Determining mechanisms of resistance to αPD-1/PD-L1 immune-checkpoint immunotherapy is key to developing new treatment strategies. Cancer-associated fibroblasts (CAF) have many tumor-promoting functions and promote immune evasion through multiple mechanisms, but as yet, no CAF-specific inhibitors are clinically available. Here we generated CAF-rich murine tumor models (TC1, MC38, and 4T1) to investigate how CAFs influence the immune microenvironment and affect response to different immunotherapy modalities [anticancer vaccination, TC1 (HPV E7 DNA vaccine), αPD-1, and MC38] and found that CAFs broadly suppressed response by specifically excluding CD8+ T cells from tumors (not CD4+ T cells or macrophages); CD8+ T-cell exclusion was similarly present in CAF-rich human tumors. RNA sequencing of CD8+ T cells from CAF-rich murine tumors and immunochemistry analysis of human tumors identified significant upregulation of CTLA-4 in the absence of other exhaustion markers; inhibiting CTLA-4 with a nondepleting antibody overcame the CD8+ T-cell exclusion effect without affecting Tregs. We then examined the potential for CAF targeting, focusing on the ROS-producing enzyme NOX4, which is upregulated by CAF in many human cancers, and compared this with TGFβ1 inhibition, a key regulator of the CAF phenotype. siRNA knockdown or pharmacologic inhibition [GKT137831 (Setanaxib)] of NOX4 “normalized” CAF to a quiescent phenotype and promoted intratumoral CD8+ T-cell infiltration, overcoming the exclusion effect; TGFβ1 inhibition could prevent, but not reverse, CAF differentiation. Finally, NOX4 inhibition restored immunotherapy response in CAF-rich tumors. These findings demonstrate that CAF-mediated immunotherapy resistance can be effectively overcome through NOX4 inhibition and could improve outcome in a broad range of cancers.
NOX4 is critical for maintaining the immune-suppressive CAF phenotype in tumors. Pharmacologic inhibition of NOX4 potentiates immunotherapy by overcoming CAF-mediated CD8+ T-cell exclusion.
Immune-checkpoint inhibitors that target PD-1/PD-L1 and CTLA-4 are used to treat an ever-expanding range of malignancies (1–3). Their success has led to a burgeoning interest in other immunotherapy approaches, such as cancer vaccines and adoptive cell transfer (4). However, most patients (∼80%) fail to respond to checkpoint monotherapy, a fact that highlights the need to identify targetable resistance mechanisms to broaden clinical effectiveness of these drugs (5, 6).
The success of most immunotherapies relies on CD8+ T cells effectively infiltrating tumors (7). Significantly, nonresponders to immunotherapy have been shown to display an “immune-excluded” tumor phenotype where lymphocytes fail to penetrate the tumor (8–10). Recent tissue analyses from αPD-1–treated tumors have also identified a prominent CAF gene signature in nonresponders, marked by upregulation of genes regulating extracellular matrix (ECM) remodeling and TGFβ1 signaling (6, 10, 11).
Although CAFs remain a relatively poorly characterized heterogeneous cell population, the term CAF most commonly refers to cells with a myofibroblast-like phenotype; similar to myofibroblasts, CAFs typically transdifferentiate through TGFβ1 signaling, generating a contractile cell, which expresses αSMA, secretes collagen-rich ECM, and promotes multiple hallmarks of malignancy (12–16). CAF-containing tumors often have low levels of lymphocytes, and recent studies suggest an emerging role for CAF in tumor immune evasion (9, 17). This raises the possibility that CAF targeting could produce immunotherapeutic benefit. Treatments designed to target CAFs, however, have not been successful clinically because the identification of specific CAF targets has proven problematic (18–20). Previously, we identified the role of NOX4 in regulating CAF differentiation (21). This ROS-producing enzyme is a downstream target of TGFβ1, and a central, and relatively specific regulator of the CAF phenotype in multiple human cancers (21). The principal aims of this study were to examine the effect of CAF on different immunotherapies, to investigate how CAFs affect immune cell phenotype, function and distribution, and to explore the use of a small-molecule NOX4 inhibitor [GKT137831 (Setanaxib)] for CAF targeting in combination with immunotherapy.
Materials and Methods
Murine tumor models
Experiments were conducted according to UK Home Office Regulations (license number P8969333C) and received appropriate institutional approval. C57BL/6J or BALB/c WT mice were originally obtained from Charles River Laboratories and colonies maintained in-house. The following tumor models were used in the study: the murine lung cancer cell line TC1 (obtained from ATCC) was derived from C57BL/6 murine lung epithelial cells immortalized with HPV16 E6 and E7 and transformed with c-Ha-ras (22). The murine colorectal cancer cell line MC38 (obtained from Charles River Laboratories) was derived from a methylcholanthrene-induced C57BL/6 murine colon adenocarcinoma (23). The murine breast cancer cell line 4T1 (obtained from ATCC) was derived from a spontaneously arising BALB/c mammary tumor (24). TC1 or MC38 cancer cells (0.5 × 105) were injected in phosphate-buffered saline (PBS) subcutaneously (s.c.) into the flank of C57BL/6 female mice ages 8 to 10 weeks. TC1 or MC38 cells were either injected alone or mixed with 2.5 × 105 C57BL/6 lung or colon fibroblasts, respectively, which had been pretreated ex vivo prior to injection with 2 ng/mL of TGFβ1 (R&D Systems) for 6 days to induce a myofibroblast CAF-like phenotype. 