Single-cell Profiling Uncovers a Muc4-Expressing Metaplastic Gastric Cell Type Sustained by Helicobacter pylori-driven Inflammation.

Mechanisms for Helicobacter pylori (Hp)-driven stomach cancer are not fully understood. In a transgenic mouse model of gastric preneoplasia, concomitant Hp infection and induction of constitutively active KRAS (Hp+KRAS+) alters metaplasia phenotypes and elicits greater inflammation than either perturbation alone. Gastric single-cell RNA sequencing showed that Hp+KRAS+ mice had a large population of metaplastic pit cells that expressed the intestinal mucin Muc4 and the growth factor amphiregulin. Flow cytometry and IHC-based immune profiling revealed that metaplastic pit cells were associated with macrophage and T-cell inflammation. Accordingly, expansion of metaplastic pit cells was prevented by gastric immunosuppression and reversed by antibiotic eradication of Hp. Finally, MUC4 expression was significantly associated with proliferation in human gastric cancer samples. These studies identify an Hp-associated metaplastic pit cell lineage, also found in human gastric cancer tissues, whose expansion is driven by Hp-dependent inflammation. Significance: Using a mouse model, we have delineated metaplastic pit cells as a precancerous cell type whose expansion requires Hp-driven inflammation. In humans, metaplastic pit cells show enhanced proliferation as well as enrichment in precancer and early cancer tissues, highlighting an early step in the gastric metaplasia to cancer cascade.

Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy.

Renal-cell carcinoma (RCC) is responsible for the majority of tumors arising from the kidney parenchyma. Although a progressive improvement in median overall survival has been observed after the introduction of anti-PD-1 therapy, many patients do not benefit from this treatment. Therefore, we have investigated T cell dynamics to find immune modification induced by anti-PD-1 therapy. Here, we show that, after therapy, RCC patients (5 responders and 14 nonresponders) are characterized by a redistribution of different subsets across the memory T cell compartment.

New interpretable machine-learning method for single-cell data reveals correlates of clinical response to cancer immunotherapy.

We introduce a new method for single-cell cytometry studies, FAUST, which performs unbiased cell population discovery and annotation. FAUST processes experimental data on a per-sample basis and returns biologically interpretable cell phenotypes, making it well suited for the analysis of complex datasets. We provide simulation studies that compare FAUST with existing methodology, exemplifying its strength. We apply FAUST to data from a Merkel cell carcinoma anti-PD-1 trial and discover pre-treatment effector memory T cell correlates of outcome co-expressing PD-1, HLA-DR, and CD28. Using FAUST, we then validate these correlates in cryopreserved peripheral blood mononuclear cell samples from the same study, as well as an independent CyTOF dataset from a published metastatic melanoma trial. Finally, we show how FAUST's phenotypes can be used to perform cross-study data integration in the presence of diverse staining panels. Together, these results establish FAUST as a powerful new approach for unbiased discovery in single-cell cytometry.

Cytomegalovirus-specific T-cell reconstitution following letermovir prophylaxis after hematopoietic cell transplantation.

Decreased cytomegalovirus (CMV)-specific immunity after hematopoietic cell transplantation (HCT) is associated with late CMV reactivation and increased mortality. Whether letermovir prophylaxis-associated reduction in viral exposure influences CMV-specific immune reconstitution is unknown. In a prospective cohort of allogeneic HCT recipients who received letermovir, we compared polyfunctional CMV-specific T-cell responses to those of controls who received PCR-guided preemptive therapy before the introduction of letermovir. Thirteen-color flow cytometry was used to assess T-cell responses at 3 months after HCT following stimulation with CMV immediate early-1 (IE-1) antigen and phosphoprotein 65 (pp65) antigens. Polyfunctionality was characterized by combinatorial polyfunctionality analysis of antigen-specific T-cell subsets. Use of letermovir and reduction of viral exposure were assessed for their association with CMV-specific T-cell immunity. Polyfunctional T-cell responses to IE-1 and pp65 were decreased in letermovir recipients and remained diminished after adjustment for donor CMV serostatus, absolute lymphocyte count, and steroid use. Among letermovir recipients, greater peak CMV DNAemia and increased viral shedding were associated with stronger CD8+ responses to pp65, whereas the CMV shedding rate was associated with greater CD4+ responses to IE-1. In summary, our study provided initial evidence that letermovir may delay CMV-specific cellular reconstitution, possibly related to decreased CMV antigen exposure. Evaluating T-cell polyfunctionality may identify patients at risk for late CMV infection after HCT.

