RESET: A TCR-coupled antigen receptor with superior targeting sensitivity and reversible drug-regulated anti-tumor activity

Jardin Leleux; Jillian Rosenberg; Olmo Sonzogni; Rebecca L Walker; Anita Venkitaraman; Sarah M Garrison; Nan Jin; Philip D Gregory; Jordan Jarjour

doi:10.1016/j.ymthe.2025.02.026

. 2025 Feb 20;33(4):1608–1620. doi: 10.1016/j.ymthe.2025.02.026

RESET: A TCR-coupled antigen receptor with superior targeting sensitivity and reversible drug-regulated anti-tumor activity

Jardin Leleux ^1,³, Jillian Rosenberg ^2,^3,^∗, Olmo Sonzogni ¹, Rebecca L Walker ², Anita Venkitaraman ², Sarah M Garrison ², Nan Jin ², Philip D Gregory ², Jordan Jarjour ¹

PMCID: PMC11997482 PMID: 39980194

Abstract

Chimeric antigen receptor (CAR) T cells are effective cancer therapies, particularly in indications with high, stable, and tumor-specific antigen expression. Other settings may require improved targeting sensitivity, controllable targeting selectivity, and/or additional potency enhancements to achieve robust efficacy. Here, we describe a novel receptor architecture called RESET (rapamycin-enabled, switchable endogenous T cell receptor) that combines (1) cell surface antigen targeting, (2) small-molecule regulation, and (3) the signaling proficiency and inherent sensitivity of native T cell receptors. RESET-T cells outperformed both constitutive and drug-regulated CAR-T cells and show hallmarks of TCR activation that suggest improved fidelity to native T cell responses. Pharmacological control then increases safety through toggling T cell activation between active and resting states and may mitigate T cell exhaustion caused by continuous antigen exposure. This convergence of drug-regulated targeting and natural immune receptor signal transduction may better replicate the kinetics and physiology of a classical T cell response and potentiate more successful and safer immunotherapies.

Keywords: chimeric antigen receptor, T cell receptor, TCR-enabled chimeric receptor, rapamycin, drug regulated, pharmacological control

Graphical abstract

RESET is a first-in-class T cell therapy approach that combines the benefits of chimeric antigen receptor (CAR) redirection of T cell reactivity, canonical T cell receptor (TCR) signaling, and pharmacological regulation to drive potent, sensitive, and tunable anti-tumor responses.

Introduction

Chimeric antigen receptor (CAR)-T cells are transformative clinical therapies for B cell malignancies and multiple myeloma, where tumor antigen density is often high and normal tissue toxicity is clinically manageable.¹ However, to date, CAR-T cells have been far less successful when antigen is downregulated or in solid tumors, owing in part to the expression patterns of putative targets.² Targetable solid tumor antigens are often expressed at low levels, heterogeneously, and/or also in essential tissues. These target expression properties have resulted in discouraging clinical outcomes ranging from candidates that lack therapeutic efficacy to those that have caused fatal adverse events.³

An additional contributor to suboptimal CAR-T performance is the deviation in signaling and transcriptional responses elicited by CAR engagement compared to the natural T cell receptor (TCR). TCR activation occurs with exquisite sensitivity in response to sparsely distributed peptide-major histocompatibility complex ligands,⁴^,⁵^,⁶^,⁷ and activated T cells integrate a myriad of parallel signaling inputs and feedback mechanisms throughout the expansion, effector, contraction, and memory phases of cellular immunity.⁷^,⁸ Therapies that mechanistically converge on natural TCR engagement, either directly or indirectly, have demonstrated significant clinical activity where CAR-T cells have failed, confirming that the core TCR program is a robust and validated foundation for immunotherapy development.⁹^,¹⁰ Therefore, engineered receptors structured around natural TCR signaling could both expand targeting opportunities and recover T cell properties inefficiently engaged, absent, or potentially even corrupted during CAR activation.

We have previously described a drug-regulated CAR architecture that enables physician-controlled toggling of a T cell therapy between active and resting states, thereby limiting potential off-tumor toxicities.¹¹ Here, we set out to develop drug-regulated receptors that leverage the naturally evolved TCR architecture. While several chimeric systems built with TCR-based signaling have been reported previously,¹⁰^,¹²^,¹³^,¹⁴ constitutive, high-sensitivity targeting carries substantial off-tumor targeting risks and exhaustion susceptibility that may limit safety and efficacy.¹⁵^,¹⁶^,¹⁷^,¹⁸^,¹⁹ Combining drug-regulated targeting with TCR-based signaling proficiency could solve these challenges.

The rapamycin-enabled, switchable endogenous T cell receptor (RESET) architecture is a two-component system that fuses rapamycin-enabled dimerization domains to (1) a CD3 protein of the endogenous TCR complex (“TCR incorporating unit”) and (2) a co-expressed tumor-targeting transmembrane protein (“targeting subunit”). The targeting subunit enables antibody-based responses to surface antigens, while the TCR incorporating subunit scavenges the endogenous TCR to propagate natural T cell activation signals upon target recognition, but only when rapamycin is co-administered. Our data demonstrate that RESET-T cells drive deeper, more sensitive anti-tumor responses in vitro and in vivo compared to regulated or canonical CAR-T cells. Furthermore, RESET-T cell signaling closely resembles transgenic TCRs, suggesting that incorporation and utilization of endogenous TCR componentry mimics native signal propagation. To our knowledge, RESET is the first system that pairs human leukocyte antigen (HLA)-independent, highly sensitive, drug-regulated antigen targeting with TCR signaling, merging the benefits of engineerable surface antigen targeting with the natural T cell physiology that follows TCR activation.

Results

RESET engineering and optimization

As described above, RESET is a two-component design comprising independent subunits that enable antibody-based surface antigen targeting and incorporation into the endogenous TCR-CD3 complex (Figure 1A). Each subunit contains interchangeable rapamycin-induced dimerization of FK506 binding protein (FKBP) or FKBP-rapamycin binding (FRB) domains that mediate reversible heterodimerization via application or withdrawal of a sub-immunosuppressive dose of the macrolide drug rapamycin. Several design iterations were tested to achieve efficient CD3 incorporation, optimal orientation, and function of each RESET component. RESET variations were evaluated using a CD33-targeting VHH nanobody²⁰ on the targeting subunit, and T cell activation and effector functions were assessed by co-culturing RESET-T cells with CD33⁺ tumor cell lines with variable antigen density (Figure S1A) in the presence or absence of rapamycin.

