified in the malignant pleural effusion of advanced lung cancer patients by single-cell analysis.

Isolation of tumor-specific T cells and their antigen receptors (TCRs) from malignant pleural effusions (MPE) may facilitate the development of TCR-transduced adoptive cellular immunotherapy products for advanced lung cancer patients. However, the characteristics and markers of tumor-specific T-cells in MPE are largely undefined. To this end, to establish the phenotypes and antigen specificities of CD8+ T cells, we performed single-cell RNA and TCR sequencing of samples from three advanced lung cancer patients. Dimensionality reduction on a total of 4,983 CD8+ T cells revealed 10 clusters including naïve, memory, and exhausted phenotypes. We focused particularly on exhausted T cell clusters and tested their TCR reactivity against neoantigens predicted from autologous cancer cell lines. Four different TCRs specific for the same neoantigen and one orphan TCR specific for the autologous cell line were identified from one of the patients. Differential gene expression analysis in tumor-specific T cells relative to the other T cells identified CXCL13, as a candidate gene expressed by tumor-specific T cells. In addition to expressing CXCL13, tumor-specific T cells were present in a higher proportion of T cells co-expressing PDCD1(PD-1)/TNFRSF9(4-1BB). Furthermore, flow cytometric analyses in advanced lung cancer patients with MPE documented that those with high PD-1/4-1BB expression have a better prognosis in the subset of 57 adenocarcinoma patients (p = .039). These data suggest that PD-1/4-1BB co-expression might identify tumor-specific CD8+ T cells in MPE, which are associated with patients' prognosis. (233 words).

Corrigendum: Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

[This corrects the article DOI: 10.3389/fimmu.2023.1265044.].

Integrative HLA typing of tumor and adjacent normal tissue can reveal insights into the tumor immune response.

BACKGROUND: The HLA complex is the most polymorphic region of the human genome, and its improved characterization can help us understand the genetics of human disease as well as the interplay between cancer and the immune system. The main function of HLA genes is to recognize "non-self" antigens and to present them on the cell surface to T cells, which instigate an immune response toward infected or transformed cells. While sequence variation in the antigen-binding groove of HLA may modulate the repertoire of immunogenic antigens presented to T cells, alterations in HLA expression can significantly influence the immune response to pathogens and cancer. METHODS: RNA sequencing was used here to accurately genotype the HLA region and quantify and compare the level of allele-specific HLA expression in tumors and patient-matched adjacent normal tissue. The computational approach utilized in the study types classical and non-classical Class I and Class II HLA alleles from RNA-seq while simultaneously quantifying allele-specific or personalized HLA expression. The strategy also uses RNA-seq data to infer immune cell infiltration into tumors and the corresponding immune cell composition of matched normal tissue, to reveal potential insights related to T cell and NK cell interactions with tumor HLA alleles. RESULTS: The genotyping method outperforms existing RNA-seq-based HLA typing tools for Class II HLA genotyping. Further, we demonstrate its potential for studying tumor-immune interactions by applying the method to tumor samples from two different subtypes of breast cancer and their matched normal breast tissue controls. CONCLUSIONS: The integrative RNA-seq-based HLA typing approach described in the study, coupled with HLA expression analysis, neoantigen prediction and immune cell infiltration, may help increase our understanding of the interplay between a patient's tumor and immune system; and provide further insights into the immune mechanisms that determine a positive or negative outcome following treatment with immunotherapy such as checkpoint blockade.

LTX-315 and adoptive cell therapy using tumor-infiltrating lymphocytes generate tumor specific T cells in patients with metastatic soft tissue sarcoma.

LTX-315 is an oncolytic peptide that elicits both local and systemic immune responses upon intratumoral injection. In the present pilot trial, we treated patients with metastatic soft tissue sarcoma with the combination of LTX-315 and adoptive T-cell therapy using in vitro expanded tumor-infiltrating lymphocytes. Six heavily pretreated patients were included in the trial and treated with LTX-315 of which four patients proceeded to adoptive T-cell therapy. Overall, the treatment was considered safe with only expected and manageable toxicity. The best overall clinical response was stable disease for 208 days, and in this patient, we detected tumor-reactive T cells in the blood that lasted until disease progression. In three patients T-cell reactivity against in silico predicted neoantigens was demonstrated. Additionally, de novo T-cell clones were generated and expanded in the blood following LTX-315 injections. In conclusion, this pilot study provides proof that it is feasible to combine LTX-315 and adoptive T-cell therapy, and that this treatment can induce systemic immune responses that resulted in stabilization of the disease in sarcoma patients with otherwise progressive disease. Further optimization of the treatment protocol is warranted to increase clinical activity. ClinicalTrials.gov Identifier: NCT03725605.

Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence.

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

The interplay between neoantigens and immune cells in sarcomas treated with checkpoint inhibition.

Introduction: Sarcomas are comprised of diverse bone and connective tissue tumors with few effective therapeutic options for locally advanced unresectable and/or metastatic disease. Recent advances in immunotherapy, in particular immune checkpoint inhibition (ICI), have shown promising outcomes in several cancer indications. Unfortunately, ICI therapy has provided only modest clinical responses and seems moderately effective in a subset of the diverse subtypes. Methods: To explore the immune parameters governing ICI therapy resistance or immune escape, we performed whole exome sequencing (WES) on tumors and their matched normal blood, in addition to RNA-seq from tumors of 31 sarcoma patients treated with pembrolizumab. We used advanced computational methods to investigate key immune properties, such as neoantigens and immune cell composition in the tumor microenvironment (TME). Results: A multifactorial analysis suggested that expression of high quality neoantigens in the context of specific immune cells in the TME are key prognostic markers of progression-free survival (PFS). The presence of several types of immune cells, including T cells, B cells and macrophages, in the TME were associated with improved PFS. Importantly, we also found the presence of both CD8+ T cells and neoantigens together was associated with improved survival compared to the presence of CD8+ T cells or neoantigens alone. Interestingly, this trend was not identified with the combined presence of CD8+ T cells and TMB; suggesting that a combined CD8+ T cell and neoantigen effect on PFS was important. Discussion: The outcome of this study may inform future trials that may lead to improved outcomes for sarcoma patients treated with ICI.

Single-cell sequencing on CD8+ TILs revealed the nature of exhausted T cells recognizing neoantigen and cancer/testis antigen in non-small cell lung cancer.

BACKGROUND: CD8+tumor infiltrating lymphocytes (TILs) are often observed in non-small cell lung cancers (NSCLC). However, the characteristics of CD8+ TILs, especially T-cell populations specific for tumor antigens, remain poorly understood. METHODS: High throughput single-cell RNA sequencing and single-cell T-cell receptor (TCR) sequencing were performed on CD8+ TILs from three surgically-resected lung cancer specimens. Dimensional reduction for clustering was performed using Uniform Manifold Approximation and Projection. CD8+ TIL TCR specific for the cancer/testis antigen KK-LC-1 and for predicted neoantigens were investigated. Differentially-expressed gene analysis, Gene Set Enrichment Analysis (GSEA) and single sample GSEA was performed to characterize antigen-specific T cells. RESULTS: A total of 6998 CD8+ T cells was analyzed, divided into 10 clusters according to their gene expression profile. An exhausted T-cell (exhausted T (Tex)) cluster characterized by the expression of ENTPD1 (CD39), TOX, PDCD1 (PD1), HAVCR2 (TIM3) and other genes, and by T-cell oligoclonality, was identified. The Tex TCR repertoire (Tex-TCRs) contained nine different TCR clonotypes recognizing five tumor antigens including a KK-LC-1 antigen and four neoantigens. By re-clustering the tumor antigen-specific T cells (n=140), it could be seen that the individual T-cell clonotypes were present on cells at different stages of differentiation and functional states even within the same Tex cluster. Stimulating these T cells with predicted cognate peptide indicated that TCR signal strength and subsequent T-cell proliferation and cytokine production was variable but always higher for neoantigens than KK-LC-1. CONCLUSIONS: Our approach focusing on T cells with an exhausted phenotype among CD8+ TILs may facilitate the identification of tumor antigens and clarify the nature of the antigen-specific T cells to specify the promising immunotherapeutic targets in patients with NSCLC.

Robust spike-specific CD4+ and CD8+ T cell responses in SARS-CoV-2 vaccinated hematopoietic cell transplantation recipients: a prospective, cohort study.

