Immune microenvironment modulation unmasks therapeutic benefit of radiotherapy and checkpoint inhibition
Immune checkpoint inhibitors (ICIs) for solid tumors, including those targeting programmed cell death 1 (PD-1) and cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), have shown impressive clinical efficacy, however, most patients do not achieve durable responses. One major therapeutic obstacle is the immunosuppressive tumor immune microenvironment (TIME). Thus, we hypothesized that a strategy combining tumor-directed radiation with TIME immunomodulation could improve ICI response rates in established solid tumors.
Using a syngeneic mouse model of human papillomavirus (HPV)-associated head and neck cancer, mEER, we developed a maximally effective regimen combining PD-1 and CTLA-4 inhibition, tumor-directed radiation, and two existing immunomodulatory drugs: cyclophosphamide (CTX) and a small-molecule inducible nitric oxide synthase (iNOS) inhibitor, L-n6-(1-iminoethyl)-lysine (L-NIL). We compared the effects of the various combinations of this regimen on tumor growth, overall survival, establishment of immunologic memory, and immunologic changes with flow cytometry and quantitative multiplex immunofluorescence.
We found PD-1 and CTLA-4 blockade, and radiotherapy alone or in combination, incapable of clearing established tumors or reversing the unfavorable balance of effector to suppressor cells in the TIME. However, modulation of the TIME with cyclophosphamide (CTX) and L-NIL in combination with dual checkpoint inhibition and radiation led to rejection of over 70% of established mEER tumors and doubled median survival in the B16 melanoma model. Anti-tumor activity was CD8+ T cell-dependent and led to development of immunologic memory against tumor-associated HPV antigens. Immune profiling revealed that CTX/L-NIL induced remodeling of myeloid cell populations in the TIME and tumor-draining lymph node and drove subsequent activation and intratumoral infiltration of CD8+ effector T cells.
Overall, this study demonstrates that modulation of the immunosuppressive TIME is required to unlock the benefits of ICIs and radiotherapy to induce immunologic rejection of treatment-refractory established solid tumors.
KeywordsImmunotherapy Tumor immune microenvironment Immune checkpoint inhibitors Programmed cell death protein-1 (PD-1) Cytotoxic T lymphocyte associated antigen-4 (CTLA-4) Cyclophosphamide (CTX) L-n6-(1-iminoethyl)-lysine (L-NIL) Radiotherapy Head and neck cancer Human papillomavirus (HPV)
CTX/L-NIL+ αPD-1/αCTLA-4 + radiation combination treatment
Cytotoxic T lymphocyte associated antigen-4
Head and neck squamous cell carcinoma
Immune checkpoint inhibitors
Inducible nitric oxide synthase
Killer cell lectin-like receptor
Myeloid-derived suppressor cells
Major histocompatibility complex
Peripheral blood mononuclear cells
Programmed cell death protein-1
Plasmacytoid dendritic cells
Stereotactic body radiotherapy
Stimulator of interferon genes
Tumor-draining lymph node
Tumor immune microenvironment
Regulatory T cells
t-stochastic neighbor embedding
Solid tumors currently account for over 90% of new cancer cases and cancer-related deaths in the U. S . Alongside conventional treatments such as chemotherapy, radiotherapy, and surgery, immunotherapy has recently emerged as a standard of care treatment for diverse recurrent/metastatic tumors. Among cancer immunotherapies, immune checkpoint inhibitors (ICIs) describe a class of drugs which block proteins that downregulate immune responses. In 2011 the first ICI, a monoclonal antibody targeting cytotoxic T lymphocyte-associated protein 4 (anti-CTLA-4 or αCTLA-4), was approved for use in advanced melanoma and followed in 2014 by another ICI targeting programmed cell death protein 1 (anti-PD-1 or αPD-1) [2, 3, 4, 5]. Both αCTLA-4 and αPD-1 are currently clinically approved or under investigation for use in numerous solid tumor malignancies . Although some patients achieve long-term, seemingly curative, responses to ICI monotherapies, approximately 60–80% of patients do not receive durable benefit from these therapies [7, 8, 9]. In an effort to potentiate the therapeutic efficacy of ICIs various combinatory approaches have been investigated, including dual ICI approaches [10, 11, 12] and combinations with standard-of-care therapies (i.e. chemotherapy and radiation) as well as other immunotherapies [13, 14]. Tumor-directed radiation, in particular, has shown promising combinatorial benefit with ICIs, driven largely by its ability to stimulate tumor cell apoptosis and antigen uptake ; increase the expression of major histocompatibility complex class I (MHCI) on cancer cells ; and promote tumor-specific clonal T cell focusing [13, 17, 18]. However, radiation also promotes substantial lymphodepletion [19, 20] and immunosuppressive effects, including impaired T cell reactivity; diminished antigen presentation; and elevation of circulating immunosuppressive cells [21, 22]. This suggests that additional therapeutic combinations may be required to unmask the maximum benefit of ICIs.
Recent advances in our understanding of the tumor-immune interaction suggest that effective anti-tumor immunity requires a complex and multi-faceted response. This includes: (i) promotion of immunogenic tumor cell death and antigen release, (ii) antigen uptake and effective presentation by antigen presenting cells (APCs), (iii) generation and priming of tumor-specific cytotoxic T cells, (iv) migration and infiltration of those T cells into the tumor environment, and (v) continuous T cell recognition and killing of tumor until clearance [23, 24]. This poses a challenge to current cancer immunotherapies, since most immunomodulators are only capable of stimulating a few of the necessary steps listed above when used as a single agent. An equally daunting challenge is the highly immunosuppressive tumor immune microenvironment (TIME). As a recently recognized hallmark of solid tumor cancers , the TIME is often characterized by the infiltration of various immunosuppressive cell types, most notably myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), and a lack of anti-tumor immune activity (often described as a “cold” tumor) [26, 27]. Numerous studies have demonstrated the profound effects that the TIME can have on treatment response, not just for immunotherapies, but for numerous oncologic modalities [27, 28, 29]. Thus, favorably remodeling the TIME could sensitize tumors to ICI therapy benefit; however, there are currently few clinically available immunomodulatory strategies capable of broadly reprogramming the various myeloid and lymphoid cellular subsets comprising the TIME.
Our group has previously shown that the combination of cyclophosphamide (CTX) and a selective small molecule inducible nitric oxide synthase (iNOS) inhibitor, L-n6-(1-iminoethyl)-lysine (L-NIL), provides potent intratumoral immunomodulatory effects. More specifically, we demonstrated that L-NIL inhibits MDSC development and intratumoral trafficking , and when combined with CTX prevents Treg tumor infiltration . Mitigation of these two immunosuppressive cells using CTX and L-NIL (CTX/L-NIL) ultimately promoted enhanced infiltration of CD8+ T cells and improved survival in a mouse model of melanoma . In an additional murine model of human papillomavirus (HPV) head and neck squamous cell carcinoma (HPV-HNSCC) we observed that CTX/L-NIL promotes even broader immunologic effects, including the upregulation of numerous anti-tumoral immune pathways such as antigen processing and presentation, myeloid trafficking and activation, and T cell function and co-stimulation . We further found that its combination with chemoradiotherapy promoted favorable alterations in both the myeloid and lymphoid intratumoral microenvironment which significantly enhanced the therapeutic benefit of standard-of-care therapy . Thus, we hypothesized that CTX/L-NIL immunomodulation could promote a “cold to hot” transition of the TIME which could enhance treatment responses to ICI and radiation therapies.
