De novo-designed transmembrane domains tune engineered receptor functions

  1. Assaf Elazar
  2. Nicholas J Chandler
  3. Ashleigh S Davey
  4. Jonathan Y Weinstein
  5. Julie V Nguyen
  6. Raphael Trenker
  7. Ryan S Cross
  8. Misty R Jenkins
  9. Melissa J Call  Is a corresponding author
  10. Matthew E Call  Is a corresponding author
  11. Sarel J Fleishman  Is a corresponding author
  1. Department of Biomolecular Sciences, Weizmann Institute of Science, Israel
  2. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Australia
  3. Department of Medical Biology, The University of Melbourne, Australia
  4. Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Australia
  5. La Trobe Institute of Molecular Science, La Trobe University, Australia

Abstract

De novo-designed receptor transmembrane domains (TMDs) present opportunities for precise control of cellular receptor functions. We developed a de novo design strategy for generating programmed membrane proteins (proMPs): single-pass α-helical TMDs that self-assemble through computationally defined and crystallographically validated interfaces. We used these proMPs to program specific oligomeric interactions into a chimeric antigen receptor (CAR) that we expressed in mouse primary T cells and found that both in vitro CAR T cell cytokine release and in vivo antitumor activity scaled linearly with the oligomeric state encoded by the receptor TMD, from monomers up to tetramers. All programmed CARs stimulated substantially lower T cell cytokine release relative to the commonly used CD28 TMD, which we show elevated cytokine release through lateral recruitment of the endogenous T cell costimulatory receptor CD28. Precise design using orthogonal and modular TMDs thus provides a new way to program receptor structure and predictably tune activity for basic or applied synthetic biology.

Editor's evaluation

This is an interesting article that uses de novo protein design to probe the effects of oligomerization state on the activity of chimeric antigen receptors (CARS). The successful design of transmembrane domains with specific oligomeric states is an impressive result on its own. After experimentally evaluating a couple rounds of designs, the investigators settled on a design protocol that also included screening of the design candidates with docking simulations in alternative oligomerization states to check that the sequences preferred the desired oligomerization state. The designs were experimentally evaluated with gel electrophoresis and X-ray crystallography. In the end, designs that adopted well-defined dimers, trimers, or tetramers were created and carried forward in experiments as CARs.

https://doi.org/10.7554/eLife.75660.sa0

Introduction

Interactions among cell-surface receptors play central roles in determining complex structures and controlling signal propagation. In immune receptors (Berry and Call, 2017; Dong et al., 2019), death receptors (Fu et al., 2016; Pan et al., 2019), and growth factor receptors (Arkhipov et al., 2013; Endres et al., 2013; Fleishman et al., 2002), the transmembrane domains (TMDs) govern key interactions involved in assembly, activation, and high-order clustering. Control over the specificity, stability, geometry, and oligomeric state of these interactions is therefore highly desirable both for mechanistic studies of natural receptors and in the engineering of synthetic receptors. Accurate control, however, is difficult to achieve using natural TMDs that have likely been evolutionarily selected for a degree of flexibility in these very attributes (Matthews et al., 2006). The importance of high precision in receptor engineering has come into particularly sharp focus with the clinical adoption of cancer immunotherapies using targeted chimeric antigen receptors (CARs) (Majzner and Mackall, 2019; June et al., 2018; Salter et al., 2018; Eshhar et al., 1993) to endow T cells with potent antitumor activity. Controlling functional outputs from these engineered single-chain immune receptors poses significant challenges in balancing antitumor CAR T cell activity against toxicities associated with high inflammatory cytokine release, known as cytokine release syndrome (CRS) (Morgan et al., 2010; Gutierrez et al., 2018; Morris et al., 2022; Maus et al., 2020).

The modular domain organization of CARs offers a prime example of a synthetic cellular system in which customizable sequences exert control over receptor structure and function. Current-generation CARs comprise an antibody single-chain variable fragment (scFv) domain for tumor antigen binding, a spacer or hinge domain for length and flexibility, a TMD controlling membrane integration and expression levels, and intracellular costimulation and activation domains that provide signals for proliferation, survival, and activation of T cell effector functions. Efforts to imbue CARs with optimal signaling properties have probed all of these domains in one way or another (Alabanza et al., 2017; James, 2018; Liu et al., 2015; Mata and Gottschalk, 2019; Rafiq et al., 2020; Balakrishnan et al., 2019; Hartl et al., 2020; Wu et al., 2020; Feucht et al., 2019; Majzner et al., 2020). The TMDs, however, have received little attention in systematic studies of CAR design. For convenience, most incorporate the TMD sequence of the protein from which the adjacent hinge or signaling domains were derived; that is, most commonly from endogenous T cell proteins such as CD4, CD8, CD28, or the T cell receptor (TCR)-associated CD3ζ chain. At least some of these TMD sequences can engage in molecular interactions that drive self-association and/or assembly with the essential T cell proteins from which they were derived and thereby impact CAR expression and functions in ways that reduce control over signaling outcomes (Bridgeman et al., 2010; Cosson et al., 1991; Leddon et al., 2020; Call et al., 2006; Hennecke and Cosson, 1993; Muller et al., 2021; Bridgeman et al., 2014). These may contribute to enhanced function by, for example, driving CAR homodimer formation (Salzer et al., 2020; Bridgeman et al., 2010; Fujiwara et al., 2020), but the involvement of natural immune receptor TMDs in native T cell signaling hampers rational design of CARs with predictable properties.

We set out to define the relationships between TMD structure, CAR oligomeric state, and signaling in CAR T cells by designing completely new TMDs with programmable self-association features and minimal risk of cross-talk with native T cell components. Despite significant recent progress (Barth and Senes, 2016; Korendovych and DeGrado, 2020), the limitations of membrane protein (MP) atomistic calculations have restricted de novo ɑ-helical MP design studies to highly predictable and rigid coiled-coil motifs (Joh et al., 2014; Lu et al., 2018) that, while stabilizing them, limited their usefulness as receptor TMDs. By contrast, we recently described an ab initio Rosetta atomistic modeling strategy (Weinstein et al., 2019) that uses a new energy function with experimentally determined membrane-solvation terms for each amino acid. This modeling strategy accurately predicts the structure of single-spanning sequences known to self-assemble (Elazar et al., 2016a; Elazar et al., 2016b). Here, we introduce a new strategy to de novo design programmable membrane proteins (proMPs), resulting in completely new sequences that form TM homo-oligomers of defined geometry and order that can be used to program cell-surface receptor structure. We used these proMPs to generate programmed CAR (proCAR) constructs and found that they endowed T cells with in vivo functional potency that scaled linearly with oligomeric state. proCARs also maintained significantly lower inflammatory cytokine release compared to an otherwise identical CAR containing the natural CD28 TMD, a property that may have safety benefits in clinical applications (Brudno et al., 2020; Ying et al., 2019; Morris et al., 2022; Rafiq et al., 2020; Alabanza et al., 2017). Our results shed new light on the importance of precision in engineered receptor structure and intermolecular associations for optimal CAR T activity and provide new design tools that may be useful for developing cellular immunotherapies with optimal safety and efficacy profiles.

Results

Atomically precise de novo-designed TMDs

In our initial design approach (Figure 1a), each design trajectory started from two fully symmetric and extended chains of 24 amino acids encoding either poly-Val or poly-Ala (Video 1). In a first, coarse-grained modeling step, backbone torsion angles were sampled from a database comprising three and nine amino acid fragments from α-helical MPs, and the two chains were symmetrically docked against one another with an energy term that disfavored large crossing angles (Equation 1Weinstein et al., 2019; Bowie, 1997). In a second, all-atom step, we refined the sequence and the structure through iterations of symmetric sequence optimization, backbone minimization, and rigid-body docking using the ref2015_memb atomistic energy function that is dominated by van der Waals packing, hydrogen bonding, and amino acid lipophilicity (Weinstein et al., 2019). We noticed that the resulting sequences were overwhelmingly biased towards the large and flexible hydrophobic amino acid Leu (Figure 1b), as expected from the dominant role of lipophilicity in the ref2015_memb potential (Weinstein et al., 2019). Forward-folding ab initio structure-prediction calculations, however, indicated that the designs were prone to form multiple alternative low-energy dimer structures that were conformationally different from the design conception (Figure 1—figure supplement 1a). To mitigate the risk of misfolding due to the high Leu content, we introduced a sequence diversification step comprising 120 iterations of single-point mutation and energy relaxation while biasing the sequence composition to match that of natural TMDs (Figure 1b; Equations 2-3). The resulting sequences were subjected to ab initio structure prediction calculations (Das et al., 2009), and this time, they converged to the design models (Figure 1—figure supplement 1b) and exhibited a large energy gap from undesired structures. Previous studies noted that natural TMDs are not optimized for thermodynamic stability (Faham et al., 2004). Our design simulations suggest that evolution might have selected sequence compositions to counter TMD misfolding.

Figure 1 with 4 supplements see all
Learning the rules for programming self-associating membrane proteins (MPs).

(a) Rosetta fold, dock, and design uses backbone fragments from natural MPs to construct symmetric, de novo architectures and an MP energy function (Weinstein et al., 2019) to optimize the amino acid sequence. (b) Round 1 designs were biased towards the hydrophobic amino acid Leu relative to naturally occurring transmembrane domains (TMDs). In round 2, we incorporated a sequence diversification step that conformed the amino acid propensities to those observed in natural TMDs. (c) The programmed membrane proteins (proMPs) strongly self-associate in the E. coli inner membrane as evidenced by high viability in the deep sequencing TOXCAT-β-lactamase (dsTβL) self-association assay (Elazar et al., 2016a). The TMDs from human quiescin sulfhydryl oxidase 2 (QSOX2) and ErbB2 provide positive controls for TMD self-association, whereas the C-terminal portion of human L-selectin (CLS) provides a negative control. (d–f) Designed positions that are buried at the interface (orange) are more sensitive to mutation according to dsTβL analysis (Elazar et al., 2016a) (y-axis) than exposed positions (blue). Mutations are predicted to be detrimental or neutral/beneficial using computational mutation scanning of the model structures (Materials and methods). Changes in self-association energies upon mutation are computed according to Equation 9. (g) proMPs produced as free peptides form SDS-stable homo-oligomers. SDS-PAGE samples containing approximately 15, 45, and 135 μg of peptide were heated to 95℃ for 1 min and run under reducing conditions. * indicates the position of a minor contaminant from the fusion protein used to generate proMP peptides (Materials and methods). Molecular weight below each gel is for a monomer of the corresponding peptide sequence with additional N-terminal EPE and C-terminal RRLC flanking sequences (Materials and methods). See additional examples in Figure 3. (h, i) The 2.55 Å resolution structure (blue ribbon) determined from crystals grown in monoolein lipid cubic phase (LCP) shows that proMP 1.2, designed to form a dimer, associates to form a trimer in a lipid bilayer environment. (i) Forward-folding ab initio prediction of proMP 1.2 in trimeric (C3) symmetry results in a model structure (h, gray ribbon) that is very close to the experimentally determined one.

Video 1
Key steps in the de novo design of a transmembrane homodimer.

The design process starts from a fully extended chain and uses symmetric fold-and-dock simulations to generate realistic coarse-grained ("centroid" mode) conformations. These are then designed in full atom mode. Finally, a Monte Carlo based sequence diversification step mutates amino acids with a bias towards the sequence propensity of natural amino acids. This diversification step reduces the bias of the full atom design step to Leu amino acids.

Twelve designs were tested in the Escherichia coli TOXCAT-β-lactamase (TβL) selection system (Elazar et al., 2016a; Langosch et al., 1996). In this dual-reporter system, survival on ampicillin and chloramphenicol reports on a design’s membrane insertion and self-association propensity, respectively (Figure 1—figure supplement 2). Remarkably, most proMPs supported high survival (Figure 1c) and two-thirds survived even at the highest chloramphenicol concentration tested, indicating a self-association strength significantly greater than the TMD from the human receptor-tyrosine kinase HER2 (also known as ErbB2), which served as a positive control. Deep mutational scanning of mutant libraries showed that the sensitivity to mutations of most designs was consistent with interfacial versus exposed positions in the design models (Figure 1d–f, Figure 1—figure supplement 3), suggesting that they indeed assembled through the designed interfaces in the bacterial inner membrane.

