Neoadjuvant chemotherapy (NACT) may stimulate anticancer adaptive immune responses in high-grade serous ovarian cancer (HGSOC), but little is known about effects on innate immunity. Using omental biopsies from HGSOC, and omental tumors from orthotopic mouse HGSOC models that replicate the human tumor microenvironment, we studied the impact of platinum-based NACT on tumor-associated macrophages (TAM). We found that chemotherapy reduces markers associated with alternative macrophage activation while increasing expression of proinflammatory pathways, with evidence of inflammasome activation. Further evidence of a shift in TAM functions came from macrophage depletion via CSF1R inhibitors (CSF1Ri) in the mouse models. Although macrophage depletion in established disease had no impact on tumor weight or survival, CSF1Ri treatment after chemotherapy significantly decreased disease-free and overall survival. This decrease in survival was accompanied by significant inhibition of adaptive immune response pathways in the tumors. We conclude that chemotherapy skews the TAM population in HSGOC toward an antitumor phenotype that may aid adaptive immune responses, and therapies that enhance or sustain this during remission may delay relapse.
Women with disseminated high-grade serous ovarian cancer (HGSOC) may be treated with neoadjuvant chemotherapy (NACT) before surgery, or receive upfront cytoreductive surgery followed by adjuvant chemotherapy. Because there is evidence that chemotherapy can stimulate host antitumor responses (1), we, and others, have used human tissues and blood samples to study this in a clinically relevant context. The known effects of NACT on the ovarian tumor microenvironment (TME) to date can be summarized as follows: T-cell activation may be enhanced; T-cell, B-cell, and natural killer–cell densities can increase, whereas T regulatory cell density decreases (2–5). Because a majority of patients with HGSOC fail to respond to current immunotherapies (6, 7), in spite of prognostic correlation between T-cell densities and survival (4, 8, 9), novel immunotherapies that augment chemotherapy-induced immune stimulation could delay relapse, increase survival, and reduce the need for chemotherapy after surgical debulking. However, these studies have not investigated the effects of NACT on the abundant myeloid cell populations found in HGSOC that may provide novel targets after NACT and surgery. We previously reported that NACT leads to significant decreases in plasma of tumor-promoting inflammatory cytokines such as TNF, IL6, and IL8, with a concomitant increase in circulating IFNγ (3), but we did not investigate local changes in inflammatory cells such as tumor-associated macrophages (TAM). Although TAMs are generally considered as tumor promoting and immunosuppressive, they may have several phenotypic and functional states, some of which may aid antitumor immune responses (10).
The role of TAMs in HGSOC is not fully defined. There is no prognostic association with TAM density in biopsies as measured by the pan-macrophage marker CD68 (11), but higher densities of alternatively activated CD163+ macrophages have negative correlations (12). These results could be explained by our previous study of the pan-macrophage marker CD68 in stromal and malignant cell areas in pretreatment primary tumor biopsies from 152 patients with HGSOC (9). We found that overall CD68+ density does not have any association with patient survival; however, high stromal CD68+ density was significantly predictive of improved overall survival (OS; ref. 9), suggesting that some TAM populations may aid antitumor immunity in HGSOC.
The aim of the work reported here, therefore, was to ask if NACT could modulate TAM populations in HGSOC. To understand how to exploit any immune-activating effects of NACT, we also needed mouse models that recapitulate the molecular and cellular features of HGSOC. We previously described orthotopic syngeneic mouse models of HGSOC that show significant correlations with the TME of human tumors, in terms of their transcriptomes, host cell immune infiltrates, matrisome, vasculature, and tissue modulus (13). In this article, we compared the effects of NACT in patient omental biopsies with the effects of chemotherapy on omental tumors in mouse models. We then asked if inhibiting TAM populations in the mouse models during remission, resulting from chemotherapy, would affect subsequent relapse. We conclude that chemotherapy in patients and mouse models decreases overall TAM populations, but those TAMs that remain may foster antitumor responses.
Materials and Methods
Patients and samples
Tissues surplus to diagnostic and therapeutic requirements were collected along with clinical data under the Barts Gynae Tissue Bank HTA license number 12199 (REC no: 10/H0304/14 and 15/EE/0151). Patients gave written informed consent, and the study was approved by the East of England (Cambridge, UK) national review board. Studies were conducted in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Biomedical Research Involving Human Subjects. Samples were kindly donated by patients with HGSOC undergoing surgery at Barts Health NHS Trust and St. George's University Hospitals NHS Foundation Trust. Omental metastases were collected prospectively before and after NACT to include samples from stage III/IV patients. Patients were treated with carboplatin (AUC 5 mg/mL/min) intravenously every 3 weeks. Paclitaxel standard treatment was at a dose of 175 mg/m2 intravenously every 3 weeks. Surgery was usually performed between 3 and 4 weeks after the last NACT. Human omental specimens (prechemotherapy and postchemotherapy) were processed for flow cytometry, cell sorting, or IHC within 5 to 7 hours from sample resection and were stored between 0°C and 4°C in PBS until processing.
For the analysis of CD68 in tumors and stomal areas, the postchemotherapy samples from interval cytoreduction surgery were contained in a tissue microarray (TMA; ref. 3), whereas prechemotherapy samples were sections from diagnostic percutaneous core omental biopsies. This cohort consisted of 26 matched pre- and post-NACT biopsy samples. Patient samples (pre- and post-NACT) were fixed in 10% formalin solution (HT501128, Sigma-Aldrich) and paraffin-embedded by the Pathology Department, Barts Cancer Institute. Sections were dewaxed, dehydrated, and incubated in Antigen Unmasking Solution (H-3300, Vector Laboratories) at 100°C in a microwave for 20 minutes. Sections were incubated in 0.3% H2O2 in methanol for 10 minutes and in blocking buffer [2.5% BSA (A4503, Sigma-Aldrich) and 2.5% goat serum (B15-035, PAA) in PBS] for 60 minutes at room temperature. Primary antibodies were applied in blocking buffer for 60 minutes at room temperature: CD68 (1:8,000, M0814, Dako), PAX-8 (1:400, NBP1-32440, Novus), TREML4 (triggering receptor expressed on myeloid cell-like 4; 1:1,100, ab204798, Abcam), cleaved caspase-3 (CC3; 1:100, 9664S, Cell Signaling Technology), F4/80 (1:100, MCA497, Bio-Rad), and CD206 (1:100, MCA2235, Bio-Rad). Washes were performed with PBS with 0.1% Tween-20 (PBST). Sections were incubated with Vector Impact Kit (H-4343, MP-63636, Vector Laboratories) for 1 hour at room temperature, washed 3 times in PBST before addition of 3, 3′-diaminobenzidine (DAB) chromogen, and counterstained with hematoxylin (GHS116, Sigma-Aldrich) for 20 seconds. Sections were imaged using the Panoramic digital slide scanner (3DHISTECH). Definiens digital analysis software was used to quantify staining within cancer cell islands and stroma. The software-determined tumor and stromal separation for the entire tissue sample was reviewed with the oversight of Consultant Gynecological Pathologist, Dr. Jacqueline McDermott (St. George's University Hospitals NHS Foundation Trust). Size and number of human macrophage “lakes” across the entire tissue section, defining them as areas of continuous CD68+ staining >4,000, 10,000, or 50,000 μm2, were measured in unmatched biopsies from 9 patients before chemotherapy and 23 patients after chemotherapy. For mouse sections, tumors were analyzed for the presence of “lakes” using QuPath image analysis software (14). Total tumor area and individual “lakes” were manually annotated.
