Skip to main content
NCBI home page
British Journal of Cancer logoLink to British Journal of Cancer
. 2025 Feb 24;132(8):744–753. doi: 10.1038/s41416-025-02962-1

Potentiation of immune checkpoint blockade with a pH-sensitizer as monitored in two pre-clinical tumor models with acidoCEST MRI

Renee L Tran 1, Tianzhe Li 1,3, Jorge de la Cerda 1, F William Schuler 1, Alia S Khaled 1, Shivanand Pudakalakatti 1, Pratip K Bhattacharya 1, Sanhita Sinharay 2, Mark D Pagel 1,✉
PMCID: PMC11997056  PMID: 39994445

Abstract

Background

Tumor acidosis causes resistance to immune checkpoint blockade (ICB). We hypothesized that a “pH-sensitizer” can increase tumor extracellular pH (pHe) and improve tumor control following ICB. We also hypothesized that pHe measured with acidoCEST MRI can predict improved tumor control with ICB.

Methods

We tested the effects of pH-sensitizers on proton efflux rate (PER), cytotoxicity, T cell activation, tumor immunogenicity, tumor growth and survival using 4T1 and B16-F10 tumor cells. We measured in vivo tumor pHe of 4T1 and B16-F10 models with acidoCEST MRI.

Results

Among the pH-sensitizers tested, someprazole caused the greatest reduction in PER without exhibiting cytotoxicity or reducing T cell activation. Esomeprazole improved 4T1 tumor control with ICB administered one day after the pH-sensitizer. Tumor pHe positively correlated with TCF-1 + CD4 effector and CD8 T cell intratumoral frequencies and predicted improved 4T1 tumor control with ICB. For comparison, esomeprazole had a mild effect on B16-F10 tumor pHe, and worsened tumor control with ICB and increased intratumoral myeloid and dendritic cell (DC) frequencies.

Conclusions

A pH-sensitizer can improve tumor control with ICB, and acidoCEST MRI can be used to measure pHe and predict tumor control, but only in the 4T1 model and not the B16-F10 model.

graphic file with name 41416_2025_2962_Figa_HTML.jpg

Subject terms: Cancer imaging, Cancer microenvironment

Introduction

Immune checkpoint blockade (ICB) treatments, such as nivolumab and ipilimumab, changed the standard of care for many cancers [1, 2]. Beginning with advanced melanoma, ICB has now been approved for the treatment of patients with hepatocellular carcinoma, prostate cancer, urothelial carcinoma, renal cell carcinoma, and ovarian cancer [3]. Despite their successes, this combination induces objective response rates of only 58% for melanoma patients [4], 35.9% for patients with non-small cell lung cancer [5], 42% for patients with renal cell carcinoma [6], 16% in patients with hepatocellular carcinoma [7], and 18% in patients with breast cancer [8]. Improving the efficacy of ICB would translate into better patient outcomes and may expand the reach of ICB to treat more resistant cancer types.

One of the many barriers to ICB response is the high acidity in the tumor microenvironment that results from high tumor cell glycolytic metabolism, known as the Warburg effect [9, 10]. Solid tumors almost always have a pHe less than that of normal tissue, at approximately pH 6.4–6.9 [11, 12]. This extracellular acidosis promotes tumor cell survival and metastasis [13]. T cells cultured at such acidic pH are inhibited, showing significantly reduced expansion, interferon gamma expression, and increased inhibitory receptor expression, tumor cell lysis, and anergy [14, 15]. As ICB depends on effector T cell activity to promote tumor control [16, 17], high tumor acidity may also hinder the antitumor effects of ICBs such as programmed death 1 antibody (αPD-1) and cytotoxic T-lymphocyte-associated protein 4 antibody (αCTLA-4), and the combination of these ICBs.

Previous studies exploring the relationship between tumor acidosis and ICB demonstrated that increasing tumor pH yields better antitumor T cell responses [5, 18, 19]. We refer to treatments that increase pHe as “pH-sensitizers”. Alkaline buffers are a class of pH-sensitizers that can be combined with αPD-1 or αCTLA-4 treatment to decrease tumor weights and improve CD8 T cell infiltration and activation [20]. Unfortunately, clinical trials using alkaline buffers such as oral bicarbonate have failed due to issues with patient compliance and grade 2 gastrointestinal events [21]. As another class, nanoparticles have been developed that can be naturally degraded, resulting in a sustained increase tumor pHe. However, the variable rates of alkalization and the heterogenous distribution of nanoparticles in solid tumors pose challenges for this treatment approach [22, 23]. Inhibitors that reduce the production or secretion of lactic acid or related hydrogen ions can act as pH-sensitizers. For example, early treatment with a proton pump inhibitor can potentiate the antitumor effects of transplanted, tumor-specific CD8 T cells by increasing T cell expansion and interferon-γ production [18]. We chose to study enzyme inhibitors as pH-sensitizers because inhibitors show more clinical promise than buffers and nanoparticles.

To further explore this class of pH-sensitizers, we compiled a panel of pH-sensitizers that can potentially increase pHe (Supplementary Table S1). We did not include inhibitors of lactate dehydrogenase, sodium-hydrogen exchangers, and anion exchangers, as previous studies have shown either inefficacy or toxicity [24, 25]. Our pH-sensitizer panel includes inhibitors of monocarboxylate transporter 1 and 4 (MCT1 and MCT4) that secrete lactate and H+; carbonic anhydrase IX and XII (CA-IX and CA-XII) that convert H2O and CO2 into H+ and HCO3-; and vacuolar ATPase (V-ATPase) that secretes H+ [24, 26, 27]. Furthermore, most of these pH-sensitizers are in clinical trials or are approved by the U.S. Food and Drug Administration [28–32]. We sought to identify the best pH-sensitizers through in vitro assays and in vivo studies with tumor models of mammary carcinoma and melanoma treated with a pH-sensitizer and ICB.

For effective combination therapy of a pH-sensitizer and ICB, it is imperative that the pH-sensitizer not interfere with T cell activity. During T cell activation, T cells have increased cellular metabolism, with as much as 85% of consumed glucose eventually secreted as lactate and only 3% undergoing oxidative phosphorylation [33]. As the pH-sensitizers screened either inhibit H+ secretion or increase pHe by increasing HCO3- uptake into cells, the pH-sensitizers may hinder T cell activity by restricting lactate secretion or promoting a low intracellular T cell pHe. The effect of the pH-sensitizer on T cells must be carefully considered to potentiate the antitumor T cell response of ICB.