We first hypothesized that RESET activity would depend on the optimal alignment of the FKBP and FRB heterodimerization domains. In the TCR incorporating subunit, one heterodimerization domain sits atop the extracellular domain of the CD3 chain. To assess variations in the complementary heterodimerization domain placement, we evaluated several hinge and transmembrane compositions in the targeting subunit. These included regions derived from CD28 (RESET-28h), CD4 (RESET-4h), and a control with minimal hinge residues (RESET-nh), which were 17, 15, or 3 amino acids in length, respectively (Figure 1B). Both the VHH and the FRB domains were efficiently detected in all RESET designs, indicating robust expression of both subunits (Figures 1C, S1A, and S1B). Co-culture of RESET-T cells with an HL-60 cell line that expresses CD33 at high levels revealed robust, rapamycin-dependent cytokine production by the RESET-28h and RESET-4h, while RESET-nh cytokine production was significantly lower (Figures 1D-E and S1D). However, when co-cultured with OCI-AML3 cells, which express ∼10-fold less CD33 than HL-60 (Figure S1C), RESET-4h significantly outperformed RESET-28h (Figure 1F), demonstrating that this hinge composition drives highly sensitive rapamycin-dependent T cell functional responses. Across all variations, RESET-T cells had minimal to no activity when rapamycin was absent.

Holding the RESET-4h targeting subunit constant, we next compared appending the FRB domain to CD3ε or CD3γ to determine whether the different stoichiometry and/or orientation of these CD3 chains could influence TCR incorporation efficiency or functional performance. Both RESET-ε and RESET-γ designs were expressed efficiently (Figures 1H, S1A, and S1B) and co-culture with HL-60 tumor cells led to similar rapamycin-dependent interferon-γ (IFN-γ) (Figure S1I) and significantly more interleukin-2 (IL-2) production (Figure 1J). RESET-ε was also significantly more reactive to OCI-AML3 cells (Figure 1K) and therefore was selected for use in combination with the RESET-4h hinge in subsequent designs.

Finally, we evaluated swapping the relative positions of the VHH and FKBP12 domains. Specifically, we tested FKBP positioned distally to the VHH domain in the targeting subunit (RESET33-flip, Figure 1L). The RESET-flip expressed efficiently (Figures 1M, S1A, and S1B), maintained rapamycin dependence, and produced comparable levels of cytokine production to the optimized RESET-ε design (Figures 1N). However, we also observed antigen-independent, rapamycin-dependent RESET-flip T cell activity (Figures 1O and S1D). We hypothesized that the FRB-FKBP interaction may occur between neighboring T cells and cause trans-activation in this configuration. In subsequent experiments, we pooled T cell populations independently expressing the RESET-flip targeting subunit and the TCR-incorporating subunit and observed hallmarks of rapamycin-dependent activation, confirming trans-activation (data not shown). Given the capacity for trans-activation and tonic signaling, the VHH domain was maintained in the distal position. We additionally analyzed cytotoxicity and T cell proliferation in response to antigen for all RESET designs, confirming proficient antigen and rapamycin-dependent activity consistent with the cytokine responses described above (Figures S1E and S1F).

Next, to broaden the applicability of RESET-T cells, we replaced the wild-type FRB dimerization domain with the T2098L mutated variant (FRB∗)²¹ that enables binding to non-immunosuppressive rapamycin analogs (“rapalogs,” e.g., AP21967). We observed no significant changes in component expression or functional outcomes and confirmed that rapamycin could be substituted with rapalogs (Figures S1G and S1H). Engineering RESET with optimal componentry led to peak function and versatility, enabling the multi-functional chimeric receptor we evaluated to move forward.

RESET-T cells require TCR expression and recapitulate TCR stimulation

We next sought to confirm that the RESET-T cell function is dependent on the TCR-incorporating subunit’s intended integration into the endogenous TCR complex. While this was suggested by the linear relationship observed when co-staining for FRB and TCR expression (Figures S2A and S2B), we used a gene editing approach to directly assess the dependence of the RESET architecture on TCR incorporation. T cells treated with a megaTAL (transcription activator-like effector) targeting the T cell receptor alpha (TRAC) locus²² (Figure S2E) ablated TCR expression (Figures S2G and S2H) but were still successfully transduced with either RESET or a rapamycin-dependent 41BB-CD3ζ CAR, dimerizing agent-regulated immunoreceptor complex (DARIC)¹¹^,²³ (Figure S2I). Whereas the non-TCR-dependent DARIC construct retained expression and function in both TCR⁺ and TCR⁻ T cells, FRB expression and RESET function were confined to the small remaining population of TCR⁺ T cells (Figures 2A and 2B), notwithstanding the proficient expression of the targeting subunit irrespective of TCR knockout (KO) status (Figure 2C). Furthermore, intracellular IFN-γ was significantly reduced in TCR-ablated RESET-T cells, despite remaining unchanged in corresponding DARIC-T cells (Figure 2D). These data definitively demonstrate that the RESET TCR-incorporating subunit requires TCR expression to complete the formation of a functional, antigen-responsive RESET architecture. Additionally, by co-transducing RESET alongside a transgenic TCR, we demonstrated that both can function concurrently (Figures S2C–S2D).