In vitro cell lines and macrophages
Human HGSOC cell lines were derived in our laboratory (G164, established in our laboratory from an omental metastasis of a patient with HGSOC), or kindly given by Professor David Bowtell (Peter MacCallum Cancer Centre, Melbourne, Australia; AOCS1, received in 2011). Both cell lines are described in ref. 15 and were cultivated in in DMEM/F12 with Glutamax (cat. no. 31331-093, Gibco) containing pen/strep (100 μg/mL; 15140-122, Gibco) supplemented with 4% human serum (H4522, Sigma-Aldrich) for G164 cells or 10% FBS (SV30160.03, lot. no. RXL35906, HyClone) and insulin, transferrin, and sodium selenite (1X; 51300-044, Gibco) for AOCS1 cells. Cells were authenticated by short tandem repeat sequencing with the ATCC (135-XV) at the beginning and at the end of the project. 60577 and 30200 cell lines were established from tumors in genetic models generated by adenoviral transfection of the ovarian surface epithelium in FVB mice and are Tp53−/−, Brca-1−/− with inactivation of the Rb pathway (16). HGS2 is Tp53−/−, Pten−/−, and Brca-2−/− and was generated from tumors derived from a genetic model established by Perets and colleagues (17) that we backcrossed to B6 mice (13). Mouse cell lines 30200, 60577 (16), and HGS2 (13) were grown in DMEM/F12 with Glutamax with 4% FBS, pen/strep (100 μg/mL; 15140-122, Gibco), and insulin, transferrin, sodium selenite (1X; 51300-044, Gibco), murine EGF (0.2 μg/mL; E4127, Sigma), hydrocortisone (0.5 μg/mL; H0135, Sigma), and antibiotic-antimycotic (1X; 15240-062, Gibco). Cell lines were used within five to six passages from thawing. Routine testing for mycoplasma contamination using the MycoAlert PLUS Mycoplasma Detection Kit (cat. no. LT07-710, Lonza) has been consistently negative.
Human blood from healthy donors was obtained from leucocyte cones from the NHS Blood and Transplant service. Peripheral blood mononuclear cells (PBMC) were isolated using Ficoll-Paque PLUS (17-1440-03 AG, GE Healthcare). Monocytes were isolated from PBMCs by magnetic isolation with CD14 microbeads (130-050-201, Miltenyi) on LS columns (130-042-401, Miltenyi) according to the manufacturer's instructions. Monocytes were differentiated to macrophages for 7 days with MCSF (100 ng/mL; 574806, BioLegend). Macrophages were polarized with IL4 (20 ng/mL; 200-04 PeproTech) and IL10 (20 ng/mL; 200-10, PeproTech) or IFNγ (10 ng/mL; 300-02 PeproTech) and LPS (100 ng/mL; L2630, Sigma Aldrich) in the presence of MCSF (100 ng/mL) for 72 hours.
Human and mouse HGSOC cell lines and human macrophages were stimulated with carboplatin (Hospira) or paclitaxel (Hospira), obtained from St. Bartholomew's Hospital pharmacy, for 48 hours at the concentrations indicated in the legends. As a positive control for inflammasome activation, human macrophages were stimulated for 48 hours with LPS (200 ng/mL; LPS from Escherichia coli O111:B4, tlrl-eblps, Invivogen) and nigericin (7.5 μmol/L; tlrl-nig, Invivogen).
Human and mouse HGSOC cells were fixed after stimulation and stained in 70% ethanol (51976, Sigma-Aldrich) + 0.5% crystal violet (C0775, Sigma-Aldrich). Crystal violet was dissolved in 10% acetic acid (W200603, Sigma-Aldrich), and the optical density at 595 nm (OD595) was quantified. Measurements were normalized to media-only control analyzed in Prism v7.0. Human monocyte–derived macrophages were plated in 6-well plates at a density of 1 × 106 cells/well and stimulated with carboplatin (Hospira) or paclitaxel (Hospira) for 48 hours at the concentrations indicated in the legends. Cells were detached using Cell Dissociation Buffer (Gibco, 13151014). Cells were washed and stained with FVD450 (1:500; 65-0863-14, eBioscience) for 25 minutes at 4°C. After fixation, cells were analyzed on a LSR Fortessa II flow cytometer (BD Biosciences). The results were analyzed using FlowJo v10.2 (Treestar Inc.). Measurements were normalized to media-only control and analyzed in Prism v7.0.
Mouse experiments were performed under the license PBE3719B3 in accordance with Animals (Scientific Procedures) Act 1986 and with the approval of our Institutional Ethics Committee.
Seven-week-old female FVB and 6-week-old C57BL/6 mice were purchased from Charles River. Mice received 1 × 107 cells (60577, 30200, or HGS2) injected i.p. in 300 μL PBS. Mice were treated with carboplatin (20 mg/kg), paclitaxel (10 mg/kg), or the combination of carboplatin (20 mg/kg, Hospira) + paclitaxel (10 mg/kg, Hospira), both from the pharmacy at St. Bartholomew's Hospital, London. Carboplatin and paclitaxel were administered to mice i.p. in 300 μL volume starting 21 days (60577), 84 days (30200), or 49 days (HGS2) after cell injection. Vehicle-treated controls received either 0.9% NaCl or cremophor (C5135, Sigma-Aldrich): ethanol (51976, Sigma-Aldrich; 1:1) dissolved in 0.9% NaCl. Mice were assessed daily and weighed twice weekly. The survival endpoint for mice was defined as a change in general health; specifically, 15% body weight loss over 72 hours or 20% over any time period, inability to ambulate, hunched posture, or difficulty breathing, as well as signs of ascites or palpable tumors exceeding an estimated size of 1.2 cm diameter. In survival experiments, assessment of mice was made by the same individual to limit interobserver variability. In the majority of cases, survival determinations were made by a trained animal technician who was not directly involved in the experimental design. AZD7507 was obtained from AstraZeneca, dissolved in 0.5% (w/v) methyl cellulose (M7027, Sigma-Aldrich) and 0.1% (v/v) Tween-80 (P4780, Sigma-Aldrich) in water, and administered at 100 mg/kg twice daily by oral gavage, 5 days/week in the 60577 and 30200 models, starting as indicated in the figures and legends. BLZ945 (HY-12768, MedChemTronica) was dissolved in 10% sulfobutylether-b-cyclodextrin (HY-17031, MedChemTronica) in water and administered at 200 mg/kg once daily by oral gavage, 5 days/week in the 60577 model, starting at 70 days after injection for 63 days.
