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Custom software for the calculation of the peripheral immunoscore.

Supplementary Figure S1: Gating strategy to identify classic peripheral immune cell types and refined immune cell subsets with phenotypes reflecting immune function that were used to generate the peripheral immunoscores of patients treated with docetaxel plus or minus PANVAC. Supplementary Figure S2: Gating strategy to identify classic peripheral immune cell types and refined immune cell subsets with phenotypes reflecting immune function that were used to generate the peripheral immunoscores of patients treated with Quadramet plus or minus PROSTVAC. Supplementary Figure S3: Point assignment method used in generating peripheral immunoscores. Supplementary Table S1: Antibodies and panels used in flow cytometry analysis to identify immune cells. Supplementary Table S2: PBMC frequencies (min, median, max) and bin cutoffs used in generating peripheral immunoscores in breast cancer patients treated with docetaxel +/- PANVAC vaccine and prostate cancer patients treated with Sm-153 (Quadramet) +/- PROSTVAC vaccine. Supplementary Table S3: Summation of points assigned to individual subsets to generate peripheral immunoscore.

Tumor immunoscore analyses, especially for primary colorectal cancer and melanoma lesions, provide valuable prognostic information. Metastatic lesions of many carcinoma types, however, are often not easily accessible. We hypothesized that immune cells in peripheral blood may differ among individual patients with metastatic disease, which, in turn, may influence their response to immunotherapy. We thus analyzed immune cell subsets within peripheral blood mononuclear cells to determine if a "peripheral immunoscore" could have any prognostic significance for patients before receiving immunotherapy. Patients with metastatic breast cancer were randomly assigned to receive docetaxel ± PANVAC vaccine. In another trial, prostate cancer patients with metastatic bone lesions were randomly assigned to receive a bone-seeking radionuclide ± PROSTVAC vaccine. Predefined analyses of "classic" immune cell types (CD4, CD8, natural killer cells, regulatory T cells, myeloid-derived suppressor cells, and ratios) revealed no differences in progression-free survival (PFS) for either arm in both trials. Predefined analyses of refined immune cell subsets for which a biologic function had been previously reported also showed no significant prognostic value in PFS for patients receiving either docetaxel or radionuclide alone; however, in patients receiving these agents in combination with vaccine, the peripheral immunoscore of refined subsets revealed statistically significant differences in PFS (P < 0.001) for breast cancer patients receiving docetaxel plus vaccine, and in prostate cancer patients receiving radionuclide plus vaccine (P = 0.004). Larger randomized studies will be required to validate these findings. These studies, however, provide the rationale for the evaluation of refined immune cell subsets to help determine which patients may benefit most from immunotherapy. Cancer Immunol Res; 4(9); 755–65. ©2016 AACR.

Purpose The aim of this study was to investigate the feasibility of measuring early changes in serum cytokine levels after intravenous diethylenetriaminepentaacetic acid (Ca-DTPA) chelation in patients manifesting either gadolinium deposition disease (GDD) or gadolinium storage condition (GSC) and the possible usefulness of this method in further research. Methods Four patients with recent-onset GDD (≤1 year) and 2 patients with long-standing GSC (4 and 9 years) underwent chelation with intravenous bolus administration of Ca-DTPA. Multiple blood draws were performed to measure serum cytokines: at T = 0 (before Ca-DTPA injection) and 1, 5, 10, 30, 60 minutes, and 24 hours after Ca-DTPA injection. Patients rated the severity of GDD symptom flare at 24 hours. The 24-hour urine Gd amounts were measured prechelation and for the 24 hours after chelation. Serum samples were analyzed blind to whether patients had GDD or GSC but with knowledge of the time points characterizing each sample. Results Urine samples for both GDD and GSC patients showed increases in Gd postchelation. All GDD patients experienced flare reactions postchelation; the 2 GSC patients did not. Two cytokines, EGF and sCD40L, peaked at 30 minutes postchelation in at least 4 of the 6 participants. Three cytokines, ENA78/CXCL5, EOTAXIN/CCL11, and LEPTIN, peaked at 24 hours in at least 4 of the 6 participants. Two participants were high outliers for a large number of cytokines across time points. No clear distinction between GDD and GSC was apparent from the cytokine patterns, although differences were present. Conclusions This pilot study describes precise temporal resolution (in the range of minutes) after a cytokine-inciting event. Select cytokines exhibited peak values at different time points. At this preliminary stage of investigation, peak cytokine release seems to reflect the amount of Gd mobilized rather than the severity of the patient symptomatic reaction. Too few subjects were studied to support statistical analysis between GDD and GSC groups, although differences were observed through visual data analysis.

Identifying associations among circulating proteins, dietary intakes, and clinically relevant indicators of cardiometabolic health during weight loss may elucidate biologically relevant pathways affected by diet, allowing for an incorporation of precision nutrition approaches when designing future interventions. This study hypothesized that plasma proteins would be associated with diet and cardiometabolic health indicators within a behavioral weight-loss intervention.This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/mA total of 16 plasma proteins were associated with ≥1 diet or health indicator at baseline (p 0.001); changes in 42 proteins were associated with changes in diet or health indicators from baseline to 3 months (p 0.005). Baseline tumor necrosis factor receptor superfamily member 10C (TNFRSF10C) was associated with intakes of dark green vegetables (r = -0.712), and fatty acid-binding protein 4 (FABP4) was associated with intakes of unsweetened coffee (r = -0.689). Changes in refined-grain intakes were associated with changes in scavenger receptor cysteine-rich type 1 protein M130 (CD163; r = 0.725), interleukin-1 receptor type 1 (IL1R-T1; r = 0.624), insulin (r = 0.656), and triglycerides (r = 0.648).Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

OBJECTIVE Identifying predictors of weight loss and clinical outcomes may increase understanding of individual variability in weight loss response. We hypothesized that baseline multiomic features, including DNA methylation (DNAme), metabolomics, and gut microbiome, would be predictive of short-term changes in body weight and other clinical outcomes within a comprehensive weight loss intervention. METHODS Healthy adults with overweight or obesity (n = 62, age 18-55 years, BMI 27-45 kg/m2 , 75.8% female) participated in a 1-year behavioral weight loss intervention. To identify baseline omic predictors of changes in clinical outcomes at 3 and 6 months, whole-blood DNAme, plasma metabolites, and gut microbial genera were analyzed. RESULTS A network of multiomic relationships informed predictive models for 10 clinical outcomes (body weight, waist circumference, fat mass, hemoglobin A1c , homeostatic model assessment of insulin resistance, total cholesterol, triglycerides, C-reactive protein, leptin, and ghrelin) that changed significantly (P

Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

BACKGROUND: Identifying predictors of preterm birth (PTB) in high-burden regions is important as PTB is the leading cause of global child mortality. METHODS: This analysis was nested in a longitudinal study of human immunodeficiency virus (HIV) incidence in Kenya. HIV-seronegative women enrolled in pregnancy had nucleic acid amplification tests (chlamydia and gonorrhea), rapid plasma reagin (syphilis), wet mount microscopy (Trichomonas and yeast), and Gram stain (bacterial vaginosis); sexually transmitted infection (STI) treatment was provided. PTB predictors were determined using log-binomial regression. RESULTS: Among 1244 mothers of liveborn infants, median gestational age at enrollment was 26 weeks (IQR, 22–31), and at delivery was 39.1 weeks (IQR, 37.1–40.9). PTB occurred in 302 women (24.3%). Chlamydia was associated with a 1.59-fold (P = .006), gonorrhea a 1.62-fold (P = .04), and incident HIV a 2.08-fold (P = .02) increased PTB prevalence. Vaginal discharge and cervical inflammation were associated with PTB, as were age ≤21 (prevalence ratio [PR] = 1.39, P = .001) and any STI (PR = 1.47, P = .001). Associations with chlamydia and incident HIV remained in multivariable models. CONCLUSIONS: STIs and incident HIV in pregnancy predicted PTB despite treatment, suggesting the need for earlier treatment and interventions to decrease genital inflammation.

HIV type 1 is associated with pulmonary dysfunction that is exacerbated by cigarette smoke. Alveolar macrophages (AM) are the most prominent immune cell in the alveolar space. These cells play an important role in clearing inhaled pathogens and regulating the inflammatory environment; however, how HIV infection impacts AM phenotype and function is not well understood, in part because of their autofluorescence and the absence of well-defined surface markers. The main aim of this study was to evaluate the impact of HIV infection on human AM and to compare the effect of smoking on their phenotype and function. Time-of-flight mass cytometry and RNA sequencing were used to characterize macrophages from human bronchoalveolar lavage of HIV-infected and -uninfected smokers and nonsmokers. We found that the frequency of CD163+ anti-inflammatory AM was decreased, whereas CD163−CCR7+ proinflammatory AM were increased in HIV infection. HIV-mediated proinflammatory polarization was associated with increased levels of inflammatory cytokines and macrophage activation. Conversely, smoking heightened the inflammatory response evident by change in the expression of CXCR4 and TLR4. Altogether, these findings suggest that HIV infection, along with cigarette smoke, favors a proinflammatory macrophage phenotype associated with enhanced expression of inflammatory molecules. Further, this study highlights time-of-flight mass cytometry as a reliable method for immunophenotyping the highly autofluorescent cells present in the bronchoalveolar lavage of cigarette smokers.

To investigate the role of serum cytokine assays to distinguish between active from treated syphilis among serofast patients, we recruited individuals into a prospective cohort study. Participants underwent routine syphilis screening. We selected specimens from a majority cohort of serofast participants with treated and active syphilis. We analyzed specimens with a 62-cytokine multiplex bead-based enzyme-linked immunosorbent assay. Cytokines, brain-derived neurotrophic factor and tumor necrosis factor β, were most predictive. We built a decision tree that was 82.4% accurate, 100% (95% confidence interval, 82%–100%) sensitive, and 45% (18%–75%) specific. Our decision tree differentiated between serum specimens from serofast participants with treated syphilis versus active syphilis.