To test this hypothesis, we used a syngeneic model of HPV-HNSCC (mEER) developed using murine pharyngeal epithelial cells transduced with HPV16 E6 and E7 viral oncogenes and H-ras [33, 34] with additional validation in models of HPV negative HNSCC and melanoma. We observed that established mEER tumors minimally respond to ICI therapies and suggest this to be due to their inability to overcome the immunosuppressive TIME. When ICIs are combined with radiation, though therapeutic benefit is improved, they remain non-curative and the TIME remains “cold”, with low effector-to-suppressor immune infiltrate. However, when ICIs and radiation are combined with CTX/L-NIL immunomodulation, the combination induces complete regression and clearance of over 70% of established tumors in a CD8+ T cell-dependent manner, accompanied by establishment of potent tumor-antigen specific memory. This dramatic improvement in treatment efficacy is attributed to broadly favorable alterations of the TIME and tumor-draining lymph node (tdLN). More specifically we observed (i) beneficial alterations of tumor-infiltrating myeloid cells at early treatment timepoints, which lead to (ii) enhanced tdLN infiltration of anti-tumor myeloid cells and T cell proliferation, which ultimately drives (iii) enhanced intratumoral T cell infiltration and activation at later treatment timepoints. Overall, these findings implicate the TIME as a major barrier to ICI therapies and demonstrate that its effective modulation can unlock their therapeutic potential in solid tumors.
The primary objectives of this study were (i) to identify tumor features that limit immune checkpoint inhibitors therapeutic benefit in established solid tumors and (ii) to develop combinatory treatment strategies to maximize their efficacy. All experiments were replicated at least two times with an average of 5–10 samples per experiment, and final representation includes either pooled data or representative experiments, as noted in the corresponding figure legends. The number of mice used per experiment was determined using power analysis (α = 5%, β = 20%) and prior knowledge of experimental variability. The precise number of mice used within each experiment is presented in the corresponding figure legend. To limit cage-specific effects, mice were randomized across experimental groups prior to treatment initiation. All measurements were recorded under proper treatment blinding. Outliers from flow cytometry analysis were determined using the ROUT (Q = 1%) method and excluded from the analysis.
C57BL/6 J male mice were purchased from The Jackson Laboratory and housed under specific pathogen-free conditions in standard temperature and lighting conditions with free access to food and water. Tumor inoculation was performed when mice reached 8–10 weeks of age. All experiments were performed with approval of the Institutional Animal Care and Use Committee (IACUC) at Baylor College of Medicine (BCM) and followed established protocols.
mEER cell line expressing HPV16 E6, E7 and hRas was obtained from Dr. John Lee at the Sanford Research center/ University of South Dakota and maintained in E-media as previously described . MOC2 cell line was obtained from Dr. Uppaluri at Brigham and Women’s Hospital/ Harvard Medical School and maintained as previously described [35, 36]. MOC2 E6/E7 cell line expressing HPV16 E6 and E7 was obtained from Dr. Simon Young at UT Health and was maintained similar to the parental MOC2 cell line . B16-F0 cell line was purchased from American Type Culture Collection (ATCC) and maintained according to manufacturer instructions (DMEM high-glucose with 10% fetal bovine serum and 1% penicillin/streptomycin). C57BL/6 J mice were injected subcutaneously (s.c.) with 1 × 106 mEER, 1 × 105 MOC2, 1 × 105 MOC2 E6/E7, or 3 × 105 B16-F0 cells in the flank. Mice were monitored 2–3 times per week for tumor growth using calipers. Tumor area (mm2) was calculated as L x W, where L is Length and W is Width, respectively. Growth curve experiments were stopped once tumors reached 225 mm2.
In vivo treatment
All mice were properly randomized prior to treatment. Once tumors become established (day 17–18 after tumor inoculation for mEER tumors and day 4 for B16-F0 tumors) treatment was initiated. Mice then received combinations of treatment including immune checkpoint inhibitors, tumor directed radiation, and/or CTX/L-NIL immunomodulation. Immune checkpoint inhibitors, InVivoMAb anti-mouse PD-1 (clone RMP1–14; BioXCell; 250 μg per dose) and/or InVivoMAb anti-mouse CTLA-4 (clone 9H10; BioXCell; 100 μg per dose), were administered using intraperitoneal (i.p.) injections for a total of 6 doses. Control mice received combination isotype antibodies to account for non-specific antibody effects; InVivoMAb rat IgG2a (clone 2A3; BioXCell; 250 μg per dose) and/or InVivoMAb Syrian Hamster IgG (polyclonal; BioXCell; 100 μg per dose). Tumor-directed radiation was delivered as a 2 X 10 Gy regimen (each dose delivered weekly). Irradiation was performed on non-anesthetized mice using a RadSource 2000 X-ray irradiator (160 kV, 25 mA) at a dose rate of 0.031 Gy/s. Each mouse was briefly confined in a plastic restrainer and tumor-directed radiation was done using lead shield with an opening that exposed the tumor-bearing flank of the mouse (BrainTree Scientific, Inc.). The immunomodulatory regimen was delivered over 2 weeks and combined a weekly cyclophosphamide (2 mg/mouse; TCI Chemicals) i.p. injection with continuous L-NIL (2 mg/mL; Enzo Life Sciences) in the drinking water (see Fig. 2b for treatment schematic).
For CD8 depletion experiments, all mice receiving the full treatment regimen were injected with 1 mg depleting InVivoMAb anti-mouse CD8α (clone 53–6.7; BioXCell) or InVivoMAb rat IgG2a isotype control (clone 2A3; BioXCell) 2 days prior the treatment, and further treated with 250 μg of depleting antibody weekly for 4 consecutive weeks (see Fig. 6a for treatment schematic).