Although in vitro studies can provide valuable information, the extracellular tumor microenvironment is best studied in an in vivo context. We sought to evaluate the effect of our best pH sensitizer using immunocompetent tumor models of 4T1 mammary carcinoma and B16-F10 melanoma. Furthermore, a pH sensitizer may require time to raise tumor pHe in vivo, so that the timing between the administration of a pH sensitizer and ICB may need to be optimized. To evaluate if and when a pH-sensitizer raises tumor pHe for ICB, it is critical to measure the tumor pHe before and after treatment with a pH-sensitizer. We have pioneered the development of acidoCEST MRI that can measure tumor pHe. AcidoCEST MRI is an advanced magnetic resonance imaging (MRI) protocol based on chemical exchange saturation transfer (CEST) that generates MR image contrast through the exchange of protons between a contrast agent and bulk water [34–36]. Our version of acidoCEST MRI uses iopamidol, a clinically-approved computed tomography agent repurposed for MRI, which has chemical exchange rates with water that are dependent on pH. We convert the pH-dependent CEST MR image contrast to a map of tumor pHe. In this study, we used acidoCEST MRI to monitor tumor pHe before and after treatment with a pH-sensitizer, to identify if and when the imaging of tumor pHe can predict an improvement in tumor control with the combination of a pH-sensitizer and ICB.

Materials and methods

Cell lines and tumor models

4T1 murine mammary epithelial cells (ATCC, CRL-2539) were grown in tissue culture-treated flasks supplemented with Roswell Park Memorial Institute (RPMI) 1640 and additional 10% FBS, 1% penicillin and streptomycin, and glutamate, but lacking HEPES and sodium pyruvate. B16-F10 murine melanoma cells (ATCC, CRL-6475) were grown in Dulbecco’s modified eagle’s medium (DMEM) supplemented with 10% FBS, 2% penicillin and streptomycin, glucose, glutamine, and sodium pyruvate. No testing was done on these cell lines, because the cell bank ATCC uses the highly reliable Promega PowerPlex 18D System to identify cell lines by Short Tandom Repeat analyses. Cells were manipulated upon reaching 90% confluence prior to twenty passages. 4T1 and B16-F10 tumor cell lines were initially selected due to their sensitivity to combination αPD-1 and αCTLA-4 treatment without complete tumor rejection [17, 37].

pH-sensitizers

For in vitro assays, our pH-sensitizers included AZD3965, acriflavine, CHC, acetazolamide, SLC-0111, and esomeprazole. Additional details regarding pH-sensitizer use are provided in Supplementary Table S1.

Seahorse assay

The proton efflux rate (PER) was measured using a Seahorse assay following the instructions for a glycolytic rate assay kit (Agilent, 103344-100). Prior to this experiment, cell density for 4T1 and B16-F10 cells was optimized. Acquisition was performed following the instructions for the ATP rate assay kit (Agilent, 130592-100), while analysis was performed using GraphPad Prism 9.0.0 (see the Statistical Analysis section). Seahorse assays were programmed to first record three baseline data points, inject a pH-sensitizer, record four data points, inject rotenone and antimycin A (inhibitors of oxidative phosphorylation), record three data points, inject 2-deoxyglucose (inhibitor of glycolysis), and lastly record four data points. The Seahorse instrument measures pH in cell media to determine the PER. Each pH-sensitizer was tested using concentrations of 0.1, 1, 10, and 100 µM in DMSO (except for acriflavine which was dissolved in water), due to preliminary studies showing the IC50 values of the different pH-sensitizers ranging from 1.48 µM to 37.3 µM (data not shown).

Cytotoxicity assay and T cell activation assay

Details regarding the assays are provided in Supplementary Information.

Pre-clinical tumor models and treatments

Female C57BL/6J (IMSR_JAX:000664) and BALB/cJ (IMSR_JAX:000651) mice were purchased from the Jackson Laboratory. To implant 4T1 cells, harvested cells were resuspended in 50 µL PBS and 40,000 cells were injected into the fat pad of the 4th inguinal nipple in BALB/c mice. B16-F10 cells were resuspended in 100–200 µL PBS and 400,000 cells injected intradermally into the right flank of C57BL/6J mice. The day of injection was known as Day 0 in this study. All tumor cells were implanted in mice between the ages of 5–8 weeks and at least 20 g in weight.

Treatment and imaging of mice began when tumors reached 3–4 mm in diameter, which was 10 days for 4T1 tumors and 8 days for B16-F10 tumors post implantation. Mice were excluded from the study if their tumors were not visible or exceeded 200 mm3 upon blind distribution prior to first treatment. For the tumor growth and survival study, a mouse was euthanized when the tumor volume reached 1000 mm3. Sample sizes ranged from 4 to 12 mice for immunogenicity studies, tumor growth kinetics studies, and survival studies (all without involving acidoCEST MRI), which were conducted in triplicate for a total of 87 mice for immunogenicity studies and 170 mice for tumor growth and survival studies. AcidoCEST MRI scans were performed with 12 mice with a 4T1 tumor and 12 mice with a B16-F10 tumor. Three pre-treatment scans of the B16-F10 model were unsuccessful, but all other scans were successful, resulting in n = 9–12 for various results. All mice were randomized into groups, mice were tested unblinded, and variances were similar between groups that were compared. The number of mice for imaging studies was determined using a statistical package, nQuery Advisor V.5.0 (Statistical Solutions) using a 2-groupT-test for full change assuming log-normal distribution, assuming equal variances between groups. The number of mice for other tests was set based on our prior experiences.

Sodium esomeprazole (Abcam, ab120500) was resuspended in PBS and injected intraperitoneally at a dose of 7.5 μg/kg. The αCTLA-4 (9H10) and αPD-1 (RMP1-14) antibodies (Leinco Technologies, C2841 and P372, respectively) were also diluted in PBS and injected intraperitoneally at doses of 5 µg/kg and 12.5 µg/kg, respectively. We treated mice with esomeprazole and ICB on the same day, with esomeprazole followed 1 day later with ICB, or with esomeprazole followed 3 days alter with ICB. PBS was injected intraperitoneally as the vehicle control on the same day as esomeprazole treatment. The treatments with ICB or PBS were then continued at 3-day intervals. The timings for the administration of esomeprazole, the ICB antibodies, and PBS are shown in Figs. S1 and S2 in the Supplementary Information.