Having explored and optimized various design configurations and confirmed the drug responsiveness and dependence on integration with the TCR complex of RESET, we carried out a series of comparative molecular analyses of RESET-T cell activation. We performed RNA sequencing (RNA-seq)and assessed the transcriptional activation signatures of T cells expressing the rapamycin-dependent TCR-based (RESET) and CAR-based (DARIC) immune receptors in comparison to their non-drug-regulated counterparts: a transgenic TCR targeting a MAGEA4 peptide:HLA-A∗0201 complex and a CD33-targeted CAR (Figures S3A and S3B). Preliminary analyses were performed to account for the influence of rapamycin exposure on gene expression, and we found minimal effects of rapamycin, seen as very small effect sizes detected in differential gene expression analysis (Figures S3C−S3E). To examine differences among constructs in their activated state, we performed differential gene expression using a model with all activated construct samples and identified a subset of >2,400 differentially expressed genes significantly associated with construct. Principal-component (PCA) and heatmap analyses on this gene subset revealed that T cells expressing non-regulated immune receptors (TCR- and CAR-T cells) clustered uniquely (Figures 2E and 2F), replicating a significant body of literature that has emphasized the signaling differences between CAR and TCR constructs.²⁴^,²⁵ Interestingly, both DARIC and RESET activation signatures clustered between the TCR and CAR signatures, with RESET resembling the TCR signature and DARIC mirroring the CAR signature (Figures 2E and 2F). These associations held true when the full gene set was analyzed via an orthogonal analysis, the weighted gene co-expression network analysis (WGCNA), on all expressed genes (transcripts per million reads [TPM] > 0 and present in >90% samples) from all samples (Figure S4). These transcriptional data illustrate that RESET signal propagation, despite being driven by antibody-based targeting nested within a rapamycin-regulated architecture, activates the T cells in a manner that more closely resembles the activation and response state of T cells activated through their TCR.

RESET-T cell activation is tightly controlled by both rapamycin and antigen

A critical feature of RESET is multi-parameter control,⁵ where T cells remain at rest when either rapamycin or antigen is withheld, and both are required for T cell activation. Multi-parameter control has been reported with DARIC-T cells, a rapamycin regulated CAR architecture¹¹ currently being investigated clinically.²³ To identify genes associated with T cell activation, we took the union of significant differentially expressed genes between untransduced and activated RESET and DARIC, respectively, resulting in a subset of >1,500 genes (Figure S3C). Principal-component clustering of all DARIC, RESET, and UTD (untransduced) sample conditions on this subset of activated genes confirmed that antigen stimulation accounted for the bulk of the variance in the transcriptional response to activation (Figure 2G, PC1). This analysis highlights the basal activation state of DARIC without antigen stimulation, as those samples cluster in between untransduced controls and antigen-stimulated samples, whereas RESET samples overlap with the same controls. WCGNA analysis revealed that modules of genes enriched for Gene Ontology (GO) terms associated with cellular proliferation, metabolic and antigen processing, and overall immune response pathways were negatively regulated in untransduced cells and unstimulated RESET-T cells, whereas unstimulated DARIC-T cells showed low but detectable activity in these processes (Figures 2H and S4A–S4D).

We further confirmed that RESET-T cells have no observable activity in the absence of antigen independent of rapamycin exposure, while DARIC-T cells exhibited low levels of basal cytokine production both in the presence and absence of rapamycin (Figure 2I). These results highlight that RESET-T cells have minimally aberrant basal signaling, and thus a more complete off-state in the absence of either rapamycin or antigen, and that both inputs are strictly required for receptor activation. Importantly, this is true without sacrificing antigen-specific potency, which is comparable to that of DARIC (Figure 2J).

RESET-T cells have comparable in vivo reactivity to clinically validated TCR-enabled architecture

TCR fusion constructs (TRuCs) comprising binders linked directly to CD3ε have been previously reported¹⁴ and studied in clinical trials.²⁶ Since the TCR incorporating subunit of RESET bears a resemblance to the TRuC receptor, we assessed the phenotypic and functional properties of RESET in comparison with a CD33 targeting TRuC (Figures 3 and S5). To remove any confounding factors of rapamycin on mammalian target of rapamycin function, cell metabolism, and proliferation,²⁷ we used a non-immunosuppressive rapamycin analog and RESET constructs bearing the T2098L mutation for these studies.

Functional comparison of RESET-T cells with TCR-, CAR-, DARIC-, and TRuC-T cells

T cells were transduced with CD33-targeting DARIC, RESET, or TRuC. (A) Phenotypic characterization of T cells that were untransduced or transduced with one of the three constructs. The pie chart is for a single representative donor. (B) IFN-γ and (C) IL-2 secretion was measured by MSD. Shown are cells cultured with HL60 tumor cells, with or without 20 nM of a non-immunosuppressive rapamycin analog (“rapalog”). Graphs show mean ± SEM of two to three donors. (D and E) (D) Cell cytotoxicity via tumor cell count and (E) T cell proliferation as measured by Incucyte. Shown is mean ± SEM of three donors. (F and G) NSG mice bearing firefly luciferase-expressing HL-60 tumors were treated with 10 × 10⁶ T cells normalized for the expression of indicated transgene on day 5 after tumor engraftment. Mice were treated three times weekly with 4 mg/kg rapalog. Mean tumor burden (F) and survival (G) are shown, with error bars representing 95% confidence interval (CI) with n = 5 mice per group (see Figure S5C). Red dotted line indicates sacrifice criteria. Statistical analyses for (B) and (C) were conducted using paired t tests. Statistical analyses for (D) and (E) were conducted using two-way ANOVA. Statistical analyses for (G) were conducted using Mantel-Cox log rank analysis.

By assessing the differentiation phenotype of T cells expressing DARIC, RESET, or TRuC molecules, we observed that the relative proportion of naive-like (CD45RA⁺CD62L⁺) vs. effector-like (CD62L⁻) T cells expressing RESET closely matched that of untransduced and TRuC-T cells. DARIC displayed more prominent effector-like populations in both CD8⁺ and CD4⁺ T cells (Figures 3A, S5A, and S5B). Cytokine production, cytotoxicity, and proliferative responses of RESET- and TRuC-T cells were improved over DARIC-T cells (Figures 3B–3E and S5C). We further assessed the in vivo activity of each architecture in mice engrafted systemically with CD33⁺ HL-60 tumors. RESET- and TRuC-T cells were able to control tumor growth more effectively than DARIC-T cells, both in the first week following T cell infusion and following the tumor rebound that is characteristic of this model (Figures 3F, 3G, S5D, and S5E). Most mice treated with DARIC-T cells were not able to reestablish tumor control. These data demonstrate greater potency of RESET-T cells compared to DARIC-T cells, and the overlapping anti-tumor responses observed between RESET- and TRuC-T cells suggest that these two architectures engage TCR signaling mechanisms similarly despite the drug control feature embedded within RESET-T cells.