Eight proMPs were produced recombinantly as free peptides and all exhibited electrophoretic mobility consistent with SDS- and heat-stable self-association (Figure 1g, Figure 1—figure supplement 3). The patterns of migration, however, were not uniform. Six proMPs had the apparent molecular weight of a dimer (e.g., proMP 1.5 and 1.6, Figure 1g) and exhibited reduced mobility as the peptide concentration was increased, similar to the SDS-stable (Lemmon et al., 1992) behavior of the well-studied glycophorin A TMD (Figure 1—figure supplement 3). By contrast, the remaining two proMPs exhibited migration patterns that were independent of the sample concentration and had apparent molecular weights more consistent with oligomers larger than the designed dimers (proMP 1.2, Figure 1g; proMP 1.3, Figure 1—figure supplement 3). To establish the molecular structures of these designs, several were screened for crystallization in monoolein lipid cubic phase, and the structure of proMP 1.2 was determined to 2.55 Å resolution (Figure 1h, Figure 1—figure supplement 4). While the positions involved in helix packing recapitulated the design model, this proMP indeed formed a trimer instead of the intended dimer, indicating good concordance between mobility in SDS-PAGE and the oligomeric state solved by X-ray crystallography. Ab initio structure prediction calculations in trimeric (C3) symmetry recapitulated the experimentally observed packing interface (RMSD 2.3 Å) (Figure 1h and i), demonstrating that it would have been possible to predict this outcome had we considered alternative oligomeric states during design calculations.

Based on this insight, we initiated a third design campaign to produce proMPs in a range of oligomeric states. We incorporated a final step in which ab initio structure prediction calculations (Weinstein et al., 2019) were performed in C2, C3, and C4 symmetries for every design. Only those proMPs that were predicted to form the target oligomeric state and none of the alternatives were selected for further analysis (Figure 2a–d). This strategy yielded two proMPs for which we obtained crystal structures confirming the target oligomeric state: a dimer with glycine-based packing interface similar to the motif observed in human glycophorin A (proMP C2.1; Figure 2a–b and e–g, Figure 2—figure supplement 1), and a trimer with an alanine-rich interface that, to the best of our knowledge, is novel in a membrane protein (proMP C3.1; Figure 2c–d and h–k, Figure 2—figure supplement 2). Interestingly, while two of the helices in the crystal structure of proMP C3.1 aligned well with the design model (Figure 2i), the third was in an antiparallel orientation (Figure 2j). Despite this arrangement, the six key interface alanine β-methyls were in near-identical positions to their counterparts in the fully parallel model (Figure 2k), leading us to suspect that the model is correct but the crystal lattice was enforcing the antiparallel binding mode of the third helix. To probe this possibility, we aligned the parallel model with the asymmetric unit seen in the crystal structure and generated crystallographic symmetry. The resulting model showed clashes for the third helix and indicated that the design model cannot be accommodated in the crystal lattice. The structure thus suggests that this proMP is unintentionally ‘reversible’ in that one of the helices can form the intended packing mode in either orientation. While this feature is of interest from a design standpoint (Woodall et al., 2015), we note that only the fully parallel trimer depicted in the model can form in a biological system where the topology of a single-spanning TMD is constrained by the biosynthetic machinery in a type I orientation.

Figure 2 with 2 supplements see all
Designed membrane proteins (MPs) of defined structure and oligomeric state.

(a) SDS-PAGE migration of programmed membrane protein (proMP) C2.1 is consistent with a dimer in gel shift assays performed as in Figure 1. Design model and peptide sequence shown for reference. (b) Rosetta ab initio structure prediction calculations predict that proMP C2.1 preferentially forms a dimer. (c) proMP C3.1 exhibits a novel Ala-dominated interface, and its migration pattern at high sample concentration suggests a complex larger than a dimer. Design model and peptide sequence shown for reference. (d) Ab initio calculations predict that it primarily forms a trimer. (e–g) The proMP C2.1-designed structure is atomically verified by a 2.7 Å crystal structure. Interfacial positions marked in orange. (h–k) The crystallographic analysis of proMP C3.1 (3.5 Å resolution) reveals a trimer that is almost identical to the design, although one of the three helices in the trimer is antiparallel relative to the other two in the crystal lattice. Alignment of the structure and model (i) shows that the antiparallel helix (green) (j) positions Ala Cβ methyls that pack into the trimer through the designed interface (gray) (k).

We conclude that the sequence diversification and the computational selection of the oligomeric state described above provide a practical approach to implement negative design principles that are critical for accurate de novo TMD design (Fleishman and Baker, 2012; Joh et al., 2014). These new insights will likely also be critical to design de novo hetero-oligomeric TMDs.

proCARs with defined oligomeric states

The availability of synthetic TMDs with defined structures provided an opportunity to address two key open questions in receptor engineering: What is the relationship between oligomeric state and functional output? And does the use of natural TMDs impart functional characteristics other than surface localization in the membrane? The hinge-TMD regions in all CARs used in FDA-approved CAR T cell products derive from CD8 or CD28 and drive disulfide-linked receptor homodimer formation (Fujiwara et al., 2020). However, the importance of the dimeric state for optimal CAR function is not well understood and alternative oligomeric forms such as trimers or tetramers have not been explored. Furthermore, both CD8 and CD28 TMDs have documented propensities to self-associate (Hennecke and Cosson, 1993; Leddon et al., 2020). Given the presence of both native receptors in CAR T cells, their use in CARs risks unintended interactions that could affect their expression and/or function. To program CARs that form specific oligomeric states and are insulated from confounding interactions with endogenous signaling proteins, we initially chose the crystallographically confirmed proMPs C2.1 and 1.2 to generate proCARs that form dimers or trimers. These were termed proCAR-2 and proCAR-3, respectively (Figure 3a). We also designed a monomeric proMP that exhibited no chloramphenicol survival in deep sequencing TβL (dsTβL) assays (Figure 3—figure supplement 1) and used it to produce a monomeric proCAR-1 in order to extend the structure–function study. Our proCAR designs incorporated an anti-HER2 scFv (FRP5; Wels et al., 1992) fused to the human CD8α hinge sequence, a proMP-derived TMD, the human CD28 costimulatory sequence, and the human CD3ζ activating tail. Our reference CAR contained the human CD28 TMD for comparison, approximating a domain configuration that has been extensively studied in vitro and in vivo (Davenport et al., 2018; Davenport et al., 2015; Haynes et al., 2002). In all proCAR constructs, a cysteine residue in the CD8α hinge that mediates disulfide-bonded dimer formation was mutated to alanine (Figure 3a) to ensure that the designed TMDs were the primary determinants of oligomeric state.

Figure 3 with 3 supplements see all
Construction and in vitro functional profiling of HER2-specific programmed chimeric antigen receptors (proCARs).

(a) Schematic showing the domain organization of the reference HER2-specific CAR constructs and modifications made to introduce programmed membrane protein (proMP) transmembrane domains (TMDs). Bold, boxed sequence indicates the human CD28 TMD in the reference CD28TM and no cys CARs and designed proMP sequences in the monomeric (proCAR-1), dimeric (proCAR-2), and trimeric (proCAR-3) receptors. (b) BW5147 murine thymoma cells stably expressing proCARs and a destabilized GFP NF-κB reporter were surface labeled with anti-Myc antibody and analyzed by flow cytometry to assess surface expression levels. (c) Live cells from (b) were coated with polyclonal anti-IgG to bind CARs through the scFv domain and immunoprecipitated using protein G beads. Products were separated by nonreducing SDS-PAGE and immunoblotted using anti-Myc antibody to visualize surface-expressed CAR proteins. Molecular weight of the unglycosylated CAR polypeptide is 55 kDa. (d, e) Cells from (b) were co-cultured with HER2+ SKBR3 human breast adenocarcinoma cells for the indicated times and analyzed by flow cytometry for upregulation of activation marker CD69 (d) and GFP expression from the NF-κB reporter (e). All activation levels are normalized to the 8 hr time point in cells expressing the CD28TM CAR (% CD28TM Max). Bars represent the mean ± SD, and dots show the individual data points for three independent experiments. (f) Maximum target killing percentage at 20:1 effector to target ratio from 4 hr 51Cr release assay. Bars show mean ± SEM with each data point representing an individual experiment (n = 3). p-Values determined from paired t-tests. (g) Cytokine production by primary mouse HER2 proCAR T cells following 24 hr co-culture with MC57-HER2 target tumor cells. Bars show mean concentration ± SEM with each data point representing an individual experiment (n = 5). Significance was determined from one-way ANOVA with multiple comparisons. Cytokine production on antigen-negative parental MC57 cells shown separately in Figure 3—figure supplement 3.

The HER2 proCARs and reference CD28TM constructs were retrovirally expressed in murine BW5147 thymoma cells. All constructs exhibited similar cell-surface levels (Figure 3b), and the reference CD28TM CAR formed disulfide-linked dimers while the cysteine mutant reference (No Cys) and proCARs did not (Figure 3c). All CARs were competent to signal when co-cultured with HER2+ SKBR3 human breast adenocarcinoma cells (Figure 3d and e). When expressed in freshly isolated mouse CD8+ T cells (Figure 3—figure supplement 2a and b), all CARs mediated antigen-dependent killing of MC57 mouse fibrosarcoma cells stably expressing HER2 in vitro (Figure 3f). Only small differences in killing potency were apparent, with proCAR-1 trending slightly less effective than the reference CARs and proCAR-2 and -3 trending slightly more effective. In vitro cytokine production (IFNγ, IL-2, TNFα, and GM-CSF), on the other hand, was significantly lower in all proCARs, reduced by 2- to 10-fold on average (Figure 3g). This effect was not apparent in the CD28TM (No Cys) background and was therefore not due to loss of the CD8α hinge-region disulfide bond.

The CD28 TMD enhances CAR-mediated cytokine release by associating with endogenous T cell CD28

The striking reduction in cytokine release in all of the proCARs led us to hypothesize that the higher levels of cytokine release in CD28 TMD-containing CARs depend primarily on CD28 sequence features rather than on CAR oligomeric state. The CD28 TMD contains a highly conserved polar YxxxxT motif that is similar to the one that drives CD3ζ dimerization (Call et al., 2006) and is required for optimal dimerization and surface expression of native CD28 (Leddon et al., 2020). A recent study showed that the CD28 YxxxxT sequence also causes CARs containing the CD28 TMD to physically associate with the CD28 protein in T cells (Muller et al., 2021), but the functional consequences of this association for CAR signaling have not been explored. We modeled this putative CD28TM interface on the ζζ structure (Call et al., 2006) and noted that tyrosine, serine, and threonine in the YSLLVT sequence could all contribute to an interhelical hydrogen-bonding network (Figure 4a). We therefore generated a CAR in which this sequence was mutated to FALLVV, selectively eliminating the key hydrogen-bonding hydroxyl groups, for comparison to our proCAR and reference constructs. This CAR was well expressed as a disulfide-linked homodimer at the cell surface (Figure 4b and c) and generated primary mouse CD8+ CAR T cells whose ability to kill HER2+ target cells in vitro was unimpaired (Figure 4d). The CD28TM mutant, however, induced lower levels of cytokine secretion (two- to sixfold lower on average; Figure 4d) that were similar to those observed for the proCARs. We therefore concluded that the low-cytokine release seen in the proCAR T cells was likely due to the proCARs being insulated from interaction with endogenous T cell signaling proteins, primarily CD28.

Functional consequences of chimeric antigen receptor (CAR)-CD28 association in CAR T cells.

(a) Model of the CD28TM interface generated by mutagenesis of the CD3ζ TMD (PDB: 2HAC). Polar residues of the CD28 dimerization motif (orange) with predicted hydrogen bonds depicted (dotted lines). (b) Surface expression and (c) SDS-PAGE and immunoblot analysis of HER2 CARs possessing WT CD28TM or CD28TM mutations depicted in (a) expressed in the BW5147 cell line. (d) Quantitation of target cell killing measured by chromium release assay and cytokine production by primary mouse CD8+ CAR T cells in response to the MC57-HER2 target cell line (n = 4). Experiments performed as in Figure 3. p-Values determined by paired t-tests. (e) Representative immunofluorescent confocal images of CAR-CD28 co-clustering in primary mouse CAR T cells. CAR clustering was induced with anti-Myc primary followed by crosslinking with fluorescent secondary antibody (magenta). Cells were then labeled for CD28 (cyan). Images are Z-projections over 12 m, scale bar represents 3 m. (f) Quantitation of CAR-CD28 co-clustering, each dot representing the percentage of CAR clusters in one cell that co-localized with a CD28 cluster. Lines show mean CAR-CD28 co-clustering percentage/per cell ± SEM, n ≥ 30 cells. p-Values determined by unpaired t-tests.