AcidoCEST MRI

AcidoCEST MRI was performed to evaluate tumor pHe with the 1-day delayed treatment protocol. The scans were performed with each mouse one day before treatment with esomeprazole, and one day after esomeprazole treatment and prior to ICB treatment (Figs. S1 and S2). Some mice were euthanized immediately after the second acidoCEST MRI scan on Days 11 and 9 for the 4T1 and B16-F10 models, respectively, and used for ex vivo tumor cell analysis as described below. The other mice were then treated with ICB and euthanized on Day 21 and Day 18 for the 4T1 and B16-F10 models, respectively, and their tumor masses were compared with the tumor pHe measurements made with acidoCEST MRI.

To perform acidoCEST MRI, a mouse was anesthetized with 2–5% isofluorane in 100% O2 breathing gas, and a 27 G catheter was placed in a tail vein. The mouse was secured to a cradle and placed in a 7 T Biospec MRI instrument (Bruker Biospin, Inc.) with a 35 mm transceiver volume coil. Mouse body temperature was maintained at 37 °C using a rectal fiber optic sensor and a feedback system that maintained warm air in the magnet bore (SA Instruments, Inc.) After localizing the tumor using T2-weighted MR images with a single 2D axial orientation, a bolus of 200 µL iopamidol was administered through the tail vein followed by a 30-min infusion of 200 µL iopamidol to maintain a high concentration in the mouse. Additional CEST MR images were acquired during the infusion. Pre-injection images were subtracted from post-injection images to remove the CEST effects from endogenous sources. The isolated CEST effects from iopamidol were then analyzed using the Bloch-McConnel equations modified to include pH as a fitting parameter, using customized software in Matlab R2021b (Mathworks, Inc.). Additional details regarding the acidoCEST MRI protocol are provided in Supplementary Information.

Ex vivo tumor immunogenicity analysis

To evaluate tumor immunogenicity without involving acidoCEST MRI, we harvested 4T1 and B16-F10 tumors at the time of esomeprazole treatment (baseline), one day following treatment, or three days following treatment. To evaluate the relationship between tumor pHe and immunogenicity, some tumors imaged with acidoCEST MRI were harvested for flow cytometry immediately after the second acidoCEST MRI scan on Days 11 and 9 for the 4T1 and B16-F10 models, respectively. We used flow cytometry to identify immune cell subset populations in the harvested tumors. The tumors were minced and digested for 30 min at 37 °C in RPMI supplemented with Collagenase H (Sigma-Aldrich, 11074059001) and DNAse (Roche, 4716728001). After digestion, tumor samples were mashed against 70 µm cell strainers (Corning Life Sciences, 352350) using the textured end of a 1 mL syringe plunger. Once a single cell solution was produced, the samples were normalized by cell count before being stained extracellularly and fixed using a fixation buffer (eBioscience, 00-5523-00). After fixing cells, they were stained intracellularly before being analyzed with a flow cytometer. Details regarding the antibodies are provided in Supplementary Information.

Statistics analysis

In vitro screening analyses, immunogenicity experiments, and tumor growth and survival experiments used three biological repeats for data analysis. AcidoCEST MRI studies were performed only once due to the long duration and high cost of in vivo imaging. Results are presented as mean ± standard deviation, while tumor growth kinetic results are presented as mean ± standard error of the mean. All statistical analyses were conducted through GraphPad Prism 9.0.0. p values and significance were determined by one-way ANOVA, while correlation studies were analyzed using Linear and Nonlinear Regression fits to determine goodness of fit and 95% confidence intervals using a profile-likelihood asymmetrical analysis.

Results

Esomeprazole reduced 4T1 tumor cell acidification

The pH-sensitizers were screened for their ability to reduce extracellular acidification of 4T1 and B16-F10 tumor cells. These two cell lines were chosen for examination as both have reported to have highly acidic tumor microenvironments and relative susceptibility to ICB therapy [13, 17, 37–41]. We used a Seahorse assay that measures PER in live cells. There were no significant differences in the changes in PER between the different dilutions for each different pH-sensitizer (Figs. S3 and S4). Esomeprazole caused the greatest reduction in PER in 4T1 cells versus the negative control and the other pH-sensitizers, and almost all of these differences between esomeprazole and the other pH-sensitizers were statistically significant (Fig. 1a). While esomeprazole also decreased PER in 4T1 cells more than AZD3065, this difference was not statistically significant. Furthermore, esomeprazole decreased PER in B16-F10 cells relative to control and some of the other pH-sensitizers, but only acriflavine decreased PER with statistical significance compared to its respective control (Fig. 1b). Based on these results, we tested 4T1 and B16-F10 tumor models in subsequent studies as representative tumors that are and are not significantly affected by esomeprazole, respectively.

Fig. 1. Esomeprazole shows the most promise as a pH-sensitizer.

Fig. 1

Open in a new tab

a, b Proton efflux rates (PER) of 4T1 and B16-F10 cells following treatment with pH-sensitizers at 100 µM, measured by Seahorse assay. c, d Cytotoxicity assays using sulforhodamine B measuring viability of 4T1 and B16-F10 cells following 10-fold dilutions of pH-sensitizer incubation for 72 h. e CFSE spectra of live CD8 T cells incubated with pH-sensitizers at 50 µM, and CD3 and CD28 activating antibody over 72 h, and (f) comparison of Mean Fluorescence Intensity (MFI) from (e). **p < 0.0021, ***p < 0.0002, ****p < 0.0001. n = 3, with three biological repeats. Error bars represent the standard deviation.

The Seahorse assay may have been influenced by DMSO, which is used as a solvent for most pH-sensitizers. The DMSO control alone negated the effect of Rot/AA, an oxidative phosphorylation inhibitor that increases glycolysis to show the glycolytic reserve of the cells through a measurable increase in PER. B16-F10 cells are typically more glycolytically active than 4T1 cells [27, 42]. Therefore, DMSO may cause PER to increase in B16-F10 cells but not in 4T1 cells. This higher glycolytic reserve in B16-F10 cells relative to the reserve in 4T1 cells may explain why 4T1 cells are more susceptible to esomeprazole.