Gene expression analysis
Tumor samples were harvested and flash frozen in liquid nitrogen. Total RNA was extracted with the RNeasy Mini Kit (Qiagen) as per the manufacturer’s instructions. Gene expression profiling was performed on 100 ng RNA using the nCounter® PanCancer Immune Profiling Panel (NanoString Technologies, Inc) containing 770 genes involved in cancer immune response. Gene expression profiling was performed using the NanoString nCounter® Gene Expression system. The process including the following steps: (i) Hybridization protocol: 100 ng of total RNA were hybridized with the NanoString Technologies nCounter® Gene Expression Mouse PanCancer Immune Profiling code set containing 770 unique pairs of 35-50 bp reporter probes and biotin-labeled capture probes, including internal reference controls. Overnight hybridization occurred for 17–22 h at 65 °C. (ii) Wash protocol: Removal of excess probes with magnetic bead purification was performed on the nCounter® Prep Station (software v188.8.131.52) on the High Sensitivity assay. Briefly, the probe-mRNA structure was affinity purified by its 3′ end to remove excess reporter probes, then by its 5′ end to remove excess capture probes. Once unbound probes were washed away, the tripartite structure was bound to the streptavidin-coated cartridge by the biotin capture probe, aligned by an electric current (negative to positive), and immobilized. Photobleaching and fluorophore degradation was prevented with the addition of SlowFade. (iii) Scan protocol: The cartridge containing immobilized samples was transferred to the nCounter® Digital Analyzer (software v184.108.40.206) and scanned at 555 field of view (FOV). An epi-fluorescent microscope and CCD camera identified sets of fluorescent spots, which were tabulated for data output. Quality control metrics were recorded using the nSolver Analysis Software v3.0.22. Raw read counts were normalized, background subtracted, and assessed for cell type score and differential gene expression using NanoString nSolver (version 3.0) following the manufacture instruction.
Flow cytometry assessment of immune microenvironment
To observe tumor immune cell infiltration, mEER tumors were harvested, digested and stained using the method previously describe . Briefly, tumors were digested in RPMI 1640 (Sigma-Aldrich) containing DNase I (20 U/ml; Sigma-Aldrich), Collagenase I (1 mg/ml; EMD Millipore) and Collagenase IV (250 U/ml; Worthington Biochemical Corporation) prior to mechanical disaggregation to form single cell suspensions. Following digestion, tumor infiltrating leukocytes were enriched using Lymphoprep™ (STEMCELL Technologies). Single cell suspensions were also prepared from tumor-draining inguinal lymph node and spleen with additional lysis of splenic red blood cells (RBC) using RBC lysis buffer (Invitrogen). For extracellular staining, all cells were first blocked with anti-mouse CD16/CD32 Fc block (BD Biosciences) and separately stained using one of various antibody panels (see Additional file 14: Table S1 for antibody panels). E7 MHCI tetramer with conjugated BV421 was used for E7-specific CD8+ T cell staining (NIH Tetramer Core Facility). For intracellular staining, cells were fixed and permeabilized with Intracellular Fixation and Permeabilization Buffer Set (eBioscience) prior to the addition of intracellular staining antibody sets. Data were acquired on a LSRII and LSRFortessa (BD Biosciences) flow cytometers, for myeloid and T cell panels respectively, and analyzed using FlowJo v10 software (FlowJo, LLC). Cellular or cellular phenotype percentage changes were often converted to Z-scores by taking the entire dataset average and standard deviation and then calculating how many dataset standard deviations a given sample was away from that population average. In some cases all single sample Z-scores for a given treatment were averaged together to give an average treatment Z-score.
Quantitative multiplex immunofluorescence
Sectioning and staining
After harvesting, tumors were immediately fixed overnight in 10% neutral-buffered formalin. Fixed tumors were dehydrated using an ethanol series, embedded in paraffin, and sections were cut at a thickness of 5 μm. Full-section slides of tumor tissues were stained using Opal multiplex 6-plex kits, according to the manufacturer’s protocol (Akoya), for DAPI, Epcam (polyclonal; Abcam, 1:100 dilution), CD3 (clone SP7; Spring Biosciences; 1:100 dilution), CD8 (clone 4SM15; Thermo Fisher; 1:500), CD4 (clone 4SM95; eBioscience, 1:50), Foxp3 (polyclonal; Thermo Fisher, 1:500), and Granzyme B (polyclonal; Abcam, 1:200). Single color controls and an unstained slide were also included for proper spectral un-mixing.
Multispectral image capture was done at 20X magnification using Vectra (Akoya). Images were analyzed using inForm software version 2.4.1 (Akoya) as previously described . Briefly, five representative areas were randomly selected. These images were factored equally into the analysis for each mouse. For spectral un-mixing, examples of each fluorophore are taken from single-stained slides for each antibody, as well as a representative autofluorescence spectrum from an unstained sample.
Images from each of these single-stained and unstained slides were used to create a multispectral library in inForm and extracted from the multispectral data using linear un-mixing. Cellular and subcellular compartments were defined by a counterstain (DAPI) to define the nucleus of each cell. Cell segmentation was adjusted based on minimum DAPI signal to accurately locate all cells and minimize hyper- and hypo-segmentation below 5% of total cells (assessed manually). Cells were then characterized using the phenotyping feature in inForm. Approximately 25–30 representative cells for each base variable were selected to train the phenotyping algorithm: tumor (EpCAM), T cells (CD3), and other (negative for EpCAM and CD3). Last the images were scored for intensity based on each individual secondary marker for further phenotyping of CD4, CD8, FoxP3, and Granzyme B. Finally, data obtained from all representative images were compiled to yield values for each mouse. Exported inForm data from all images were processed in separate software designed in RStudio (version 0.99.896). In this software, images were combined and analyzed to concatenate variables (i.e., CD3+CD8+Granzyme B+) and determine density and distance of distinct phenotypes. Densities were all calculated as counts per total nucleated cells.
Data sets were tested for Gaussian distribution using the D’Agostino-Pearson normality test. For parametric data sets, statistical significance was determined by: unpaired t test for two-tailed data or ANOVA test followed by selected comparison using Tukey’s multiple comparison tests with multiple comparison correction. For non-parametric data sets, statistical significance was determined by: Mann-Whitney test for two tailed data and Kruskal-Wallis test followed by selected comparison by Dunn’s multiple comparison tests with multiple comparison correction. Survival was analyzed by the Kaplan– Meier method using Log-rank test. (*p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, non-significant). Outliers from flow cytometry analysis were determined using ROUT (Q = 1%) method.
