Research Article

Targeting Hypoxia-Induced Carbonic AnhydraseIX Enhances Immune-Checkpoint BlockadeLocally and SystemicallyShawn C. Chafe1, Paul C. McDonald1, Saeed Saberi2, Oksana Nemirovsky1,Geetha Venkateswaran1, Samantha Burugu3, Dongxia Gao3, Alberto Delaidelli2,Alastair H. Kyle1, Jennifer H.E. Baker1, Jordan A. Gillespie1, Ali Bashashati4,Andrew I. Minchinton1, Youwen Zhou5, Sohrab P. Shah4, and Shoukat Dedhar1,6

Abstract

Treatment strategies involving immune-checkpoint block-ade (ICB) have significantly improved survival for a subset ofpatients across a broad spectrum of advanced solid cancers.Despite this, considerable room for improving response ratesremains. The tumor microenvironment (TME) is a hurdle toimmune function, as the altered metabolism-related acidicmicroenvironment of solid tumors decreases immune activity.Here, we determined that expression of the hypoxia-induced,cell-surface pH regulatory enzyme carbonic anhydrase IX(CAIX) is associated with worse overall survival in a cohortof 449 patients with melanoma. We found that targetingCAIX with the small-molecule SLC-0111 reduced glycolytic

metabolism of tumor cells and extracellular acidification,resulting in increased immune cell killing. SLC-0111 treatmentin combination with immune-checkpoint inhibitors ledto the sensitization of tumors to ICB, which led to anenhanced Th1 response, decreased tumor growth, andreduced metastasis. We identified that increased expressionof CA9 is associated with a reduced Th1 response inmetastatic melanoma and basal-like breast cancer TCGAcohorts. These data suggest that targeting CAIX in the TME incombination with ICB is a potential therapeutic strategy forenhancing response and survival in patientswith hypoxic solidmalignancies.

IntroductionImmune-checkpoint blockade (ICB) with antibodies blocking

CTLA-4 or PD-1 has now received FDA approval formultiple solidtumor types (1). Considerable response rates have been achievedin a subset of patients with metastatic melanoma (2, 3) and, to alesser degree, patients with triple-negative breast cancer (TNBC;refs. 4, 5) when treated with ICB alone. Response rates in TNBC

have seen considerable improvement when ICB was combinedwith chemotherapy (6). Despite this, many patients fail torespond or develop resistance, suggesting that a significant pop-ulation of patients stand to benefit from enhancing response ratesto ICB. Identifying determinants of response is critical to extend-ing ICB benefit to a greater patient population. Althoughincreased tumor mutational burden has been found to correlatewith response to immunotherapy, it alone fails to explain theheterogeneity in responses (7–9). It is critical to understand thelevel of immune infiltration in the tumor and the environment inwhich the lymphocytes reside to predict which patients stand toachieve the greatest benefit from ICB (10, 11).

The tumor microenvironment (TME) has been identified as acritical hurdle to immune infiltration and activity (12). Physicalconstraints within the TME lead to decreases in perfusion andoxygendelivery, triggering the hypoxic response and stimulating afurther increase in glycolytic activity (13). Elevated glycolyticactivity of solid tumors results in increased nutrient competitionbetween the tumor and immune cells (14), and increased extra-cellular accumulation of glycolytic metabolites, such as lactate,protons, and carbonic acids, leads to the acidification of the TME,further reducing normal immune function (13, 15–17). Indeed,the accumulation of lactate in the TME inhibits the efflux of lactatefrom cytotoxic T cells and blunts the production of cytotoxiceffectors (15). Neutralization of acidity with bicarbonate withinthe TME or inhibiting lactate production in the tumor by reducingLDHA expression has been shown to increase immune activityand enhance the efficacy of ICB (18, 19). However, theseapproaches lack specificity for the tumor and are faced with

1Department of Integrative Oncology, BC Cancer Research Centre, Vancouver,British Columbia, Canada. 2Department of Molecular Oncology, BC CancerResearch Centre, Vancouver, British Columbia, Canada. 3Genetic PathologyEvaluation Centre, University of British Columbia, Vancouver, British Columbia,Canada. 4Department of Pathology and Laboratory Medicine, University ofBritish Columbia, Vancouver, British Columbia, Canada. 5Department of Der-matology and Skin Science, Vancouver Coastal Health Research Institute,University of British Columbia, Vancouver, British Columbia, Canada. 6Depart-ment of Biochemistry and Molecular Biology, University of British Columbia,Vancouver, British Columbia, Canada.

Note: Supplementary data for this article are available at Cancer ImmunologyResearch Online (http://cancerimmunolres.aacrjournals.org/).

Current address for S.P. Shah: Department of Epidemiology and Biostatistics,Memorial Sloan Kettering Cancer Center, New York, NY.

Corresponding Author: Shoukat Dedhar, BC Cancer Research Centre, 675West10th Avenue, Vancouver, BC V5Z 1L3, Canada. Phone: 604-675-8029; Fax: 604-675-8099; E-mail: sdedhar@bccrc.ca

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doi: 10.1158/2326-6066.CIR-18-0657

�2019 American Association for Cancer Research.

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challenges clinically (13, 20). Thus, there remains an unmet needto specifically restore pH homeostasis within the TME, whileleaving the immune system unaffected.

Carbonic anhydrase IX (CAIX) is a hypoxia-induced, extracel-lular facing, cell-surface enzyme involved in pH regulation ofhypoxic solid tumors (17). CAIX hydrates carbon dioxide toproduce bicarbonate and protons. The protons accumulate inthe extracellular space and contribute to the acidification of theTME, while the bicarbonate, via bicarbonate transporters, isreturned inside the cell, facilitating the titration of intracellularpH. CAIX plays a role in tumor growth and survival, invasion andmetastasis, and mobilization of immune-suppressive myeloid-derived suppressor cells, making it an attractive therapeutic tar-get (21). Therapeutically targeting CAIX in hypoxic solid tumorswith the specific small-molecule SLC-0111 inhibits tumor growthand metastases in preclinical models of breast and brain can-cer (22–24). SLC-0111 has now entered clinical evaluation andhas, thus far, been found to bewell tolerated (25).Given its role inpH regulation within the hypoxic niche, targeting CAIX mayrestore pH homeostasis of the TME, relieving a critical hurdle toimmune activity (23, 24, 26–28).

Herein, we investigated whether CAIX inhibition wouldincrease the efficacy of ICB.We identified thatCAIXwas associatedwith increased tumor grade, risk of metastasis, and was indepen-dently predictive of worse overall survival in a melanoma patientcohort. We determined that CAIX inhibition reduced the capacityof melanoma and breast cancer cells to acidify the extracellularenvironment, leading to enhanced immune activity. We foundthat CAIX inhibition augmented anti–PD-1 and anti–CTLA-4blockade to reduce melanoma tumor growth and breast cancermetastasis. Finally, we identified that CA9 expression was asso-ciated with decreased immune activity in the tumors of patientswith a broad spectrumof solidmalignancies, includingmetastaticmelanoma and basal-like breast cancer.

Materials and MethodsCell lines

The murine mammary adenocarcinoma 4T1 (CRL-2539) andmurine skin melanoma B16F10 (CRL-6475) cell lines were pur-chased from the American Type Culture Collection and validatedby STR analysis. 4T1 cells were maintained in DMEM (Gibco,#11995-065) plus 10% FBS (Gibco, #12483020) (D10) and1� nonessential amino acids (Gibco, #11140-050). B16F10 cellswere maintained in D10. Splenocytes were maintained in RPMI-1640 (Gibco, #11835-030) plus 10% FBS, 50 mmol/L b-mercap-toethanol (Sigma, #M3148), 1� penicillin and streptomycin(Gibco, #15140-122), and IL2 (50 U/mL; PeproTech, #200-02-10UG). Stable knockdown of CAIX was achieved by lentiviraltransduction of short hairpin RNAs toward Car9 (23). Sequencescontained within the constructs utilized are shNS: CTTACTCT-CGCCCAAGCGAGAG; shCar9_1: V2LMM_48299—TAACTTCA-GGTGGATCCTC; shCar9_2: V2LMM_57078—TTTCTTCCAAA-TGGGACAG. Cells were incubated with lentivirus for 24 hoursprior to undergoing selection with puromycin for 72 hours. Allcultures were maintained at 37�C under a 5% CO2 atmosphere,and cell lines were cultured for a week prior to the initiation of anexperiment. For studies involving hypoxia, cells were maintainedin a 37�C incubator in a nitrogen-balanced atmosphere of 1%O2

and 5% CO2 and were routinely monitored for hypoxia-inducedCAIX expression. Cell lines were routinely tested for Mycoplasma

using the LookOut Mycoplasma PCR detection kit (Sigma-Aldrich; MP0035).

ReagentsAntibodies to PD-1 (RMP1-14) and CTLA-4 (9H10) and

isotype controls were purchased from Bio X Cell. Antibodies to4-1BB/CD137 were purchased from R&D Systems (Supplemen-tary Table S1). The ureido-sulfonamide CAIX inhibitor, SLC-0111was previously described and provided by Welichem BiotechInc. (23).

Animal studiesAll studies involving mice were performed in accordance with

and with the approval of the University of British ColumbiaInstitutional Animal Care and Use Committee under approvedanimal study protocol A14-0058.