Background: Associations between the microbiome and the immune system are well documented. However, elucidating specific mechanisms is an active research area. High-dimensional "omic" assays such as 16S ribosomal RNA (rRNA) sequencing and CyTOF immunophenotyping support hypotheses generation, identifying possible interactions that can be further explored experimentally. Linear regression between microbial and host immune features is useful for quantifying relationships between mi-crobes and immune readouts. But, vetting dozens of significant associations can be cumbersome, especially when a project involves experts from different disciplines. In order to facilitate communication and sense-making across disciplines, we performed a design study for visual analysis of these relationships with a goal of helping researchers prioritize results for experimental follow-up. Results: Using data from paired 16S ribosomal RNA (rRNA) sequencing and CyTOF immunophenotyping on gut biopsy samples from people with and without HIV, we fit a regression model to each microbe:immune cell pair, accounting for differences by disease status. We used permutation testing to control the false discovery rate, resulting in a top table of microbe:immune cell pairs. After identifying essential tasks in the further analysis of this top table, we designed VOLARE (Visualization Of LinEar Re-gression Elements), a web application that integrates a searchable top table, a network summarizing this table, sparkline-inspired graphs of fitted regression models, and detailed regression plots showing sample-level detail. We applied this application to two case studies--microbiome:immune cell data from gut biopsies and microbiome:cytokine data from fecal samples. Conclusions: Systematically integrating microbiome-immune system data through linear regressions and presenting the top table results in an interactive environment supports the Shneiderman mantra ("Overview first, filter, details-on-demand"). Our approach allows domain experts to control the analysis of their results, empowering them to screen dozens of candidate relationships with ease. Our contributions include characterizing the exploration of microbiome-immune system data in a team science context, and the support of an associated workflow by integrating existing visualization approaches. Availability: R scripts, the web application, and sample data are available at https://sourceforge.net/projects/cytomelodics

Background Current syphilis tests cannot distinguish between active and past syphilis among patients with serofast rapid plasma reagin (RPR) titers. We investigated whether cytokine profiles might provide insight in the differentiation of active and treated syphilis. Methods We collected quarterly serum samples from participants at risk for incident syphilis in a prospective cohort study of men and male-to-female transgender women. We defined incident syphilis as a new RPR titer ≥ 1:8 or a fourfold increase from a prior RPR titer and a positive Treponema pallidum particle agglutination assay. We measured cytokine expression using a 63-multiplex bead-based Luminex assay (eBiosciences/Affymetrix, San Diego, California, USA). We used tertile bins and Chi square tests to identify differences in proportions of cytokines between samples from patients with active and treated syphilis. We constructed a network of cytokine profiles from those findings. We used R software (R version 3.4.1, R, Vienna, Austria) to fit models. Results We identified 20 pairs of cytokines (out of 1953 possible pairs) that differed between active and treated syphilis. From those, we identified three cytokine networks of interest: an Eotaxin–Rantes–Leptin network, a Mig-IL1ra-Trail-CD40L network, and an IL12p40-IL12p70 network. Conclusions Differences in cytokine profiles are present among men and male-to-female transgender women with active and treated syphilis. Cytokine assays may be a potentially useful tool for identifying active syphilis among patients with serologic syphilis reactivity. Electronic supplementary material The online version of this article (10.1186/s12967-019-1947-7) contains supplementary material, which is available to authorized users.

Vitiligo is an autoimmune disease in which melanocyte destruction causes skin depigmentation, with 49 loci known from previous GWAS. Aiming to define vitiligo subtypes, we discovered that age-of-onset is bimodal; one-third of cases have early onset (mean 10.3 years) and two-thirds later onset (mean 34.0 years). In the early-onset subgroup we found novel association with MHC class II region indel rs145954018, and independent association with the principal MHC class II locus from previous GWAS, represented by rs9271597; greatest association was with rs145954018del-rs9271597A haplotype (P = 2.40 × 10−86, OR = 8.10). Both rs145954018 and rs9271597 are located within lymphoid-specific enhancers, and the rs145954018del-rs9271597A haplotype is specifically associated with increased expression of HLA-DQB1 mRNA and HLA-DQ protein by monocytes and dendritic cells. Thus, for vitiligo, MHC regulatory variation confers extreme risk, more important than HLA coding variation. MHC regulatory variation may represent a significant component of genetic risk for other autoimmune diseases., GWAS have led to the identification of 49 genetic loci associated with vitiligo. Here, the authors observe a bimodal distribution of age-of-onset and find a novel genetic locus specifically associated with early-onset vitiligo, located in a regulatory element in the MHC class II region.

Background And Aims Acute pancreatitis (AP) is an inflammatory disease with mild to severe course that is associated with local and systemic complications and significant mortality. Uncovering inflammatory pathways that lead to progression and recovery will inform ways to monitor and/or develop effective therapies. Methods We performed single-cell mass Cytometry by Time Of Flight (CyTOF) analysis to identify pancreatic and systemic inflammatory signals during mild AP (referred to as AP), severe AP (SAP), and recovery using 2 independent experimental models and blood from patients with AP and recurrent AP. Flow cytometric validation of monocytes subsets identified using CyTOF analysis was performed independently. Results Ly6C+ inflammatory monocytes were the most altered cells in the pancreas during experimental AP, recovery, and SAP. Deep profiling uncovered heterogeneity among pancreatic and blood monocytes and identified 7 novel subsets during AP and recovery, and 6 monocyte subsets during SAP. Notably, a dynamic shift in pancreatic CD206+ macrophage population was observed during AP and recovery. Deeper profiling of the CD206+ macrophage identified 7 novel subsets during AP, recovery, and SAP. Differential expression analysis of these novel monocyte and CD206+ macrophage subsets revealed significantly altered surface (CD44, CD54, CD115, CD140a, CD196, podoplanin) and functional markers (interferon-γ, interleukin 4, interleukin 22, latency associated peptide-transforming growth factor–β, tumor necrosis factor-α, T-bet, RoRγt) that were associated with recovery and SAP. Moreover, a targeted functional analysis further revealed distinct expression of pro- and anti-inflammatory cytokines by pancreatic CD206+ macrophage subsets as the disease either progressed or resolved. Similarly, we identified heterogeneity among circulating classical inflammatory monocytes (CD14+CD16-) and novel subsets in patients with AP and recurrent AP. Conclusions We identified several novel monocyte/macrophage subsets with unique phenotype and functional characteristics that are associated with AP, recovery, and SAP. Our findings highlight differential innate immune responses during AP progression and recovery that can be leveraged for future disease monitoring and targeting.

Objectives The aim of this study was to determine the following in patients who have undergone magnetic resonance imaging with gadolinium-based contrast agents (GBCAs) and meet the proposed diagnostic criteria for gadolinium deposition disease (GDD): (1) the effectiveness of chelation therapy (CT) with intravenous Ca-diethylenetriaminepentaacetic acid in removing retained gadolinium (Gd) and factors affecting the amount removed; (2) the frequency of CT-induced Flare, that is, GDD diagnostic symptom worsening, and factors affecting Flare intensity; (3) whether, as reported in a separate cohort, GDD patients' serum cytokine levels differ significantly from those in healthy normal controls and change significantly in response to CT; and (4) whether urine Gd, Flare reaction, and serum cytokine findings in GDD patients are mimicked in non-ill patients described as having gadolinium storage condition (GSC). Materials and methods Twenty-one GDD subjects and 3 GSC subjects underwent CT. Patients provided pre-CT and post-CT 24-hour urine samples for Gd content determination along with pre-CT and 24-hour post-CT serum samples for cytokine analysis. Patients rated potential Flare 24 hours after CT. Pre-CT and post-CT 24-hour urine Gd analyses and Luminex serum cytokine assays were performed blind to patients' GDD and GSC status and all other data except age and sex. Serum cytokine levels in a healthy normal control group of age- and sex-matched subjects drawn from Stanford influenza vaccination studies were measured once, contemporaneously with those of GDD and GSC patients, using the same Luminex assay. Results Urine Gd amounts increased post-CT by 4 times or more after 87% of the 30 CT sessions. The most important factors appeared to be the time since the last GBCA dose and the cumulative dose received. Urine Gd amounts for GDD and GSC patients fell in the same ranges. All GDD patients, and no GSC patient, reported a Flare 24 hours post-CT. Linear regression found that Flare intensity was significantly predicted by a model including pre- and post-CT Gd amounts and the number of GBCA-enhanced magnetic resonance imaging. Post-CT, multiple cytokines showed strong positive relationships with GDD patients' Flare intensity in multivariable models. The pre-CT serum levels of 12 cytokines were significantly different in GDD patients compared with healthy flu vaccine controls. The small number of GSC patients precluded analogous statistical testing. Post-CT, GDD patients' serum levels of 20 cytokines were significantly decreased, and 2 cytokines significantly increased. These cytokines did not exhibit the same change pattern in the 3 GSC patients. The small number of GSC patients precluded statistical comparisons of GSC to GDD patients' results. Conclusions In this preliminary study, 24-hour urine Gd content increased markedly and similarly in GDD and GSC patients after Ca-diethylenetriaminepentaacetic acid CT. Post-CT Flare reaction developed only in GDD patients. The current study is the second finding significantly different serum cytokine levels in GDD patients compared with healthy normal controls. These differences and the difference between GDD and GSC patients' Flare and cytokine responses to CT suggest some inflammatory, immunologic, or other physiological differences in patients with GDD. Further research into the treatment and physiological underpinnings of GDD is warranted.

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Vitiligo is an autoimmune disease in which depigmented skin results from destruction of\ud skin melanocytes, with strong epidemiologic association with several other autoimmune\ud diseases. In previous linkage and genome-wide association studies (GWAS1, GWAS2),\ud we identified 27 vitiligo susceptibility loci in patients of European (EUR) ancestry. We\ud carried out a third GWAS (GWAS3) of vitiligo in EUR subjects, with augmentation of\ud GWAS1 and GWAS2 controls, genome-wide imputation, and meta-analysis of all three\ud vitiligo GWAS, followed by an independent replication study. The combined analyses,\ud with 4,680 vitiligo cases and 39,586 controls, identified 23 novel replicated loci, as well as\ud 7 new suggestive loci, most encoding immune regulators, apoptotic regulators, and\ud melanocyte regulators, several of which are also associated with other autoimmune\ud diseases. Functional analyses indicate a predominance of causal regulatory variation, in\ud some cases corresponding to eQTL at these loci. Together, the identified genes provide\ud a framework for vitiligo genetic architecture and pathobiology, highlight genetic\ud relationships to other autoimmune diseases and melanoma, and offer potential targets\ud for vitiligo treatment.