Poor overall survival of hematopoietic stem cell transplantation (HSCT) recipients who developed COVID-19 underlies the importance of SARS-CoV-2 vaccination. Previous studies of vaccine efficacy have reported weak humoral responses but conflicting results on T cell immunity. Here, we have examined the relationship between humoral and T cell response in 48 HSCT recipients who received two doses of Moderna's mRNA-1273 or Pfizer/BioNTech's BNT162b2 vaccines. Nearly all HSCT patients had robust T cell immunity regardless of protective humoral responses, with 18/48 (37%, IQR 8.679-5601 BAU/mL) displaying protective IgG anti-receptor binding domain (RBD) levels (>2000 BAU/mL). Flow cytometry analysis of activation induced markers (AIMs) revealed that 90% and 74% of HSCT patients showed reactivity towards immunodominant spike peptides in CD8+ and CD4+ T cells, respectively. The response rate increased to 90% for CD4+ T cells as well when we challenged the cells with a complete set of overlapping peptides spanning the entire spike protein. T cell response was detectable as early as 3 months after transplant, but only CD4+ T cell reactivity correlated with IgG anti-RBD level and time after transplantation. Boosting increased seroconversion rate, while only one patient developed COVID-19 requiring hospitalization. Our data suggest that HSCT recipients with poor serological responses were protected from severe COVID-19 by vaccine-induced T cell responses.

People who use drugs show no increase in pre-existing T-cell cross-reactivity toward SARS-CoV-2 but develop a normal polyfunctional T-cell response after standard mRNA vaccination.

People who use drugs (PWUD) are at a high risk of contracting and developing severe coronavirus disease 2019 (COVID-19) and other infectious diseases due to their lifestyle, comorbidities, and the detrimental effects of opioids on cellular immunity. However, there is limited research on vaccine responses in PWUD, particularly regarding the role that T cells play in the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, we show that before vaccination, PWUD did not exhibit an increased frequency of preexisting cross-reactive T cells to SARS-CoV-2 and that, despite the inhibitory effects that opioids have on T-cell immunity, standard vaccination can elicit robust polyfunctional CD4+ and CD8+ T-cell responses that were similar to those found in controls. Our findings indicate that vaccination stimulates an effective immune response in PWUD and highlight targeted vaccination as an essential public health instrument for the control of COVID-19 and other infectious diseases in this group of high-risk patients.

The T Cell Epitope Landscape of SARS-CoV-2 Variants of Concern.

During the COVID-19 pandemic, several SARS-CoV-2 variants of concern (VOC) emerged, bringing with them varying degrees of health and socioeconomic burdens. In particular, the Omicron VOC displayed distinct features of increased transmissibility accompanied by antigenic drift in the spike protein that partially circumvented the ability of pre-existing antibody responses in the global population to neutralize the virus. However, T cell immunity has remained robust throughout all the different VOC transmission waves and has emerged as a critically important correlate of protection against SARS-CoV-2 and its VOCs, in both vaccinated and infected individuals. Therefore, as SARS-CoV-2 VOCs continue to evolve, it is crucial that we characterize the correlates of protection and the potential for immune escape for both B cell and T cell human immunity in the population. Generating the insights necessary to understand T cell immunity, experimentally, for the global human population is at present a critical but a time consuming, expensive, and laborious process. Further, it is not feasible to generate global or universal insights into T cell immunity in an actionable time frame for potential future emerging VOCs. However, using computational means we can expedite and provide early insights into the correlates of T cell protection. In this study, we generated and revealed insights on the T cell epitope landscape for the five main SARS-CoV-2 VOCs observed to date. We demonstrated using a unique AI prediction platform, a significant conservation of presentable T cell epitopes across all mutated peptides for each VOC. This was modeled using the most frequent HLA alleles in the human population and covers the most common HLA haplotypes in the human population. The AI resource generated through this computational study and associated insights may guide the development of T cell vaccines and diagnostics that are even more robust against current and future VOCs, and their emerging subvariants.

Personalized HLA typing leads to the discovery of novel HLA alleles and tumor-specific HLA variants.

Accurate and full-length typing of the HLA region is important in many clinical and research settings. With the advent of next generation sequencing (NGS), several HLA typing algorithms have been developed, including many that are applicable to whole exome sequencing (WES). However, most of these solutions operate by providing the closest-matched HLA allele among the known alleles in IPD-IMGT/HLA Database. These database-matching approaches have demonstrated very high performance when typing well characterized HLA alleles. However, as they rely on the completeness of the HLA database, they are not optimal for detecting novel or less well characterized alleles. Furthermore, the database-matching approaches are also not adequate in the context of cancer, where a comprehensive characterization of somatic HLA variation and expression patterns of a tumor's HLA locus may guide therapy and clinical outcome, because of the pivotal role HLA alleles play in tumor antigen recognition and immune escape. Here, we describe a personalized HLA typing approach applied to WES data that leverages the strengths of database-matching approaches while simultaneously allowing for the discovery of novel HLA alleles and tumor-specific HLA variants, through the systematic integration of germline and somatic variant calling. We applied this approach on WES from 10 metastatic melanoma patients and validated the HLA typing results using HLA targeted NGS sequencing from patients where at least one HLA germline candidate was detected on Class I HLA. Targeted NGS sequencing confirmed 100% performance for the 1st and 2nd fields. In total, five out of the six detected HLA germline variants were because of Class I ambiguities at the third or fourth fields, and their detection recovered the correct HLA allele genotype. The sixth germline variant let to the formal discovery of a novel Class I allele. Finally, we demonstrated a substantially improved somatic variant detection accuracy in HLA alleles with a 91% of success rate in simulated experiments. The approach described here may allow the field to genotype more accurately using WES data, leading to the discovery of novel HLA alleles and help characterize the relationship between somatic variation in the HLA region and immunosurveillance.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goal of this study was to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3,400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA haplotypes of approximately 22,000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population; the approach identified an optimal constellation of epitope hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have profiled the entire SARS-CoV-2 proteome and identified a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.