For studies done using the 4T1model, 1� 106 tumor cells in 50mL PBS were inoculated subcutaneously into the left fourthmammary fat pad of female Balb/c mice (7–9-week-old; Simon-sen Laboratories). Tumor growth was tracked by digital caliperthree times per week, and treatments were initiated once anaverage tumor volume for the cohort reached 100 mm3 usingthe modified ellipsoid formula (l � w2 � p/6). Mice were thenrandomly distributed among groups in a manner to maintainequal size distributions across each treatment group. Treatmentwith SLC-0111 (50mg/kg) or drug vehicle was initiated on day 1,and treatmentswere administered daily until study completionbyoral gavage. The oral formulation (drug vehicle) of SLC-0111consisted of 55.6% (w/w) phospholipon (Lipoid), 7.3% vitaminE TPGS (Antares Health Products Inc., #TG0101NF), 11.9%polyethylene glycol (PEG) 200 (Sigma, #P3015-500G), 16.3%PEG400 (Sigma, #202398-500G), and 8.9% propylene glycol(Sigma, #P4347-500ML). Antibodies to PD-1 (10 mg/kg),CTLA-4 (10 mg/kg), or isotype controls (10 mg/kg) were admin-istered intraperitoneally (i.p.) on days 1, 3, 5, 7, 9, and 11.

For studies done using the B16F10 model, 5 � 105 tumorcells in 100 mL PBS were inoculated subcutaneously onto theback on female C57Bl/6J mice (7–9 week-old; The JacksonLaboratory). Tumor growth was tracked as stated above. Due tothe growth kinetics of the B16F10 tumor model and the rapidonset of signs of morbidity in the mice bearing these tumors,treatment was initiated when tumors were palpable in order toobtain reliable tumor growth measurements prior to the micebecoming moribund. When tumors became palpable, micewere randomized to treatment group by random numbergeneration. Administration of SLC-0111(S) and vehicle (V)was performed as described above. Treatment with antibodiesto PD-1 (P), CTLA-4 (C), and isotype (I) controls was per-formed on days 1, 3, 5, 7, 9 and days 1 and 6 for 4-1BB/CD137(BB) (1 mg/kg; Supplementary Table S1). Tumor growth wastracked until permitted size limits (1,200 mm3) were reachedahead of animals becoming moribund.

To assess the growth of CAIX-depleted cell lines, cell lines wereinoculated subcutaneously on the back of female C57Bl/6J mice(5� 105 B16F10) or in the left fourthmammary fat pad of femaleNOD/SCID (BC Cancer Research Centre in house breeding ofJackson Laboratory mice) mice (1 � 106 4T1), and tumors wereallowed to grow until the endpoints were reached (mentionedabove for subcutaneous B16F10 on the back and 500 mm3 fororthtopic 4T1 in the mammary fat pad). Tumor growth wasmeasured as mentioned above.

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Flow cytometryFor flow cytometry experiments profiling immune popula-

tions, B16F10 tumor-bearing mice were treated with immune-checkpoint or isotype control antibodies on days 1, 3, 5, and 7,or SLC-0111 or vehicle daily and tumors were harvested 24hours following the final dose of immune-checkpoint anti-body. This included a reduction of antibody treatments to fourin an effort to maximize recovery of tumor material fromtreated groups. Spleens were removed from mice and mashedthrough a 0.45-mm-mesh filter with the plunger of a 3-mLsyringe. Tumors were removed and minced with a razor bladeprior to tissue disaggregation using the mouse tumor dissoci-ation kit, according to the manufacturer's instructions (MiltenyiBiotec, #130-096-730). Peripheral blood was collected bycardiac bleed into a potassium EDTA-coated tube. Followingdisaggregation, all collected tissues were subjected to erythro-cyte lysis with ammonium chloride buffer (Stem Cell Technol-ogies, #07800). Samples were stained in HBSS containing 2%FBS for 30 minutes at 4�C.

For detection of transcription factors and intracellularcytokines, cells were fixed with FoxP3/transcription factorstaining buffer (Thermo Fisher; #00-5523-00). Viability wastracked using the LIVE/DEAD fixable yellow viability stain(Thermo Fisher; #L34959). Antibodies and dilutions utilizedare listed in Supplementary Table S1. Gating strategy usedfor all main cell subsets can be found in Supplementary Fig.S1. All samples were analyzed using the BD LSRFortessa (BDBiosciences), and data were analyzed using FlowJo v10(FlowJo LLC).

Measurement of extracellular pHB16F10 cells were seeded in 6-well dishes (BD Falcon) at

5,000/cm2 and incubated at 21% O2 overnight in D10 media.The following morning, media were replaced with fresh D10growth medium or medium containing 100 mmol/L SLC-0111.Cells were then incubated in 21% or 1% O2 for 72 hours.Following the 72-hour incubation, media were collected, andpH measured immediately with an Accumet pH meter (FisherScientific) and Accumet pH electrode (Fisher Scientific; #13-620-299A) as previously described (23).

Intracellular pH measurementsB16F10 cells were seeded into 96-well plates (BD Falcon;

5,000 cells/well) in D10 media. The following day, fresh D10media were added and the cells were cultured in 1% O2 for 72hours in the presence or absence of 100 mmol/L SLC-0111.Intracellular pH (pHi) measurements were carried out using theFluorometric Intracellular pH Assay Kit (Sigma; cat no:MAK150) according to the manufacturer's instructions. Briefly,the growth media were removed and cells were loaded with50 mL of BCFL-AM dye loading solution for 30 minutes at 1%O2. After loading, the cells were treated with 100 mmol/LSLC-0111 and incubated for additional 15 minutes. Ratio-metric measurements were carried out using a SpectraMaxi3x microplate reader (Molecular Devices). A nigericin-basedcalibration (Sigma; N7143) standard curve was prepared aspreviously described (29) with buffers of pH ranging from pH5.5–8.0 in 0.5 pH unit increments. A sigmoidal 4PL nonlinearregression model was used to fit the calibration curve andinterpolate the experimental pHi values using GraphPadPrism 7.

In vitro immune cell cytotoxicity assaysSplenocytes (5� 105) fromna€�ve C57Bl/6Jmice were obtained

as described above, and T cells were stimulated with plate boundanti-CD3/anti-CD28 (5 mg/mL and 1 mg/mL, respectively;Supplementary Table S1) for 48 hours and then expanded foran additional 48 hours with fresh medium provided every 24hours. B16F10 cells were cultured for 72hours in 21%or 1%O2 at5,000/cm2. Cells were then harvested and seeded onto 96-wellplates at the same density overnight in D10 medium containingthe nuclear tracking dye, Nuclight Rapid Red (1:2000; EssenBiosciences, #4717), in 21% or 1% O2. The following morning,T cells and indicated treatments were added in culture mediumcontaining the membrane permeability dye, Sytox green(250 nmol/L; Thermo Fisher, #S7020), to track dead cells. Cellswere treated with SLC-0111 (100 mmol/L) dissolved in culturemedium. Cultures were incubated in 21% or 1% O2 and imagedlongitudinally (four images per well per time point) using theIncuCyte live-cell imaging system (EssenBioscience). Cytotoxicityindices were calculated by measuring the number of deadcancer cells as a percentage of total cancer cells (Sytox green/mm2/Nuclight Rapid Red/mm2).

Western blotCell lines were seeded at 5 � 103 cells/cm2 and incubated in

21% or 1% O2 for 72 hours. Following this incubation period,cells were lysed in RIPA buffer in 21%or 1%O2 and quantified byBCA assay. Lysate (30 mg) was separated on a 4% to 12% Bis-Trisgel, transferred to PVDF, and incubated overnight with antibodyto CAIX and CAXII (Supplementary Table S1). Blots were devel-oped using the Super SignalWest femtomaximum sensitivity ECLreagent (Thermo Fisher Scientific; #34096).

Immunohistochemical and histochemical staining of tissuesTwo hours prior to tumor excision, mice were injected intra-

peritoneally (i.p.) with a saline solution containing bromodeox-yuridine (BrdUrd; 1,500 mg/kg; Sigma; B5002) and pimonida-zole (60mg/kg; Hypoxyprobe; HP2-100Kit). Sevenminutes priorto tumor excision, mice were injected intravenously (i.v.) withDiOC7 as previously described (23). Formalin-fixed paraffin-embedded sections were deparaffinized in xylene, gradually rehy-drated with incubations in ethanol baths containing decreasingethanol concentrations, and incubated in PBS. For B16F10 tumorsections, melanin was bleached by 40-minute incubation in 65�Cin10%H2O2 (30). Antigen retrieval was performedby incubationin 0.01 mol/L citrate, pH 4.0, by microwaving on high for 10minutes. Tissue sections were incubated with primary antibodyovernight (GLUT1 1 mg/mL; CAIX 2 mg/mL, MCT4—2 mg/mL;pimonidazole—1:1,500, CD3—1:100; Supplementary Table S1)and incubated with species-specific ImmPRESS HRP secondaryAbs (MP-7405 andMP-7500, Vector Laboratories) for 30minutesthe following morning, according to the manufacturer's instruc-tions. Staining for and detection of pimonidazole (hypoxia),BrdUrd (proliferation), CD31 (blood vessels), and DiOC7 (per-fusion) in the 4T1 model are previously described (23). Detec-tion was carried out using the DAB Peroxidase (HRP) SubstrateKit (SK-4100; Vector Laboratories). Immunofluorescent stain-ing of tumor tissue required 4 mg/mL CAIX and 300 mg/mL anti-pimonidazole (Hypoxyprobe) antibody concentrations. Tissueswere treated with the TrueView autofluorescence quenching kit(SP-8400; Vector Laboratories) per the manufacturer's instruc-tions following incubation with Hoechst 33342 (5 mg/mL).