Immune checkpoint inhibitors alone and in combination weakly inhibit mEER tumor growth
The combination of ICIs with radiation fails to reverse the “cold” tumor immune microenvironment
Radiation has been previously shown to stimulate a variety of immunologic effects that can improve ICI responses [13, 15, 16, 17]. Thus, we combined αPD-1 and αCTLA-4 (denoted as αPD-1/αCTLA-4) with tumor-directed radiation as a method to potentiate its therapeutic benefit. Mice bearing similarly established mEER tumors were treated with tumor-directed radiation delivered as 2 weekly 10 Gy fractions with concurrent αPD-1/αCTLA-4 treatment (see Fig. 1c for treatment schematic). Assessment of tumor growth and survival showed a significant treatment improvement in mice receiving αPD-1/αCTLA-4 and radiation compared to mice receiving ICIs alone (Fig. 1d and e; for individual tumor growth curves see Additional file 2: Figure S2A and B). Despite this improvement, the combinatory regimen remained incapable of promoting complete tumor regressions in this established tumor model. To better understand this limitation, we used flow cytometry at day 23 of treatment (5 days post-radiation) to characterize changes in the lymphoid and myeloid TIME. At this timepoint tumor sizes are similar between all treatment groups, thereby minimizing immunologic effects influenced by tumor size and allowing better comparison of treatment-related effects. As previously discussed, the TIME of αPD-1/αCTLA-4 treated tumors is very similar to that of untreated control tumors (Fig. 1f). The addition of radiation to αPD-1/αCTLA-4, appears to promote both lymphodepleting and general inflammatory effects as indicated by the modest decrease in various T cell subsets and a 1.8-fold increase in monocytic myeloid cells (Fig. 1g). Overall, these data suggest that even in the context of αPD-1/αCTLA-4 and tumor-directed radiation, the TIME remains relatively “cold”, with limited anti-tumoral immune cell infiltration and high levels of various immunosuppressive cell subsets such as granulocytic myeloid-derived suppressor cells (G-MDSC) and Tregs.
CTX/L-NIL immunomodulation renders tumors responsive to the combination of αPD-1/αCTLA-4 and radiation (CPR)
CPR combination therapy promotes tumor antigen specific immunologic memory
Development of tumor-specific immunologic memory capable of long-term immune surveillance is a major theoretical benefit of cancer immunotherapies, and numerous reports suggest that ICIs can enhance this effect [46, 47]. Thus, we investigated whether the CPR regimen promoted development of tumor-specific memory. First, we assessed this using a tumor rechallenge approach, in which CPR treated mice were re-injected approximately 100 days after initial tumor clearance with 5-fold the original tumor inoculum in the opposing flank. We observed that 70% of mice that rejected the initial tumor challenge were capable of fully clearing the secondary tumor rechallenge, suggesting the development of tumor-specific immunologic memory (Fig. 2e). To further assess the antigen specificity of the immune memory response we utilized a dual flank rechallenge model using a HPV-negative HNSCC tumor model, MOC2, made with or without exogenous expression of E6 and E7 HPV antigens [35, 36, 37]. In CPR treated mice 100 days post-clearance, we re-challenged with parental MOC2 tumors on the initial tumor-bearing flank and MOC2 tumors transfected with E6 and E7 HPV viral oncoproteins (MOC2-E6/E7) on the opposing flank. We observed 90% complete clearance of the MOC2-E6/E7 tumors and minor, though significant, delays in the growth of MOC2 tumors lacking HPV antigen compared to age-matched control mice (Fig. 2f). These data suggest that the CPR regimen stimulated the development of potent immunologic memory to the original mEER tumor, including strong reactivity to E6 and E7 HPV viral antigens.
CPR combination therapy promotes favorable changes in TIME and lymph node myeloid populations
CPR combination therapy improves CD8+ T cell infiltration and activation
CPR combination therapy stimulates proliferation, tumor infiltration, and activation of CD8+ T cells
Due to the favorable lymphoproliferation effects within the lymph node, we next performed lymphocyte subset analysis within tumors over the same treatment time-course. Early (day 23) time-point analysis of CPR treated tumors showed a largely myeloid-dominated tumor, with small and approximately equal fractions of CD8+, CD4+, and Tregs (Fig. 5c). However, at the intermediate (day 33) and late (day 37) timepoints, we observed more than a 13-fold increase in the percentage of CD8+ T cells and a greater than 2-fold increase in CD4+ T cells infiltrating CPR treated tumors compared to tumor size-matched control mice (Fig. 5d). We additionally observed consistently low levels of tumor infiltrating immunosuppressive Tregs over the full course of treatment, which contributed to the 15-fold improvement in the CD8+ T cell to Treg ratio (Fig. 5e and Additional file 8: Figure S8A and C). Based on a consensus nomenclature for CD8+ T cell phenotypes , phenotyping of tumor infiltrating CD8+ T cells at each day of CPR treatment revealed a strongly proliferating (i.e. Ki67+) CD8+ T cell subset expressing numerous molecules associated with both effector (i.e. Perforin, killer cell lectin-like receptor-KLRG1) and memory (i.e. Eomes, low PD-1) T cell status (Fig. 5f) . Towards the end (day 37) of treatment CD8+ T cells appear to have entered a late stage of tumor killing due to the loss of numerous effector markers including Eomes and perforin (Fig. 5f; see Additional file 10: Figure S10 for CD8+ T cell phenotypes for all groups and tissues) , and further supported by the fact that tumors rapidly regress and clear between days 37 to 50 (see Fig. 2c and e). In addition, we observed elevations in E7 specific CD8+ T cells by E7 tetramer staining both in the tumor and tdLN of CPR treated mice at days 33 and 37 of treatment (Additional file 7: Figure S7B and Additional file 9: Figure S9B) but only observed minor levels at day 23 for any groups (Additional file 6: Figure S6B and Additional file 8: Figure S8B; for representative tetramer staining see Additional file 6: Figure S6E, Additional file 7: Figure S7E, Additional file 8: Figure S8E, and Additional file 9: Figure S9E). Overall, these observations suggest the CPR regimen is capable of activating the lymphoid TIME at least partially by driving strong T cell proliferation in both the tumor and tdLN; limiting intratumoral infiltration and development of Tregs; and enhancing the activation status and specificity of tumor-infiltrating CD8+ T cells.
CD8+ T cells are necessary for tumor clearance after CPR combination treatment
Our previous and current evidence of the immunomodulatory potential of CTX [31, 32] are consistent with a wide-body of literature describing its immune stimulating effects . Examples include its ability to decrease Treg levels , increase cytotoxic T cell activity [59, 60, 61], and enhance APC activation . As a result, CTX has been proposed as a potential combination with ICIs; however, few studies have directly tested this approach in preclinical models [14, 63, 64]. To further drive beneficial immunomodulation, we combined CTX with selective iNOS inhibition using L-NIL . iNOS has been implicated in a variety of immunosuppressive and therapeutic resistance mechanisms in solid tumors [65, 66] and as a critical mediator in the recruitment and suppressive function of G-MDSC . Results from this study further suggest that CTX and L-NIL in combination not only function as a potent immunomodulator to target immunosuppressive cells types commonly associated with solid tumors (i.e. Tregs and G-MDSC), but also stimulate the generation, infiltration, and activation of both myeloid and lymphoid anti-tumor immune subsets. Additionally, these results further validate the importance of combination therapies targeting both myeloid and lymphoid tumor compartments, as both of these compartments are now well known to play a role in ICI efficacy . This has been further suggested by various reports which show enhanced ICI efficacy when combined with myeloid-focused therapies including combinations of GM-CSF activated DCs loaded with tumor-antigen [44, 68] stimulator of interferon genes (STING) vaccine strategies , and indolamine 2,3 dioxygenase inhibitors [29, 70].