Esomeprazole did not affect cell viability

The reduction of PER by pH-sensitizers may result from cell lysis. While this effect is advantageous for tumor treatment, a reduction in tumor cell viability may indicate toxicity against normal cells. Therefore, we tested for the effect of pH-sensitizers on cell viability by using a sulforhodamine B assay that is dependent on the amount of protein (Fig. 1c, d). For comparison, the more commonly used MTT assay for cell viability is sensitive to cell metabolism, which would potentially bias our results [43]. Only acriflavine significantly reduced cell viability at 50 µM concentration compared to the unstimulated control in both 4T1 and B16-F10 cells. The cytotoxicity caused by acriflavine indicates that acriflavine decreases PER in the Seahorse assay due to a loss of viable cells rather than a reduction of the cell acidification. For comparison, esomeprazole truly reduces tumor acidification rather than relying on cell lysis to reduce PER.

Esomeprazole did not inhibit T cell activation

For our purposes, the pH-sensitizer must not interfere with T cell function for the pH-sensitizer to be effective in combination with ICB [16, 17]. To test whether these pH-sensitizers impact T cell activity, we conducted a T cell activation assay incubating T cells with each pH-sensitizer. There was a significant difference in the MFI of the CFSE dye for AZD3965 and acroflavin, but not for esomeprazole or the other pH-sensitizers (Fig. 1e, f). Furthermore, the assay with different concentrations of each pH-sensitizer showed that acriflavine was toxic to T cells at the highest concentration (Fig. S5). Based on this assay, as well as the Seahorse assay and the cytotoxicity assay described above, esomeprazole is the most suitable candidate to combine with ICB to improve tumor control.

Immune cell populations responded differently in either 4T1 or B16-F10 tumors due to esomeprazole

For esomeprazole to potentiate tumor control by ICB, esomeprazole must promote tumor immunogenicity. Previous studies have shown that esomeprazole increases pHe [44, 45] and this increase is durable for at least 48 h [46, 47]. This evidence hinted that the maximal effect of esomeprazole may span multiple days following treatment. We tested whether esomeprazole can promote tumor immunogenicity immediately after, one day after, or three days after esomeprazole treatment (without also involving acidoCEST MRI) by harvesting tumors at these time points. We used flow cytometry to identify immune cell subset populations in the tumors (Fig. S6). One day after esomeprazole treatment, both 4T1 and B16-F10 tumors showed significantly increased ratios of CD8 T cell frequencies to monocytic myeloid-derived suppressor cell (M-MDSC) frequencies compared to baseline tumors or tumors harvested three days after esomeprazole (Fig. 2). However, this effect was mediated by reduced frequencies of M-MDSCs and not increasing CD8 T cell frequencies in 4T1 tumors, whereas B16-F10 tumors responded by increasing the frequencies of CD8 T cells and not reducing M-MDSC frequencies. These results demonstrated that esomeprazole increased tumor immunogenicity in 4T1 and B16-F10 in different ways, which justified our in vivo studies with esomeprazole and ICB.

Fig. 2. 4T1 and B16-F10 tumors increase tumor immunogenicity differently when primed with esomeprazole.

Fig. 2

Open in a new tab

The ratios of Ly6C+ myeloid cell frequencies to CD8 + T cell frequencies (left), Ly6C+ myeloid cell frequencies alone (middle), and CD8 + T cell frequencies alone (right) in (a) 4T1 and (b) B16-F10 tumors at time of esomeprazole treatment (baseline), one day after esomeprazole treatment (+1), and three days after esomeprazole treatment (+3). *p < 0.0332, and **p < 0.0021. n = 4–5, with three biological repeats. Error bars represent the standard deviation.

Esomeprazole induced tumor control when delivered one day prior to starting the ICB regimen in 4T1 tumors

We monitored tumor growth and survival after treatment with esomeprazole in combination αPD-1 and αCTLA-4 ICB using ICB treatment concurrently with esomeprazole, one day afterwards, or three days after esomeprazole treatment (Figs. S1 and S2). We also tested a single treatment of esomeprazole, a separate treatment of ICB, or a separate vehicle control. In mice bearing 4T1 tumors, we observed a statistically significant delay in tumor growth and increase in survivability when mice were treated with esomeprazole and ICB given one day afterwards, compared to the other esomeprazole and ICB timing combinations, single agent controls and vehicle control (Fig. 3a). No 4T1 tumors were completely rejected. For comparison, while mice bearing B16-F10 tumors did not show significant changes in tumor growth, there was a significant increase in survival of B16-F10-bearing mice receiving combination of esomeprazole one day prior to starting ICB (Fig. 3b). The combination of esomeprazole and ICB delivered one day afterwards promoted increased B16-F10 tumor rejection (Fig. S7), which manifests in the significant increase in the survival of these mice compared to those receiving other treatments. The different responses between 4T1 and B16-F10 tumors with esomeprazole and 1-day-delayed ICB matches the in vitro PER results from the Seahorse assay and the immunogenicity report, reinforcing that 4T1 and B16-F10 are affected by esomeprazole differently.

Fig. 3. Effects of combination treatment of esomeprazole and ICB injected one day later on tumor growth for 4T1 and B16-F10 tumors.

Fig. 3

Open in a new tab

a Tumor growth kinetics (right) and pooled survival (left) of BALB/c mice orthotopically implanted with 4T1 tumors. b Tumor growth kinetics (right) and pooled survival (left) of C57BL/6 mice implanted with B16-F10 tumors. ICB treatment days (“ICB:”) corresponding to treatment groups (identified by treatment group color in legend) are shown as arrows above the x-axis, while the red arrow below the x-axis corresponds to the esomeprazole treatment day (“eso:”). *p < 0.0332, and **p < 0.0021. n = 4–5, with three biological repeats. Error bars represent the standard deviation.

Different pHe responses were indicative of tumor control in 4T1 and B16-F10 tumors

We next asked whether pHe measured with acidoCEST MRI can serve as a biomarker one day after esomeprazole treatment to indicate the success of esomeprazole and one-day delayed ICB on tumor control at study endpoint (Fig. S8). We treated mice bearing 4T1 and B16-F10 tumors with esomeprazole and one-day delayed ICB, and measured tumor pHe with acidoCEST MRI before and 1 day after esomeprazole treatment (before ICB treatment on that day). We measured tumor volume with both MRI scans. A comparison of the initial volumes of 4T1 and B16-F10 tumors versus their respective pre-treatment pHe also showed moderately negative correlations (Fig. 4). This observation corresponds with previous observations that tumors with a faster growth rate are more metabolically active, and more glycolytic metabolism produces more acidic tumors [48].

Fig. 4. Baseline pHe reflects tumor growth rate.