4T1 cancer cells (0.25 × 105) in PBS were injected s.c. into the upper mammary fat pad of female mice ages 8 to 10 weeks. Cells were either injected alone or mixed with 1.25 × 105 BALB/c breast CAF isolated from transgenic BALB-neuT spontaneous breast tumors (25). Tumors were measured every 2 to 3 days by electronic skin caliper from longest width and length, and tumor volume was calculated using the formula 4/3π × r3, where the radius (r) was calculated from tumor width and length measurement to provide an average diameter value. Mice were placed into groups based on tumor volume so that there was no statistical difference in mean tumor volumes between groups before treatment commenced. Vaccination with DNA vaccine encoding tetanus Fragment C domain 1 (Dom; ref. 26) fused to the immunodominant CD8 epitope of E7 HPV RAHYNIVTF (RAH, E749-57) p.Dom-RAH was administered via intramuscular (i.m.) injection when tumors were palpable. P.Dom without the epitope served as a control. One injection containing 50 μg of DNA in PBS was given and any repeat doses were given 3 weeks after initial immunization (27). NOX4 inhibitor GKT137831 (Setanaxib, Genkyotex SA) was reconstituted in 1.2% methyl cellulose (Sigma) with 0.1% Polysorbate (Sigma) and administered to mice by oral gavage 5×/week at 40 mg/kg when tumors were palpable. Control mice received vehicle alone. For longer term dosing, 15 initial doses were given as stated, but reduced to 3×/week for 3 weeks at 50 mg/kg, and finally 2×/week for 3 weeks at 60 mg/kg to comply with UK home office project license procedure limits for repeated oral gavage. αPD-1 antibodies (Bio X Cell; RMP1-14) were given via intraperitoneal (i.p.) injection. Antibody or IgG2a isotype control (300 μg; Bio X Cell) was given when tumors were palpable every other day, totaling 3 doses. αCSF-1 antibodies (Bio X Cell; 5A1) were given via i.p. injection. Initially 1 mg of antibody or IgG1 isotype control (Bio X Cell) was given when tumors were palpable, then a further 4 doses were given at 500 μg spaced 4 days apart. αCTLA-4 blocking antibodies (Bioxcell; 9D9) were given via i.p. injection. Two hundred μg of antibody or IgG2b isotype control antibodies (Bioxcell) were given when tumors were palpable every three days, totaling 3 doses. αTGFβ1 antibodies (Bioxcell; 1D11) were given via i.p. injection. Two hundred μg of antibody or IgG1 isotype control antibodies (Bioxcell) were given when tumors were palpable every other day totaling 5 doses.
Cell culture and treatments
TC1 cells (ATCC) were cultured in RPMI-1640 (Sigma) supplemented with 10% FBS and 2 mmol/L L-glutamine (Sigma G7513). MC38 cells (Charles River Laboratories) were cultured in DMEM (Sigma D5671) with 10% FBS, HEPES (Gibco 15630-056), nonessential amino acids (Sigma M7145), sodium pyruvate (Sigma 58636), and L-glutamine. 4T1 (ATCC) were cultured in DMEM with 10% FBS and L-glutamine. Lung fibroblasts were isolated from C57BL/6 female mice; pieces of tissue were washed 3× in fresh PBS containing 4% penicillin/streptomycin and 0.25 μg/mL amphotericin B (Sigma) and grown in 12-well tissue culture plates (Corning) in 750 μL of DMEM containing 20% FBS, 4% penicillin/streptomycin, and amphotericin B. Tissue pieces were cultured for 5 days, and the medium was changed twice weekly. As cells appeared from the tissue, penicillin/streptomycin concentration was lowered to 2%. At 80% confluence, cells were detached with 0.05% trypsin/EDTA solution (Sigma-Aldrich). Cells from adjacent wells were pooled and expanded into one T75 tissue culture vented flask (Corning) containing DMEM, 10% FBS, L-glutamine 1% penicillin/streptomycin, and amphotericin B. BALB/c breast CAF and normal fibroblasts were isolated similarly from BALB-neuT transgenic spontaneous breast tumors or WT breast tissue, respectively. Primary C57BL/6 colon fibroblasts (Cell Biologics) were maintained in DMEM containing 10% FBS, 2 mmol/L L-glutamine, 1% penicillin/streptomycin, and amphotericin B. All fibroblasts were cultured at 37°C, 5% CO2 and 3% O2. Fibroblasts (1 × 105) were seeded per 6-well for PCR analysis or 5 × 103 per 8-well chamber slides for immunofluorescence analysis and left to culture for 48 hours. Cells were treated with 40 μmol/L GKT137831 for 1 hour followed by 2 ng/mL TGFβ1 treatment. To assess the effect of drugs on myofibroblastic CAF differentiation, fibroblasts were treated with 40 μmol/L GKT137831 or 25 μg/mL of αTGFβ (Bioxcell) for 1 hour, followed by 2 ng/mL TGFβ1 treatment. To test the effect of drug on the established CAF phenotype, inhibitors were directly applied to CAF. Cells were then cultured for 48 hours for PCR analysis, 72 hours for ROS analysis, or 6 days for immunofluorescence (unless otherwise stated). For 6-day experiments, cells were re-treated with agents as above after 3 days. All cell lines were routinely PCR-tested for Mycoplasma.
Lentiviral-mediated shRNA transduction was used to stably knock down NOX4 in fibroblasts. To generate lentiviral particles, HEK-293-T cells were transiently transfected with 3 μg pLKO.1 lentiviral vector (Sigma) containing shNOX4 or sh nontargeting control (Sigma) plus 3 μg of pCMVDR8.91 and 0.75 μg of pMD.2G plasmids (Addgene) using transfection reagent Lipofectamine 2000 (Invitrogen). Fibroblasts were transduced with lentiviral particles plus 0.4 μg/mL polybrene (Sigma) and infected overnight. Virally transduced fibroblasts were selected by adding 0.75 μg/mL puromycin (Sigma).