Tumor responses to programmed cell death protein 1 (PD-1) blockade therapy are mediated by T cells, which we characterized in 102 tumor biopsies obtained from 53 patients treated with pembrolizumab, an antibody to PD-1. Biopsies were dissociated, and single-cell infiltrates were analyzed by multicolor flow cytometry using two computational approaches to resolve the leukocyte phenotypes at the single-cell level. There was a statistically significant increase in the frequency of T cells in patients who responded to therapy. The frequency of intratumoral B cells and monocytic myeloid-derived suppressor cells significantly increased in patients' biopsies taken on treatment. The percentage of cells with a regulatory T-cell phenotype, monocytes, and natural killer cells did not change while on PD-1 blockade therapy. CD8+ memory T cells were the most prominent phenotype that expanded intratumorally on therapy. However, the frequency of CD4+ effector memory T cells significantly decreased on treatment, whereas CD4+ effector T cells significantly increased in nonresponding tumors on therapy. In peripheral blood, an unusual population of blood cells expressing CD56 was detected in two patients with regressing melanoma. In conclusion, PD-1 blockade increases the frequency of T cells, B cells, and myeloid-derived suppressor cells in tumors, with the CD8+ effector memory T-cell subset being the major T-cell phenotype expanded in patients with a response to therapy. Cancer Immunol Res; 4(3); 194–203. ©2016 AACR.

Background Dysbiosis occurs in spondyloarthritis (SpA) and inflammatory bowel disease (IBD), which is subdivided into Crohn’s disease (CD) and ulcerative colitis (UC). The immunologic consequences of alterations in microbiota, however, have not been defined. Intraepithelial lymphocytes (IELs) are T cells within the intestinal epithelium that are in close contact with bacteria and are likely to be modulated by changes in microbiota. We examined differences in human gut-associated bacteria and tested correlation with functional changes in IELs in patients with axial SpA (axSpA), CD, or UC, and in controls. Methods We conducted a case-control study to evaluate IELs from pinch biopsies of grossly normal colonic tissue from subjects with biopsy-proven CD or UC, axSpA fulfilling Assessment of SpondyloArthritis International Society (ASAS) criteria and from controls during endoscopy. IELs were harvested and characterized by flow cytometry for cell surface markers. Secreted cytokines were measured by ELISA. Microbiome analysis was by 16S rRNA gene sequencing from rectal swabs. Statistical analyses were performed with the Kruskal-Wallis and Spearman’s rank tests. Results The total number of IELs was significantly decreased in subjects with axSpA compared to those with IBD and controls, likely due to a decrease in TCRβ+ IELs. We found strong, significant negative correlation between peripheral lymphocyte count and IEL number. IELs secreted significantly increased IL-1β in patients with UC, significantly increased IL-17A and IFN-γ in patients with CD, and significantly increased TNF-α in patients with CD and axSpA as compared to other cohorts. For each disease subtype, IELs and IEL-produced cytokines were positively and negatively correlated with the relative abundance of multiple bacterial taxa. Conclusions Our data indicate differences in IEL function among subjects with axSpA, CD, and UC compared to healthy controls. We propose that the observed correlation between altered microbiota and IEL function in these populations are relevant to the pathogenesis of axSpA and IBD, and discuss possible mechanisms. Trial registration ClinicalTrials.gov, NCT02389075. Registered on 17 March 2015. Electronic supplementary material The online version of this article (10.1186/s13075-018-1639-3) contains supplementary material, which is available to authorized users.

The autoimmune condition is a primary obstacle to inducing tolerance in type 1 diabetes patients receiving allogeneic pancreas transplants. It is unknown how autoreactive T cells that recognize self-MHC molecules contribute to MHC-disparate allograft rejection. In this report, we show the presence and accumulation of dual-reactive, that is autoreactive and alloreactive, T cells in C3H islet allografts that were transplanted into autoimmune diabetic NOD mice. Using high-throughput sequencing, we discovered that T cells prevalent in allografts share identical TCRs with autoreactive T cells present in pancreatic islets. T cells expressing TCRs that are enriched in allograft lesions recognized C3H MHC molecules, and five of six cell lines expressing these TCRs were also reactive to NOD islet cells. These results reveal the presence of autoreactive T cells that mediate cross-reactive alloreactivity, and indicate a requirement for regulating such dual-reactive T cells in tissue replacement therapies given to autoimmune individuals.

This report summarizes the symposium, ‘Immunotherapy Biomarkers 2016: Overcoming the Barriers’, which was held on April 1, 2016 at the National Institutes of Health in Bethesda, Maryland. The symposium, cosponsored by the Society for Immunotherapy of Cancer (SITC) and the National Cancer Institute (NCI), focused on emerging immunotherapy biomarkers, new technologies, current hurdles to further progress, and recommendations for advancing the field of biomarker development.

Autoimmune Addison's disease (AD) is the major cause of primary adrenal failure in developed nations. Autoantibodies to 21-hydroxylase (21OH-AA) are associated with increased risk of progression to AD. Highest genetic risk is associated with the Major Histocompatibility region (MHC), specifically human leukocyte antigen (HLA)-DR3 haplotypes (containing HLA-B8) and HLA-DR4.The objective of the study was the further characterization of AD risk associated with MHC alleles.MHC genotypes were determined for HLA-DRB1, DQA1, DQB1, MICA, HLA-B, and HLA-A in 168 total individuals with 21OH-AA (85 with AD at referral and 83 with positive 21OH-AA but without AD at referral).Genotype was evaluated in 21OH-AA-positive individuals. Outcomes were compared with general population controls and type 1 diabetes patients.In HLA-DR4+ individuals, HLA-B15 was found in only one of 55 (2%) with AD vs. 24 of 63 (40%) 21OH-AA-positive nonprogressors (P = 2 × 10(-7)) and 518 of 1558 (33%) HLA-DR4 patients with type 1 diabetes (P = 1 × 10(-8)). On prospective follow-up, none of the HLA-B15-positive, 21-hydroxylase-positive individuals progressed to AD vs. 25% non-HLA-B15 autoantibody-positive individuals by life table analysis (P = 0.03). Single nucleotide polymorphism analysis revealed the HLA-DR/DQ region associated with risk and HLA-B15 were separated by multiple intervening single-nucleotide polymorphism haplotypes.HLA-B15 is not associated with protection from 21OH-AA formation but is associated with protection from progression to AD in 21OH-AA-positive individuals. To our knowledge, this is one of the most dramatic examples of genetic disease suppression in individuals who already have developed autoantibodies and of novel dominant suppression of an autoimmune disease by a class I HLA allele.

