Renold J. Capocasale

Monoclonal antibody-induced cytokine-release syndrome

Monoclonal antibodies (mAbs) are widely used in anti-inflammatory and tumor therapy. Although effective, mAbs can cause a variety of adverse effects. An important toxicity seen with a few mAbs is cytokine-release syndrome (CRS). These mAbs include: alemtuzumab, muromonab-CD3, rituximab, tosituzumab, CP-870,893, LO-CD2a/BTI-322 and TGN1412. By contrast, over 30 mAbs used clinically are not associated with CRS. In this review, the clinical aspects of CRS, the mAbs associated with CRS, the cytokines involved and putative mechanisms mediating cytokine release will be discussed. This will be followed by a discussion of the poor predictive value of studies in animals and the prospects for creating in vitro screens. Finally, approaches to decreasing the probability of CRS, decreasing the severity or treating CRS, should it occur, will be described.

A statistical pattern recognition approach for determining cellular viability and lineage phenotype in cultured cells and murine bone marrow

Cellular binding of annexin V and membrane permeability to 7‐aminoactinomycin D (7AAD) are important tools for studying apoptosis and cell death by flow cytometry. Combining viability markers with cell surface marker expression is routinely used to study various cell lineages. Current classification methods using strict thresholds, or “gates,” on the fluorescent intensity of these markers are subjective in nature and may not fully describe the phenotypes of interest. We have developed objective criteria for phenotypic boundary recognition through the application of statistical pattern recognition. This task was achieved using artificial neural networks (ANNs) that were trained to recognize subsets of cells with known phenotypes, and then used to determine decision boundaries based on statistical measures of similarity. This approach was then used to test the hypothesis that erythropoietin (EPO) inhibits apoptosis and cell death in erythroid precursor cells in murine bone marrow.

Myelodysplasia and anemia of chronic disease in human tumor necrosis factor‐α transgenic mice

TNF‐α is a pleitropic cytokine that expresses both pro‐ and anti‐inflammatory activity and transgenic mice expressing human tumor necrosis factor‐α (TNF‐α) exhibit a progressive polyarthritis that models rheumatoid arthritis (RA). One of the common comorbidities of RA is anemia of chronic disease (ACD). The purpose of these experiments was to study the changes in the bone marrow and peripheral blood that accompany polyarthritis in TNF‐α transgenic mice in an effort to better understand the pathogenesis of myelodysplasia and ACD. Polychromatic cytometry, hematology and serum cytokine analysis were used to study the pathogenesis of ACD in human TNF‐α transgenic mice. Our hematological evaluation revealed a mild, compensated, microcytic hypochromic anemia, and monocytosis. In the bone marrow, we observed alterations in cell kinetics, decreased relative expression of transferrin receptor and increased apoptosis and cell death in several late precursor cell populations. Although significant levels of human TNF‐α were found in the serum, neither change in serum murine erythropoietin nor any significant difference observed in serum levels of murine IL‐β, IL‐5, IL‐6, IL‐10, IL‐12(p70), IL‐17, TNF‐α, IFNγ, GM‐CSF, MIP‐1αJE, MCP‐5 was observed. Tg197 mice develop a compensated, microcytic, hypochromic anemia, and a functional iron deficiency by 9 weeks of age. Changes in peripheral blood are reflected in alterations in cell kinetics, transferrin receptor expression and markedly increased apoptosis and cell death in the bone marrow indicating that TNF‐α may contribute to myelodysplasia in ACD. Moreover, since human TNF‐α can interact only with murine TNFR1, our data suggest that TNFR1 may play an important role in the development of ACD

Identification and Characterization of Novel Bone Marrow Myeloid DEC205+Gr-1+ Cell Subsets That Differentially Express Chemokine and TLRs

Bone marrow-derived immunomodulatory cytokines impart a critical function in the regulation of innate immune responses and hemopoiesis. However, the source of immunomodulatory cytokines in murine bone marrow and the cellular immune mechanisms that control local cytokine secretion remain poorly defined. Herein, we identified a population of resident murine bone marrow myeloid DEC205+CD11c−B220−Gr1+CD8α−CD11b+ cells that respond to TLR2, TLR4, TLR7, TLR8, and TLR9 agonists as measured by the secretion of proinflammatory and anti-inflammatory cytokines in vitro. Phenotypic and functional analyses revealed that DEC205+CD11b+Gr-1+ bone marrow cells consist of heterogeneous populations of myeloid cells that can be divided into two main cell subsets based on chemokine and TLR gene expression profile. The DEC205+CD11b+Gr-1low cell subset expresses high levels of TLR7 and TLR9 and was the predominant source of IL-6, TNF-α, and IL-12 p70 production following stimulation with the TLR7 and TLR9 agonists CpG and R848, respectively. In contrast, the DEC205+CD11b+Gr-1high cell subset did not respond to CpG and R848 stimulation, which correlated with their lack of TLR7 and TLR9 expression. Similarly, a differential chemokine receptor expression profile was observed with higher expression of CCR1 and CXCR2 found in the DEC205+CD11+Gr-1high cell subset. Thus, we identified a previously uncharacterized population of resident bone marrow cells that may be implicated in the regulation of local immune responses in the bone marrow.

Sequential univariate gating approach to study the effects of erythropoietin in murine bone marrow.

