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Targeting immunosuppressive macrophages overcomes PARP inhibitor resistance in BRCA1-associated triple-negative breast cancer

Nature Cancer volume 2, pages 66–82 (2021)Cite this article

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Abstract

Despite objective responses to poly(ADP-ribose) polymerase (PARP) inhibition and improvements in progression-free survival (PFS) compared to standard chemotherapy in patients with BRCA-associated triple-negative breast cancer (TNBC), benefits are transitory. Using high-dimensional single-cell profiling of human TNBC, here we demonstrate that macrophages are the predominant infiltrating immune cell type in breast cancer susceptibility (BRCA)-associated TNBC. Through multi-omics profiling, we show that PARP inhibitors enhance both anti- and pro-tumor features of macrophages through glucose and lipid metabolic reprogramming, driven by the sterol regulatory element-binding protein 1 (SREBF1, SREBP1) pathway. Combining PARP inhibitor therapy with colony-stimulating factor 1 receptor (CSF1R)-blocking antibodies significantly enhanced innate and adaptive antitumor immunity and extended survival in mice with BRCA-deficient tumors in vivo, and this was mediated by CD8+ T cells. Collectively, our results uncover macrophage-mediated immune suppression as a liability of PARP inhibitor treatment and demonstrate that combined PARP inhibition and macrophage-targeting therapy induces a durable reprogramming of the tumor microenvironment (TME), thus constituting a promising therapeutic strategy for TNBC.

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Fig. 1: TNBC with mutated BRCA is highly infiltrated by T cells and macrophages.
Fig. 2: PARP inhibition modulates the TME and increases intratumoral macrophage levels in BRCA1-deficient TNBC.
Fig. 3: PARP inhibition modulates the macrophage phenotype.
Fig. 4: PARP inhibition modulates the metabolic phenotype of differentiating macrophages.
Fig. 5: PARP inhibition modulates the glycolytic capacity of macrophages.
Fig. 6: Anti-CSF1R therapy enhances PARP inhibitor therapy in BRCA1-deficient TNBC.
Fig. 7: Olaparib-treated macrophages suppress T cell function, which is overcome with anti-CSF1R therapy in BRCA1-deficient TNBC.

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Data availability

The data that support the findings of this study are available upon reasonable request from the corresponding author (J.L.G.). The data are not publicly available due to IRB restrictions of data containing information that could compromise research participant privacy and/or consent. With controlled use, the MS proteomic data were deposited in the ProteomeXchange Consortium via the PRIDE partner repository under the dataset identifier PXD015804. With controlled use, the RNA-seq data were deposited in Synapse (syn23018992). All CyCIF images are available at https://www.cycif.org/data/mehta-2020/. Source data are provided with this paper.

Code availability

Static copies of analysis versions are available as follows.

For CyCIF, code repositories are available for ongoing improvements to Ashlar (https://github.com/labsyspharm/ashlar) and for segmentation and analysis (https://github.com/sorgerlab/cycif). A static copy of the analysis version can be found at https://github.com/breasttumorimmunologylab/TAM-PARP-2019. All tumors analyzed in this study may be viewed at https://www.cycif.org/data/mehta-2020/.

A static copy of the RNA-seq analysis version can be found at https://github.com/breasttumorimmunologylab/TAM-PARP-2019.

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Acknowledgements

This work was supported by the DFCI/Eli Lilly & Co. Research Collaboration (J.L.G.), the Dana-Farber/Harvard Cancer Center (DF/HCC) Specialized Program of Research Excellence (SPORE) in Breast Cancer P50 CA1685404 Career Enhancement Award (J.L.G.), the Susan G. Komen Foundation Career Catalyst Award CCR18547597 (J.L.G.), the Terri Brodeur Breast Cancer Foundation (J.L.G.), the Breast Cancer Research Foundation (N.T.), the Ludwig Center at Harvard (J.L.G., S.S., P.K.S. and G.I.S.), the Center for Cancer Systems Pharmacology NCI U54-CA225088 (J.-R.L., P.K.S., S.S., M.K., S.A.B. and J.L.G), Eli Lilly (J.L.G.) and NIH/NHLBI K08 HL128802 (W.M.O.). S.J. was the recipient of R01 CA090687 and P50 CA1685404 Diversity Supplements. E.A.M. acknowledges the Rob and Karen Hale Distinguished Chair in Surgical Oncology for support. J.Y. acknowledges funding from the Spanish Ministerio de Economia, Industria y Competitividad (grant SAF2017-83565-R) and the Fundación Cientifica de la Asociacion Española Contra el Cancer (AECC) (grant PROYEI6018YELA). We thank A. Letai for his guidance and input during early experiments and J. Agudo for support and discussions related to the preparation of this manuscript. We thank G. Wulf and J. Jonkers for providing reagents for animal experiments, S. Mei for technical assistance with CyCIF and S. Lazo for technical help in setting up flow cytometry panels. We are grateful for expertise and help from the following core facilities: the Dana-Farber Animal Research Facility, the Dana-Farber Flow Cytometry Core, Brigham and Women’s Seahorse Core, the Brigham and Women’s Center for Advanced Molecular Diagnostics Research Core Lab, the Harvard Medical School Rodent Pathology Core and the Proteomics Platform, Sequencing Platform and Multiplex Imaging Platform of the Laboratory of Systems Pharmacology (LSP) at Harvard Medical School. We thank K. Shaw for assistance in drawing summary cartoons.

Author information

Authors and Affiliations

  1. Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, USA

    Anita K. Mehta, Emily M. Cheney, Christina A. Hartl, Madisson Oliwa, Jessica A. Castrillon, Nathan T. Johnson, Elizabeth A. Mittendorf & Jennifer L. Guerriero

  2. Department of Medical Oncology, Dana-Farber Cancer Institute and Department of Medicine, Harvard Medical School, Boston, MA, USA

    Constantia Pantelidou, Aditi Kothari, Shawn Johnson, Judy E. Garber & Geoffrey I. Shapiro

  3. Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Jia-Ren Lin, Nathan T. Johnson, Marian Kalocsay, Matthew J. Berberich, Sarah A. Boswell, Sandro Santagata, Peter K. Sorger & Jennifer L. Guerriero

  4. Department of Orthopedics and Physical Medicine, Medical University of South Carolina, Charleston, SC, USA

    Katie E. Hurst & Jessica E. Thaxton

  5. Department of Microbiology & Immunology, Medical University of South Carolina, Charleston, SC, USA

    Katie E. Hurst & Jessica E. Thaxton

  6. Division of Hematology/Oncology, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

    Mateus de Oliveira Taveira & Nadine Tung

  7. Department of Imaging, AC Camargo Cancer Center, São Paulo, Brazil

    Mateus de Oliveira Taveira

  8. Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    William M. Oldham

  9. Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Deborah A. Dillon, Mikel Lipschitz, Scott Rodig & Sandro Santagata

  10. Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Mikel Lipschitz & Scott Rodig

  11. Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, USA

    Sandro Santagata, Peter K. Sorger, Geoffrey I. Shapiro & Jennifer L. Guerriero

  12. Cancer Research Program, Hospital del Mar Medical Research Institute, Barcelona, Spain

    José Yélamos

  13. Hollings Cancer Center, Charleston, SC, USA

    Jessica E. Thaxton

  14. Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA, USA

    Elizabeth A. Mittendorf & Jennifer L. Guerriero

  15. Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA, USA

    Elizabeth A. Mittendorf

  16. Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    Geoffrey I. Shapiro

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  1. Anita K. Mehta
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  2. Emily M. Cheney
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  28. Jennifer L. Guerriero
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A.K.M., J.E.T., G.I.S. and J.L.G. conceived and designed the studies. A.K.M., E.M.C., C.A.H., C.P., M.O., J.A.C., J.-R.L., K.E.H., M.d.O.T., N.T.J., W.M.O., M.K., M.J.B., S.A.B., A.K., S.J., M.L., J.E.T. and J.L.G. performed experiments and analyzed data. W.M.O. performed Seahorse assays. M.K. and M.J.B. performed proteomic experiments and corresponding analyses. N.T.J. and S.A.B. performed RNA-seq and corresponding analyses. J.E.G. and N.T. obtained and provided clinical samples. J.Y. obtained and provided Parp2 knockout bone marrow. D.A.D. and S.R. provided pathology support. S.S., J.E.T., E.A.M., P.K.S., G.I.S. and J.L.G. provided oversight. A.K.M. and J.L.G. prepared the manuscript with input from co-authors.

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Correspondence to Jennifer L. Guerriero.

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Competing interests

J.L.G. is a consultant for GlaxoSmithKline (GSK), Codagenix, Verseau, Kymera and Array BioPharma and receives sponsored research support from GSK, Array BioPharma and Eli Lilly. G.I.S. has served on advisory boards for Pfizer, Eli Lilly, G1 Therapeutics, Roche, Merck KGaA/EMD Serono, Sierra Oncology, Bicycle Therapeutics, Fusion Pharmaceuticals, Cybrexa Therapeutics, Astex, Almac, Ipsen, Bayer, Angiex, Daiichi Sankyo, Seattle Genetics, Boehringer Ingelheim, ImmunoMet, Asana, Artios, Atrin, Concarlo Holdings, Syros and Zentalis and has received sponsored research support from Merck, Eli Lilly, Merck/EMD Serono and Sierra Oncology. Clinical trial support from Pfizer and Array Biopharma was provided to the DFCI for the conduct of investigator-initiated studies led by G.I.S. He holds a patent entitled, ‘Dosage regimen for sapacitabine and seliciclib’, also issued to Cyclacel Pharmaceuticals and a pending patent entitled ‘Compositions and methods for predicting response and resistance to CDK4/6 inhibition’, together with L. Cornell. E.A.M. is on the scientific advisory board (SAB) for AstraZeneca/MedImmune, Celgene, Genentech, Genomic Health, Merck, Peregrine Pharmaceuticals, SELLAS Life Sciences and TapImmune and has clinical trial support for her former institution (MDACC) from AstraZeneca/MedImmune, EMD Serono, Galena Biopharma and Genentech, as well as Genentech support from an SU2C grant, as well as sponsored research support for the laboratory from GSK and Eli Lilly. S.R. receives research funding from Merck, Bristol Myers Squibb, Gilead and Affimed and is on the SAB for Immunitas. S.S. is a consultant for RareCyte, Inc. N.T. receives research support from AstraZeneca. P.K.S. serves on the SAB or board of directors of Glencoe Software, Applied BioMath and RareCyte, Inc. and has equity in these companies; he is a member of the NanoString SAB and is also a co-founder of Glencoe Software, which contributes to and supports the open-source OME/OMERO image informatics software used in this paper. D.A.D. consults for Novartis and is on the advisory board for Oncology Analytics, Inc. S.J. receives consulting fees from Venn Therapeutics.

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Extended data

Extended Data Fig. 1 BRCA1-associated TNBC are highly infiltrated with T-cells and macrophages.

CyCIF was performed on BRCA-WT (n = 6) and BRCA1-associated (n = 10) triple negative breast cancer tumors from consented patients. a, Antibody panel for CyCIF shows the staining strategy for each cycle of CyCIF. b, Representative images for each cycle are shown. c, Representative images of merged antibodies for each cycle. d-f, The overview of image processing and data analysis workflow. d, The multiplexed images were segmented, and single-cell data were collected via customized ImageJ scripts. Digital representation of Keratin staining is shown. e, An example of gating for positive cells for CD163 are shown. Note that the distribution is similar for Keratin and CD163, but number/level is different due to different background subtraction methods. The CD163 distribution is continuous whereas the Keratin distribution is more bi-modal. f, Individual antibodies corresponding to Fig. 1a. g, T-regulatory cells were identified using FoxP3 positivity of CD3+CD4+ cells. h, Cytotoxic T-cells were identified using Granzyme B positivity of CD3+CD8+ cells. i, Macrophages were identified by CD68 and CD163.

Extended Data Fig. 2 PARP inhibition modulates the tumor microenvironment and increases intratumoral macrophages in BRCA1-deficient TNBC.

Mice bearing BRCA-deficient TNBC tumors were treated with either vehicle or 50 mg kg−1 of Olaparib for 5 days. a, Mice maintained their body weight during the treatment. b, Gating strategy for flow cytometry used throughout manuscript. c, Representative images of MAC2 immunohistochemistry, percentage of positive cells were assessed using ImageJ quantification (n = 6 mice). Representative images are shown at 20x magnification and are representative of the 6 mice. Error bars represent standard error of mean (±SEM). Statistical analyses were performed using one-tailed t-test. Exact p values indicated in each panel for each comparison.

Source data

Extended Data Fig. 3 PARP inhibition modulates the tumor microenvironment and increases intratumoral macrophages in BRCA1-deficient TNBC.

Mice bearing BRCA-deficient TNBC tumors were treated with either vehicle or 50 mg kg−1 of Olaparib for 5 days and tumors were harvested and RNA was isolated for gene expression analysis using NanoString. a-b, Heatmap of the raw counts (a) and heatmap of the normalized data (b) scaled to give all genes equal variance, generated via unsupervised clustering using the NanoString advance analysis tools. Orange indicates high expression; blue indicates low expression. c-h, Box plots represent cell type scores (the minimum, the maximum, the sample median are shown) for CD45 (c) and macrophages (d) as well as pathway analysis for antigen presentation (e), chemokine signaling (f), cytokine signaling (g) and TLR signaling (h). i-j, Plots represent the normalized mRNA expression of genes associated with itgax (CD11c; (i)) and interferon signaling (irf5, irf8), and il1r1 (j). k, Gene expression measured by qPCR. Error bars represent standard error of mean (±SEM) with n = 5 mice per group. Statistical analyses were performed using two-tailed t-test. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 4 PARP inhibition modulates the phenotype of differentiating macrophages.

a, Schematic representation of ex vivo differentiation of CD14+ human monocytes. b, Gating strategy used in flow cytometric analysis of ex vivo differentiated human monocytes. c-e, GM-CSF plus IL-4 differentiation of ex vivo cultured macrophages treated with vehicle or Olaparib. c, There was no change in proportion of CD45+ cells, CD11b+ cells or DCs (CD11b(neg)). d, Olaparib significantly increased CD11b(neg) (dendritic cell) expression of pTBK1. e, M-CSF differentiation of ex vivo cultured macrophages treated with vehicle or Olaparib. Olaparib did not change the proportion of macrophages (CD11b+) or dendritic cells (CD11b(neg)). The frequency of cells expressing CSF-1R increased after Olaparib treatment. Data represent n = 5 human donors. Error bars represent standard error of mean (±SEM). Statistical analyses were performed using two-tailed t-test. f-g, CD14+ cells from healthy human donors were isolated and differentiated to mature myeloid cells with IL-4 and GM-CSF for 5 days at which point Olaparib was added for 4 additional days. Cells were then collected for immunophenotyping by flow cytometry f, Schematic representation of ex vivo differentiation of CD14+ human monocytes to mature macrophages. g, Olaparib did not affect the viability of mature macrophages in ex vivo cultures as shown by total viable cells. No significant changes were observed in the phenotypic markers after Olaparib was added on the differentiated macrophages. Statistical analysis was performed using unpaired one-tailed t-. Error bars represent standard error of the mean (±SEM) with n = 5 healthy human donors. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 5 Role of PARP1 in differentiating macrophages.