RESET-T cells are highly sensitive to low antigen density

It is widely accepted that, relative to CARs, TCRs require significantly fewer targets on the tumor surface to drive activation and subsequent anti-tumor responses.⁴^,⁶^,²⁴^,²⁸^,²⁹^,³⁰ This has also been corroborated by others in the field who have devised chimeric strategies to retarget TCR signaling.¹²^,¹³^,¹⁴ Our initial results when optimizing RESET componentry suggested that this receptor architecture is highly sensitive to the CD33-low OCI-AML3 cell line. To further characterize the sensitivity of RESET-T cells in comparison with CAR-formatted receptors, we transfected an antigen-negative cell line with titrated concentrations of mRNA expressing CD33 (Figure S6A; see materials and methods). This resulted in a panel of cell lines expressing >10⁵ to ∼10³ CD33 molecules (Figure 4A) at the time of co-culture initiation, with CD33 targeting RESET-, DARIC-, or CAR-T cells. Across all antigen densities, RESET-T cells produced significantly more IL-2 than either DARIC- or CAR-T cells. Notably, at the very lowest antigen densities, RESET-T cells were significantly more active than DARIC- and CAR-T cells, which showed little to no activity at the lowest end of the CD33 expression range (Figure 4B).

RESET-T cells display enhanced sensitivity compared to DARIC- or CAR-T cells

(A) Antigen density of HL-60.CD33KO cells electroporated with titrated levels of CD33 mRNA, as well as wild-type HL60 and OCI-AML3 cell lines. See also Figure S6A. (B) IL2 secretion as measured by MSD following 24 h of co-culture with RESET-, DARIC- or CAR-T cells. (C and D) NSG mice bearing firefly luciferase-expressing OCI-AML3 tumors were treated with (C) 5 × 10⁶ CD33-directed T cells or (D) 25 × 10⁶ CLL1-directed T cells 8 days after tumor engraftment. Mice were treated three times weekly with 0.5 mg/kg rapamycin (Rapamune). (B) Mean ± SEM of three donors in technical duplicates. Statistical analyses were conducted using Tukey’s multiple comparisons two-way ANOVA, where ∗ denotes the p value significance of RESET vs. DARIC and $ denotes the p value significance of RESET vs. CAR. (C and D) Mean tumor burden is shown, with error bars representing 95% CI, with n = 5 mice per group (see Figures S6B–S6E).

To understand how this improved sensitivity to antigen may impact RESET activity in vivo, we established a systemic xenograft model in NSG mice using the OCI-AML3 cell line, which expresses two relevant antigens at very low levels: CD33 (∼2,000 copies) and CLL1 (∼200 copies). To test the limits of RESET sensitivity, we generated RESET- and DARIC-T cells targeting CLL1 to test alongside the CD33-targeting RESET- and DARIC-T cells that we evaluated previously. These cells were able to secrete cytokines in response to multiple cell lines (Figures S6B and S6C). Strikingly, while CD33-targeted RESET and DARIC were both able to control systemic OCI-AML3 tumors (Figures 4C and S6D, top panels), RESET-CLL1 controlled OCI-AML3 growth significantly better than DARIC-CLL1, despite the extremely low availability of antigen (Figures 4D and S6D, bottom panels). Later time points have been excluded from the OCI-AML3 study due to alloreactivity, as evidenced by non-specific tumor control in the UTD condition, but the initial in vivo responsiveness of RESET-CLL1 against a model with a vanishingly low CLL1 antigen density remains compelling. These data confirm the superior sensitivity of RESET, further emphasizing its ability to leverage both the qualitative and quantitative benefits of TCR signaling. Finally, we confirmed the versatility of RESET to be expressed and functional with a well-characterized single-chain variable fragment binder against CD19, FMC63 (Figure S6E).

Discussion

Expanding CAR-T cell successes beyond B cell cancers has been a persistent challenge in the cell therapy field. Intriguingly, TCR-based therapies have achieved some provocative clinical responses across these more formidable settings.³¹^,³²^,³³^,³⁴ This may be due to the functional fidelity of engineered TCRs relative to native immune receptor biology, co-opting endogenous CD3 chains and recapitulating the natural TCR’s highly sensitive and pleiotropic signaling events. Likewise, the integrated downstream responses underlying T cell expansion, cytolytic activity, and the subsequent phases of physiological contraction and immunological memory are more likely to remain intact. In developing the next generation of T cell therapies, technologies that engage TCR signaling in response to cell surface antigens, such as we have described with the RESET architecture, have the unique potential of mirroring the full complexity of natural immunological processes. In doing so, the possibility of achieving more potent and durable responses across a wider spectrum of targets and disease states may be realized.

Despite the advantages described above, operationalizing native TCRs may provoke their intrinsic susceptibilities. Robust targeting sensitivity could lead to unwanted off-target or on-target/off-tumor activity, and chronic receptor stimulation in association with continuous on-target activity could result in terminal T cell exhaustion. For these reasons, layering in pharmacological control over the on-state of engineered immune receptors is a powerful countermeasure that could enable both safer and broader clinical translation of T cell therapies. While drug regulation through a variety of approaches and designs has been reported for CAR architectures, to our knowledge RESET is the first architecture that integrates native TCR-based signaling with a facile and clinically proven pharmacological control mechanism. The reversibility of RESET-T cell activation could therefore be used to safeguard against long-lasting normal tissue toxicity, such as B cell aplasia in patients who receive T cell therapies targeting B cell lymphomas³⁵^,³⁶ or myeloid aplasia following treatment for acute myeloid leukemia.²³ Reversibility may be even more critical in T cell therapies for solid tumor malignancies, which often have a greater risk of target antigen overlap with healthy tissues. Regulation may enable optimization toward a therapeutic window of on-target activity and, similarly, intermittent application and withdrawal of rapamycin may allow for T cell rest and rejuvenation, mitigating exhaustion and prolonging T cell persistence.³⁷

