Dynamic quantification of antigen molecules with flow cytometry

Differential expression of immune-regulatory proteins C5AR1, CLEC4A and NLRP3 on peripheral blood mononuclear cells in early-stage non-small cell lung cancer patients

Changes in gene expression profiling of peripheral blood mononuclear cells (PBMC) appear to represent the host's response to the cancer cells via paracrine signaling. We speculated that protein expression on circulating T-lymphocytes represent T-lymphocyte trafficking before infiltration into the tumor microenvironment. The possibility of using protein expression on circulating T-lymphocytes as a biomarker to discriminate early-stage non-small cell lung cancer (NSCLC) was explored. Four independent PBMC gene expression microarray datasets (GSE12771, GSE13255, GSE20189 and GSE3934) were analyzed. We selected C5AR1, CLEC4A and NLRP3 based on their significant protein expression in tumor-infiltrating lymphocytes, but not in normal lymphoid tissue. A validation study using automated flow cytometry was conducted in 141 study participants including 76 treatment-naive early-stage non-small cell lung cancer patients (NSCLC), 12 individuals with non-malignant pulmonary diseases, and 53 healthy individuals. Median ratios of C5AR1, CLEC4A and NLRP3 specific antibody staining to CD3 positive cells in early-stage NSCLC patients compared to healthy controls were 0.014 [0-0.37] vs. 0.01 [0-0.07, p = 0.13], 0.03 [0-0.87] vs. 0.02 [0-0.13, p = 0.10] and 0.19 [0-0.60] vs. 0.09 [0.02-0.31, p < 0.0001], respectively. Median fluorescence intensity (MFI) of CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ expression in early-stage NSCLC patients compared to healthy volunteers was 185 [64.2-4801] vs. 107.5 [27-229, p < 0.0001], 91.2 [42.4-2355] vs. 71.25 [46.2-103, p = 0.0005], and 1585 [478-5224] vs. 758.5 [318-1976, p < 0.0001], respectively. NLRP3:CD3 ratio, CD3⁺C5AR1⁺, CD3⁺CLEC4A⁺ and CD3⁺NLRP3⁺ MFI were significantly higher in early-stage NSCLC than healthy volunteers with an area under the ROC curve of 0.69-0.76. The CD3⁺NLRP3⁺ MFI provided the most distinguishable expression at 71.5% sensitivity and 70% specificity. Furthermore, CD3⁺NLRP3⁺ MFI potentially discriminated between early-stage NSCLC from malignant-mimic inflammation and infection pulmonary disease. Further validation in various pulmonary inflammatory disease might be warranted. Our proof-of-principle findings strengthen the hypothesis that malignancies generate distinctive protein expression fingerprints on circulating T-lymphocytes.

Phase I Trial: Cirmtuzumab Inhibits ROR1 Signaling and Stemness Signatures in Patients with Chronic Lymphocytic Leukemia

Cirmtuzumab is a humanized monoclonal antibody (mAb) that targets ROR1, an oncoembryonic orphan receptor for Wnt5a found on cancer stem cells (CSCs). Aberrant expression of ROR1 is seen in many malignancies and has been linked to Rho-GTPase activation and cancer stem cell self-renewal. For patients with chronic lymphocytic leukemia (CLL), self-renewing, neoplastic B cells express ROR1 in 95% of cases. High-level leukemia cell expression of ROR1 is associated with an unfavorable prognosis. We conducted a phase 1 study involving 26 patients with progressive, relapsed, or refractory CLL. Patients received four biweekly infusions, with doses ranging from 0.015 to 20 mg/kg. Cirmtuzumab had a long plasma half-life and did not have dose-limiting toxicity. Inhibition of ROR1 signaling was observed, including decreased activation of RhoA and HS1. Transcriptome analyses showed that therapy inhibited CLL stemness gene expression signatures in vivo. Cirmtuzumab is safe and effective at inhibiting tumor cell ROR1 signaling in patients with CLL.

FISIK: Framework for the Inference of In Situ Interaction Kinetics from Single-Molecule Imaging Data

Recent experimental and computational developments have been pushing the limits of live-cell single-molecule imaging, enabling the monitoring of intermolecular interactions in their native environment with high spatiotemporal resolution. However, interactions are captured only for the labeled subset of molecules, which tends to be a small fraction. As a result, it has remained a challenge to calculate molecular interaction kinetics, in particular association rates, from live-cell single-molecule tracking data. To overcome this challenge, we developed a mathematical modeling-based Framework for the Inference of in Situ Interaction Kinetics (FISIK) from single-molecule imaging data with substoichiometric labeling. FISIK consists of (I) devising a mathematical model of molecular movement and interactions, mimicking the biological system and data-acquisition setup, and (II) estimating the unknown model parameters, including molecular association and dissociation rates, by fitting the model to experimental single-molecule data. Due to the stochastic nature of the model and data, we adapted the method of indirect inference for model calibration. We validated FISIK using a series of tests in which we simulated trajectories of diffusing molecules that interact with each other, considering a wide range of model parameters, and including resolution limitations, tracking errors, and mismatches between the model and the biological system it mimics. We found that FISIK has the sensitivity to determine association and dissociation rates, with accuracy and precision depending on the labeled fraction of molecules and the extent of molecule tracking errors. For cases where the labeled fraction is too low (e.g., to afford accurate tracking), combining dynamic but sparse single-molecule imaging data with almost-whole population oligomer distribution data improves FISIK's performance. All in all, FISIK is a promising approach for the derivation of molecular interaction kinetics in their native environment from single-molecule imaging data with substoichiometric labeling.

Quantification of number of CD38 sites on bone marrow plasma cells in patients with light chain amyloidosis and smoldering multiple myeloma

BACKGROUND

Recent approaches in multiple myeloma (MM) treatment have targeted CD38. As antigen expression levels on plasma cells (PCs) were demonstrated to affect response to monoclonal antibody (mAb) treatment, a precise characterization of PC phenotype is warranted.

METHODS

Anti-CD38 mAb (isatuximab) was tested for antibody-dependent cellular cytotoxicity (ADCC) in MM cell lines. Quantification of the number of sites (NOS) of CD38 on bone marrow PCs and other immune cells obtained from light chain (AL) amyloidosis (n = 46) and smoldering multiple myeloma (SMM) patients (n = 19) was performed with two different quantitative flow cytometry (QFCM) applications.

RESULTS

ADCC activity of isatuximab was observed in cell lines with >100 × 10³ CD38-NOS only. The average PC CD38-NOS was 153 ± 53 × 10³ in AL amyloidosis and 138.7 ± 53 × 10³ in SMM patients. Eight (17%) AL amyloidosis and 4 (21%) SMM patients showed a PC CD38-NOS level <100 × 10³ . In four AL amyloidosis and two SMM patients <10% of PCs had a CD38-NOS ≥100 × 10³ . The CD38-NOS identified on bone marrow lymphocytes, monocytes, and granulocytes was two log units below the CD38-NOS on PCs (P < 0.001). No significant differences in CD38-NOS expression levels on any of the analyzed PC subpopulations in AL amyloidosis and SMM patients were identified.

CONCLUSION

Levels of CD38 expression affect the isatuximab-mediated ADCC in vitro. As PCs of patients with AL amyloidosis and SMM do not homogenously express high CD38 our data provide a rationale for assessment of CD38-NOS in patients with PC disorders prior to anti-CD38 treatment. © 2018 International Clinical Cytometry Society.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.