Antibodies and clones used were anti-CD45.5-percp-Cy5.5 (104), anti-CD3-eFluor-450 (17A2), anti-CD4-eFluor-450 (GK1.5), anti-CD8a-APC-Cy7 (53-6.7), anti–PD-1-PE (RMP1-30), anti–CTLA-4-PE (UC10-4B9), anti-CD11b-PE (M1/70), anti-F480-APC-Cy7 (BM8), anti-TNFα-PE-Cy7 (MP6-XT22), anti-granzyme B-APC (GB11) viability dye eFluor-450 and appropriate isotype controls (all from eBioscience/Thermo Fisher) as well as anti-CD3-FITC (17A2), anti-IRF4-AF488 (IRF4.3E4), anti-CD137-APC (17B5), and anti-IFNγ-FITC (XMG1.2) and appropriate isotype controls (all BioLegend), anti-Ki67-FITC (BD Pharmingen), and DAPi for staining dead cells (Invitrogen). E7 tetramer was made in-house using the RAHYNIVTF 9-mer peptide and PE labeled. For analysis of total tumor-infiltrating immune cells, tumors were cut into small pieces and incubated at 37°C for 20 minutes in a shaking incubator in complete RPMI-1640 medium containing 0.15 Wünsch Unit/mL Liberase TL (Sigma-Aldrich) and 50 μg/mL DNAse 1 (Sigma-Aldrich). Tumors were mashed through a 100-μm strainer (Greiner Bione) and pelleted by centrifugation at 500 × g for 5 minutes to achieve a single-cell suspension. Red blood cells were lysed for 5 minutes at room temperature with RBC lysis solution (eBioscience) before staining. For all flow cytometry, single-cell suspensions were incubated with 10 μg/mL anti-Fc receptor mAb (2.4G2, BD) for 10 minutes at 4°C prior to surface staining. Antibodies were added at a concentration of 10 μg/mL and incubated for 30 minutes at 4°C in the dark. Samples were washed with FACS buffer containing PBS with 0.1% BSA, centrifuged for 5 minutes at 200 × g and run on a flow cytometer (BD FACSCanto). For intracellular/intranuclear staining, cells were fixed and permeabilized post surface staining using FOXP3 staining buffer set (eBioscience) and antibodies applied for 30 minutes at 4°C in the dark. Cells were washed for 5 minutes at 200 × g in permeabilization buffer and analyzed. For detection of intracellular cytokines, tumor single-cell suspension was restimulated for 5 hours with 1 μg/mL of relevant peptide (E7 predefined epitope) in the presence of GolgiPlug and GolgiStop (BD Pharmingen). After surface staining, cells were fixed, permeabilized with BD cytofix/perm (BD Pharmingen), and intracellular cytokine staining was performed in permeabilization buffer. Single-stain control tubes were used to set appropriate voltages, and isotype controls were used to aid the gating of positive populations. Stained cells or fluorescent beads (BD Pharmingen) were used for compensation set up. FlowJo (Tree Star) software was used for analysis.
RNA extraction and RT-qPCR
RNA extraction was performed using an RNeasy Mini Kit (Qiagen) following the standard protocol. mRNA was retro-transcribed using RevertAid First-Strand cDNA Synthesis Kit (Thermo Scientific) following the manufacturer's instructions. RT-qPCR was performed with SYBR green reagent (Life Technologies). Analysis of relative gene expression was performed in ΔΔCT by comparing the gene of interest CT value to housekeeping gene HPRT CT value. Data were normalized to TGFβ1-treated positive control. Primer sequences and concentrations used were ACTA2, 0.1 μmol/L, F:CCTCATGCCATCATGCGTCT / R:AATCTCACGCTCGGCAGTAG, COL1A1, 0.4 μmol/L, F:GTGTTCCCTACTCAGCCGTC / R:ACTCGAACGGGAATCCATCG, COL3A1, 0.2μmol/L, F:TCCTGGTGGTCCTGGTACTG / R:AGGAGAACCACTGTTGCCTG, FN1, 0.1 μmol/L, F:GAAGACAGATGAGCTTCCCCA/R:GGTTGGTGATGAAGGGGGTC, NOX4, 0.4 μmol/L, F:TGCCCCAGTGTATCAGCATT / R:CCGGAATCGTTCTGTCCAGT, HPRT1, 0.1 μmol/L, F:GTTGGGCTTACCTCACTGCT / R:TCATCGCTAATCACGACGCT.
Bulk RNA sequencing and analysis
Total RNA was purified using a miRNeasy Micro Kit (Qiagen) and quantified as described (28). Purified total RNA was amplified following the smart-seq2 protocol. cDNA was purified using AMPure XP beads (0.9:1 ratio, Beckman Coulter). From this step, 1 ng of cDNA was used to prepare a standard Nextera XT sequencing library (Nextera XT DNA sample preparation kit and index kit, Illumina; ref. 29). Samples were sequenced using HiSeq2500 (Illumina) to obtain 50-bp single-end reads. Quality control steps were included to determine total RNA quality and quantity, optimal number of PCR preamplification cycles, and cDNA fragment size. Samples that failed quality control were eliminated from further analysis. Bulk RNA-seq data were mapped against the mm10 reference using TopHat (v1.4.1; –library-type fr-unstranded –no-coverage-search; ref. 30) with FastQC (v0.11.2), Bowtie (v1.1.2; ref. 31), Samtools (0.1.18; ref. 32), and we used htseq-count -m union -s no -t exon -i gene_id (part of the HTSeq framework, version 0.7.1; ref. 33). Values throughout are displayed as log2 TPM (transcripts per million) counts; a value of 1 was added prior to log transformation. To identify genes differentially expressed between 2 groups, we performed negative binomial tests for unpaired comparisons by using the Bioconductor package DESeq2 (v1.14.1) disabling the default options for independent filtering and Cooks cutoff (34). We considered genes differentially expressed between any comparison when the DESeq2 analysis resulted in a Benjamini–Hochberg–adjusted P value <0.05. The Qlucore Omics Explorer 3.2 software package was used for visualization and representation (heat map) of RNA-seq data (28). Gene set enrichment analyses (GSEA) were performed as previously described (35, 36). Genes used in the GSEA analysis are shown (Supplementary Table S2; ref. 37).
