Tonic Signaling Leads to Off-Target Activation of T Cells Engineered with Chimeric Antigen Receptors That Is Not Seen in T Cells Engineered with T Cell Antigen Coupler (TAC) Receptors
Bibliographic record
Abstract
Chimeric antigen receptors (CARs) are powerful tools that enable MHC-independent activation of T cells. Recent reports have indicated that constitutive, low-level (tonic) signaling by CARs can impair the utility of the engineered T cells. The single-chain antibody (scFv) binding domain was one of the features determined to promote tonic signaling. We have recently developed a novel chimeric receptor, known as the T cell antigen coupler (TAC), that is less prone to tonic signaling than second-generation CARs. The TAC consists of a scFv-based antigen binding domain, a CD3-binding domain that couples the TAC to endogenous T cell receptor (TCR), and a transmembrane and cytoplasmic coreceptor (CD4) domain. In contrast to CARs, this design enables TAC-T cells to signal through the endogenous TCR, which we propose provides a fidelity to natural T cell signal regulation. Interestingly, we have recently reported that CAR-T cells have a greater propensity for off-target activation than TAC-T cells, suggesting a safety advantage to TAC-T cells (Helsen et al., Nat. Comm., 2019). Further characterization of the differences between CAR- and TAC-T cell signal initiation and activation is required to understand how their design affects sensitivity, specificity and regulation of T cell activation. Examination of the activation requirements for BCMA-specific CAR-T cells and TAC-T cells confirmed that TAC-T cells are reliant upon the endogenous TCR for T cell activation whereas CAR-T cells are TCR-independent. TRAC knock-out CAR-T cells retained potent effector function at levels similar to CAR-T cells with intact TCR expression, whereas TRAC knock-out TAC T-cells showed significant impairment in effector function. Consistent with TCR-dependence, the immunological synapse produced by TAC-T cells displays all the hallmarks of a conventional immunological synapse, whereas CAR-T cells form unconventional synapses. Unlike TAC-T cells, immunological synapses formed by CAR-T cells display non-uniform central supramolecular activation clusters, disperse Lck distribution, a lack of an LFA-1 associated adhesion ring (Figure), as well as more disperse delivery of perforin to the cell interface. CAR-T cells also formed synapses faster than TAC-T cells. This suggests that while TAC T-cells are beholden to the requirement of organized, mature synapse formation, CAR T-cells can rapidly form less structurally organized synapses. Transcriptional profiling of CAR-T cells in the absence of antigen stimulation revealed a basal activation status associated with upregulation of Nur77, a transcription factor that is downstream of TCR activation. Transcriptional profiling of TAC-T cells failed to reveal evidence of TCR signaling in the absence of stimulation. Further evaluation of CAR- and TAC- T cells in the absence of stimulation revealed elevated levels of CD69, PD-1 and LAG-3 in CAR-T cells compared with TAC-T cells, as well as higher expression of IL-2, IFNγ, and TNF in CAR-T cells. Interestingly, the level of tonic signaling was dependent on the antigen-binding scFV, as otherwise identical BCMA-specific CAR- and TAC-T cells displayed different levels of CD69, PD-1 and LAG-3 depending on the identity of the BCMA-specific scFv. Despite different levels of basal activation, both CAR- and TAC-T cells displayed comparable activation kinetics as measured by upregulation of CD69 and Ki-67, as well as proliferation. However, the elevated level of basal activation rendered the CAR-T cells more easily activated by a cross-reactive off-target antigen that failed to stimulate TAC-T cells carrying the same binding domain. These data suggest that the TAC receptor offers a valuable alternate platform to CAR-T cells. The antigen-binding scFv domain has a direct impact on tonic signaling and basal activation in CAR-T cells. Conversely, TAC-T cells are less susceptible to basal activation and this works suggests that the TAC receptor can deploy scFv binding domains that are not suitable for CARs. This work was supported by Triumvira Immunologics and Genome Canada. Figure 1 Disclosures Bramson: McMaster University: Current Employment, Patents & Royalties; Triumvira Immunologics: Current Employment, Current equity holder in private company, Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".