a-h, CD14+ cells from healthy human donors were isolated and differentiated to mature myeloid cells for 5 days with IL-4 and GM-CSF in presence or absence of the PARP inhibitors: Olaparib, Niraparib, and Talazoparib, and then collected for immunophenotyping by flow cytometry. a,b, PARP inhibitor treatment did not affect the viability (a) or proportion of CD45+ cells (b). PARP inhibitors decreased the proportion of CD14+ (c) and CD163+ (d) cells and increased the proportion of CD80+ (e), pTBK1+ (f), PD-L1+ (g) and CSF-1R+ (h) macrophages. Statistical analyses were performed using unpaired one-tailed t-test: Error bars represent standard error of mean (±SEM) with n = 5 healthy human donors per group. Exact p values indicated in each panel for each comparison. i-o, Bone marrow from wild-type (wt) and parp1-/- mice was isolated and differentiated to mature myeloid cells for 5 days with IL-4 plus GM-CSF in the presence or absence of Olaparib, then collected for immunophenotyping by flow cytometry. i, Olaparib did not affect the viability of differentiated macrophages. Olaparib increased the differentiation to mature myeloid cells (j), and macrophages (k), and increased PD-L1 expression on macrophages (l), independent of PARP1 status. However, Olaparib-induced expression of CSF-1R on macrophages (m) and increased expression of pTBK1 on myeloid cells (n) and macrophages (o) was PARP1-dependent. Statistical analyses were performed using one-way ANOVA with uncorrected Fisher’s LSD. Error bars represent standard error of the mean (±SEM) with n = 5 mice per group. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 6 The role of PARP1 and PARP2 in PARP inhibitor treated differentiating macrophages.

a-g, Bone marrow cells from wild-type (wt) and parp1-/- mice was isolated and differentiated to mature myeloid cells for 5 days with IL-4 plus GM-CSF in the presence or absence of talazoparib, and immunophenotyping was performed by flow cytometry. h-n, Bone marrow cells from wild-type (wt) and parp2-/- mice was isolated and differentiated to mature myeloid cells as described above in presence or absence of either Olaparib or talazoparib and immunophenotyping was performed. Statistical analyses were performed using one-way ANOVA with Uncorrected Fisher’s LSD. a-n Statistical analyses were performed using one-way ANOVA with uncorrected Fisher’s LSD. Error bars represent standard error of the mean (±SEM) with n = 5 mice per group. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 7 PARP inhibition modulates the metabolic phenotype of differentiating macrophages.

CD14+ cells from healthy human donors were isolated and differentiated to mature myeloid cells with IL-4 plus GM-CSF in presence or absence of Olaparib for 5 days (n = 5 donors). Proteomics was performed and the 100 most significantly upregulated proteins (FDR < 0.05) and the 100 most highly upregulated proteins were used to identify GO terms associated with Olaparib treatment (a) and proteins that are associated with the GO-terms shown in (b) are highlighted in the same color.

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Extended Data Fig. 8 Role of the STING and SREBP1 pathways on the Olaparib-induced macrophage phenotype.

CD14+ cells from healthy human donors were isolated and differentiated to mature myeloid cells with IL-4 and GM-CSF for 5 days in the presence or absence of Olaparib and then collected for immunophenotyping by flow cytometry. a-e, A STING inhibitor or a SREBP1 inhibitor (fatostatin) was added to the ex vivo macrophage differentiation assay for 5 days, cells were collected and then analyzed by flow cytometry. f, A STING agonist was added to differentiating myeloid cells 24 hours before flow cytometry analysis. The STING agonist did not affect the viability or proportion of CD45+, CD14+, CD163+, or CSF-1R+ cells of the differentiated myeloid cells but did increase CD80+, PD-L1+ and pTBK1 expression on CD11b+ cells. Statistical analysis was performed using unpaired one-tailed t-test for subfigure f and One-way ANOVA with Uncorrected Fisher’s LSD for subfigure a-e. Error bars represent standard error of the mean (±SEM) with 3-7 healthy human donors per group, as shown. Exact p values indicated in each panel for each comparison. g-l, Bone marrow cells from wild-type and sting-/- mice was isolated and differentiated to mature myeloid cells as described above in presence or absence of Olaparib and immunophenotyping by flow cytometry was performed. Olaparib-induced phenotypes were independent of STING. Statistical analysis was performed using One-way ANOVA with uncorrected Fisher’s LSD. Error bars represent standard error of the mean (±SEM) with n = 5 mice per group. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 9 Nanostring validation by flow cytometry.