Due to the expansive number ICI clinical trials in combination with tumor-directed radiation and/or chemotherapeutics, the translational implications of our findings are significant . Our findings not only demonstrate potential readouts of tumor ICI reactivity but also provide recommendations for treatment schedule design. Nevertheless, further investigation is required prior to clinical implementation of the full CPR regimen. The first is a better understanding of the dosing and schedule for CTX, which has been shown to strongly influence its immunologic effects [72, 73]; however, despite numerous prospective and retrospective clinical reports, the optimal CTX dosing schedule for immunomodulatory benefit remains unclear . Similar to CTX, the optimal radiation schedule remains similarly uncertain and requires further investigation. During these studies, we investigated alterative radiation schedules within the CPR regimen and observed that hypo-fractionated “stereotactic body radiotherapy (SBRT)-like” schedules (higher dose with fewer fractions) provide optimal treatment benefit in the mEER tumor model compared to more fractionated regimens; however, further assessment in B16 melanoma models generated opposing results (data not shown). This reflects the existing literature, in which conflicting reports claim enhanced immunomodulatory benefit using different radiation dosing strategies [74, 75, 76]. A final translational hurdle relates to the iNOS inhibitor used in this study, L-NIL. Although L-NIL has been assessed in clinical trials for inflammatory diseases , it requires additional study before utilization as a cancer therapeutic. Nevertheless, iNOS inhibition has been demonstrated using other clinically available drugs such as phosphodiesterase 5 inhibitors (i.e. sildenafil and tadalafil) and doxycycline [78, 79, 80], which may provide an alternative for faster translation to clinical trials.
Overall, our results provide a broad immunologic investigation of the factors in the TIME which limit response to ICIs and radiotherapy, and demonstrate that their reversal with the CPR regimen greatly enhances treatment efficacy. One potential limitation of our study is the absence of more thorough cellular characterization using additional markers of activation status and cellular sub-types. For example, B cells are known to be present at elevated levels in tdLN of tumor bearing mice, yet their role as pro- or anti-tumor remains unclear [81, 82]. Upon treatment, we did observe significant B cell depletion, which may have contributed to the treatment efficacy, however, we were unable to determine whether this plays a role in treatment responsiveness (data not shown). Additionally, there exist numerous sub-classes of the various cell types we detail in this study. For example, among DCs, plasmacytoid DCs (pDC) are potent inducers of Th1 immune responses [83, 84] and our prior gene-expression analysis suggested that pDCs may be upregulated following CTX/L-NIL immunomodulation . Thus, future studies will be necessary to more thoroughly characterize the full immune landscape of this immunomodulatory treatment combination.
A final limitation of our study exists in the lack of analysis in primary patient samples, which remains challenging due to current capabilities of ex vivo model systems. Despite some recently published methods which can provide a more accurate representation of the tumor microenvironment , a major advantage compared to most ex vivo systems, these platform remain incapable of recapitulating the systemic consequences of radiation . In addition, these systems fail to recapitulate the tumor-tdLN interaction which we found to be a critical feature of the CPR regimen. Therefore, future work will focus on alternative methods to assess the translatability of the CPR regimen in primary patients samples and data.
In conclusion, multi-component remodeling of the TIME has the potential to significantly expand the fraction of patients responding to ICI and radiation therapies. We believe that the clinical relevance of these findings and the therapeutic interventions used could potentially be applied to diverse solid tumor malignancies where the immunosuppressive TIME impedes effective anti-tumor immunologic responses.
The authors thank Dr. Ravindra Uppaluri for providing the MOC2 tumor cell line. We further acknowledge the following core facilities: Cytometry and Cell Sorting Core facility at Baylor College of Medicine with funding from NIH (P30 A1036211, P30 CA125123, and S10 RR024 574) and the expert assistance of Joel M. Sederstrom, Genomic and RNA Profiling Core Facility at Baylor College of Medicine with funding from P30 Digestive Disease Center Support Grant (NIDDK-DK56338) and P30 Cancer Center Support Grant (NCI-CA125123) and the expert assistance of Mylinh Bernardi, Pathology and Histology core at Baylor College of Medicine, Flow Cytometry and Cellular Imaging Core Facility (FCCICF) at MD Anderson partially funded by NCI Cancer Center Support Grant P30CA16672 and the expert assistance of Jared K. Burks, Ph.D. We acknowledge Columbia University Irving Medical Center’s Human Immune Monitoring Core (HIMC) for quantitative multiplex immunofluorescence and Vectra imaging. We acknowledge the NIH Tetramer Core Facility for providing the E7 tetramer used in these studies.
JMN, AH, and AGS designed the study, JMN, AH, H-CL, AG, and FP performed the immunological experiments, WCS provided mEER tumor cell line. ND and SY provided the MOC2 E6/E7 cell line. DL and JAvG contributed myeloid expertise. JMN and AH conducted gene expression analysis. JMN, RDG, TDH, ND, SY, and YMS contributed to the multiplex analyses. JMN and AH analyzed data, including statistical analysis, and with AGS interpreted the data. JMN wrote the manuscript and AH and AGS contributed to the manuscript corrections. All authors read and approved the final manuscript.
JMN acknowledges financial support from the National Institute of General Medical Sciences T32 predoctoral training grant (T32GM088129) and the National Institute of Dental & Craniofacial Research F31 NRSA training grant (F31DE026682) both of the National Institutes of Health. RDG-C acknowledges support from Swim Across America and the National Center for Advancing Translational Sciences of the National Institutes of Health (KL2TR001874). JAvG and DL acknowledge support from Kom op tegen Kanker (Stand Up against Cancer) and FWO (Science Foundation Flanders). JAvG also acknowledges support from Foundation against Cancer. SY acknowledges support from the National Institutes of Health (NIDCR R00 grant DE023577). AGS. acknowledges support from the Caroline Weiss Law Endowment for Academic Excellence; the Owens Foundation; and grants from the Cancer Research Institute (Team Strategy Grant), and the National Institutes of Health (NCI/NIDCR 1U01DE028233–01). This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Ethics approval and consent to participate
All mice were housed and treated in accordance with Institutional Animal Care and Use Committee guidelines at Baylor College of Medicine.
Consent for publication
RDG-C has received travel support from PerkinElmer and has consulted on an advisory board for Northwest Biotherapeutics. AGS receives support in the form of investigational drug from Advaxis for an investigator-initiated clinical trial. AGS receives funds for clinical trial development from Tessa Therapeutics in the form of grant support.
