Melanoma Evolves Complete Immunotherapy Resistance through the Acquisition of a Hypermetabolic Phenotype

Abstract

Despite the clinical success of T cell checkpoint blockade, most cancer patients still fail to have durable responses to immunotherapy. The molecular mechanisms driving checkpoint blockade resistance, whether pre-existing or evolved, remain unclear. To address this critical knowledge gap, we treated B16 melanoma with the combination of CTLA-4, PD-1, and PD-L1 blockade and a Flt3 ligand vaccine (≥75% curative), isolated tumors resistant to therapy, and serially passaged them in vivo with the same treatment regimen until they developed complete resistance. Using gene expression analysis and immunogenomics, we determined the adaptations associated with this resistance phenotype. Checkpoint resistance coincided with acquisition of a “hypermetabolic” phenotype characterized by coordinated upregulation of the glycolytic, oxidoreductase, and mitochondrial oxidative phosphorylation pathways. These resistant tumors flourished under hypoxic conditions whereas metabolically starved T cells lost glycolytic potential, effector function, and the ability to expand in response to immunotherapy. Further, we found that checkpoint resistant versus sensitive tumors could be separated by non-invasive MRI imaging based solely on their metabolic state. In a cohort of melanoma patients resistant to both CTLA-4 and PD-1 blockade, we observed upregulation of pathways indicative of a similar hypermetabolic state. Together these data indicated that melanoma can evade T cell checkpoint blockade immunotherapy by adapting a hypermetabolic phenotype.

Introduction

T cell checkpoint blockade antibodies are now approved for the treatment of a majority of adult cancers either alone or in various combinations(1). Despite this remarkable progress, most cancer patients still show intrinsic or naturally acquired resistance to immune checkpoint blockade leading to treatment failure. In addition, certain immunologically “cold” tumors, such as pancreatic ductal adenocarcinoma, have no appreciable response to these therapies(2–4). Before we can identify biomarkers of checkpoint antibody response, or rationally plan to circumvent immunotherapy resistance, we must understand the underlying processes responsible for lack of and/or loss of response.

The mechanisms employed by tumors to escape host immunity have been extensively studied even prior to clinical adoption of checkpoint blockade(5–9). Most of the initial research addressing checkpoint blockade resistance mechanisms focused on the upregulation of alternative immune checkpoints such as TIM3, TIGIT and VISTA(10–12). Mutational load, or lack thereof(13–15), loss of interferon response(16,17), and copy number loss of components of the antigen presentation machinery(18,19) by tumor cells are also described as mechanisms of resistance to αPD-1 and αCTLA-4 monotherapies. However, these pathways fail to account for the majority of non-responders. Additionally, little is known of the transcriptomic states of tumor cells which favor immunotherapeutic sensitivity versus resistance. To address this critical gap in knowledge, we established an immunotherapy-resistant mouse model of melanoma. We employed an approach rooted in both ‘cancer immunoediting’ theory(9), postulating that immune pressure fosters suppressive adaptations by tumors, and on the in vivo serial passage approach pioneered by Fidler et. al. (20–22). Mice were treated for B16 melanoma using a triple-checkpoint blockade and vaccination approach which cures ≥75% (23), and progressive tumors were then isolated and inoculated into new mice that received the same therapy. These serial immune selections were performed until a completely checkpoint blockade resistant melanoma emerged. Using gene expression analysis and immunogenomics, flow cytometry, confocal imaging and metabolic assays, we showed that resistant tumors acquired a “hypermetabolic” phenotype characterized by coordinated upregulation of the glycolytic, oxidoreductase, and mitochondrial oxidative phosphorylation pathways, thus creating a hostile metabolic microenvironment in which cytotoxic CD8⁺ T cells are energetically starved and rendered dysfunctional. Heterologous overexpression of the key genes driving enhanced metabolic fitness in the resistant melanoma back into the parental B16 line conferred substantial resistance to triple checkpoint therapy, thus validating their role in immunotherapy resistance. In a cohort of melanoma patients who failed both CTLA-4 and PD-1 blockade, we identified similar metabolic enhancement in progressing versus responding patients, suggesting clinical relevance of these findings. Overall, our data demonstrate that melanoma is capable of evolving resistance to simultaneous blockade of CTLA-4, PD-1 and PD-L1 through acquisition of a hypermetabolic state characterized by enhanced glycolysis, oxidoreductase activity and mitochondrial OxPhos.

Materials and Methods

Mice

Male C57BL/6J and B6.129S7-Rag1tm1Mom/J mice were purchased from The Jackson Laboratory. Mice were housed in our pathogen-free facility which is fully accredited by the Association for Assessment and Accreditation of Laboratory Animals Care. All experiments were performed according to protocols approved by the Institutional Animal Care and Use Committee.

Therapeutic antibodies

Anti-CTLA-4 (9H10), anti-PD-1 (RMP1-14), and anti-PD-L1 (10F.9G2) were purchased from BioXCell (West Lebanon, NH) and administered intraperitoneally

Patient cohort

A cohort of 9 melanoma patients were included in the analysis. Surgical samples were from metastatic melanoma patients treated with ipilimumab (αCTLA-4) and/or pembrolizumab or nivolumab (αPD-1) at the UT MD Anderson Cancer Center between April 2014 and September 2015 on IRB protocol 2012-0846 prior to therapy or at time of progression (Supplementary Table 1). Clinical responses were assessed based on RECIST 1.1(24). This study was designed and monitored in accordance with sponsor procedures in compliance with the ethical principles of Good Clinical Practice, International Conference on Harmonization guidelines, the Declaration of Helsinki, and applicable local regulatory requirements. All patients provided written, informed consent. The protocol, any amendments, and informed consent forms were reviewed and approved by the institutional review boards/independent ethics committees.

Cell lines

The B16/BL6 cell line was originally obtained from I. J. Fidler (MD Anderson Cancer Center) in 2012. The B16-sFlt3L-Ig (FVAX) and B16-tdTomato cell lines have been described previously(25). The cells were maintained in RPMI media with 10% Fetal Bovine Serum (FBS). Panc02 cells, originally obtained from the lab of Dr. Elizabeth Jaffee in 2010, were maintained in DMEM supplemented with 10% FBS. Cells were used within two weeks of thaw from their master tumor banks. These cells were tested mycoplasma negative but not authenticated in the past year.

Harvesting B16 melanoma

To harvest mouse tumor single cell suspensions, tumors were removed post sacrifice and were treated with 0.25 mg ml⁻¹ collagenase H (Sigma- Aldrich (St. Louis, MO) and 25 U ml⁻¹ DNase (Roche Diagnostics, Indianapolis, IN) for 20 min at 37°C; the dissociated cells were then passed through a sterile 70 μm filter (Fisher Scientific, Catalog #352350) The resulting dissociated cells were collected by centrifugation and washed twice in phosphate-buffered saline (PBS). The cells were then cultured and/or used for flow cytometry analysis and/or flow sorting.

