SVE NEWS & SCIENCE.ORG Sharing Series — Fc glycoengineering of a PD-L1 antibody harnesses Fcγ receptors for increased antitumor efficacy

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Tweaking the scaffold

Immune checkpoint blockade using anti–PD-L1 monoclonal antibodies (mAbs) is used for treatment of multiple cancers. Whether these mAbs have the optimal Fc scaffold to induce Fc-mediated effector functions is unclear. Here, Cohen-Saban et al. investigate how FcγR engagement by anti–PD-L1 contributes to antitumor immunity. They demonstrate that beneficial FcγR signaling pathways are not engaged by FDA-approved mAbs and use two approaches to increase the ratio of activating to inhibitory FcγR pathway activation. Blockade of the inhibitory FcγRIIB in combination with anti–PD-L1 improved antitumor responses. Similarly, afucosylation of the IgG1 Fc region improved antitumor responses, and this was due to alterations in the tumor microenvironment. These findings identify two approaches to improve anti–PD-L1 therapy and suggest that an afucosylated IgG1 scaffold renders anti–PD-L1 mAbs more effective. —HMI

Abstract

FDA-approved anti–PD-L1 monoclonal antibodies (mAbs) bear the IgG1 isotype, whose scaffolds are either wild-type (e.g., avelumab) or Fc-mutated and lacking Fcγ receptor (FcγR) engagement (e.g., atezolizumab). It is unknown whether variation in the ability of the IgG1 Fc region to engage FcγRs renders mAbs with superior therapeutic activity. In this study, we used humanized FcγR mice to study the contribution of FcγR signaling to the antitumor activity of human anti–PD-L1 mAbs and to identify an optimal human IgG scaffold for PD-L1 mAbs. We observed similar antitumor efficacy and comparable tumor immune responses in mice treated with anti–PD-L1 mAbs with wild-type and Fc-mutated IgG scaffolds. However, in vivo antitumor activity of the wild-type anti–PD-L1 mAb avelumab was enhanced by combination treatment with an FcγRIIB-blocking antibody, which was co-administered to overcome the suppressor function of FcγRIIB in the tumor microenvironment (TME). We performed Fc glycoengineering to remove the fucose subunit from the Fc-attached glycan of avelumab to enhance its binding to the activating FcγRIIIA. Treatment with the Fc-afucosylated version of avelumab also enhanced antitumor activity and induced stronger antitumor immune responses compared with the parental IgG. The enhanced effect by afucosylated PD-L1 antibody was dependent on neutrophils and associated with decreased frequencies of PD-L1+ myeloid cells and increased infiltration of T cells in the TME. Our data reveal that the current design of FDA-approved anti–PD-L1 mAbs does not optimally harness FcγR pathways and suggest two strategies to enhance FcγR engagement to optimize anti–PD-L1 immunotherapy.

INTRODUCTION

Therapeutic blockade of the PD-1/PD-L1 axis using anti–PD-1 and PD-L1 monoclonal antibodies (mAbs) is a hallmark of cancer immunotherapy (1). In addition to the Fab-mediated activity of these immune checkpoint mAbs, their Fc domain composition may affect their antitumor activity because of engagement of Fcγ receptor (FcγR) pathways (2). Differences in the activity of FcγR pathways lead to distinct in vivo outcomes induced by anti–PD-1 versus anti–PD-L1 mAbs. Mouse anti–PD-1 mAbs are FcγR independent, and the presence of FcγR-interactions compromises their antitumor activity (24). In contrast, anti–PD-L1 mouse mAbs show augmented antitumor effects when activating FcγR-binding activity is introduced (25). This FcγR-dependent pathway synergizes with the FcγR-independent PD-1/L1–blocking activity of anti–PD-L1 mAbs, thereby augmenting their therapeutic efficacy.
Now, three PD-L1 mAbs have been granted U.S. Food and Drug Administration (FDA) approval for the treatment of various types of skin, lung, bladder, breast, liver, and kidney cancers (68). Avelumab is an anti–PD-L1 human immunoglobulin G1 (IgG1) mAb capable of interacting with and activating various human FcγR signaling pathways, resulting in antibody-dependent cell-mediated cytotoxicity/phagocytosis (ADCC/P). Two other PD-L1 mAbs used in the clinic, atezolizumab and durvalumab, are mutated in their Fc domain, which abolishes FcγR binding, thereby preventing Fc-mediated effector functions (9). Although all three mAbs exhibit clinical activity, no data are available to directly compare their relative efficacy. It is therefore not known whether avelumab benefits from FcγR-mediated mechanisms, as was described for mouse PD-L1 mAbs. Furthermore, the optimal IgG scaffold for human PD-L1 mAbs is currently unclear.
In this work, we characterized the effector functions engaged by the Fc domain of two anti–PD-L1 drugs, avelumab and atezolizumab, in a humanized FcγR mouse model (10). In addition, we evaluated whether FcγR pathways can be harnessed to further improve their antitumor activity in vivo by directly comparing the in vivo activity of several human IgG scaffolds. These results allowed us to identify the optimal Fc variant for the therapeutic activity of human anti–PD-L1 mAbs.

RESULTS

IgG scaffolds of the FDA-approved PD-L1 mAbs do not engage beneficial FcγR pathways

To evaluate both Fab- and Fc-mediated activities of human PD-L1 mAbs, we first assessed the mouse and human PD-1/PD-L1 binding and blocking activity of avelumab (wild type) and atezolizumab (Fc mutated). Both mAbs cross-reacted with murine PD-L1, and the degree of blocking of the PD-1/PD-L1 axis was comparable between species and similar to that of the commercial anti-murine PD-L1 clone, which is widely used in preclinical studies (Fig. 1, A and B, and fig. S1A) (2). Considering the cross-reactivity of these human mAbs with the mouse ligand and the high human-mouse similarity of the PD-1/PD-L1 pathways, we anticipated that mice expressing murine PD-1/PD-L1 could be used to model the effect of PD-L1 inhibition by these human PD-L1 mAbs.
Fig. 1. IgG scaffolds of the FDA-approved PD-L1 mAbs do not engage beneficial FcγR pathways.
(A) Binding ELISA of avelumab or atezolizumab to plate-bound recombinant human (left) and mouse (right) PD-L1 protein. Data are shown from one of three independent experiments with similar results. (B) PD-1/L1–blocking activity of avelumab and atezolizumab. ELISA-based assay to determine binding of human (left) and mouse (right) soluble PD-1 protein to plate-bound recombinant human (left) and mouse (right) PD-L1 protein in the presence of increasing concentrations of the indicated anti–PD-L1 mAbs. Data shown are from one of two independent experiments with similar results. (C) IgG scaffolds of anti–PD-L1 mAbs avelumab, atezolizumab, and durvalumab with glycosylation at asparagine (Asn) 297. Mutation to alanine (Aln) results in deglycosylation. Glycan residues are as follows: core glycan in dash: GlcNac (blue rectangle); mannose (green circle); non-core: fucose (red triangle); galactose (yellow circle); sialic acid (purple diamond). Image was created with Biorender.com. (D) huFcγR mice were inoculated with MC38 tumor cells and treated with IgG1 and IgG1-N297A variants (100 μg) of avelumab and atezolizumab as indicated. Data are graphed as means ± SEM and show one experiment representative of three repeats. One-way ANOVA with Tukey’s post hoc test. *P ≤ 0.05. n = 9 or 10. (E) Flow cytometry analysis of immune cell composition in MC38 tumors dissected from tumor-bearing huFcγR mice 8 days after initiation of treatment with anti–PD-L1 Fc variants. Data are graphed as bar plots with means ± SEM and represent one of two independent experiments. One-way ANOVA with Tukey’s post hoc test. *P ≤ 0.05. n = 5. Gating strategy and individual mice are shown in figs. S1 (G to I).

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Nevertheless, mice and humans differ in their FcγR structures and expression profiles and in the binding affinities of IgG subclasses to FcγRs (1112). Therefore, the previously identified role of Fc-FcγR interactions of mouse PD-L1 mAbs has limited relevance for human clinical translation. To overcome this constraint, we used a previously described humanized FcγR mouse strain, in which human FcγRs are expressed instead of their mouse orthologs in a manner that more precisely recapitulates human cellular and tissue expression patterns in humans (101317). To assess the effect of the IgG backbones on the in vivo antitumor activity of human PD-L1 mAbs, we generated two Fc versions of avelumab and atezolizumab (Fig. 1C) by switching atezolizumab to the IgG1 wild-type scaffold and avelumab to the FcγR-binding null IgG1 (N297A) scaffold. Switching the IgG backbone for these two mAbs did not alter their Fab-mediated PD-L1 binding (fig. S1B). We compared the treatment efficacy of these two Fc variants in huFcγR mice with established MC38 adenocarcinoma tumors, which revealed a similar effect on tumor growth control for both IgG scaffolds of each PD-L1 mAb (Fig. 1D). To test whether the similar efficacy of huIgG1 and huIgG1-N297A was due to residual binding of the latter to FcγRI, we compared the antitumor activity of avelumab IgG1-N297A in huFcγR mice and FcγR-knockout (KO) mice bearing MC38 tumors. Similar levels of tumor growth control were observed in both mouse strains, suggesting that residual engagement of FcγRs by this Fc variant does not mediate its antitumor activity (fig. S1C). Phenotyping of the tumor microenvironment (TME) composition after IgG1 and IgG-N297A anti–PD-L1 treatments revealed similar effects on the frequencies of different immune cell populations (Fig. 1E and fig. S1, D to H).
These results suggest that both the IgG1 wild-type and IgG1-N297A scaffolds of human PD-L1 mAbs result in a similar potency without affecting their mode of action. Of clinical relevance, these data suggest that despite the potential of avelumab (IgG1) to engage Fc effector function in vitro (18), it does not engage beneficial FcγR pathways as part of its antitumor activity in vivo.

FcγRIIB is an inhibitory checkpoint of antitumor activity by anti–PD-L1 IgG1

Our results characterizing the similar antitumor activity of human IgG1 and IgG1-N297A variants of PD-L1 mAbs are in contrast to previous observations for mouse PD-L1 mAbs in the same tumor models. In prior studies, the mouse IgG2a subclass (associated with potent activation of mouse FcγRs) results in enhanced antitumor activity compared with the Fc-silent version of the same PD-L1 mAb clone (2). We next explored the factors that limit engagement of FcγR pathways by the human IgG1 subclass. FcγRIIB represents the sole inhibitory FcγR that directly antagonizes the immunostimulatory intracellular signals of activating FcγRs (11). To assess the potential involvement of FcγRIIB in the activity of anti–PD-L1 mAbs at the tumor site, we evaluated its expression pattern on different immune cells in the TMEs, spleens, and draining lymph nodes (dLNs) of tumor-bearing huFcγR mice. We found that FcγRIIB is highly expressed on the surfaces of dendritic cells (DCs), macrophages, monocytes, and neutrophils and is significantly up-regulated on DCs and macrophages in the TME compared with the peripheral organs (Fig. 2A and fig. S2, A to C).
Fig. 2. FcγRIIB is an inhibitory checkpoint of antitumor activity by anti–PD-L1 IgG1.
(A) dLNs, spleens, and MC38 tumors were dissected 7 days after tumor inoculation in huFcγR mice and analyzed by flow cytometry. Gating strategy is shown in fig. S2 (A to C). ΔMFIs of huFcγRIIB are shown. Each dot represents an individual mouse (n = 6), and data are graphed as means ± SEM. Kruskal-Wallis with Dunn’s post hoc test or one-way ANOVA with Tukey’s post hoc test was performed on the basis of normality. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. (B) huFcγR mice were inoculated with MC38 tumor cells and treated with avelumab alone or in combination with huFcγRIIB blocker (clone 2B6). Left: Tumor progression over time. Data are graphed as means ± SEM. One-way ANOVA with Tukey’s post hoc test. **P ≤ 0.01, ****P < 0.0001. Middle: Individual mice on day 21 after treatment onset. Data are graphed as means ± SEM. Mann-Whitney test. *P ≤ 0.05. Right: Survival over time. Log-rank test with Bonferroni corrected threshold. **P ≤ 0.01, ***P ≤ 0.001. Data shown are from one experiment representative of two independent repeats, n = 10 or 11. (C) CD16A+/+CD16B+/+ and huFcγR mice were inoculated with MC38 tumor cells and treated with avelumab alone or in combination with huFcγRIIB blocker (clone 2B6), respectively. Left: Tumor progression over time. Mann-Whitney test. *P ≤ 0.05. Right: Individual mice on day 14 after treatment initiation. Mann-Whitney test. *P ≤ 0.05. Data are presented as means ± SEM. n = 30 to 38. (D) Paraffin sections of Merkel cell carcinoma or renal cell carcinoma tumors before treatment with avelumab were stained using DAPI (cyan), anti-CD32b (magenta), and anti-CD68 (yellow). Rectangles show magnification of representative cells: CD68 (yellow), CD32b (magenta), and costaining of CD68 and CD32b (white). Similar analysis of biopsies from three more patients is shown in fig. S2D.

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This expression pattern supports the possibility of a negative effect of the inhibitory FcγRIIB on the FcγR-dependent activity of anti–PD-L1 mAbs in the TME and the lack of enhanced activity by the human IgG1 isotype. To test this hypothesis, we combined systemic administration of avelumab (IgG1 wild type) with intratumoral injections of anti-huFcγRIIB–blocking mAb (clone 2B6). This combination significantly increased the in vivo therapeutic effect compared with avelumab monotherapy (Fig. 2B), implying that FcγRIIB inhibits the antitumor activity of PD-L1 IgG1 in the TME.
We hypothesized that blocking the interaction of anti–PD-L1 IgG1 with inhibitory FcγRIIB might increase its engagement with activating FcγRs in the TME and thereby potentiate its antitumor activity. Three activating FcγRs are expressed in human cells—FcγRI, FcγRIIA, and FcγRIIIA. We wished to determine which members of the activating FcγRs mediate the Fc-dependent activity of avelumab when its engagement of the inhibitory FcγRIIB is blocked. To address this question, we compared avelumab activity in mice expressing all the activating FcγRs with blockade of the inhibitory FcγR (huFcγR mice that were treated with FcγRIIB blocker mAb) with its activity in mice expressing only the activating FcγRIIIA and FcγRIIIB (CD16A+/+CD16B+/+ transgenic mice) (Fig. 2C). Superior control of tumor growth was achieved by avelumab treatment in mice expressing all activating FcγRs, supporting a potential role for multiple activating FcγR pathways. Thus, FcγRI and/or FcγRIIA, presumably in addition to FcγRIIIA, participate in the FcγR-mediated enhanced antitumor response after combination treatment with avelumab together with anti-FcγRIIB.
To predict whether a similar suppressive FcγRIIB-dependent mechanism may occur in human patients, we evaluated the expression of FcγRIIB in biopsies of patients with Merkel cell carcinoma or with renal cell carcinoma before initiation of their treatment with avelumab. We found high expression of FcγRIIB in the TME at these baseline biopsies (Fig. 2D and fig. S2D). CD68+ macrophages were abundant in these tumors, and we detected a subset of macrophages that coexpress FcγRIIB on their cell surface. This high expression of FcγRIIB in patient tumors at baseline, immediately before avelumab treatment, suggests that the FcγRIIB expression may also reduce avelumab efficacy in human patients.

Fc afucosylation of anti–PD-L1 mediates FcγR-dependent increased antitumor activity

An alternative approach to increase signaling of activating over inhibitory FcγRs by the anti–PD-L1 IgG1 is to modify its Fc region to provide an increased activating/inhibitory FcγR-binding affinity ratio (A/I). To this end, we generated a glycoengineered version of avelumab by removing the fucose subunit from the Fc-attached glycan (aFuc-IgG1), a modification known to selectively increase IgG1 binding affinity by 11-fold to the activating FcγRIIIA/B (19). We enriched the afucosylated Fc glycoform portion of avelumab to 71.86% compared with 5.47% in the parental IgG1 preparation (Fig. 3A and fig. S3, A and B). Binding of the aFuc-IgG1, IgG1, and IgG1-N297A Fc versions of avelumab to all human FcγRs was compared. The aFuc-IgG1 exhibited increased binding to only FcγRIIIA and FcγRIIIB relative to IgG1, whereas no binding of IgG1-N297A was detected for any FcγRs other than slight binding to the high-affinity FcγRI (Fig. 3B). These Fc modifications of avelumab did not affect its Fab-mediated PD-L1 binding (figs. S1A and S3C) or its in vivo half-life (fig. S3D). Thus, comparing the in vivo activity of these Fc variants provides a direct and precise readout of the contribution of FcγR engagement to the tested activity. To this end, we compared the antitumor activity in huFcγR mice of the IgG1 and aFuc-IgG1 versions of avelumab as monotherapies to treat MC38 tumors, and as a combination treatment for B16-F10 melanoma, together with a B16-F10 tumor–based vaccine that secretes granulocyte-macrophage colony-stimulating factor (GM-CSF) (GVAX). We observed significant improvement in antitumor activity in mice treated with aFuc-IgG1 compared with those treated with IgG1 (Fig. 3C and fig. S3D). This superior activity of aFuc-IgG avelumab versus IgG1 avelumab was abrogated in KO mice deficient for all FcγRs (Fig. 3D), indicating that the improved activity of aFuc-IgG1 avelumab is dependent on FcγR interactions.
Fig. 3. Fc afucosylation of anti–PD-L1 mediates FcγR-dependent increased antitumor activity.
(A) IgG1 and aFuc-IgG variants of avelumab were subjected to mass spectrometry analysis to evaluate the Fc glycan composition. Pie charts represent the distribution of the different glycoforms shown on the right. Glycan residues are as follows: GlcNac (blue rectangular); mannose (green circle); fucose (red triangle); galactose (yellow circle); sialic acid (purple diamond). Image was created with Biorender.com. (B) Binding ELISA of anti–PD-L1 IgG1, IgG1-N297A, and aFuc-IgG1 avelumab to plate-bound recombinant huFcγR proteins. Data are graphed as means ± SEM and show one experiment representative of two independent repeats. (C) huFcγR mice were inoculated with MC38 tumor cells and treated with IgG1 or aFuc-IgG1 avelumab. Left: Tumor progression over time. Kruskal-Wallis with Dunn’s post hoc test. *P ≤ 0.05, ***P ≤ 0.001, ****P < 0.0001. Right: Individual mice on day 17 after treatment initiation. Unpaired, two-tailed Student’s t test. *P ≤ 0.05. Data are graphed as means ± SEM. n = 20 or 21. (D) huFcγR KO mice were inoculated with MC38 tumor cells and treated with IgG1 or aFuc-IgG1 avelumab. Left: Tumor progression over time. Kruskal-Wallis with Dunn’s post hoc test. ***P ≤ 0.001. Right: Individual mice on day 17 after treatment initiation. Mann-Whitney test. ns: not significant. Data are graphed as means ± SEM; n = 14 to 16. (E) huFcγR mice were inoculated with MC38 tumor cells and treated with IgG1 or aFuc-IgG1 avelumab in combination with huFcγRIIB blocker (clone 2B6). Tumor progression over time is shown. Data are graphed as means ± SEM. n = 26.

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Next, we tested whether combining aFuc-IgG1 avelumab with the huFcγRIIB-blocking mAb can further improve its therapeutic activity as observed for the IgG1 variant. There was no additive antitumor response obtained by aFuc-IgG1 avelumab compared with the parental IgG1 avelumab when both variants are combined with anti-huFcγRIIB (Fig. 3E). This suggests that the combination of anti–PD-L1 with anti-huFcγRIIB, as well as Fc modification by afucosylation, works through a redundant FcγR-dependent mechanism to enhance anti–PD-L1 IgG1 antitumor activity, both allowing for improved antitumor in vivo efficacy. Our results identified aFuc-IgG1 as an improved human IgG scaffold for anti–PD-L1, which results in an increased FcγR-mediated therapeutic effect.

Afucosylated avelumab decreases frequencies of PD-L1+ myeloid cell subsets in the TME

Myeloid-derived suppressor cells (MDSCs) are potent suppressors of cancer immunity and the response to immunotherapy. We evaluated the effect of avelumab Fc variants on granulocytic MDSCs (Ly6ClowLy6G+) and monocytic MDSCs (Ly6Clow or Ly6Chigh Ly6G). We identified a significant Fc-dependent effect on the frequencies of the suppressive populations by anti–PD-L1 treatment. Treatment with aFuc-IgG1 resulted in a significant increase in granulocytic MDSCs and in a significant decrease in monocytic MDSCs in these mice compared with untreated mice, and this was observed in the TME but not in the spleen (Figs. 4A and fig. S4A). To better characterize the Fc effector functions mediated by the aFuc-IgG1 avelumab variant, and to understand its direct effect on its target cells, we compared the effect of treatment on different PD-L1+ cells in the TME. We used an anti–PD-L1 mAb that does not compete with avelumab for PD-L1 binding (fig. S4B) to evaluate the frequencies of PD-L1+ cells in the TME after treatment with avelumab Fc variants.
Fig. 4. Afucosylated avelumab decreases frequencies of PD-L1+ myeloid cell subsets in the TME.
(A) Flow cytometry analysis of Ly6G Ly6C MDSC percentages in MC38 tumors dissected from huFcγR mice 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. Each dot represents an individual mouse average from two technical repeats, and data are graphed as a violin plot. One-way ANOVA with Tukey’s post hoc test was performed. *P ≤ 0.05, **P ≤ 0.01. n = 6. (B) Flow cytometry analysis of PD-L1+ nonimmune cells (CD45), PD-L1+ total immune cells (CD45+), and PD-L1+ myeloid cells in MC38 tumors dissected from tumor-bearing huFcγR mice 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. Each dot represents an individual mouse, and data are graphed as violin plots. Kruskal-Wallis test or one-way ANOVA with Tukey’s post hoc test was performed on the basis of normality. *P ≤ 0.05, ***P ≤ 0.001. n = 12 or 13. (C) MFIs of PD-L1 are shown for different cells in the TME of MC38 dissected from tumor-bearing huFcγR mice 8 days after inoculation. Each dot represents an individual mouse, and data are graphed as means ± SEM; n = 13.

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The percentages of PD-L1+ CD45 tumor and stroma cells did not change with either IgG1 or aFuc-IgG1 variants of avelumab, negating the possibility of FcγR-mediated elimination of PD-L1+ stroma and cancer cells by the parental or Fc-enhanced avelumab variants (Fig. 4B and fig. S4D). In contrast, we found a significant decrease in the frequencies and absolute numbers of PD-L1+ CD45+ immune cells in tumors of huFcγR mice treated with aFuc-IgG1 avelumab, which was mainly attributed to a significant decrease in the frequencies of CD11b+ myeloid cells. We analyzed different myeloid populations on the basis of their PD-L1 surface expression and found that the most significant effect of aFuc-IgG1 avelumab treatment was a decrease in frequencies of PD-L1+ DCs. Frequencies of other immune cells, including PD-L1+ monocytes, macrophages, and natural killer (NK) cells, were not significantly affected by the treatment. This decrease in PD-L1+ myeloid cells after treatment with the aFuc-IgG1 variant is consistent with higher levels of PD-L1 expression on CD45+ myeloid immune cells compared with CD45 tumor and stromal cells at treatment baseline (Fig. 4C).
Together, these findings indicate that the enhanced antitumor activity of aFuc-IgG1 avelumab is associated with the Fc-mediated decrease in frequencies of specific PD-L1+ immune myeloid subsets with suppressive phenotypes in the TME (2023) and suggest a mechanism involving ADCC/P enhanced by aFuc-IgG1 avelumab mediating the elimination of these PD-L1hi immune cells.

Afucosylated avelumab induces FcγR-dependent TME immune modulation

To assess the factors leading to the superior activity of afucosylated avelumab, we analyzed the immune cell composition in tumors after treatment with aFuc-IgG1 or the parental IgG1 variant of avelumab. Treatment with aFuc-IgG1 resulted in an increase in CD45+ leukocyte infiltration to the tumor site manifested mainly by increased T cell infiltration (Fig. 5A). Increases in absolute (Fig. 5B) and relative (fig. S5A) T cell numbers in the TME were mainly attributed to an increase in effector CD4+ and CD8+ T cells, but not regulatory T cells (Tregs), suggesting engagement of an antitumor effector T cell response mediated by the treatment. The increased T cell abundance in the TME was associated with a simultaneous decrease in CD4+ and CD8+ T cells in the dLNs (Fig. 5C) but not in the spleen (fig. S5B), suggesting T cell migration from the lymph nodes to the tumor upon T cell activation. Myeloid cell subset composition in the TME was also affected by treatment with afucosylated avelumab (Fig. 5D and fig. S5, C and D and H) compared with IgG1. A decrease in the frequencies of DCs and macrophages was observed in the TME, which was associated with the relative increased frequencies of T cells. The most significant effect specific to afucosylated avelumab was found in neutrophils, a scarce population in the TME accounting for about 0.5% of immune cells, which were significantly increased both in their relative frequencies and in absolute numbers (Fig. 5D and fig. S5, C and D). This Fc-dependent effect on TME composition was abrogated when treating tumor-bearing FcγR KO mice, supporting the dependence on engagement of FcγR pathways (Fig. 5E and fig. S5, E and F and I).
Fig. 5. FcγR-dependent TME immune modulation after treatment with afucosylated avelumab.
Flow cytometry analysis of (A) absolute immune cell numbers in MC38 tumors, (B) absolute lymphocyte numbers in MC38, and (C) lymphocyte composition in dLNs dissected from tumor-bearing huFcγR mice 8 days after IgG1 or aFuc-IgG1 avelumab treatment initiation. Each dot represents an individual mouse, and data are graphed as a violin plot. (D) Flow cytometry analysis of relative frequencies of immune cell populations in MC38 tumors dissected from tumor-bearing huFcγR mice (left) or FcγR KO mice (right) 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. Data are graphed as bar plots with means ± SEM. For all plots, one-way ANOVA with Tukey’s post hoc test was performed. *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001. n = 5 to 7.

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Neutrophils mediate the superior antitumor activity of afucosylated avelumab

The TME contains heterogeneous populations of neutrophils, which can play a dual role in tumors by either promoting tumor growth or mediating antitumor immunity through several mechanisms (2429). We therefore sought to characterize the changes to the neutrophil phenotype driven by aFuc-IgG1 avelumab treatment. We performed single-cell RNA sequencing (scRNA-seq) of immune cells (CD45+) isolated from tumors of mice treated with either IgG1 or aFuc-IgG1 avelumab at day 8 after treatment onset and from tumors of untreated controls. Unsupervised clustering revealed distinct immune cell populations, annotated on the basis of marker genes (fig. S6, A and B), including neutrophils. Differential gene expression analysis comparing the two Fc variant treatments for each cell population showed that anti–PD-L1 treatments had divergent effects on neutrophils, two subpopulations of monocytes, and CD8 T cells (Fig 6A and fig. S6C). Neutrophils isolated from aFuc-IgG1 avelumab–treated tumors had a less suppressive phenotype, as indicated by lower PD-L1 expression (Fig. 6B). Up-regulation of genes related to complement and major histocompatibility complex II (MHC-II) antigen presentation–related genes such as C1qbCd74, and H2-Aa (Fig. 6C) in aFuc-IgG1 avelumab–treated tumors further demonstrated a shift from neutrophils with a suppressive state toward a more proinflammatory one. Accordingly, by gene set enrichment analysis (GSEA), we identified antigen presentation, complement, and adaptive immune responses as associated with aFuc-IgG1 avelumab neutrophils, whereas neutrophils from IgG1 avelumab–treated tumors up-regulated programs related to suppression of nuclear factor κB (NF-κB) signaling and wound healing (Fig. 6D). In line with the scRNA-seq data, protein-level analysis using flow cytometry revealed that a relatively larger fraction of neutrophils in aFuc-IgG1 avelumab–treated tumors were negative for expression of proteins associated with the suppressive neutrophil phenotypes, PD-L1 (fig. S6D) and Arg1 (fig. S6E).
Fig. 6. Neutrophils mediate the superior antitumor activity of afucosylated avelumab.
(A) Number of DEGs (y axis) by cell type (x axis). IgG1 is shown in red, and IgG1-aFuc is shown in blue. (B) Log-normalized gene expression of PD-L1 on neutrophils in MC38 tumors dissected from tumor-bearing huFcγR mice 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. *P ≤ 0.05, Wilcoxon rank sum test. (C) Highly variable genes in neutrophils in MC38 tumors dissected from tumor-bearing huFcγR mice 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. Graphs are color-coded for z-score gene expression. (D) GSEA performed on neutrophils in MC38 tumors dissected from tumor-bearing huFcγR mice 8 days after IgG1 and aFuc-IgG1 avelumab treatment initiation. Graphs are color-coded for normalized enrichment score (NES); bubble size depicts −log10 false discovery rate–adjusted P value. For (A) to (D), n = 3 to 5. (E) Tumor challenge and treatment scheme for neutrophil depletion experiment. (F and G) huFcγR mice were inoculated with MC38 tumor cells and treated with aFuc-IgG1 (F) or IgG1 avelumab (G) in combination with anti-Gr1 or IC. Left: Tumor progression over time. Right: Individual mice on day 11 after treatment onset. Data are graphed as means ± SEM. Mann-Whitney test. *P ≤ 0.05. n = 15. i.v., intravenous; s.c., subcutaneous; i.p., intraperitoneal.

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To validate the RNA data showing enrichment of antigen presentation–related genes in tumor neutrophils, we also measured MHC-II protein expression in dLN neutrophils. We observed a significant increase in MHC-II+/MHC-II ratio in dLN neutrophils after treatment with afucosylated anti–PD-L1 mAb versus untreated controls (fig. S6F). This supports a possible role of neutrophils in promoting antitumor immunity while acting as antigen-presenting cells to activate CD4+ T cells, as previously demonstrated under certain conditions (2830). These data are also in line with the increased frequencies of intratumoral CD4+ T cells we observed in aFuc-IgG1–treated mice.
To test whether the phenotypic changes observed in neutrophils contribute to the increased efficacy of afucosylated avelumab, we depleted neutrophils in huFcγR mice using a commercially available rat anti-Gr1–depleting Ab. We validated the depletion of Ly6Clow Ly6G+ neutrophils in blood, dLN, and tumors both 4 hours and 4 days after injection (fig. S6, G and H). Because anti-Gr1 Ab targets Ly6C/Ly6G, we evaluated whether Ly6C+ monocytes were affected by this protocol and found that the percentages of Ly6C+ cells were not affected (fig. S6, I and J), implying a neutrophil-specific depletion in this setting. We did not observe side effects, such as weight loss, in the treated mice (fig. S6K). Next, huFcγR tumor–bearing mice were treated with either IgG1 or afucosylated avelumab in combination with anti-Gr1 or isotype control (IC), and tumor volume kinetics were tracked (Fig. 6E). The absence of neutrophils compromised the superior antitumor activity of afucosylated IgG1 avelumab, but not of the parental IgG1 avelumab (Fig. 6, F and G). This implies an essential role for neutrophils in mediating the enhanced activity of the afucosylated IgG1 variant.
Human neutrophils express FcγRI, FcγRIIA, and FcγRIIIB; the latter is a glycosyl-phosphatidylinositol–linked human neutrophil–specific receptor with no intracellular signaling domain. FcγRIIIB by itself cannot engage IgG-mediated cytotoxicity by neutrophils but can increase the local concentration of IgG-antigen immune complexes on the neutrophil membrane, which may activate intracellular signaling and cytotoxic activity by the other activating FcγRs on these cells, FcγRI or FcγRIIA. In addition to FcγRIIIA binding, Fc afucosylation enhances the IgG binding to the neutrophil-specific FcγRIIIB. We wished to determine whether the enhanced binding of aFuc-IgG1 avelumab to neutrophil FcγRIIIB is needed for its enhanced antitumor activity. We therefore compared aFuc-IgG1 avelumab activity in humanized FcγR mice treated with FcγRIIB-blocking mAb, which have all the activating FcγRs available for interactions with the PD-L1 mAb, relative to CD16A+/+CD16B+/+ mice. We observed similar activity in these different settings, suggesting that FcγRIIA and/or FcγRI do not enhance the antitumor activity of aFuc-IgG1 avelumab (fig. S6L). This suggests that the enhanced tumor control of aFuc-IgG1 avelumab versus IgG1 avelumab does not involve direct FcγR signaling in neutrophils and that the beneficial role of neutrophils is induced by the FcγR-mediated changes in the TME described above.