Fig. 4

Open in a new tab

Comparison between the pHe before esomeprazole and the initial tumor volume of 4T1 (a) and B16-F10 (b) tumors when the pHe was measured with acidoCEST MRI. Dotted lines represent the 95% confidence interval while the solid lines represent the linear best fit line. n = 9–12.

For our acidoCEST MRI study, we also measured tumor mass on Day 21 and Day 18 for the 4T1 model and B16-F10 model, respectively, as an indication of tumor control. Interestingly, both the pHe after esomeprazole treatment (Day 11) and the change in pHe (ΔpHe = pHe 1 day after esomeprazole treatment on Day 11 – pHe 1 day before esomeprazole treatment on Day 9) negatively correlated with a lower tumor mass in the 4T1 model at Day 21 (Fig. 5a). Conversely, the pHe on Day 9 (one day after esomeprazole treatment and before ICB treatment) was not correlated with B16-F10 tumor mass at Day 16, although ΔpHe was positively correlated with the final B16-F10 tumor mass on Day 16 (Fig. 5b). These results indicated that acidoCEST MRI at an early time point can predict tumor control at study endpoint in response to esomeprazole and one-day-delayed ICB in 4T1 tumors, but not in B16-F10 tumors.

Fig. 5. pHe response to esomeprazole predicts tumor control by esomeprazole treatment followed by ICB.

Fig. 5

Open in a new tab

a A comparison between tumor mass on Day 22 vs. the pHe on Day 11 and change in pHe caused by esomeprazole (Day 11 vs. Day 9) in the 4T1 model. b A comparison between tumor mass on Day 18 vs. the pHe on Day 9 and change in pHe caused by esomeprazole (Day 9 vs. Day 7) in the B16-F10 model. The r values are the Pearson correlation coefficients, the dotted lines represent the 95% confidence intervals, and the solid lines represent the exponential best fit line. n = 9–12.

Post-esomeprazole pHe in 4T1 tumors was correlated with T cell stimulation

For pHe to be a biomarker for the success of the combination of esomeprazole and ICB delayed by one day, pHe must also be related to tumor immunogenicity. To depict the relationship between pHe and immune cell subsets, we harvested tumors after the second acidoCEST MRI scan and used flow cytometry to assess the tumor immunophenotype. In 4T1 tumors, post-esomeprazole pHe at Day 11 positively correlated with frequencies of progenitor T cell subsets on that same day, denoted as TCF-1 + CD4 effector and CD8 T cells (Fig. 6a and Table S2). As post-esomeprazole pHe also predicted tumor control, the effect of esomeprazole and ICB given one day afterwards must be driven by a heightened T cell response.

Fig. 6. AcidoCEST MRI measurements of pHe correlates with intratumoral immune cell frequencies.

Fig. 6

Open in a new tab

a Intratumoral frequencies of TCF-1 + CD4+FoxP3- (left) and TCF-1 + CD8 T cells (right) compared to post-esomeprazole pHe in the 4T1 model, n = 12. b Intratumoral frequencies of CD11b+ myeloid cells (left), and CD11c+ DCs (right) compared to post-esomeprazole pHe in the B16-F10 model, n = 9. The r values are the Pearson correlation coefficients, the dotted lines represent the 95% confidence intervals, and the solid lines represent the exponential best fit line.

A change in pHe correlated with myeloid subset frequencies in B16-F10 tumors

Using the B16-F10 tumors harvested after the second acidoCEST MRI scan, we observed that the change in pHe after administering esomeprazole (Day 9 vs. Day 7) moderately correlated with CD11b+ and CD11c + CD11b+ DC myeloid frequencies in B16-F10 tumors on Day 9 (Fig. 6b and Table S2). Interestingly, there was no correlation between the change in pHe of B16-F10 tumors and either activated or suppressive T cell populations on Day 9. Therefore, B16-F10 tumors responded to an esomeprazole-mediated increase in pHe by increasing the frequencies of myeloid and DC populations, which may cumulatively reduce antitumor T cell activity and hinder the effect of ICB.

Discussion

The results of this study found that esomeprazole can inhibit tumor cell acidification in vitro, leading to tumor control when combined with ICB given one day afterwards for treating the 4T1 tumor model and promoting survival in the B16-F10 tumor. We also observed an impactful role of pHe in determining immunophenotype and response to esomeprazole and one-day delayed ICB. However, different results between the two tumor models suggests that a better understanding of individual tumor biology and tumor response to therapy is necessary to translate our findings into the clinic.

4T1 tumors are poorly immunogenic with relatively high frequencies of MDSCs and low frequencies of T cells [49]. Esomeprazole control of the MDSC population is thus ideal (Fig. 2a), as MDSCs play an important role in inhibiting the antitumor T cell responses induced by immunotherapy. Our finding corresponds to previous data that showed low pHe causes an increase in macrophage expression of the arginase 1, which induces T cell suppression [50]. However, esomeprazole may not be able to promote significant increases in T cell frequencies as the population of T cells to stimulate and expand in 4T1 tumors is already low. For TCF-1 expression to be correlated to post-esomeprazole pHe in both CD4+FoxP3- and CD8 + T cells, TCF-1 may have the potential to be another biomarker for immunotherapy response. Several studies have reported that TCF-1 + T cells and other progenitor T cell subsets as more active than their exhausted counterparts, and are mediators of antitumor T cell response [51, 52].

B16-F10 tumors are understood to be more susceptible to ICB than 4T1 tumors, as depletion of T cells significantly decreases survival and increases the rate of tumor growth [53, 54]. Because T cells are sensitive to pH, the B16-F10 response to esomeprazole by increasing the frequency of CD8 T cells is expected (Fig. 2b). Furthermore, ICB promotes such a robust antitumor response that the effect of ICB may mask the additive effect of combination esomeprazole given one day before ICB. However, the biggest contrast to the 4T1 and B16-F10 tumor response is the capability of pHe as a biomarker for combination treatment efficacy. Because esomeprazole controls the M-MDSC population in 4T1 tumors, the difference may indicate that pHe is only a viable biomarker for tumors that respond to esomeprazole by reducing the frequency of M-MDSCs, and not CD8 T cells. To corroborate this notion, we observed that an increase in the change in pHe correlates with increased frequencies of total myeloid cells in B16-F10 tumors, which can then inhibit ICB-mediated T cell tumor control. (Fig. 6b and Supplementary Table S2). If esomeprazole cannot reduce the frequency of M-MDSCs in the tumor, it may be imperative to control the myeloid cell compartment for pHe to predict treatment success. An early indicator for tumor response may need to encompass both tumor biopsy for reduction in Ly6C+ myeloid cell frequencies and post-esomeprazole pHe to ascertain tumor control by ICB.