- 2.Hodi FS, Mihm MC, Soiffer RJ, Haluska FG, Butler M, Seiden MV, Davis T, Henry-Spires R, MacRae S, Willman A, Padera R, Jaklitsch MT, Shankar S, Chen TC, Korman A, Allison JP, Dranoff G. Biologic activity of cytotoxic T lymphocyte-associated antigen 4 antibody blockade in previously vaccinated metastatic melanoma and ovarian carcinoma patients. Proc Natl Acad Sci. 2003;100:4712–7.PubMedCrossRefGoogle Scholar
- 3.Hodi FS, O’Day SJ, McDermott DF, Weber RW, Sosman JA, Haanen JB, Gonzalez R, Robert C, Schadendorf D, Hassel JC, Akerley W, van den Eertwegh AJM, Lutzky J, Lorigan P, Vaubel JM, Linette GP, Hogg D, Ottensmeier CH, Lebbé C, Peschel C, Quirt I, Clark JI, Wolchok JD, Weber JS, Tian J, Yellin MJ, Nichol GM, Hoos A, Urba WJ. Improved survival with Ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010;363:711–23.PubMedPubMedCentralCrossRefGoogle Scholar
- 4.Brahmer JR, Drake CG, Wollner I, Powderly JD, Picus J, Sharfman WH, Stankevich E, Pons A, Salay TM, McMiller TL, Gilson MM, Wang C, Selby M, Taube JM, Anders R, Chen L, Korman AJ, Pardoll DM, Lowy I, Topalian SL. Phase I study of single-agent anti–programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol. 2010;28:3167–75.PubMedPubMedCentralCrossRefGoogle Scholar
- 8.Schadendorf D, Hodi FS, Robert C, Weber JS, Margolin K, Hamid O, Patt D, Chen T-T, Berman DM, Wolchok JD. Pooled analysis of long-term survival data from phase II and phase III trials of Ipilimumab in unresectable or metastatic melanoma. J Clin Oncol. 2015;33:1889–94.PubMedPubMedCentralCrossRefGoogle Scholar
- 9.Topalian SL, Sznol M, McDermott DF, Kluger HM, Carvajal RD, Sharfman WH, Brahmer JR, Lawrence DP, Atkins MB, Powderly JD, Leming PD, Lipson EJ, Puzanov I, Smith DC, Taube JM, Wigginton JM, Kollia GD, Gupta A, Pardoll DM, Sosman JA, Hodi FS. Survival, durable tumor remission, and long-term safety in patients with advanced melanoma receiving Nivolumab. J Clin Oncol. 2014;32:1020–30.PubMedPubMedCentralCrossRefGoogle Scholar
- 10.Wolchok JD, Kluger H, Callahan MK, Postow MA, Rizvi NA, Lesokhin AM, Segal NH, Ariyan CE, Gordon R-A, Reed K, Burke MM, Caldwell A, Kronenberg SA, Agunwamba BU, Zhang X, Lowy I, Inzunza HD, Feely W, Horak CE, Hong Q, Korman AJ, Wigginton JM, Gupta A, Sznol M. Nivolumab plus Ipilimumab in advanced melanoma. N Engl J Med. 2013;369:122–33.PubMedPubMedCentralCrossRefGoogle Scholar
- 13.Twyman-Saint Victor C, Rech AJ, Maity A, Rengan R, Pauken KE, Stelekati E, Benci JL, Xu B, Dada H, Odorizzi PM, Herati RS, Mansfield KD, Patsch D, Amaravadi RK, Schuchter LM, Ishwaran H, Mick R, Pryma DA, Xu X, Feldman MD, Gangadhar TC, Hahn SM, Wherry EJ, Vonderheide RH, Minn AJ. Radiation and dual checkpoint blockade activate non-redundant immune mechanisms in cancer. Nature. 2015;520:373–7.CrossRefGoogle Scholar
- 15.Apetoh L, Ghiringhelli F, Tesniere A, Obeid M, Ortiz C, Criollo A, Mignot G, Maiuri MC, Ullrich E, Saulnier P, Yang H, Amigorena S, Ryffel B, Barrat FJ, Saftig P, Levi F, Lidereau R, Nogues C, Mira J-P, Chompret A, Joulin V, Clavel-Chapelon F, Bourhis J, André F, Delaloge S, Tursz T, Kroemer G, Zitvogel L. Toll-like receptor 4–dependent contribution of the immune system to anticancer chemotherapy and radiotherapy. Nat Med. 2007;13:1050–9.PubMedCrossRefGoogle Scholar
- 16.Reits EA, Hodge JW, Herberts CA, Groothuis TA, Chakraborty M, Wansley EK, Camphausen K, Luiten RM, de Ru AH, Neijssen J, Griekspoor A, Mesman E, Verreck FA, Spits H, Schlom J, van Veelen P, Neefjes JJ. Radiation modulates the peptide repertoire, enhances MHC class I expression, and induces successful antitumor immunotherapy. J Exp Med. 2006;203:1259–71.PubMedPubMedCentralCrossRefGoogle Scholar
- 17.Marciscano AE, Ghasemzadeh A, Nirschl TR, Theodros D, Kochel CM, Francica BJ, Muroyama Y, Anders RA, Sharabi AB, Velarde E, Mao W, Chaudhary KR, Chaimowitz MG, Wong J, Selby MJ, Thudium KB, Korman AJ, Ulmert D, Thorek DLJ, DeWeese TL, Drake CG. Elective nodal irradiation attenuates the combinatorial efficacy of stereotactic radiation therapy and immunotherapy. Clin Cancer Res. 2018;24:5058–72.PubMedGoogle Scholar
- 20.Crocenzi T, Cottam B, Newell P, Wolf RF, Hansen PD, Hammill C, Solhjem MC, Y.-Y. To, Greathouse A, Tormoen G, Jutric Z, Young K, Bahjat KS, Gough MJ, Crittenden MR. A hypofractionated radiation regimen avoids the lymphopenia associated with neoadjuvant chemoradiation therapy of borderline resectable and locally advanced pancreatic adenocarcinoma. J Immunother Cancer. 2016;4:45.PubMedPubMedCentralCrossRefGoogle Scholar
- 21.van Meir H, Nout RA, Welters MJP, Loof NM, de Kam ML, van Ham JJ, Samuels S, Kenter GG, Cohen AF, Melief CJM, Burggraaf J, van Poelgeest MIE, van der Burg SH. Impact of (chemo) radiotherapy on immune cell composition and function in cervical cancer patients. Oncoimmunology. 2017;6:e1267095.PubMedCrossRefGoogle Scholar
- 29.Spranger S, Koblish HK, Horton B, Scherle PA, Newton R, Gajewski TF. Mechanism of tumor rejection with doublets of CTLA-4, PD-1/PD-L1, or IDO blockade involves restored IL-2 production and proliferation of CD8+ T cells directly within the tumor microenvironment. J Immunother Cancer. 2014;2:3.PubMedPubMedCentralCrossRefGoogle Scholar
- 30.Jayaraman P, Parikh F, Lopez-Rivera E, Hailemichael Y, Clark A, Ma G, Cannan D, Ramacher M, Kato M, Overwijk WW, Chen S-H, Umansky VY, Sikora AG. Tumor-expressed inducible nitric oxide synthase controls induction of functional myeloid-derived suppressor cells through modulation of vascular endothelial growth factor release. J Immunol. 2012;188:5365–76.PubMedPubMedCentralCrossRefGoogle Scholar
- 31.Jayaraman P, Alfarano MG, Svider PF, Parikh F, Lu G, Kidwai S, Xiong H, Sikora AG. iNOS expression in CD4+ T cells limits Treg induction by repressing TGF 1: combined iNOS inhibition and Treg depletion unmask endogenous antitumor immunity. Clin Cancer Res. 2014;20:6439–51.PubMedCrossRefGoogle Scholar
- 32.Hanoteau A, Newton JM, Krupar R, Huang C, Liu H-C, Gaspero A, Gartrell RD, Saenger YM, Hart TD, Santegoets SJ, Laoui D, Spanos C, Parikh F, Jayaraman P, Zhang B, Van der Burg SH, Van Ginderachter JA, Melief CJM, Sikora AG. Tumor microenvironment modulation enhances immunologic benefit of chemoradiotherapy. J Immunother Cancer. 2019;7:10.PubMedPubMedCentralCrossRefGoogle Scholar
- 34.Vermeer DW, Coppock JD, Zeng E, Lee KM, Spanos WC, Onken MD, Uppaluri R, Lee JH, Vermeer PD. Metastatic model of HPV+ oropharyngeal squamous cell carcinoma demonstrates heterogeneity in tumor metastasis. Oncotarget. 2016;7. https://doi.org/10.18632/oncotarget.8254.