Improved HLA typing of Class I and Class II alleles from next-generation sequencing data.

Precise HLA genotyping is of great clinical importance, albeit a challenging bioinformatics endeavor because of the hyper polymorphism of the HLA region. The ever-increasing availability of next-generation sequencing (NGS) solutions has spurred the development of several computational methods for predicting HLA genotypes from NGS data. Although some of these tools genotype HLA Class I alleles reasonably well, there is a need to incorporate integrative parameters related to ethnicity frequency information, in order to improve performance for both Class I and Class II alleles. Here, we present a bioinformatics method that addresses some of the current shortfalls in HLA genotyping from NGS. First, reads that map to the HLA region is aligned against a comprehensive library of reference HLA alleles. The allele type was then subsequently determined on the basis of the distribution of aligned reads, and the prior probabilities of the ethnic frequencies of alleles. Three public NGS datasets were used to benchmark the approach against six similar tools. The method outlined in this manuscript displayed an overall accuracy of 98.73% for Class I and 96.37% for Class II alleles. We illustrate an improved integrative approach that outperforms existing tools and is able to predict HLA alleles with improved fidelity for both Class I and Class II alleles.

NeoMutate: an ensemble machine learning framework for the prediction of somatic mutations in cancer.

BACKGROUND: The accurate screening of tumor genomic landscapes for somatic mutations using high-throughput sequencing involves a crucial step in precise clinical diagnosis and targeted therapy. However, the complex inherent features of cancer tissue, especially, tumor genetic intra-heterogeneity coupled with the problem of sequencing and alignment artifacts, makes somatic variant calling a challenging task. Current variant filtering strategies, such as rule-based filtering and consensus voting of different algorithms, have previously helped to increase specificity, although comes at the cost of sensitivity. METHODS: In light of this, we have developed the NeoMutate framework which incorporates 7 supervised machine learning (ML) algorithms to exploit the strengths of multiple variant callers, using a non-redundant set of biological and sequence features. We benchmarked NeoMutate by simulating more than 10,000 bona fide cancer-related mutations into three well-characterized Genome in a Bottle (GIAB) reference samples. RESULTS: A robust and exhaustive evaluation of NeoMutate's performance based on 5-fold cross validation experiments, in addition to 3 independent tests, demonstrated a substantially improved variant detection accuracy compared to any of its individual composite variant callers and consensus calling of multiple tools. CONCLUSIONS: We show here that integrating multiple tools in an ensemble ML layer optimizes somatic variant detection rates, leading to a potentially improved variant selection framework for the diagnosis and treatment of cancer.

Optimization of Salmonella enterica serovar typhi DeltaaroC DeltassaV derivatives as vehicles for delivering heterologous antigens by chromosomal integration and in vivo inducible promoters.

Novel candidate live oral vaccines based on a Salmonella enterica serovar Typhi ZH9 (Ty2 DeltaaroC DeltassaV) derivative that directed the expression of either the B subunit of Escherichia coli heat-labile toxin or hepatitis B virus core antigen from the bacterial chromosome using the in vivo inducible ssaG promoter were constructed. The levels of attenuation of the two S. enterica serovar Typhi ZH9 derivatives were similar to that of the parent as assessed by measuring the replication of bacteria within human macrophage-like U937 cells. The expression of heterologous antigen in the respective S. enterica serovar Typhi ZH9 derivatives was up-regulated significantly within U937 cells compared to similar S. enterica serovar Typhi ZH9 derivative bacteria grown in modified Luria-Bertani broth supplemented with aromatic amino acids. Immunization of mice with these S. enterica serovar Typhi ZH9 derivatives stimulated potent antigen-specific serum immunoglobulin G responses to the heterologous antigens.

