Impact of Diverse Immune Evasion Mechanisms of Cancer Cells on T Cells Engaged by EpCAM/CD3-Bispecific Antibody Construct AMG 110

<div><p>Background</p><p>Bispecific T cell engager (BiTE^®) are single-chain bispecific antibody constructs with dual specificity for CD3 on T cells and a surface antigen on target cells. They can elicit a polyclonal cytotoxic T cell response that is not restricted by T cell receptor (TCR) specificity, and surface expression of MHC class I/peptide antigen complexes. Using human EpCAM/CD3-bispecific BiTE^® antibody construct AMG 110, we here assessed to what extent surface expression of PD-L1, cytoplasmic expression of indoleamine-2,3-deoxygenase type 1, Bcl-2 and serpin PI-9, and the presence of transforming growth factor beta (TGF-β), interleukin-10 (IL-10) and adenosine in culture medium can impact redirected lysis by AMG 110-engaged T cells.</p><p>Methods</p><p>The seven factors, which are all involved in inhibiting T cell functions by cancer cells, were tested with human EpCAM-expressing Chinese hamster ovary (CHO) target cells at levels that in most cases exceeded those observed in a number of human cancer cell lines. Co-culture experiments were used to determine the impact of the evasion mechanisms on EC₅₀ values and amplitude of redirected lysis by AMG 110, and on BiTE^®-induced proliferation of previously resting human peripheral T cells.</p><p>Findings</p><p>An inhibitory effect on redirected lysis by AMG 110-engaged T cells was seen upon overexpression of serpin PI-9, Bcl-2, TGF-βand PD-L1. An inhibitory effect on induction of T cell proliferation was only seen with CHO cells overexpressing IDO. In no case, a single evasion mechanism rendered target cells completely resistant to BiTE^®-induced lysis, and even various combinations could not.</p><p>Conclusions</p><p>Our data suggest that diverse mechanisms employed by cancer cells to fend off T cells cannot inactivate AMG 110-engaged T cells, and that inhibitory effects observed <i>in vitro</i> may be overcome by increased concentrations of the BiTE^® antibody construct.</p></div

Impact of diverse immune evasion mechanisms on AMG 110-mediated T cell proliferation.

<p>Human CD3⁺ T cells were labeled with CFSE and co-cultured at effector to target (E:T) ratios of 1:8, 1:1 and 4:1 in 48-well plates with CHO cell lines expressing human EpCAM and one of the six stably transfected immune evasion proteins (PI-9, Bcl-2, IDO, IL-10, TGF-β or PD-L1) (red), or with parental EpCAM⁺ CHO cells in the presence of 1 mM adenosine (ADO) in the culture medium (black: parental cells). CFSE signals in cells were analyzed by flow cytometry. For each evasion protein one representative experiment is shown. Co-culture in the absence of AMG 110 did not gave CSFE signals, whereas co-culture in the presence of 1 μg/ml AMG 110 showed cycles of cell division affected by E:T ratio. Three different human PBMC were used.</p

Reversal of inhibitory effects of PD-L1, IDO and TGF-β by pharmacological means.

<p>(A) Analysis of CD3⁺ T cell proliferation after 120 h of co-culture with control EpCAM⁺ CHO cells and IDO-expressing, EpCAM⁺ CHO cells in the presence or absence of 500 μM tryptophane (Trp) with and without 1 μg/ml AMG 110. (B) Dose-dependent, redirected target cell lysis of parental EpCAM⁺ CHO cells and EpCAM⁺ CHO TGF-β transfectants +/- 50 μg/ml TGF-β neutralizing anti-human TGF-β antibody in presence of AMG 110 and CD3⁺ T cells in an E:T ratio of 4:1 after 72 h incubation. (C) Dose-dependent redirected target cell lysis of control EpCAM⁺ CHO cells and EpCAM⁺ CHO PD-L1 transfectants with and without 5 μg/ml of an anti-human PD-L1-neutralizing antibody in the presence of AMG 110 and pre-stimulated CD8⁺ T cells. The E:T ratio was 1:1, the assay duration 24 h.</p

Impact of diverse immune evasion mechanisms on AMG 110-mediated redirected lysis of target cell.

<p>AMG 110 dose-dependent lysis was compared between parental EpCAM⁺ CHO cells and EpCAM⁺ CHO cells stably transfected with one of six human evasions proteins or in the presence of 1 mM adenosine. Human CD8⁺ T cells were used as effector cells at an E:T ratio of 1:1. Percentage of target cell lysis after co-culture for 24 h and EC₅₀ values from sigmoidal dose-response response curves were determined in a FACS-based cytotoxicity assay. (A) Representative dose response curves are shown for parental EpCAM⁺ CHO cells (black) and EpCAM⁺ CHO cells expressing evasion proteins or incubated in the presence of 1 mM adenosine (red). (B) Mean EC₅₀ values for redirected lysis were calculated from the indicated number of independent experiments. The relative change in EC₅₀ values for lysis was determined by dividing the mean of EC₅₀ values from evasion conditions by the mean of EC₅₀ values from control conditions. Error bars represent SEM values. (C) Relative changes in the percentage of target cell lysis after 24 h at the plateau of dose response curves. The mean amplitude of lysis under evasion conditions was divided by the mean amplitude of lysis under control conditions. Error bars represent SEM values.</p

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

ABSTRACTOver the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA’s recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches – one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features – to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, [Formula: see text], of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task ([Formula: see text] 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs