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. 2019 Dec 19;4(24):e131882.
doi: 10.1172/jci.insight.131882.

CD47 blockade augmentation of trastuzumab antitumor efficacy dependent on antibody-dependent cellular phagocytosis

Affiliations

CD47 blockade augmentation of trastuzumab antitumor efficacy dependent on antibody-dependent cellular phagocytosis

Li-Chung Tsao et al. JCI Insight. .

Abstract

The HER2-specific monoclonal antibody (mAb), trastuzumab, has been the mainstay of therapy for HER2+ breast cancer (BC) for approximately 20 years. However, its therapeutic mechanism of action (MOA) remains unclear, with antitumor responses to trastuzumab remaining heterogeneous and metastatic HER2+ BC remaining incurable. Consequently, understanding its MOA could enable rational strategies to enhance its efficacy. Using both murine and human versions of trastuzumab, we found its antitumor activity dependent on Fcγ receptor stimulation of tumor-associated macrophages (TAMs) and antibody-dependent cellular phagocytosis (ADCP), but not cellular cytotoxicity (ADCC). Trastuzumab also stimulated TAM activation and expansion, but did not require adaptive immunity, natural killer cells, and/or neutrophils. Moreover, inhibition of the innate immune ADCP checkpoint, CD47, significantly enhanced trastuzumab-mediated ADCP and TAM expansion and activation, resulting in the emergence of a unique hyperphagocytic macrophage population, improved antitumor responses, and prolonged survival. In addition, we found that tumor-associated CD47 expression was inversely associated with survival in HER2+ BC patients and that human HER2+ BC xenografts treated with trastuzumab plus CD47 inhibition underwent complete tumor regression. Collectively, our study identifies trastuzumab-mediated ADCP as an important antitumor MOA that may be clinically enabled by CD47 blockade to augment therapeutic efficacy.

Keywords: Breast cancer; Immunology; Immunotherapy; Macrophages; Oncology.

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Conflict of interest statement

Conflict of interest: An invention disclosure at Duke University’s Office of Licensing and Ventures has been generated for “identifying phagocytic macrophage as a biomarker” by the following authors: LT, ZH, and HKL (patent T-006575).

Figures

Figure 1
Figure 1. Generation of murine trastuzumab and its antitumor dependence on antibody-dependent cellular phagocytosis (ADCP) by tumor-associated macrophages (TAMs).
(A) Cartoon representation of trastuzumab and 4D5 antibodies used in this study. (B) MM3MG cells expressing human HER2Δ16 were implanted into the mammary fat pads (1 × 106 cells) of BALB/c mice. Trastuzumab (human IgG1) or 4D5 (mouse IgG2A) was administered weekly (200 μg per mouse). n = 8–10. (C) Tumors (>1000 mm3 volume) were processed into single-cell suspensions and TAMs (percentage CD11b+F4/80+LY6GLY6C of CD45+ cells) were analyzed by FACS. n = 8–10. (D) Experiment as in B was repeated in SCID-beige animals. n = 8. (E) Experiment in SCID-beige was repeated using neutrophil-depleting anti-LY6G antibodies (clone IA8, 300 μg per mouse biweekly). (F and G) To deplete macrophages, SCID-beige mice were pretreated with anti-CSF1R antibody (clone AFS98, 300 μg, 3 times per week) for 2 weeks. (F) Macrophage depletion was verified by FACS. (G) 4D5-IgG2A was injected with anti-CSF1R treatment maintained throughout the experiment. n = 8. (H) Trastuzumab/4D5 induced ADCP of HER2+ breast cancer (BC) cells by bone marrow–derived macrophages (BMDMs). MM3MG-HER2Δ16 cells were labeled with Brilliant Violet 450 Dye and cocultured with BMDMs (3:1 ratio) with control or anti-HER2 antibodies (10 μg/mL). ADCP rates were measured as percentage of BMDM uptake of labeled tumor cells (CD45+ and BV450+), and antibody-dependent cellular cytotoxicity (ADCC) rates were measured as percentage of dying free tumor cells (CD45 and LIVE/DEAD+). ADCP inhibitor (latrunculin A) or ADCC inhibitor (concanamycin A) was added as assay control. n = 3; experiment was repeated 3 separate times. In B, D, E, and G, tumor growth was determined with caliper-based tumor measurement over time. Significance was determined by 2-way ANOVA with Tukey’s multiple-comparisons test (C, F, and H) or 1-way ANOVA test with Tukey’s multiple-comparisons test. All data represent the mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Figure 2
Figure 2. Antibody-dependent cellular phagocytosis (ADCP) of mouse trastuzumab (4D5) requires engagement with Fcγ receptors (FCGRs) and is IgG2A isotype dependent.
(A) FCGRs are required for 4D5-induced ADCP of HER2+ breast cancer cells by bone marrow–derived macrophages (BMDMs) in vitro. BMDMs were generated from wild-type and Fcer1g–/– mice, and ADCP experiments were performed with the conditions described in Figure 1E. n = 3. (B and C) FCGR is required for the antitumor activity of 4D5 therapy. (B) Wild-type or Fcer1g–/– BALB/c mice were implanted with MM3MG-HER2Δ16 cells as before (Figure 1B). 4D5-IgG2A or control antibodies were administered weekly (200 μg per mouse intraperitoneally) and tumor growth was measured. n = 5. (C) Tumor-associated macrophages (TAMs) from tumors in Figure 2B were analyzed by FACS. n = 4 or 5. (DF) The ADCP activity of 4D5 is IgG2A isotype dependent. (D) MM3MG-HER2Δ16 tumor growth in mice was repeated using 4D5 antibodies containing mouse IgG1 as a comparison to the previously utilized IgG2A isotype. n = 8–10. (E) ADCP experiments with BMDM cultures were performed using 4D5-IgG1 versus 4D5-IgG2A antibody isotypes. n = 4. (FH) Mouse FCGR signaling activation assay. MM3MG breast cancer cells expressing HER2 were plated and treated with indicated antibody concentrations for 1 hour. Jurkat cells containing NFAT-luciferase reporter and expressing mouse FCGR1 (F), FCGR3 (G), or FCGR4 (H) were added to the target cells containing antibodies and cocultured for 4 hours. FCGR signaling activation was assessed by luciferase activity quantification. n = 4. Significance was determined by 1-way ANOVA with Tukey’s multiple-comparisons test (A, C, and E) or 2-way ANOVA with Tukey’s multiple-comparisons test vs. control IgG group (B, D, and FH). All data represent the mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Figure 3
Figure 3. CD47 suppresses the antitumor activity of mouse trastuzumab (4D5).
(A) CD47-KO cells were generated from MM3MG-HER2Δ16 cells using CRISPR/Cas9 technology. A control GFP-KO line was generated in parallel. Control and CD47-KO MM3MG-HER2Δ16 cells were labeled with Brilliant Violet 450 Dye and incubated with bone marrow–derived macrophages (BMDMs) at a 3:1 ratio with control or 4D5 antibodies (10 μg/mL). Antibody-dependent cellular phagocytosis (ADCP) and cytotoxicity (ADCC) were measured as described in Figure 1H. n = 3. Experiment was repeated 2 separate times using CD47-KO clones containing a different guide RNA. (B) Cytokines and chemokines secreted by macrophages from coculture experiment with HER2+ BC were analyzed using the Luminex platform. Additional cytokines detected can be found in Supplemental Figure 5. n = 3. (C and D) Control and CD47-KO MM3MG-HER2Δ16 cells were implanted into mouse mammary fat pads and treated with 4D5-IgG2A or control antibodies as described before (Figure 1B). TAMs were analyzed by FACS after tumor volume reached >1000 mm3. n = 5. (E and F) Cd47-overexpressing cells (CD47-OE) were generated in MM3MG-HER2Δ16 cells after transduction with Cd47 cDNA under the control of the EF1s promoter. CD47-OE tumor cell growth was compared to parental MM3MG-HER2Δ16 cells in mice treated with control antibody or 4D5-IgG2A. TAMs were analyzed by FACS. n = 5. Significance was determined by 1-way ANOVA with Tukey’s multiple-comparisons test (A, B, D, and F) or 2-way ANOVA with Tukey’s multiple-comparisons test (C and E). All data represent the mean ± SEM. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 4
Figure 4. CD47 blockade increases therapeutic efficacy of mouse trastuzumab and augments tumor-associated macrophage (TAM) expansion and phagocytosis.
(A) Tumor growth experiment (as in Figure 1B) was repeated using CD47 blockade antibody (MIAP410, 300 μg per mouse) alone or in combination with 4D5-IgG2a. (B) TAM populations were analyzed by FACS after tumor volume reached >1000 mm3. Analysis of additional immune cell types are shown in Supplemental Figure 4D. Data represent the mean ± SEM. n = 8–10. (C) Repeat of similar tumor growth experiment and treatments in SCID-beige mice. (D) TAM populations from SCID-beige experiment were analyzed by FACS. n = 10. (E) Schematic representation of in vivo antibody-dependent cellular phagocytosis (ADCP) experiment. MM3MG-HER2Δ16 cells were labeled with Vybrant DiD dye and implanted (1 × 106 cells) into mammary fat pads of BALB/c mice. Once tumor volume reached approximately 1000 mm3, mice were treated with control antibody, 4D5-IgG2A (200 μg), or 4D5-IgG2A in combination with MIAP410 (300 μg). On the next day, tumors were harvested and tumor-phagocytic macrophages were quantified by FACS. (F) Representative FACS plots and graphical summary showing frequency of macrophages (CD11b+F4/80+LY6GLY6C) that have phagocytosed DiD-labeled tumor cells. n = 6. (G) A similar in vivo ADCP experiment was repeated in Fcer1g–/– mice. n = 8. Significance was determined by 2-way ANOVA with Tukey’s multiple-comparisons test (A and C) or 1-way ANOVA with Tukey’s multiple-comparisons test (B, D, F, and G). All data represent the mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.
Figure 5
Figure 5. CD47 blockade synergizes with mouse trastuzumab therapeutic activity in a transgenic human HER2+ breast cancer (BC) mouse model.
(A) Schematic representation of experiment using the endogenous human HER2-transgenic mouse model. Spontaneous breast tumors in the transgenic animals were induced with doxycycline diet. Four treatment arms were set up: control IgG (200 μg weekly, n = 15); CD47 blockade (MIAP410, 300 μg weekly, n = 14); 4D5-IgG2A (200 μg weekly, n = 16); and 4D5-IgG2A combined with MIAP410 (n = 16). Individual animals were consecutively enrolled into a specific treatment arm as soon as palpable breast tumors were detected (~200 mm3). (B) Survival of mice in each treatment arm; time of start is on the day of palpable tumor detection and treatment enrollment. Log-rank (Mantel-Cox) test for survival analysis: ****P < 0.0001 for treatment vs. control group; ##P < 0.01 for difference observed between 4D5 group and 4D5 plus αCD47 group. (C) Tumor burden in animals from each treatment arm was measured over time after enrollment in treatment arm. Each animal developed 1–4 total tumors in their mammary fat pads. The total tumor burden per mouse is shown. Animals were euthanized when their total tumor volume reached more than 2000 mm3. (D) Tumors in the transgenic mice were harvested, processed into single-cell suspensions, and analyzed by FACS. Each tumor was treated as an individual measurement. Data are shown as the mean ± SEM. Control IgG n = 23, αCD47 n = 27, 4D5 n = 38, 4D5+αCD47 n = 32. *P < 0.05, ***P < 0.001 by 1-way ANOVA with Tukey’s multiple-comparisons test.
Figure 6
Figure 6. Single-cell transcriptome analysis of immune clusters within HER2+ breast cancer after trastuzumab with CD47 blockade therapy.
HER2+ tumors from HER2Δ16-transgenic animals were isolated for single-cell RNA sequencing using the 10× Genomics platform. Data from all tumors were pooled for clustering and gene expression analysis. (A) tSNE plots showing distinct clusters of immune cells in tumors from 4 treatment groups: control IgG, αCD47, 4D5-IgG2A, or combination. (B and C) Heatmap of relevant gene markers confirmed the various immune cell clusters in control tumors (B) and the expansion of macrophage clusters in the combination therapy–treated tumors (C). Macrophages that contained tumor-specific transcripts (e.g., hERBB2, Epcam, Krt8) were categorized as tumor-phagocytic macrophages (Phag MΦ, predominantly found in the combination treatment group).
Figure 7
Figure 7. Differential gene expression analysis of TAM clusters in HER2+ breast cancer after trastuzumab with CD47 blockade therapy.
(A and B) Differential gene expression analysis of gene signatures for IFN, proinflammation, chemotaxis, and TLR/MyD88/NF-κB pathways in M1-like MΦ clusters (A) and M2-like MΦ clusters (B) revealed how they were affected by the treatment regimens. (C) Differential gene expression analysis of immunoregulatory gene signatures (wound healing, ECM remodeling, growth factors, antiinflammation) versus immunostimulatory gene signatures (proinflammation, chemotaxis, antigen presentation, phagocytosis/opsonization) among the 3 distinct macrophage clusters in the combined data set.
Figure 8
Figure 8. Human CD47 gene expression is a prognostic factor in HER2+ breast cancer and limits the therapeutic activity of trastuzumab.
(A and B) Kaplan-Meier survival curve for breast cancer (BC) patients’ METABRIC data set. (A) Stratified into low and high groups based on average expression of CD47 in all patients. (B) The same patient stratification based on disease subtype (ER+, HER2+, and triple-negative BC [TNBC]). (C) CD47 knockout in human HER2+ BC line KPL-4 was generated using the CRISPR/Cas9 approach. Control and CD47-KO KPL-4 cells were labeled with Brilliant Violet 450 Dye, and incubated with human monocyte–derived macrophages (hMDMs) at a 3:1 ratio, in the presence of control IgG or trastuzumab (10 μg/mL). Antibody-dependent cellular phagocytosis (ADCP) was measured by percentage of hMDM uptake of labeled KPL-4 cells (CD45+ and BV450+). Data are shown as the mean ± SEM; n = 4 biological replicates. Experiment was repeated using hMDMs generated from 3 healthy PBMC donors. (D) Control or CD47-KO KPL-4 cells were implanted into mammary fat pads of SCID-beige BALB/c mice (5 × 105 cells). Trastuzumab (50 μg) or control human IgG1 was administered weekly and tumor volume was measured. ****P < 0.0001 by 2-way ANOVA with Tukey’s multiple-comparisons test. (E) Tumor-infiltrating macrophage (F4/80+Gr1CD11b+) populations were analyzed by FACS, except for the CD47-KO plus trastuzumab group, as no tumor growth occurred. Data are shown as the mean ± SEM. n = 7. (F) Tumor-associated macrophages from control-treated and trastuzumab-treated tumors were sorted by FACS (F4/80+Gr1CD11b+CD45+) and analyzed with RT-qPCR for the expression of pro- and antiinflammatory genes. Data are shown as the mean ± SEM. n = 7. Multiple 2-sided t test. In C and E, significance was determined by 1-way ANOVA with Tukey’s multiple-comparisons test. *P < 0.05; **P < 0.01; ***P < 0.001.

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