DISCUSSION

Although the role of the Fc region of anti–PD-L1 mAbs in enhancing the antitumor response has been highlighted in several preclinical studies (23), the relevance of these observations to the activity of human PD-L1 IgGs in clinical use has not been fully addressed. Here, we demonstrate that the IgG1 subclass of avelumab does not optimally engage beneficial human FcγR pathways in the TME. We further demonstrate that increasing the ratio of activating to inhibitory FcγR pathway engagement by PD-L1 mAbs can potentiate the antitumor immunity mediated by this IgG subclass. We characterized two such practical approaches to potentiate FcγR pathways by anti–PD-L1 treatments: (i) combined treatment with FcγRIIB-blocking mAb and (ii) IgG Fc scaffold modification, in the form of afucosylated IgG1, to increase FcγRIIIA binding.
The similar antitumor efficacy seen in the Fc-silent N297A mutated compared with the human IgG1 subclass of the same PD-L1 mAb clones is in contrast to our previous observation of enhanced antitumor activity by mouse IgG2a and rat IgG2b subclasses compared with their Fc-silent variant or with mice with genetic deficiency of activating FcγRs (2). The mouse IgG2a and human IgG1 isotypes are widely considered as homologous subclasses because of their superior ability to engage Fc effector function, such as ADCC and ADCP, relative to the other IgG subclasses in each species. However, these subclasses exhibit significant differences in their FcγR-binding properties. The mouse IgG2a and rat IgG2b isotypes exhibit significant preferential binding capacity to the mouse activating FcγRs (A/I of 69 and 40, respectively) (231), whereas the binding ratio of human IgG1 is lower (FcγRIIIA/FcγRIIB binding ratio of 5) (10). This A/I ratio dictates the cytotoxic activity of many types of mAb and likely contributes to these observed cross-species differences due to a dominant effect of the inhibitory FcγRIIB in the TME on human IgG compared with mouse IgG2a. Moreover, the expression and distribution of the mouse versus human FcγRs on the effector monocytes and macrophages in the TME are significantly different (1232). These cross-species differences in IgG-FcγR interactions and FcγR distribution and function highlight the advantage of studies using a fully humanized FcγR-IgG in vivo system to better mimic the expected activity of IgGs in humans (3334).
The importance of FcγRs in enhancing the antitumor activity of additional mAb targeting immune checkpoints has been highlighted by several previous studies (2133539). Engagement of both activating and/or inhibitory FcγRs pathways was shown to contribute to the potency of these immunotherapies. Fc engagement of activating FcγRs was shown to contribute through induction of Ab-mediated elimination of intratumoral Treg by anti-CTLA4 (3537384041), anti–OX-40 (39), anti–4-1BB (42), and anti-GITR (38) mAbs. Conversely, engagement of the inhibitory FcγRIIB provides an inert scaffold for Ab cross-linking to enhance agonistic mAb activities, mainly those targeting tumor necrosis factor receptors, such as anti-CD40 mAbs (364344). Thus, many checkpoint mAbs can work through multiple mechanisms in vivo (e.g., checkpoint blockade and Treg depletion), which can synergize their activities for optimal therapeutic efficacy. Similarly, our study implies that Fab-mediated PD-1/L1–blocking and Fc-mediated modulation of the myeloid subsets are two modes of action engaged by anti–PD-L1 mAbs.
The importance of the Fc region in several FcγR-mediated pathways that compromise antitumor activity, as in the case of anti–PD-1 mAbs, is highlighted by our findings and those of others (23). Hence, the selection of the appropriate Ab scaffold (IgG isoform or Fc variant that selectively engages or avoids specific FcγR pathways) for each mAb is extremely important to achieve optimal antitumor activity. Moreover, these identified FcγR pathways provide opportunities to Fc-engineer checkpoint mAbs to improve their activity. Examples in clinical testing include an afucosylated variant of ipilimumab, an anti–CTLA-4 mAb modified for enhanced Treg depletion (45), and an Fc-modified variant of selicrelumab (2141-V11), a CD40 agonist enhanced for FcγRIIB-mediated cross-linking (13). Our study supports the rationale of clinical development and evaluation of an afucosylated variant of avelumab and other PD-L1 mAbs as second-generation checkpoint inhibitory mAbs with enhanced Fc effector function for enhanced control of tumor growth.
FcγRIIB reduces the efficacy of mAb-mediated immunotherapy (46), and combining an FcγRIIB antagonist with anticancer mAbs was demonstrated to increase the FcγR-dependent antitumor effect of anticancer cytotoxic mAb immunotherapy (47). Our results demonstrating FcγRIIB expression by macrophages in human Merkel cell and renal cell carcinoma lesions are consistent with previous reports on human melanoma biopsies (37). The dominant role of macrophages in Fc-mediated activity of therapeutic mAbs and their high abundance and FcγRIIB up-regulation in mouse and human tumors implies a possible role of macrophages in the Fc-mediated activity of avelumab and highlights these macrophage-associated pathways as potential biomarkers for a treatment response (48). This FcγRIIB expression pattern, and the enhanced antitumor activity of avelumab in combination with intratumoral injection of FcγRIIB blockers, indicates that this FcγR has an inhibitory effect on avelumab activity. Therapeutic FcγRIIB-specific antagonistic mAbs have been developed, and our study supports clinical evaluation of these available reagents for coadministration with avelumab or other anti–PD-L1 IgG1 mAbs (49).
Survival benefit for patients with advanced urothelial carcinoma treated with avelumab is associated with the presence of two or more of the FCGR2AH131 and FCGR3AV158 FcγR alleles with high affinity to IgG1 binding (50). Moreover, FcγR-expressing cells in the TME, including NK cells, monocytes, and macrophages, were associated with enhanced survival in the avelumab-treated group in this trial, further supporting the potential of beneficial FcγR engagement by avelumab in a subset of patients with these Fc effector–permissive conditions. The huFcγR mice used in our study carry the low-affinity FCGR2AR131 and FCGR3AF158 FcγR alleles. We show that avelumab as a monotherapy does not engage beneficial FcγR pathways in tumor models established in these mice unless the Ab is afucosylated. Together, our preclinical findings and the cited clinical data consistently imply that the binding intensity of anti–PD-L1 IgG1 with low-affinity alleles of FcγRs is insufficient to provide antitumor Fc effector function by this mAb. An increase in the activating FcγR-IgG1 binding interactions, either with FcγRs carrying higher-affinity alleles for IgG1 binding, by Fc-engineering of the IgG scaffold, or by blocking the competing binding to the inhibitory FcγRIIB, is needed to provide improved antitumor response. It is also predicted that treatment with afucosylated avelumab will benefit the patient subset with no or a low number of high-affinity FcγR alleles, compensating for the reduced treatment efficacy by the parental IgG1 mAb in these patients’ subset.
The improved activity of aFuc-IgG1 avelumab is characterized by alteration of the myeloid subsets in the TME. The most notable effects include the reduction of the PD-L1+ subset of DCs and MDSCs and modulation of neutrophil frequencies and cell states. Tumor-associated inflammatory PD-L1+ DCs can contribute to immune suppression and play a critical role in the antitumor response (225152). Hence, we suggest that afucosylated avelumab promotes a less suppressive TME by Fc-mediated reduction of these PD-L1–suppressive cell types through engagement with FcγRIIIA-expressing effector cells, such as macrophages and NK cells. This treatment leads to an increase in proinflammatory lymphocyte infiltration to the tumors, likely mediating the killing of tumor cells.
In contrast to the reduced frequencies of suppressive DCs and MDSCs, neutrophil frequencies increased after treatment with aFuc-IgG1 avelumab. This suggests that neutrophils are not targeted for elimination by the Fc-engineered PD-L1 mAb. Moreover, our data imply that they are not affected directly by their FcγRIIIB engagement and activation of intracellular FcγR signaling. Instead, we propose that neutrophils are affected by bystander effects of the proinflammatory response mediated in the TME by the aFuc-IgG1 avelumab treatment. Neutrophils have been reported to contribute to either protumor or antitumor activities. Antitumor functions of neutrophils include preventing metastasis formation (24), mediating the inhibition of early tumor growth (25), stimulating T cell responses (2627), and antigen processing and presenting of tumor peptides to T cells (2829). Our data imply that neutrophils could be indirectly affected by aFuc-IgG1 avelumab Fc-FcγR interactions with other immune cells. These interactions lead to a decrease in PD-L1+ suppressive cells and a subsequent increase in the proinflammatory state of the TME, which modulates the neutrophil population.
A limitation of this study is the use of mouse preclinical models to predict the clinical response of fully human Abs. To minimize this caveat, we used humanized FcγR mice to better recapitulate human IgG-FcγR interactions and validated the up-regulation of FcγRIIB we observed in mice tumors in relevant clinical tumor biopsies. However, additional significant cross-species differences in immunity to the tumors exist and may compromise the clinical translation potential of our preclinical finding.
Together, we have addressed a yet unresolved question in the field regarding the relevance of previously identified FcγR pathways in mice to the activity of human PD-L1 Abs used in the clinic. We studied the contribution of human FcγRs and Fc-mediated effector function to the activity of human FDA-approved PD-L1 mAbs. Our study concludes that Fc engineering of PD-L1 mAbs to increase affinity to activating FcγRs is a valid strategy to optimize tumor immunotherapy. This is opposite to the Fc engineering strategy previously applied in the design of this class of therapeutics (atezolizumab and durvalumab) to eliminate their interaction with FcγRs. Future studies should address whether additional types of therapeutic IgG1 can optimally engage Fc effector functions in the solid TME and whether they can be further optimized by Fc engineering to potentiate their engagement with activating FcγRs.