To directly interrogate potential CAR-CD28 associations in primary CD8+ T cells, we examined the four CAR constructs we expect to form dimers in a co-clustering experiment by fluorescence microscopy; these dimers included the CD28TM reference and CD28TM mutant as well as those that ablate the disulfide linkage for comparison (No Cys and proCAR-2). We found that receptors containing the WT CD28 TMD frequently co-clustered endogenous surface CD28, while the CD28TM mutant and proCAR-2 did so significantly less frequently (Figure 4e and f). These experiments clearly link the CD28TM interaction motif YSxxxT to high cytokine production in CARs that incorporate this sequence and implicate the recruitment of additional co-stimulatory signaling via endogenous T cell CD28 as the cause. They further substantiate that the de novo-designed TMDs are insulated from these specific interactions.

In vivo antitumor potency scales with proCAR oligomeric state

Short-term in vitro tumor cell killing assays do not account for variations in proliferation, survival, and cytokine activity that are critical for antitumor activity in a living animal. To evaluate the in vivo antitumor potential of proCAR T cells as a function of receptor oligomeric state, we engrafted NOD-SCID-IL2RG-/- (NSG) mice with the aggressive MC38 mouse colon adenocarcinoma cell line engineered to stably express HER2 and treated them 1 day later with a single intravenous injection of CD8+ CAR T cells (Figure 5a). Tumors in mice that received empty vector-transduced T cells grew to ethical endpoint (1000 mm3) within 14 days, while mice that received proCAR-1, -2, and -3T cells slowed tumor growth with potency that increased with oligomeric state (Figure 5b, Figure 5—figure supplement 1). proCAR-3 provided control that most closely resembled the CD28TM reference CAR T cells. The CD28TM mutant CAR tracked with proCAR-2 and proCAR-3 (Figure 5c, Figure 5—figure supplement 1), confirming that mutation of the YSxxxT association motif recapitulated the general proCAR functional profile in vivo as well as in vitro. Analysis of mean tumor size at day 14 post-tumor inoculation (the last day all mice were alive) shows a strong inverse correlation with proCAR oligomeric state (Figure 5d). These data show for the first time that, all other features being equal, the potency of antitumor CAR T cell activity scales directly with the oligomeric state of the engineered receptor.

Figure 5 with 1 supplement see all
In vivo antitumor potency scales directly with programmed chimeric antigen receptor (proCAR) oligomeric state.

(a) Treatment schedule and experimental setup. NOD-SCID-IL2RG-/- (NSG) mice were injected subcutaneously with MC38-HER2 tumor cells and treated the following day with CD8+ T cells delivered intravenously. Mice were supplemented with daily intraperitoneal injections of recombinant human IL-2 from days 1–3, and tumors measured daily until they reached ethical limits. (b) Tumor growth over time for No CAR (empty vector), CD28TM WT, and proCAR T cell groups (n = 5–6 mice/group). Data points represent mean ± error bars showing SEM. Statistical analysis performed using a two-way ANOVA at day 14. (c) Tumor growth over time of the CD28TM mutant group superimposed on (b). (d) Linear correlation of tumor size on day 14 from (b) vs. proCAR oligomeric state, where the ‘0’ point is provided by empty vector (EV)-transduced T cells. Individual data points are colored, mean values in white box and error bars indicate SEM. p-Values indicate the confidence that the slope of the linear regression is nonzero.

Tetrameric proCAR-4 matches CD28TM CAR tumor control in vivo with substantially lower cytokine release in vitro

This striking correlation between receptor oligomeric state and functional potency prompted us to push the limits further by designing a tetrameric proMP (proMP C4.1), which features extensive alanine-based complementary packing (Figure 6a). The free proMP C4.1 peptide migrates on SDS-PAGE predominantly as a single species at a position indicative of a tetramer (Figure 6b), consistent with the observation that complementary apolar packing alone can drive stable MP assembly (Mravic et al., 2019). HER2 proCAR-4 containing the tetrameric proMP C4.1 TMD sequence was well expressed at the surface of freshly isolated mouse CD8+ T cells (Figure 6c, Figure 6—figure supplement 1) and supported strong tumor cell killing in vitro (Figure 6d). This live-cell imaging assay at low effector:target ratio confirmed that oligomeric proCAR T cells and the T cells expressing the reference CD28TM CAR were all potent killers in vitro, but the monomeric proCAR-1 T cells clearly segregated with weaker killing. Interestingly, the scaled killing we observed in vivo was not evident here, consistent with observations by others that in vitro killing is easily saturated and some differences in CAR activity are only observed at very low target antigen density (Majzner et al., 2020). In vivo, proCAR-4 T cells displayed a level of MC38-HER2 tumor control that was indistinguishable from the CD28TM reference CAR-T cells (Figure 6e, Figure 6—figure supplement 2), thereby closing the functional gap that was apparent between proCAR-3 and the CD28TM CAR in the previous experiment.

Figure 6 with 3 supplements see all
In vitro cytokine production scales with programmed chimeric antigen receptor (proCAR) oligomeric state including tetramers.

(a) SDS-PAGE migration of programmed membrane protein (proMP) C4.1 is consistent with a tetramer. Design model and peptide sequence shown for reference. (b) Rosetta ab initio structure prediction calculations predict that proMP C4.1 preferentially forms a tetramer. (c) CAR surface expression on primary mouse CD8+ T cells stably expressing CD28TM and proCAR-4 analyzed by c-Myc staining on flow cytometry. HER2 proCAR-4 was designed using the proMP C4.1 sequence without the final C-terminal leucine as a transmembrane domain (TMD), inserted as shown in Figure 3a. (d) IncuCyte killing assay over 24 hr of no CAR, CD28TM, and proCAR1-4 T cells on MC57-HER2 target cells at 1:1 effector to target ratio. Comparison of maximum killing for n = 6 independent experiments shown between CD28TM vs. ProCAR-4. Data points represent individual experiments, with mean ± SEM error bars plotted. (e) Tumor growth over time using the same experimental design in Figure 5a for No CAR (empty vector), CD28TM WT, and proCAR-4 T cell groups (n = 5–6 mice/group). Data points represent mean ± error bars showing SEM. Statistical analysis performed using a two-way ANOVA at day 10. (f, g) Linear correlation of proCAR oligomeric state vs. IFNγ, IL-2, TNFα, and GM-CSF cytokine production (normalized to CD28TM reference) from 24 hr co-culture with (e) MC57-HER2 and (f) antigen-negative MC57 tumor cells. Individual data points are colored, mean values in white box and error bars indicate SEM.

Despite this functional equivalence in vivo, proCAR-4 T cells still released significantly lower levels of cytokines than the CD28TM reference CAR T cells in vitro (Figure 6—figure supplement 3). However, the tetrameric design trended towards higher levels of all cytokines than the other proCARs. When we normalized cytokine release to the CD28TM reference across all experiments for all proCAR T cells, the combined data revealed strong linear correlations with receptor oligomeric state for all cytokines tested (Figure 6f), reflecting a similar relationship to that identified in the in vivo tumor control data. Taken together, our results reveal that the high cytokine release stimulated by the CD28TM CAR is largely determined by recruiting native CD28 through the TMD. Yet, amongst the proCAR designs that all eliminate this unintended interaction and thereby reduce cytokine release, the relative cytokine levels scale directly with the receptors’ oligomeric state. This is consistent with a sensitivity to the number of CAR-encoded CD28 and CD3 tail sequences that can be engaged by a single antigen-binding event. As expected, cytokine production in response to HER2-negative tumor cells was very low in all constructs (Figure 6g), showing that pre-assembled higher-order oligomers did not cause spontaneous antigen-independent activation of cytokine production and still required stimulation. These data confirm a robust linear correlation between CAR oligomeric state and CAR T cell functional output, both in vivo and in vitro, that extends at least to the tetrameric state.

Discussion

This work establishes new de novo TMD design principles that have direct applications in synthetic biology. Starting from a general methodology for the de novo design of membrane-spanning homodimers, we learned that the lowest-energy-designed structures systematically exhibited features that are related to protein misfolding, such as self-assembly through multiple alternative interfaces. Furthermore, biochemical and structural analysis noted a surprising tendency of the designs to self-assemble into higher-order oligomers. To counter these unexpected problems, we developed a new strategy that incorporated negative design principles into an automated design workflow and generated highly expressed and atomically accurate single-span oligomers of defined order. Their formation of SDS-resistant peptide complexes consistent with the target oligomeric state is indicative of high stability. While this analysis does not rule out unintended weak interactions that are disrupted by denaturants, its excellent agreement with design models and experimentally determined structures indicates that the designed interfaces dominate. This paved the way to apply de novo TMD design to the rapidly developing field of engineered receptors, shedding new light on fundamental structure–function relationships in engineered immune receptors.

The outcomes of the proCAR design experiments revealed two specific mechanistic insights into engineered immune receptor function. First, our results highlight how using natural TMDs can confound predictability and control by encoding unexpected functions. In the HER2 CAR used here, CD28 costimulation is explicitly encoded through the CD28 signaling tail incorporated into the CAR protein but is also amplified through a specific sequence signature in the CD28 TMD that recruits endogenous CD28 into activated CAR complexes. A dimeric CAR incorporating the CD28 TMD thus has the potential to recruit two dimers of endogenous CD28, contributing as many as four additional costimulatory sequences to a receptor that carries only two within the CAR sequence itself. This additional costimulatory signaling, which supports both cell division and cytokine production, likely explains a large portion of the enhanced potency and higher toxicity of CD28 TMD-containing CARs compared to those that use TMDs from CD8 or other proteins (Majzner et al., 2020; Fujiwara et al., 2020; Brudno et al., 2020; Cappell and Kochenderfer, 2021; Davey et al., 2020) and underscores the importance of fully understanding the structure–function relationships in natural TMDs when repurposing them for receptor engineering.

The second major mechanistic insight from this study is that CAR T cell functional potency scales directly with the immune receptor’s oligomeric state when all other features are equal. Systematic and robust interrogation of this relationship has never before been possible because type I single-spanning TMDs with well-characterized oligomeric structures are limited (Trenker et al., 2016) and functional outcomes that depend strictly on oligomeric state are not easily separated from other features and functions of TMDs (Bridgeman et al., 2010; Bridgeman et al., 2014; Wan et al., 2020). Our de novo-designed proMPs provided a panel of well-characterized, orthogonal TMDs that enabled this finding. The in vitro cytokine production and in vivo tumor control experiments reported here revealed a striking linear correlation between proCAR oligomeric state and the magnitude of T cell responses. Other structural aspects of the receptor complexes, such as the TMD geometry or conformational changes transmitted from the extracellular ligand-binding domains to the intracellular signaling domains, could in principle have roles in receptor activity levels; however, the observed linear relation between oligomeric state and activation suggests that these other aspects play minor roles, if any. Notably, our proMP design workflow can now provide the tools to directly test the role of TMD geometry in CAR signaling by generating different structures of the same oligomeric state for systematic comparisons in future studies.

The ability to broadly attenuate CAR T cell cytokine release while providing a predictable range of functional potencies may have important implications for the development of future cellular immunotherapies. The most effective CAR T cell therapies are accompanied by dangerously high levels of inflammatory cytokine production that cause CRS, which is characterized by fever, hypotension, respiratory distress, and multiorgan failure that can be fatal if not carefully managed (Morgan et al., 2010; Gutierrez et al., 2018). The current clinical practice is to manage CRS symptoms with cytokine-blocking antibodies and corticosteroids (Maus et al., 2020; Morris et al., 2022), but approaches to prevent CRS altogether using cytokine gene disruption or modified CAR constructs are areas of active research (Brudno et al., 2020; Ying et al., 2019; Sterner et al., 2019; Sachdeva et al., 2019). The lessons we learned from analysis of monomeric, dimeric, and trimeric proCAR designs led to the generation of a tetrameric proCAR with in vivo antitumor activity that precisely matched the potent CD28TM design while still providing a 40–60% reduction in inflammatory cytokine release. Other proCAR designs offer greater reductions in cytokine release but also exhibit concomitant loss of antitumor activity in vivo. The complete proCAR panel thus provides an opportunity to better balance safety and efficacy in CAR T cell therapies by selecting from a spectrum of designs with defined structure–function relationships. Fully assessing their clinical potential will require further testing in mouse models that more closely approximate treatment of established disease in patients, support longer-term persistence studies, and allow direct measurement of cytokine toxicity in vivo.