Accordingly, RESET consistently outperformed both regulated and constitutively active CAR architectures, particularly when challenged with tumors expressing low levels of target antigen. We attribute this to the properties of the RESET architecture we have demonstrated herein: increased antigen sensitivity,⁴^,⁶^,²⁹^,³⁰ minimal tonic signaling, maintenance of preferred phenotypic qualities during manufacturing, and engagement of high-fidelity signaling cascades through TCR signaling. Although some CAR redesign efforts have resulted in marginally improved sensitivity,³⁸^,³⁹ we and others⁹^,¹⁴ have demonstrated that sensitivity is far more dramatically enhanced by architectures that engage TCR signaling. Furthermore, the qualitative properties of TCR signaling are fundamentally divergent from CARs and, to date, have not been recapitulated through CAR engineering.²⁴^,²⁵ Our results demonstrate that resting RESET-T cells retain a phenotype that is highly similar to untransduced cells and are transcriptionally grouped with T cells exhibiting minimal activation when antigen and/or rapamycin are not present. Activated RESET-T cells have a transcriptional profile that more closely resembles T cells activated through a TCR vs. activation through a CAR. These patterns are particularly evident for gene sets related to cytokine stimulus, regulation of activation, and metabolic processes (Figure S4).

We hypothesize that unlike CAR-T cells, RESET and other TCR-enabled architectures can leverage the highly sensitive, potent, and naturally regulated features of the TCR itself, which has developed protective negative feedback-signaling mechanisms that optimize immune function.⁴⁰^,⁴¹ Our data indicate that RESET-T cells recapitulate critical features of natural TCR signal transduction that culminate in both effector and memory T cell functions. The convergence of these properties with drug-regulated targeting may better replicate the kinetics and physiology of T cell responses and result in more successful immunotherapies across a growing landscape of disease settings addressable by this powerful therapeutic modality.

Materials and methods

Plasmid construction

RESETs were generated by coexpressing the FRB-CD3 subunit fusion and either an anti-CD33 or anti-CLL1 single-domain antibody (VHH, Inhibrx) linked to FKBP12 with unique hinge, transmembrane, and intracellular signaling domains using a bicistronic vector linked with a P2A cleavage domain. The constructs were cloned in a lentiviral backbone described previously⁴² using Mlu1 and Kpn1 restriction sites and Gibson assembly.

Cell line generation

HL-60.FP tumor cell suspensions were manufactured and characterized by 2seventy bio (Cambridge, MA). The HL-60.FP tumor cell line was derived by transducing parental HL-60.FP tumor cells with a lentiviral vector for the stable expression of both firefly luciferase and a puromycin-resistance gene. Cells were selected using puromycin in Iscove’s modified Dulbecco’s medium, and engineered cell stocks were cultured at 37°C with 5% CO₂ in RPMI supplemented with 10% fetal bovine serum (FBS). HL-60.CD33KO cells were manufactured and characterized by 2seventy bio.

OCI-AML3.GFP.ffluc tumor cell suspensions were a generous gift from Dr. Michael Jenson at Seattle Children’s Research Institute (Seattle, WA). The bulk transduced cell line was first sorted and then clonally expanded to generate the 2G2 clonal line, which was the best performing in terms of luciferase expression and showed comparable CD33 and CLL1 expression to the parental OCI-AML3. Cells were cultured at 37°C with 5% CO₂ in RPMI supplemented with 10% FBS.

A549 (American Type Culture Collection [ATCC], catalog no. CCL-185) was engineered to express HLA-A∗02:01, MAGEA4, and/or CD33 by 2seventy bio. Expression verification was conducted by flow cytometry. Cells were cultured at 37°C with 5% CO₂ in DMEM supplemented with 10% FBS.

MV-411 (ATCC, catalog no. CRL-9591) was cultured at 37°C with 5% CO₂ in RPMI supplemented with 10% FBS.

All cell lines are regularly tested for mycoplasma, and sterility was verified by IDEXX.

Lentivirus production

HEK293T (ATCC, catalog no. CRL-3216) cells were seeded at >80% confluency in a 10-cm dish (Falcon, catalog no. 353003). Viral transfection was carried out with a standard lentiviral packaging plasmid DNA mix complexed with Transit-2020 (Mirus, catalog no. MIR5404/5400/5405/5406) according to the manufacturer’s instructions. The supernatant was replaced 18 h later with X-Vivo 15 (Lonza, catalog no. BEBP04-744Q) supplemented with 10% human AB serum (HABS; Valley Biomedical, catalog no. HP1022HI). Viral supernatant was harvested 36–48 h later, spun at 500 × g for 5 min, filtered through a 0.45-μm PES filter, and placed at −80°C for long-term storage.

T cell production

Healthy donor peripheral blood mononuclear cells were purchased from BioIVT or AllCells. Cells were activated with 0.05 μg/mL human anti-CD3 (Miltenyi, catalog no. 130-093-387) and 0.05 μg/mL human anti-CD28 (Miltenyi, catalog no. 130-093-375) in X-Vivo 15 (Lonza, catalog no. BEBP04-744Q) with 10% HABS (Valley Biomedical, catalog no. HP1022HI), GlutaMAX (Thermo Fisher Scientific, catalog no. 35050061), 1 M HEPES (Thermo Fisher Scientific, catalog no. 15630-080), and 250 IU/mL recombinant human IL-2 (CellGenix, catalog no. 1020-1000). T cells were transduced 24 h later with 1:1 v/v lentivirus. On days 2–3 post-activation, T cells were counted and seeded into G-Rex (Wilson Wolf) at the manufacturer’s recommended seeding density. Four days later, half of the media was removed and replaced with fresh cytokine-containing media. T cells were harvested on days 7–10, stained for expression and memory markers for flow cytometry, and incorporated into assays. Vector incorporation was assessed via PCR by Strateos.