� Abstract Understanding cytokine profiles of disease states has provided researchers with great insight into immunologic signaling associated with disease onset and progression, affording opportunities for advancement in diagnostics and therapeutic intervention. Multiparameter flow cytometric assays support identification of specific cytokine secreting subpopulations. Bead-based assays provide simultaneous measurement for the production of ever-growing numbers of cytokines. These technologies demand appropriate analytical techniques to extract relevant information efficiently. We illustrate the power of an analytical workflow to reveal significant alterations in T-cell cytokine expression patterns in type 1 diabetes (T1D) and breast cancer. This workflow consists of population-level analysis, followed by donor-level analysis, data transformation such as stratification or normalization, and a return to population-level analysis. In the T1D study, T-cell cytokine production was measured with a cytokine bead array. In the breast cancer study, intracellular cytokine staining measured T cell responses to stimulation with a variety of antigens. Summary statistics from each study were loaded into a relational database, together with associated experimental metadata and clinical parameters. Visual and statistical results were generated with custom Java software. In the T1D study, donor-level analysis led to the stratification of donors based on unstimulated cytokine expression. The resulting cohorts showed statistically significant differences in poststimulation production of IL-10, IL-1b, IL-8, and TNFb. In the breast cancer study, the differing magnitude of cytokine responses required data normalization to support statistical comparisons. Once normalized, data showed a statistically significant decrease in the expression of IFNg on CD41 and CD81 T cells when stimulated with tumor-associated antigens (TAAs) when compared with an infectious disease antigen stimulus, and a statistically significant increase in expression of IL-2 on CD81 T cells. In conclusion, the analytical workflow described herein yielded statistically supported and biologically relevant findings that were otherwise unapparent. ' 2007 International Society for Analytical Cytology

The overall prevalence with which endogenous tumor Ags induce host T cell responses is unclear. Even when such responses are detected, they do not usually result in spontaneous remission of the cancer. We hypothesized that this might be associated with a predominant phenotype and/or cytokine profile of tumor-specific responses that is different from protective T cell responses to other chronic Ags, such as CMV. We detected significant T cell responses to CEA, HER-2/neu, and/or MAGE-A3 in 17 of 21 breast cancer patients naive to immunotherapy. The pattern of T cell cytokines produced in response to tumor-associated Ags (TAAs) in breast cancer patients was significantly different from that produced in response to CMV or influenza in the same patients. Specifically, there was a higher proportion of IL-2-producing CD8+ T cells, and a lower proportion of IFN-γ-producing CD4+ and/or CD8+ T cells responding to TAAs compared with CMV or influenza Ags. Finally, the phenotype of TAA-responsive CD8+ T cells in breast cancer patients was almost completely CD28+CD45RA− (memory phenotype). CMV-responsive CD8+ T cells in the same patients were broadly distributed among phenotypes, and contained a high proportion of terminal effector cells (CD27−CD28−CD45RA+) that were absent in the TAA responses. Taken together, these results suggest that TAA-responsive T cells are induced in breast cancer patients, but those T cells are phenotypically and functionally different from CMV- or influenza-responsive T cells. Immunotherapies directed against TAAs may need to alter these T cell signatures to be effective.

While many age-associated immune changes have been reported, a comprehensive set of metrics of immune aging is lacking. Here we report data from 243 healthy adults aged 40–97, for whom we measured clinical and functional parameters, serum cytokines, cytokines and gene expression in stimulated and unstimulated PBMC, PBMC phenotypes, and cytokine-stimulated pSTAT signaling in whole blood. Although highly heterogeneous across individuals, many of these assays revealed trends by age, sex, and CMV status, to greater or lesser degrees. Age, then sex and CMV status, showed the greatest impact on the immune system, as measured by the percentage of assay readouts with significant differences. An elastic net regression model could optimally predict age with 14 analytes from different assays. This reinforces the importance of multivariate analysis for defining a healthy immune system. These data provide a reference for others measuring immune parameters in older people.

Bioanalysts and immunologists can interrogate the immune system with a variety of high-throughput technologies such as gene expression, multiplex bead arrays and flow cytometry. Conceptually, these assays support systems immunology studies, in which phenomena can be measured and correlated across biological compartments. First, however, the resulting high-dimensional data must be combined in a consistent fashion that supports analysis of the data as an integrated whole. Next, analytical methods must be applied to the hundreds or thousands of readouts. We recommend the use of a four-part analytical pipeline, consisting of data integration, hypothesis generation, prediction and hypothesis testing, and validation. We describe a variety of established methods appropriate for these integrated datasets, and highlight their application to human immunological studies. Our goal is to provide bioanalysts, immunologists and data analysts with a valuable perspective with which to approach the multiassay high-dimensional datasets generated by contemporary immunological studies.