Weighted Gene Correlation Network Analysis (WGCNA) was performed on publicly available HNSCC RNA-seq data (data are available at ArrayExpress accession E-MTAB-4546; ref. 38) using the wgcna R package (39). Raw counts were transformed as described above [log2(TPM + 1)], and batch effects between data sets were removed using the limma package in R (40). Genes used as input for WGCNA were determined by performing principal components analysis and selecting components that accounted for ∼95% of the variance within the data. Genes were ranked by highest absolute principal components analysis score to one of these components, and the top 8,000 were selected for use in WGCNA. Modules were identified from unsupervised hierarchical clustering of genes using 1-Topological overlap, within a scale-free adjacency matrix, as a distance measure. Each module was then summarized by the first principal component gene (module eigengene, ME; representing a suitably defined average of the gene module). GO Biological processes and Kyoto Encyclopedia of Genes and Genomes Pathway enrichment analyses were carried using GSEA where all genes were ranked by their correlation to the module eigengene using the fgsea package in R (25).
For IHC of αSMA (Sigma-Aldrich; 1A4), tissues were fixed in 4% paraformaldehyde and embedded in paraffin. Sections (4 μm) were deparaffinized, rehydrated, and antigen retrieved for 20 minutes at 97°C using a predefined program on the Dako PT links. Antigen retrieval was performed using Envision FLEX High pH. Endogenous peroxidase activity was blocked using 3% hydrogen peroxide. Mouse Ig blocking reagent (M.O.M. kit; Vector Laboratories) was applied for 1 hour. Primary antibody incubation (1:100) in M.O.M. diluent (Vector Laboratories) was for 20 minutes. For secondary amplification, M.O.M. Biotinylated anti-Mouse IgG reagent was applied for 10 minutes followed by Vectorstain elite ABC reagent (Vector Laboratories). Chromogenic visualization was completed with 2 × 5 minute washes in DAB and counterstaining with hematoxylin. For IHC of CD8 (in-house; YTS169), CD4 (BD; RM4-5), PD-L1 (Thermo Fisher; MIH5), and F480 (AbD Serotec; Cl:A3-1) tissues were frozen in OCT. Sections (8 μm) were fixed in 100% acetone for 10 minutes. Endogenous peroxidase activity was blocked using neat peroxidase suppressor (Peirce) for 15 minutes. Sections were blocked for 30 minutes with 2.5% goat serum. Primary antibodies were applied for 2 hours: CD8 (1:800), CD4 (1:100), and F480 (1:100). Appropriate species HRP polymer (ImmunoPress, Vector Laboratories) was applied to sections for 30 minutes. Vector NovaRED chromagen substrate (Vector Laboratories) was applied for 2 to 10 minutes and then counterstained with hematoxylin (Vector Laboratories) for 30 seconds. Images were captured using an Olympus CKX41 microscope with Cell B imaging software. The percentage of area of staining was calculated using Fiji software (java) by running the “color deconvolution” tool analyzing H DAB. The image was appropriately thresholded based on the “color 2” image. The same thresholding was applied to all images from the same experiment. Regions of interest (margin and core) were confirmed by a pathologist, and each point plotted represents the mean of a minimum of 4 independent fields of view (field diameter = 2 mm) from margin and core (one section per mouse). The mean was calculated from 2 or 3 mice / experimental group.
Multiplex immunohistochemistry (MxIHC) was performed on FFPE samples from the HNSCC cohort (Bulk RNA-seq and analysis; ref. 38), where tissue was available (n = 16; Research Ethics Committee reference 09/H0501/90). Staining and image processing was performed as previously with minor alterations (41). Deparaffinization, rehydration, antigen retrieval, and IHC staining were performed using a Dako PT Link Autostainer and EnVision FLEX Target Retrieval Solution, High pH (Agilent Dako). The section was first stained with anti-Pan-Cytokeratin (prediluted, Clone AE1/AE3; Agilent Dako), followed by biotinylated anti-Mouse IgG and Vectorstain elite ABC reagent (Vector Laboratories) as described above. Chromogenic visualization was completed with DAB and counterstaining with hematoxylin. The staining was imaged using a Zeiss AxioScan.Z1 with 20× air immersion objective. Following this staining iteration, the section was sequentially stained as above, except using AEC for chromogenic visualization, with anti-αSMA, anti-GZMB, and anti-CD8α (prediluted Kit IR62361-2; clone C8/144B; Agilent Dako) and scanned. Between each staining iteration, removal of the labile AEC staining (50% ethanol for 2 minutes; 100% ethanol for 2 minutes; 100% xylene for 2 minutes; 100% ethanol for 2 minutes; 50% ethanol for 2 minutes) and denaturation of the preceding antibodies through repeated antigen retrieval were performed. Image processing was performed in Fiji image analysis software. The PanCK alone image was used as a reference for registering each iteration of staining, using the linear stack alignment with SIFT Fiji plugin. Color deconvolution for hematoxylin, DAB, and AEC staining was performed using the Fiji plugin and images processed applying a positive staining threshold for each marker based on the initial (AEC-negative) scan. Finally, processed images were combined to generate pseudo-IF multichannel 8-bit TIFF images. Definiens Tissue Studio software (Definiens) was used to analyze the pseudo-IF multichannel images for nuclear segmentation, cellular simulation, and tumor boundary identification. CAFs were identified as αSMA+ PanCK− CD8− cells after excluding vessels using a machine learning classifier. Tumor center and margin regions were identified by a consultant pathologist (G.J. Thomas). For comparing cell numbers across samples, αSMA+ CAFs or CD8+ T cells were quantified as the percentage of stromal/immune cells (nonepithelial PanCK− cells) within each region. For distance measurements from CAFs to tumor cells, 6 independent representative regions from within the tumor were selected by a consultant pathologist (GJT), and quantified as the average across all regions.