DNA Priming Increases Frequency of T-Cell Responses to a Vesicular Stomatitis Virus HIV Vaccine with Specific Enhancement of CD8+ T-Cell Responses by Interleukin-12 Plasmid DNA.

The HIV Vaccine Trials Network (HVTN) 087 vaccine trial assessed the effect of increasing doses of pIL-12 (interleukin-12 delivered as plasmid DNA) adjuvant on the immunogenicity of an HIV-1 multiantigen (MAG) DNA vaccine delivered by electroporation and boosted with a vaccine comprising an attenuated vesicular stomatitis virus expressing HIV-1 Gag (VSV-Gag). We randomized 100 healthy adults to receive placebo or 3 mg HIV-MAG DNA vaccine (ProfectusVax HIV-1 gag/pol or ProfectusVax nef/tat/vif, env) coadministered with pIL-12 at 0, 250, 1,000, or 1,500 µg intramuscularly by electroporation at 0, 1, and 3 months followed by intramuscular inoculation with 3.4 × 107 PFU VSV-Gag vaccine at 6 months. Immune responses were assessed after the prime and boost and 6 months after the last vaccination. High-dose pIL-12 increased the magnitude of CD8+ T-cell responses postboost compared to no pIL-12 (P = 0.02), while CD4+ T-cell responses after the prime were higher in the absence of pIL-12 than with low- and medium-dose pIL-12 (P ≤ 0.05). The VSV boost increased Gag-specific CD4+ and CD8+ T-cell responses in all groups (P < 0.001 for CD4+ T cells), inducing a median of four Gag epitopes in responders. Six to 9 months after the boost, responses decreased in magnitude, but CD8+ T-cell response rates were maintained. The addition of a DNA prime dramatically improved responses to the VSV vaccine tested previously in the HVTN 090 trial, leading to broad epitope targeting and maintained CD8+ T-cell response rates at early memory. The addition of high-dose pIL-12 given with a DNA prime by electroporation and boosted with VSV-Gag increased the CD8+ T-cell responses but decreased the CD4+ responses. This approach may be advantageous in reshaping the T-cell responses to a variety of chronic infections or tumors. (This study has been registered at ClinicalTrials.gov under registration no. NCT01578889.).

Higher T-Cell Responses Induced by DNA/rAd5 HIV-1 Preventive Vaccine Are Associated With Lower HIV-1 Infection Risk in an Efficacy Trial.

Background: It is important to identify vaccine-induced immune responses that predict the preventative efficacy of a human immunodeficiency virus (HIV)-1 vaccine. We assessed T-cell response markers as correlates of risk in the HIV Vaccine Trials Network (HVTN) 505 HIV-1 vaccine efficacy trial. Methods: 2504 participants were randomized to DNA/rAd5 vaccine or placebo, administered at weeks 0, 4, 8, and 24. Peripheral blood mononuclear cells were obtained at week 26 from all 25 primary endpoint vaccine cases and 125 matched vaccine controls, and stimulated with vaccine-insert-matched peptides. Primary variables were total HIV-1-specific CD4+ T-cell magnitude and Env-specific CD4+ polyfunctionality. Four secondary variables were also assessed. Immune responses were evaluated as predictors of HIV-1 infection among vaccinees using Cox proportional hazards models. Machine learning analyses identified immune response combinations best predicting HIV-1 infection. Results: We observed an unexpectedly strong inverse correlation between Env-specific CD8+ immune response magnitude and HIV-1 infection risk (hazard ratio [HR] = 0.18 per SD increment; P = .04) and between Env-specific CD8+ polyfunctionality and infection risk (HR = 0.34 per SD increment; P < .01). Conclusions: Further research is needed to determine if these immune responses are predictors of vaccine efficacy or markers of natural resistance to HIV-1 infection.

Pooled-Peptide Epitope Mapping Strategies Are Efficient and Highly Sensitive: An Evaluation of Methods for Identifying Human T Cell Epitope Specificities in Large-Scale HIV Vaccine Efficacy Trials.