Background Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy. Methods and Findings Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, TRAF3IP3 transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy. Conclusions Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.

Preclinical models suggest that focal high-dose radiation can make tumors more immunogenic. We performed a pilot study of stereotactic body radiation therapy (SBRT) followed by high-dose interleukin-2 (IL-2) to assess safety and tumor response rate and perform exploratory immune monitoring studies. Patients with metastatic melanoma or renal cell carcinoma (RCC) who had received no previous medical therapy for metastatic disease were eligible. Patients received one, two, or three doses of SBRT (20 Gy per fraction) with the last dose administered 3 days before starting IL-2. IL-2 (600,000 IU per kilogram by means of intravenous bolus infusion) was given every 8 hours for a maximum of 14 doses with a second cycle after a 2-week rest. Patients with regressing disease received up to six IL-2 cycles. Twelve patients were included in the intent-to-treat analysis, and 11 completed treatment per the study design. Response Evaluation Criteria in Solid Tumors criteria were used to assess overall response in nonirradiated target lesions. Eight of 12 patients (66.6%) achieved a complete (CR) or partial response (PR) (1 CR and 7 PR). Six of the patients with PR on computed tomography had a CR by positron emission tomography imaging. Five of seven (71.4%) patients with melanoma had a PR or CR, and three of five (60%) with RCC had a PR. Immune monitoring showed a statistically significantly greater frequency of proliferating CD4(+) T cells with an early activated effector memory phenotype (CD3(+)CD4(+)Ki67(+)CD25(+)FoxP3(-)CCR7(-)CD45RA(-)CD27(+)CD28(+/-)) in the peripheral blood of responding patients. SBRT and IL-2 can be administered safely. Because the response rate in patients with melanoma was significantly higher than expected on the basis of historical data, we believe that the combination and investigation of CD4(+) effector memory T cells as a predictor of response warrant further study.

Measuring cytokine production is an integral part of measuring immune response during immunotherapy. Current technologies allow the simultaneous quantification of multiple cytokines in a variety of tissues. Patterns of cytokine response can be referred to as cytokine profiles. This article discusses the experimental design and data analysis of a number of studies that examined cytokine profiles in humans. We highlight potential sources of variability, both due to assay nuances and the diversity of human populations. We present strategies for analyzing data, emphasizing both multidimensional analysis and the value of treating each donor as his or her own control.

Background The complex data sets generated by higher-order polychromatic flow cytometry experiments are a challenge to analyze. Here we describe Exhaustive Expansion, a data analysis approach for deriving hundreds to thousands of cell phenotypes from raw data, and for interrogating these phenotypes to identify populations of biological interest given the experimental context. Methods We apply this approach to two studies, illustrating its broad applicability. The first examines the longitudinal changes in circulating human memory T cell populations within individual patients in response to a melanoma peptide (gp100209-2M) cancer vaccine, using 5 monoclonal antibodies (mAbs) to delineate subpopulations of viable, gp100-specific, CD8+ T cells. The second study measures the mobilization of stem cells in porcine bone marrow that may be associated with wound healing, and uses 5 different staining panels consisting of 8 mAbs each. Results In the first study, our analysis suggests that the cell surface markers CD45RA, CD27 and CD28, commonly used in historical lower order (2-4 color) flow cytometry analysis to distinguish memory from naïve and effector T cells, may not be obligate parameters in defining central memory T cells (TCM). In the second study, we identify novel phenotypes such as CD29+CD31+CD56+CXCR4+CD90+Sca1-CD44+, which may characterize progenitor cells that are significantly increased in wounded animals as compared to controls. Conclusions Taken together, these results demonstrate that Exhaustive Expansion supports thorough interrogation of complex higher-order flow cytometry data sets and aids in the identification of potentially clinically relevant findings.

Context: Multiple autoimmune disorders (e.g. Addison’s disease, type 1 diabetes, celiac disease) are associated with HLA-DR3, but it is likely that alleles of additional genes in linkage disequilibrium with HLA-DRB1 contribute to disease. Objective: The objective of the study was to characterize major histocompatability complex (MHC) haplotypes conferring extreme risk for autoimmune Addison’s disease (AD). Design, Setting, and Participants: Eighty-six 21-hydroxylase autoantibody-positive, nonautoimmune polyendocrine syndrome type 1, Caucasian individuals collected from 1992 to 2009 with clinical AD from 68 families (12 multiplex and 56 simplex) were genotyped for HLA-DRB1, HLA-DQB1, MICA, HLA-B, and HLA-A as well as high density MHC single-nucleotide polymorphism (SNP) analysis for 34. Main Outcome Measures: AD and genotype were measured. Result: Ninety-seven percent of the multiplex individuals had both HLA-DR3 and HLA-B8 vs. 60% of simplex AD patients (P = 9.72 × 10−4) and 13% of general population controls (P = 3.00 × 10−19). The genotype DR3/DR4 with B8 was present in 85% of AD multiplex patients, 24% of simplex patients, and 1.5% of control individuals (P = 4.92 × 10−191). The DR3-B8 haplotype of AD patients had HLA-A1 less often (47%) than controls (81%, P = 7.00 × 10−5) and type 1 diabetes patients (73%, P = 1.93 × 10−3). Analysis of 1228 SNPs across the MHC for individuals with AD revealed a shorter conserved haplotype (3.8) with the loss of the extended conserved 3.8.1 haplotype approximately halfway between HLA-B and HLA-A. Conclusion: Extreme risk for AD, especially in multiplex families, is associated with haplotypic DR3 variants, in particular a portion (3.8) but not all of the conserved 3.8.1 haplotype.