K14-Cre Brca1f/fTrp53f/f tumor bearing mice were treated for 5 days (n = 6 mice/group). a, Tumor volume after 5 days of treatment. b, Therapy was well tolerated. Statistical analysis was performed using a 2-way ANOVA with Turkey test *P < 0.05. c-j, Tumors were collected and immunophenotyped by flow cytometry. c, Olaparib and the combination of anti-CSF-1R plus Olaparib significantly increased total leukocyte infiltration (CD45+). Anti-CSF-1R significantly decreased the macrophage population as indicated by F480+ cells. d, The proportion of neutrophils (Gr1+) and myeloid derived suppressor cells (CD11b+Gr1+) are shown. e, Anti-CSF-1R plus Olaparib increased the number of macrophages (CD45+F480+) that expressed the pro-inflammatory cytokines IL-1α and its receptors (IL-1R1+, IL-1R2+) whereas Olaparib treatment increased macrophages expressing IL-1β. f, Olaparib, anti-CSF-1R and the combination of anti-CSF-1R plus Olaparib increased the frequency of macrophages (CD45+F480+) expressing TNFα, yet induced variable expression of its receptors CD120a and CD120b. The frequency of myeloid cells CD11b+ (g) and dendritic cells (i) expressing the pro-inflammatory cytokines IL-1β and IL-1α and their receptors (IL-1R1 and IL-1R2) increased after Olaparib treatment and further increased with anti-CSF-1R plus Olaparib treatment. h,j, Similar changes were seen for TNFα and its receptors CD120a and CD120b. Error bars represent standard error of mean (±SEM). Statistical analyses were performed using two-way ANOVA with uncorrected Fisher’s LSD. Exact p values indicated in each panel for each comparison.

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Extended Data Fig. 10 Olaparib-treated macrophages suppress T-cell function, which is overcome with anti-CSF-1R therapy in BRCA-deficient TNBC.

a, BT20 or MCF7 human breast tumor cells were treated with conditioned media from IL-4 plus GM-CSF differentiated myeloid cells in the presence or absence of Olaparib. b, Control media was generated similar to conditioned media but was not incubated with monocytes; it did not induce tumor cell killing. Error bars represent standard error of mean (±SEM). Statistical analyses were performed using one-tailed t-test. Only one replicate for data in B. c,d, OT-1 T cells cultured in supernatants collected from media with vehicle (red), media with Olaparib (blue), human macrophages treated with vehicle (black, donors 1-3), or human macrophages treated with Olaparib (light blue, donors 1-3) were assessed for (c) live cell number and (d) AnnexinV (n = 3 human donors). Error bars represent standard error of mean (±SEM). Statistical analyses were performed using paired t-test or one-way ANOVA as indicated on graphs. e-g, CD8 T-cells are effectively depleted with anti-CD8 antibodies, corresponding to Fig. 6d (n = 5 mice/group). Frequency of CD8+T-cells in tumors (e) and (f) are shown. Gating strategy to gate CD8+T-cells is shown (g). Error bars represent standard error of mean (±SEM). Statistical analyses were performed using one-tailed t-test. h, Flow plots corresponding to Fig. 6h-j. Exact p values indicated in each panel for each comparison.

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Mehta, A.K., Cheney, E.M., Hartl, C.A. et al. Targeting immunosuppressive macrophages overcomes PARP inhibitor resistance in BRCA1-associated triple-negative breast cancer. Nat Cancer 2, 66–82 (2021). https://doi.org/10.1038/s43018-020-00148-7

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