Importantly, TMD modifications do not directly impact either the antigen-binding or signaling domains. This modularity means that proMP TMDs may be easily implemented on the background of any existing single-chain receptor design and can be combined with other modifications in extracellular or intracellular sequences to expand the combinatorial space available for fine-tuning signaling outputs. This flexibility should facilitate screening for an optimal design for each tumor type and target antigen. We anticipate that the proMP design methods and sequences will find additional applications for controlling intermolecular cell-surface protein interactions in a variety of synthetic and biological systems.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Strain, strain background (Escherichia coli)E.cloni 10GLucigen60107High-transformation efficiency electrocompetent cells
Cell line(Mus musculus)BW5147Kind gift from McClusky labCVCL_3896
Cell line(Homo sapiens)SKBR3Kind gift from Jenkins labCVCL_0033
Cell line(M. musculus)MC57Kind gift from Jenkins labCVCL_4985
Cell line(M. musculus)MC57-HER2Kind gift from Jenkins labCVCL_4985
Cell line(M. musculus)MC38-HER2Kind gift from Jenkins labCVCL_B288
Cell line(H. sapiens)HEK293TCellbank AustraliaCat# 12022001
AntibodyPacific Blue anti-mouse CD69 Armenian hamster IgG monoclonal antibody(clone: H1.2F3)BioLegendCat# 104524Flow cytometry (1:200)
AntibodyPE anti-mouse CD3ε Armenian hamster IgG monoclonal antibody(clone: 145-2C11)BioLegendCat# 100308Flow cytometry (1:800)
AntibodyAPC-Cyanine7 anti-mouse CD8α rat IgG2a, κ monoclonal antibody(clone: 53-6.7)BioLegendCat# 100714Flow cytometry (1:800)
AntibodyAlexa Fluor 647 anti-human c-Myc-tag mouse IgG2a monoclonal antibody(clone: 9B11)Cell SignalingCat# 2233SFlow cytometry (neat)
AntibodyAPC anti-mouse CD28 Syrian hamster IgG monoclonal antibody(clone: 37.51)BioLegendCat# 102110Fluorescence microscopy (1:400)
AntibodyAnti-human c-Myc-tag mouse IgG2a monoclonal antibody(clone: 9B11)Cell SignalingCat# 2276IP (1:2000)Fluorescence microscopy (1:100)
AntibodyHRP anti-mouse IgG polyclonal antibodySigma-AldrichCat# A0168IP (1:20,000)
AntibodyAlexa Fluor 488 goat anti-mouse IgG polyclonal antibodyAbcamCat# ab150113Fluorescence microscopy (1:200)
AntibodyLEGENDplex mouse cytokine panel 2 detection antibodiesBioLegendCat# 740149(1:4)Reagent used for cytokine detection in Figures 3, 4 and 6
AntibodyLEGENDplex MU Th panel detection antibodies V02BioLegendCat# 741045(1:4)Reagent used for cytokine detection in Figures 3, 4 and 6
AntibodyAlexa Fluor647 anti-human CD340 (erbB2/HER2) mouse IgG1, κ antibody(clone: 24D2)BioLegendCat# 324412Flow cytometry (1:3200)
Recombinant DNA reagentpMAL-dsTbL(plasmid)Elazar et al., 2016aAddgene:73805TOXCAT β-lactamase assays
Recombinant DNA reagentDesigned proCARsThis paperSee Experimental Methods - DNA sequences of designs
Sequence-based reagentDeep sequencing primersElazar et al., 2016aDeep sequencing library preparation and a protocol and analysis as described
Peptide, recombinant proteinRetronectinTakara BioCat# T100BFinal concentration (32 μg/ml)
Peptide, recombinant proteinRecombinant mouse IL-2 (ELISA Std.)BioLegendCat# 575409
Peptide, recombinant proteinRecombinant mouse TNFα (ELISA Std.)BioLegendCat# 575209
Peptide, recombinant proteinRecombinant mouse IFNγ (ELISA Std.)BioLegendCat# 575309
Peptide, recombinant proteinRecombinant mouse GM-CSF (ELISA Std.)BioLegendCat# 576309
Peptide, recombinant proteinRecombinant human IL-2PeproTechCat# 200-02-1Dose(5 × 104 IU/injection)Media concentration(100 IU/ml)
Commercial assay or kitMouse T-activator CD3/CD28 DynabeadsGibcoCat# 11456D
Commercial assay or kitEasySep mouse CD8a positive kit IIStem Cell TechnologiesCat# 18953
Chemical compound, drugRoswell Park Memorial Institute (RPMI) 1640 Medium +Pen/StrepGibcoIn-house
Chemical compound, drugDulbecco′s Modified Eagle Medium (DMEM)LonzaCat# BE12-707F
Chemical compound, drugDulbecco′s phosphate buffered salineGibcoIn-house
Chemical compound, drugFetal bovine serumBovogen BiologicalsCat# 423101Final concentration (10% v/v)
Chemical compound, drugPolybreneSigma-AldrichTR-1003-GFinal concentration (8 µMg/ml)
Chemical compound, drugZombie AquaBioLegendCat# 423101Flow cytometry (1:500)
Chemical compound, drugPropidium iodideSigma-AldrichCat# P4170Final concentration (50 µM)
Chemical compound, drugl-GlutamineGibcoCat# 25030081Final concentration (2 mM)
Chemical compound, drugSodium pyruvateGibcoCat# 11360070Final concentration (1 mM)
Chemical compound, drugNon-essential amino acidsGibcoCat# 11140050Final concentration (1×)
Chemical compound, drugβ-MercaptoethanolSigma-AldrichCat# M3148Final concentration (50 µM)
Chemical compound, drugSaponinSigma-AldrichCat# 47036Final concentration (1.2% w/v)
Chemical compound, drugSodium azideSigma-AldrichCat# 71289Final concentration (0.1% w/v)
Software, algorithmGraphPad Prism v9.0GraphPad Software
Software, algorithmFlowJo v10FlowJo Software
Software, algorithmIncuCyte Analysis SoftwareIncuCyte Analysis Software
OtherRosetta macromolecular modeling suiteRosettaGit version: b210d6d5a0c21208f4f874f62b2909f926379c0fFor documentation, see https://www.rosett acommons.org/
OtherCr51PerkinElmer100 µCi
OtherLEGENDplex mouse IL-2 capture bead A7BioLegendCat# 740054Reagent used for cytokine detection in Figures 3, 4 and 6
OtherLEGENDplex mouse TNFα capture bead A6BioLegendCat# 740066Reagent used for cytokine detection in Figures 3, 4 and 6
OtherLEGENDplex IFNγ capture bead A4BioLegendCat# 740065Reagent used for cytokine detection in Figures 3, 4 and 6
OtherLEGENDplex GM-CSF capture bead B7BioLegendCat# 740146Reagent used for cytokine detection in Figures 3, 4 and 6

Computational methods

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Command lines and RosettaScripts (Fleishman et al., 2011) are available in Supplementary file 1. Rosetta is available at http://www.rosettacommons.org. We used git version b210d6d5a0c21208f4f874f62b2909f926379c0f for all Rosetta calculations.

Membrane-protein energy function

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All atomistic calculations used the Rosetta ref2015_memb energy function Weinstein et al., 2019. This energy function is based on the recent Rosetta energy function 2015 (ref2015) energetics, which is dominated by van der Waals packing, electrostatics, hydrogen bonding, and water solvation, with the difference that in ref2015_memb the solvation terms are replaced with splines that recapitulate the amino acid-based lipophilicity contributions observed in the dsTβL insertion profiles (Elazar et al., 2016a). The centroid-level energy function was similarly based on ref2015 with amino acid lipophilicity preferences and a biasing potential that disfavors large interhelical crossing angles that are rarely observed in natural TMDs:

(1) penalty=1.51×10-4×θ3-8.925×10-3×θ2+0.187×θ-0.532

where θ is the crossing angle between the helix and the membrane normal.

TMD de novo design

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3- and 9-mer backbone fragments were generated for a 24 amino acid poly valine extended chain using the Rosetta fragment picker (Gront et al., 2011). The fold and dock protocol was used in all design simulations (Das et al., 2009). Briefly, depending on the type of symmetry (C2, C3, or C4), the chains were symmetrically duplicated and each move was applied identically to all chains. Moves included centroid-level fragment insertion and docking, followed by all-atom sequence optimization, and backbone, sidechain, and rigid-body minimization. 50,000 independent trajectories were run and the structure models were filtered using structure and energy-based criteria (the best 1% by system energy, solvent-accessible surface area>700Å; shape complementarity (Sc) > 0.6 [Lawrence and Colman, 1993]; ΔΔGbinding < –15 R.e.u.; helicality< 0.1R.e.u. [Weinstein et al., 2019]). Resulting models were visually inspected and selected for further computational design.

Sequence diversification

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De novo-designed sequences exhibited a high propensity of the amino acid Leu. To reduce this bias, we implemented 120 steps of Monte Carlo simulated annealing sequence design. In each step, a random single-amino acid change was introduced in any position (mutations were restricted to Gly, Ala, Val, Ile, Leu, Met, Phe, Tyr, or Trp). Following relaxation, the mutant was evaluated on three criteria: ΔΔGbinding, system energy, and the difference between the amino acid propensities in the design versus natural TMDs (Liu et al., 2002) using the following equation (RMSDsequence comp):

(2) RMSDsequence comp=aa(f(aadesign)f(aanatural))2L

where f is the frequency of a given amino acid, and L is the amino acid sequence length.

The three criteria were then transformed using the ‘fuzzy’-logic design sigmoidal function (Warszawski et al., 2014):

(3) fx=11+e(x-o)s

where x is each of the three criteria, and o and s take the following values: for ΔΔGbinding 3 R.e.u. and 1 R.e.u.–1, respectively, for system energy 20 R.e.u. and 0.5 R.e.u.–1, respectively, and for RMSDsequence comp 0.05 and 50, respectively. The o thresholds on binding and system energy were computed relative to the energies of the starting model in each design.

The resulting functions were then integrated into a ‘fuzzy’-logic optimization objective function (Warszawski et al., 2014):

(4) fΔΔGbindingfsystem energyfRMSDsequence comp

Ab initio structure prediction

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Designed sequences were subjected to the membrane fold and dock method essentially as described in Weinstein et al., 2019. Structure models were filtered using structure and energy-based filters: solvent-accessible surface area > 600 Å; energy < 0; the distance between the TMD ends along the membrane normal, TMsSpanMembrane > 25 Å; fractional agreement between the desired topology for each position (cytosolic, membrane, external) and the designed topology SpanTopologyMatchPos > 0.1.

To evaluate whether the ab initio structure predictions are funneled, we computed the Z-score:

(5) Z=Elowest - E_STD(E)

where Elowest is the lowest-energy model with an RMSD of less than 2 Å to the original design model, and E represents energies of models with an RMSD > 2 Å and less than 50 R.e.u. from Elowest. A cutoff of Z > 2.5 was typically used to determine whether an energy landscape was funneled.

Rosetta mutational-scanning calculations

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In order to characterize the effects of mutations on the designs’ binding energy, we conducted computational mutation scanning using the FilterScan protocol in RosettaScripts (see XMLs section below). If the difference in total energy for a mutation was >2.5 R.e.u., the mutation was predicted to be detrimental, otherwise it is defined as neutral/beneficial.