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Histochemical analysis of lung and tumor sections was done bythe Centre for Translational and Applied Genomics (CTAG) atthe BC Cancer Centre.

Melanoma tumor tissue microarray (TMA)Sample procurement and TMA construction are previously

described (31). The TMA interrogated in this study now differsby 10 patients due to 6 being removed because of misdiagnosisand 4 additional patients being lost to follow-up. The TMAcontains samples from the following melanoma subtypes: SS:superficial spreading, S: spindle-like, AL: acral lentiginous, N:nodular, DES: desmoplastic, U: unspecified. Note that all meta-static cases within the TMA had a primary diagnosis of unspec-ified, so this classification defines metastatic melanoma cases.Sections were stained for CAIX using the M75 monoclonal anti-body from Bioscience on the Ventana system as previouslydescribed (ref. 23; Supplementary Table S1). Scoring of CAIXexpression either 0 (no staining) or 1 (any staining) was done by aboard-certifiedpathologist (D.Gao) and confirmed independent-ly by S.C. Chafe.

LC-MS/MS analysis of tumor homogenatesOrthotopic breast tumors and plasma from mice treated with

25, 50, or 100mg/kg SLC-0111were recovered 24hours followingthe final dose and flash frozen in liquid nitrogen and stored at–70�Cuntil processing. Tumorswerehomogenizedby combiningtwo parts blank plasma with one part tumor using the T10 basicUltra-Turrax tissue homogenizer (IKA Works Inc.). Twenty-fivemicroliters of plasma or 50 mL of tumor homogenate wereseparated by reversed phase ultrahigh-performance liquid chro-matography on a Kinetix C18 column (2.6 mm, 75 � 3 mm;Phenomenex) at a flow rate of 0.4 mL/min and analyzed by LC-MS/MS on an API 4000 LC-MS/MS system (Sciex). For massspectrometric detection, electrospray ionization and multiplereaction monitoring in positive ionization mode was used.SLC-0111 concentrations were determined in each sample byback calculation from standard curves generated with SLC-0111solutions of known concentration (25–30,000 ng/mL).

Image acquisitionBright field immunohistochemical images were captured on a

Leica DM2500 microscope attached to a CCD camera. Imageswere processed using Photoshop CS5. Whole lung and tumorhematoxylin and eosin (H&E)–stained sections were scannedusing the Panoramic Midi (3D Histec) using a 20� objective andimages analyzed using Imagescope (3D Histec). IHC imagequantitation of CAIX expression on whole tumor sections wasdone using thresholding in ImageJ. Fluorescent images werecaptured on an LSM Airyscan 800 Zeiss confocal microscopeusing a 63� oil immersion objective using Zen Blue software.

Extracellular flux measurementsTo assess the impact of CAIX inhibition onmetabolic activity of

melanoma cells, B16F10 cells (5,000/cm2) were treatedwith SLC-0111 and incubated in 1%or 21%O2 for 72 hours, and glycolyticand respiratory function were measured using a glycolysis stresstest and cell mito stress test assays on the Seahorse ExtracellularFlux Analyzer (Agilent). Cells (5,000) were seeded per well in anXFe96 assay plate. Cellswere exposed to 1mmol/L oligomycin and0.5 mmol/L FCCP (carbonyl cyanide-p-trifluoromethoxyphenyl-hydrazone) where indicated. Data were collected with the Wave

software (Agilent), were normalized to cell number using Cell-Titer-Glo (Promega, #G7570), and plotted in GraphPad Prismv7.0. Rot/AA: cocktail of rotenone and antimycin A.

Analysis of TCGA expression and clinical correlationsRaw count data from the TCGA project were retrieved using the

TCGA biolinks package (v2.6.12) in Bioconductor. HTSeq countsgenerated from transcriptomes were downloaded from the GDCharmonized database for primary tumor samples. These datawerecombinedwith clinical data separately for each cancer type. Breastcancer data were generated for each molecular subtype based onPAM50 classification (32, 33).

Expression count values were normalized by the DESEQ2variance stabilization method and adjusted to z-score for eachgene (34). Z-scores were capped at 6 and �6 for increased anddecreased expression, respectively. This arbitrary threshold waschosen to reduce the contribution of outliers during clustering.Hierarchical clustering was performed using Euclidean distanceand "Ward.D2" clusteringmethod inR.Heatmapswere generatedusing the heatmap functionbasedon a gene set representing Th1-,cytotoxicity-, and HLA-related genes and tumor-infiltrating lym-phocyte (TIL) abundance at the tumor site (9, 35). Correlationanalysis assessing the relationship of expression between twogenes was generated using Spearman correlation.

To show clinical correlation between these clusters and theclinical outcome, patient clinical data were downloaded andplotted for each patient across each hierarchical cluster. Analysiswas performed after dividing the patients into two or three trees.Survival analysis was performed using "TCGAanalyze_survival" Rfunction, and survival curves were plotted for each group ofpatients.

Differential expression analysisThe transcriptome fastq files for the TCGA provisional data sets

for breast invasive carcinoma (TCGA-BRCA), skin cutaneousmelanoma (TCGA-SKCM), pancreatic adenocarcinoma (TCGA-PAAD), lung adenocarcinoma (TCGA-LUAD), lung squamouscell carcinoma (TCGA-LUSC), bladder urothelial carcinoma(TCGA-BLAD), and sarcoma (TCGA-SARC) were aligned to hg18using STAR aligner and bam files were generated with recom-mended settings from the STAR-Fusionuser guide (https://github.com/STAR-Fusion/STAR-Fusion/wiki). Cufflink was used to callthe FPKM values and expression counts. The matrix of countvalues was sent to DESEQ2 for differential analysis. This matrixplus a conditions file annotating the condition and treatment ofeach sample of interest was then input into the BioconductorDESEQ2 package (v1.10.1) in R, and differential analysis wasconducted as instructed by the user guide (https://bioconductor.org/packages/devel/bioc/html/DESeq2.html). After filtering fornonempty samples and keeping only annotated samples of inter-est, the three key DESeq2 commands were executed as suggestedin the user guide. A table of significantly differentially expressedgenes between the two conditions of interestwas generated,whichspecified both fold change and significance value for each gene.The results were written to a text output for the subsequent geneset enrichment and pathway analysis with the BioconductorReactomePA (v1.14.4) package in R.

Statistical analysisStatistical analyseswere performedusingGraphPadPrismv7.0.

Tests for normality were calculated using the D'Agostino and

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Pearson test. Nonparametric comparisons for two groups werecalculated byMann–WhitneyU test, and analyses were two sided.Comparisons for more than two groups were calculated byANOVA followed by Tukey multiple comparisons test for datawith normal distribution or the Kruskal–Wallis test and Dunnmultiple comparison test for data with nonnormal distribution.Survival analyses by the Kaplan–Meiermethodwere compared bylog-rank test. Clinicopathologic associations were comparedusing the Fisher exact test. Multivariate survival analysis by Coxregression was performed using R version 3.5.2 with statisticalpackages "survival" and "survminer." Comparisons of patientgene-expression profiles were calculated using Student t test withBonferroni correction. P < 0.05 was deemed the threshold forsignificance in all statistical tests performed.

ResultsCAIX expression is an independent biomarker of worse overallsurvival in melanoma

To determine whether CAIX expression at the protein levelplayed a role in malignant melanoma, we stained a tissue micro-array (TMA) comprised of 449 patient tumor samples for CAIXexpression (31). Membranous staining for CAIX was identified in9% of cases interrogated (Fig. 1A). The clinicopathologic andfollow-up data linked to the TMA identified that CAIX expressionwas associated with increased grade (P ¼ 0.0003) and risk ofmetastasis (P ¼ 0.0003; Supplementary Table S2–S3). CAIXexpression was detected across all melanoma subtypes containedwithin the TMA, although expression was predominantly associ-ated with cases that were metastatic and did not contain infor-mation on subtype classification of the primary lesion (Fig. 1B).Multivariate analysis, including known prognostic factors andthose associated with CAIX expression, categorized CAIX as anindependent marker of worse patient survival with a hazard ratioof 2.03 (Fig. 1C; Supplementary Table S4). These data suggest thatCAIX may be an important therapeutic target for patients withmetastatic melanoma.

CAIX expression is required for melanoma tumor growthTo understand the role that CAIX plays in melanoma tumor

growth, we interrogated CAIX expression in B16F10 melanomatumors by IHC (Fig. 1D). B16F10 tumors contained regions ofhypoxia-induced CAIX expression, as seen by colocalization withthe glucose transporter GLUT1 (Fig. 1D, middle) and upregula-tion in pimonidazole-positive niches (Supplementary Fig. S2A).Upregulation of both GLUT1 and CAIX was also accompanied byupregulation of the lactate transporter MCT4 (Fig. 1D, right),suggesting that these tumors are glycolytic and contain an acidicextracellular milieu within the hypoxic TME.

We stably depleted CAIX expression in B16F10 cells with twoindependent short hairpin RNAs (Supplementary Fig. S2B) andassessed their ability to form tumors (Fig. 1E). Depletion of CAIXwith shCar9_2 resulted in a 90% reduction in tumor growthrelative to control shRNA (shNS) and a less effective shRNA(shCar9_1; Fig. 1E). Analysis of the tumors revealed significantCAIX expression in the tumors of shNS and shCar9_1 mice andthe effective reduction of CAIX expression for the shCar9_2tumors (Supplementary Fig. S2C), suggesting that CAIX expres-sion in the tumor was required for growth. We also demonstratedthat the closely related carbonic anhydrase isoformCAXII was notcompensating for loss of CAIX expression in this model (Supple-

mentary Fig. S2B–S2C). Because shCar9_2 tumorswere regressingat the point when control tumors reached endpoint, we assessedwhether this coincided with increased T-cell infiltration andidentified increased CD3þ infiltration upon CAIX depletion(Fig. 1F–G).

