Sandra Andorf

ImmPort: disseminating data to the public for the future of immunology

The immunology database and analysis portal (ImmPort) system is the archival repository and dissemination vehicle for clinical and molecular datasets created by research consortia funded by the National Institute of Allergy and Infectious Diseases Division of Allergy, Immunology, and Transplantation. With nearly 100 datasets now publicly available and hundreds of downloads per month, ImmPort is an important source for raw data and protocols from clinical trials, mechanistic studies, and novel methods for cellular and molecular measurements. To facilitate data transfer, templates for data representation and standard operating procedures have also been created and are also publicly available. ImmPort facilitates transparency and reproducibility in immunology research, serves as an important resource for education, and enables newly generated hypotheses and data-driven science.

Anti-IgE treatment with oral immunotherapy in multifood allergic participants: a double-blind, randomised, controlled trial

Summary BACKGROUND Despite progress in single food oral immunotherapy (OIT), there is little evidence concerning the safety and efficacy of treating individuals with multiple food (multifood) allergies. We conducted a pilot study testing whether anti-IgE (omalizumab) combined with multifood OIT benefitted multifood allergic patients. METHODS In this blinded, phase 2 clinical trial conducted at Stanford University, 48 participants, aged 4-15 years, with multifood allergies validated by double-blind, placebo-controlled food challenges (DBPCFCs) to their offending foods were block randomized (3:1) to receive multifood OIT to 2-5 foods, together with omalizumab (n=36) or placebo (n=12). Omalizumab or placebo was administered subcutaneously for 16 weeks with OIT starting at week 8; omalizumab or placebo was stopped 20 weeks before exit DBPCFCs (week 36) to determine the primary endpoint: the proportion of participants who passed DBPCFCs to at least 2 of their offending foods. This completed trial is registered with ClinicalTrials.gov, . FINDINGS At week 36, a significantly greater proportion of the omalizumab (30/36, 83%) vs. placebo (4/12, 33%) participants passed DBPCFCs to 2 g protein for ≥ 2 of their offending foods (odds ratio (OR): 10, 95% confidence interval (CI): 1·8, 58·3, P=0·004). The same individuals also tolerated 4 g protein of ≥ 2 foods (secondary endpoint, P=0·004). A greater proportion of omalizumab (13/17, 77%) vs. placebo (0/5, 0%) participants passed a DBPCFC to 2 g protein for ≥ 4 of their offending foods (OR: 33, 95% CI: 1·9, ∞, P=0·01). All participants completed the study. There were no serious or severe (≥ grade 3) adverse events. INTERPRETATION In multifood allergic patients, omalizumab improves the efficacy of multifood OIT and enables safe and rapid desensitization. FUNDING NIH U19 AADCRC and Opportunity Fund, Sean N. Parker Center for Allergy and Asthma Research at Stanford University, Simons Foundation, Myra Reinhard Foundation, FARE Center of Excellence, Department of Pathology, and Department of Pediatrics, Stanford University.

Association of Clinical Reactivity with Sensitization to Allergen Components in Multifood-Allergic Children

BACKGROUND Thirty percent of children with food allergies have multiple simultaneous allergies; however, the features of these multiple allergies are not well characterized serologically or clinically. OBJECTIVE We comprehensively evaluated 60 multifood-allergic patients by measuring serum IgE to key allergen components, evaluating clinical histories and medication use, performing skin tests, and conducting double-blind, placebo-controlled food challenges (DBPCFCs). METHODS Sixty participants with multiple food allergies were characterized by clinical history, DBPCFCs, total IgE, specific IgE, and component-resolved diagnostics (IgE and IgG4) data. The food allergens tested were almond, egg, milk, sesame, peanut, pecan, walnut, hazelnut, cashew, pistachio, soy, and wheat. RESULTS Our data demonstrate that of the reactions observed during a graded DBPCFC, gastrointestinal reactions occurred more often in boys than in girls, as well as in individuals with high levels of IgE to 2S albumins from cashew, walnut, and hazelnut. Certain food allergies often occurred concomitantly in individuals (ie, cashew/pistachio and walnut/pecan/hazelnut). IgE testing to components further corroborated serological relationships between and among these clustered food allergies. CONCLUSIONS Associations of certain food allergies were shown by DBPCFC outcomes as well as by correlations in IgE reactivity to structurally related food allergen components. Each of these criteria independently demonstrated a significant association between allergies to cashew and pistachio, as well as among allergies to walnut, pecan, and hazelnut.

Oral immunotherapy for food allergy

Food allergy is a pathological, potentially deadly cascade of immune responses to molecules or molecular fragments that are normally innocuous when encountered in foods, such as milk, egg, or peanut. As the incidence and prevalence of food allergy rise, the standard of care is poised to advance beyond food allergen avoidance coupled with injectable epinephrine treatment of allergen-induced systemic reactions. Recent studies provide evidence that oral immunotherapy may effectively redirect the atopic immune responses of food allergy patients as they ingest small but gradually increasing allergen doses over many months, eliciting safer immune responses to these antigens. Research into the molecular and cellular bases of pathological and therapeutic immune responses, and into the possibilities for their safe and effective modulation, is generating tremendous interest in basic and clinical immunology. We synthesize developments, innovations, and key challenges in our understanding of the immune mechanisms associated with atopy and oral immunotherapy for food allergy.