Flow cytometry and cell sorting
Samples were minced and incubated with collagenase D (1 mg/mL; 11088866001, Roche) and DNAse I (25 mg/mL; D4513, Sigma-Aldrich) in 5% FBS RPMI (R8758, Sigma-Aldrich) media for 30 minutes under agitation at 37°C, filtered through a 70 μm strainer (352350, Falcon), followed by red blood cell lysis (555899, BD Biosciences) for 5 minutes. Mouse omental samples were digested in Hank's Balanced Salt Solution (9374543, Gibco) supplemented with collagenase (2 mg/mL; C9263, Sigma) and DNAase I for 20 minutes at 37°C and filtered through a 70 μm cell strainer. Cells were washed and resuspended in FACS buffer (PBS + 2% heat-inactivated FBS + 2 mmol/L EDTA) and blocked for 15 minutes in Trustain blocker (1:200; 101319, BioLegend). The cell suspension was stained in FACS buffer for 30 minutes at 4°C using the antibodies specified in Supplementary Table S1. Mouse and human cells were washed and stained with FVD450 or FVD506 (1:500; 65-0863-14, 65-0866-14, eBioscience) for 25 minutes at 4°C. After fixation in 1:1 10% formalin:FACS buffer for 10 minutes at 4°C, fixative was washed out and cells were analyzed on an LSR Fortessa II flow cytometer (BD Biosciences). The results were analyzed using FlowJo v10.2 (Treestar Inc.). Mean fluorescence intensity (MFI) was normalized according to this formula: SI = [MFI(positive)-MFI(negative)]/[2*SD(negative)], where SI is the staining index.
For sorting, dissociated cells were stained in FACS buffer for 30 minutes at 4°C. Cells were stained using the lineage antibodies listed for flow cytometry diluted 1:100. Cells were stained with DAPI (2.5 μg/mL; 422801, BioLegend) immediately prior to sorting. FITC-HLA-DR+CD14+ cells were sorted using a FACSAria II cell sorter (BD Bioscience), lysed in RLT buffer (1015750, QIAGEN) with 1% β-mercaptoethanol (M-6250, Sigma-Aldrich), and stored at −80°C. Cells (0.25–2 × 106) were sorted per sample, and cell purities of >96% were obtained.
RNA extraction and sequencing
Mouse tumor RNA was isolated from frozen omental tumors from the 60577 model, treated with and without AZD7507 after carboplatin, and collected at endpoint, as described in ref. 13 with the RNeasy Mini Kit (74104, QIAGEN), according to the manufacturer's instructions. Three vehicle and three AZD7507-treated tumors were processed and analyzed. Human macrophage RNA was extracted from the sorted TAM population (HLA-DR+CD14+Lin− cells) using the RNeasy Micro Kit (74004, QIAGEN) according to the manufacturer's instructions. Five pre- and seven post-NACT unmatched samples were processed and analyzed. RNA purity and integrity were assessed using a Nanodrop and an RNA NanoChip (5067-1511, Agilent), and RNA integrity number values were ≥5.6 for mouse RNA and ≥8.6 for human TAM RNA.
RNA sequencing (RNA-seq) was carried out at the Wellcome Trust Centre for Human Genetics. Library preparation was carried out using ribosomal depletion, and sequencing was performed with HiSeq4000 for human samples and NovaSeq6000 for mouse samples, with 150 bp read-length, paired-end, strand specific and a coverage of, on average, 54 million reads for human samples and 68 million for mouse. Raw reads were aligned to the reference genome GRCh37 (hg19) or GRCm38 (mm10). The number of reads aligned to the exonic region of each gene was counted using htseq-count (18) based on the Ensembl annotation. Only genes that achieved at least one read count per million reads in at least 25% of the samples were kept. This led to 16,253 filtered genes, in total, for human and 16,344 for mouse. Conditional quantile normalization (19) was performed counting for gene length and GC content, and log2-transformed reads per kilobase per million reads (RPKM) expression matrices were generated. RNA-seq data have been deposited in Gene Expression Omnibus under the accession numbers GSE158739 (human data) and GSE158812.
Differential gene expression and pathway analysis
Differential gene expression analysis was performed using edgeR and limma R packages (20, 21). For human samples, the generalized linear model with a 0+group+batch design was used. For mouse samples, voom normalization and the linear model with a 0+group design was used. Gene set enrichment analysis (GSEA) was performed using the publicly available bioinformatics platform GenePattern (https://cloud.genepattern.org/, v3.9.9 and v3.9.11; ref. 22) with GSEA preranked (23) for Canonical Pathways (c2.cp.v6.2.symbols.gmt for human; c2.cp.v7.0.symbols.gmt for mouse data) and Gene Ontology Biological Processes (c5.go.bp.v7.2.symbols.gmt for human; c5.bp.v7.0.symbols.gmt for mouse data). Single sample GSEA was performed using the R package GSVA (24).
Human macrophages (stimulated as indicated in the “In vitro cell line and macrophage” section) were lysed with a RIPA buffer (R0278, Sigma-Aldrich) containing 1:10 complete mini EDTA protease inhibitor (11836153001, Roche) and 1:100 phosphatase inhibitor cocktail (P5726, Sigma-Aldrich). The protein concentration in the extracts was calculated using a bicinchoninic acid (BCA) assay. Cell extracts (25 μg) were run on a NuPAGE Novex 4% to 12% Bis-Tris Gel (1.5 mm, NP0335BOX, Invitrogen) and transferred to a nylon membrane. The membrane was blocked overnight (4°C in PBS with 0.1% Tween and 5% milk powder) and probed using CC3 (1:1,000, 9664S, Cell Signaling Technology), gasdermin E (1:1,000, ab215191, Abcam), and β-actin (1:2,000, A1978, Sigma). A rabbit or mouse horseradish peroxidase–conjugated secondary antibody (NXA931, NA9340V, GE Healthcare) incubation allowed visualization using ECL Prime (RPN2232, GE Healthcare) or the Luminata Forte (WBLUF0500, Millipore) and the Amersham Imager 600 (GE Healthcare).
