Differential effects of antiretroviral treatment on immunity and gut microbiome composition in people living with HIV in rural versus urban Zimbabwe.

BACKGROUND: The widespread availability of antiretroviral therapy (ART) has dramatically reduced mortality and improved life expectancy for people living with HIV (PLWH). However, even with HIV-1 suppression, chronic immune activation and elevated inflammation persist and have been linked to a pro-inflammatory gut microbiome composition and compromised intestinal barrier integrity. PLWH in urban versus rural areas of sub-Saharan Africa experience differences in environmental factors that may impact the gut microbiome and immune system, in response to ART, yet this has not previously been investigated in these groups. To address this, we measured T cell activation/exhaustion/trafficking markers, plasma inflammatory markers, and fecal microbiome composition in PLWH and healthy participants recruited from an urban clinic in the city of Harare, Zimbabwe, and a district hospital that services surrounding rural villages. PLWH were either ART naïve at baseline and sampled again after 24 weeks of first-line ART and the antibiotic cotrimoxazole or were ART-experienced at both timepoints. RESULTS: Although expected reductions in the inflammatory marker IL-6, T-cell activation, and exhaustion were observed with ART-induced viral suppression, these changes were much more pronounced in the urban versus the rural area. Gut microbiome composition was the most highly altered from healthy controls in ART experienced PLWH, and characterized by both reduced alpha diversity and altered composition. However, gut microbiome composition showed a pronounced relationship with T cell activation and exhaustion in ART-naïve PLWH, suggesting a particularly significant role for the gut microbiome in disease progression in uncontrolled infection. Elevated immune exhaustion after 24 weeks of ART did correlate with both living in the rural location and a more Prevotella-rich/Bacteroides-poor microbiome type, suggesting a potential role for rural-associated microbiome differences or their co-variates in the muted improvements in immune exhaustion in the rural area. CONCLUSION: Successful ART was less effective at reducing gut microbiome-associated inflammation and T cell activation in PLWH in rural versus urban Zimbabwe, suggesting that individuals on ART in rural areas of Zimbabwe may be more vulnerable to co-morbidity related to sustained immune dysfunction in treated infection. Video Abstract.

Proteomics, dietary intake, and changes in cardiometabolic health within a behavioral weight-loss intervention: A pilot study.

OBJECTIVE: 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. METHODS: This secondary data analysis included participants (n = 20, mean [SD], age: 40.1 [9.5] years, BMI: 34.2 [4.0] kg/m2 ) who completed a 1-year behavioral weight-loss intervention. Cardiovascular disease-related plasma proteins, diet, and cardiometabolic health indicators were evaluated at baseline and 3 months. Associations were determined via linear regression and integrated networks created using Visualization Of LineAr Regression Elements (VOLARE). RESULTS: A 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). CONCLUSIONS: Circulating cardiovascular disease-related proteins were associated with diet and cardiometabolic health indicators prior to and in response to weight loss.

Dynamic Serial Cytokine Measurements During Intravenous Ca-DTPA Chelation in Gadolinium Deposition Disease and Gadolinium Storage Condition: A Pilot Study.

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.

Elevated inflammatory fecal immune factors in men who have sex with men with HIV associate with microbiome composition and gut barrier function.

Introduction: People living with HIV infection (PLWH) exhibit elevated levels of gastrointestinal inflammation. Potential causes of this inflammation include HIV infection and associated immune dysfunction, sexual behaviors among men who have sex with men (MSM) and gut microbiome composition. Methods: To better understand the etiology of gastrointestinal inflammation we examined levels of 28 fecal soluble immune factors (sIFs) and the fecal microbiome in well-defined cohorts of HIV seronegative MSM (MSM-SN), MSM with untreated HIV infection (MSM-HIV) and MSM with HIV on anti-retroviral treatment (MSMART). Additionally, fecal solutes from these participants were used to stimulate T-84 colonic epithelial cells to assess barrier function. Results: Both MSM cohorts with HIV had elevated levels of fecal calprotectin, a clinically relevant marker of GI inflammation, and nine inflammatory fecal sIFs (GM-CSF, ICAM-1, IL-1ß, IL-12/23, IL-15, IL-16, TNF-ß, VCAM-1, and VEGF). Interestingly, four sIFs (GM-CSF, ICAM-1, IL-7 and IL-12/23) were significantly elevated in MSM-SN compared to seronegative male non-MSM. Conversely, IL-22 and IL-13, cytokines beneficial to gut health, were decreased in all MSM with HIV and MSM-SN respectively. Importantly, all of these sIFs significantly correlated with calprotectin, suggesting they play a role in GI inflammation. Principal coordinate analysis revealed clustering of fecal sIFs by MSM status and significant associations with microbiome composition. Additionally, fecal solutes from participants in the MSM-HIV cohort significantly decreased colonic transcellular fluid transport in vitro, compared to non-MSM-SN, and this decrease associated with overall sIF composition and increased concentrations of eight inflammatory sIFs in participants with HIV. Lastly, elevated levels of plasma, sCD14 and sCD163, directly correlated with decreased transcellular transport and microbiome composition respectively, indicating that sIFs and the gut microbiome are associated with, and potentially contribute to, bacterial translocation. Conclusion: Taken together, these data demonstrate that inflammatory sIFs are elevated in MSM, regardless of HIV infection status, and are associated with the gut microbiome and intestinal barrier function.