In all rapamycin-dependent assays, cells were either treated with vehicle (media), 1 nM rapamycin (Thermo Fisher Scientific, catalog no. NC0544882), or 20 nM rapalog (AP21967, Takara, catalog no. 635095).

Flow cytometry

Cells harvested as described above were stained with antibodies according to the manufacturer’s instructions. Briefly, cells were washed with PBS, stained with LIVE/DEAD Fixable Near-IR Dead Cell Stain Kit (Thermo Fisher Scientific, catalog no. L10119), and then washed with fluorescence-activated cell sorting (FACS) buffer (PBS + 2% FBS). Cells were then blocked in Fc Block (BD Biosciences, catalog no. 564220), stained with antibody panel at recommended dilutions, washed 2× with FACS buffer, and then run on a cytometer. Intracellular staining for IFN-γ (BioLegend, catalog no. 506518) was conducted with the BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences, catalog no. 554714) according to the manufacturer’s instructions. The antibodies used throughout the paper include anti-FRB (custom antibody by Olympic Protein Technologies), anti-FKBP (Thermo Fisher, catalog no. Mab24953), anti-CD45RA (BioLegend, catalog no. 304148), anti-CCR7 (BioLegend, catalog no. G043H7), anti-CD62L (BD Biosciences, catalog no. 563862), anti-CD4 (BD Biosciences, catalog no. 562970), anti-CD8 (BD Biosciences, catalog no. 557945), anti-CD3 (BioLegend, catalog no. 317322), anti-TCRα/β (BioLegend, catalog no. 306712), anti-VHH (GenScript, catalog no. A01994), and anti-CD33 (Acro, catalog no. CD3-H82E7). Compensation was conducted using UltraComp eBeads Plus (Invitrogen, catalog no. 01-3333-42). All antibodies were used at concentrations recommended by manufacturers.

Cytokine assays (Meso Scale Discovery)

Supernatants were harvested from cells at time point indicated, diluted with Diluent 2 (Meso Scale Discovery [MSD], catalog no. R51BB-2, 1:100 for IFN-γ, 1:10 for IL-2), and stored at −20°C until time of assay. IFN-γ was measured by V-PLEX Human IFN-γ Kit (catalog no. K151QOD-4), and IL-2 was measured by V-PLEX Custom Human Biomarkers (catalog no. K151A9H-4). Kits were used according to the manufacturer’s instructions, with the following exceptions: (1) standard curves were created at 4× the recommended concentration; (2) standard curves were run in triplicate instead of the recommended duplicate; and (3) suggested “alternative protocol” was utilized such that plates were incubated overnight at 4°C.

Cytotoxicity and proliferation (Incucyte) assays

Cells produced as described above were plated with tumor cells indicated in the figure legends at a 0.3:1 effector:tumor (E:T) cell ratio in moat plates (Thermo Fisher Scientific, catalog no. 167574). Adherent lines were allowed to attach to the plate for 1–2 h before the addition of T cells. Plates were coated with poly-l-ornithine (Millipore Sigma, catalog no. A-004-C) according to the manufacturer’s protocol prior to allowing suspension tumor cell lines to adhere to the plate for 1–2 h. Cells were cultured in X-Vivo 15 (Lonza, catalog no. BEBP04-744Q) supplemented with 10% HABS (Valley Biomedical, catalog no. HP1022HI), GlutaMAX (Thermo Fisher Scientific, catalog no. 35050061), and 1 M HEPES (Thermo Fisher Scientific, catalog no. 15630-080). Plates were put in the Incucyte instrument (Sartorius) and imaged at 20× every 4–6 h for 4–7 days. A cell-by-cell analysis was conducted in Incucyte software.

Transcriptomics assay

Cells were generated as described above. Upon harvest, cells were washed to remove IL-2 and then added to a co-culture in an E:T ratio of 2:1. UTD, DARIC, RESET, and CAR conditions were co-cultured with an A549 line engineered to overexpress low-density CD33 (A549.LDOE-CD33), and TCR conditions were co-cultured with an A549 line engineered to overexpress HLA-A∗02:01 and MAGEA4. For the purposes of this experiment, conditions where T cells are cultured with tumor cells are considered “stimulated,” and conditions where T cells are cultured alone are considered “at rest” or “no stim.” Rapamycin was added to appropriate conditions at a final concentration of 1 nM (see Figure S3A). Following an 18-h incubation, plates were rinsed thoroughly to remove and collect suspension T cells without disrupting adherent tumor cells. REAlease CD4/CD8 (TIL) MicroBead Kit (Miltenyi, catalog no. 130-121-561) was used according to the manufacturer’s instructions to isolate T cells from any tumor cells that may have been collected. Post-sort purity was assessed by flow cytometry, and then the miRNeasy 96 Kit (Qiagen, catalog no. 217061) was used to isolate RNA. RNA was assessed for integrity via a 4200 TapeStation System (Agilent) using a D1000 ScreenTape (Agilent, catalog no. 5067-5582). Library prep was completed using KAPA RNA HyperPrep Kit (Roche, catalog no. 07962363001) according to the manufacturer’s instructions, with an RNA input of 100 ng. The sequencing run was prepared with a NextSeq 500/550 version 2.5 High Output 150 cycle kit (Illumina, catalog no. 20024907) and loaded onto a NextSeq 500/550 High Output Kit version 2.5 (Illumina) according to the manufacturer’s instructions. Each read was 76 bp and each index was 8 bp. There were 2 runs, each with 16 samples, an output of 560 million reads, and 10% PhiX.

RNA-seq data processing

RNA-seq fastq files were aligned to GRCh38.13 (hg38) reference genome via STAR 2.5.4b. Gene quantifications were calculated using RSEM (version 1.3.1). Quality control (QC) metrics were calculated using FastQC and RNA-SeQC. All analysis was carried out in R (version 4.2.1).

Differential gene expression

Genes were filtered to include only those with TPM >0.1 in at least 20% of the samples, resulting in 16,773 genes. All differential gene expression analysis was performed using the glmmSeq R package (https://cran.r-project.org/web/packages/glmmSeq/vignettes/glmmSeq.html#glmmSeq) to fit a general linear mixed-effects model with negative binomial distribution. Models were fit on TPM data parametrized with gene dispersion estimates. The regression models included donor identification (ID) as a random effect and our variable of interest (construct, rapamycin, stimulation) as a fixed effect.