Generation of checkpoint blockade immunotherapy-resistant melanoma cells

We implanted 15 mice with 25,000 B16/BL6-td cells subcutaneously in 100μL of PBS and treated them with a combination of three T cell checkpoint blockade antibodies. On days 3, 6, and 9, post implantation, mice were injected subcutaneously with 1 × 10⁶ irradiated (150 Gy) FVAX cells resuspended in 100μL of PBS on the contralateral flank and an intraperitoneal injection of combination αCTLA-4 (100 μg), αPD-1 (250 μg), and αPD-L1 (100 μg) diluted to 100μL or 200μL of PBS. Mice developing tumors regardless of treatment were euthanized when tumors reached 200-500 mm³ and their tumors harvested. Tumors from non-responder mice were pooled and a cell line (3I-F1) was generated and maintained in RPMI media with 10% FBS. This 3I-F1 cell line was used to inoculate a new set of 15 mice followed by the same immunotherapy regimen. For this second cycle and all subsequent cycles, only 10,000 cells were implanted to increase the rigor of the screen. We repeated these serial passages until ≥90% of the animals became resistant to therapy. B16 melanoma cell lines were thus named 3I-F1, 3I-F2, 3I-F3, and 3I-F4 (completely resistant). For the untreated control group, we implanted 5 mice with parental tumor cells and with tumor cells from each cycle of selection.

Treatment strategies and monitoring tumor growth

For Panc02 studies, C57BL6 mice were subcutaneously implanted with 5 × 10⁵ control vector, PGAM2 or ADH7 overexpressing tumor cells and treated with either αCTLA-4 or αPD-1 as mentioned above. Metformin (50 mg/kg; every other day) and 2-DG (500mg/kg; daily) were given i.p. beginning one day following tumor challenge. For metformin water cohorts, drinking water containing 1g/L metformin was provided post tumor implantation. LDH-A inhibitor GSK2837808A (4mg/kg) was prepared in 100 μL polyethylene glycol (PEG) base and given through oral gavage everyday post implantation. Tumors were measured using calipers every other day and tumor volume was calculated as length x width x height. Death was scored as tumor volume of 1000 mm³.

RNA extraction

Tumors were harvested and sorted using a BD FACSAria cell sorter and BD FACSDiva Software on td-tomato fluorescence in tumor versus tumor microenvironment (TME). Total RNA was extracted with the RNeasy Mini Kit (Qiagen, Catalog #74106), MD). For Adh7 and Pgam2 expression, cells were lysed and RNA was extracted using the RNeasy Mini Kit. cDNA was generated using the Invitrogen SuperScript IV reverse transcriptase kit (ThermoFisher, Catalog #18090050). TaqMan real-time PCR was performed on a Via 7 RT-PCR System (Applied Biosystems) as previously described(26). For patient biopsies, the presence of tumor was confirmed by a pathologist, and total RNA was extracted from the tumor tissue using the RNeasy Mini kit. (Qiagen, MD)

Microarray analysis

Tumor cells and non-tumor cells of the TME were sorted by flow cytometry and RNA was isolated from both as above. Microarray analysis was done on both tumor cells and TME from RNA samples (n=2) from parental tumors and from 3I-F4 tumors (n=4). Each RNA sample was isolated from tumors pooled from three mice. Microarray analysis was also done on RNA from patients’ tumor biopsies. MouseRef-8 and HumanHT-2 bead chip arrays (Illumina) were used. For resistant B16 melanoma analysis, MouseRef-8 microarray analysis was performed on RNA from a 15cm plate from each generational line at the earliest passage freeze available. Microarray data is deposited into GEO with the record number GSE122222.

Quantitative RT-PCR (qRT-PCR)

RNA was reverse transcribed using the SuperScript IV First-strand Synthesis kit (ThermoFisher, Catalog #18091050). RNA concentration was quantified using a NanoDrop™ 8000 Spectrophotometer (ThermoFisher. Catalog #ND-8000-GL). 1µg of RNA template was used as input for cDNA synthesis. Samples were run in triplicates on a ViiA 7 Real-Time PCR System and gene expression was normalized to Hprt (ThermoFisher, Assay ID: Mm03024075_m1). Fold change in gene expression of Adh7 (ThermoFisher, Assay ID: Mm03121387_m1) or Pgam2 (ThermoFisher, Assay ID: Mm01187768_m1) was calculated using the 2^−ΔΔCT method.

Bioinformatic analyses

Microarray data was normalized per manufacturer’s instructions and processed in R. Low intensity probes that were not significantly expressed above background level (detection p-value≥0.05 in at least one of the samples) were excluded. Differential expression between resistant and parental for tumor and TME were determined by fold-change in absolute value ≥1.1 and p-value from the moderated t-statistic from LIMMA package ≤0.05. To support visual data exploration, we employed R to generate volcano plots, as well as heatmaps using the heatmap.2 function of gplots library.

For TCGA analysis the data were obtained from TCGA portal and lymphocyte score (LS score) table was obtained from publisher’s website (Supplemental Table S1D: Patient Centric Table). The LS scores were used to categorize patients into two groups: LS high and low patients. The LS high group included the patients whose LS score were between 3 and 6, and LS low group contained patients of LS scores between 0 and 3. The Student’s t-test was applied to compare gene expression between LS-High and Low groups.

Gene set enrichment analysis (GSEA) and ingenuity pathway analysis (IPA) were applied to the data sets in an unbiased fashion to compare resistant tumors with parental tumors and responding patients with non-responders.

Cell growth kinetics and viability

Parental and resistant cells were plated at a density of 5000 cells per well on 96-well plates in 10 replicates. Photomicrographs were taken every hour using an Incucyte cell imager (Essen Biosciences, Ann Arbor, MI) and confluency was determined using Incucyte software. Cells were grown in the HypOxystation® H35, (Hypoxygen, Frederick, MD) under 1% oxygen and 85% humidity. Parental and resistant cells were plated as above and cell viability was measured by Cell Titer 96 Aqueous One Solution Cell Proliferation Assay (MTS) (Promega, Catalog # G3582).

Extracellular flux analyses

Resistant and parental cell lines were seeded at a density of 25,000 cells per well 24 hours prior to the assay. Oxygen consumption rate (OCR) (Seahorse XF Cell Mito Stress Test Kit, Agilent Technologies, Part #103015-100) and extra cellular acidification rate (ECAR) (Seahorse XF Glycolysis Stress Test Kit, Agilent Technologies, Part # 103020-100) were measured as per the manufacturer’s protocols on an XF96 Analyzer (Agilent Technologies).