Terminal deoxynucleotidyl transferase–mediated dUTP nick end labeling assay
The Novus Biologicals APO-BRDU-IHC reagent kit (cat. no. NBP2-31164, Novus Biologicals) was used in accordance with the manufacturer's instructions following the staining protocol for paraffin-embedded tissue. Sections (4 μm) of formalin-fixed, paraffin-embedded (FFPE) tissue were cut by the Barts Cancer Institute Pathology Department. Human HGSOC sections after chemotherapy known to contain macrophage “lakes” were stained. Kit-positive controls, in addition to a tissue-positive control section, produced by incubating sections for 30 minutes with 1 μg/mL DNAse in 1x PBS + 1 mmol/L MgSO4, were stained. Sections were deparaffinized in xylene (cat. no. X/0100/17, Fisher Scientific), rehydrated through an ethanol (cat. no. E/0650DF/17, Fisher Scientific) series (100%, 90%, 80%, and 70%), and washed in PBS. Slides were then incubated with proteinase K solution for 20 minutes at room temperature. Slides were then washed in PBS, followed by endogenous peroxidase blocking by incubating slides with 3% hydrogen peroxide in methanol for 5 minutes at room temperature and then washing in PBS. Slides where then equilibrated in reaction buffer. Following addition of labeling reaction (reaction buffer, TdT enzyme, Br-dUTP, and distilled water), the sections were incubated in a humidified chamber for 1.5 hours at 37°C. The slides were washed in 1x PBS followed by incubation with blocking buffer for 10 minutes at room temperature. Slides were then incubated with biotinylated BrdU antibody for 1.5 hours at room temperature, washed in PBS, and incubated with 1x conjugate solution for 30 minutes at room temperature. After washing in PBS, the slides were incubated with DAB for 15 minutes and counterstained with methyl green. Slides were then mounted (cat. no. 06522, Sigma-Aldrich) and imaged on an Axiophot microscope (Zeiss).
Ascites from the 60577 model at endpoint were collected and centrifuged in a refrigerated centrifuge at 1,300 rpm for 5 minutes and stored at −20°C. ELISAs for IL1ß were performed using the Mouse IL1ß Duoset (DY401, R&D) with undiluted ascites according to the manufacturer's instructions. Concentration was extrapolated from a standard curve. Abs450 was read using a BMG Labtech FLUOstar Optima reader (Labtech).
Proximity ligation assay
Human macrophages were grown on coverslips and fixed in 10% formal saline (HT501128, Sigma-Aldrich) for 10 minutes at room temperature, washed with PBS, and stored at 4°C. Tissue sections from paraffin-embedded tumors underwent antigen retrieval similarly to the IHC protocol described. Samples were permeabilized in 0.5% Triton-X100 (T8787, Sigma-Aldrich) in PBS for 10 minutes at room temperature. Specimens were then washed and blocked in 5% BSA (A4503, Sigma-Aldrich, or 1% BSA and 2% FCS for tissue) in PBS for 60 minutes at room temperature. Specimens were incubated with primary antibodies NLRP3 (1:50) and ASC (1:50; AG-20B-0014-C100, AG-25B-0006-C100, AdipoGen) overnight at 4°C, followed by washing 3 times in 0.1% Triton-X100 in PBS for 10 minutes. Samples were then incubated with proximity ligation assay (PLA) probes (1:5; DUO92002, DUO92004, Sigma-Aldrich) for 60 minutes at room temperature (90 minutes for tissue slides), followed by washing 3 times with 0.1% triton-X100 for 5 minutes. Ligation reaction was applied at 37°C in a humidity chamber for 30 minutes (1 hour for tissue slides). After washing twice in 0.1% Triton X-100 for 3 minutes each, polymerization reaction was applied at 37°C in a humidity chamber for 100 minutes in the dark (overnight for tissue slides) and washed 3 times for 5 minutes. Samples were then stained with DAPI (1 μg/mL; 40043, Biotium) for 3 minutes (30 minutes for tissue slides), followed by three 5-minute washes in PBS, and mounted using FluorSave reagent (345789, Calbiochem). Imaging was done on an LSM 710 confocal microscope. Approximately 30 cells were imaged using the 63x objective under oil immersion. LSM files were exported and analyzed in Fiji v1.2/ImageJ software. For analysis of cells, regions of interest were drawn around each cell. The “find maxima” process was used to isolate the green specs individual points and counted. For the analysis of mouse tumors, 10 fields for each tumor were analyzed. Signal was thresholded to remove background, and the process “analyse particles” with a size = 4 to 20 pixels was used to count. For the analysis of human tumors, 6 to 10 fields were analyzed, and positive signal was counted manually.
All data are expressed as the mean of the individual experiments ± the SEM unless otherwise specified. Differences were considered significant at P < 0.05, using a Student t test, ANOVA test followed by Bonferroni posttest, or nonparametric test as appropriate, performed with the statistical analysis software Graphpad Prism 7/8. P values are specified. If not specified, t test or ANOVA was applied. For survival curves, log-rank (Mantel–Cox) P value is shown.
NACT changes TAM populations in HGSOC omental biopsies
We first used a cohort of 26 matched pre- and post-NACT omental biopsy samples, for which we had previously studied T- and B-cell infiltrates and activity (2, 3). Using IHC, we stained the sections for the pan-macrophage marker CD68 and quantified staining within malignant cell islands and stromal areas. Because CD68+ cells often have multiple cytoplasmic extensions, we quantified CD68 staining as a percentage of the malignant cell area, the stromal area, or the overall TMA core/tissue area. Supplementary Fig. S1A shows a representative analysis of a single TMA core, illustrating tumor and stroma separation and CD68 staining. No difference in CD68+ area between pretreatment and post-NACT biopsies was observed when the entire sample was analyzed (Fig. 1A and B). However, NACT treatment caused a significant reduction in the mean CD68+ area in the malignant cell regions (7.88% pretreatment vs. 1.01% post-NACT P < 0.0001). The stromal CD68+ area was increased after NACT in four of the patient samples (Fig. 1A and B), suggesting that CD68+ cells may not be uniformly distributed after treatment.
We, therefore, used dual-color IHC to investigate the distribution of CD68+ cells in tissue sections rather than the smaller TMAs in a different unmatched cohort of 9 pretreatment and 23 post-NACT patients. We observed large stromal clusters of CD68+ cells surrounding or in close proximity to viable-appearing malignant cells (Fig. 1C). There were significantly more macrophage “lakes” within post-NACT tissue sections compared with before chemotherapy (Fig. 1D). Because we previously reported that high stromal CD68+ cell densities in pretreatment primary tumor biopsies from 226 patients with HGSOC had a significant positive association with OS (9), this suggested that some TAM populations in HGSOC may aid host antitumor responses. We, therefore, looked for correlations between CD68+ density and OS. In patients for whom we had follow-up data, OS was greater in patients with high compared with low CD68+ areas following chemotherapy (log-rank P = 0.05), and a near-significant trend (P = 0.08) to improved survival associated with a high CD68+ area within the stroma before chemotherapy (Supplementary Fig. S1B and S1C). Taken with our published data (9), the survival curves shown here, albeit on a small cohort of patients, suggest that high stromal TAM density may confer positive influence on OS and that TAMs within the stromal and malignant cell areas of the HGSOC TME may have different functions and impacts on cancer growth. In summary, results show that chemotherapy depletes macrophages from the malignant cell islands of the HGSOC TME and alters the overall phenotype of the residual macrophages which are largely resident in the stroma.