Experimental methods

TOXCAT β-lactamase assays

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DNA encoding the designs and controls were cloned into the pMAL_dsTβL vector (Elazar et al., 2016a) (available at AddGene #73805) using XhoI and SpeI restriction sites and selected by growth on spectinomycin and ampicillin in standard concentrations. For positive controls, the natural ErbB2 and QSOXS2 TM domains were chosen (representing strong and weak homo-oligomers, respectively; Schanzenbach et al., 2017). The monomeric C-terminal portion of human L-selectin (CLS) (Srinivasan et al., 2011; Elazar et al., 2016a) was chosen as a negative control. Resulting plasmids were transformed into E. coli cloni cells (Lucigen), plated on agar plates containing 50 μl/ml spectinomycin followed by overnight growth in a 37°C at 200 rpm. Cultures were then inoculated into fresh LB + 50 μl/ml spectinomycin medium to OD600 1 and then plated on Petri dishes containing 50 μl/ml spectinomycin, 100 μl/ml ampicillin, or 100 μl/ml ampicillin with a range of different chloramphenicol concentrations. For single-clone growth assays, 2 μl of cultures at OD 0.1 were diluted and plated on square Petri dishes containing different chloramphenicol concentrations (extended data in Figure 1—figure supplement 2).

Deep sequencing analysis

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A library encoding all of the designed sequences, controls, and single-point mutations in defined positions (using NYS codons to encode hydrophobic and small, mildly polar amino acids) was transformed and grown in large 12cm Petri dishes on different chloramphenicol concentrations (0, 60, 80, 100, and 120μl/ml for data in Figure 1C and extended Figure 3—figure supplement 1 and 0, 21, 27, 34, 42, 52, 66, 82, 102, 128, 160, and 200 μl/ml for data in Figure 3—figure supplement 1C) overnight. Chloramphenicol concentration of 60 μl/ml was selected for the analysis for Figure 1D-F and Figure 1—figure supplement 3. Bacteria were harvested and subjected to deep sequencing library preparation and a protocol and analysis as described in Elazar et al., 2016a.

Deriving changes in free energy of self-association from the deep mutational scanning data

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From the deep sequencing analysis, we compute the propensity p of each mutant j at position i relative to the wild type as described in Elazar et al., 2016a:

(6) pi,j=counti,jcountwt

where count is the number of reads for each variant, adding a pseudo-count of 1 if no reads were detected for the wild type. We then obtain selection coefficients s by comparing the selected and reference populations:

(7) si,j=pselectedi,jprefi,j

where the selected population is selected on ampicillin + chloramphenicol plates (selection for insertion and self-association, respectively) and the reference population is selected only on ampicillin plates (insertion only). At each position i, the selection coefficients are transformed to changes in free energy of self-association from the wildtype identity wt to the single-point mutation j through the Gibbs free-energy equation:

(8) ΔΔGi,wtjmeasured=RTln(si,jsi,wt)

where R is the gas constant, T is the absolute temperature (310 K), and ln is the natural logarithm.

In the TOXCAT-β-lactamase construct, bacterial viability on chloramphenicol depends on the activity of the ToxR chloramphenicol acetyltransferase moiety, which in turn depends on oligomer concentrations (Langosch et al., 1996; Russ and Engelman, 1999; Elazar et al., 2016a). Oligomer concentrations depend on both membrane insertion and self-association energy (Duong et al., 2007; Elazar et al., 2016a). Therefore, the energy computed in Equation 8 comprises contributions from both membrane insertion (doubled in the case of homodimers) and self-association energy. Thus, to extract the self-association energies for each point mutation, the apparent free energy of self-association subtracts the apparent contribution from insertion:

(9) ΔΔGi,wtjapp,assoc.=ΔΔGi,wtjmeasured-2ΔΔGi,wtjapp,ins.

where the apparent change in free energy of insertion is computed according to the per amino acid, membrane depth-dependent insertion energies derived from the dsTβL assay in Elazar et al., 2016a.

DNA sequences of designs

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  • >proMP 1.1

  • CCTTTATCTTTCCTCTTAGGGATACTAGCTGCGCTGGTGGGGTTCATCATTGGCTTTTTAGCGGCCTTGATT

  • >proMP 1.2 (trimer; used in proCAR-3)

  • CCTTTGTTATTTATTCTCGTCGCAATACTTGGAGGCTTATTTGGGGCGATTGTTGCATTCCTTTTGGCGTTA

  • >roMP 1.3

  • CCGATCCTGTTCGCAATACTGGCGGCTTTCATCGGGGCATTTATAGCTGCCCTGTTCGTGCTAGTATTGGCA

  • >proMP 1.4

  • CCCTTTGGAGCTTTACTAGCAATCATAGCATTCGTCGTAGGAATGTTATTCTCAGCATTCGTTTTACTCATC

  • >proMP 1.5

  • CCCTTTAGCTTGTTTTTGGGCGTTATAGCCGGCATTATTGCTGCATTCATCGTTTTATTCCTGGCATTACTA

  • >proMP 1.6

  • CCTTTTTTATCGCTTGTTGGTGCGCTAATCGGGGCTTTCATAGCATTTATCTTGGCTTTGTTCATTTTGGTT

  • >proMP 1.7

  • CCGATTCTGATCACTTTGGCAATGCTTACGGGAGCAGTGATTGGGGCGATCTCGTCTTTTCTCCTAGTGTAT

  • >roMP 1.8

  • CCAGCCTTTTATATTATATTGGCAATTCTCACCTCGTTCATAGCCTATTTGGTGGGTCTACTCGTGTCTTTT

  • >proMP 1.9

  • CCTATTTACGTTATACTAGCCATCTTGGCCGCGGTATTCACTTGGTTCATAGTCCTTATAACTAGCCTGAGT

  • >roMP 1.10

  • CCTACGGTTACGAGTGCGATTCTTGGCGTGTCATTCGGTACCTTTATTAGCCTCGTAGCTCTGTGGCTTGCA

  • >proMP 1.11

  • CCAGTGATTGCAATCTTAACTTTTATAGTCCTCACTGCGATTTCGGGAGCGCTGCTCGCTGTTTGGTTCTCC

  • >roMP 1.12

  • CCCATCGTCTTGCTCCTCAGTCTACTCGCCAGTGTATTTGGGGCGTTCATCACATTTATTTGGGCTTACTTG

  • >proMP C1 (monomer; used in proCAR-1)

  • CTGGTGCTGATTCTGCTGACCTTTGTGCTGTTTGTGTTTATTCTGTATTGGGTGATTACCTGGTATCTGATT

  • >proMP C2.1 (dimer; used in proCAR-2)

  • CCGCTGACCGTGGCGCTGATTCTGGGCATCTTCCTGGGCACCTTTATTGCGTTTTGGGTGGTGTATCTGCTG

  • >proMP C3.1

  • ACCGCGCTGCTGGTGGCGTTTGTGGCGTATTATACCGCGCTGATTGCGCTGATTTTTGCGATTCTGGCGACC

  • >proMP C4.1 (tetramer; used in proCAR-4)

  • CCCCTTTTAGTCGCCTTATTGGCGCTGCTTGCTGTAATCGCCGCATTATTAGCAGCTATCTTTGCATTGCTG

  • >CLS

  • CCGCTGTTCATCCCGGTTGCAGTTATGGTTACCGCTTTTAGTGGATTGGGGTTTATCATCTGGCTGGCTAC

  • >ErbB2

  • TCTATCATCTCTGCGGTGGTTGGCATTCTGCTGGTCGTGGTCTTGGGCGTGGTCTTTGGCATCCTGAT

  • >QSOX2

  • AGCCTATGCGTTGTTTTATACGTGGCATCTAGTTTATTTATGGTCATGTACTTCTTC

proMP peptide production

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Peptides were produced recombinantly as 9His-trpLE fusion proteins in E. coli following a previously published protocol (Sharma et al., 2013). To aid purification, analysis, and crystallization, all designed sequences were modified to include Glu-Pro-Glu at the amino terminus and Arg-Arg-Leu-Cys at the carboxy terminus based on the favorable properties of the glycophorin A TMD fragment whose structure has been previously determined by X-ray crystallography (Trenker et al., 2015). Dissolved fusion protein from inclusion bodies was purified on nickel affinity resin, cyanogen bromide digested, and reverse-phase HPLC purified following the published procedure (Sharma et al., 2013) with the following modifications: the C-terminal Cys sulfhydryl group was protected using 10 mM S-methyl methanethiosulfonate (MMTS, Sigma-Aldrich) during lysis and inclusion body solubilization and peptides were at no time disulfide linked. HPLC-purified peptides were stored as lyophilized products at room temperature (RT) until needed.

SDS-PAGE analysis

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Samples were prepared by drying indicated amounts of each purified peptide taken from dried and weighed product redissolved in 1,1,1,1,1,1-hexafluoroisopropanol (HFIP, Merck). Samples were lyophilized, redissolved in 25 μl 1× NuPAGE LDS sample buffer (Thermo Fisher Scientific), and heated for 1 min at 95°C. Cooled samples were separated on 12% NuPAGE Bis-Tris gels (Thermo Fisher Scientific) at 200 V for 40 min and visualized by staining with Coomassie Blue R-250 (Bio-Rad).

Crystallization screening and structure determination

proMP crystallization in LCP

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For reconstitution into LCP, lyophilized peptide was weighed and co-dissolved with appropriate amounts of monoolein (Nu-Chek Prep) in HFIP. Solvent was removed under streaming nitrogen, followed by lyophilization overnight. Peptide-monoolein mix was heated (52°C) until liquid and mixed 3:2 with 10 mM Tris pH 8.0 for LCP formation using coupled 100 μl gastight Hamilton syringes (Formulatrix) at RT. For screening, LCP mixture was dispensed in 100 nl drops onto 96-well glass plates (Molecular Dimensions) with 1000 µl of precipitant solution using a Mosquito LCP robot (TTP Labtech) at RT. Plates were sealed and kept at 20°C in a Rock Imager 1000 (Formulatrix) for incubation and monitoring of crystal formation.

proMP crystalliszation in detergent

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For reconstitution with detergent, lyophilized peptide was weighed and dissolved in 30 mM detergent (C8E4; Anatrace, C8E5; Anatrace) in HFIP. Solvent was removed under streaming nitrogen followed by lyophilization overnight. Peptide-detergent mix was reconstituted in 10 mM Tris pH 8.0. For screening, peptide-detergent mixture was dispensed in 150 nl drops onto SD-2 plates (IDEX Corp) with 150 nl of precipitant solution using a Crystal Phoenix robot (Art Robins Instruments) at RT. Droplets were equilibrated against 50 nl of crystallant in the reservoir. Plates were sealed and kept at 20°C in a Rock Imager 1000 (Formulatrix) for incubation and monitoring of crystal formation.

Data collection and structure determination

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Data were collected on the MX2 beamline of the Australian Synchrotron at a wavelength of 0.9537 Å and a temperature of 100 K. Data were indexed and scaled using XDS (Kabsch, 2010) and Aimless (Winn et al., 2011). Structure factor amplitudes were obtained using cTruncate (Davenport et al., 2015). 6W9Y was solved with Phaser (McCoy et al., 2007) by molecular replacement using the GpA monomer helix as a search model (PDB code 5EH6 Trenker et al., 2015). 6W9Z was solved with Phaser by molecular replacement using 5EH6 mutated to the proMP C2.1 sequence in Coot (Emsley et al., 2010). 6WA0 was solved with Phaser by molecular replacement using the designed trimer as a search model. This resulted in a model that contained good density for two chains, with the final chain of the trimer considerably worse. The third chain was removed and a second molecular replacement job was performed with the first two chains fixed in place and a single helix from the model trimer used as a search model. This resulted in placement of the third helix in an antiparallel direction with respect to the other two chains, and this was judged as correct based on comparison of overall Rfree of each model, average B factors of each chain, and visual inspection of the electron density in Coot (Emsley et al., 2010). Iterative rounds of refinement and model building were performed in PHENIX (Liebschner et al., 2019) and Coot (Dong et al., 2019).

proCAR construct preparation

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The HER2-specific CAR used was based on a previously described construct (Haynes et al., 2002). Restriction digest sites were removed and human sequences were Gibson cloned together and inserted into EcoRI/XhoI digested pMSCV-IRES-mCherry-II vector (NEB Gibson Assembly Master Mix, Cat# E2611L). The CAR construct contains the FRP5 anti-HER2 scFv, Myc tag, human CD8ɑ stalk, human CD28 TM/ tail, and human CD3ζtail sequences. PCR primers were used to generate a cysteine to alanine mutation in the CD8ɑ stalk region to prevent covalent dimerization. Overlapping PCR was used to generate CARs with altered TM domains on the background of the cysteine-mutated CD8ɑ stalk. These constructs were inserted into the pMSCV-IRES-mCherry-II vector via EcoRI/XhoI restriction sites.