The interferon gamma, enzyme-linked immunospot (IFN-γ ELISpot) assay is widely used to identify viral antigen-specific T cells is frequently employed to quantify T cell responses in HIV vaccine studies. It can be used to define T cell epitope specificities using panels of peptide antigens, but with sample and cost constraints there is a critical need to improve the efficiency of epitope mapping for large and variable pathogens. We evaluated two epitope mapping strategies, based on group testing, for their ability to identify vaccine-induced T-cells from participants in the Step HIV-1 vaccine efficacy trial, and compared the findings to an approach of assaying each peptide individually. The group testing strategies reduced the number of assays required by >7-fold without significantly altering the accuracy of T-cell breadth estimates. Assays of small pools containing 7-30 peptides were highly sensitive and effective at detecting single positive peptides as well as summating responses to multiple peptides. Also, assays with a single 15-mer peptide, containing an identified epitope, did not always elicit a response providing validation that 15-mer peptides are not optimal antigens for detecting CD8+ T cells. Our findings further validate pooling-based epitope mapping strategies, which are critical for characterizing vaccine-induced T-cell responses and more broadly for informing iterative vaccine design. We also show ways to improve their application with computational peptide:MHC binding predictors that can accurately identify the optimal epitope within a 15-mer peptide and within a pool of 15-mer peptides.

T Cell Responses against Mycobacterial Lipids and Proteins Are Poorly Correlated in South African Adolescents.

Human T cells are activated by both peptide and nonpeptide Ags produced by Mycobacterium tuberculosis. T cells recognize cell wall lipids bound to CD1 molecules, but effector functions of CD1-reactive T cells have not been systematically assessed in M. tuberculosis-infected humans. It is also not known how these features correlate with T cell responses to secreted protein Ags. We developed a flow cytometric assay to profile CD1-restricted T cells ex vivo and assessed T cell responses to five cell wall lipid Ags in a cross-sectional study of 19 M. tuberculosis-infected and 22 M. tuberculosis-uninfected South African adolescents. We analyzed six T cell functions using a recently developed computational approach for flow cytometry data in high dimensions. We compared these data with T cell responses to five protein Ags in the same cohort. We show that CD1b-restricted T cells producing antimycobacterial cytokines IFN-γ and TNF-α are detectable ex vivo in CD4(+), CD8(+), and CD4(-)CD8(-) T cell subsets. Glucose monomycolate was immunodominant among lipid Ags tested, and polyfunctional CD4 T cells specific for this lipid simultaneously expressed CD40L, IFN-γ, IL-2, and TNF-α. Lipid-reactive CD4(+) T cells were detectable at frequencies of 0.001-0.01%, and this did not differ by M. tuberculosis infection status. Finally, CD4 T cell responses to lipids were poorly correlated with CD4 T cell responses to proteins (Spearman rank correlation -0.01; p = 0.95). These results highlight the functional diversity of CD1-restricted T cells circulating in peripheral blood as well as the complementary nature of T cell responses to mycobacterial lipids and proteins. Our approach enables further population-based studies of lipid-specific T cell responses during natural infection and vaccination.

COMPASS identifies T-cell subsets correlated with clinical outcomes.

Advances in flow cytometry and other single-cell technologies have enabled high-dimensional, high-throughput measurements of individual cells as well as the interrogation of cell population heterogeneity. However, in many instances, computational tools to analyze the wealth of data generated by these technologies are lacking. Here, we present a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets (COMPASS). COMPASS uses a Bayesian hierarchical framework to model all observed cell subsets and select those most likely to have antigen-specific responses. Cell-subset responses are quantified by posterior probabilities, and human subject-level responses are quantified by two summary statistics that describe the quality of an individual's polyfunctional response and can be correlated directly with clinical outcome. Using three clinical data sets of cytokine production, we demonstrate how COMPASS improves characterization of antigen-specific T cells and reveals cellular 'correlates of protection/immunity' in the RV144 HIV vaccine efficacy trial that are missed by other methods. COMPASS is available as open-source software.

OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis.