TAM phenotypes pretreatment and post-NACT
We next asked if NACT affected TAM phenotype. Using flow cytometry in 16 omental samples from patients having upfront surgery and 20 unmatched samples from patients receiving interval reduction surgery post-NACT, we defined TAMs as CD45+Lin−CD14+HLA-DR+ cells, excluding CD3+, CD19+, CD20+, CD56+, CD66b+, and Siglec8+ cells (Supplementary Fig. S1D). In support of the data from Fig. 1A and B, we found a significant reduction in the number of TAMs, as a percentage of CD45+ leukocytes, in the post-NACT group compared with biopsies from patients receiving upfront surgery (Fig. 1E). There was also a decrease in CD163+ TAMs, considered a marker of alternatively activated macrophages (Fig. 1F; Supplementary Fig. S1E) and associated with poorer prognosis in HGSOC (12). The MFI of CD163 was also lower in the post-NACT cohort (Supplementary Fig. S1F). Although differences in numbers of CD206+ TAMs were not significant, there was a reduction in cell surface expression (Fig. 1G; Supplementary Fig. S1G). The MFI for HLA-DR was unchanged, although it showed a trend for decreased expression (Supplementary Fig. S1H). The data thus far show that 3 to 4 weeks after the last dose of neoadjuvant platinum and taxane chemotherapy, TAM density was reduced in the malignant cell areas of the omental TME, with a decrease in cell surface markers associated with alternatively activated macrophages.
NACT alters the transcriptome of TAMs in omental tumors
To further investigate changes in macrophage cell function and activity after NACT, we used RNA-seq to study the transcriptomes of CD45+HLA-DR+Lin−CD14+ cells sorted as a bulk population, from five pretreatment and seven post-NACT unmatched omental HGSOC biopsies, obtaining between 250,000 and 750,000 cells per sample. Transcript expression of a total of 16,253 genes was quantified, of which 12,614 were protein-coding genes. Supplementary Fig. S2A demonstrates high expression of macrophage genes relative to lineage genes of other cell types. Pre- and post-NACT samples segregated by unsupervised clustering with some pre- and post-samples interspersed between the two groups, indicating discrete features in transcriptomes of the macrophages with some heterogeneity (Supplementary Fig. S2B).
Differential expression analysis revealed 858 protein-coding genes with different expression post-NACT versus prechemotherapy TAMs (Supplementary Table S2). Of these, 81 were upregulated and 777 downregulated post-NACT (Fig. 2A and B). The gene that showed the greatest fold-change increase in expression was TREML4, a positive regulator of Toll-like receptor 7 (TLR7) and other TLR signaling (25) that is upregulated in M1-polarized macrophages (Fig. 2B; ref. 26). FGFR2 was among the genes showing the greatest fold-change decrease in expression after chemotherapy and was of interest considering the reported role of FGF2 in polarizing TAMs to a tumor-promoting phenotype (27).
GSEA for canonical pathways and gene ontology biological processes (Fig. 2C; Supplementary Table S3A and S3B) showed that among the top 20 significantly upregulated pathways in the post-NACT samples were activation of AKT/PI3K signaling, IL6 signaling, IL8 signaling, activation of the inflammasome, and IL1β signaling (Fig. 2C and D). We also observed upregulation of lipid metabolism processes, suggesting altered lipid profiles in postchemotherapy TAMs (Supplementary Table S3B). The top significantly downregulated pathways in the post-NACT TAMs were extracellular matrix (ECM) formation and remodeling, ECM interactions and signaling, and cell-cycle and proliferation pathways. This reduction in ECM pathway genes is of particular interest and may suggest a decrease in cells analogous to the subset of profibrotic TAMs found in pancreatic cancer biopsies and mouse models (28). In summary, bulk RNA-seq data of CD45+HLA-DR+Lin−CD14+ cells from HGSOC omental metastases showed that 3 to 4 weeks after NACT, there was an increase in expression of inflammatory pathways, evidence of activation of the inflammasome, and a decrease in markers and pathways associated with tumor-promoting TAMs. This also suggested that pyroptosis, a form of programmed cell death characterized by cell membrane lysis and the release of the proinflammatory cytokines IL1β and IL18 (29), may be occurring in macrophages after chemotherapy.
NACT activates the inflammasome in TAMs
Because the RNA-seq data provided evidence of macrophage cell death after NACT, we reanalyzed the flow cytometry data for evidence of cytotoxicity, finding a significant increase in nonviable TAMs as a percentage of all TAMs in post-NACT samples compared with pretreatment samples (Fig. 3A). To study this further, we conducted in vitro viability assays on monocytes isolated from normal PBMCs differentiated to classically activated (M1) or alternatively activated (M2) phenotypes in vitro (see Materials and Methods) and treated with doses of chemotherapy that were toxic for malignant HGSOC cells. The cells were then treated with carboplatin at different concentrations for 48 hours, and cell viability and phenotype were assessed by flow cytometry. Macrophages were killed by carboplatin at concentrations in the range of 50 to 200 μmol/L (Fig. 3B). This was within the range of IC50 value for the two human HGSOC cell lines tested (Fig. 3C). The IC50 was lower for M1/classically activated macrophages (93 μmol/L) than M2-polarized (203 μmol/L) macrophages. Paclitaxel was also toxic for macrophages at similar doses to human HGSOC cell lines (Supplementary Fig. S3A and S3B).
We next looked for evidence of TAM death in the macrophage “lakes” in human HGSOC sections with dual-color IHC for CD68 and PAX8 and in consecutive sections, CC3 as a marker of apoptosis (Fig. 3D). There was clear overlap of CC3 and CD68 staining patterns, most notably within postchemotherapy macrophage “lakes” (Fig. 1C). Despite the fact that CC3 is frequently regarded as a marker of apoptosis, assessment of cell and nuclear morphology suggested that the CC3+ areas were not apoptotic (Fig. 3D). The distribution of the staining was also unusual for apoptosis; CC3 positivity is normally seen within individual cells rather than large clusters of cells. In keeping with the morphologic appearances of these cells, they were not terminal deoxynucleotidyl transferase–mediated dUTP nick end labeling (TUNEL) positive (Fig. 3E).
Published data suggest that caspase-3 can cleave gasdermin E to activate pyroptosis following chemotherapy drug treatment (30), either directly by inducing cell lysis or indirectly by triggering membrane pore–mediated potassium efflux that in turn activates the classical NLRP3/ASC/caspase-1 inflammasome and pyroptosis (31). Consistent with this, we observed caspase-3 and gasdermin E cleavage in macrophages following carboplatin treatment (Fig. 3F). To test if cleavage of gasdermin E could activate the inflammasome, we treated differentiated macrophages in the presence of 250 and 500 μmol/L carboplatin and performed a PLA to test for the interaction of NLRP3 (NOD-, LRR-, and pyrin domain–containing 3) and ASC (apoptosis-associated speck-like protein containing a CARD; Fig. 3G), as a readout of inflammasome activation, finding a significant increase in the number of PLA signals/cell in treated macrophages (Fig. 3G and H). We concluded that carboplatin treatment resulted in formation of the NLRP3 inflammasome complex, which was most likely triggered by gasdermin E–mediated potassium efflux. Therefore, we then used the PLA assay to look for NLRP3 inflammasome activation in HGSOC tissue sections pre- and post-NACT. Complex formation was detected more frequently in posttreatment biopsies (Fig. 3I and J).These results support the pathway analysis of the RNA-seq data. Collectively, these data provide evidence of inflammasome activation in human macrophages in response to carboplatin treatment.