- 37.Dharmaraj N, Piotrowski SL, Huang C, Newton JM, Golfman LS, Hanoteau A, Koshy ST, Li AW, Pulikkathara MX, Zhang B, Burks JK, Mooney DJ, Lei YL, Sikora AG, Young S. Anti-tumor immunity induced by ectopic expression of viral antigens is transient and limited by immune escape. Oncoimmunology. 2019;8(4):e1568809.PubMedCrossRefGoogle Scholar
- 38.Newton JM, Hanoteau A, Sikora AG. Enrichment and characterization of the tumor immune and non-immune microenvironments in established subcutaneous murine tumors. J Vis Exp. 2018. https://doi.org/10.3791/57685.
- 39.Gartrell RD, Marks DK, Hart TD, Li G, Davari DR, Wu A, Blake Z, Lu Y, Askin KN, Monod A, Esancy CL, Stack EC, Jia DT, Armenta PM, Fu Y, Izaki D, Taback B, Rabadan R, Kaufman HL, Drake CG, Horst BA, Saenger YM. Quantitative analysis of immune infiltrates in primary melanoma. Cancer Immunol Res. 2018;6:481–93.PubMedPubMedCentralCrossRefGoogle Scholar
- 40.Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Powderly JD, Carvajal RD, Sosman JA, Atkins MB, Leming PD, Spigel DR, Antonia SJ, Horn L, Drake CG, Pardoll DM, Chen L, Sharfman WH, Anders RA, Taube JM, McMiller TL, Xu H, Korman AJ, Jure-Kunkel M, Agrawal S, McDonald D, Kollia GD, Gupta A, Wigginton JM, Sznol M. Safety, activity, and immune correlates of anti–PD-1 antibody in cancer. N Engl J Med. 2012;366:2443–54.PubMedPubMedCentralCrossRefGoogle Scholar
- 44.van Elsas A, Hurwitz AA, Allison JP. Combination immunotherapy of B16 melanoma using anti-cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and granulocyte/macrophage colony-stimulating factor (GM-CSF)-producing vaccines induces rejection of subcutaneous and metastatic tumors accompanied. J Exp Med. 1999;190:355–66.PubMedPubMedCentralCrossRefGoogle Scholar
- 46.Gubin MM, Zhang X, Schuster H, Caron E, Ward JP, Noguchi T, Ivanova Y, Hundal J, Arthur CD, Krebber W-J, Mulder GE, Toebes M, Vesely MD, Lam SSK, Korman AJ, Allison JP, Freeman GJ, Sharpe AH, Pearce EL, Schumacher TN, Aebersold R, Rammensee H-G, Melief CJM, Mardis ER, Gillanders WE, Artyomov MN, Schreiber RD. Checkpoint blockade cancer immunotherapy targets tumour-specific mutant antigens. Nature. 2014;515:577–81.PubMedPubMedCentralCrossRefGoogle Scholar
- 47.Mok S, Duffy CR, Allison JP. Abstract 2984: effects of anti-CTLA-4 and anti-PD-1 on memory T-cell differentiation and resistance to tumor relapse. Cancer Res. 2018;78:2984.Google Scholar
- 48.Frey B, Rückert M, Weber J, Mayr X, Derer A, Lotter M, Bert C, Rödel F, Fietkau R, Gaipl US. Hypofractionated irradiation has immune stimulatory potential and induces a timely restricted infiltration of immune cells in colon cancer tumors. Front Immunol. 2017;8:231.PubMedPubMedCentralCrossRefGoogle Scholar
- 50.Apetoh L, Smyth MJ, Drake CG, Abastado J-P, Apte RN, Ayyoub M, Blay J-Y, Bonneville M, Butterfield LH, Caignard A, Castelli C, Cavallo F, Celis E, Chen L, Colombo MP, Comin-Anduix B, Coukos G, Dhodapkar MV, Dranoff G, Frazer IH, Fridman W-H, Gabrilovich DI, Gilboa E, Gnjatic S, Jäger D, Kalinski P, Kaufman HL, Kiessling R, Kirkwood J, Knuth A, Liblau R, Lotze MT, Lugli E, Marincola F, Melero I, Melief CJ, Mempel TR, Mittendorf EA, Odun K, Overwijk WW, Palucka AK, Parmiani G, Ribas A, Romero P, Schreiber RD, Schuler G, Srivastava PK, Tartour E, Valmori D, van der Burg SH, van der Bruggen P, van den Eynde BJ, Wang E, Zou W, Whiteside TL, Speiser DE, Pardoll DM, Restifo NP, Anderson AC. Consensus nomenclature for CD8 + T cell phenotypes in cancer. Oncoimmunology. 2015;4:e998538.PubMedPubMedCentralCrossRefGoogle Scholar
- 51.Herndler-Brandstetter D, Ishigame H, Shinnakasu R, Plajer V, Stecher C, Zhao J, Lietzenmayer M, Kroehling L, Takumi A, Kometani K, Inoue T, Kluger Y, Kaech SM, Kurosaki T, Okada T, Flavell RA. KLRG1 + effector CD8 + T cells lose KLRG1, differentiate into all memory T cell lineages, and convey enhanced protective immunity. Immunity. 2018;48:716–729.e8.PubMedPubMedCentralCrossRefGoogle Scholar
- 55.Ozao-Choy J, Ma G, Kao J, Wang GX, Meseck M, Sung M, Schwartz M, Divino CM, Pan P-Y, Chen S-H. The novel role of tyrosine kinase inhibitor in the reversal of immune suppression and modulation of tumor microenvironment for immune-based cancer therapies. Cancer Res. 2009;69:2514–22.PubMedPubMedCentralCrossRefGoogle Scholar
- 59.Rahir G, Wathelet N, Hanoteau A, Henin C, Oldenhove G, Galuppo A, Lanaya H, Colau D, Mackay CR, Van den Eynde B, Moser M. Cyclophosphamide treatment induces rejection of established P815 mastocytoma by enhancing CD4 priming and intratumoral infiltration of P1E/H-2K d -specific CD8 + T cells. Int J Cancer. 2014;134:2841–52.PubMedCrossRefGoogle Scholar