CAIX inhibition enhances T-cell killing in vitroBecause acidic pH and lactate accumulation has been shown

to reduce immune activity (15, 18, 19, 36, 37) and CAIXcontributes to acidification of the TME (26, 28), we determinedwhether CAIX inhibition with the small-molecule inhibitorSLC-0111 (22–24) impacted the ability of B16F10 cells toacidify the extracellular milieu (Fig. 2A). Growth of B16F10cells in hypoxic conditions resulted in a substantive increase inglycolytic flux, leading to a significant reduction in extracellularpH (pHe) relative to normoxic conditions. Treatment withSLC-0111 reduced the extent to which the pHe was acidifiedin hypoxic conditions but was ineffective when CAIX wasabsent in normoxic conditions (Fig. 2A). These observationsare consistent with our previous findings in murine basal-likebreast cancer cells (23). To determine whether CAIX inhibitionaltered the metabolic activity of melanoma cells grown inhypoxic conditions, we measured the extracellular flux ofprotons and oxygen. SLC-0111 treatment reduced the abilityof melanoma cells to rely on glycolysis for energy production,known as their glycolytic reserve, when energy production byoxidative phosphorylation was inhibited (Fig. 2B and C).When cellular respiration was measured, SLC-0111–treatedcells had a reduced basal respiration rate compared withcontrols (Fig. 2D and E). The decreased metabolic activity ofCAIX-inhibited melanoma cells was exacerbated when the cellsrespired at maximal rates, known as their spare respiratorycapacity, revealing a metabolic defect (Fig. 2F).

To explain the impact of CAIX inhibition onmetabolic activity,we assessed whether CAIX inhibition altered intracellular pH(pHi; Fig. 2G). Enzymatic function is dependent on narrow pHranges for optimal activity, and glycolytic enzymes are impactedby changes in intracellular pH (38). Melanoma cells treated withSLC-0111 had a more acidic pHi than controls (Fig. 2G). Todetermine whether these conditions would lead to enhancedT-cell activity, we cocultured activated T cells at multiple ratioswith B16F10 cells (Fig. 2H and I) in the presence and absence ofSLC-0111. In the presence of SLC-0111, a dose-dependentincrease in T cell–mediated killing of the cancer cells was seen(Fig. 2H and I).

CAIX inhibition in combination with immune-checkpointinhibitors improves responses

To assess whether inhibiting CAIX activity in the TME wouldenhance the efficacy of ICB, we treated B16F10 tumors with acombination consisting of SLC-0111 and antibodies to PD-1 andCTLA-4 (Fig. 3A). In agreementwith previous data, treatmentwithsingle-agent anti–PD-1 or anti–CTLA-4 failed to provide signif-icant benefit (Supplementary Fig. S3A; refs. 39, 40). Single-agenttreatment with SLC-0111 was as effective as both antibodies indelaying growth relative to controls (Fig. 3B; Supplementary Fig.S3A). Combining SLC-0111 with either immune-checkpointantibody offered no additional benefit over either single agent(Supplementary Fig. S3A).

As also demonstrated previously by others, combining anti–PD-1 and anti–CTLA-4 delayed tumor growth and progression

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Figure 1.

CAIX is an independent marker of poor patient outcome in melanoma. A, IHC staining of CAIX and Melan-A expression in melanoma TMA cores (n¼ 400). B,CAIX expression across melanoma subtypes in the cohort. SS: superficial spreading, S: spindle-like, AL: acral lentiginous, N: nodular, DES: desmoplastic, U:unspecified. C, Kaplan–Meier curve for 5-year overall survival for the melanoma patient cohort according to CAIX expression; 363 CAIX negative, 37 CAIXpositive, P < 10�14 by log-rank test.D, IHC staining of CAIX, GLUT-1, and MCT-4 in B16F10 tumors. E, Tumor growth of the indicated B16F10 cell lines.���� , P < 0.0001 by two-way ANOVA and Sidak multiple comparisons test. F, IHC staining of CD3 in the tumors from E. Shown are representative images fromeach of the indicated groups. For D and F, bottom plots encompass areas captured at higher magnification. Scale bar, 100 mm, top; 20 mm, bottom. G,Quantitation of the IHC analysis from F. 4–20� fields were quantified/mouse/group. Bars represent the mean number of CD3þ cells per 20� field. Circlesindicate individual mice/group; n¼ 6. � , P < 0.05; �� , P < 0.01 by Kruskal–Wallis test and Dunnmultiple comparisons test. Bars, mean� SEM.

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compared with controls and either antibody alone (Fig. 3B and C;Supplementary Fig. S3A; ref. 41). Because ICB has been shown toresult in vascular normalization in certainpreclinicalmodels (42),we assessed CAIX expression after treatment and found no changefrom control across the indicated treatment groups (Fig. 3D;Supplementary Fig. S3B). The addition of SLC-0111 to the com-bination of anti–PD-1 and anti–CTLA-4 further delayed tumor

growth and progression (Fig. 3B and C; Supplementary Fig. S3Aand S3C), and this triple combination increased the number ofcomplete responders to 30% (3/10), resulting in the extension ofsurvival of these mice (Fig. 3C, E, and F).

To determine whether CAIX inhibition could be combinedwith additional interventions, we assessed combination treat-ment of melanoma tumors with an antibody to CD137/4-1BB,

Figure 2.

CAIX inhibition decreases acidification of the extracellular milieu and increases T-cell activity. A, Extracellular pH of B16F10 cells grown in the presence (S: SLC-0111) or absence (NT: no treatment) of 100 mmol/L SLC-0111. � , P < 0.05 by ANOVA and Tukey multiple comparisons test. Shown is a representative experimentfrom n¼ 3 experiments. B, Glycolytic function of B16F10 cells treated as in A. Oligo: oligomycin, 2-DG: 2-deoxyglucose. Shown is a representative experimentfrom n¼ 2 experiments. C,Glycolytic reserve available to B16F10 cells treated with SLC-0111; �� , P¼ 0.0079 by Mann–Whitney U test. D,Mitochondrial respirationof B16F10 cells treated as in A. FCCP: carbonyl cyanide-p-trifluoromethoxyphenylhydrazone, Rot/AA: cocktail of rotenone and antimycin A. Shown is arepresentative experiment from n¼ 2 experiments. E, Basal respiration (BR) and (F) spare respiratory capacity (SRC) of B16F10 cells in response to treatmentwith SLC-0111. � , P < 0.05 by Mann–Whitney U test.G, B16F10 cells were grown and treated as in B and intracellular pH (pHi) measured; n¼ 12/condition. n¼ 2experiments; ���� , P < 0.0001 by Mann–Whitney U test. H, B16F10 cells grown and treated as in A in the presence of increasing concentrations anti-CD3/anti-CD28–stimulated splenocytes. n¼ 12/condition and shown is a representative experiment from n¼ 3 experiments; � , P < 0.05; ���, P < 0.001 by three-wayANOVA and Tukey multiple comparisons test. I, Representative micrographs from hypoxic cultures in H. Cells were cultured in the presence of an indicator ofcellular cytotoxicity, Sytox green, and then exposed to a nuclear dye (red) at endpoint. Scale bar, 100 mm. Bars, mean� SEM.

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which stimulates proliferation and survival of T cells (Supple-mentary Fig. S3D–S3G; refs. 43, 44). Anti–4-1BB treatment aloneresulted in a complete response rate of 10% and combinationwith anti–PD-1 failed to result in any complete responses(Supplementary Fig. S3F). The addition of SLC-0111 to anti–PD-1 and anti–4-1BB increased the complete response rate to20% (Supplementary Fig. S3F).

To determine whether infiltrating T cells into the hypoxic TMEexpressed CAIX, we stained tumor sections for CAIX and CD3simultaneously (Supplementary Fig. S3H). CAIXwas upregulatedon the tumor cells in the hypoxic TME, but CD3þ T cells localizedto these areas did not produce any detectable CAIX, suggestingthat targeting CAIX with SLC-0111 would not adversely impactthe responding immune cells when treated in combinationwith ICB.

CAIX inhibition in combination with ICB reduces breast cancermetastasis

To extend our melanoma observations to other hypoxic solidtumors, we assessed CAIX inhibition in combination with ICB inthe 4T1 model of basal-like breast cancer. The 4T1 model is aglycolytic and hypoxic tumor model that expresses a substantiveamount ofCAIX in areas that are bothpositive andnegative for thehypoxia marker pimonidazole (Fig. 4A). To this end, we previ-ously demonstrated that shRNA-mediated depletion of CAIX inthis model results in significant inhibition of tumor growth (23).We demonstrated here that this was dependent upon the presenceof a functional immune system, as growth of these cell lines inimmunocompromised mice results in only a minor growth delayof the tumors formed fromCAIX-depleted cell lines relative to thenonsilenced control tumors (Supplementary Fig. S4A). These data

Figure 3.