Flow cytometry is used increasingly in clinical research for cancer, immunology and vaccines. Technological advances in cytometry instrumentation are increasing the size and dimensionality of data sets, posing a challenge for traditional data management and analysis. Automated analysis methods, despite a general consensus of their importance to the future of the field, have been slow to gain widespread adoption. Here we present OpenCyto, a new BioConductor infrastructure and data analysis framework designed to lower the barrier of entry to automated flow data analysis algorithms by addressing key areas that we believe have held back wider adoption of automated approaches. OpenCyto supports end-to-end data analysis that is robust and reproducible while generating results that are easy to interpret. We have improved the existing, widely used core BioConductor flow cytometry infrastructure by allowing analysis to scale in a memory efficient manner to the large flow data sets that arise in clinical trials, and integrating domain-specific knowledge as part of the pipeline through the hierarchical relationships among cell populations. Pipelines are defined through a text-based csv file, limiting the need to write data-specific code, and are data agnostic to simplify repetitive analysis for core facilities. We demonstrate how to analyze two large cytometry data sets: an intracellular cytokine staining (ICS) data set from a published HIV vaccine trial focused on detecting rare, antigen-specific T-cell populations, where we identify a new subset of CD8 T-cells with a vaccine-regimen specific response that could not be identified through manual analysis, and a CyTOF T-cell phenotyping data set where a large staining panel and many cell populations are a challenge for traditional analysis. The substantial improvements to the core BioConductor flow cytometry packages give OpenCyto the potential for wide adoption. It can rapidly leverage new developments in computational cytometry and facilitate reproducible analysis in a unified environment.

Critical assessment of automated flow cytometry data analysis techniques.

Traditional methods for flow cytometry (FCM) data processing rely on subjective manual gating. Recently, several groups have developed computational methods for identifying cell populations in multidimensional FCM data. The Flow Cytometry: Critical Assessment of Population Identification Methods (FlowCAP) challenges were established to compare the performance of these methods on two tasks: (i) mammalian cell population identification, to determine whether automated algorithms can reproduce expert manual gating and (ii) sample classification, to determine whether analysis pipelines can identify characteristics that correlate with external variables (such as clinical outcome). This analysis presents the results of the first FlowCAP challenges. Several methods performed well as compared to manual gating or external variables using statistical performance measures, which suggests that automated methods have reached a sufficient level of maturity and accuracy for reliable use in FCM data analysis.

Gene-expression profiling of microdissected breast cancer microvasculature identifies distinct tumor vascular subtypes.

INTRODUCTION: Angiogenesis represents a potential therapeutic target in breast cancer. However, responses to targeted antiangiogenic therapies have been reported to vary among patients. This suggests that the tumor vasculature may be heterogeneous and that an appropriate choice of treatment would require an understanding of these differences. METHODS: To investigate whether and how the breast tumor vasculature varies between individuals, we isolated tumor-associated and matched normal vasculature from 17 breast carcinomas by laser-capture microdissection, and generated gene-expression profiles. Because microvessel density has previously been associated with disease course, tumors with low (n = 9) or high (n = 8) microvessel density were selected for analysis to maximize heterogeneity for this feature. RESULTS: We identified differences between tumor and normal vasculature, and we describe two subtypes present within tumor vasculature. These subtypes exhibit distinct gene-expression signatures that reflect features including hallmarks of vessel maturity. Potential therapeutic targets (MET, ITGAV, and PDGFRß) are differentially expressed between subtypes. Taking these subtypes into account has allowed us to derive a vascular signature associated with disease outcome. CONCLUSIONS: Our results further support a role for tumor microvasculature in determining disease progression. Overall, this study provides a deeper molecular understanding of the heterogeneity existing within the breast tumor vasculature and opens new avenues toward the improved design and targeting of antiangiogenic therapies.

Hypoxia promotes ligand-independent EGF receptor signaling via hypoxia-inducible factor-mediated upregulation of caveolin-1.