To visualize ECM, wells were decellularized with 0.25 mol/L NH4OH in 50 mmol/L Tris buffer at 37°C for 30 minutes, washed in PBS, and fixed with ice-cold 100% methanol at −20°C for 30 minutes and incubated with blocking solution containing PBS 1% BSA for 30 minutes. Collagen I primary antibody (abcam; 34710; 1:500) was applied for 1.5 hours. Secondary antibody anti-goat 488 (Invitrogen; 1:200) was applied for 45 minutes. To visualize cell-associated proteins, cells were fixed in 4% paraformaldehyde solution and permeabilized in PBS containing 0.5% tritonX for 10 minutes. Cells were blocked with PBS 0.1% tritonX containing 2% BSA for 1.5 hours. αSMA primary antibody (Sigma-Aldrich; 1A4; 1:100) was applied for 1 hour. Secondary antibody anti-mouse 546 (Invitrogen; 1:200) was added to cells for 45 minutes. DAPI was used as a counterstain to visualize cell nuclei (Invitrogen 1 in 1,000). Fluorescence was visualized using an Olympus IX81 fluorescent microscope with Xcellence imaging software (Olympus). For ECM, a 4× objective was used with 3,000-ms exposure time. For cells, a 20× objective was used with 1,000-ms exposure for αSMA and 500 ms for DAPI. Mean fluorescence intensity of staining was calculated with Fiji software using the “analyze” tool with the mean pixel intensity selected. Data were normalized to TGFβ1-treated positive control. Each point plotted represents the mean of 4 independent fields of view from one experimental condition. The mean was calculated from a minimum of 2 independent experiments.
Statistical analyses were performed on a minimum of three independent experiments or biological replicates unless stated otherwise. Data are presented as mean ± SEM for all experiments. For normalized data where variance was equal to 0 for one group but significantly differed for the comparison group, Welch correction was applied to the calculated P value. For tumor growth curves, statistical testing was performed on the mean area under the curve (AUC) values for each curve. For cell proliferation curves, statistical tests were performed on the mean data from the final time point. Statistical tests were performed in GraphPad Prism v. 7. All statistical tests were two-sided, and P values less than 0.05 were considered to be statistically significant. Significant values are marked with asterisks and represented the following: P ≥ 0.05 (ns, nonsignificant); *, P < 0.05; **, P < 0.01; ***, P ≤ 0.001; ****, P < 0.0001.
CAFs suppress tumor responses to anticancer vaccination and αPD-1 immunotherapies
We found that commonly used syngeneic murine tumors typically have low CAF content compared with human tumors. Immunostaining of CAFmod/high lung, colorectal and breast human tumors for αSMA (12, 15, 21) revealed that CAF content ranged from 15% to 60%. Comparatively, murine lung (TC1), colorectal (MC38), and breast (4T1) tumor models were CAFlow (<10%; Fig. 1A). Therefore, we developed CAF-rich murine tumor models by coinjecting CAF with tumor cells, by either treating anatomically matched fibroblasts with TGFβ1 (TC1, MC38) to produce cells with a typical myofibroblast CAF-like phenotype (upregulated Acta2, Fn1, Col1A1, Col3A1; αSMA-positive stress fibers; collagen I secretion; Supplementary Fig. S1A–S1C), or isolating CAF directly from BALB-neuT transgenic breast tumors (4T1; Supplementary Fig. S1A–S1C). This technique increased tumor growth (Supplementary Fig. S1D) and produced tumors with an αSMA-positive, CAF-rich stroma more typical of human cancers (Fig. 1B). Then we investigated whether CAF influenced tumor responses to different immunotherapies. First, we tested a vaccine model using HPV E6/E7-expressing TC1 cells (42); mice were treated with DNA vaccine encoding the immunodominant CD8 epitope of HPV E7 (RAH, E749-57). Vaccination significantly reduced the volume of control tumors (3/8 mice showed complete tumor regression; Fig. 1C and D). No significant volume reduction was seen in CAF-rich tumors where all of the mice retained tumors (Fig. 1C and E). Next, we explored whether CAF also promoted resistance to αPD-1 inhibition; treatment of mice with αPD-1 mAbs resulted in a significant reduction in MC38 (43) control tumor volume (4/8 mice had complete tumor regression; Fig. 1F and G). αPD-1 treatment did not significantly reduce volume of MC38 CAF-rich tumors where all of the mice retained tumors (Fig. 1F and H).
CAFs exclude CD8+ T cells from tumors
To investigate the mechanism by which CAF promote immunotherapy resistance, we first compared the immune cell composition of control and CAF-rich TC1 tumors using flow cytometry; this showed that CAF-rich tumors contained more macrophages, but there were no significant differences in CD8+ or CD4+ T cells (Fig. 2A). We used immunochemistry to examine the localization of immune cells in TC1, MC38, and 4T1 tumors. This showed that CAFs markedly altered the distribution of CD8+ T cells, which accumulated at the tumor periphery and were excluded from the tumor center (Fig. 2B and Supplementary Fig. S2A–S2C). CAF produced no change in CD4+ T-cell localization (Fig. 2C and Supplementary Fig. S2D and S2E) but did promote macrophage accumulation in and around the tumors (Fig. 2D and Supplementary Fig. S2F and S2G). Analysis of vaccine-treated TC1 tumors and αPD-1-treated MC38 tumors confirmed that CAFs promoted CD8+ T-cell exclusion from tumors (Supplementary Fig. S3A and S3B).
To examine the spatial and functional relationships between CAF and CD8+ T cells in human tumors, we used MxIHC analysis [pan-cytokeratin (tumor cells), αSMA (CAF), CD8 and GZMB] of a cohort of head and neck cancers that had previously undergone RNA-seq (Fig. 3A–C; ref. 38). Initial analysis showed CAFhigh tumors have significantly fewer CD8+ T cells in the center of the tumor compared with CAFlow tumors (Fig. 3C). In contrast, the number of CD8s at the margin of these tumors was not significantly different (Fig. 3C). To further examine the effect of CAFs on CD8+ T cells, we analyzed the relationships between spatial features and gene-expression profiles. WGCNA was used to identify correlated gene modules, which represent prominent biological processes activated in these samples (Supplementary Fig. S4A). This identified a module of genes involved in lymphocyte costimulation [GSEA: normalized enrichment score (NES) = 3.1; FDR, Q = 0.003], which correlated with the density of CD8+ tumor-infiltrating lymphocytes (r = 0.64, P = 0.01; Supplementary Fig. S4B and Supplementary Table S2). MxIHC staining showed CD8+ T-cell exclusion in tumors where CAF directly abutted the tumor cells (i.e., low distance between CAF and tumor cells; Fig. 3D). To quantify this observation, we measured the distance of CAF to tumor cells and examined how this related to expression of the lymphocyte costimulation module (summarized by the first principle component/eigengene). This showed a significant correlation between lymphocyte costimulation eigengene expression and the distance of CAF to tumor cells (i.e., decreased costimulation where CAF directly abutted the tumor; Fig. 3E).