Animals

All mice were of an inbred C57B/6J or NOD.Cg‐PrkdcscidIL2rgtmWjl/SzJ (NSG) genetic background. All animal experiments were approved and performed in accordance with the regulatory standards of the Walter and Eliza Hall Institute Animal Ethics Committee (approval: WEHI-2019.020).

Mouse CD8+ T cell isolation and culture

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Single-cell suspensions of peripheral lymph nodes from 6- to 8-week-old C57B/6 mice were prepared by mechanically dissociating through a 70 µm cell strainer (BD Biosciences) into cold phosphate-buffered saline (PBS). CD8+ T cells were subsequently selected using the EasySep mouse CD8a positive Kit II (Stem Cell Technologies) according to the manufacturer’s instructions. Purity was confirmed as >95% using LSR II Fortessa (BD Bioscience), FACSymphony (BD Biosciences), or Aurora (Cytek). CD8+ T cells were subsequently activated by incubating overnight with Mouse T-Activator CD3/CD28 Dynabeads (Gibco) at a bead-to-cell ratio of 1:1 in mouse T cell medium (mTCM) consisting of Roswell Park Memorial Institute (RPMI) 1640 Medium (Gibco) supplemented with fetal bovine serum (10%; Bovogen Biologicals), l-glutamine (2 mM; Gibco), sodium pyruvate (1 mM; Gibco), nonessential amino acids (1×; Sigma-Aldrich), β-mercaptoethanol (50 μM; Sigma-Aldrich), and recombinant human IL-2 (100 IU/ml; PeproTech). Following removal of magnetic beads, T cells were maintained at 1 × 106 cell/ml in mTCM.

Cell lines

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293T, MC57, MC57-HER2, SKBR3, and MC38-HER2 cell lines were cultured in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with fetal bovine serum (10%; Bovogen Biologicals) and l-glutamine (2 mM; Gibco), incubated at 37°C, 10% CO2. BW5147 cells were cultured in RPMI supplemented with fetal bovine serum (10%; Bovogen Biologicals) and l-glutamine (2 mM; Gibco), incubated at 37°C, 5% CO2. Cell line identity was not independently verified by genetic testing. HER2 expression on tumor target and control cell lines was confirmed via anti-HER2 surface staining (BioLegend, Cat# 324412) and flow cytometry. All cell lines were regularly confirmed mycoplasma negative using the Stratagene Mycosensor PCR Assay Kit (Agilent, Cat# 302108).

BW5147 and primary mouse CAR-T cell generation

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Retrovirus for all T cells was produced using calcium phosphate transfection of HEK293T cells. BW5147 cells expressing a destabilized-GFP NFkB reporter element were mixed 1:1 with filtered viral supernatant at a final density of 2.5 × 105 cells/ml. Polybrene transfection reagent (Merck) was added to a final concentration of 8 µg/ml polybrene prior to spinfection (2500 rpm, 37°C, 45 min). For primary mouse T cells, plates were coated with 32 µg/ml retronectin (Takara Bio) for 24 hr before plating of 1 × 106 cells in 1 ml viral supernatant and performing a spinfection (2500 rpm, 37°C, 45 min). Viral supernatant was removed after 16 hr and replaced with RPMI supplemented with fetal bovine serum (10%; Bovogen Biologicals) and l-glutamine (2 mM; Gibco) for BW5147 cells, or mTCM for primary T cells.

Surface IP and immunoblot analysis

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2 × 107 cells per sample were pelleted and washed twice with PBS prior to coating with 20 µg/ml polyclonal anti-mouse IgG for 45 min on ice. Cells were washed twice with PBS and lysed in 200 µl PBS/1% IGEPAL-640/P8340 protease inhibitor/10 mM iodoacetamide for 30 min on ice. Lysate was centrifuged at 20,000 × g for 10 min, 10 µl of cleared lysate was taken for 5% input controls with remainder being added to 20 µl Thermo Fisher Protein G agarose beads and rotated in cold room for 2 hr. Beads were washed with lysis buffer twice then eluted with LDS and boiled. Samples were run on SDS-PAGE and transferred for blotting with 1:2000 anti-Myc primary antibody (Cell Signaling #2276) and 1:20,000 anti-mouse IgG HRP secondary (Sigma-Aldrich A0168).

CAR T cell SKBR3 co-culture assay

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5 × 104 cells/cell line were aliquoted onto a confluent layer of SKBR3 cells in a 96-well plate at specified time points. After 8 hr, all time points were removed from plate and stained with 1:200 anti-CD69 (BioLegend #104524) on ice for 45 min. Samples were analyzed on an LSR Fortessa X20 (BD Biosciences), and data were analyzed using FlowJo v10 software.

Flow cytometry

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For CD8+ T cell selection and transduction efficiency verification, single-cell suspensions were washed and stained with Live/Dead marker Zombie Aqua (BioLegend) for 15 min at RT in PBS, before washing and labeling for at least 30 min on ice with a panel of monoclonal antibodies (mAbs), including anti-mouse CD3ε PE (clone 145-2C11, BioLegend), anti-mouse CD8α APC-Cy7 (clone 53-6.7, BioLegend), and anti-mouse Myc-Tag Alexa Fluor 647 (clone 9B11, Cell Signaling). All samples were analyzed with an LSR II Fortessa (BD Biosciences), FACSymphony (BD Biosciences), or Aurora (Cytek), and data were analyzed using FlowJo v10 software.

Chromium release killing assay

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Standard 51Cr release assays were conducted to assess CAR T cell cytotoxicity by measuring release of radioactivity into culture supernatants as cells are lysed. Target MC57 mouse fibrosarcoma cells stably expressing human HER2 (MC57-HER2) were pre-loaded with 100 μCi 51Cr for 1 hr at 37°C, washed three times, and then 2 × 104 tumor cells were co-incubated with CAR T cells at effector-to-target (E:T) ratios ranging from 40:1 to 1.25:1. Supernatants were harvested after 4 hr of co-incubation, plated onto a 96-well scintillator-coated LumaPlate (PerkinElmer), and 51Cr release quantified using a MicroBeta2 Microplate Counter (PerkinElmer). Target tumor cells incubated in a 5% Triton X-100 solution were used as a maximum release control, while tumor cells incubated in mTCM alone were used as a spontaneous release control. Percent lysis was calculated as follows: % lysis = ((Experimental release – Spontaneous release) ÷ (Maximum release – Spontaneous release)) 100. Data in Figures 3f and 4d were derived from the 20:1 E:T ratio where killing was maximal for all constructs.

IncuCyte killing assay

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To measure tumor cell death over time, the live-cell imaging system IncuCyte SX3 or SX5 was used. In this assay, 8 × 103 target tumor cells per well were plated in a 96-well plate in triplicate, and the following day CD8+ T cells were added at an effector to target ratio of 1:1 in mTCM media. 50 µM propidium iodide (PI; Sigma-Aldrich) was added to each well as a surrogate marker of cell death. Wells were subsequently imaged every hour for 24 hr, with phase and PI fluorescence recorded. All images were analyzed using the IncuCyte Analysis Software program, where the average PI area (µm) was calculated for each individual well from at least two images per time point. Target tumor cells incubated in a 1.2% (w/v) Saponin (Sigma-Aldrich) solution were used as a positive PI release control while tumor cells incubated in mTCM alone were used as a background PI release control for PI area calculations. Data in Figure 6d shows all biological replicates and time points graphed as PI area (y-axis) vs. time (x-axis) using GraphPad Prism v 9.0.0.

Cytokine bead array

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To assess cytokine secretion by CAR T cells, cytokine bead arrays on co-culture supernatants were performed. Murine CAR T cells (1 × 105 cells) were washed once in PBS and co-incubated with either mTCM alone, a 1:1 bead-to-cell ratio of Mouse T-Activator CD3/CD28 Dynabeads (Gibco) as a positive control, nontarget MC57 parental tumor cells (2 × 104 cells) as a negative control, or target MC57-HER2 tumor cells (2 × 104 cells) in triplicate. After 24 hr, supernatants of co-cultures were collected and used in a LEGENDplex Mouse T Helper Cytokine Panel Version 2 Flexi Kit (BioLegend) for IFN-γ, IL-2, and TNFα, and LEGENDplex Mouse Cytokine Panel 2 Flexi Kit (BioLegend) for GM-CSF according to the manufacturer’s instructions. All samples were analyzed using an LSR II Fortessa or FACSVerse (BD Biosciences) and concentration determined against a standard curve of each analyte using FlowJo v10 software.

Confocal microscopy and cluster analysis

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8 × 105 cells were labeled with unconjugated anti-Myc primary antibody (Cell Signaling) in PBS/0.5% BSA for 30 min on ice. Cells were washed twice in PBS and further incubated with Alexa Fluor 488 anti-mouse IgG secondary antibody (Abcam) in 50 μl ice-cold RPMI for 10 min on ice. 50 μl pre-warmed RPMI was added and samples were transferred to a 37°C water bath for 10 min to induce CAR clustering. CAR clustering was halted via addition of ice-cold PBS/0.1% sodium azide. Cells were washed in PBS/0.1% sodium azide then stained on ice for 45 min with anti-CD28 APC (BioLegend) diluted in PBS/0.1% sodium azide. Cells were fixed with 3% paraformaldehyde, transferred to eight-well chamber slides (Ibidi), and stored at 4°C overnight until imaging. 3D confocal image data was collected using a Zeiss LSM 980 microscope, with 55–60 slices collected per image at a z-step size of 0.23 μm. The pinhole size used was 1 airy unit, resulting in a slice thickness of 600 nm. Image analysis was conducted using the cluster-picking function within the Imaris software package. CAR clusters (Alexa Fluor488) and CD28 clusters (APC) were counted, with the percentage of CAR clusters co-localizing with a CD28 cluster reported per cell with at least 30 cells per construct analyzed.

In vivo tumor growth

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For in vivo tumor growth analysis, 5 × 105 MC38 colon adenocarcinoma cells stably expressing human HER2 (MC38-HER2) were injected subcutaneously into the left flank of 5- to 6-week-old NSG mice and 5–6 mice were randomly assigned to each treatment group. One day later, mice were injected intravenously via the tail vein with 1 × 107 CD8+ T cells transduced with the indicated CAR constructs. On days 1, 2 and 3, mice were injected intraperitoneally with 5 × 104 IU recombinant human IL-2. Mice were weighed weekly and tumors measured daily until each individual tumor reached a maximum tumor volume of 1000 mm3 as per ethical guidelines, after which mice were euthanized.

Data availability

Diffraction data have been deposited in the Protein Data Bank under the PDB accession codes 6WA0, 6W9Y and 6W9Z.

References

    1. Emsley P
    2. Lohkamp B
    3. Scott WG
    4. Cowtan K
    (2010) Features and development of Coot
    Acta Crystallographica. Section D, Biological Crystallography 66:486–501.
    https://doi.org/10.1107/S0907444910007493
    1. Kabsch W
    (2010) XDS
    Acta Crystallographica. Section D, Biological Crystallography 66:125–132.
    https://doi.org/10.1107/S0907444909047337
    1. Korendovych IV
    2. DeGrado WF
    (2020)
    De Novo Protein Design, a Retrospective
    Quarterly Reviews of Biophysics 53:e3.
    1. Lemmon MA
    2. Flanagan JM
    3. Hunt JF
    4. Adair BD
    5. Bormann BJ
    6. Dempsey CE
    7. Engelman DM
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    Glycophorin A dimerization is driven by specific interactions between transmembrane alpha-helices
    The Journal of Biological Chemistry 267:7683–7689.