Existing analysis tools for flow cytometry data offer specialised but limited functionality. This work presents advantages of combining the cytometer's data with sample-specific information. Data is loaded into a relational database, where the analyst can query based on sample characteristics such as species, gender, diet type or sample stain type.

The Phase I/II clinical trial randomized patients to GVAX immunotherapy (two prostate cancer cell lines that secrete GM-CSF, Cell Genesys Inc.) alone or in combination with non-myeloablative chemotherapy and adoptive transfer of PBMC. Pre and week 11 aphereses were obtained to elutriate monocytes and cryopreserve PBMC for immune monitoring. We used ProtoArrays (8,217 proteins spotted arrays, Invitrogen) to evaluate the week 11 post treatment antibody responses of 11 patients who completed this IRB-approved DAMD-funded clinical trial for men with androgen-independent prostate cancer. Analysis of pre and week 11 sera identified increased antibody responses to a number of proteins and the patients with an increase in PSA-doubling time (PSA-DT) exhibited a strong response to a significantly (p 1.5 fold compared to a normal prostate tissue control. Current efforts are aimed at evaluating whether targets of the antibody response were contained in the oligonucleotide array and whether proteins for genes that decreased in intensity were contained on the protein array. The methods we describe may help assess whether a given treatment is inducing strong immune response and whether this immune response is sculpting the repertoire of antigens expressed by the tumor. Such tests are currently not available and development of such a test may aid the development and refinement of the novel combination immunotherapy strategies.

Background The precise mechanisms involved in the initiation and progression of rheumatoid arthritis (RA) are not known. Early stages of RA often have non-specific symptoms, delaying diagnosis and therapy. Additionally, there are currently no established means to predict clinical responsiveness to therapy. Immune cell activation is a critical component therefore we examined the cellular activation of peripheral blood mononuclear cells (PBMCs) in the early stages of RA, in order to develop a novel diagnostic modality. Methods and Findings PBMCs were isolated from individuals diagnosed with early RA (ERA) (n = 38), longstanding RA (n = 10), osteoarthritis (OA) (n = 19) and from healthy individuals (n = 10). PBMCs were examined for activation of 15 signaling effectors, using phosphorylation status as a measure of activation in immunophenotyped cells, by flow cytometry (phospho-flow). CD3+CD4+, CD3+CD8+ and CD20+ cells isolated from patients with ERA, RA and OA exhibited activation of multiple phospho-epitopes. ERA patient PBMCs showed a bias towards phosphorylation-activation in the CD4+ and CD20+ compartments compared to OA PBMCs, where phospho-activation was primarily observed in CD8+ cells. The ratio of phospho (p)-AKT/p-p38 was significantly elevated in patients with ERA and may have diagnostic potential. The mean fluorescent intensity (MFI) levels for p-AKT and p-H3 in CD4+, CD8+ and CD20+ T cells correlated directly with physician global assessment scores (MDGA) and DAS (disease activity score). Stratification by medications revealed that patients receiving leflunomide, systemic steroids or anti-TNF therapy had significant reductions in phospho-specific activation compared with patients not receiving these therapies. Correlative trends between medication-associated reductions in the levels of phosphorylation of specific signaling effectors and lower disease activity were observed. Conclusions Phospho-flow analysis identified phosphorylation-activation of specific signaling effectors in the PB from patients with ERA. Notably, phosphorylation of these signaling effectors did not distinguish ERA from late RA, suggesting that the activation status of discrete cell populations is already established early in disease. However, when the ratio of MFI values for p-AKT and p-p38 is >1.5, there is a high likelihood of having a diagnosis of RA. Our results suggest that longitudinal sampling of patients undergoing therapy may result in phospho-signatures that are predictive of drug responsiveness.

Background Osteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities. Methods We analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform. Results Potential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion. Conclusions This profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention.

Background Systems-level approaches are increasingly common in both murine and human translational studies. These approaches employ multiple high information content assays. As a result, there is a need for tools to integrate heterogeneous types of laboratory and clinical/demographic data, and to allow the exploration of that data by aggregating and/or segregating results based on particular variables (e.g., mean cytokine levels by age and gender). Methods Here we describe the application of standard data warehousing tools to create a novel environment for user-driven upload, integration, and exploration of heterogeneous data. The system presented here currently supports flow cytometry and immunoassays performed in the Stanford Human Immune Monitoring Center, but could be applied more generally. Results Users upload assay results contained in platform-specific spreadsheets of a defined format, and clinical and demographic data in spreadsheets of flexible format. Users then map sample IDs to connect the assay results with the metadata. An OLAP (on-line analytical processing) data exploration interface allows filtering and display of various dimensions (e.g., Luminex analytes in rows, treatment group in columns, filtered on a particular study). Statistics such as mean, median, and N can be displayed. The views can be expanded or contracted to aggregate or segregate data at various levels. Individual-level data is accessible with a single click. The result is a user-driven system that permits data integration and exploration in a variety of settings. We show how the system can be used to find gender-specific differences in serum cytokine levels, and compare them across experiments and assay types. Conclusions We have used the tools and techniques of data warehousing, including open-source business intelligence software, to support investigator-driven data integration and mining of diverse immunological data.