Immunofluorescence staining and imaging

Mice were injected i.v. with Pimonidazole (Hypoxyprobe, Catalog # HP-500mg, Burlington, MA) 30’ prior to euthanasia so that hypoxia could be imaged in tumor sections by immunofluorescence staining with a pimonidazole adduct FITC antibody (Hypoxyprobe). Mouse tissues were collected and embedded in Tissue-Tek® OCT Compound (Sakura, Torrance, CA). Embedded tissues were frozen in liquid nitrogen and sectioned at the MD Anderson Histology Core. Sectioned tissue was fixed with acetone for 10’, permeabilized with the FoxP3 staining kit (eBioscience, Catalog #00-5521-00, San Diego, CA) for 10’, and blocked with Superblock (Thermo Fisher, Catalog #37515) for 15’. The samples were stained with antibodies in 2% bovine serum albumin, 0.2% Triton-X100 in PBS for 30’ and, after being washed in PBS, mounted with Prolong® Gold anti-fade reagent (Invitrogen, Catalog #P10144 Carlsbad, CA). Fluorescence microscopy was performed using a TCS SP8 confocal microscope equipped with lasers for 405nm, 458nm, 488nm, 514nm, 568nm, and 642nm wavelengths (Leica Microsystems, Inc., Bannockburn, IL). The ImageJ software was used to analyzed the images.

Extraction of metabolites and NMR analysis

Cells were trypsinized and washed twice with PBS and frozen in liquid nitrogen. Tumors from mice with and without immunotherapy treatment were collected on day 12-16 post implantation and frozen in liquid nitrogen. Cells were counted, and tumor tissues were weighed before extraction of metabolites. Cells and tumor tissues were homogenized and added with 2:1 methanol and ceramic beads. The tissues/cells were then vortexed for 40 – 60 seconds followed by freezing in liquid nitrogen and thawing on ice. Water soluble proteins and other biopolymers were precipitated in methanol solvent leaving the small molecular weight metabolites in the solution which were then extracted using ultra-centrifugation. Remaining residual solvent was removed by overnight lyophilization.

The lyophilized sample was dissolved in 800 µl of 2H₂O and centrifuged at 10,000 rpm. The 600 µl of sample was added with 40 µl of 8 mM 4,4-dimethyl-4-silapentane-1-sulfonic acid (DSS) before acquisition on NMR. NMR data were collected on an Avance Bruker spectrometer operating at 500 MHz proton (¹H) resonance frequency, equipped with cryogenically cooled triple resonance (¹H, ¹³C, ¹⁵N) TXI probe. All one-dimensional (1D) ¹H NMR spectra were acquired with suppressed solvent signal achieved by pre-saturation during longitudinal relaxation time. The inter-scan delay of 6 seconds is used to rule out the longitudinal relaxation related signal attenuation. The 900-radio frequency (r.f) pulse of 12 µs, spectral width of 8,000 Hz and 256 transients were used to acquire the 1D ¹H NMR. All spectra were processed in topspin 3.1 and metabolites were assigned with the help of Chenomx and Human Metabolomics Database (HMDB). The intensities of metabolites were taken with respect to NMR reference compound of 0.5 mM 2, 2 Dimethyl-2-Silapentane-5-sulfonate-d6 (DSS) appearing at 0 ppm. Intensities (area under the curve) of the metabolites were normalized to the cell numbers and tumor mass. The normalized intensities were used to calculate the Z score expressing relative expression of metabolite in resistant tumors/cell lines compared to parental tumors/cell line.

Hyperpolarized pyruvate to lactate flux imaging of tumors

The mixture of 20 µl 1-¹³C, 10 µl of 15 mM trityl radical OX63 and 0.4 µl Gd2+ was hyperpolarized for an hour with microwave irradiation at 94 GHz at low temperature 1.5 K in Oxford Hypersense instrument. The hyperpolarized pyruvate was dissolved at high temperature in 4 ml of TRIS/EDTA buffer at physiological pH 7.8 to a final concentration of 80 mM of pyruvate. 200 µL of the solution was injected into the mice via tail vein injection in a horizontal bore 7 T Bruker MR Scanner(27).

The anatomical proton image and ¹³C Magnetic Resonance Spectroscopy (MRS) were acquired using a surface transceiver ¹³C-¹H coil (Doty Scientifics). Anatomical images of coronal, axial and sagittal were acquired with T2 weighted Rapid Imaging with Refocused Echo (RARE) sequence to determine the size and location of tumors. The ¹³C enriched urea phantom was used as spectroscopic reference and to locate the tumor. The single pulse Fast Low Angle Shot (FLASH) was used to acquire 1D ¹³C magnetic resonance spectroscopy (MRS) with repetition time of 2 seconds, flip angle 200, image size 2048 X 90 and single slice of thickness 5-10 mm and acquired over a period of 180 seconds(27).

Flow cytometric characterization of resistant tumors

Following gradient separation, samples were fixed using the Foxp3 Buffer Set (eBioscience) and then incubated with anti-mouse CD15/CD32 Ab (clone 2.4G2, BioXcell, Catalog #BE0307) for 15 minutes before being stained with up to 18 antibodies from Abcam, Biolegend, BD Biosciences, eBioscience, or Life Technologies. Antibodies were used against the following mouse proteins in different combinations: CD45 (30-F11), CD45.2 (104), CD3 (17A2), CD8a (53-6.7), CD4 (GK1.5), CD11b (M1/70), F4/80 (BM8), IDO (mIDO-48), GLUT1 (EPR3915), PD-1 (RMP1-30), Granzyme B (NGZB), 4-1BB (17B5), IL-2 (JES6-5H4), TNF-α (MP6-XT22), IFNγ (XMG1.2), FoxP3 (FJK-16s), Gr-1 (RB6-8C5), Arginase 1 (A1exF5), . Flow cytometry data was collected on a 5-laser BD LSR II cytometer and analyzed using FlowJo (Treestar)(26,28).

For metabolic characterization of lymphocytes, fluorescently labeled glucose (2-NBDG) (Cayman Chemical Company, Catalog #11046) was injected i.v. 30’ prior to sacrificing mice for tumor harvest. For characterization of T cell effector function, mice were injected s.c. with a single cell suspension of 250K parental or resistant tumor cells in 100µL of PBS. Mice were treated on days 3, 6, 9 post implantation with FVAX and triple immune checkpoint blockade as above. On day 11, mice were euthanized, and viable immune cells from tumor digests wereenriched through density gradient separation over Histopaque 1119 (Sigma-Aldrich, Catalog #111-91-100ML). CD8⁺ T cells were isolated from immune cells via negative selection through magnetic beads using a MACS CD8⁺ T cells Isolation Kit (Miltenyi Biotec, Catalog #130-104-075). CD8⁺ T cells were activated for 6 hours using eBioscience™ Cell Stimulation Cocktail (plus protein transport inhibitors) (Catalog #00-4975-93) and analyzed by flow cytometry.