Decision letter

  1. Nir Ben-Tal
    Reviewing Editor; Tel Aviv University, Israel
  2. José D Faraldo-Gómez
    Senior Editor; National Heart, Lung and Blood Institute, National Institutes of Health, United States
  3. Brian Kuhlman
    Reviewer; University of North Carolina at Chapel Hill, United States
  4. Dieter Langosch
    Reviewer; The Technical University of Munich, Germany
  5. James J Chou
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

Thank you for submitting your article "de novo designed transmembrane domains tune engineered receptor functions" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by Nir Ben-Tal as Reviewing Editor and José Faraldo-Gómez as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Brian Kuhlman (Reviewer #1); Dieter Langosch (Reviewer #2); James J. Chou (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. in vitro experiments showed a clear correlation between oligomerization state of the CAR and cytokine secretion when CAR T cells were exposed to HER2+ cancer cells. The higher order oligomers also were more effective at slowing tumor growth in mice injected with HER2 tumor cells. These results confirm previous observations that dimeric CARs (via disulfide formation) are more effective than monomeric CARs. One exciting finding was that the designed CAR tetramer was as effective at suppressing tumor growth in mice as a standard CAR construct used in the field (transmembrane domain derived from CD28), but the tetramer CAR stimulated less cytokine release than the CD28TM CAR in vitro. The CAR T therapies currently used in the clinic frequently stimulate dangerous levels of cytokines, if a CAR T cell can be created that is as effective as current treatments but overall cytokine release is lowered, this could be an improvement over current treatment options. One caveat about the efficacy data presented in this paper is that CAR T cells were administered to the mice only a single day after injection of the cancer cells. More rigorous tests of efficacy will be needed to determine if the tetrameric CAR is on par with standard constructs used in therapy.

2. The protein design process contained no consideration for how activation signals are transmitted from the extracellular domain (ECD) of the CAR to the intracellular activation domains. Does this suggest that specific conformational changes are not a part of the activation process? It would be great if the authors could comment on this. Do the results say anything about how binding to the ECD does or does not activate signaling?

3. TMH oligomer design. It appears that the crystal structures and the designed structures agree with rather high success rate. Wouldn't you be able to predict the oligomeric structure of a given TMH sequence? Can your design protocol be revised to predict the assumed dimeric structure of CD28 TMH? We are asking because there is no direct biochemical evidence that the CD28 TM mutations have abolished TMH dimerization.

4. A potential concern of analyzing TMD oligomeric state using SDS-PAGE is that the strong assembly interactions can survive the harsh condition of SDS-PAGE but the weak ones don't. You should comment on the possible issue, if any, of TMD forming higher-order clusters in the membrane in the discussion. Has this issue been eliminated in the design phase?

5. An observation of profound impact is the beautiful linear correlation between the proCAR TMD oligomeric state and tumor killing in vivo. However, the increase is not obvious for in vitro cell cytotoxicity assay. Did the two studies use two different cancel cell lines? Please explain.

6. For readers not working on CAR-T, can you discuss why heterotypic mixing of CAR-CD28TM and endogenous CD28 via TM-TM dimerization can cause much greater cytokine release given both contain the same CD28 signaling tail? Is it because CAR-CD28TM mixing with CD28 would increase the total amount of CD28 that can engage HER2 and activate? If so, does the 2-6 fold increase in cytokine release make qualitative sense? Do CAR and endogenous CD28 induce different cytokine release?Reviewer #1:

This is an interesting paper that uses de novo protein design to probe the effects of oligomerization state on the activity of chimeric antigen receptors (CARS). The successful design of transmembrane domains with specific oligomeric states is an impressive result on its own. The proteins were designed using rotamer-based sequence optimization in Rosetta with an energy function specific for the membrane environment. During the design process it was important to explicitly reward sequence diversity as low diversity sequences (i.e. many leucines) produced the lowest energies when evaluated on the target backbone, but showed little specificity for a single conformation when docking simulations were performed with the designs. After experimentally evaluating a couple rounds of designs, the investigators settled on a design protocol that also included screening of the design candidates with docking simulations in alternative oligomerization states to check that the sequences preferred the desired oligomerization state. The designs were experimentally evaluated with gel electrophoresis and X-ray crystallography. In the end, designs that adopted well-defined dimers, trimers, or tetramers were created and carried forward in experiments as CARs.

In vitro experiments showed a clear correlation between oligomerization state of the CAR and cytokine secretion when CAR T cells were exposed to HER2+ cancer cells. The higher order oligomers also were more effective at slowing tumor growth in mice injected with HER2 tumor cells. These results confirm previous observations that dimeric CARs (via disulfide formation) are more effective than monomeric CARs. One exciting finding was that the designed CAR tetramer was as effective at suppressing tumor growth in mice as a standard CAR construct used in the field (transmembrane domain derived from CD28), but the tetramer CAR stimulated less cytokine release than the CD28TM CAR in vitro. The CAR T therapies currently used in the clinic frequently stimulate dangerous levels of cytokines, if a CAR T cell can be created that is as effective as current treatments but overall cytokine release is lowered, this could be an improvement over current treatment options. One caveat about the efficacy data presented in this paper is that CAR T cells were administered to the mice only a single day after injection of the cancer cells. More rigorous tests of efficacy will be needed to determine if the tetrameric CAR is on par with standard constructs used in therapy.

One thing that struck me is that the protein design process contained no consideration for how activation signals are transmitted from the extracellular domain (ECD) of the CAR to the intracellular activation domains. Does this suggest that specific conformational changes are not a part of the activation process? It would be great if the authors could comment on this. Do the results say anything about how binding to the ECD does or does not activate signaling?Reviewer #2:

The authors designed and computationally refined a set of novel self-associating TM helices which were shown to form dimers and trimers by X-ray crystallography. The dimer (Car2) and trimer (Car3) as well as a modelled tetramer (Car4) were then fused to extracellular antigen-recognition and intracellular signalling domains. They were then compared to a monomer (Car1) and the original CD28 TMD in their ability to kill cancer cells in vitro and in vivo and to release various cytokines. Also, the authors show that a specific amino acid motif within the wild-type CD28 TMD of the CD28 CAR mediates interaction with the endogenous CD28 and that this is responsible for cytokine release, an undesired side reaction associated with cancer cell killing. Since their CARs with de novo designed TMDs show much reduced cytokine release, this confirms their notion that non-natural TMDs would isolate CARs from endogeneous CD28. The novel functional aspect about the designer CARs presented here is thus that cytokine secretion is less troubling when compared with the original CD28 CAR28. Most interestingly, they find that the antitumor activity of the constructs tested in an engineered mouse tumour model as well as induced cytokine release scales with the oligomeric state.

In sum, the work is not only very elegant from a membrane protein engineering point of view. Rather, it represents a fine example of how protein design can be translated into medical applications.

Reviewer #3:

The paper by Elazar et al., describes a highly significant and rigorously performed study that addresses the influence of TMD-mediated chimeric antigen receptor (CAR) oligomerization on the antitumor activity and cytokine release of CAR-T cells.

The transmembrane helices (TMHs) of most single-pass transmembrane receptors have long been considered as mere membrane anchors, and hence their function in receptor signaling has often been neglected. In recent years, however, several studies have found that TMH oligomerization can play an active and often essential role in the signaling mechanism of receptors including growth factor receptors, death receptors, and immune receptors. In this study, the authors have demonstrated that designed TMH oligomerization, which mediates CAR oligomerization, can have profound impact on the activity and cytokine release of CAR-T cells. Their findings have illuminated another dimension for CAR-T/NK engineering and optimization.

Specifically, the authors first performed de novo design of completely new TMHs that can form stable parallel dimer, trimer, or tetramers, as validated by SDS-PAGE and crystallography. Then, by replacing the TMH of the most used CARs, which is the TMH of CD28, with the designed TMHs, they found that the antitumor activity of the CAR-T cells in mice scaled linearly with CAR oligomeric state encoded by the designed TMH. More strikingly, their in vitro assays showed that the CARs with the designed TMHs all induced 2-10-fold less cytokine release than the CAR with CD28 TMH, raising the suspicion that the CD28 TMH may pair CAR with endogenous T cell CD28 leading to higher level of CD28 signaling independent of the CARs. Indeed, they have shown that a set of mutations in the CD28 TM of the regular CAR that is likely to disrupt TMH homodimerization led to 2-6-fold reduction in CAR-T cell cytokine release. Collectively, their results suggest that there is ample room for improving the CAR-T antitumor activity / cytokine release ratio by optimizing the CAR TM sequence, and it remains to be seen if this approach can be used to achieve better outcomes in higher animals.

https://doi.org/10.7554/eLife.75660.sa1

Author response

Essential revisions:

1. In vitro experiments showed a clear correlation between oligomerization state of the CAR and cytokine secretion when CAR T cells were exposed to HER2+ cancer cells. The higher order oligomers also were more effective at slowing tumor growth in mice injected with HER2 tumor cells. These results confirm previous observations that dimeric CARs (via disulfide formation) are more effective than monomeric CARs.

This is an important point and we had not explicitly acknowledged that dimer formation was already thought to be important for optimal CAR function in the original manuscript. We have clarified this in the Introduction (lines 74-75) with relevant references.

One exciting finding was that the designed CAR tetramer was as effective at suppressing tumor growth in mice as a standard CAR construct used in the field (transmembrane domain derived from CD28), but the tetramer CAR stimulated less cytokine release than the CD28TM CAR in vitro. The CAR T therapies currently used in the clinic frequently stimulate dangerous levels of cytokines, if a CAR T cell can be created that is as effective as current treatments but overall cytokine release is lowered, this could be an improvement over current treatment options. One caveat about the efficacy data presented in this paper is that CAR T cells were administered to the mice only a single day after injection of the cancer cells. More rigorous tests of efficacy will be needed to determine if the tetrameric CAR is on par with standard constructs used in therapy.

We agree with the Reviewer that this is an important caveat. The MC38-HER2 tumor model was used here because it is relatively rapid and it is well suited to quantitative comparisons of relative functional potency, but the extremely aggressive growth of MC38 necessitates very early CAR T cell administration in order to observe tumor control. The revised Discussion (lines 475-478) now makes it clear that testing the clinical potential of proCARs will require additional experiments in models that more closely approximate treatment of cancer patients.

2. The protein design process contained no consideration for how activation signals are transmitted from the extracellular domain (ECD) of the CAR to the intracellular activation domains. Does this suggest that specific conformational changes are not a part of the activation process? It would be great if the authors could comment on this. Do the results say anything about how binding to the ECD does or does not activate signaling?

This is a very good point. We can’t rule out dynamics or conformational changes in response to ligand binding to the ECD driving the CAR-T signaling, but as the Reviewer notes, our designs don’t consider such possibilities yet increase activation. The revised Discussion (lines 452-458) explains that our results, particularly the linear correlation between oligomeric state and activation, favor the view that the specific geometry of interaction and conformational changes or dynamics play a minor role if any in receptor activation.

3. TMH oligomer design. It appears that the crystal structures and the designed structures agree with rather high success rate. Wouldn't you be able to predict the oligomeric structure of a given TMH sequence? Can your design protocol be revised to predict the assumed dimeric structure of CD28 TMH? We are asking because there is no direct biochemical evidence that the CD28 TM mutations have abolished TMH dimerization.

Rather than rely on an independent model of this interface, we based our approach on Tox-Luc data from Leddon et al., (Front Immunol 2020, cited in the manuscript) showing that the Y-T interactions drive TMD dimerisation in a manner very similar to CD3 zeta. We, therefore, used the latter structure as a template for introducing the mutations to the CD28 TMD. We agree that the results do not provide “proof” that CD28 associates through the interface observed in the CD3 zeta structure. However, the fact that the mutations were isosteric, yet abolished association and reversed the amplification of T cell functions, is evidence in favour of this view.

4. A potential concern of analyzing TMD oligomeric state using SDS-PAGE is that the strong assembly interactions can survive the harsh condition of SDS-PAGE but the weak ones don't. You should comment on the possible issue, if any, of TMD forming higher-order clusters in the membrane in the discussion. Has this issue been eliminated in the design phase?

The reviewer is correct to point out that SDS-PAGE migration does not report on weak self-association and therefore does not rule out unintended secondary interfaces that could drive higher-order interactions in the membrane. We now clearly state this is the revised Discussion (lines 420-423). We do note, however, that the striking linearity in the correlations between the designed proMP oligomeric state and the measured proCAR functional output suggests that the designed interfaces drive the dominant interactions, and we see no evidence of influence by unintended higher-order assemblies. Indeed, the design process favors the desired oligomeric state (up to tetramers) but we would not think that computational design could “guarantee” that higher-order clusters were eliminated.