This study can serve as a template for additional studies. With the different results of 4T1 and B16-F10 tumors in this study, more tumor types must be investigated with pH-sensitizers and ICB. Because tumor acidification by lactic acid secretion is related to aerobic glycolysis, hypoxia and other biomarkers of tumor metabolism could be interrogated in addition to measuring tumor pHe with acidoCEST MRI. Furthermore, ex vivo histology and immunohistochemistry may further substantiate the effects of various treatment regimens, in addition to monitoring tumor growth and survival. In particular, the change in expression of PD-L1, PD-1, and CTLA-4 could be monitored before and after treatment with esomeprazole to provide additional mechanistic evidence for improved response to ICB caused by a pH-sensitizer [15, 55–59]. Many future studies can further optimize pH-sensitizer treatment.

As esomeprazole is delivered daily to patients as the active ingredient in Nexium®, an obvious next step is to test multiple doses of esomeprazole in combination with ICB during pre-clinical studies. Other pH-sensitizers can also be tested, and acidoCEST MRI can improve the understanding of the magnitudes and timings of in vivo tumor pHe caused by these pH-sensitizers. Furthermore, additional studies can combine esomeprazole with another pH-sensitizer to bolster the modulation of acidity. These combination studies could be supported by an analysis of target protein expressions within tumor models, to ensure that combinations of pH-sensitizers are tested with appropriate models. Finally, future studies can evaluate tumor pHe vs. ICB without a pH sensitizer as an additional control experiment, and also to further establish that tumor acidosis causes resistance to ICB.

In conclusion, our study found that esomeprazole reduces the acidification of 4T1 tumor cells without inducing cell lysis. Esomeprazole does not interfere with T cell activation and sensitizes tumors to ICB tumor control by increasing tumor immunogenicity. Increased post-esomeprazole pH correlates with improved combination ICB tumor control mediated by TCF-1 + CD4 effector and CD8 T cells. However, without myeloid cell control, the increase in pHe after esomeprazole confers worsened combination ICB tumor control mediated by a compensatory increase in the intratumoral frequency of tumor-associated myeloid cells and dendritic cells.

Supplementary information

Supplenental Material (19.7MB, docx)

Acknowledgements

The authors thank the MD Anderson Cancer Center Small Animal Imaging Facility and the MD Anderson Cancer Center Advanced Cytometry & Sorting Facility for use of their resources.

Author contributions

RLT, SS, and MDP developed the experimental design. RLT and SS performed Seahorse studies. RLT and FWS developed the in vitro cell cultures and tumor models. RLT performed tumor growth and immunogeneicity studies. RLT and JDLC performed acidoCEST MRI studies. RLT, TL, and ASK performed image analyses. RLT, SP, PKB, SS, and MDP interpreted the results. RLC and MDP wrote the manuscript and all co-authors approved the manuscript.

Funding

This work was supported by a CPRIT grant (RP220270).

Data availability

All data is available upon reasonable request to the corresponding author. The AcidoCEST code for Matlab 2022b is available on GitHub at https://github.com/CAMEL-MartyPagel/acidoCEST_MRI_Matlab.

Competing interests

The authors declare no competing interests.

Ethics approval

All experiments involving mice were conducted according to Protocol 00001998 approved by the Institutional Animal Care and Use Committee at the UT MD Anderson Cancer Center. All mice were housed in a pathogen-free facility fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

The online version contains supplementary material available at 10.1038/s41416-025-02962-1.

