Unexpected interference in cell surface staining by monoclonal antibodies to unrelated antigens

Combined Effect of Anti-SSEA4 and Anti-Globo H Antibodies on Breast Cancer Cells

The globo-series glycosphingolipids (SSEA3, SSEA4, and Globo H) were shown to express in many cancers selectively, and a combination of anti-SSEA4 and anti-Globo H antibodies was able to suppress tumor growth in mice inoculated with breast cancer cell lines. To further understand the effect, we focused on the combined effect of the two antibodies in target binding and antibody-dependent cellular cytotoxicity (ADCC) in vitro. Here, we report that the binding of anti-Globo H antibody (VK9) to MDA-MB231 breast cancer cells was influenced by anti-SSEA4 antibody (MC813-70), and a combination of both antibodies induced a similar effect as did anti-SSEA4 antibodies alone in a reporter-based ADCC assay, indicating that SSEA4 is a major target in breast cancer due to its higher expression than Globo H. Furthermore, we showed that a homogeneous anti-SSEA4 antibody (chMC813-70-SCT) designed to maximize the ADCC activity can be used to isolate a subpopulation of natural killer (NK) cells that exhibit an ∼23% increase in killing the target cells as compared to the unseparated NK cells. These findings can be used to predict a therapy outcome based on the expression levels of antigens and evaluate therapeutic antibody development.

Lot-to-lot reproducibility, stability and life cycle management of antibody reagents for flow cytometry

The increasing number of biopharmaceuticals, gene and cell therapies in development has seen a growing use of flow cytometry to measure biomarkers, generate pharmacokinetic data, assess immunogenicity and investigate target engagement. The importance of these data types and their inclusion in regulatory submissions mean that flow cytometry analyses are now expected to demonstrate robust performance and comply with both regulatory and scientific recommendations during their validation and subsequent use in sample analysis. The control of the 'critical reagents' commonly used in flow cytometry presents some specific challenges, particularly when an assay is required for use over a long period of time across different phases of a drug development program, or where it is deployed in complex, multisite clinical studies. This paper highlights some key challenges in flow cytometry reagent management with some of the strategies employed to control and monitor flow cytometry critical reagents.

A human receptor occupancy assay to measure anti-PD-1 binding in patients with prior anti-PD-1

Receptor occupancy (RO) assessment by flow cytometry is an important pharmacodynamic (PD) biomarker in the clinical development of large molecules such as monoclonal therapeutic antibodies (mAbs). The total‐drug‐bound RO assay format directly assesses mAb binding to cell surface targets using anti‐drug detection antibodies. Here, we generated a flow cytometry detection antibody specifically binding to mAbs of the IgG₁ P329GLALA backbone. Using this reagent, we developed a total‐drug‐bound RO assay format for RG7769, a bi‐specific P329GLALA containing mAb targeting PD‐1 and TIM3 on T cells. In its fit‐for‐purpose validated version, this RO assay has been used in the Phase‐I dose escalation study of RG7769, informing on peripheral T cell RO and RG7769 antibody binding capacity (ABC). We assessed RG7769 RO in checkpoint‐inhibitor (CPI) naïve patients and anti‐PD‐1 CPI experienced patients using our novel assay. Here, we show that in both groups, complete T cell RO can be achieved (~100%). However, we found that the maximum number of T cell binding sites for RG7769 pre‐dosing was roughly twofold lower in patients recently having undergone anti‐PD‐1 treatment. We show that this is due to steric hindrance exerted by competing mAbs masking the available drug binding sites. Our findings highlight the importance of quantitative mAb assessment in addition to relative RO especially in the context of patients who have previously received anti‐PD‐1 treatment.

Measuring Extracellular Vesicles by Conventional Flow Cytometry: Dream or Reality?

Intense research is being conducted using flow cytometers available in clinically oriented laboratories to assess extracellular vesicles (EVs) surface cargo in a variety of diseases. Using EVs of various sizes purified from the HT29 human colorectal adenocarcinoma cell line, we report on the difficulty to assess small and medium sized EVs by conventional flow cytometer that combines light side scatter off a 405 nm laser with the fluorescent signal from the EVs general labels Calcein-green and Calcein-violet, and surface markers. Small sized EVs (~70 nm) immunophenotyping failed, consistent with the scarcity of monoclonal antibody binding sites, and were therefore excluded from further investigation. Medium sized EVs (~250 nm) immunophenotyping was possible but their detection was plagued by an excess of coincident particles (swarm detection) and by a high abort rate; both factors affected the measured EVs concentration. By running samples containing equal amounts of Calcein-green and Calcein-violet stained medium sized EVs, we found that swarm detection produced false double positive events, a phenomenon that was significantly reduced, but not totally eliminated, by sample dilution. Moreover, running highly diluted samples required long periods of cytometer time. Present findings raise questions about the routine applicability of conventional flow cytometers for EV analysis.

Strategies for calibrating models of biology

Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling but is often underappreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here, we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple-component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage a deeper consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.