- 61.Scurr M, Pembroke T, Bloom A, Roberts D, Thomson A, Smart K, Bridgeman H, Adams R, Brewster A, Jones R, Gwynne S, Blount D, Harrop R, Hills R, Gallimore A, Godkin A. Low-dose cyclophosphamide induces antitumor T-cell responses, which associate with survival in metastatic colorectal cancer. Clin Cancer Res. 2017;23:6771–80.PubMedPubMedCentralCrossRefGoogle Scholar
- 62.Nakahara T, Uchi H, Lesokhin AM, Avogadri F, Rizzuto GA, Hirschhorn-Cymerman D, Panageas KS, Merghoub T, Wolchok JD, Houghton AN. Cyclophosphamide enhances immunity by modulating the balance of dendritic cell subsets in lymphoid organs. Blood. 2010;115:4384–92.PubMedPubMedCentralCrossRefGoogle Scholar
- 63.Pfirschke C, Engblom C, Rickelt S, Cortez-Retamozo V, Garris C, Pucci F, Yamazaki T, Poirier-Colame V, Newton A, Redouane Y, Lin Y-J, Wojtkiewicz G, Iwamoto Y, Mino-Kenudson M, Huynh TG, Hynes RO, Freeman GJ, Kroemer G, Zitvogel L, Weissleder R, Pittet MJ. Immunogenic chemotherapy sensitizes tumors to checkpoint blockade therapy. Immunity. 2016;44:343–54.PubMedPubMedCentralCrossRefGoogle Scholar
- 65.Matsunaga T, Yamaji Y, Tomokuni T, Morita H, Morikawa Y, Suzuki A, Yonezawa A, Endo S, Ikari A, Iguchi K, El-Kabbani O, Tajima K, Hara A. Nitric oxide confers cisplatin resistance in human lung cancer cells through upregulation of aldo-keto reductase 1B10 and proteasome. Free Radic Res. 2014;48:1371–85.PubMedCrossRefGoogle Scholar
- 67.Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, Noguchi T, Arthur CD, Meng W, Alspach E, Medrano RFV, Fronick C, Fehlings M, Newell EW, Fulton RS, Sheehan KCF, Oh ST, Schreiber RD, Artyomov MN. High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell. 2018;175:1014–1030.e19.PubMedCrossRefGoogle Scholar
- 68.Li B, VanRoey M, Wang C, Chen TT, Korman A, Jooss K. Anti–programmed death-1 synergizes with granulocyte macrophage colony-stimulating factor–secreting tumor cell immunotherapy providing therapeutic benefit to mice with established tumors. Clin Cancer Res. 2009;15:1623–34.PubMedCrossRefGoogle Scholar
- 69.Fu J, Kanne DB, Leong M, Glickman LH, McWhirter SM, Lemmens E, Mechette K, Leong JJ, Lauer P, Liu W, Sivick KE, Zeng Q, Soares KC, Zheng L, Portnoy DA, Woodward JJ, Pardoll DM, Dubensky TW, Kim Y. STING agonist formulated cancer vaccines can cure established tumors resistant to PD-1 blockade. Sci Transl Med. 2015;7:283ra52.PubMedPubMedCentralCrossRefGoogle Scholar
- 75.Morisada M, Clavijo PE, Moore E, Sun L, Chamberlin M, Van Waes C, Hodge JW, Mitchell JB, Friedman J, Allen CT. PD-1 blockade reverses adaptive immune resistance induced by high-dose hypofractionated but not low-dose daily fractionated radiation. Oncoimmunology. 2018;7:e1395996.PubMedCrossRefPubMedCentralGoogle Scholar
- 79.Weed DT, Vella JL, Reis IM, De la Fuente AC, Gomez C, Sargi Z, Nazarian R, Califano J, Borrello I, Serafini P. Tadalafil reduces myeloid-derived suppressor cells and regulatory T cells and promotes tumor immunity in patients with head and neck squamous cell carcinoma. Clin Cancer Res. 2015;21:39–48.PubMedCrossRefPubMedCentralGoogle Scholar
- 82.Amaria RN, Reddy SM, Tawbi HA, Davies MA, Ross MI, Glitza IC, Cormier JN, Lewis C, Hwu W-J, Hanna E, Diab A, Wong MK, Royal R, Gross N, Weber R, Lai SY, Ehlers R, Blando J, Milton DR, Woodman S, Kageyama R, Wells DK, Hwu P, Patel SP, Lucci A, Hessel A, Lee JE, Gershenwald J, Simpson L, Burton EM, Posada L, Haydu L, Wang L, Zhang S, Lazar AJ, Hudgens CW, Gopalakrishnan V, Reuben A, Andrews MC, Spencer CN, Prieto V, Sharma P, Allison J, Tetzlaff MT, Wargo JA. Neoadjuvant immune checkpoint blockade in high-risk resectable melanoma. Nat Med. 2018;24:1649–54.PubMedPubMedCentralCrossRefGoogle Scholar
- 83.Amakawa R, Takebayashi M, Yoshimura T, Inaba K, Hori T, Ota M, Nakamura K, Ito T, Fukuhara S, Inaba M, Miyaji M. Plasmacytoid dendritic cells regulate Th cell responses through OX40 ligand and type I IFNs. J Immunol. 2014;172:4253–9.Google Scholar
- 84.Liu C, Lou Y, Lizée G, Qin H, Liu S, Rabinovich B, Kim GJ, Wang Y-H, Ye Y, Sikora AG, Overwijk WW, Liu Y, Wang G, Hwu P. Plasmacytoid dendritic cells induce NK cell–dependent, tumor antigen–specific T cell cross-priming and tumor regression in mice. J Clin Invest. 2008;118:1165–75.PubMedPubMedCentralGoogle Scholar
- 85.Majumder B, Baraneedharan U, Thiyagarajan S, Radhakrishnan P, Narasimhan H, Dhandapani M, Brijwani N, Pinto DD, Prasath A, Shanthappa BU, Thayakumar A, Surendran R, Babu GK, Shenoy AM, Kuriakose MA, Bergthold G, Horowitz P, Loda M, Beroukhim R, Agarwal S, Sengupta S, Sundaram M, Majumder PK. Predicting clinical response to anticancer drugs using an ex vivo platform that captures tumour heterogeneity. Nat Commun. 2015;6:6169.PubMedPubMedCentralCrossRefGoogle Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.