Chemotherapy alters TAM numbers in mouse models of HGSOC
We then asked whether we could see similar effects of chemotherapy in mouse models of HGSOC that recapitulate many aspects of the human omental TME (13). We used three of these syngeneic orthotopic murine models, 60577, 30200, and HGS2, that harbor the genetic features of some human HGSOCs and recapitulate key features of the human TME, although there are distinct differences between them (ref. 13; see Materials and Methods). When injected intraperitoneally, mice develop tumors within the omentum, as well as other peritoneal sites. We first assessed the sensitivity of the 60577, 30200, and HGS2 mouse cell lines to carboplatin and paclitaxel in vitro (Supplementary Fig. S4A and S4B). IC50 values were similar between the three cell lines and within the range described for two human ovarian cancer cell lines (Fig. 3C).
In all in vivo experiments, mice were treated once peritoneal disease was established (approximately 3, 10, and 7 weeks after intraperitoneal injection of cells for 60577, 30200, and HGS2, respectively). As shown in survival experiments (Fig. 4A–C), established peritoneal tumors from all three models showed some therapeutic response to three doses of carboplatin given weekly, in a regime to mimic the platinum component of NACT in the patients. However, there were obvious differences between the models. 60577 tumors were the most sensitive to carboplatin treatment, with extension of survival in excess of 20 weeks, whereas in 30200 and HGS2 tumors, the difference in median survival was about 3 weeks. Omental weights were used as a surrogate measure of tumor burden as previously described (13). Nine and 14 days after the start of treatment, when mice had received two doses of carboplatin, there was a significant reduction in omental weight in mice bearing 60577 tumors (Supplementary Fig. S4C). Two doses of paclitaxel as a single agent did not have appreciable effects on omental weight in 60577-bearing mice (Supplementary Fig. S4D). There was a significant effect on omental weight 48 hours after the third dose of treatment in carboplatin-treated 30200 tumors, but again paclitaxel had no activity (Supplementary Fig. S4E). A trend for a reduction in tumor weight was observed in HGS2 tumors treated with three doses of carboplatin, but the difference was not significant (Supplementary Fig. S4F), suggesting that this model is the least sensitive to this chemotherapeutic treatment.
We next used flow cytometry to study macrophage cell populations in the omental tumors, defining murine TAMs as CD45+Ly6C−Ly6G−F4/80+CD11b+ cells (Supplementary Fig. S4G). The number of these cells, as a percentage of CD45+ cells, was significantly increased in the omental tumors compared with normal omentum from age-matched mice in the 60577 model (Fig. 4D and E). In established omental tumors, we found a significant decline in TAMs, as a percentage of CD45+ cells, 2 days after both the first and second weekly doses of carboplatin in the 60577 model (Fig. 4D; Supplementary Fig. S4H) but not after paclitaxel (Fig. 4E). The same decline was seen after the third weekly dose of carboplatin in the 30200 model but not after paclitaxel (Fig. 4F). There was also a decline in TAMs with the HGS2 model at the same time point, but this did not reach significance (Fig. 4G). The decline in TAMs shown by the flow cytometry data was confirmed in carboplatin-treated 60577 tumors using IHC for the macrophage marker F4/80 (Fig. 4H and I).
Chemotherapy alters TAM phenotypes in mouse models of HGSOC
We next asked if there was a change in phenotype of the murine TAMs after carboplatin treatment. Because there was no reliable commercially available antibody for murine CD163 at the time these experiments were conducted, we used CD206 as a marker of alternatively activated macrophages. CD206+ TAMs were significantly increased in established 60577 omental tumors compared with age-matched normal mouse omentum (Fig. 5A). CD206+ TAMs (as a percentage of the TAM population) decreased in carboplatin- compared with vehicle-treated 60577 omental tumors after the first and second doses (Fig. 5A; Supplementary Fig. S4H), whereas there was an increase in paclitaxel-treated tumors (Fig. 5B). We also found significant reductions in CD206+ TAMs in the 30200 model after three doses of carboplatin but not paclitaxel (Fig. 5C). The finding was, however, not replicated in the HGS2 model (Fig. 5D). The CD206 data were confirmed by IHC in 60577 omental tumors (Fig. 5E). Because MHC class II was not one of our positive gating criteria in the mouse tumor experiments as HLA-DR had been in the patient samples, we were able to measure the frequency of MHC class II–expressing TAMs (Fig. 5F–I). There were significant reductions in the 60577 model at the indicated time points with carboplatin but not with paclitaxel, and no significant differences were seen in 30200 and HGS2 models.
The data suggest that carboplatin, but not paclitaxel, results in loss of TAMs from the TME of murine HGSOC sensitive to carboplatin and changes their phenotype in a way that largely replicates the human data we described. In line with the patient data, we found significantly more nonviable TAMs in carboplatin-treated compared with control tumors at all time points in 60577 tumors (Fig. 5J). There was also evidence of inflammasome activation after NACT in the 60577 model, with a positive signal for ASC–NLRP3 complex formation observed at higher frequency in carboplatin-treated tumors (Fig. 5K and L) and increased size of macrophage “lakes” 14 days after carboplatin treatment (Fig. 5M). Thus, data so far show that chemotherapy treatment of omental metastases, either platinum/taxane NACT in human HGSOC or single-agent carboplatin in murine HGSOC tumors, decreases the number of TAMs, with a decreased positivity for alternative activation markers and an increase in nonviable TAMs in carboplatin-susceptible models. We also presented evidence that the mechanism of action of carboplatin involves inflammasome activation.
Inhibition of TAM populations after chemotherapy decreases disease-free survival
Because we concluded that chemotherapy depleted TAMs in both murine and human HGSOC at the same time as augmenting TAM populations that may aid host antitumor responses, we hypothesized that targeting TAMs after chemotherapy may modulate disease-free survival (DFS). We tested this hypothesis in the 60577 model where the mice relapsed 20 weeks after three doses of chemotherapy. We speculated that if chemotherapy increases antitumor TAMs, depletion of TAM populations after chemotherapy would decrease DFS, whereas it may have an opposite effect later during remission. First, we asked if alternatively activated TAMs were increased in relapsed tumors. We compared TAMs from omental tumors that reached humane endpoint in control-untreated groups and in mice that had relapsed after chemotherapy. TAM numbers were similar in untreated tumors and relapsed tumors (Fig. 6A), but there was a significant increase CD206+ TAMs (Fig. 6B), with no change in numbers with high MHC expression (Fig. 6C), suggesting that there may be more alternatively activated TAMs at relapse.
To test the above-stated hypothesis, we treated mice with two orally available inhibitors of the pan-macrophage receptor CSF1R (CSF1Ri), AZD7507 and BLZ945, both selective and potent inhibitors of CSF1R kinase activity (32, 33) in order to inhibit TAM recruitment to murine tumors (the reported biochemical IC50 values are 3 nmol/L for AZD7507 and 1 nmol/L BLZ945; both have negligible activity against other kinases).