5. An observation of profound impact is the beautiful linear correlation between the proCAR TMD oligomeric state and tumor killing in vivo. However, the increase is not obvious for in vitro cell cytotoxicity assay. Did the two studies use two different cancel cell lines? Please explain.

We agree that the lack of scaled activity when in vitro cytotoxicity is used as a measure is striking. The revised manuscript addresses this directly in the Results section (lines 369-371) and offers a possible explanation. Others (see Majzner et al., 2020, cited in the manuscript) have observed that CARs with apparently equivalent killing activity against high-antigen targets can display very different activities against low-antigen targets in the same assay, suggesting that in vitro killing is easily saturated at high antigen densities. The cell lines used for the in vitro (MC57 mouse fibrosarcoma cells) and in vivo (MC38 mouse colon carcinoma cells) experiments are both engineered to express high levels of HER2 and we have no evidence that the difference cell lines is the source of this discrepancy. We believe that the oligomeric proCARs segregate in vivo but not in vitro because in vivo tumor control over days to weeks is a function of multiple parameters that are all affected by CAR signal strength (T cell proliferation, survival, cytokine production and cytotoxic potential) whereas short-term in vitro killing primarily measures release of cytotoxic effector molecules.

6. For readers not working on CAR-T, can you discuss why heterotypic mixing of CAR-CD28TM and endogenous CD28 via TM-TM dimerization can cause much greater cytokine release given both contain the same CD28 signaling tail? Is it because CAR-CD28TM mixing with CD28 would increase the total amount of CD28 that can engage HER2 and activate? If so, does the 2-6 fold increase in cytokine release make qualitative sense? Do CAR and endogenous CD28 induce different cytokine release?

We agree that this is especially complicated for the non-specialist and have clarified in the revised Discussion (lines 433-437) that we believe the effect of the CD28 TMD interaction on CAR signaling to be a direct consequence of recruitment of additional costimulatory signaling tails into activated receptor complexes: each CD28 TMD in a CAR dimer could theoretically recruit an endogenous CD28 dimer via domain-swapped TMD interactions. While this is entirely consistent with several-fold amplification of cytokine output by the CD28 TMD, it is not known whether signaling from a CD28 tail in the context of a CAR is qualitatively or quantitatively equivalent to that in the context of the native protein. Thus we hesitate to draw direct connections between the stoichiometry of the interaction and the observed fold-differences in cytokine release.

https://doi.org/10.7554/eLife.75660.sa2

Article and author information

Author details

  1. Assaf Elazar

    Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
    Contribution
    Conceived and developed the proMP modelling and design methods and computed the designs, Conceived and developed the proMP modelling and design methods and computed the designs, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Writing – review and editing
    Contributed equally with
    Nicholas J Chandler and Ashleigh S Davey
    Competing interests
    Author is an inventor on a related patent WO2021229581A2
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5281-0908
  2. Nicholas J Chandler

    1. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    Contribution
    Data curation, Formal analysis, Investigation, Methodology, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures, Writing – review and editing
    Contributed equally with
    Assaf Elazar and Ashleigh S Davey
    Competing interests
    Author is an inventor on a related patent WO2021229581A2
  3. Ashleigh S Davey

    1. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    Contribution
    Data curation, Designed and performed in vitro primary T cell experiments and in vivo tumor control experiments, Formal analysis, Investigation, Methodology, Writing – review and editing
    Contributed equally with
    Assaf Elazar and Nicholas J Chandler
    Competing interests
    No competing interests declared
  4. Jonathan Y Weinstein

    Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
    Contribution
    Conceived and developed the proMP modelling and design methods and computed the designs, Investigation, Methodology, Software, Writing – review and editing
    Competing interests
    Author is an inventor on a related patent WO2021229581A2
  5. Julie V Nguyen

    Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    Contribution
    Investigation, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures
    Competing interests
    No competing interests declared
  6. Raphael Trenker

    1. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    Contribution
    Investigation, Methodology, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures, Produced and purified proMP peptides and performed crystallization screening. Determined crystal structures
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1748-0517
  7. Ryan S Cross

    1. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    2. Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    Contribution
    Methodology, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  8. Misty R Jenkins

    1. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    2. Immunology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    3. La Trobe Institute of Molecular Science, La Trobe University, Bundoora, Victoria, Australia
    Contribution
    Funding acquisition, Methodology, Resources, Writing – review and editing
    Competing interests
    No competing interests declared
  9. Melissa J Call

    1. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    Contribution
    Conceived and supervised proMP peptide production and structure determination strategies. Conceived and supervised proCAR T cell design and testing strategies. Determined crystal structures, Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    mjcall@wehi.edu.au
    Competing interests
    Author is an inventor on a related patent WO2021229581A2
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7684-5841
  10. Matthew E Call

    1. Structural Biology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia
    2. Department of Medical Biology, The University of Melbourne, Parkville, Victoria, Australia
    Contribution
    Conceived and supervised proMP peptide production and structure determination strategies. Conceived and supervised proCAR T cell design and testing strategies. Produced and purified proMP peptides and performed crystallization screening, Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    mecall@wehi.edu.au
    Competing interests
    Author is an inventor on a related patent WO2021229581A2
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5846-6469
  11. Sarel J Fleishman

    Department of Biomolecular Sciences, Weizmann Institute of Science, Rehovot, Israel
    Contribution
    Conceived and developed the proMP modelling and design methods. Supervised the design process and bacterial expression and self-association experiments, Conceptualization, Funding acquisition, Methodology, Project administration, Software, Supervision, Writing – original draft, Writing – review and editing
    For correspondence
    sarel.fleishman@weizmann.ac.il
    Competing interests
    Reviewing editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3177-7560

Funding

H2020 European Research Council (Consolidator Grant 815379)

  • Sarel Fleishman

Dr Barry Sherman Institute for Medicinal Chemistry (Institutional Funding)

  • Sarel Fleishman

Sam Switzer and Family (Charitable Donation)

  • Sarel Fleishman

National Health and Medical Research Council (Project Grant 1158249)

  • Misty R Jenkins
  • Melissa J Call
  • Matthew E Call
  • Sarel Fleishman

Harry Secomb Foundation (Charitable Donation)

  • Matthew E Call

Percy Baxter Charitable Trust (Charitable Donation)

  • Matthew E Call

Harold and Cora Brennen Benevolent Trust (Charitable Donation)

  • Matthew E Call

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We acknowledge the use of the CSIRO Collaborative Crystallisation Centre (C3) for crystallization screening. X-ray diffraction data were collected at the MX2 beamline of the Australian Synchrotron, and we thank the beamline scientists for their technical support. Research was supported in part by the NHMRC IRIISS Infrastructure Support and Victorian State Government Operational Infrastructure Support (to WEHI).

Ethics

All animal experiments were approved and performed in accordance with the regulatory standards of the Walter and Eliza Hall Institute Animal Ethics Committee (Approval: WEHI-2019.020).

Senior Editor

  1. José D Faraldo-Gómez, National Heart, Lung and Blood Institute, National Institutes of Health, United States

Reviewing Editor

  1. Nir Ben-Tal, Tel Aviv University, Israel

Reviewers

  1. Brian Kuhlman, University of North Carolina at Chapel Hill, United States
  2. Dieter Langosch, The Technical University of Munich, Germany
  3. James J Chou

Publication history

  1. Preprint posted: July 26, 2020 (view preprint)
  2. Received: November 18, 2021
  3. Accepted: April 14, 2022
  4. Version of Record published: May 4, 2022 (version 1)

Copyright

© 2022, Elazar et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Assaf Elazar
  2. Nicholas J Chandler
  3. Ashleigh S Davey
  4. Jonathan Y Weinstein
  5. Julie V Nguyen
  6. Raphael Trenker
  7. Ryan S Cross
  8. Misty R Jenkins
  9. Melissa J Call
  10. Matthew E Call
  11. Sarel J Fleishman
(2022)
De novo-designed transmembrane domains tune engineered receptor functions
eLife 11:e75660.
https://doi.org/10.7554/eLife.75660

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    2. Medicine
    Iago de Castro Silva, Anna Bianchi ... Jashodeep Datta
    Short Report Updated

    Background:

    Partial/complete pathologic response following neoadjuvant chemotherapy (NAC) in pancreatic cancer (PDAC) patients undergoing pancreatectomy is associated with improved survival. We sought to determine whether neutrophil-to-lymphocyte ratio (NLR) dynamics predict pathologic response following chemotherapy in PDAC, and if manipulating NLR impacts chemosensitivity in preclinical models and uncovers potential mechanistic underpinnings underlying these effects.

    Methods:

    Pathologic response in PDAC patients (n=94) undergoing NAC and pancreatectomy (7/2015-12/2019) was dichotomized as partial/complete or poor/absent. Bootstrap-validated multivariable models assessed associations between pre-chemotherapy NLR (%neutrophils÷%lymphocytes) or NLR dynamics during chemotherapy (ΔNLR = pre-surgery—pre-chemotherapy NLR) and pathologic response, disease-free survival (DFS), and overall survival (OS). To preclinically model effects of NLR attenuation on chemosensitivity, Ptf1aCre/+; KrasLSL-G12D/+;Tgfbr2flox/flox (PKT) mice and C57BL/6 mice orthotopically injected with KrasLSL-G12D/+;Trp53LSL-R172H/+;Pdx1Cre(KPC) cells were randomized to vehicle, gemcitabine/paclitaxel alone, and NLR-attenuating anti-Ly6G with/without gemcitabine/paclitaxel treatment.

    Results:

    In 94 PDAC patients undergoing NAC (median:4 months), pre-chemotherapy NLR (p<0.001) and ΔNLR attenuation during NAC (p=0.002) were independently associated with partial/complete pathologic response. An NLR score = pre-chemotherapy NLR+ΔNLR correlated with DFS (p=0.006) and OS (p=0.002). Upon preclinical modeling, combining NLR-attenuating anti-Ly6G treatment with gemcitabine/paclitaxel—compared with gemcitabine/paclitaxel or anti-Ly6G alone—not only significantly reduced tumor burden and metastatic outgrowth, but also augmented tumor-infiltrating CD107a+-degranulating CD8+ T-cells (p<0.01) while dampening inflammatory cancer-associated fibroblast (CAF) polarization (p=0.006) and chemoresistant IL-6/STAT-3 signaling in vivo. Neutrophil-derived IL-1β emerged as a novel mediator of stromal inflammation, inducing inflammatory CAF polarization and CAF-tumor cell IL-6/STAT-3 signaling in ex vivo co-cultures.

    Conclusions:

    Therapeutic strategies to mitigate neutrophil-CAF-tumor cell IL-1β/IL-6/STAT-3 signaling during NAC may improve pathologic responses and/or survival in PDAC.

    Funding:

    Supported by KL2 career development grant by Miami CTSI under NIH Award UL1TR002736, Stanley Glaser Foundation, American College of Surgeons Franklin Martin Career Development Award, and Association for Academic Surgery Joel J. Roslyn Faculty Award (to J. Datta); NIH R01 CA161976 (to N.B. Merchant); and NCI/NIH Award P30CA240139 (to J. Datta and N.B. Merchant).

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    During obesity and high fat-diet (HFD) feeding in mice, sustained low-grade inflammation includes not only increased pro-inflammatory macrophages in the expanding adipose tissue, but also bone marrow (BM) production of invasive Ly6Chigh monocytes. As BM adiposity also accrues with HFD, we explored the relationship between the gains in BM white adipocytes and invasive Ly6Chigh monocytes by in vivo and ex vivo paradigms. We find a temporal and causal link between BM adipocyte whitening and the Ly6Chigh monocyte surge, preceding the adipose tissue macrophage rise during HFD in mice. Phenocopying this, ex vivo treatment of BM cells with conditioned media from BM adipocytes or bona fide white adipocytes favoured Ly6Chigh monocyte preponderance. Notably, Ly6Chigh skewing was preceded by monocyte metabolic reprogramming towards glycolysis, reduced oxidative potential and increased mitochondrial fission. In sum, short-term HFD changes BM cellularity, resulting in local adipocyte whitening driving a gradual increase and activation of invasive Ly6Chigh monocytes.