Retroviral vectors and virus production

Murine PGAM2 and ADH7 cDNAs were cloned into the pMG-rtNGFr retroviral vector. This vector resembles pGC-IRES except that for a truncated form of rat p75 nerve growth factor receptor (rtNGFr) is used for selection(29). Recombinant virus production and infection were performed as described (30) to generate B16-BL6-td or Panc02 cells overexpressing either ADH7 or PGAM2.

Generation of CRISPR and shRNA clones

PGAM2 (sgPGAM2), ADH7 (sgADH7) or control (sgScramble) KO clones were generated using the all-in-one CRISPR/Cas9 system (Genecopoeia, pCRISPR-CG04). Vectors encoding a scramble guide RNA sequence or guides targeting exon 1 or 2 of each gene were transfected into 3I-F4 cells. GFP positive cells were sorted 24 hours later, plated, and loss of either ADH7 or PGAM2 expression was validated by qRT-PCR. To generate PGAM2 knockout, ADH7 knockdown cells (sgPGAM2/shADH7), a lentiviral short hairpin (shRNA, LVRH1GH) construct was introduced into sgPGAM2 cells. Control sgScramble/shScramble and sgPGAM2/shScramble were generated by transduction of a plasmid containing a scrambled shRNA. GFP positive cells were sorted, and knockdown of ADH7 was validated by qRT-PCR as described above..

Statistical analysis

All statistics were calculated using Graphpad Prism. Statistical significance was determined using a two-tailed Student’s t test applying Welch’s correction for unequal variance. Graphs show mean ± standard deviation unless otherwise indicated. P-values less than 0.05 were considered significant.

Results

Serially in vivo passaged B16/BL6 melanoma cells acquired resistance to immunotherapy

We generated the immunotherapy resistant melanoma cell line (3I-F4) by in vivo passaging a B16/BL6-tdTomato melanoma in the presence of the combination of a B16-Fms-like tyrosine kinase 3 ligand vaccine (FVAX) and antibody blockade of CTLA-4, PD-1 and PD-L1, a therapy with initial survival benefit in ≥75% of animals (Fig. 1, A and B) (23).

Figure 1: Generation and characterization of checkpoint blockade immunotherapy resistant tumor cells through serial in vivo passage.

(A) Experimental model showing how immunotherapy-resistant tumor cells (3I-F4) were evolved through in vivo passaging. (B) This bar graph shows the percentage of mice resistant to immunotherapy after each in vivo passage. Data label on the bar indicates name and number of tumor cells implanted for the respective passages. (C) The in vitro growth kinetics of resistant tumor cell line compared to parental tumor cell line were determined using IncuCyte^TM confluency assay (data points represent average percent confluence for a representative experiment). (D) Survival of mice challenged with 2.5×10⁴ parental or resistant tumor cells with and without immunotherapy treatment in (D) wild type C57BL/6 and (E) Rag1−/− mice. Statistical significance was calculated using the log-rank (Mantel-Cox) test. *P < 0.05, **P< 0.01, ***P < 0.001, ****P < 0.0001.

To ensure that the lack of immunotherapy response in the resistant B16/3I-F4 clone did not result from accelerated proliferation, we compared in vitro and in vivo proliferation of B16/3I-F4 and B16/BL6 (Parental). Using the IncuCyteTM confluency assay, we found no significant difference in proliferation between parental and resistant B16 (Fig. 1C). We also compared in vivo tumor growth and survival of mice with parental and resistant tumors in normal C57BL6 and immune-deficient B6.Rag−/− mice. Untreated parental and resistant tumors showed no significant difference in tumor growth rate or host survival in C57BL6 (Fig. 1D and Supplementary Fig. S1A) or B6.Rag−/− mice (Fig. 1E and Supplementary Fig. S1B). In the presence of triple checkpoint blockade; however, C57BL6 mice with parental tumors showed reduced tumor growth and significant survival benefit (Fig. 1D and Supplementary Fig. S1A). In contrast, in B6.Rag−/− mice, both parental and resistant melanomas grew at the same rate, even in the presence of immunotherapy, demonstrating that resistance depends on differential insensitivity to adaptive immunity and does not result from amplified proliferation.

Gene expression changes in tumor metabolic pathways correlated with acquired resistance

We next probed the underlying acquired genetic changes within immunotherapy-resistant tumors responsible for their resistance. We harvested resistant 3I-F4 tumors, sorted tumor cells from non-tumor cells based on td-Tomato expression as shown in the histogram (Fig. 2A), and performed gene expression profiling on tumor cells . Expression of numerous genes was significantly altered during acquisition of triple checkpoint resistance; however, top candidate genes generally clustered into metabolic pathways, in particular, glycolysis, oxidative phosphorylation, oxidative stress, and hypoxia (Fig. 2B–2D).

Figure 2: Gene expression profiling and immunogenomics of immunotherapy resistant tumor cells.

(A) Experimental schema showing how resistant tumors and parental tumors were flow cytometry sorted into tdTomato-positive versus negative tumor cells (included histogram shows td-Tomato expression).). for microarray analysis.. (B) A volcano plot representing log fold change in gene expression in immunotherapy-resistant (3I-F4) tumor cells compared to immunotherapy-sensitive parental tumor cells. (C) Positively enriched curated (C2 MsigDB|GSEA) and GO (C5 MsigDB|GSEA) gene sets in #3I-F4 tumor cells compared to parental B16. (D) Representative Gene Set Enrichment Analysis (GSEA) plots from tumors (OxPhos, hypoxia and oxidative stress related gene sets). (E) Taqman (Invitrogen) gene expression analysis of Adh7 and Pgam2 expression in the resistant 3I-F4 line versus parental B16.

Gene set enrichment analysis (GSEA) and an Ingenuity Pathway Analysis (IPA) were performed and showed that immunotherapy resistant tumors augmented biological pathways involving mitochondrial OxPhos, oxidoreductase activity, hypoxia response, and glycolysis (Fig. 2C). Resistant tumors increased OxPhos as suggested by the ‘MOOTHA_VOXPHOS’ gene set and these increases in OxPhos appeared to be associated with deepening hypoxia as suggested by ‘MANALO_HYPOXIA_DN’ (Fig. 2D). The gene set ‘NFE2L2.V2’, representing genes that are critical for oxidative stress responses, was also positively enriched in 3I-F4. This suggested that resistant tumor cells altered their oxidative damage response to become better adapted to the cellular stress caused by hypermetabolism and its associated worsening of hypoxia (Fig. 2D). Upregulation of the key metabolic regulatory genes, Adh7 and Pgam2, associated with acquired checkpoint blockade resistance from the microarray was validated by Taqman qRT-PCR analysis comparing resistant to parental lines (Fig. 2E). Gene expression analysis of microarray data from cell lines generated at each passage (i.e. 3I-F1-3I-F4) shows progressive genetic evolution of each line as they move toward more complete immunotherapy resistance (Supplementary Fig. S2). Taken together, these data suggested that resistant tumors deplete oxygen and other nutrients in the TME and nucleate a state of hypoxia in which they can flourish in their metabolically-adapted state, while lymphocytes face a harsh milieu in which they are metabolically unfit to thrive.