Upregulation of CTLA-4 in CD8+ T cells from CAF-rich tumors
To investigate potential mechanisms promoting CD8+ T-cell exclusion, we performed RNA-seq on flow cytometry–sorted CD8+ T cells from CAF-rich and control TC1 tumors [adjusted P value of <0.05 (DESeq2 analysis; Benjamini–Hochberg test; Fig. 4A and Supplementary Table S1)]. One of the most highly upregulated genes was Ctla4, a CD28 homologue that acts as a negative regulator of T-cell response (44, 45). Other upregulated genes included Tnfrsf9 (41bb), a marker of antigen experience, and Irf4, a transcription factor implicated in T-cell exhaustion. GSEA of CD8+ T cells from CAF-rich versus CAFlow tumors significantly correlated with the cytokine and cytotoxic (NES 1.54; *, P = 0.02 Kolmogorov–Smirnov test) and exhaustion signatures (NES 1.55; *, P = 0.05 Kolmogorov–Smirnov test; Fig. 4B; ref. 37). We used flow cytometry to examine CD8+ T-cell exhaustion in CAF-rich tumors in further detail and confirmed increased expression of CTLA-4 (and TNFRSF9 and IRF4; Fig. 4C), but found no differences in expression of PD-1, granzyme B, and Ki67 (Fig. 4D). Consistent with this finding, CD8+ T cells isolated from control and CAF-rich tumors following vaccination (E7 DNA vaccine) functioned similarly, with flow cytometry showing no differences in expression of IFNγ, TNFα, or granzyme B effector cytokines following E7 peptide restimulation ex vivo (Fig. 4E).
Upregulation of CTLA-4 in the absence of additional “classic” T-cell exhaustion markers raised the possibility that CD8+ T-cell exclusion could be mediated by CTLA-4 regulation of lymphocyte adhesion/migration (44, 45). Analysis of human HNSCC (38) using multiplexed immunochemistry similarly showed that a proportion of excluded CD8+ T cells expressed CTLA-4 (range, 5%–34%; mean = 15.3%; Fig. 4F). Notably, inhibiting CTLA-4 in CAF-rich TC1 tumors using blocking (nondepleting) antibodies reduced tumor growth (Fig. 4G), decreased CD8+ T-cell exclusion (Fig. 4H; bottom), and increased infiltration (Fig. 3H; top). CTLA-4 blocking had no effect on intratumoral FOXP3+ Treg levels (Supplementary Fig. S4C) or growth of CAFlow TC1 tumors (Fig. 4G).
NOX4 inhibition promotes tumor CD8+ T-cell infiltration
Macrophages have been reported to promote T-cell exclusion from tumors, and therefore we investigated whether this was the case in our models (46). Macrophage depletion using mAbs against CSF-1, however, did not affect tumor growth or CD8+ T-cell distribution in CAF-rich TC1 tumors (Supplementary Fig. S4D–S4G). Next, we investigated whether targeting the CAF phenotype could promote CD8+ T-cell infiltration into tumors. TGFβ1 regulates the myofibroblast-like CAF phenotype and we have shown previously that its downstream target, NOX4, regulates CAF differentiation in multiple human cancers (21). First, we tested the effect of TGFβ inhibition on CAF differentiation, phenotype, and CD8+ T-cell exclusion. In primary fibroblasts, TGFβ inhibition suppressed TGFβ1-induced myofibroblast differentiation (αSMA and collagen expression; Supplementary Fig. S5A–S5D) but had no effect on established CAF (Supplementary Fig. S5E–S5H). In vivo, TGFβ1 inhibition did not reduce intratumoral CAF levels (Supplementary Fig. S5I and S5J) or prevent CD8+ T-cell exclusion (Supplementary Fig. S5K and S5L, bottom). Despite this, TGFβ1 inhibition reduced the volume of CAF-rich TC1 and MC38 tumors (Supplementary Fig. 5M and S5N) and increased intratumoral CD8+ T cells (Supplementary Fig. S5K and S5L, top). However, TGFβ1 inhibition similarly decreased tumor growth and increased CD8+ T cells in control (CAFlow) tumors (Supplementary Fig. S5O–S5R), suggesting the effect was CAF independent.
The enzyme NOX4 generates intracellular ROS associated with myofibroblast differentiation (Supplementary Figs. S5A and S5B; S6A and S6B). Differentiated αSMA-positive CAF cultured ex vivo also showed increased ROS and NOX4 levels (Supplementary Fig. S6C and S6D). Similar to TGFβ1, the suppression of NOX4 activity using a NOX4/1 inhibitor [GKT137831 (Setanaxib)] suppressed TGFβ1-induced myofibroblast differentiation (Supplementary Fig. S6E–S6H), but also “normalized” fully differentiated CAF to a quiescent phenotype, downregulating expression of functional CAF markers, αSMA and collagen 1; Fig. 5A–D). Similar findings were observed using NOX4 shRNA (Supplementary Fig. S6I–S6L). In vivo, GKT137831 reduced CAF levels (Fig. 5E and F). The accumulation of CD8+ T cells at the tumor margin was no longer apparent, and infiltration of CD8+ T cells into the tumor significantly increased (Fig. 5G and H), resulting in reduced tumor volume of CAF-rich TC1 and MC38 tumors (Fig. 5I and J). shNOX4 knockdown in CAF produced similar effects (Fig. 5K–M). GKT137831 had no effect on the growth of control (CAFlow) tumors (Supplementary Fig. S6M and S6N). Intriguingly, GKT137831 treatment also resulted in the reexpression of PD-L1 by MC38 cells, which was downregulated in CAF-rich tumors (Supplementary Fig. S6O).