CAIX inhibition augments ICB to improve therapeutic efficacy in the B16F10 model of melanoma.A, Experimental design and treatment regimen of B16F10tumors with ICB in combination with CAIX inhibition with SLC-0111. SLC-0111 was delivered daily from treatment initiation until endpoint. SQ: subcutaneous. B,Tumor growth of the indicated treatment cohorts at 19 days after inoculation. Groups defined in the Materials and Methods. Vþ I: n¼ 9; S: n¼ 12; Pþ C: n¼ 10; Sþ Pþ C: n¼ 11. NS: not significant; � , P < 0.05; ��� , P < 0.001 by two-way ANOVA and Tukeymultiple comparisons test. C, Tumor size frequencies at day 40. D,Quantification of whole tumor sections stained for CAIX by IHC and expressed as percentage positive area/tumor area. n¼ 5/group; �� , P < 0.01 by Kruskal–Wallis test and Dunnmultiple comparisons test. E, Frequency of tumor-free mice in the indicated treatment groups at day 40. F, Kaplan–Meier curve depictingsurvival proportions across all treatment groups at the study endpoint. ��� , P < 0.001; ���� , P < 0.0001 by log-rank test. Bars, mean� SEM.

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provide additional evidence that CAIX expression in the TMEnegatively impacted immune function.

We treatedmice bearingorthotopic 4T1 tumorswith SLC-0111,anti–PD-1, and anti–CTLA-4 (Fig. 4B).We first demonstrated thatSLC-0111 preferentially accumulated in hypoxic breast tumorsover theplasma in adose-dependentmanner (Supplementary Fig.S4B), and this correlated directly with decreases in tumor volume(Supplementary Fig. S4C). In agreement with previous findings,single-agent anti–PD-1 and anti–CTLA-4 treatment had very littleimpact on tumor growth (Supplementary Fig. S4D–S4E; ref. 45).Combining CAIX inhibition with anti–PD-1, anti–CTLA-4, orboth inhibitors simultaneously only marginally provided addi-tional therapeutic capacity in controlling tumor growth (Supple-mentary Fig. S4D–S4E), despite equivalent CAIX expression pres-ent across all treatment cohorts (Fig. 4C; Supplementary Fig. S4F).Because we failed to achieve tumor growth delays in mice withthese treatments, we assessed the therapeutic efficacy histologi-cally and evaluated necrosis (Fig. 4D; Supplementary Fig. S4G).The combination of anti–PD-1 and anti–CTLA-4 increased centralnecrosis within the tumors relative to controls (Fig. 4D; Supple-mentary Fig. S4G), and the addition of SLC-0111 to this combi-nation increased intratumoral necrosis even further, suggestingthat this combination was effectively activating the immunesystem to target the tumor (Fig. 4D). Because the 4T1 modelrepresents a basal-type/triple-negative tumor with a high meta-static propensity, we examined metastatic burden in the mice(Fig. 4E). As shown in Fig. 4F and Table 1, we observed up to a90% reduction in gross lung metastatic burden across the treat-

ment cohort relative to controls. CAIX inhibition in combinationwith anti–PD-1 or anti–CTLA-4 was more effective in reducinglung metastasis than either single-agent treatment (Table 1).Triple combination treatment had the greatest effect in reducingthe lung metastatic burden (Fig. 4F; Table 1) and resulted inextension of median survival over control mice (Fig. 4G). Histo-logic examination of lung tissues from mice across the treatmentcohort confirmed the findings of the macroscopic evaluation ofthe lungs (Supplementary Fig. S4H).

Immune profiling reveals an increased Th1 response followingICB and CAIX inhibition

To evaluate changes in immune cell composition in micetreatedwith SLC-0111, anti–PD-1, and anti–CTLA-4, we collectedtumors, blood, and spleens (Fig. 5A; Supplementary Fig. S4A).

Figure 4.

CAIX inhibition in combination with ICB reduces breast cancer metastasis. A, IHC of 4T1 tumors stained for the indicated markers. Shown are representativeimages from two separate mice. Scale bar, 150 mm. B, Experimental design and treatment regimen of 4T1 mammary tumors with ICB in combination with SLC-0111. C, Total CAIXþ tumor areas from the indicated treatment groups. n¼ 5/group; ns: not significant by Kruskal–Wallis test and Dunnmultiple comparisons test.Groups defined in the Materials and Methods. Vþ I: n¼ 9; S: n¼ 10; Pþ C: n¼ 10; Sþ Pþ C: n¼ 9. D, Tumor necrotic area as a percentage of total tumor areaacross each group; n¼ 4–5/group. � , P < 0.05; �� , P < 0.01 by Kruskal–Wallis test and Dunnmultiple comparisons test. E, Images of lungs frommice from theindicated treatment groups. Arrows indicate visible metastatic nodules. Bar, 5 mm. F,Macroscopic lung burden upon necropsy across the indicated treatmentgroups. Symbols represent individual mice within each group. � , P < 0.05; ��, P < 0.01 by Kruskal–Wallis test and Dunnmultiple comparisons test.G, Kaplan–Meiercurves for the indicated groups. NS: not significant; �� , P¼ 0.0081; ��� , P¼ 0.0003 by log-rank test. Bars, mean� SEM.

Table 1. Lung metastatic burden of orthotopic 4T1 tumor–bearing mice treatedwith SLC-0111 in combination with immune-checkpoint inhibitors

Group Mean number of metastatic nodules

Vehicle 51Isotype 55Vehicle þ isotype 47SLC-0111 41Anti–PD-1 32SLC-0111 þ anti–PD-1 16Anti–CTLA-4 20SLC-0111 þ anti–CTLA-4 10Anti–PD-1 þ anti–CTLA-4 13SLC-0111 þ anti–PD-1 þ anti–CTLA-4 8

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Figure 5.

CAIX inhibition leads to increased immune activity of TILs. A, Experimental treatment and harvest scheme for B16F10 tumors (n¼ 2 independent experiments).B, TIL frequency across the indicated treatment groups expressed as a proportion of CD45þ cells. C, Phenotypic frequency of intratumoral CD4 T cells.D, Representative flow plot depicting the frequency of T-betþ CD8þ TILs in the indicated groups. E, Frequencies of intratumoral T-betþCD8þ TILs. � , P < 0.05 byKruskal–Wallis test and Dunnmultiple comparisons test. F, Representative flow plots depicting the frequency of EOMESþ CD8þ TILs. G, Frequency of EOMESþ

CD8þ TILs; � , P < 0.05 by ANOVA and Tukey multiple comparisons test.H, Representative flow plots depicting frequencies of CD44þPD-1þ CD8þ TILs. I,Representative histograms of PD-1 fluorescence intensity on CD8þ TILs for VþI (black), S (red), Pþ C (blue), and Sþ Pþ C (green) treatment groups. J,Meanfluorescence intensities (MFI) of PD-1 on CD8þ TILs in the indicated treatment groups; � , P < 0.05 by ANOVA and Tukeymultiple comparisons test. K,Representative flow plots depicting the frequency of granzyme B–producing CD3þ TILs. L, Frequency of granzyme Bþ intratumoral CD3þ TILs.M, Frequency ofgranzyme Bþ CD3þ T cells in blood; �� , P¼ 0.0043; ��� , P¼ 0.0002; ���� , P < 0.0001 by ANOVA and Holm–Sidak multiple comparisons test.N, The CD8:Tregratio within tumors.O, Frequency of ICOSþCD4þ cells within the circulation of mice. Vþ I: n¼ 5; S: n¼ 7; Sþ P: n¼ 9; Sþ C: n¼ 9; Sþ Pþ C: n¼ 12; � , P < 0.05;�� , P < 0.01 by ANOVA and Tukeymultiple comparisons test. Bars, mean� SEM.

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Analysis of the leukocyte composition within the tumors revealedno change in the frequency of T cells and B cells (Fig. 5B). Becauseoverall T-cell frequency did not change with the combination ofSLC-0111 with anti–PD-1 and anti–CTLA-4, we assessed thephenotype of the CD4þ T-helper cells present within the TME(Fig. 5C). The CD4 profile of control-treated tumors was splitbetween T-betþ (Th1) and RORgtþ (Th17) cells, whereas thepresence of regulatory T cells (Treg) and GATA-3þ Th2 cells wasalso detected. Treatment with SLC-0111 reduced the presence ofTregs and Th17 cells and increased the frequency of Th1 cells.Combining anti–PD-1 with anti–CTLA-4 further reduced thepresence of Tregs within these tumors, but had a similar Th1frequency to SLC-0111 treatment alone. Treatment with all threeinhibitors maintained the SLC-0111-induced Th1 cells, whilemaintaining low frequencies of Tregs and eliminating Th2 cells,suggesting that the enhanced therapeutic efficacy achieved in thecombination of all three inhibitors may be due to the removal ofimmune-suppressive cell populations within the TME.

To assess whether the increased presence of T-betþ Th1 cells ledto an increase inCD8þ TILs skewed in that direction, we evaluatedT-bet expression on the CD8þ TILs (Fig. 5D and E). Similar to theobservations in the CD4þ TILs, T-bet expression was increased inSLC-0111-treated tumors relative to control treatments. Anti–PD-1 and anti–CTLA-4 treatment resulted in a similar increase in thepresence of T-bet–expressing CD8þ TILs, whereas treatment withall three led to an enhanced frequency of T-betþCD8þ TILswithinthe TME. We also assessed whether this was accompanied by anincrease in EOMES expression on the CD8þ TILs (Fig. 5F and G).Similar to the T-bet observations, SLC-0111 treatment alone or incombination with anti–PD-1 and anti–CTLA-4 increased thefrequency of EOMES-expressing CD8þ TILs to a similar degreerelative to control treatments, suggesting the skewing of T cellstoward a cytotoxic phenotype.