Using AZD7507, we confirmed that this agent depleted TAM in established 60577 omental tumors (Fig. 6D). AZD7507 did not change the frequency of TAMs expressing CD206 (Fig. 6E), but there was a small but significant decrease in the frequency of MHCII-expressing TAMs (Fig. 6F). In spite of the decrease in macrophage cell number and change in phenotype, treatment of established 60577 tumors had no impact on mouse survival when the CSF1Ri was given as a single agent (Fig. 6G). The CFS1Ri BLZ945 also depleted TAMs in established 60577 tumors without a significant impact on tumor weight after 4-day treatment (Supplementary Fig. S5A and S5B). Treatment of mice with AZD7507 as a single agent also led to a significant reduction of CD4+ cells and CD19+ B cells infiltrating the tumors (Supplementary Fig. S5C and S5D). A similar effect was seen with treatment with BLZ945 (Supplementary Fig. S5E and S5F).
Having shown that these agents could deplete TAMs in untreated tumors, we treated cohorts of 60577 mice with three doses of carboplatin and then with AZD507, BLZ945, or appropriate vehicle controls to see if this would influence time to relapse and OS. In support of our hypothesis, long-term treatment with these two inhibitors significantly decreased mouse survival (P = 0.0006 and P = 0.012 for AZD7507 and BLZ945, respectively, 30 days after the end of carboplatin; Fig. 6H and I). In another experiment, we started treatment with AZD7507 immediately after the end of carboplatin treatment and again observed a significant decrease in DFS and OS (P = 0.025; Fig. 6J). However, if AZD7507 started later, 10 weeks after carboplatin treatment ceased, there was no impact on DFS (Fig. 6K). This provides further evidence that carboplatin induced changes in TAM populations toward an antitumor phenotype. These data also suggest that TAM populations persist in the TME after the end of chemotherapy.
We also studied the TAM populations when mice reached humane endpoint in the experiment shown in Fig. 6H and J. For mice shown in Fig. 6H, there was a trend for a decrease in density of F4/80+ cells in the 60577 omental tumors by IHC (Fig. 6L). Flow cytometry of endpoint tumors from the AZD7507 experiment shown in Fig. 6J similarly showed a decline in F4/80+ macrophages, but this was not significant (Fig. 6M). Within the remaining TAMs, CD206+ cells did not decrease significantly in AZD7507-treated tumors (Fig. 6N). There was, however, a significant and pronounced decrease in MHC class II+ TAMs (Fig. 6O; Supplementary Fig. S5G) after long-term AZD7507 treatment, suggesting that antigen presentation was decreased. Because there was evidence that chemotherapy induced inflammatory cell death in both human and mouse tumors, we assayed IL1β as a marker of inflammasome activation in ascites from the AZD7507 experiment (Fig. 6J) at endpoint and found a significant decrease in IL1ß (Fig. 6P). We also tested the effects of AZD7507 after carboplatin therapy in the 30200 model, which had less sensitivity to three doses of chemotherapy, and there was no effect on DFS or OS (Supplementary Fig. S5H).
In summary, although CSF1Ris were able to decrease F4/80+ cells in established omental tumors and those that had relapsed after chemotherapy, our experiments suggested that this did not augment host antitumor responses. Indeed, single-agent CSF1Ri treatment also depleted cells of the adaptive immune system in the omental tumors, and some schedules decreased DFS and OS when given in an attempt to prevent relapse. We conclude that long-term depletion of CSF1R+ cells was preventing potential antitumor actions of myeloid cells that had been induced by the carboplatin.
CSF1Ri treatment reprograms the microenvironment of relapsing tumors
We next conducted RNA-seq on omental biopsies from AZD7507 or vehicle-treated tumors at endpoint (Fig. 6J). Principal component analysis illustrated differences in the transcriptomes of the vehicle- compared with AZD7507-treated tumors (Supplementary Fig. S6A). Continued suppression of myeloid cells was confirmed with a significant decrease of F4/80 (Adgre1) in the AZD7507-treated group (Fig. 7A, left). Expressions of murine orthologs of the human TAM signature determined by Cassetta and colleagues (34) were also significantly decreased in the AZD7507-treated tumors (Fig. 7A, right). Differential expression analysis of AZD7507- versus vehicle-treated tumors revealed 352 genes (P value < 0.05 and log2FC > |1|), of which 336 genes were downregulated in AZD7507-treated tumors and 16 were upregulated (Fig. 7B; Supplementary Table S4). The top downregulated genes included innate and adaptive immune genes, such as Tlr9, Cx3cr1, Il10ra, Cd72, C1qb, Cd83, Irf8, Ifi207, and Slc11a1, as well as expected downregulation of Csf1r. The top upregulated genes included the IFN-inducible GTPase Iigp1, malignant cell–related genes such as Snx31 and Neto1, and adhesion molecules such as Sdk2. GSEA showed significant decreases in B- and T-cell activation in CSF1Ri-treated tumors and an upregulation of malignant cell processes such as developmental pathways, DNA damage response, and DNA replication (Fig. 7C and D; Supplementary Table S5). Figure 7D illustrates core enrichment genes in the GSEA pathways relating to the upregulation of DNA replication pathways and the downregulation of T- and B-cell activation. These results point to an important role of macrophages after treatment in the activation of adaptive immunity.
We found that TAM populations in omental samples from patients and mouse orthotopic models were altered by carboplatin chemotherapy, both in terms of density and phenotype. Because we found that pan-macrophage depletion starting shortly after chemotherapy in the mouse models reduced DFS and adaptive immunity, we concluded that chemotherapy enhanced the antitumor actions of TAMs and their ability to support adaptive immune responses.
A majority of patients with HGSOC have a good response to initial chemotherapy but, even with the use of more targeted treatments such as PARP inhibitors in an adjuvant setting (35), many patients will eventually relapse with increasingly drug-resistant disease. The work described here, taken together with published investigations of the adaptive immune response in this context, suggests that therapies that can enhance this nascent chemotherapy-induced immune response could increase DFS and even reduce the amount of chemotherapy given. As a majority of TAMs are thought to be tumor-promoting, one approach would be to reduce TAM recruitment to postchemotherapy micrometastases. There are several antibody and small-molecule approaches to macrophage depletion currently in early-phase clinical trials (10), but our model would suggest that this could be detrimental because it would also reduce TAMs capable of fostering antitumor immune responses. On the basis of our current results, and our previously published data showing stimulation of adaptive immunity after NACT in HGSOC (2, 3) as well as a positive association between stromal TAM density and survival (9), we suggest that macrophage-repolarizing approaches may maintain and even enhance the immune-stimulatory effects of chemotherapy in HGSOC and other cancers.
We do not know why only the stromal macrophages are associated with a better prognosis. It is known that both ECM components and stiffness of the matrix can influence the phenotype of macrophages (reviewed in ref. 36). High infiltration of macrophages at the invasive front of colorectal cancer sections correlates with a better response to chemotherapy (37). Similarly, in human pancreatic ductal adenocarcinoma, a high density of TAMs at the tumor–stroma interface positively predicts responsiveness to gemcitabine adjuvant chemotherapy (38). Stromal macrophages may have functions distinct from those in tumor islands, and/or may be predictive of response, rather than simply prognosis.