For the purposes of this experiment, conditions where T cells are cultured with tumor cells are considered stimulated, and conditions where T cells are cultured alone are considered at rest or no stim.

To determine the difference in signaling between DARIC and RESET (Figure 2G), we identified an activation signature by comparing UTD samples to both constructs in their activated state (stim + rapamycin vs. UTD stim + rapamycin; Figure S3B), Raw p values were corrected for multiple testing by the qvalue package (R package version 2.32.0, http://github.com/jdstorey/qvalue) using a false discovery rate (FDR). Genes were considered significant if they had an FDR <0.05 and an effect size (abs(log2(fold change)) >1.5) across any of the stimulated construct vs. UTD models, identifying 1,528 genes. PCA was performed on these activated signature genes by first regressing out donor effects using ComBat from the sva R package version 3.46.0, plotting the first two PCs using ggplot2 version 3.4.2 (Figure 2E).

To examine the effects of rapamycin on gene expression, we ran two models—one for samples in the stimulated state and one in the non-stimulated state, where there were both positive and negative rapamycin conditions (Figures S3C and S3D). For the non-stimulated state, UTD, RESET, DARIC, and CAR constructs with and without rapamycin were used to fit a model with rapamycin and construct as a fixed effect and donor ID as a random effect. For the stimulated state, TCR, and CAR constructs with and without rapamycin were used to fit a model with rapamycin and construct as a fixed effect and donor ID as a random effect. Raw p values were corrected for multiple testing by the qvalue package using an FDR. Genes were considered significantly associated with rapamycin if they had an FDR <0.05. However, none of the genes with an FDR <0.05 and an effect size (abs(rapamycin coefficient) > 1.5), indicating very small effects of rapamycin. Volcano plots of all genes rapamycin coefficient by –log10(p value) colored by significance were generated for both results using ggplot2. For significantly identified genes, heatmaps of log2(fold change) of gene expression between rapamycin and no rapamycin per donor were generated using the heatmap.2 function from the gplots R package version 3.1.3 (Figure S3E).

To identify genes associated with construct, activated samples for all constructs (RESET and DARIC = stim + rapamycin, TCR and CAR = stim + no rapamycin) were used to fit a model with construct and rapamycin as a fixed effect and donor ID as a random effect. Raw p values were corrected for multiple testing by the qvalue package using an FDR. Genes were considered significantly associated with construct if they had an FDR <0.06, identifying 2,457 genes. PCA was performed on these construct-associated genes by first regressing out donor effects using ComBat, plotting the first two PCs using ggplot2 (Figure 2D). Additionally, a heatmap of construct-associated genes was generated using the pheatmap R package version 1.0.12 (Figure 2F).

Network analysis

We performed weighted gene coexpression network analysis using the WGCNA R package using signed networks⁴³ on all constructs (RESET, DARIC, TCR, CAR, and UTD) and in all stimulated and rapamycin conditions. Genes were defined as expressed if TPM >0 in at least 90% of the samples, resulting in 7,643 genes. The data were normalized for GC content gene length using the cqn R package version 1.44.0, donor effects were regressed out using ComBat, followed by outlier removal. A soft-threshold power of 26 was chosen as it was the smallest threshold that was as close to a scale free R² fit of 0.8 and pairwise topological overlap matrix (TOM) similarity between all genes was calculated (Figure S4A). The network dendrogram was created using hierarchical clustering of the topological overlap dissimilarity matrix (1-TOM, Figure S4B). Modules are defined as branches of the dendrogram using hybrid dynamic tree-cutting using the cutreeHybrid function in WGCNA, with a minimum module size of 50 genes, deepSplit of 4, merge threshold of 0.1, and pamStage set to false. Modules are labeled by color and summarized by their first PC (module eigengene [ME]) (Figure S4C).

ME associations with construct, rapamycin, and stimulation were performed by linear regression. Module functional enrichment of GO pathways was performed using the clusterProfile R package version 4.6.2 enrichGO function. The background set of genes consisted of the 7,643 expressed genes used for module construction. The top pathways reaching a significance of FDR <0.05 are shown in Figure S4D.

TCR KO assay and indel analysis

The TCR megaTAL enzyme was designed by bluebird bio using a proprietary method that relies on directed evolution, as previously described.⁴⁴ The TAL array binds to 5′-TAAATCCAGTGAC-3′, and the meganuclease recognizes 5′-TGTCTGCCTATTCACCGATTTT-3′. T cells were electroporated (Lonza, catalog no. V4XC-2032), with megaTAL mRNA (manufactured via proprietary methods) 24 h following transduction, and were assessed for KO efficiency by flow cytometry. KO efficiency was also validated using indel sequencing. Briefly, DNA was extracted using the DNeasy Blood and Tissue Kit (Qiagen, catalog no. 69504) and then PCR amplified for the region of interest (forward primer: 5′-AACCCTGACCCTGCCGTGTAC-3′; reverse primer: 5′-CTTGAAGTCCATAGACCTCATGTCTAG-3′; adaptor sequences not included) using the Q5 Polymerase Master Mix (NEB, catalog no. M0492L) according to the manufacturer’s instructions. A second PCR amplification step was performed using the same product to add adapter sequences for next-generation sequencing indexing. The final PCR product was sequenced on a MiSeq Series (Illumina) according to the manufacturer’s instructions. The indel percentage was determined computationally in-house. Electroporated T cells were then added to a co-culture with HL60 tumor cells in a 1:1 ratio and incubated for 2 h before the addition of GolgiPlug (BD Biosciences, catalog no. 555029). Incubation continued for another 18 h, and then cells were assessed for cytokine production by intracellular flow cytometry. Cells were gated for size, singlets, live cells, VHH⁺ then gated under TCR^+/−, followed by IFN-γ.

