Janet Siebert

Functional T Cell Responses to Tumor Antigens in Breast Cancer Patients Have a Distinct Phenotype and Cytokine Signature

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.

Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

BackgroundOsteosarcoma (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.MethodsWe 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.ResultsPotential 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.ConclusionsThis profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention.

Multiparameter Phospho-Flow Analysis of Lymphocytes in Early Rheumatoid Arthritis: Implications for Diagnosis and Monitoring Drug Therapy

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.

Large-Scale and Comprehensive Immune Profiling and Functional Analysis of Normal Human Aging

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.

An analytical workflow for investigating cytokine profiles

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‐1β, IL‐8, and TNFβ. 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 IFNγ on CD4+ and CD8+ 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 CD8+ T cells. In conclusion, the analytical workflow described herein yielded statistically supported and biologically relevant findings that were otherwise unapparent.

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.

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

BackgroundSystems-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).MethodsHere 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.ResultsUsers 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.ConclusionsWe 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.

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.

Immunotherapy biomarkers 2016: overcoming the barriers

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.

Congruence as a measurement of extended haplotype structure across the genome

BackgroundHistorically, 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.MethodsThe 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.ResultsCongruent 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.ConclusionsLong-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.