NOX4 inhibition resensitizes CAF-rich tumors to anticancer vaccination and αPD-1 checkpoint inhibition
To investigate whether GKT137831 resensitized CAF-rich tumors to immunotherapy, mice with CAF-rich TC1 tumors were dosed with vaccine/GKT137831 combination. The combination treatment significantly reduced tumor volume (Fig. 6A), increased CD8+ T-cell infiltration (Fig. 6B), and showed a nonsignificant trend for increased HPV-reactive CD8+ T cells (Fig. 6C). Although cessation of GKT137831 after vaccine (day 31) resulted in tumor relapse (Fig. 6D), mice treated with the drug combination showed increased median survival (58 days vs. 22 days) compared with vaccine monotherapy; Fig. 6E). Next, we repeated the experiment, extending the duration of GKT137831 treatment and incorporating a second vaccine dose (Fig. 6F and G). Extending GKT137831 treatment following single vaccination partially prevented tumor relapse (2/7 mice relapsing compared with 5/8 mice in the group with earlier GKT137831 withdrawal; Fig. 6F) but did not significantly improve progression-free survival (Fig. 6G). However, combining a second dose of vaccine with extended GKT137831 treatment resulted in long-term clearance of tumors in all mice (Fig. 6F and G).
Similarly, GKT137831 resensitized tumors to αPD-1 therapy; treatment of CAF-rich MC38 tumors with GKT137831/αPD-1 combination resulted in smaller tumors (Fig. 7A), higher tumor infiltration of CD8+ T cells (Fig. 7B), and increased overall survival compared with αPD-1 monotherapy [median survival 76.5 days (3/8 mice tumor-free) vs. 39.5 days (1/8 mice tumor-free); Fig. 7C and D].
Checkpoint immunotherapy is revolutionizing the treatment of a broad range of cancers, but a significant proportion of patients (∼80%) fail to respond. There are many mechanisms by which tumors evade the immune system, including suppression of lymphocyte infiltration into the tumor mass. The “immune-excluded” phenotype is now recognized as a feature associated with poor response to checkpoint inhibition (10) and the identification of druggable mechanisms that regulate this effect could significantly improve clinical outcome. Here we show that CAF promote resistance to different immunotherapies by specifically excluding CD8+ T cells (but not CD4+ T cells) from the tumor mass, which then accumulate at the tumor margin. We found that CAF can be precisely targeted through the inhibition of NOX4 to both suppress and reverse CAF differentiation, thereby promoting intratumoral CD8+ T-cell infiltration and resensitizing CAF-rich tumors to immunotherapy. Notably, we found that the excluded CD8+ T cells upregulate the expression of CTLA-4 in the absence of other exhaustion markers, and that the inhibition of CTLA-4 with blocking (nondepleting) antibodies also overcomes this exclusion effect.
Studies using single-cell RNA-seq have begun to characterize the CAF population in some detail, identifying specific CAF subpopulations with likely functional differences (47, 48). Although the population is evidently heterogeneous with no single marker identifying all CAFs, the term “CAF” is used most commonly to describe a cell that phenotypically and functionally resembles a wound-healing myofibroblast, albeit with some distinct characteristics; that is, a contractile, αSMA-positive cell that secretes collagen-rich ECM as well as numerous growth factors and cytokines (49, 50). In contrast to myofibroblasts, however, which during wound resolution either revert to a normal fibroblast or undergo apoptosis and elimination, CAFs appear to be perpetually active (51). A significant proportion of most solid cancers are CAF-rich (including metastasis); for example, over 50% of cases of head and neck, esophageal, colorectal, and pancreatic cancers are dominated by the presence of CAFs, and these are associated with poor prognosis (21).
Recent analyses of melanoma and urothelial cancer patients treated with αPD-1/PD-L1 have identified nonresponse gene signatures that are characterized by prominent CAF ECM gene profiles (6, 10). CAFs can potentially promote tumor immune evasion through multiple potential mechanisms (52); CAF differentiation is TGFβ1-dependent, and CAFs also amplify TGFβ1 signaling in tumors, promoting the secretion and activation of TGFβ1. This cytokine has numerous immunosuppressive effects such as inhibiting CD8+ T-cell proliferation and cytotoxicity (53), and also induction of CD8+ T-cell apoptosis through expression of PD-L2 and FasL (54). Therefore, TGFβ1 would seem an attractive target in CAF-rich tumors. Indeed, Mariathasan and colleagues have shown that TGFβ1-neutralizing mAbs promote a response to αPD-L1 therapy and facilitate lymphocyte infiltration into the tumor mass (10). TGFβ1, however, is a pleiotropic cytokine with both tumor-promoting and tumor-suppressive effects, and plays an important role in tissue homeostasis. Upstream targeting of the TGFβ pathway, therefore, is a potentially risky strategy. The use of small-molecule TGFβ pathway inhibitors, for example, has been impeded by on-target toxicities, including cardiac effects and the development of cutaneous squamous cell carcinoma (55). In the present study, we found that treatment of CAF-rich tumors with a TGFβ1 inhibitor did not reduce CAF levels, or overcome CD8+ T-cell exclusion, but did result in increased intratumoral CD8 T cells. However, this response was also seen in control (CAFlow) tumors, suggesting that the effect was not mediated through CAF inhibition.