Resistant melanoma cells acquired a hypermetabolic phenotype to evade immunotherapeutic pressure

To validate the metabolic adaptations of resistant tumors, we assessed their glycolytic metabolism by measuring their extracellular acidification rate (ECAR), and their rate of oxidative phosphorylation by measuring their oxygen consumption rate (OCR) using a Seahorse XF analyzer (Agilent). The immunotherapy resistant 3I-F4 line demonstrated higher basal ECAR and OCR relative to parental B16 with elevated maximum glycolytic capacity and mitochondrial respiration (Fig. 3, A and B). This enhancement of both glycolysis and OxPhos is a departure from the expected Warburg effect, in which tumor cells rely primarily on glycolysis for ATP production in the oxygen-depleted TME(31). To further validate the hypermetabolic phenotype of immunotherapy resistant tumors, we analyzed their cellular metabolites using nuclear magnetic resonance (NMR) spectroscopy. The resistant cells showed relative increases in lactate and other TCA cycle metabolites compared to the parental line (Supplementary Fig. S3A). We also compared metabolites extracted from whole tumor lysates of resistant versus parental tumors with and without treatment. Consistent with the cell line data, ex vivo resistant tumors also showed increased relative expression of lactate and other TCA cycle metabolites with and without therapy (Fig. 3C). The observed increase in these metabolites was more profound in the presence of immunotherapy treatment, suggesting that treatment itself directly or indirectly triggers these metabolic changes in resistant tumors.

Figure 3: Resistant melanoma cells acquired a hypermetabolic phenotype to evade checkpoint blockade mediated immunotherapeutic pressure.

Immunotherapy resistant 3I-F4 and immunotherapy sensitive parental cells were analyzed using seahorse extracellular flux assays. (A) Extracellular acidification rate (ECAR) measured using the Seahorse XF Glycolysis Stress Test. (B) Oxygen consumption rate (OCR) measured using Seahorse XF Cell Mito Stress Test. (C) A heat map depicting relative changes in metabolites’ intensities from resistant to parental tumors with or without treatment. Tumors from mice with and without immunotherapy treatments were collected on day 12-16 post implantation and frozen in liquid nitrogen. Metabolites were extracted and analyzed on NMR The intensities of metabolites were taken with respect to NMR reference compounds. A heat map was generated using Z score, which represents the relative intensities of extracted metabolites from resistant compared to parental tumors with and without treatment. (D) A metabolic signature of resistant tumors was visualized using non-invasive MRI. Hyperpolarized pyruvate was injected in tumor-bearing mice which were then analyzed using MRI to calculate pyruvate to lactate conversion ratio. (E) Normalized lactate to pyruvate ratio was calculated [nLAC= (Lactate + Pyruvate)/Lactate)] and used as a surrogate read out of glycolysis rate in resistant tumor compared to parental tumor. (F) Hypoxic zones were quantified from 12-15 fields from 4-5 mice treated with triple checkpoint blockade. (G) Cell survival assay (MTS) on resistant and parental tumors in hypoxic chamber (pO2=1%). Statistical significance was calculated using a Student’s T test. ns, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

Based on our in vitro and ex-vivo metabolic analyses, we hypothesized that the increase in lactate production in resistant tumors could serve as a marker to separate immunotherapy-sensitive and resistant tumors by visualizing conversion of hyperpolarized pyruvate into lactate utilizing noninvasive magnetic resonance imaging (MRI). Using this approach, we showed that the rate of pyruvate to lactate conversion was significantly higher in immunotherapy resistant tumors (Fig. 3, D and E). This demonstrated the potential to segment immunotherapy sensitive B16 melanoma away from checkpoint-resistant 3I-F4 tumors in live, untreated animals. Together, these data suggest that checkpoint blockade immunotherapy-resistant tumors acquired a hypermetabolic state where they upregulated both glycolysis and OxPhos to evade the host immune response.

Resistant melanoma tumors adapted to thrive in hostile hypoxic conditions

We further investigated the role of hypoxia in mediating resistance to checkpoint blockade immunotherapy. Using confocal microscopy and the hypoxia-specific dye Pimonidazole, we observed how resistant and parental tumors (shown in red) interact with hypoxic zones in the TME. No difference was observed in the extent of hypoxia in untreated resistant and parental tumors (Supplementary Fig. S3B); however, following treatment, hypoxia appeared more prevalent in resistant compared to parental tumors (Supplementary Fig. S3C). Also, td-Tomato positive cancer cells in resistant tumors grew in equal or greater density within hypoxic zones compared to parental. Quantitative analysis across multiple parental and resistant tumors confirmed a significant increase in the number of discrete zones of hypoxia within treated, resistant tumors relative to both untreated resistant and treated parental melanomas (Fig. 3F). An in vitro survival assay comparing resistant and parental tumors in a hypoxic chamber (pO2=1%) showed increased accumulation of the resistant 3I-F4 cell line, further illustrating that these cells can thrive under adverse metabolic conditions (Fig. 3G). Thus, immunotherapy-resistant 3I-F4 cells have acquired a hypermetabolic phenotype and help to propagate a metabolically hostile TME in which they have adapted to flourish.

The resistant tumor microenvironment demonstrated hypoxic stress and reduced immune fitness

We next assayed the impact of the hypermetabolic adaptations of the tumor cells themselves on the gene signature of the surrounding TME. In this case, we separated the TME (td-Tomato-) away from the tumor (td-Tomato+) by FACS and analyzed gene expression of the td-Tomato- fraction (Fig. 4A). In contrast to our prior observation for the tumor itself (Fig. 2D), the GSEA analysis of the resistant TME showed gene signatures indicative of a failure to adapt to hypoxia (Fig. 4, B and C). Perhaps due to this inability to adapt to increasing hypoxic stress, TME gene expression data suggested diminished antitumor immune function as indicated by negative enrichment of gene sets encompassing T cell effector function, myeloid (DC and microphages) cell activation and DC maturation (Fig. 4, D and E).

Figure 4: Gene expression profiling and immunogenomics of the immunotherapy-resistant tumor microenvironment.

(A) Experimental schema showing how the TME of resistant tumors and control parental tumors were flow cytometry sorted as tdTomato-negative. Data is presented for microarray analysis of this pure sorted TME population. (B) Negatively enriched curated (C2 MsigDB|GSEA) and GO (C5 MsigDB|GSEA) gene signatures in immunotherapy-resistant TME compared to immunotherapy-sensitive parental TME and (C) representative GSEA plots from tumors (hypoxia and oxidative phosphorylation). (D) Negatively-enriched immunological gene signatures (C7 MsigDB|GSEA) in immunotherapy-resistant TME compared to immunotherapy-sensitive parental TME and (E) representative GSEA plots from TME (DC Activation and CD8⁺ T cell effector function).