References

  • 1.Butterfield LH, Najjar YG. Immunotherapy combination approaches: mechanisms, biomarkers and clinical observations. Nature Rev Immunology 2024;24:399–416. [DOI] [PMC free article] [PubMed]
  • 2.Lahiri A, Maji A, Potdar PD, Singh N, Parikh P, Bisht B, et al. Lung cancer immunotherapy: progress, pitfalls, and promises. Molec Cancer 2023;22:40. [DOI] [PMC free article] [PubMed]
  • 3.Sharma P, Allison JP. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 2015;161:205–14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hodi FS, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Cowey CL, et al. Nivolumab plus ipilimumab or nivolumab alone versus ipilimumab alone in advanced melanoma (CheckMate 067): 4-year outcomes of a multicentre, randomised, phase 3 trial. Lancet Oncol. 2018;19:1480–92. [DOI] [PubMed] [Google Scholar]
  • 5.Hellmann MD, Paz-Ares L, Bernabe Caro R, Zurawski B, Kim SW, Costa EC, et al. Nivolumab plus ipilimumab in advanced non-small-cell lung cancer. N. Engl J Med. 2019;381:2020–31. [DOI] [PubMed] [Google Scholar]
  • 6.Motzer RJ, Tannir NM, McDermott DF, Frontera OA, Melichar B, Choueiri TK, et al. Nivolumab plus ipilimumab versus sunitinib in advanced renal-cell carcinoma. N. Engl J Med. 2018;378:1277–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wong JSL, Kwok GGW, Tang V, Li BCW, Leung R, Chiu J, et al. Ipilimumab and nivolumab/pembrolizumab in advanced hepatocellular carcinoma refractory to prior immune checkpoint inhibitors. J Immunother Cancer. 2021;9:e001945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Adams S, Othus M, Patel SP, Miller KD, Chugh R, Schuetze SM, et al. A multicenter phase II trial of ipilimumab and nivolumab in unresectable or metastatic metaplastic breast cancer: cohort 36 of dual anti-CTLA-4 and anti-PD-1 blockade in rare tumors (DART, SWOG S1609). Clin Cancer Res. 2022;28:271–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Warburg O. The metabolism of carcinoma cells. J Cancer Res. 1925;9:148–63. [Google Scholar]
  • 10.Vaupel P, Multhoff G. Revisiting the Warburg effect: historical dogma versus current understanding. J Physiol. 2021;599:1745–57. [DOI] [PubMed] [Google Scholar]
  • 11.Gerweck LE, Seetharaman K. Cellular pH gradient in tumor versus normal tissue: potential exploitation for the treatment of cancer. Cancer Res. 1996;56:1194–8. [PubMed] [Google Scholar]
  • 12.Gillies RJ, Raghunand N, Karczmar GS, Bhujwalla ZM. MRI of the tumor microenvironment. J Magn Reson Imaging. 2002;16:430–50. [DOI] [PubMed] [Google Scholar]
  • 13.Anemone A, Consolino L, Conti L, Irrera P, Hsu MY, Villano D, et al. Tumour acidosis evaluated in vivo by MRI-CEST pH imaging reveals breast cancer metastatic potential. Br J Cancer 2021;124:207–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Huntington KE, Louie A, Zhou L, Seyhan AA, Maxwell AW, El-Deiry WS. Colorectal cancer extracellular acidosis decreases immune cell killing and is partially ameliorated by pH-modulating agents that modify tumor cell cytokine profiles. Am J Cancer Res. 2022;12:138–51. [PMC free article] [PubMed] [Google Scholar]
  • 15.Davern M, Donlon NE, O’Connell F, Gaughan C, O’Donovan C, Habash M, et al. Acidosis significantly alters immune checkpoint expression profiles of T cells from oesophageal adenocarcinoma patients. Cancer Immunol Immunother. 2023;72:55–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAAS, Andrews MC, et al. Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 2017;170:1120–1133.e17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Wei SC, Anang NAS, Sharma R, Andrews MC, Reuben A, Levine JH, et al. Combination anti-CTLA-4 plus anti-PD-1 checkpoint blockade utilizes cellular mechanisms partially distinct from monotherapies. Proc Natl Acad Sci USA. 2019;116:22699–709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Calcinotto A, Filipazzi P, Grioni M, Iero M, De Milito A, Ricupito A, et al. Modulation of microenvironment acidity reverses anergy in human and murine tumor-infiltrating T lymphocytes. Cancer Res. 2012;72:2746–56. [DOI] [PubMed] [Google Scholar]
  • 19.Knight FC, Gilchuk P, Kumar A, Becker KW, Sevimli S, Jacobson ME, et al. Mucosal immunization with a pH-responsive nanoparticle vaccine induces protective CD8+ lung-resident memory T cells. ACS Nano 2019;13:10939–60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ibrahim-Hashim A, Estrella V. Acidosis and cancer: from mechanism to neutralization. Cancer Metastasis Rev. 2019;38:149–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Gillies RJ, Ibrahim-Hashim A, Ordway B, Gatenby RA. Back to basic: trials and tribulations of alkalizing agents in cancer. Front Oncol. 2022;12:981718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Shin S, Lee JY, Han J, Li FY, Ling DS, Park W. Tumor microenvironment modulating functional nanoparticles for effective cancer treatments. Tissue Eng. Regen Med. 2022;19:205–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.de Maar JS, Sofias AM, Siegel TP, Vreeken RJ, Moonen C, Bos C, et al. Spatial heterogeneity of nanomedicine investigated by multiscale imaging of the drug, the nanoparticle and the tumour microenvironment. Theranostics 2020;10:1884–909. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Bailey KM, Wojtkowiak JW, Hashim AI, Gillies RJ. Targeting the metabolic microenvironment of tumors. Adv Pharmacol. 2012;65:63–107. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Feng Y, Xiong Y, Qiao T, Li X, Jia L, Han Y. Lactate dehydrogenase a: a key player in carcinogenesis and potential target in cancer therapy. Cancer Med. 2018;7:6124–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Pastorekova S, Gillies RJ. The role of carbonic anhydrase IX in cancer development: links to hypoxia, acidosis, and beyond. Cancer Metastasis Rev. 2019;38:65–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bailey KM, Wojtkowiak JW, Cornnell HH, Ribeiro MC, Balagurunathan Y, Hashim AI, et al. Mechanisms of buffer therapy resistance. Neoplasia 2014;16:354–64.e1-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.McDonald PC, Chia S, Bedard PL, Chu Q, Lyle M, Tang L, et al. A phase 1 study of SLC-0111, a novel inhibitor of carbonic anhydrase IX, in patients with advanced solid tumors. Am J Clin Oncol. 2020;43:484–90. [DOI] [PMC free article] [PubMed]
  • 29.Nehme R, Hallal R, El Dor M, Kobeissy F, Gouilleux F, Mazurier F, et al. Repurposing of acriflavine to target chronic myeloid leukemia treatment. Curr Med. Chem. 2021;28:2218–33. [DOI] [PubMed] [Google Scholar]
  • 30.Scarpignato C, Gatta L, Zullo A, Blandizzi C. Effective and safe proton pump inhibitor therapy in acid-related diseases - A position paper addressing benefits and potential harms of acid suppression. BMC Medicine 2016;14:179. [DOI] [PMC free article] [PubMed]
  • 31.Halford S, Veal GJ, Wedge SR, Payne GS, Bacon CM, Sloan P, et al. A phase I dose-escalation study of AZD3965, an oral monocarboxylate transporter 1 inhibitor, in patients with advanced cancer. Clin Cancer Res. 2023;29:1429–39. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kulterer OC, Pfaff S, Wadsak W, Garstka N, Remzi M, Vraka C, et al. A microdosing study with (99m)Tc-PHC-102 for the SPECT/CT imaging of primary and metastatic lesions in renal cell carcinoma patients. J Nucl Med. 2021;62:360–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Brand K, Aichinger S, Forster S, Kupper S, Neumann B, Nurnberg W, et al. Cell-cycle-related metabolic and enzymatic events in proliferating rat thymocytes. Eur J Biochem. 1988;172:695–702. [DOI] [PubMed] [Google Scholar]