Salmonella enterica serovar Typhimurium interaction with dendritic cells: impact of the sifA gene.

Salmonella enterica serovar Typhimurium (S. Typhimurium) and several mutant derivatives were able to enter efficiently murine bone marrow-derived dendritic cells using mechanisms predominantly independent of the Salmonella pathogenicity island 1 type III secretion system. The levels of intracellular bacteria did not increase significantly over many hours after invasion. Using fluid endocytic tracers and other markers, S. Typhimurium-containing vacuoles (SCVs) were physically distinguishable from early endocytic compartments. Fifty to eighty per cent of SCVs harbouring wild-type S. Typhimurium or aroA, invH and ssaV mutant derivatives were associated with late endosome markers. In contrast, S. Typhimurium sifA was shown to escape the SCVs into the cytosol of infected dendritic cells. S. Typhimurium aroC sifA was more efficient than S. Typhimurium aroC in delivering a eukaryotic promoter-driven green fluorescent protein reporter gene for expression in dendritic cells. In contrast, S. Typhimurium aroC sifA did not detectably increase the efficiency of MHC class I presentation of the model antigen ovalbumin to T cells compared to a similar aroC derivative. Mice infected with the S. Typhimurium aroC sifA expressing ovalbumin did not develop detectably enhanced levels of cytotoxic T cell or interferon-gamma production compared to S. Typhimurium aroC derivatives.

Novel strains of Salmonella typhimurium as potential vectors for gene delivery.

DNA vaccines are known to induce long-term antigen specific cellular responses. We tested two new strains of Salmonella typhimurium, one carrying a mutation in a SPI-2 gene and the aroC-gene and another carrying mutations in the sifA- and aroC-genes, as potential DNA vaccine delivery vehicles. We compared them with the SL7207 strain and found that the new strains were more invasive, and that they were efficient mediators of gene transfer in vitro using EGFP as reporter gene. We tested the ability of the new strains to survive within the spleen, liver and mesenteric lymph nodes and evaluated their safety in C57/BL/6J mice.

Expression of heterologous antigens in Salmonella Typhimurium vaccine vectors using the in vivo-inducible, SPI-2 promoter, ssaG.

DNA derived from regions upstream of the Salmonella enterica serovar Typhimurium ssaG gene were used to drive expression of different reporter genes in putative Salmonella vaccine strains. Expression from ssaG was shown to be significantly upregulated once Salmonella had entered murine or human macrophages, and levels of expression were dependent on the length of the ssaG 5' sequence incorporated. S. Typhimurium derivatives harbouring the Escherichia coli heat labile toxin B subunit (LT-B) fused to various lengths of the ssaG promoter region were also constructed as single copy chromosomal integrations. Expression of LT-B by these Salmonella derivatives was detected at significant levels after intra-macrophage survival and mice immunised with these derivatives mounted marked anti-LT-B humoral antibody responses.

Salmonella typhi and S typhimurium derivatives harbouring deletions in aromatic biosynthesis and Salmonella Pathogenicity Island-2 (SPI-2) genes as vaccines and vectors.

The S. typhimurium strain (TML deltaaroC deltassaV) WT05, harbouring defined deletions in genes involved in both the aromatic biosynthesis pathway (aroC) and the Salmonella Pathogenicity Island-2 (SPI-2) (ssaV) was shown to be significantly attenuated in C57 BL/6 interferon gamma knockout mice following oral inoculation. Similarly, the S. typhi strain (Ty2 deltaaroC deltassaV) ZH9 harbouring the aroC and ssaV mutations propagated less efficiently than wild type in human macrophages. These studies demonstrated the attractive safety profile of the aroC ssaV mutant combination. Strains S. typhimurium (TML deltaaroC deltassaV ) WT05 and S. typhi (Ty2 deltaaroC deltassaV) ZH9 were subsequently tested as vaccine vectors to deliver E. coli heat-labile toxin (LT-B) mucosally to mice. Mice inoculated orally with S. typhimurium (TML deltaaroC deltassaV) WT05 expressing LT-B (WT05/LT-B) elicited high titres of both LT-specific serum IgG and intestinal IgA, although no specific IgA was detected in the vagina. Similarly, intranasal inoculation of mice with S. typhi (Ty2 deltaaroC deltassaV) ZH9 expressing LT-B (ZH9/LT-B) elicited even higher titres of LT-specific serum antibody as well as LT-specific Ig in the vagina. We conclude that deltaaroC deltassaV strains of Salmonella are highly attenuated and are promising candidates both as human typhoid vaccines and as vaccine vectors for the delivery of heterologous antigens.