We observed differences in the tissue localization of CD68+ macrophages in HGSOC sections 3 weeks following NACT compared with untreated samples. In chemotherapy-treated samples, we frequently observed large clusters of macrophages (macrophage “lakes”) often in close proximity to or surrounding islands of viable malignant cells. In contrast, prechemotherapy samples, macrophages, were scattered in the stroma or malignant cell islands. A fibroinflammatory response including infiltrating macrophages and “foam” cells has been described as a tissue response to chemotherapy within omental HGSOC metastases (39). Indeed, assessment of these infiltrates forms a part of the chemotherapy response score and is associated with a favorable response to chemotherapy when associated with a concomitant tumor cell response (39).
There are a number of limitations to our work. Postchemotherapy changes in the TME are dynamic, but we studied just one time point—3 to 4 weeks after the last treatment cycle in our patients. However, because we were able to replicate the effects of chemotherapy in the HGSOC mouse models, we could study dynamic changes. Also, we focused on a single disease site, the omentum, because diagnostic omental biopsies are readily available to match biopsies taken at post-NACT surgery from the same patient, and our mouse cell lines develop large omental tumor deposits (13). However, other groups have confirmed our initial studies on the effects of NACT on T- and B-cell responses in other HGSOC disease sites (5). We should also note that heterogeneity of TAM populations and the effect of chemotherapy in the different TAM subsets are important factors in understanding the dynamics of TAMs, and single-cell RNA-seq will be valuable in future analysis. We studied platinum and taxane treatments individually, which of course was not possible in the patients, and further study of combination treatments in the mice is warranted.
The NLRP3 inflammasome is a multiprotein complex that mediates maturation and release of cytokines such as IL1β, as well as initiating pyroptosis. We found evidence for increased NLRP3 inflammasome activation in carboplatin-treated macrophages and post-NACT mouse and human tumors as a result of chemotherapy-induced, caspase-3–mediated activation of gasdermin E. Gasdermin E in turn forms pores on the plasma membrane that may either directly cause pyroptosis or potassium efflux that in turn activates the NLRP3 inflammasome. Once formed, the inflammasome promotes the maturation of IL1β and other cytokines, along with the induction of pyroptosis. Activation of the inflammasome and gasdermin E–induced pyroptosis in the TME has been reported to have a critical role in the activation of antitumor immunity in some cancer models (40, 41).
Our data support a model, at least in vitro, whereby carboplatin induces caspase-3–dependent cleavage of gasdermin E that in turn forms pore in the plasma membrane. Similarly to that described in ref. 30, we observed CC3-positive/TUNEL-negative macrophages, and NLRP3 inflammasome formation, in biopsies from carboplatin-treated patients. Because NLRP3-inflammasome formation is known to mediate caspase-1 autoproteolytic activation and IL1β cleavage and release, we hypothesize that this also occurs in macrophages. We do not know whether this exclusively happens in macrophages or also in cancer cells. Because we observed PLA-positive cells that morphologically looked like tumor cells, it is possible that inflammasome activation also occurs in malignant cells.
Pathways associated with IL1, IL6, and IL8 signaling were enhanced in the post-NACT human TAM RNA-seq data. The actions of these inflammatory cytokines in cancer are likely to be context dependent—in early disease, neutralizing these inflammatory cytokines may be beneficial, but acute and high levels of these inflammatory cytokines generated by the dying malignant cells after chemotherapy may help stimulate longer-lasting adaptive immune responses as our results suggest. In terms of antitumor actions of IL1ß, a study highlights a critical role for myeloid cells in the stimulation of immunogenic cell death after oxaliplatin and mitoxantrone chemotherapy in mouse cancer models. The mechanism of this involves myeloid cell PTEN-promoting inflammasome activation and production of IL1β. They were also able to find evidence for this mechanism in patients with breast cancer treated with anthracycline-based adjuvant chemotherapy (42). However, there is also abundant preclinical evidence that inflammatory cytokines such as IL1ß, IL6, and IL8 are targets for cancer therapy, rather than being involved in host antitumor responses (43–45). In the CANTOS trial of 10,061 patients with cardiovascular disease, treatment with the IL1ß antibody canakinumab reduces incidence of lung cancer in a dose-dependent manner (46). One interpretation of this result is that the patients (many of whom were smokers) had undetected early lung cancers and that, in this context, chronic IL1β production was promoting these nascent tumors. However, these important findings need to be replicated in further clinical trials.
We believe that the mouse models of treatment and relapse that we have described here will allow us to ask if neutralizing inflammatory cytokines, inflammasome inhibitor drugs, or therapies that repolarize TAMs will prolong or reduce remission or act together with chemo- or immunotherapies.
S.T. Barry is an employee and shareholder of AstraZeneca. F.R. Balkwill reports personal fees from Verseau Therapeutics, Inc., Novartis, and GlaxoSmithKline outside the submitted work. No disclosures were reported by the other authors.
O. Heath: Conceptualization, formal analysis, funding acquisition, investigation, visualization, writing–original draft, writing–review and editing. C. Berlato: Conceptualization, resources, formal analysis, investigation, visualization, writing–original draft, writing–review and editing. E. Maniati: Conceptualization, data curation, formal analysis, investigation, writing–original draft, writing–review and editing. A. Lakhani: Investigation, writing–review and editing. C. Pegrum: Investigation, methodology, writing–review and editing. P. Kotantaki: Formal analysis, investigation, writing–review and editing. S. Elorbany: Formal analysis, investigation, writing–review and editing. S. Böhm: Resources, writing–review and editing. S.T. Barry: Resources, writing–review and editing. A. Annibaldi: Conceptualization, resources, methodology, writing–review and editing. D.P. Barton: Resources, writing–review and editing. F.R. Balkwill: Conceptualization, formal analysis, supervision, funding acquisition, writing–original draft, writing–review and editing.
The authors thank the patients for donating samples and doctors and nurses at St. Bartholomew's Gynaecological Cancer Centre and St. George University Hospital for their support. They also thank the Barts Gynae Tissue Bank and the BCI Flow Cytometry Facility, which is funded by a CORE SERVICE GRANT at Barts Cancer Institute (Core Award C16420/A18066). In particular, the authors thank Dr. Rebecca Pike for her advice and support in flow cytometry and cell sorting. This work was funded by Cancer Research UK Programme Grants C587/A16354 and C587/A25714 (C. Berlato, E. Maniati, A. Lakhani, P. Kotantaki, and F.R. Balkwill); Cancer Research UK Clinical Bursary A21222, Wellcome Trust Clinical Research Training Fellowship 201118/Z/16/Z (O. Heath); and Wellbeing of Women RTF1013 and ELS906 (S. Elorbany). A. Annibaldi was funded by Barts Charity Grant CIF9035B.
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