  • 34.Chen LQ, Randtke EA, Jones KM, Moon BF, Howison CM, Pagel MD. Evaluations of tumor acidosis within in vivo tumor models using parametric maps generated with acidoCEST MRI. Mol Imaging Biol. 2015;17:488–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Moon BF, Jones KM, Chen LQ, Liu P, Randke EA, Howison CM, et al. A comparison of iopromide and iopamidol, two acidoCEST MRI contrast media that measure tumor extracellular pH. Contrast Media Mol Imaging 2015;10:446–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Chen LQ, Howison CM, Jeffery JJ, Robey IF, Kuo PH, Pagel MD. Evaluations of extracellular pH within in vivo tumors using acidoCEST MRI. Magn Reson Med. 2014;72:1408–17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Rupp T, Genest L, Babin D, Legrand C, Hunault M, Froget G, et al. Anti-CTLA-4 and anti-PD-1 immunotherapies repress tumor progression in preclinical breast and colon model with independent regulatory T cells response. Transl Oncol. 2022;20:101405. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Pilon-Thomas S, Kodumudi KN, El-Kenawi AE, Russell S, Weber AM, Luddy K, et al. Neutralization of tumor acidity improves antitumor responses to immunotherapy. Cancer Res. 2016;76:1381–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Anemone A, Consolino L, Arena F, Capozza M, Longo DL. Imaging tumor acidosis: a survey of the available techniques for mapping in vivo tumor pH. Cancer Metastasis Rev. 2019;38:25–49. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Kim K, Skora AD, Li Z, Liu Q, Tam AJ, Blosser RL, et al. Eradication of metastatic mouse cancers resistant to immune checkpoint blockade by suppression of myeloid-derived cells. Proc Natl Acad Sci USA. 2014;111:11774–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Nakagawa Y, Negishi Y, Shimizu M, Takahashi M, Ichikawa M, Takahashi H. Effects of extracellular pH and hypoxia on the function and development of antigen-specific cytotoxic T lymphocytes. Immunol Lett. 2015;167:72–86. [DOI] [PubMed] [Google Scholar]
  • 42.Dowling CM, Hollinshead KER, Di Grande A, Pritchard J, Zhang H, Dillon ET, et al. Multiple screening approaches reveal HDAC6 as a novel regulator of glycolytic metabolism in triple-negative breast cancer. Sci Adv. 2021;7:eabc4897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Skrzydlewski P, Twaruzek M, Grajewski J. Cytotoxicity of mycotoxins and their combinations on different cell lines: a review. Toxins 2022;14:244. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.De Milito A, Canese R, Marino ML, Borghi M, Iero M, Villa A, et al. pH-dependent antitumor activity of proton pump inhibitors against human melanoma is mediated by inhibition of tumor acidity. Int J Cancer. 2010;127:207–19. [DOI] [PubMed] [Google Scholar]
  • 45.Irrera P, Roberto M, Consolino L, Anemone A, Villano D, Navarro-Tableros V, et al. Effect of esomeprazole treatment on extracellular tumor pH in a preclinical model of prostate cancer by MRI-CEST tumor pH imaging. Metabolites 2023;13:48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Lindner K, Borchardt C, Schöpp M, Bürgers A, Stock C, Hussey DJ, et al. Proton pump inhibitors (PPIs) impact on tumour cell survival, metastatic potential and chemotherapy resistance, and affect expression of resistance-relevant miRNAs in esophageal cancer. J Exp Clin Cancer Res. 2014;33:73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Rong A, Yao Y, Guo X, Jiang W, Jiang M, Yang J, et al. Precise cancer anti-acid therapy monitoring using pH-densitive MnO(2)@BSA nanoparticles by magnetic resonance imaging. ACS Appl Mater Interfaces 2021;13:18604–18. [DOI] [PubMed] [Google Scholar]
  • 48.Estrella V, Chen T, Lloyd M, Wojtkowiak J, Cornnell HH, Ibrahim-Hashim A, et al. Acidity generated by the tumor microenvironment drives local invasion. Cancer Res. 2013;73:1524–35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Lechner MG, Karimi SS, Barry-Holson K, Angell TE, Murphy KA, Church CH, et al. Immunogenicity of murine solid tumor models as a defining feature of in vivo behavior and response to immunotherapy. J Immunother. 2013;36:477–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Bohn T, Rapp S, Luther N, Klein M, Bruehl TJ, Kojima N, et al. Tumor immunoevasion via acidosis-dependent induction of regulatory tumor-associated macrophages. Nat Immunol. 2018;19:1319–29. [DOI] [PubMed] [Google Scholar]
  • 51.Miller BC, Sen DR, Al Abosy R, Bi K, Virkud YV, LaFleur MW, et al. Subsets of exhausted CD8(+) T cells differentially mediate tumor control and respond to checkpoint blockade. Nat Immunol. 2019;20:326–36. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Chen Z, Ji Z, Ngiow SF, Manne S, Cai Z, Huang AC, et al. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity. 2019;51:840–855.e5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Fan X, Quezada SA, Sepulveda MA, Sharma P, Allison JP. Engagement of the ICOS pathway markedly enhances efficacy of CTLA-4 blockade in cancer immunotherapy. J Exp Med. 2014;211:715–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Ricca JM, Oseledchyk A, Walther T, Liu C, Mangarin L, Merghoub T, et al. Pre-existing immunity to oncolytic virus potentiates its immunotherapeutic efficacy. Mol Ther. 2018;26:1008–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Knopf P, Stowbur D, Hoffmann SHL, Hermann N, Maurer A, Bucher V, et al. Acidosis-mediated increase in IFN-γ-induced PD-L1 expression on cancer cells as an immune escape mechanism in solid tumors. Mol Cancer. 2023;22:207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Kwon YJ, Seo EB, Jeong AJ, Lee SH, Noh KH, Lee S, et al. The acidic tumor microenvironment enhances PD-L1 expression via activation of STAT3 in MDA-MB-231 breast cancer cells. BMC Cancer. 2022;22:852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Rahman A, Janic B, Rahman T, Singh H, Ali H, Rattan R, et al. Immunotherapy enhancement by targeting extracellular tumor pH in triple-negative breast cancer mouse model. Cancers. 2023;15:4931. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Huntington KE, Louie A, Zhou LL, Seyhan AA, Maxwell AWP, El-Deiry WS. Colorectal cancer extracellular acidosis decreases immune cell killing and is partially ameliorated by pH-modulating agents that modify tumor cell cytokine profiles. Am J Cancer Res. 2022;12:138–51. [PMC free article] [PubMed] [Google Scholar]
  • 59.Giatromanolaki A, Koukourakis IM, Balaska K, Mitrakas AG, Harris AL, Koukourakis MI. Programmed death-1 receptor (PD-1) and PD-ligand-1 (PD-L1) expression in non-small cell lung cancer and the immune-suppressive effect of anaerobic glycolysis. Med. Oncol. 2019;36:76. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplenental Material (19.7MB, docx)

Data Availability Statement

All data is available upon reasonable request to the corresponding author. The AcidoCEST code for Matlab 2022b is available on GitHub at https://github.com/CAMEL-MartyPagel/acidoCEST_MRI_Matlab.

Articles from British Journal of Cancer are provided here courtesy of Cancer Research UK

RESOURCES