The Gut Microbiota during a Behavioral Weight Loss Intervention.

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.

Novel Circulating and Tissue Monocytes as Well as Macrophages in Pancreatitis and Recovery.

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-ß, 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.

Multiomic Predictors of Short-Term Weight Loss and Clinical Outcomes During a Behavioral-Based Weight Loss Intervention.

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 < 0.05). For eight of these, adjusted R2 ranged from 0.34 to 0.78. Our models identified specific DNAme sites, gut microbes, and metabolites that were predictive of variability in weight loss, waist circumference, and circulating triglycerides and that are biologically relevant to obesity and metabolic pathways. CONCLUSIONS: These data support the feasibility of using baseline multiomic features to provide insight for precision nutrition-based weight loss interventions.

CANTARE: finding and visualizing network-based multi-omic predictive models.

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.

Acute Chelation Therapy-Associated Changes in Urine Gadolinium, Self-reported Flare Severity, and Serum Cytokines in Gadolinium Deposition Disease.

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.

The Application of Cytokine Expression Assays to Differentiate Active From Previously Treated Syphilis.

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 ß, 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.

VOLARE: visual analysis of disease-associated microbiome-immune system interplay.

BACKGROUND: Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. "Omic" methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between "omes", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments. RESULTS: To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies-microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results. CONCLUSIONS: Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Systematic evaluation of immune regulation and modulation.

Cancer immunotherapies are showing promising clinical results in a variety of malignancies. Monitoring the immune as well as the tumor response following these therapies has led to significant advancements in the field. Moreover, the identification and assessment of both predictive and prognostic biomarkers has become a key component to advancing these therapies. Thus, it is critical to develop systematic approaches to monitor the immune response and to interpret the data obtained from these assays. In order to address these issues and make recommendations to the field, the Society for Immunotherapy of Cancer reconvened the Immune Biomarkers Task Force. As a part of this Task Force, Working Group 3 (WG3) consisting of multidisciplinary experts from industry, academia, and government focused on the systematic assessment of immune regulation and modulation. In this review, the tumor microenvironment, microbiome, bone marrow, and adoptively transferred T cells will be used as examples to discuss the type and timing of sample collection. In addition, potential types of measurements, assays, and analyses will be discussed for each sample. Specifically, these recommendations will focus on the unique collection and assay requirements for the analysis of various samples as well as the high-throughput assays to evaluate potential biomarkers.

Analyses of Pretherapy Peripheral Immunoscore and Response to Vaccine Therapy.

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.

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates.

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.

Integrating and mining diverse data in human immunological studies.

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.

Phase 1 study of stereotactic body radiotherapy and interleukin-2--tumor and immunological responses.

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.

The Stanford Data Miner: a novel approach for integrating and exploring heterogeneous immunological data.

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.

Congruence as a measurement of extended haplotype structure across the genome.

BACKGROUND: Historically, extended haplotypes have been defined using only a few data points, such as alleles for several HLA genes in the MHC. High-density SNP data, and the increasing affordability of whole genome SNP typing, creates the opportunity to define higher resolution extended haplotypes. This drives the need for new tools that support quantification and visualization of extended haplotypes as defined by as many as 2000 SNPs. Confronted with high-density SNP data across the major histocompatibility complex (MHC) for 2,300 complete families, compiled by the Type 1 Diabetes Genetics Consortium (T1DGC), we developed software for studying extended haplotypes. METHODS: The software, called ExHap (Extended Haplotype), uses a similarity measurement we term congruence to identify and quantify long-range allele identity. Using ExHap, we analyzed congruence in both the T1DGC data and family-phased data from the International HapMap Project. RESULTS: Congruent chromosomes from the T1DGC data have between 96.5% and 99.9% allele identity over 1,818 SNPs spanning 2.64 megabases of the MHC (HLA-DRB1 to HLA-A). Thirty-three of 132 DQ-DR-B-A defined haplotype groups have > 50% congruent chromosomes in this region. For example, 92% of chromosomes within the DR3-B8-A1 haplotype are congruent from HLA-DRB1 to HLA-A (99.8% allele identity). We also applied ExHap to all 22 autosomes for both CEU and YRI cohorts from the International HapMap Project, identifying multiple candidate extended haplotypes. CONCLUSIONS: Long-range congruence is not unique to the MHC region. Patterns of allele identity on phased chromosomes provide a simple, straightforward approach to visually and quantitatively inspect complex long-range structural patterns in the genome. Such patterns aid the biologist in appreciating genetic similarities and differences across cohorts, and can lead to hypothesis generation for subsequent studies.