MATERIALS AND METHODS

Study design

The objective of this study was to elucidate how human PD-L1 mAb therapy recruits FcγR+ immune cells in the TME to enhance their antitumor activity. Our goal was to generate and characterize next-generation Fc variants of current FDA-approved anti–PD-L1 drugs with improved efficacy that can be readily advanced to clinical evaluation. We designed and performed experiments using flow cytometry, immunofluorescence staining, scRNA-seq, and in vivo tumor challenge. In this study, we used FcγR humanized mice that recapitulate the human Fc receptor activity and expression pattern and allowed us to overcome cross-species reactivity between human Abs and murine receptors. We also used human tumor sections for clinical relevance. The sample size and number of biological replicates are indicated in each of the figure legends. In several cases, experiments were pooled. Any experiment with n < 5 in treatment groups is a representative experiment of several repeats with similar results obtained. No data were excluded from the study except in the following: (i) In tumor challenge experiments, samples were excluded in the following cases: (a) outlier mice in the tumor model experiments that had significantly higher tumor volume compared with all other mice in the same experiment (Fig. 2C and fig. S3E), (b) micethat unexpectedly died at an early time point (Fig. 3D), and (c) mice that did not have a tumor at the time of sacrifice (fig. S6, G and I). (ii) In flow cytometry experiments, samples were excluded from analysis when (a) the population was too small for reliable gating (fewer than 300 cells), (b) when cell quantification was not possible because of technical issues (discrepancy in bead count), and (c) in rare cases of outlier samples that differed significantly from the group’s trend (Fig. 4B and figs. S4D and 5S, A to C). (iii) In enzyme-linked immunosorbent assay (ELISA) experiments, two concentrations were excluded because of technical problems (fig. S1A). In all in vivo experiments shown in the study, animals were randomized and assigned to experimental groups on the basis of tumor size, sex, and age, as denoted in Materials and Methods. Tumor measurements were performed by a researcher blinded to each animal’s treatment group. In all in vivo experiments shown in the study, data collection was performed either until the humane end point approved by the relevant Institutional Animal Care and Use Committee (IACUC) was reached or until mice were tumor free for up to 100 days after tumor inoculation.

Mice

All animal studies were approved by the IACUC of Weizmann Institute of Science under license numbers 05620720-2 and 03910422-3. FcγR KO (mFcγRanull), huFcγR mice (mFcγRAnull, hFcγRI+, hFcγRIIAR131+, hFcγRIIB+, hFcgRIIIAF158+, and hFcγRIIIB+), and CD16A+/+CD16B+/+ mice (mFcγRanull, hFcγRInull, hFcγRIIAnull, hFcγRIIBnull, hFcγRIIIAF158+, and hFcγRIIIB+) were provided by J. Ravetch from the Rockefeller University and maintained in-house. All in vivo experiments were performed in the Weizmann Institute of Science specific pathogen–free facility, and animals of both sexes were used between the ages of 8 and 12 weeks, randomly assigned to experimental groups.

Cell lines

MC38 and B16-F10 GVAX were provided by J. Ravetch from the Rockefeller University. B16-F10 were purchased from the American Type Culture Collection [Research Resource Identifier (RRID): CVCL_0159].

Tumor challenge and treatment

Tumor cell lines were maintained in a humidified incubator at 37°C and 5% CO2 and cultured in complete RPMI 1640 medium containing 25 mM Hepes, 1% l-glutamine, 10% fetal bovine serum, 1% penicillin-streptomycin, 1% nonessential amino acids, and 1% pyruvate. MC38 (2 × 106) or B16F10 (4 × 105) cells were implanted subcutaneously on the left flanks of the mice, and tumor volumes were measured every 2 to 3 days with an electronic caliper by an investigator blinded to treatment group. Volume was calculated using the formula (L2 * l)/2, where L is the largest diameter and l is the smallest diameter. Mice were randomized by tumor size (day 0) and were treated as described in the legend of each experiment. huFcγR mice were treated with 50 μg of anti–PD-L1 mAb IgG1, IgG1-N297A, afucosylated IgG1 (avelumab or atezolizumab), 25 μg of anti–human FcγRIIB, or control phosphate-buffered saline (PBS) on days 0, 3, and 6 unless indicated otherwise. GVAX (1 × 106) were irradiated (150 Gy) and implanted subcutaneously on the right flanks of B16-F10 tumor–bearing mice on days 0 and 6. Mice were monitored after treatment initiation and sacrificed when tumor size reached the Weizmann Institute of Science IACUC limit.

Neutrophil depletion in vivo

Anti-mouse Ly6G/Ly6C (clone RB6-8C5) (Bio X Cell, catalog no. BE0075, RRID: AB_10312146) and the corresponding rat IgG2b IC (clone LTF-2) (Bio X Cell, catalog no. BE0090, RRID: AB_1107780) were injected intravenously through the lateral tail vein, as indicated in the figure and/or legend. Depletion efficiency was assessed by flow cytometry analysis.

Human tumor tissues

Meckel cell carcinoma and renal cell carcinoma paraffin tumor sections were provided by A. Alva from the University of Michigan. Sections were 4 μm thick. Personal patient information was not reported.

Immunofluorescence staining

Immunofluorescence staining of paraffin-embedded sections was performed after deparaffinization with xylene, EtOH, and PBS. All slides were blocked with CAS-Block (Thermo Fisher Scientific, ZY-008120) for 60 min in a humidified chamber at room temperature (RT). All Abs were diluted in CAS-Block. Slides were incubated overnight at 4°C with primary Abs against CD68 (Abcam, catalog no. ab213363, RRID: AB_2801637), CD66b (Abcam, catalog no. ab197678), anti-LY75/DEC-205 (Abcam, catalog no. ab124897, RRID: AB_10976058), and anti-CD32B (Abcam, catalog no. ab77093, RRID: AB_1523300) at 1:200 dilution. The slides were incubated with biotin-conjugated anti-goat IgG (Jackson ImmunoResearch Labs, catalog no. 705-065-147, RRID: AB_2340397) at 1:100 dilution for 90 min in humidified chambers at RT. Slides were then incubated with Cy3-conjugated donkey anti-rabbit secondary Ab (Jackson ImmunoResearch Labs, catalog no. 711-165-152, RRID: AB_2307443) at 1:200 dilution or streptavidin Cy5 (Jackson ImmunoResearch Labs, catalog no. 016-170-084, RRID: AB_2337245) at 1:150 dilution for 60 min at RT. DAPI (4′,6-diamidino-2-phenylindole) staining was performed at a dilution of 1:1000 for 60 min at RT. All slides were mounted with Thermo Fisher Scientific Shandon Immu-Mount. Slides were imaged by using a Zeiss LSM 880 Airyscan confocal microscope with a 20×/0.8 M27 Plan-Apochromat objective.