CAIX inhibition in combinationwith ICB increases granzyme Bproduction

We next assessed the expression of PD-1 (Fig. 5H–J), LAG-3(Supplementary Fig. S1B), and TIM-3 (Supplementary Fig. S1C)on antigen-experienced CD8þ TILs. Treatment with controls orSLC-0111 was ineffective at reducing PD-1, LAG-3, or TIM-3expression. PD-1 was decreased with triple treatment, but thiswas not seen for LAG-3 or TIM-3 (Fig. 5H–J; Supplementary Fig.S1B–S1C). To assess whether this reduction impacted the releaseof cytolytic molecules from the CD8þ TILs, we assessed the TILproduction of granzyme B in these tumors (Fig. 5K and L). Theheterogeneity in theproduction of granzymeB in the TMEmaskedany changes between groups, so we assessed granzyme B–pro-ducing T cells in the blood and identified that the addition of SLC-0111 increased the frequency of granzyme B–producing T cells incirculation (Fig. 5M). Together, these data suggest that CAIXinhibition in the TME enhanced antitumor Th1 responses.

CAIX inhibition in combination with ICB increases thefrequency of CD4þICOSþ T cells

Profiling the CD4 abundance within the TME showed a reduc-tion in the frequency of Tregs in all treatments relative to thecontrols (Supplementary Fig. S1D–S1E). We further investigatedthe relationship of this reductionwith respect to the global ratio ofCD8:CD4 T cells, and this was unchanged (Supplementary Fig.S1F). We next assessed whether the CD8:Treg ratio changed(Fig. 5N). The CD8:Treg ratio was greatest in the triple combi-

nation but was not significant. We also assessed the frequency ofan effector CD4þ population associated with response to anti–CTLA-4 therapy (46). The triple combination resulted in increasedcirculating ICOSþCD4þ T cells (Fig. 5O). This suggests that theenhanced efficacy achieved by the triple combination was due toan increased Th1 response in a less suppressive TME.

CA9 is inversely associatedwith an immune activity signature inpatients

We next assessed CA9 expression in patient tumors available inTCGA data sets for association with a T cell–inflamed genesignature consisting of immune activity markers (Fig. 6). Usingthe skin cutaneous melanoma (TCGA-SKCM) data set (n¼ 479),we performed hierarchical clustering according to high or lowintartumoral expression of CD3E, CD8A, and CD4 (Fig. 6A).Higher CA9 expression was significantly associated with lowerexpression of CD3E, CD8A, and CD4 (Fig. 6B). To evaluatewhether this was attributable to the metabolic phenotype of thetumors, we assessed whether this was also true for expression oflactate dehydrogenase (LDHA), the monocarboxylate transporterMCT-1 (SLC16A1), and glucose transporter GLUT-4 (SLC2A4),gene products involved in glucose uptake, utilization, and lactateextrusion (Supplementary Fig. S5A). We identified that the reduc-tion in expression of CD3E, CD8A, and CD4 in the TME wasassociated with worse outcome (Fig. 6C).

We then used an immune gene set previously demonstrated topredict colorectal cancer metastasis and to identify T-cell–inflamed TMEs to address the immune activity of T cells withintumors and their associationwithCA9 expression (Fig. 6D; refs. 9,34). HigherCA9 expressionwas associatedwith decreased expres-sion of genes associated with an effective immune response(Fig. 6E). This observation was also true for expression of LDHAand SLC2A4 (Supplementary Fig. S5B). Reduced expression ofgenes associated with an effective antitumor immune responsewas predictive ofworse survival in these patients (Fig. 6F). BecauseCAIX is associated with the hypoxic TME and the glycolyticphenotypes associated within these regions of solid tumors, weassessed how the expression of CA9, SLC2A1, SLC2A4, LDHA,SLC16A1, SLC16A3, and PDK1 correlated with the expression ofgenes in the immune gene set (Fig. 6G). Our analysis identifiedthat increased expression of CA9, SLC2A1, SLC2A4, LDHA, andSLC16A1 were the most significantly associated with decreasedexpression of the gene set. We next performed hierarchical clus-tering of patients according to the expression of CA9, SLC2A1,SLC2A4, LDHA, SLC16A1, SLC16A3, and PDK1 and assessedsurvival (Supplementary Fig. S5C–S5D). Although the expressionof these genes clustered together, a glycolytic phenotype of thetumors alone did not offer further predictive value in determiningpatient survival in our analyses (Supplementary Fig. S5C–S5D).

We next assessed whether these findings were restricted tomelanoma, and assessed additional TCGA solid tumor data sets,in particular, basal-like breast cancer where we have previouslydemonstrated CAIX to be an independent biomarker for worseoverall survival (23). Stratifying the TCGA-BRCA data set accord-ing to the PAM50 classifier, we confirmed thatCA9 expression hadthe highest expression in the basal-like subtype, followed by theHER2, Luminal A, and Luminal B subtypes (Supplementary Fig.S5E) and thesefindings agreewithourpreviousobservations (23).We then evaluated the basal-like subset identically to our mela-noma analyses (Fig. 6H–N). Increased CA9 expression was asso-ciated with reduced expression of CD3E, CD8A, and CD4 in the

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Figure 6.

CA9 expression is associated with decreased immune activity in tumors from patients with metastatic melanoma and basal-like breast cancer.A, Heat map ofTCGA-SKCM data set (n¼ 480) depicting CD3E, CD8A, and CD4 expression. Hierarchical clustering according to high (red) and low (black) T-cell expression. B,Log10 expression values for CA9 across each cluster. � , P < 0.05 by t test. C, Kaplan–Meier curves for patients stratified according to hierarchical clustering in A;P < 0.0001 by log-rank test. D, Heat map summarizing expression of a 31-Th1 gene set in the TCGA-SKCM data set. E, Log10 CA9 expression across both clusters.F, Kaplan–Meier curve for patients stratified according to hierarchical clustering in D; P < 0.0001 by log-rank test. G, Spearman correlation plot between theindicated genes. H, Heat map of the TCGA-BRCA basal-like data set (n¼ 113) depicting CD3E, CD8A, and CD4 expression. I, Log10 CA9 expression across eachcluster. � , P < 0.05 by t test. J, Kaplan–Meier curve for patients stratified according to hierarchical clustering in H; P¼ 0.0034 by log-rank test. K, Heat mapsummarizing expression of the Th1 gene set in TCGA-BRCA basal-like data set. L, Log10 CA9 expression across each cluster. � , P < 0.05 by t test.M, Kaplan–Meiercurve for patients stratified according to hierarchical clustering in K; P¼ 0.015 by log-rank test.N, Spearman correlation plot between the indicated genes.

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tumors of these patients (Fig. 6H and I). This was also true forSLC2A1, suggesting that glucose utilization in the basal-likesubtype may negatively affect immune infiltration (Supplemen-tary Fig. S5F). Reduced expression of these genes within the TMEwas associated with worse overall survival (Fig. 6J).

We next evaluated the expression of the immune gene setagainst the basal-like stratified patients (Fig. 6K) and identifiedthat higher CA9 expression was associated with decreased expres-sion of genes associated with immune activation (Fig. 6L).Decreased immune activity was associated with worse overallsurvival in this cohort (Fig. 6M). We explored whether thereduction in the immune gene set was associated with a glyco-lytic/acidic gene-expression signature similar to melanoma, weidentified that again the expression of SLC2A1, LDHA, and CA9was negatively associated with markers of immune activation(Fig. 6N), suggesting that the acidic TME was associated withdecreased immune activity in breast cancer. The expression ofSLC2A4 and SLC16A1was not predictive in this analysis, suggest-ing cancer-specific metabolic alterations need to be considered(Supplementary Fig. S5G). However, together, the data demon-strated that increased glucose utilization and conversion to lactateby the tumor and the concomitant acidification of the TMEassociates with immune dysfunction in metastatic melanomaand basal-like breast cancer patients, and further demonstratesthat CA9 expression could be a promising target to improveantitumor immune responses in cancers with similar character-istics (Supplementary Fig. S6A–S6J).

DiscussionThe suppressive TME is recognized as a critical hurdle to

antitumor immunity (12, 47). Aberrant vasculature and accom-panying deficiencies in perfusion and oxygen diffusion create ametabolically challenging environmentwhere nutrients are scarceand pH is critically low. Interventions to restore tissue oxygen-ation and perfusion have been shown to be beneficial to immunefunction (42, 48–50). Here, we demonstrated that therapeuticallytargeting CAIX altered metabolism of the tumor cells, decreasingglycolytic output and respiratory capacity. We demonstrated thatCAIX inhibition reduced the capacity of melanoma and breastcancer cells to acidify the extracellular environment, leading toenhanced immune activity. CAIX inhibition in models of mela-noma and metastatic breast cancer improved the efficacy of anti–PD-1 and anti–CTLA-4.We have also demonstrated that althoughCAIX is negatively associated with expression of Th1, cytotoxic,and HLA-related genes in patients withmetastatic melanoma andbasal-like breast cancer, its inhibition increased the Th1 responsein the preclinical models evaluated.