Analysis of multicolor flow cytometric data is traditionally based on the judgment of an expert, generally time consuming, sometimes incomplete and often subjective in nature. In this article, we investigate another statistical method using a Sequential Univariate Gating (SUG) algorithm to identify regions of interest between two groups of multivariate flow cytometric data. The metric used to differentiate between the groups of univariate distributions in SUG is the Kolmogorov‐Smirnov distance (D) statistic. The performance of the algorithm is evaluated by applying it to a known three‐color data set looking at activation of CD4+ and CD8+ lymphocytes with anti‐CD3 antibody treatment and comparing the results to the expert analysis. The algorithm is then applied to a four‐color data set used to study the effects of recombinant human erythropoietin (rHuEPO) on several murine bone marrow populations. SUG was used to identify regions of interest in the data and results compared to expert analysis and the current state‐of‐the‐art statistical method, Frequency Difference Gating (FDG). Cluster analysis was then performed to identify subpopulations responding differently to rHuEPO. Expert analysis, SUG and FDG identified regions in the data that showed activation of CD4+ and CD8+ lymphocytes with anti‐CD3 treatment. In the rHuEPO treated data sets, the expert and SUG identified a dose responsive expansion of only the erythroid precursor population. In contrast, FDG resulted in identification of regions of interest both in the erythroid precursors as well as in other bone marrow populations. Clustering within the regions of interest defined by SUG resulted in identification of four subpopulations of erythroid precursors that are morphologically distinct and show a differential response to rHuEPO treatment. Greatest expansion is seen in the basophilic and poly/orthochromic erythroblast populations with treatment. Identification of populations of interest can be performed using SUG in less subjective, time efficient, biologically interpretable manner that corroborates with the expert analysis. The results suggest that basophilic erythroblasts cells or their immediate precursors are an important target for the effects of rHuEPO in murine bone marrow. The MATLAB implementation of the method described in the article, both experimental data and other supplemental materials are freely available at http://web.mac.com/acidrap18.

Richter's syndrome associated with loss of response to transforming growth factor-beta☆

A patient with chronic lymphocytic leukemia developed a large cell lymphoma apparently derived from the same neoplastic B-cell clone (Richters syndrome). At the same time, mitogen-stimulated proliferation of the patients circulating leukemic B-cells was no longer inhibited by the regulatory cytokine transforming growth factor-beta (TGF-beta), suggesting that such loss of inhibition might be contributing to the clinical and biological progression of the disease.

Abstract 3518: Flow cytometry as a single platform tool to evaluate multiple mechanisms of actions of therapeutic antibodies

The unique ability of flow cytometry to simultaneously examine intricate details of multiple cell subsets is unparalleled and the platform is now a dominant tool for measuring antibody mediated effector functions on cancer cells. The objective of this study is to illustrate the utility of flow cytometry as a single platform for concurrently measuring the multiple mechanisms of actions of therapeutic antibodies such as ADCC, ADCP, Apoptosis, CDC and Trogocytosis on human cancer cells and immune functioning cells. Rituximab® was tested with the Burkett9s Lymphoma cell line (Daudi) for all assays between the ranges of 0.01 and 100ug/mL. Apoptosis was evaluated by measuring Caspase 3/7 staining. ADCC assays were established with Rituximab®/CFSE labeled Daudi cells and human PBMC’s. ADCP was assessed with Rituximab®/ CFSE labeled Daudi cells and CD14+/CD11b+ in vitro generated phagocytes using a 4 hour exposure. CDC assays were established with Daudi cells/ 10% pooled human serum using 7-AAD as the cytotoxic indicator in a 4 hour assay. Trogocytosis was measured up to 4 hours with Rituximab®/ Daudi and PBMC9s and CD19/20 and CD14. All assays were analyzed using a FACS ARIA III flow cytometer and replicates of 3 were evaluated for statistical relevance. Results demonstrated that Rituximab® induced effects were observed in all 5 assays using flow cytometry. Apoptosis was induced when 0.01ug/mL Rituximab® was present. ADCP occurred at levels as low as 0.01ug/mL Rituximab®. ADCC and CDC occurred at levels as low as 0.1ug/mL Rituximab®. Trogocytosis, indicated by the transfer of CD19+ B cells to CD14+ monocytes, occurred at 0.1ug/ml Rituximab®. In summary, we demonstrated the clinical applicability of flow cytometry as a single platform to simultaneously evaluate five different mechanisms of actions that can occur when therapeutic antibodies are used to treat cancer cells. Citation Format: Antony R. Chadderton, Shane Harvey, Brandy Strake, Jill Giles-Komar, Renold J. Capocasale, T. Shantha Raju. Flow cytometry as a single platform tool to evaluate multiple mechanisms of actions of therapeutic antibodies. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3518.

A neural network approach to determining cellular viability

Determination of cellular viability is a frequent goal of flow cytometry assays, and most published methods for creating boundaries that separate live, apoptotic, and dead cells are based on heuristics. We describe a method of determining these boundaries by training neural networks to learn the intensity patterns of a subset of cells with known viability, and then produce decision boundaries based on the networks measure of similarity. Five networks were studied and a radial basis perceptron was found to be the most accurate. We have shown that these neural networks provide an objective rationale for classification using all available data.

ANALYSIS OF THE EFFECTS OF RECOMBINANT HUMAN ERYTHROPOIETIN (EPO) ON MURINE BONE MARROW POPULATIONS USING MIXTURE MODELING TECHNIQUES

By applying mixture modeling techniques to flow cytometry data, we revealed that, as expected, treatment with EPO increased the fraction of erythroid precursors in murine bone marrow. However, we also found the fraction of apoptotic and dead late stage erythroid precursors increased. Furthermore, we also demonstrated that EPO increased the ratio of dead polyploid to dead diploid erythroid cells, providing evidence that EPO increased the probability that dividing erythroid precursors will enter apoptosis, a finding not evident by expert human analysis. These results indicate that mathematical analysis can be a powerful approach for analyzing flow cytometry data and understanding complex biological systems.