Generation of anti–human PD-L1 and anti–human FcγRIIB Abs and their Fc variants

The variable heavy and light regions of avelumab or atezolizumab were synthesized on the basis of their published sequences. The variable heavy region of anti–human FcγRIIB (clone 2B6) was provided by J. Ravetch (Rockefeller University). The variable region sequences of the parental Abs were cloned and inserted into mammalian expression vectors with human IgG1 or human kappa (atezolizumab and anti–human FcγRIIB)/lambda (avelumab) Fc backbones. For the generation of the human IgG1 N297A Fc domain variant, site-directed mutagenesis using specific primers (avelumab: 5′-GAGAAGAGCAGTACGCCTCAACTTACCGCGTAG-3′, 5′-CTACGCGGTAAGTTGAGGCGTACTGCTCTTCTC-3′; atezolizumab: 5′-GAGAAGAACAATACGCCTCAACCTATCGCGTTG-3′, 3′-CAACGCGATAGGTTGAGGCGTATTGTTCTTCTC-5′) was performed by polymerase chain reaction (PCR) (Agilent Technologies) according to the manufacturer’s instructions. Mutated plasmid sequences were validated by direct sequencing (Life Science Core Facility, Weizmann Institute of Science). To produce Abs, Ab heavy and light chain expression vectors were transiently transfected into Expi293 cells (Thermo Fisher Scientific, catalog no. A14527).
For the generation of the human IgG1 afucosylated Fc domain variant, 200 μM 2-deoxy-2-fluoro-l-fucose (Biosynth Carbosynth) was added 1 day after transfection. The secreted Abs in the supernatant were purified by Protein G Sepharose 4 Fast Flow (GE Healthcare). Purified Abs were dialyzed in PBS and sterile-filtered (0.22 μm). Purity was assessed by SDS–polyacrylamide gel electrophoresis and Coomassie staining. Percentage of afucosylated forms was assessed by mass spectrometry and was estimated to be 75%.

Enzyme-linked immunosorbent assay

Binding specificity and affinity of anti–PD-L1 mAbs were determined by ELISA using recombinant mouse PD-L1 (Sino Biological, no. 50010-M08H or BPS Bioscience, no. 71117-1), human PD-L1 (Sino Biological, no. 10084-H08H or BPS Bioscience no. 71104-1), and human FcγRs (human FCGR1A, no. 10256-H08H; human FCGR2A, no. 10374-H08H; human FCGR2B, no. 10259-H08H; human FCGR3A, no. 10389-H08H; and human FCGR3B, no.11046-H08H1, Sino Biological). The 96-well ELISA Half Area High Binding plates (Greiner Bio-One, no. 675061) were coated overnight at 4°C with replicates of recombinant PD-L1 proteins (1 μg/ml) or human FcγRs (2 μg/ml). All subsequent steps were performed at RT. After washing, the plates were blocked for 1 hour with 1× PBS containing 2% bovine serum albumin (BSA; for PD-L1 proteins) or 10% BSA (for human FcγRs) and were then incubated for 1 hour (for PD-L1 proteins) or 2 hours (for human FcγRs) with serially diluted IgGs (dilutions are indicated in the figures and were prepared in the relevant blocking solution). After washing, plates were incubated for 1 hour with horseradish peroxidase–conjugated anti-human IgG (Jackson ImmunoResearch Labs, catalog no. 109-035-088, RRID: AB_2337584). For ELISA inhibition assay, after blocking nonspecific sites, plates were incubated for 1 hour with serially diluted IgGs with 1 μg/ml of mouse (BPS Bioscience, no. 71118) or human PD-1-biotin (BPS Bioscience, no. 71109-1) in 1× PBS with 2% BSA. After washing, plates were incubated for 1 hour with horseradish peroxidase–labeled streptavidin (no. 405210, BioLegend). Detection was performed using a one-component substrate solution (trimethylboron), and reactions were stopped with the addition of 0.18 M sulfuric acid. Absorbance at 450 nm was immediately recorded using a SpectraMax Plus spectrophotometer (Molecular Devices), and background absorbance from negative control samples was subtracted. The following Abs were tested: avelumab (prepared in-house), atezolizumab (prepared in-house), and anti-mouse PD-L1 (clone 10F.9G2) (Bio X Cell, catalog no. BE0101, RRID: AB_10949073).

Pharmacokinetics assay

MC38 tumor–bearing huFcγR mice were injected with anti–PD-L1 IgG1 (avelumab), IgG1-N297A, or afucosylated IgG1 (300 μg per mouse) and bled at indicated time points. Serum was stored at −80°C until all time points were collected. Ab concentration in serum was determined using a standard colorimetric ELISA assay. Briefly, assay plates were coated with recombinant human PD-L1 (1μg/ml; Sino Biological, no. 10084-H08H) and incubated overnight at 4°C. Plates were then blocked for 2 hours with 1× PBS with 10% fetal calf serum. Serial dilutions of serum were added to the plates, and plates were incubated for 2 hours. After washing, plates were incubated for 1 hour with horseradish peroxidase–conjugated anti-human IgG (Jackson ImmunoResearch Labs, catalog no. 109-035-088, RRID: AB_2337584). Absorbance at 450 nm was immediately recorded using a SpectraMax Plus spectrophotometer (Molecular Devices), and background absorbance from negative control samples was subtracted. Ab concentration in the serum was calculated for each time point based on a calibration curve of known concentrations and the dilution factor.

Tissue processing

Peripheral blood was collected into K2E EDTA tubes (Becton Dickinson), and cells were then stained at RT for 30 min. Erythrocytes were then lysed by 10-min incubation with 1 ml of BD FACS Lysing Solution (Becton Dickinson) followed by washes with fluorescence-activated cell sorting (FACS) buffer (1× PBS with 0.5% BSA and 2 mM EDTA) at 1200 rpm before analysis. Spleens and LNs were dissected through a 70-μm nylon cell strainer. For spleen, erythrocytes were lysed by 5-min incubation with red blood cell lysis buffer (Zymo Research) and washed with PBS. Tumors were mechanically dissected into small fragments and transferred to GentleMACS C tubes (Miltenyi Biotec) with deoxyribonuclease I (0.33 mg/ml; Roche) and Liberase TL (0.27 mg/ml; Roche) in Dulbecco’s modified Eagle’s medium (DMEM) (Biological Industries). Next, tumors were dissociated twice in the GentleMACS Octo Dissociator (Miltenyi Biotec), and the cell suspension was then further incubated at 37°C, 25 rpm, for 40 min. After incubation, tumors were subjected to two additional dissociation cycles in the GentleMACS. After the second dissociation cycle, tumors were dispersed through a 70-μm nylon cell strainer and washed with PBS.

Flow cytometry

Single-cell suspensions were prepared as described above. For surface staining, cells were plated in U-shaped 96-well plates (Thermo Fisher Scientific) in PBS. Cells were first stained with LIVE/DEAD Fixable blue dead cell stain (Thermo Fisher Scientific), followed by one wash with PBS, and then resuspended in 25 μl of FACS buffer with human TruStain Fc block (BioLegend) and incubated in the dark for 15 min at RT. Surface antigens were stained in FACS buffer for 30 min in the dark on ice. Then, the cells were washed twice with FACS buffer, resuspended in 150 μl of FACS buffer, and analyzed by flow cytometry. For intracellular Foxp3 and Arginase 1 staining, an additional staining step was performed using the True-Nuclear Transcription Factor Buffer Set Kit (BioLegend) according to the manufacturer’s instructions. All samples were analyzed by CytoFLEX LX (Beckman Coulter). For cell quantification, we used CountBright absolute counting beads for flow cytometry (Thermo Fisher Scientific, catalog no. C36950), and calculation of absolute number was done according to manufacturer instructions. Unless otherwise specified, cell populations were defined by the following markers: DCs: CD45+, NK1.1, CD11b+, CD11c+, MHC-II+, F4/80; macrophages: CD45+, NK1.1, CD11b+, MHC-II+, F4/80+, Ly6C, Ly6G; monocytes: CD45+, NK1.1, CD11b+, Ly6C+, Ly6G, F4/80, CD11c; neutrophils: CD45+, NK1.1, CD11b+, Ly6G+, Ly6Clow, F4/80; NK cells: CD45+, NK1.1+; CD8 T cells: CD45+, CD3+, CD8+, CD4; CD4 T cells: CD45+, CD3+, CD4+, CD8, Foxp3; Tregs: CD45+, CD3+, CD4+, CD8, Foxp3+. To determine Arginase 1, PD-L1, and CD32B expression, the following Abs were used: arginase 1 (A1exF5), PD-L1 (10F.9G2), and CD32B (2B6). The following commercial Abs were used for determining cell populations by flow cytometry: anti-mouse CD45 (clone 30-F11), BioLegend, catalog no. 103151, RRID: AB_2565884; anti-mouse CD11c (clone N418), BioLegend, catalog no. 117320, RRID: AB_528736; anti-mouse MHC-II (clone M5/114.15.2), BioLegend, catalog no. 107636, RRID: AB_2734168; anti-mouse F4/80 (clone BM8), BioLegend, catalog no. 123118, RRID: AB_893477; anti-mouse CD11b (clone M1/70), BioLegend, catalog no. 101256, RRID: AB_2563648; anti-mouse Ly6C (clone HK1.4), BioLegend, catalog no. 128012, RRID: AB_1659241; anti-mouse Ly6G (clone 1A8), BioLegend, catalog no. 127645, RRID: AB_2566317; anti-mouse NK1.1 (clone PK136), BioLegend, catalog no. 108741, RRID: AB_2562561; anti-mouse CD3 (clone 17A2), BioLegend, catalog no. 100220, RRID: AB_1732057; anti-mouse CD8α (clone 53-6.7), BioLegend, catalog no. 100730, RRID: AB_493703; anti-mouse CD4 (clone RM4-5), BioLegend, catalog no. 100526, RRID: AB_312727; anti-mouse Foxp3 (clone 53-6.7), BioLegend, catalog no. 126404, RRID: AB_1089117; anti-mouse Arginase 1 (clone A1exF5), Thermo Fisher Scientific, catalog no. 12-3697-82, RRID: AB_2734839; and anti-mouse PD-L1 (clone 10F.9G2), BioLegend, catalog no. 124308, RRID: AB_2073556.