CAIX is associated with poor prognosis in many solid tumortypes, including breast cancer (51). We have previously shownthat CAIX is an independent marker of worse overall survival andis associated with the occurrence of distant metastasis inTNBC (23).We established that CAIXwas an independentmarkerof worse overall survival in melanoma and was associated withincreased metastasis. In support of these findings, increased CAIXexpression has been detected previously in melanoma patientswith distant metastasis compared with those with lymph nodemetastasis (52). Thus, CAIX is a critical therapeutic target in bothTNBCandmetastaticmelanoma. The identificationof biomarkersor signatures of response/resistance to ICB is critical to stratifyingpatients into those most likely to derive maximal benefit from

therapy. We identified that CA9 expression was associated withdecreased expression of CD3E, CD8A, and CD4 genes and 31genes of a T cell–inflamed signature in tumors from patients withmetastatic melanoma, basal-like breast cancer, pancreatic ductaladenocarcinoma, bladder cancer, lung adenocarcinoma, lungsquamous cell carcinoma, and sarcoma. The broader implicationsthatCA9 expression is negatively correlated with the expression ofT-cell activationmarkers across a range of solid tumor types,manyof which are currently the subject of interventions with ICB,strengthen our rationale for combining CAIX inhibition with ICBto improve immune activity clinically.Wedemonstrated that SLC-0111 treatment in combination with anti–PD-1 and anti–CTLA-4leads to an increased Th1 response, in part, by reducing PD-1expression on antigen-experienced T cells within the TME. Chron-ic exposure to antigens in models of chronic viral infection orcancer leads to epigenetic changes in T cells that ultimately lead toan unrecoverable state of exhaustion (53, 54). However, a win-dow of opportunity exists whereby T cells can be reinvigorated bytherapeutic intervention with anti–PD-1 (55, 56).

Tumor hypoxia is an appreciated impediment to immunefunction (21). The hypoxic niche contains numerous nodes ofimmune suppression, including enhanced expression of PD-L1on tumor cells (21, 57). A study investigatingmarkers of resistanceto anti–PD-1 therapy identified hypoxic gene signatures amongthose expressed in tumors of patients who failed to respond totherapy (58). Interestingly, mining their data set revealedincreasedCA9 expression in those patients who failed to respond.These findings were further supported in a study that also iden-tified increased CAIX expression, at both the RNA and proteinlevels, in patients failing to respond to anti–PD-1 therapy (59).Combined, these studies provide evidence that a clinical cohortstands to potentially benefit from CAIX inhibition in combina-tion with PD-1 blockade. CAIX is a well-known downstreameffector of the hypoxia-inducible factor (HIF)-1, and its expres-sion in relationship to exogenous markers of hypoxia, as shownhere, is well documented (23). HIF1, although known for itsstabilization under conditions of low oxygen, is also induced byothermeans, e.g., oncogenic activation (60),which could lead to ahypoxia-independent activation of the HIF program and subse-quent expression of downstream HIF effectors. Thus, there arecircumstances where CAIX expression may be induced in a hyp-oxia-independent manner (51). In support of this, there is evi-dence available that CAIX expression is induced in acidic pHenvironments independent of hypoxia, which suggests that CAIXexpression and exogenous markers of hypoxia may not alwaysdirectly correlate (61). Nevertheless, elimination of the CAIX-positive fraction of the tumor between the perfused vessels andthe pimonidazole area through CAIX inhibition, in combinationwith a reinvigorated immune response, is an effective approachfor restoring immune function in the TME. Our findings furthersupport the importance of neutralizing the acidic pH within theTME by exploiting the tumor-specific expression of CAIX, whileoffering a clinically viable therapeutic option using the small-molecule SLC-0111 (25).

Disclosure of Potential Conflicts of InterestP.C. McDonald has ownership interest (including stock, patents, etc.)

in SignalChem Lifesciences Corporation. S.P. Shah has ownership interest(including stock, patents, etc.) in Contextual Genomics Inc. and is a consul-tant/advisory board member for the same. S. Dedhar has ownership interest(including stock, patents, etc.) in SignalChem Lifesciences Corp and is a

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consultant/advisory board member for Welichem Biotech Inc. No potentialconflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: S.C. Chafe, P.C. McDonald, S. DedharDevelopment of methodology: S.C. Chafe, P.C. McDonald, S. Saberi,O. Nemirovsky, G. Venkateswaran, A.H. Kyle, Y. Zhou, S.P. Shah, S. DedharAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): S.C. Chafe, P.C. McDonald, S. Saberi,O. Nemirovsky, G. Venkateswaran, S. Burugu, A. Delaidelli, A.H. Kyle,J.H.E. Baker, J.A. Gillespie, Y. ZhouAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): S.C. Chafe, P.C.McDonald, S. Saberi,O.Nemirovsky,G. Venkateswaran, D. Gao, A. Delaidelli, J.A. Gillespie, A.I. Minchinton,S.P. Shah, S. DedharWriting, review, and/or revision of the manuscript: S.C. Chafe, S. Saberi,S. Dedhar

Administrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): S. Saberi, Y. Zhou, S. DedharStudy supervision: S.C. Chafe, A. Bashashati, S. Dedhar

AcknowledgmentsThis work was supported by research grants from the Canadian Institutes of

Health Research (FDN-143318) and the Canadian Cancer Society ResearchInstitute (CCSRI #703191) to S. Dedhar.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received November 2, 2018; revised February 19, 2019; accepted May 9,2019; published first May 14, 2019.

References1. Wei SC, Duffy CR, Allison JP. Fundamental mechanisms of immune

checkpoint blockade therapy. Cancer Discov 2018;8:1069–86.2. Hodi FS,O'Day SJ,McDermott DF,Weber RW, Sosman JA,Haanen JB, et al.

Improved survivalwith ipilimumab in patients withmetastaticmelanoma.N Engl J Med 2010;363:711–23.

3. Larkin J, Chiarion-Sileni V, Gonzalez R, Grob JJ, Cowey CL, Lao CD, et al.Combined nivolumab and ipilimumab or monotherapy in untreatedmelanoma. N Engl J Med 2015;373:23–34.

4. Nanda R, Chow LQ, Dees EC, Berger R, Gupta S, Geva R, et al. Pembro-lizumab in patients with advanced triple-negative breast cancer: phase IbKEYNOTE-012 study. J Clin Oncol 2016;34:2460–7.

5. Savas P, Salgado R, Denkert C, Sotiriou C, Darcy PK, Smyth MJ, et al.Clinical relevance of host immunity in breast cancer: fromTILs to the clinic.Nat Rev Clin Oncol 2016;13:228–41.

6. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al.Atezolizumab andnab-paclitaxel in advanced triple-negative breast cancer.N Engl J Med 2018;379:2108–21.

7. Alexandrov LB,Nik-Zainal S,WedgeDC,Aparicio SA, Behjati S, BiankinAV,et al. Signatures of mutational processes in human cancer. Nature 2013;500:415–21.

8. Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ,et al. Cancer immunology. Mutational landscape determines sensitivityto PD-1 blockade in non-small cell lung cancer. Science 2015;348:124–8.

9. Spranger S, Luke JJ, Bao R, Zha Y, Hernandez KM, Li Y, et al. Density ofimmunogenic antigens does not explain the presence or absence of theT-cell-inflamed tumormicroenvironment inmelanoma. ProcNatl Acad SciU S A 2016;113:E7759–68.

10. ChenDS,Mellman I.Elements of cancer immunity and the cancer-immuneset point. Nature 2017;541:321–30.

11. Galon J, Angell HK, Bedognetti D,Marincola FM. The continuum of cancerimmunosurveillance: prognostic, predictive, and mechanistic signatures.Immunity 2013;39:11–26.

12. Anderson KG, Stromnes IM, Greenberg PD. Obstacles posed by the tumormicroenvironment to T cell activity: a case for synergistic therapies.Cancer Cell 2017;31:311–25.

13. CorbetC, FeronO. Tumour acidosis: from the passenger to the driver's seat.Nat Rev Cancer 2017;17:577–93.

14. Chang CH, Qiu J, O'Sullivan D, Buck MD, Noguchi T, Curtis JD, et al.Metabolic competition in the tumormicroenvironment is a driver of cancerprogression. Cell 2015;162:1229–41.

15. FischerK,HoffmannP, Voelkl S,MeidenbauerN,Ammer J, EdingerM, et al.Inhibitory effect of tumor cell-derived lactic acid on human T cells. Blood2007;109:3812–9.

16. Kim JW, Tchernyshyov I, Semenza GL, Dang CV. HIF-1-mediatedexpression of pyruvate dehydrogenase kinase: a metabolic switchrequired for cellular adaptation to hypoxia. Cell Metab 2006;3:177–85.

17. Parks SK, Chiche J, Pouyssegur J. Disrupting proton dynamics and energymetabolism for cancer therapy. Nat Rev Cancer 2013;13:611–23.

18. Brand A, Singer K, Koehl GE, Kolitzus M, Schoenhammer G, Thiel A, et al.LDHA-associated lactic acidproductionblunts tumor immunosurveillanceby T and NK cells. Cell Metab 2016;24:657–71.

19. Pilon-Thomas S, Kodumudi KN, El-Kenawi AE, Russell S, Weber AM,Luddy K, et al. Neutralization of tumor acidity improves antitumorresponses to immunotherapy. Cancer Res 2016;76:1381–90.

20. Damgaci S, Ibrahim-Hashim A, Enriquez-Navas PM, Pilon-Thomas S,Guvenis A, Gillies RJ. Hypoxia and acidosis: immune suppressors andtherapeutic targets. Immunology 2018;154:354–62.

21. McDonald PC, Chafe SC, Dedhar S. Overcoming hypoxia-mediated tumorprogression: combinatorial approaches targeting pH regulation, angiogen-esis and immune dysfunction. Front Cell Dev Biol 2016;4:27.

22. Lock FE, McDonald PC, Lou Y, Serrano I, Chafe SC, Ostlund C, et al.Targeting carbonic anhydrase IXdepletes breast cancer stem cells within thehypoxic niche. Oncogene 2013;32:5210–9.

23. Lou Y, McDonald PC, Oloumi A, Chia S, Ostlund C, Ahmadi A, et al.Targeting tumor hypoxia: suppression of breast tumor growth and metas-tasis by novel carbonic anhydrase IX inhibitors. Cancer Res 2011;71:3364–76.