Sensitivity assays (mRNA titration)

HL60.CD33.KO cells were electroporated (Lonza, catalog no. V4XC-2032) according to the manufacturer’s instructions, with a 2-fold dilution series of mRNA encoding CD33 (TriLink, custom). Antigen incorporation was assessed 18 h later by flow cytometry and quantified for antibodies bound per cell using Quantibrite Beads (BD Biosciences, catalog no. 340495) according to the manufacturer’s instructions antibodies used were aCD33 (BioLegend, catalog no. 266608) with isotype control (BioLegend, catalog no. 4001114). T cells produced as described above were incubated with electroporated tumor cells in the presence of rapamycin for 18 h. Supernatants were harvested for analysis by MSD, as described above.

In vivo studies

All in vivo studies were conducted with the approval and oversight of the 2seventy bio’s Institutional Animal Care and Use Committee. Six- to 10-week-old NOD-SCID-γ^−/− (NSG) mice sourced from JAX were injected with 5 × 10⁶ HL-60.FP human tumor cells/mouse intravenously on day −4 or 2 × 10⁶ OCI-AML3.GFP.ffluc human tumor cells/mouse intravenously on day −7. Tumor volume was measured prior to treatment until target total flux reached ∼9 × 10⁷ photons/s (p/s; HL-60.FP) or 5 × 10⁷ p/s (OCI-AML3.GFP.ffluc) and twice per week during the study. Since the tumors express firefly luciferase, mice were imaged to determine bioluminescent activity using the IVIS Lumina Series and Living Image Software (PerkinElmer), a non-invasive bioluminescent imaging system. Animals were subsequently randomized into treatment groups to achieve an equivalent average tumor size across groups on treatment day. Intravenous administration of drug product was performed on day 0 and animals were humanely euthanized at respective endpoints (1 × 10¹⁰ p/s [HL-60.FP] or 3 × 10¹⁰ p/s [OCI-AML3.GFP.ffluc]).

Animals treated with “rapamycin” were dosed with an oral formulation of rapamycin, called Rapamune. Rapamune Oral Solution was sourced from Pfizer (NDC 0008-1030-06) and diluted in sterile water for oral gavage injection. Rapamune oral gavage dosing was based on individual animal weight at the time of dosing. Animals were either dosed with 0.5 mg/kg Rapamune solution at a dose volume of 5 mL/kg or Rapamune vehicle (sterile water) at a dose volume of 5 mL/kg. Rapamune and vehicle oral gavage dosing were scheduled every Monday, Wednesday, and Friday during the course of the study (3 times per week).

Rapalog (AP21967, Takara, catalog no. 635095) lyophilized stock was prepared in DMSO and further diluted to a working concentration with PBS for intraperitoneal administration. Rapalog was administered based on individual animal weight at the time of dosing. Animals were either dosed with 4 mg/kg rapalog solution at a dose volume of 10 mL/kg or rapalog vehicle (sterile water) at a dose volume of 10 mL/kg. Rapalog product and respective vehicle were dosed every Monday to Friday during the course of the study (5 times per week).

Statistical analysis

Statistical analyses were performed in GraphPad Prism, with specific tests associated with each figure indicated in the legend.

Data availability

All experimental and computational results are available in the supplemental information. RNA Sequencing data can be found at NCBI GEO: GSE284664.

Acknowledgments

The authors would like to acknowledge the innumerable contributions from colleagues and collaborators that made this work possible, specifically, Tram Lam for core operation; Brady Mistretta for collecting RNA-seq data; Marlene Frias, Andreya Gatling, and Tina Jackson for in vivo study support; and Molly Jenkins for manuscript preparation support. 2seventy bio would also like to acknowledge the Jensen lab at Seattle Children’s Research Institute for the generous gift of the firefly luciferase engineered OCI-AML3 cell line.

Author contributions

J.L. and J.J. invented the RESET concepts and designs. J.J. cloned all the RESET constructs. J.L., J.R., S.M.G., and N.J. generated the in vitro data and performed all subsequent analysis. O.S. and A.V generated the in vivo data and performed all subsequent analysis. R.L.W. analyzed the RNA-seq data. J.L., J.R., A.V., and R.L.W. prepared the manuscript, and O.S., P.D.G., and J.J. contributed to editing.

Declaration of interests

J.L., J.R., O.S., R.L.W., A.V., S.M.G., N.J., P.D.G., and J.J. are current employees of Regeneron Pharmaceuticals and/or former employees of 2seventy bio.

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.ymthe.2025.02.026.

Supplemental information

Document S1. Figures S1–S6

mmc1.pdf^{(2.7MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(6.4MB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Document S1. Figures S1–S6

mmc1.pdf^{(2.7MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(6.4MB, pdf)}

Data Availability Statement

All experimental and computational results are available in the supplemental information. RNA Sequencing data can be found at NCBI GEO: GSE284664.

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PERMALINK

RESET: A TCR-coupled antigen receptor with superior targeting sensitivity and reversible drug-regulated anti-tumor activity

Jardin Leleux

Jillian Rosenberg

Olmo Sonzogni

Rebecca L Walker

Anita Venkitaraman

Sarah M Garrison

Nan Jin

Philip D Gregory

Jordan Jarjour

Abstract

Graphical abstract

Introduction

Results

RESET engineering and optimization

Figure 1.

RESET-T cells require TCR expression and recapitulate TCR stimulation

Figure 2.

RESET-T cell activation is tightly controlled by both rapamycin and antigen

RESET-T cells have comparable in vivo reactivity to clinically validated TCR-enabled architecture

Figure 3.

RESET-T cells are highly sensitive to low antigen density

Figure 4.

Discussion

Materials and methods

Plasmid construction

Cell line generation

Lentivirus production

T cell production

Flow cytometry

Cytokine assays (Meso Scale Discovery)

Cytotoxicity and proliferation (Incucyte) assays

Transcriptomics assay

RNA-seq data processing

Differential gene expression

Network analysis

TCR KO assay and indel analysis

Sensitivity assays (mRNA titration)

In vivo studies

Statistical analysis

Data availability

Acknowledgments

Author contributions

Declaration of interests

Footnotes

Supplemental information

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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