ECM proteins may also play a role in suppressing the response to immunotherapy. ECM production is a central CAF function, and CAF-rich tumors are often characterized by a desmoplastic stroma that is rich in collagen, fibronectin, and various proteoglycans (hyaluronan and versican), which have been shown to “trap” T cells and inhibit T-cell motility (56). A dense network of collagen fibers has also been shown to limit T-cell access to tumors (57) and enhance matrix density; the protease-independent nature of T-cell migration leads to contact guidance where T cells follow a path of least resistance along collagen fibers (58). Likewise, in fibrotic pancreatic cancers, T cells accumulate in areas of low-density collagen (59, 60). Therefore, drugs targeting ECM proteins such as hyaluronidase or LOXL2 inhibitors, which suppress collagen cross linking, may have some utility for improving responses to immunotherapy. In the present study, CTLA4 was one of the most upregulated genes in the excluded CD8+ T cells from CAF-rich tumors (in the absence of other exhaustion markers or evidence of suppressed activity). Although CTLA-4 classically negatively regulates T-cell function, it has also been shown to promote T-cell adhesion and suppresses migration by modulating integrin activation (44, 45). αCTLA-4 antibodies have been shown to enhance T-cell motility and overcome matrix-dependent stop signals (61). Similarly, we found that blocking CTLA-4 with nondepleting antibodies promoted CD8+ T-cell tumor infiltration (sparing Tregs), suggesting that CTLA-4 targeting could also have benefit in CAF-rich tumors, and perhaps contributes to the increased efficacy of αPD-1/αCTLA-4 combinations (62).
In the past, attempts to therapeutically target CAF have been unsuccessful and, possibly, compounded by poor understanding of CAF heterogeneity and lack of specific CAF targets. Depletion of fibroblast activation protein (FAP)-positive CAF in murine models enhances antitumor immunity (63) but clinically targeting FAP in colorectal cancer was not successful (19, 20). Furthermore, FAP expression has been identified on bone marrow stem cells and therefore off-target effects resulting from FAP-targeting cannot be excluded (64). Hedgehog inhibition has also been used to target CAF effectively in preclinical models (65) but has produced disappointing results in phase II clinical testing in metastatic pancreatic cancer (18–20). Recently, Chen and colleagues pharmacologically inhibited CXCR4 using Plerixafor in murine models of breast cancer and demonstrated that the decreased fibrosis increased T-cell infiltration and improved response to checkpoint inhibition (65). CXCR4 is expressed on a wide variety of immune cells, including T cells, B cells, and hematopoietic stem cells, and has been shown to promote αPD-1 response through inhibition of myeloid-derived suppressor cells (65).
We have shown previously that NOX4, a ROS-producing enzyme and downstream target of TGFβ1, promotes CAF activation in human cancers and is relatively CAF specific (21). GKT137831 (Setanaxib) is an orally available, small organic molecule of the pyrazolopyridine dione chemical class; it is a selective inhibitor of NOX4/1 and the first drug in this class of NOX inhibitors to enter the clinic [phase II clinical testing treating fibrotic disease (liver, kidney, lung fibrosis); NCT03226067 and NCT02010242, respectively]. In the present study, we found that GKT137831 not only prevents CAF differentiation, but “normalizes” established CAFs to a more quiescent fibroblast-like cell, downregulating classic CAF markers such as αSMA and ECM proteins. These findings suggest that CAFs are not fixed in a terminally differentiated state, but require continuous NOX4-dependent ROS generation to maintain their phenotype. CAFs can therefore be specifically targeted with GKT137831 to reshape the CAF-regulated immune microenvironment.
In summary, CAF-rich tumors respond poorly to αPD-1/PD-L1 immunotherapy, and currently there are no pharmacologic means of targeting this cell-type specifically (6, 10). The results of the present study show that CAF-mediated immunotherapy resistance results from the exclusion of CD8+ T cells from the tumor mass. This phenomenon can be successfully overcome by reversing the CAF phenotype through NOX4 inhibition using GKT137831 (Setanaxib), a clinically tested drug with an excellent safety profile. A significant proportion of solid cancers are CAF-rich, and our data suggest that the combination of NOX4 inhibition and immunotherapy would improve clinical outcome in these tumors.
Disclosure of Potential Conflicts of Interest
K. Ford, C.J. Hanley, and G.J. Thomas have ownership interest (including patents) in WO2019086579. C. Szyndralewiez is head of pharmacology at Genkyotex. F. Heitz is employed at Genkyotex. P. Wiesel is chief medical officer at Genkyotex and has ownership interest (including patents) in Genkyotex. A. Chakravarthy has ownership interest (including patents) in University Health Network. P. Vijayanand reports receiving a commercial research grant from Pfizer. A. Al-Shamkhani reports receiving commercial research grants from Talix Therapeutics and Celldex Therapeutics. No potential conflicts of interest were disclosed by the other authors.
Conception and design: K. Ford, M. Mellone, A. Al-Shamkhani, N. Savelyeva, G.J. Thomas
Development of methodology: K. Ford, M. Mellone, C. Szyndralewiez, P. Wiesel, C. Wang, T.R. Fenton, P. Vijayanand, N. Savelyeva, G.J. Thomas
Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): K. Ford, O. Wood, A.-P. Ganesan, A. Chakravarthy, E.V. King, P. Vijayanand, C.H. Ottensmeier, G.J. Thomas
Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): K. Ford, C.J. Hanley, F. Heitz, M. Machado, A.-P. Ganesan, C. Wang, A. Chakravarthy, P. Vijayanand, C.H. Ottensmeier, A. Al-Shamkhani, G.J. Thomas
Writing, review, and/or revision of the manuscript: K. Ford, C.J. Hanley, M. Mellone, C. Szyndralewiez, F. Heitz, P. Wiesel, C. Wang, T.R. Fenton, E.V. King, P. Vijayanand, C.H. Ottensmeier, A. Al-Shamkhani, N. Savelyeva, G.J. Thomas
Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): O. Wood, M. Machado, M.-A. Lopez, C.H. Ottensmeier
Study supervision: M. Mellone A. Al-Shamkhani, G.J. Thomas
This study was funded through Cancer Research UK (grant nos. A203904, A20256, and A27989) and Medical Research Council UK (grant no. MR/P013414/1 and CASE Studentship for K. Ford; with Genkyotex).
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.