24. BoydNH,Walker K, Fried J,Hackney JR,McDonald PC, BenavidesGA, et al.Addition of carbonic anhydrase 9 inhibitor SLC-0111 to temozolomidetreatment delays glioblastoma growth in vivo. JCI Insight 2017;2:e92928.

25. McDonald PC, Swayampakula M, Dedhar S. Coordinated regulation ofmetabolic transporters and migration/invasion by carbonic anhydrase IX.Metabolites 2018;8. pii: E20.

26. Tapmeier TT, Moshnikova A, Beech J, Allen D, Kinchesh P, Smart S, et al.The pH low insertion peptide pHLIP variant 3 as a novel marker of acidicmalignant lesions. Proc Natl Acad Sci U S A 2015;112:9710–5.

27. Swietach P, Patiar S, Supuran CT, Harris AL, Vaughan-Jones RD. The role ofcarbonic anhydrase 9 in regulating extracellular and intracellular ph inthree-dimensional tumor cell growths. J Biol Chem 2009;284:20299–310.

28. Lee SH, McIntyre D, Honess D, Hulikova A, Pacheco-Torres J, Cerdan S,et al. Carbonic anhydrase IX is a pH-stat that sets an acidic tumourextracellular pH in vivo. Br J Cancer 2018;119:622–30.

29. Lucien F, Harper K, Pelletier PP, Volkov L, Dubois CM. Simultaneous pHmeasurement in endocytic and cytosolic compartments in living cells usingconfocal microscopy. J Vis Exp 2014;28(86). doi: 10.3791/51395.

30. Liu CH, Lin CH, Tsai MJ, Chen YH, Yang SF, Tsai KB. Melanin bleachingwith warm hydrogen peroxide and integrated immunohistochemicalanalysis: an automated platform. Int J Surg Pathol 2018;26:410–6.

31. Khosravi S, Martinka M, Zhou Y, Ong CJ. Prognostic significance of theexpression of nuclear eukaryotic translation initiation factor 5A2 in humanmelanoma. Oncol Lett 2016;12:3089–100.

32. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al.Supervised risk predictor of breast cancer based on intrinsic subtypes. J ClinOncol 2009;27:1160–7.

33. Comprehensive molecular portraits of human breast tumours. Nature2012;490:61–70.

34. Love MI, Huber W, Anders S. Moderated estimation of fold change anddispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.

CAIX Inhibition Enhances Immune-Checkpoint Blockade

www.aacrjournals.org Cancer Immunol Res; 7(7) July 2019 1077

on July 24, 2020. © 2019 American Association for Cancer Research. cancerimmunolres.aacrjournals.org Downloaded from

Published OnlineFirst May 14, 2019; DOI: 10.1158/2326-6066.CIR-18-0657

35. Mlecnik B, Bindea G, Kirilovsky A, Angell HK, Obenauf AC, Tosolini M,et al. The tumor microenvironment and Immunoscore are critical deter-minants of dissemination to distant metastasis. Sci Transl Med 2016;8:327ra26.

36. Calcinotto A, Filipazzi P, Grioni M, Iero M, De Milito A, Ricupito A, et al.Modulation of microenvironment acidity reverses anergy in human andmurine tumor-infiltrating T lymphocytes. Cancer Res 2012;72:2746–56.

37. Walton ZE, Patel CH, Brooks RC, Yu Y, Ibrahim-Hashim A, Riddle M, et al.Acid suspends the circadian clock in hypoxia through inhibition of mTOR.Cell 2018;174:72–87.

38. Persi E, Duran-Frigola M, Damaghi M, Roush WR, Aloy P, Cleveland JL,et al. Systems analysis of intracellular pH vulnerabilities for cancer therapy.Nat Commun 2018;9:2997.

39. Chen S, Lee LF, Fisher TS, Jessen B, Elliott M, EveringW, et al. Combinationof 4-1BB agonist and PD-1 antagonist promotes antitumor effector/mem-ory CD8 T cells in a poorly immunogenic tumor model. Cancer ImmunolRes 2015;3:149–60.

40. Sundstedt A, Celander M, Eriksson H, Torngren M, Hedlund G. Mono-therapeutically nonactive CTLA-4 blockade results in greatly enhancedantitumor effects when combined with tumor-targeted superantigens in aB16 melanoma model. J Immunother 2012;35:344–53.

41. Curran MA, Montalvo W, Yagita H, Allison JP. PD-1 and CTLA-4 combi-nation blockade expands infiltrating T cells and reduces regulatory T andmyeloid cellswithin B16melanoma tumors. ProcNatl Acad SciU SA2010;107:4275–80.

42. Fukumura D, Kloepper J, Amoozgar Z, Duda DG, Jain RK. Enhancingcancer immunotherapy using antiangiogenics: opportunities and chal-lenges. Nat Rev Clin Oncol 2018;15:325–40.

43. Hurtado JC, Kim YJ, Kwon BS. Signals through 4–1BB are costimulatory topreviously activated splenic T cells and inhibit activation-induced celldeath. J Immunol 1997;158:2600–9.

44. Shuford WW, Klussman K, Tritchler DD, Loo DT, Chalupny J, Siadak AW,et al. 4-1BB costimulatory signals preferentially induce CD8þ T cellproliferation and lead to the amplification in vivo of cytotoxic T cellresponses. J Exp Med 1997;186:47–55.

45. Kim K, Skora AD, Li Z, Liu Q, Tam AJ, Blosser RL, et al. Eradication ofmetastatic mouse cancers resistant to immune checkpoint blockade bysuppression of myeloid-derived cells. Proc Natl Acad Sci U S A 2014;111:11774–9.

46. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, et al.Distinct cellular mechanisms underlie anti-CTLA-4 and Anti-PD-1 check-point blockade. Cell 2017;170:1120–33.

47. Pitt JM, Vetizou M, Daillere R, Roberti MP, Yamazaki T, Routy B, et al.Resistancemechanisms to immune-checkpoint blockade in cancer: tumor-intrinsic and -extrinsic factors. Immunity 2016;44:1255–69.

48. Tian L, Goldstein A, Wang H, Ching Lo H, Sun Kim I, Welte T, et al. Mutualregulation of tumour vessel normalization and immunostimulatory repro-gramming. Nature 2017;544:250–4.

49. Hatfield SM, Kjaergaard J, Lukashev D, Schreiber TH, Belikoff B, Abbott R,et al. Immunologicalmechanisms of the antitumor effects of supplementaloxygenation. Sci Transl Med 2015;7:277ra30.

50. Cascone T, McKenzie JA, Mbofung RM, Punt S, Wang Z, Xu C, et al.Increased tumor glycolysis characterizes immune resistance to adoptiveT cell therapy. Cell Metab 2018;27:977–87.

51. McDonald PC, Winum JY, Supuran CT, Dedhar S. Recent developments intargeting carbonic anhydrase IX for cancer therapeutics. Oncotarget 2012;3:84–97.

52. Pinheiro C, Miranda-Goncalves V, Longatto-Filho A, Vicente AL, Berardi-nelli GN, Scapulatempo-Neto C, et al. Themetabolicmicroenvironment ofmelanomas: prognostic value of MCT1 and MCT4. Cell Cycle 2016;15:1462–70.

53. Philip M, Fairchild L, Sun L, Horste EL, Camara S, Shakiba M, et al.Chromatin states define tumour-specific T cell dysfunction and reprogram-ming. Nature 2017;545:452–6.

54. Pauken KE, Sammons MA, Odorizzi PM, Manne S, Godec J, Khan O, et al.Epigenetic stability of exhausted T cells limits durability of reinvigorationby PD-1 blockade. Science 2016;354:1160–5.

55. Barber DL, Wherry EJ, Masopust D, Zhu B, Allison JP, Sharpe AH, et al.Restoring function in exhausted CD8 T cells during chronic viral infection.Nature 2006;439:682–7.

56. Schietinger A, PhilipM, Krisnawan VE, Chiu EY, Delrow JJ, BasomRS, et al.Tumor-specific T cell dysfunction is a dynamic antigen-driven differenti-ation program initiated early during tumorigenesis. Immunity 2016;45:389–401.

57. Noman MZ, Desantis G, Janji B, Hasmim M, Karray S, Dessen P, et al.PD-L1 is a novel direct target of HIF-1alpha, and its blockade underhypoxia enhanced MDSC-mediated T cell activation. J Exp Med 2014;211:781–90.

58. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al.Genomic and transcriptomic features of response to anti-PD-1 therapy inmetastatic melanoma. Cell 2016;165:35–44.

59. Gide TN, Quek C, Menzies AM, Tasker AT, Shang P, Holst J, et al.Distinct immune cell populations define response to anti-PD-1 mono-therapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell2019;35:238–55.

60. Brahimi-Horn MC, Pouyssegur J. HIF at a glance. J Cell Sci 2009;122:1055–7.

61. Andreucci E, Peppicelli S, Carta F, Brisotto G, Biscontin E, Ruzzolini J, et al.Carbonic anhydrase IX inhibition affects viability of cancer cells adapted toextracellular acidosis. J Mol Med (Berl) 2017;95:1341–53.

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2019;7:1064-1078. Published OnlineFirst May 14, 2019.Cancer Immunol Res   Shawn C. Chafe, Paul C. McDonald, Saeed Saberi, et al.   Immune-Checkpoint Blockade Locally and SystemicallyTargeting Hypoxia-Induced Carbonic Anhydrase IX Enhances

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