Antitumor immunity was compromised by the nutrient-depleted microenvironment of resistant tumors

Next, we investigated the effects of metabolic adaptation by resistant tumor cells on the composition and phenotype of immune cells in the TME. We established both parental and resistant tumors with or without treatment and then isolated them on day 14 and performed 18-parameter flow cytometry analysis to characterize immune infiltration (Supplementary Fig. S4). While baseline CD8⁺ T cell infiltration of all untreated B16 melanoma tumors was poor, vaccination and triple checkpoint blockade elicited profound expansion of the tumor infiltrating CD8⁺ compartment in the parental tumors (Fig. 5A). In contrast, CD8⁺ T cell density failed to significantly increase in response to the same therapy in resistant 3I-F4 melanomas. The treatment expanded CD4⁺ effector cells and improved the CD8⁺ T cell to Treg ratios but failed to do so in resistant tumors (Supplementary Fig. S5,A and B). The baseline proliferation of CD8⁺ T cells in the TME of resistant tumors was significantly depressed; however, treatment elicited similar Ki67 expression in both melanomas suggesting that reduced CD8⁺ T cell density in the resistant setting likely resulted from impaired T cell persistence and/or emigration (Fig. 5B and Supplementary Fig. S4). Granzyme B, a marker for cytolytic potential, and Glut1, a glucose transporter and maker of glycolytic potential(35), showed attenuated therapeutic induction in the resistant melanomas relative to parental (Fig. 5, C and D and Supplementary Fig. S4).

Figure 5: Effects of metabolic adaptation by resistant tumors on cytotoxic T cell infiltration and function.

(A) CD8⁺ T cell density per tumor weight was determined by counting and flow cytometry. Resistant and parental tumors were implanted in mice and treated on day 3, 6 and 9. Tumors were weighed before harvesting for flow cytometric analysis. Data are expressed as the total number of CD8 positive cells per milligram of tumor. T cell functional marker are shown as mean fluorescence intensity (MFI) of (B) Ki-67. (C) Granzyme, (D) Glut1 and (E) PD-1. ( (F) Resistant and parental tumors were implanted in mice and treated on day 3, 6 and 9. Thirty minutes prior to tumor harvest, mice were intravenously injected with fluorescently labeled glucose (NBDG). Following tumor isolation and infiltrate purification, cells were stained with antibodies for cell classification and functional assessment, as well as with the mitochondrial dye Mito Red (Sigma). (G) Mean fluorescent intensity of NBDG, and Mito Red on tumor infiltrating CD8⁺ T cells, splenic CD8⁺ T cells from tumor-bearing mice and tdTomato positive tumor cells (n=12-15). (H) The antigen specific 4-1BB⁺ subset of CD8⁺ tumor-infiltrating T cells was isolated from established parental or resistant melanomas (with and without treatment) and restimulated using the eBioscience cell stimulation cocktail, and analyzed by flow cytometry. Statistical significance was calculated using Student’s t test. ns, not significant; *P< 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001.

In the CD4⁺ compartment, effectors had lower baseline Glut1 in 3I-F4 but could still respond to immunotherapy, whereas Treg cells lost Glut1 responsiveness to treatment (Supplementary Fig. S5, C and D). CD8⁺ T cells in resistant melanoma showed significantly increased expression of the exhaustion marker PD-1 suggesting reduced fitness in the presence of hypermetabolic melanoma, although PD-1 remained unchanged on CD4 T cells perhaps reflecting differing metabolic demands on each subset (Fig. 5E and Supplementary Fig. S5, E and F).

Effector capacity of cytotoxic T cells is tightly linked to their metabolic fitness, particularly to their glycolytic capacity. In order to test the effect of metabolic adaptation of resistant tumors on CD8⁺ T cell function, we measured glucose uptake using the fluorescently labeled glucose analog 2-NBDG (ThermoFisher) and mitochondrial membrane potential using MitoTracker Deep Red FM (ThermoFisher) in tumor infiltrating T cells. CD8⁺ T cells demonstrated reduced glucose uptake and showed high Mito FM staining in resistant compared to parental tumors (Fig. 5, F and G). This diminished glycolytic metabolism coupled with a compensatory increase in OxPhos is emblematic of CD8⁺ T cells which lack significant effector function and are metabolically focused on survival. Of note, splenic CD8⁺ T cell metabolism was unchanged between parental and resistant melanomas showing that diminished glycolysis in the resistant-tumor resident CD8⁺ T was a result of localized conditioning by 3I-F4 tumors. Consistent with our earlier observations of resistant tumor hypermetabolism under therapeutic pressure, we observed both enhanced glycolytic potential and OxPhos in treated 3I-F4 melanomas relative to parental (Fig. 5G).

Next, we examined a population enriched for tumor antigen specific CD8⁺ T cells which express the costimulatory molecule 4-1BB(32). CD8⁺ T cells extracted from parental tumors that received combination immunotherapy produced significantly increased levels of the cytokines TNFα and Interleukin (IL)2, and expressed more CD107a, a marker of T cell degranulation. In contrast, 4-1BB⁺ CD8⁺ T cells extracted from treated resistant tumors failed to receive benefit from immunotherapy treatment across CD107a, TNFα, IL2 and IFNγ (Fig. 5H and Supplementary Fig. S4).

We also investigated the effects of tumor hypermetabolism on the frequency and phenotype of tumor-infiltrating Myeloid Derived Suppressor cells (MDSC). While the overall frequency of MDSC did not increase in resistant versus parental tumors (Supplementary Fig. S6A), the expression of the T cell suppressive enzymes IDO and Arginase both significantly increased in 3I-F4 tumor MDSC in response to treatment (Supplementary Fig. S6, B and C). Together, these data suggested that metabolic adaptation of immunotherapy resistant tumors created a hostile TME where antitumor CD8⁺ T cells failed to accumulate or develop enhanced effector capacity in response to checkpoint blockade, whereas the immunosuppressive potential of resident of MDSC increased.