Caveolin-1 (CAV1) is an essential structural constituent of caveolae, specialized lipid raft microdomains on the cell membrane involved in endocytosis and signal transduction, which are inexplicably deregulated and are associated with aggressiveness in numerous cancers. Here we identify CAV1 as a direct transcriptional target of oxygen-labile hypoxia-inducible factor 1 and 2 that accentuates the formation of caveolae, leading to increased dimerization of EGF receptor within the confined surface area of caveolae and its subsequent phosphorylation in the absence of ligand. Hypoxia-inducible factor-dependent up-regulation of CAV1 enhanced the oncogenic potential of tumor cells by increasing the cell proliferative, migratory, and invasive capacities. These results support a concept in which a crisis in oxygen availability or a tumor exhibiting hypoxic signature triggers caveolae formation that bypasses the requirement for ligand engagement to initiate receptor activation and the critical downstream adaptive signaling during a period when ligands required to activate these receptors are limited or are not yet available.

In silico ascription of gene expression differences to tumor and stromal cells in a model to study impact on breast cancer outcome.

Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures.

Regulation of endocytosis via the oxygen-sensing pathway.

Tumor hypoxia is associated with disease progression, resistance to conventional cancer therapies and poor prognosis. Hypoxia, by largely unknown mechanisms, leads to deregulated accumulation of and signaling via receptor tyrosine kinases (RTKs) that are critical for driving oncogenesis. Here, we show that hypoxia or loss of von Hippel-Lindau protein--the principal negative regulator of hypoxia-inducible factor (HIF)--prolongs the activation of epidermal growth factor receptor that is attributable to lengthened receptor half-life and retention in the endocytic pathway. The deceleration in endocytosis is due to the attenuation of Rab5-mediated early endosome fusion via HIF-dependent downregulation of a critical Rab5 effector, rabaptin-5, at the level of transcription. Primary kidney and breast tumors with strong hypoxic signatures show significantly lower expression of rabaptin-5 RNA and protein. These findings reveal a general role of the oxygen-sensing pathway in endocytosis and support a model in which tumor hypoxia or oncogenic activation of HIF prolongs RTK-mediated signaling by delaying endocytosis-mediated deactivation of receptors.

Stromal gene expression predicts clinical outcome in breast cancer.

Although it is increasingly evident that cancer is influenced by signals emanating from tumor stroma, little is known regarding how changes in stromal gene expression affect epithelial tumor progression. We used laser capture microdissection to compare gene expression profiles of tumor stroma from 53 primary breast tumors and derived signatures strongly associated with clinical outcome. We present a new stroma-derived prognostic predictor (SDPP) that stratifies disease outcome independently of standard clinical prognostic factors and published expression-based predictors. The SDPP predicts outcome in several published whole tumor-derived expression data sets, identifies poor-outcome individuals from multiple clinical subtypes, including lymph node-negative tumors, and shows increased accuracy with respect to previously published predictors, especially for HER2-positive tumors. Prognostic power increases substantially when the predictor is combined with existing outcome predictors. Genes represented in the SDPP reveal the strong prognostic capacity of differential immune responses as well as angiogenic and hypoxic responses, highlighting the importance of stromal biology in tumor progression.

Gene expression signatures of morphologically normal breast tissue identify basal-like tumors.

INTRODUCTION: The role of the cellular microenvironment in breast tumorigenesis has become an important research area. However, little is known about gene expression in histologically normal tissue adjacent to breast tumor, if this is influenced by the tumor, and how this compares with non-tumor-bearing breast tissue. METHODS: To address this, we have generated gene expression profiles of morphologically normal epithelial and stromal tissue, isolated using laser capture microdissection, from patients with breast cancer or undergoing breast reduction mammoplasty (n = 44). RESULTS: Based on this data, we determined that morphologically normal epithelium and stroma exhibited distinct expression profiles, but molecular signatures that distinguished breast reduction tissue from tumor-adjacent normal tissue were absent. Stroma isolated from morphologically normal ducts adjacent to tumor tissue contained two distinct expression profiles that correlated with stromal cellularity, and shared similarities with soft tissue tumors with favorable outcome. Adjacent normal epithelium and stroma from breast cancer patients showed no significant association between expression profiles and standard clinical characteristics, but did cluster ER/PR/HER2-negative breast cancers with basal-like subtype expression profiles with poor prognosis. CONCLUSION: Our data reveal that morphologically normal tissue adjacent to breast carcinomas has not undergone significant gene expression changes when compared to breast reduction tissue, and provide an important gene expression dataset for comparative studies of tumor expression profiles.