Poster
2

Evaluation of Checkpoint Inhibitor Therapies using a Mixed Lymphocyte Reaction (MLR) Assay

Authors

K Mcbain1; Z Liu1; B Tyrell1; T Dale1; C Szybut1
1 Sartorius, UK

Abstract

Checkpoint
inhibitors block the ‘off’ signals that are engaged when T cells interact with
antigen-presenting cells, such as dendritic cells (DCs). This increases T cell
activation and enhances the anti-tumor response. These drugs can be evaluated in
vitro
using a mixed lymphocyte reaction (MLR) model, which is a co-culture
of immune cells from two donors. Here we have quantified T cell response in MLR
using advanced flow cytometry.

Assays were
set up as either one-way or two-way MLR co-cultures. One-way assays combined unlabeled
DCs and encoder dye labeled CD4+ T cells. Two-way assays combined PMBCs from two donors. Cells were plated in
96 or 384-well formats for 3-6 days, alongside a range of checkpoint inhibitor
drugs. Supernatant samples were taken throughout the time course for cytokine
analysis using iQue Qbeads®. T cell subsets were analyzed using the iQue® Advanced
Flow Cytometry Platform and Human T Cell Activation Kit.

CD4+ T cells co-cultured for 6 days
with DCs showed roughly 3-fold greater expression of activation marker CD25 (62
± 2 %) when compared to T cells in monoculture (22 ± 9 %). The addition of an anti-PD1 checkpoint inhibitor to the co-culture induced a further, concentration dependent
increase in CD25 expression. This was accompanied by increased production
of cytokines indicative of activation, such as TNFα, and inflammatory cytokines, such as CCL2. Throughout the
study, large differences in sensitivity to drug response were observed between
donor pairs. For example, the increase in IFNγ release upon addition of anti-CTLA4
(17ng/mL) was 13.5-fold with one donor pair, compared to only 1.3-fold with another.
This highlights the need to characterize drug activity against multiple donor
pairs.

These data
exemplify the use of the iQue® to generate pharmacological data for checkpoint
inhibitor activity on T cells in MLR, with the potential to profile libraries of novel therapeutics in minimal time.
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