PGAM2 and ADH7 were drivers of enhanced metabolic activity leading to checkpoint blockade resistance

Within the metabolic pathways induced in the resistant 3I-F4 melanoma cells, gene expression analysis revealed certain key nodes which were among the most highly and significantly induced (Fig. 2B–2E). Among these, we investigated Phosphoglycerate mutase 2 (PGAM2), a key enzyme in both glycolysis and the synthesis of nucleotide and amino acid precursors, and Alcohol Dehydrogenase 7 (ADH7), which can decrease oxidative stress (oxidoreductase pathway) by reducing NAD to NADH and can also contribute to retinoic acid synthesis, as a potential contributor to 3I-F4 resistance(33,34). To test the capacity of PGAM2 and ADH7 to confer checkpoint blockade resistance, we retrovirally overexpressed each, or a control vector, in the parental B16 melanoma cell line. Next, we implanted these vector control and PGAM2 or ADH7-overexpressing melanomas and followed tumor growth and survival in the recipient mice with and without immunotherapy. In the absence of treatment, both PGAM2 and ADH7-overexpressing tumors did not show significant differences in tumor growth or survival relative to parental (Fig. 6, A–D and Supplementary Fig. S4). When treated with triple T cell checkpoint blockade, however, PGAM2 and ADH7 overexpressing tumors both conferred significant immunotherapy resistance relative to the vector-transduced parental tumor. Similarly, we found that both Panc02-PGAM2 and Panc02-ADH7 also showed significant resistance to PD-1 blockade and a trend towards resistance to CTLA-4 blockade relative to the vector transduced Panc02 line, further validating the capacity of these genes to confer checkpoint blockade resistance (Supplementary Fig. S7, A–H and Supplementary Fig. S4).

Figure 6: Monogenic assessment of the candidate immunotherapy resistance genes PGAM2 and ADH7.

Mice were implanted with either PGAM2-overexpressing or control cells (empty vector) and (A) survival and (B) tumor growth was monitored with and without treatment. PGAM2 was knocked out in the 3I-F4 cells using a transient CRISPR/Cas9 system and series of PGAM2 guide RNAs (control cells with scrambled guide RNAs (sgScramble)). (C) Survival and (D) tumor growth were monitored in the mice implanted with PGAM2-KO and control tumors with and without treatment. Mice were implanted with either ADH7-overexpressing or control cells (empty vector) and (E) survival and (F) tumor growth were monitored with and without treatment. ADH7 was knocked out in the 3I-F4 cells using a transient CRISPR/Cas9 system and series of ADH7 guide RNAs (control cells with sgScramble). (G) Survival and (H) tumor growth were monitored in the mice implanted with ADH7-KO and control tumors with and without treatment. Statistical significance was calculated using the log-rank (Mantel-Cox) test for 2 independent experiments with 5 mice per group. *P < 0.05, **P < 0.01, ***P < 0.001,****P < 0.0001.

Using CRISPR/Cas9 targeting (Genecopeia) to knockout PGAM2 and ADH7 in resistant 3I-F4 melanoma, in turn, produced tumors that showed increased checkpoint sensitivity (Fig. 6, E–H and Supplementary Fig. S8A). We also attempted to knock out both PGAM2 and ADH7 using CRISPR/CAS9 but were unable to obtain viable double knockout cells. We were able to create an ADH7 knockdown using shRNA in the 3I-F4 PGAM2 knockout cell line; however, these cells showed no greater sensitivity to triple checkpoint blockade than PGAM2 knockout alone (Supplementary Fig. S8, A and B).

Although specific inhibitors of these key genes are not available, we speculated that general inhibition of glycolysis or OxPhos might help restore sensitivity to immunotherapy in 3I-F4. We therefore treated resistant tumors and control parental tumors with 2-Deoxy-D-glucose (2DG), a structural analogue of glucose that inhibits glycolysis, and with a selective lactate dehydrogenase-A (LDHA) inhibitor (GSK2837808A) that both can inhibit glycolytic activity (Supplementary Fig. S9A) (35–37). Unexpectedly, both drugs failed to provide any therapeutic advantage to resistant tumors when given in combination with immunotherapy. Metformin diminishes OxPhos and decreases hypoxia in a manner which can complement immunotherapy(29). In this case, however, neither oral (drinking water) nor injected Metformin was able to sensitize resistant tumors to immunotherapy (Supplementary Fig. S9B).

Induction of similar metabolic pathways in double checkpoint resistant melanoma patients

We sought to validate the role of metabolic adaptation in modulating the response to checkpoint blockade in melanoma patients. We therefore performed gene expression analysis on mRNA samples from a cohort consisting of metastatic melanoma patients who progressed on CTLA-4 blockade and then were treated with αPD-1(13). Patients were biopsied prior to αPD-1 therapy and responses were assessed with serial CT scan after initiation of therapy. In this cohort, there were four patients who responded and five who did not respond to therapy (Fig. 7A). Detailed information on this cohort and the samples profiled is available in the GEO database as record GSE122222. Ingenuity® Pathway Analysis (IPA®) showed that, compared to responders, non- responders enriched similar metabolic pathways to those identified in our resistant mouse models, including elevated oxidative phosphorylation, glycolysis, and buffering of oxidative stress (Fig. 7B). Similarly, GSEA analysis showed enhanced hypoxia gene set induction, higher OxPhos, related alterations associated with enhanced mitochondrial respiration, and similar signatures of CD8⁺ T cell exhaustion (Fig. 7C). Our confidence in this cohort was increased based on finding alterations in PI3K/AKT and mTOR expression, as well as VEGF and IL-8, as major non-metabolic pathways associated with checkpoint resistance, as the significant role of these changes in mediating PD-1 resistance in melanoma patients has previously been described(15,38).

Figure 7: Validation of immunotherapy resistant genetic signature in human melanoma.

(A) Metastatic melanoma patients were treated with anti-CTLA-4 (ipilimumab) and non-responders were biopsied and then subsequently treated with anti-PD-1 (nivolumab or pembrolizumab). Patients were then evaluated for clinical outcome. Gene expression analyses were performed on biopsies from 4 anti-PD-1 responders and 5 non-responders. Significantly enriched metabolic pathways based on either (B) IPA and (C) GSEA analysis are shown. (D) Cutaneous melanoma patients whose information was included in TCGA were stratified in to two groups, high and low lymphocyte infiltration score. Gene Set enrichment analysis was performed and metabolic gene signature pathways (C2 MsigDB|GSEA) and GO (C5 MsigDB|GSEA) found enriched in patients with low compared to high lymphocytic score are presented.

To determine the potential breadth of this observation, we divided the melanoma TCGA cohort into high T cell versus low T cell infiltration fractions based on lymphocyte score (L-Score), which summarizes the lymphocyte distribution and density based on pathological review(39). GSEA analysis shows a similar pathway of enhanced metabolic activity in melanoma tumors with poor immune infiltration (Fig. 7D). Aside from a modest signal in IPA analysis, we did not find prominent induction of gene sets associated with enhanced glycolysis compared to those for OxPhos in either patient cohort. In contrast to our murine system, patient tumor RNA is isolated from whole tumor including the TME. In this setting, enhanced tumor glycolytic activity is likely balanced by reduced glycolysis in the surrounding TME resulting from depletion of critical nutrients. Melanoma tumor cell lines from patients who were resistant versus sensitive to adoptive transfer of ex vivo amplified tumor infiltrating lymphocytes (TIL therapy) showed that enhanced glycolytic metabolism mediates resistance to TIL immunotherapy(40). In an in vitro screen, knockout of PGAM2 was one of two most potent hits in augmenting sensitivity to TIL lysis in patient melanoma cell lines, and in TIL-resistant patient-derived cell lines, PGAM4, an ortholog of mouse PGAM2 was identified as a significant contributor to resistance(40). Overall, these findings suggest that the murine model we generated to study checkpoint immunotherapy resistance provides key insights with direct relevance to human disease.