Single-cell sorting

After staining, cells were washed and resuspended in cold FACS buffer (0.5% BSA and 2 mM EDTA in PBS), stained with fluorophore-conjugated anti-mouse CD45 Ab, and filtered through a 70-μm strainer. Before sorting, cells were stained with propidium iodide to exclude dead/dying cells. Cell sorting was performed using a BD FACSAria Fusion flow cytometer (BD Biosciences), gating for CD45+ cells after exclusion of dead cells and doublets. Single cells were sorted into 384-well capture plates containing 100 μl of lysis solution, 3 μl of mineral oil, and 20 nM barcoded poly(T) reverse transcription primers for scRNA-seq as described previously (PubMed identifier: 34017133). Immediately after sorting, plates were spun down to ensure cell immersion into the lysis solution, snap-frozen on dry ice, and stored at −80°C until further processing. Cells were analyzed using BD FACSDIVA software (BD Bioscience) and FlowJo software (FlowJo LLC).

Single-cell library preparation

scRNA-seq libraries from sorted cells were prepared with a modified version of the massively parallel scRNA-seq method (MARS-seq) (PMID:31101904). In brief, polyadenylated mRNA from single cells sorted into 384-well capture plates was barcoded during reverse transcription into cDNA. Each plate was pooled, and cDNA was fragmented and amplified to generate Illumina sequencing-ready libraries. Each plate library was tested for quality and DNA concentration.

Read alignment

Sequencing libraries were pooled at equimolar concentrations and sequenced using an Illumina NextSeq 500 or NovaSeq 6000 sequencer at a sequencing depth of 10,000 to 50,000 reads per cell. Reads were condensed into original molecules by counting same unique molecular identifiers (UMIs). We used statistics on empty-well spurious UMI detection to ensure that the batches we used for analysis showed a low level of cross single-cell contamination (less than 3%). Reads were processed as previously described for MARS-seq. Reads were mapped to murine reference genome mm10 using HISAT (version 0.1.6); reads with multiple mapping positions were excluded. Reads were associated with genes if they were mapped to an exon using the UCSC genome browser for reference. Exons of different genes that shared genomic position on the same strand were considered a single gene with a concatenated gene symbol.

Quality control

Cells were filtered on the basis of count depth (400 < #UMI), number of expressed genes (200 < #genes), and mitochondrial gene content (20 > %Mt-). Cells not meeting the latter threshold were usually nonviable or apoptotic.

Clustering and annotation

Seurat package (version 4.0) (53) standard workflow was used to cluster. Briefly, the UMI matrix was log-normalized, highly variable genes were detected, and principal components analysis (PCA) was applied, followed by K-nearest neighbors graph construction on 50 leading components. The Louvain algorithm was used for community detection using resolution parameter 1. Clusters were manually annotated on the basis of marker genes.

Dimensionality reduction

We used Uniform Manifold Approximation and Projection (UMAP) with Seurat (53) functions RunUMAP(). We applied it to the batch-corrected data. The PCs used to calculate the embedding were the same as those used for clustering. When calculating the UMAP coordinates, we used a = 0.5, b = 2 for optimal spread.

Gene set enrichment analysis

We applied GSEA (54) developed in the Broad Institute to find gene sets enriched in different treatment arms. We provided gene log2FC generated by FindMarkers() from Seurat and sorted. We used the Fast GSEA (“fgsea”) package implemented in R (55). Gene sets were drawn from the mouse C5 v5p2 gene ontology (GO) collection.

Differential expression testing

To find differentially expressed genes (DEGs) between treatments, we pooled together cells from all samples and used FindMarkers() from Seurat (53) with two-sided Wilcoxon rank-sum test on uncorrected log-transformed normalized counts. We considered genes that were expressed in >0.05 of cells and with >5 cells with >2 UMIs.
Marker genes (fig. S6A) were selected to be fold change > 1.5, Benjamini-Hochberg adjusted P value < 0.05, of which the top scoring genes were presented. DEGs in fig. S6B were selected to be |fold change > 1.25|, Benjamini-Hochberg adjusted P value < 0.05, of which the top scoring genes were labeled.

Software and visualization

All statistical analyses were conducted using R software (R Foundation for Statistical Computing, Vienna, Austria). ggplot2 code package was used for most graphics.

Statistical analysis

Flow cytometry data analysis was performed using FlowJo software. All other data analyses were performed using GraphPad Prism 9 (GraphPad Software). Quantitative data are presented as means ± SEM unless otherwise indicated. When the value is the florescence intensity of the Ab deduced from an IC, delta geometric mean intensity is presented (ΔMFI). For each dataset, normality of the population and/or population residuals (Gaussian distribution) was confirmed using Shapiro-Wilk and/or D’Agostino-Pearson tests. For normal distributions, when two groups were compared, an unpaired two-tailed Student’s t test (two-tailed, unequal variance) was used to determine statistical significance; when three groups were compared, one-way analysis of variance (ANOVA) with Tukey’s post hoc test was used to determine statistical significance. When data were not normally distributed, a nonparametric test was used, Mann-Whitney test was used when two groups were compared, or Kruskal-Wallis with Dunn’s post hoc test was used for multiple comparisons. For survival curves, log-rank test with Bonferroni corrected threshold was performed. Statistical significance is represented in figures as follows: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P < 0.0001, ns: not significant.

Acknowledgments

We thank I. Sher for artwork and S. Schwarzbaum for editorial assistance.
Funding: This work was supported by Rina Gudinski Career Development Chair (R.D.), the Israel Cancer Association (R.D.), Israel Science Foundation (R.D.), Moross Integrated Cancer (R.D.), Flight Attendant Medical Research Institute (R.D.), Dwek Institute for Cancer Therapy Research (R.D.), David E. Stone and Sheri Hirschfield Stone 75th Anniversary Fund (R.D.), Rising Tide Foundation (R.D.), Mexican Association of Friends of the Weizmann Institute (R.D.), Miel de Botton (R.D.), Garvan-Weizmann Partnership donors (R.D.), Elie Hirschfeld and Dr. Sarah Schlesinger (R.D.), an NFBI-Teva Ph.D. fellowship (N.C.S.), Maccabim Foundation Scholarship (N.C.S.), and Cancer-Immunology MICC Ph.D. program (N.C.S.).
Author contributions: R.D. supervised this study and obtained its funding. R.D. and N.C.S. conceived and designed the study. R.D. and N.C.S. wrote the manuscript. N.C.S., A.Y., T.L., R.S., and T.F. performed experiments and/or analyzed data. A.A. provided resources for this study. I.A. supervised work and obtained funding.
Competing interests: The Weizmann Institute has filed a PCT patent application related to this work, on which N.C.S. and R.D. are inventors. R.D. receives research funding from Teva Pharmaceuticals and holds consultancy roles for Teva Pharmaceuticals, Nucleai, and Immunai. The other authors declare that they have no competing interests.
Data and materials availability: Single-cell RNA sequencing data were deposited to NCBI GEO, accession GSE224011. All data needed to evaluate the conclusions in the paper are present in the paper or the Supplementary Materials. All datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request.

Supplementary Materials

This PDF file includes:

Figs. S1 to S6

Other Supplementary Material for this
manuscript includes the following:

Data file S1
MDAR Reproducibility Checklist

REFERENCES AND NOTES

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