Development of a tool predicting severity of allergic reaction during peanut challenge

BACKGROUND Reliable prognostic markers for predicting severity of allergic reactions during oral food challenges (OFCs) have not been established. OBJECTIVE To develop a predictive algorithm of a food challenge severity score (CSS) to identify those at higher risk for severe reactions to a standardized peanut OFC. METHODS Medical history and allergy test results were obtained for 120 peanut allergic participants who underwent double-blind, placebo-controlled food challenges. Reactions were assigned a CSS between 1 and 6 based on cumulative tolerated dose and a severity clinical indicator. Demographic characteristics, clinical features, peanut component IgE values, and a basophil activation marker were considered in a multistep analysis to derive a flexible decision rule to understand risk during peanut of OFC. RESULTS A total of 18.3% participants had a severe reaction (CSS >4). The decision rule identified the following 3 variables (in order of importance) as predictors of reaction severity: ratio of percentage of CD63hi stimulation with peanut to percentage of CD63hi anti-IgE (CD63 ratio), history of exercise-induced asthma, and ratio of forced expiratory volume in 1 second to forced vital capacity (FEV1/FVC) ratio. The CD63 ratio alone was a strong predictor of CSS (P < .001). CONCLUSION The CSS is a novel tool that combines dose thresholds and allergic reactions to understand risks associated with peanut OFCs. Laboratory values (CD63 ratio), along with clinical variables (exercise-induced asthma and FEV1/FVC ratio) contribute to the predictive ability of the severity of reaction to peanut OFCs. Further testing of this decision rule is needed in a larger external data source before it can be considered outside research settings. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT02103270.

Food allergy and omics

Food allergy (FA) prevalence has been increasing over the last few decades and is now a global health concern. Current diagnostic methods for FA result in a high number of false-positive results, and the standard of care is either allergen avoidance or use of epinephrine on accidental exposure, although currently with no other approved treatments. The increasing prevalence of FA, lack of robust biomarkers, and inadequate treatments warrants further research into the mechanism underlying food allergies. Recent technological advances have made it possible to move beyond traditional biological techniques to more sophisticated high-throughput approaches. These technologies have created the burgeoning field of omics sciences, which permit a more systematic investigation of biological problems. Omics sciences, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, and exposomics, have enabled the construction of regulatory networks and biological pathway models. Parallel advances in bioinformatics and computational techniques have enabled the integration, analysis, and interpretation of these exponentially growing data sets and opens the possibility of personalized or precision medicine for FA.

MetaCyto: A Tool for Automated Meta-analysis of Mass and Flow Cytometry Data

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of ten heterogeneous cytometry studies totaling 2,926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between demographic groups. Software is released to the public through Bioconductor (http://bioconductor.org/packages/release/bioc/html/MetaCyto.html).

Towards the characterization of normal peripheral immune cells with data from ImmPort

To date, our understanding of a normal immune system is far behind that of other healthy organ systems. One reason for this is the lack of standardization in the lab techniques, especially flow cytometry. To take a step towards the characterization of a normal immune system, we re-analyzed and combined data that was made publicly available through the Immunology Database and Analysis Portal (ImmPort, immport.niaid.nih.gov) [1]. ImmPort is a public warehouse for the management and analysis of clinical and mechanistic data from NIAID/DAIT-funded research studies. Currently, 108 studies are made publicly available in ImmPort of which 27 contain raw FCS files from flow cytometry experiments run on samples of adults. Here we use ImmPort as a source of publicly available raw flow cytometry files from hundreds of participants in several trials, to study immune cells from the blood of healthy individuals. To characterize well-defined cells in a normal immune system, we used an unbiased method to compare data from different cytometers and antibody staining panels. As an initial step, we obtained the marker information from each raw FCS file in an automated fashion and made their nomenclature consistent. We applied and evaluated various transformation strategies and normalized the data on a per-channel basis using the R function warpSet [2] from the flowStats package of Bioconductor to make the flow cytometry data more comparable across studies. Initial promising results were observed for the cell-surface markers used to define T and B cells in general in automatically gated lymphocyte populations. Using our pipeline, the distribution of percentages of B cells as well as CD4+ and CD8+ T cells of subjects is in the range as immunologically expected and mostly comparable across the different studies originating from different laboratories. We plan to extend this approach to more cell types and eventually to studies of healthy individuals separated by gender, age group or ethnicity. Our approach promises to give further insights into the normal immune system.

Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).

RImmPort: an R/Bioconductor package that enables ready-for-analysis immunology research data

Summary: Open access to raw clinical and molecular data related to immunological studies has created a tremendous opportunity for data‐driven science. We have developed RImmPort that prepares NIAID‐funded research study datasets in ImmPort (immport.org) for analysis in R. RImmPort comprises of three main components: (i) a specification of R classes that encapsulate study data, (ii) foundational methods to load data of a specific study and (iii) generic methods to slice and dice data across different dimensions in one or more studies. Furthermore, RImmPort supports open formalisms, such as CDISC standards on the open source bioinformatics platform Bioconductor, to ensure that ImmPort curated study datasets are seamlessly accessible and ready for analysis, thus enabling innovative bioinformatics research in immunology. Availability and Implementation: RImmPort is available as part of Bioconductor (bioconductor.org/packages/RImmPort). Contact: rshankar@stanford.edu Supplementary information: Supplementary data are available at Bioinformatics online.