Discussion

Despite the success of T cell checkpoint blockade across a wide range of human cancers, our understanding of the factors driving both innate and acquired resistance to these therapies remains limited. Using B16 melanoma, we performed an unbiased investigation of acquired resistance to multi-checkpoint blockade immunotherapy. To our knowledge, we are the first to use a system in which evolved changes in gene expression associated with increasing immunotherapy resistance could be analyzed separately in the tumor cells versus compensatory alterations in the surrounding TME. In this case, through augmenting glycolysis, oxidoreductase, and OxPhos, resistant melanoma was able to escape the initially curative immunotherapeutic pressure of cellular vaccination coupled with blockade of CTLA-4, PD-1 and PD-L1.

This immunotherapy-resistant melanoma defied Warburg theory, which states that tumor cells rely primarily on glycolysis for generation of ATP and downregulate mitochondrial oxidative phosphorylation(41). Resistant 3I-F4 tumors increased both glycolysis and OxPhos concordantly, which we define as a hypermetabolic state. Phosphoglycerate mutase 2 (PGAM2), a glycolytic enzyme, was highly upregulated in immunotherapy-resistant tumor cells compared to parental cells. PGAM2 converts 2-phosphoglycerate to 3-phosphoglycerate, which is an essential step in glycolysis as well as contributing to anabolism (biosynthesis) of amino acids and nucleotides(42). Induction of PGAM2 activity may be a result of the oxidative stress response, which was enriched in both our resistant tumor cell line and melanoma patients that do not respond to immunotherapy . Previous reports suggest that reactive oxygen species decrease PGAM2 acetylation at the lysine 100 (K100) site, which promotes its interaction with the cytosolic protein deacetylase sirtuin 2 (SIRT2) and activates PGAM2(42). Whether there are epigenetic, transcriptional, or other post-transcriptional mechanisms involved in engaging metabolic pathways in immunotherapy resistant tumors remains to be studied.

The overactive glycolysis pathway in resistant tumor cells can induce oxidative stress, which may be counterbalanced by upregulation of oxidoreductase pathways. Alcohol dehydrogenase-7 (ADH7), a gene in the oxidoreductase family, is an NAD(P)+/NAD(P)H coupling agent(43). Highly upregulated ADH7 in resistant tumor cells offers several advantages to highly glycolytic, immune-resistant tumors(43,44). It reduces oxidative stress, generates reduced glutathione, a known scavenger of reactive oxygen species, and NAD(P)H, a substrate in mitochondrial OxPhos. We propose that upregulation of these glycolytic nodes and oxidoreductase pathways provides metabolic advantages to tumor cells, allowing them to increase mitochondrial OxPhos and foster a hypoxic microenvironment in which they have adapted to flourish.

Baseline CD8⁺ T cell infiltration of B16 melanoma is poor (23); however, in response to triple- checkpoint blockade therapy, cytotoxic effector density increased substantially. Resistant tumors blunted this capacity of immunotherapy to increase CD8⁺ T cell density, likely due to the increased metabolic hostility of the TME. These hypermetabolic melanoma tumor cells can deplete nutrients in the TME, increase tumor-derived lactate and help promote a state of hypoxia. In this hostile microenvironment, cytotoxic CD8⁺ T cells, which require glycolytic metabolism for peak effector function, lose their metabolic fitness and associated effector function(45–48). We have also seen an increase in the suppressive capacity of Treg and MDSC in resistant tumors, which could also be a consequence of low glucose and the presence of tumor derived lactate as these conditions expand immunosuppressive Treg and MDSC (37,49).

In our resistant tumor line we did not see evidence of substantial increased expression of alternative checkpoint pathways, nor did we see any changes in the IFNγ and JAK1 pathways; however, B16 melanoma, which is poorly immunogenic, may have a baseline insensitivity to Interferons(14,50). We also did not observe downregulation of MHC class I or II complexes on the surface of resistant tumors(14,51,52). In the resistant tumors, we instead found increased class I and II MHC expression at both genetic and protein levels, perhaps reflecting loss of environmental immune pressure. Whereas metabolic adaptation appeared dominant in our system, we cannot deny that other biological processes may contribute to resistance to immunotherapy such as mutational load(13–15), neoantigen load(13), and copy number loss(18,19). Future studies may analyze the role of the mutational landscape in our resistant tumor model, although this was not the focus of the current study.

We therapeutically targeted metabolic adaptation of resistant tumors with inhibitors of glycolysis (2DG and an LDH-A inhibitor) and mitochondrial complex 1 (metformin) but failed to reverse resistance to therapy. As we found that genes representing key nodes in both elevated glycolysis and OxPhos could individually confer resistance when introduced into the parental B16 melanoma, it is not surprising that blocking single pathways with relatively weak inhibitors was incapable of restoring checkpoint sensitivity. Whereas tumor cells rely on glycolysis and mitochondrial OxPhos, both metabolic pathways are equally important to the function of antitumor lymphocytes. Ideal therapeutic targets in this setting will likely be enzymes or pathway elements selectively induced by the tumor, rather than broadly active repressors of glycolytic or OxPhos metabolism which will also cripple T cells

Finally, in a small cohort of melanoma patients who failed to respond to both CTLA-4 and PD-1 blockade, we found upregulation of similar pathways associated with OxPhos and oxidoreductase activity. While we did not find strong signs of enhanced glycolysis in these resistant tumors, Data from T cell therapy-resistant melanoma cell lines suggests that enhanced glycolytic activity generally, including PGAM family upregulation specifically, is associated with acquisition of immunotherapy resistance(40). Given this, it is likely that analyzing whole tumor samples including the TME in our patient cohort caused us to lose glycolytic signal originating from the tumor due to compensatory dampening of glycolysis in the nutrient-starved stroma. Overall, the suggestive data from our patient cohort coupled with the published study of TIL-therapy resistance(40) and the presented TCGA infiltrate data all suggested that the hypermetabolic state we observed to confer checkpoint resistance in our murine melanoma system was also a relevant pathway of immune escape in human melanoma patients.

Synopsis:

Under immune pressure from T cell checkpoint blockade, melanoma evolves a hypermetabolic phenotype conferring complete immunotherapy resistance. Key genes driving enhanced glycolysis and oxidative phosphorylation confer resistance when transferred to the parental melanoma or to a pancreatic cancer.