Abnormal pH is a common feature of malignant tumors and has been associated clinically with suboptimal outcomes. Amide proton transfer magnetic resonance imaging (APT MRI) holds promise as a means to noninvasively measure tumor pH, yet multiple factors collectively make quantification of tumor pH from APT MRI data challenging. The purpose of this study was to improve our understanding of the biophysical sources of altered APT MRI signals in tumors. Combining in vivo APT MRI measurements with ex vivo histological measurements of protein concentration in a rat model of brain metastasis, we determined that the proportion of APT MRI signal originating from changes in protein concentration was approximately 66%, with the remaining 34% originating from changes in tumor pH. In a mouse model of hypopharyngeal squamous cell carcinoma (FaDu), APT MRI showed that a reduction in tumor hypoxia was associated with a shift in tumor pH. The results of this study extend our understanding of APT MRI data and may enable the use of APT MRI to infer the pH of individual patients' tumors as either a biomarker for therapy stratification or as a measure of therapeutic response in clinical settings.

Significance:

These findings advance our understanding of amide proton transfer magnetic resonance imaging (APT MRI) of tumors and may improve the interpretation of APT MRI in clinical settings.

The metabolic phenotype of tumor cells is such that they reverse the pH gradient across the cell membrane with respect to normal cells, with a slightly alkaline intracellular pH (pHi) and an acidic extracellular pH (pHe; ref. 1). The severity of the acidosis of pHe has been shown to correlate with resistance to chemo- and radiotherapy treatments; tumors with a very acidic pHe are more resistant to radiotherapy (2), and ion-trapping of weakly basic chemotherapy agents prevents their entry to the cell (3). An alkalotic pHi also makes tumor cells less susceptible to cell death via apoptotic pathways (4) and promotes tumor proliferation (5). Determination of pHe or pHi in a noninvasive, reliable manner may, therefore, aid in the stratification of patients into personalized therapeutic strategies, or inform on treatment response. Thus, there is a clear need for noninvasive, reliable quantification of tumor pH in the clinic.

In addition to pH changes, tumors often have regions of acute and chronic hypoxia as a result of both an increased oxygen consumption rate of tumor cells compared with normal cells, and an abnormal vasculature reducing delivery of oxygen (6, 7). Tumors with larger fractions of hypoxia are more resistant to chemotherapy as a result of this abnormal vasculature, and are more resistant to radiotherapy because of a reduced oxygen enhancement effect of radiotherapy. Consequently, measurement of tumor hypoxia may also aid in the stratification of patients into optimal therapies, and reduction of tumor hypoxia is a common target for therapies. Importantly, a close link between tumor hypoxia and pH has been demonstrated (8). If tumor hypoxia is reduced then a shift in the metabolic phenotype of tumor cells that were previously hypoxic would be expected, with a resultant change in their pHi and pHe.

Chemical exchange saturation transfer (CEST) is an MRI contrast mechanism that uses the chemical exchange of labile protons in biomolecules with solvent water protons (9). Amide proton transfer (APT) is a variant of CEST MRI that is sensitive to the exchange of amide protons resident on the backbone and sidechains of proteins. APT MRI has been used previously in the imaging of tumors for noninvasive staging (10, 11), differentiation of radiation necrosis from recurrent tumor (12, 13) and definition of infiltrating tumor rim tissue (14). In addition, because the APT signal is dependent on the exchange rate of amide protons with solvent water protons, and this exchange rate is base catalyzed (15), APT is sensitive to the pH of tissue. Previous studies have measured the pH of tissue in ischemic stroke lesions using calibrated APT signals (16, 17). Quantification of tumor pH from APT MRI data, however, remains a significant challenge.

Preclinical and clinical APT MRI studies have usually attributed the altered APT signal in tumors to an increase in cytosolic protein concentration because the cells are rapidly proliferating (10, 18). This interpretation is contested, however, by work suggesting that APT signal changes in tumors are a result of alterations in water T1 relaxation time in these tissues (19). Despite the metabolic phenotype of tumor cells altering the intra- and extracellular pH of their microenvironment, this factor tends to be ignored when interpreting the APT signal change in tumors. The reason for this apparent oversight is that APT MRI is weighted toward measuring the intracellular compartment (18) and the intracellular pH change in tumors is less marked than the extracellular pH change (20, 21). The combination of these factors has limited, to date, the use of APT MRI for quantitation of biophysical parameters such as protein concentration or pH in tumors, and this limitation will remain until the complex interplay between these factors is deconvolved.

The aim of this study, therefore, was to determine the proportion of APT signal change between normal appearing and tumor tissue that is caused by protein concentration and pH changes, respectively, using a preclinical model of brain metastasis. Subsequently, we tested the sensitivity of our approach to pharmacological modulation of tumor pH following alleviation of tumor hypoxia in subcutaneous tumors. By understanding the biophysical sources of altered APT signals in tumors, our goal was to improve the interpretation of APT signal changes in tumors.

In vivo brain tumor models

All animal experiments were approved by the UK Home Office [Animals (Scientific Procedures) Act 1986] and conducted in accordance with the Guidelines for the Welfare and Use of Animals in Cancer Research (22). For the brain metastasis model, female Berlin Druckrey IX (BDIX) rats (180—340 g; n = 15; Charles River Laboratories) were focally microinjected with 1000 ENU1564 cells (kind gift from Prof. G. Stoica, Texas A&M University) in 1 μL PBS into the left striatum (co-ordinates 1 mm anterior, 3 mm lateral from bregma, 3.5 mm depth), as described previously (23). MRI experiments were performed four weeks post-injection. This animal model has been previously shown to exhibit similar histological characteristics to human brain metastatic growth, with an infiltrating tumor rim and necrotic tumor core, allowing observation of these two distinct areas (23).

Subcutaneous model of hypopharyngeal squamous carcinoma

Female BALB/c nude mice (age 55–70 d; n = 18; Charles River Laboratories) were implanted with subcutaneous tumors on their right flank. Tumors were induced by injection of 1 × 106 FaDu hypopharyngeal carcinoma cells in Matrigel. Once tumors reached a volume of 100 mm3 measured by callipers, mice were randomly split into Atovaquone-treated or control groups. Atovaquone is an anti-malarial drug that has recently been shown to alter the oxygen consumption rate of cancer cells in vitro and in vivo, reducing tumor hypoxia (24). Atovaquone was administered in drinking water (50 mg/kg/d) with 2% dimethyl sulfoxide (DMSO) and 0.1% carboxymethylcellulose (CMC). Control mice were treated with DMSO and CMC only. After 7 days of treatment, MRI was performed on each mouse.

MRI experiments

All MRI experiments were performed using a 9.4 T Varian Inova spectrometer (Agilent Technologies). Animals were anesthetized with 2% to 3% isoflurane in a mixture of 30% oxygen and 70% nitrogen. Respiration and rectal temperature were monitored and maintained at 40 to 60 breaths/min and 37°C, respectively. For imaging of rats with brain tumors, a 72-mm diameter volume transmit coil and 4-channel surface receive array (Rapid Biomedical) were used, with the rat head immobilized using a custom cradle. Before placement of the rats in the MRI scanner, a tail vein was cannulated to allow for injection of contrast agents during imaging. For a subset of the rats (n = 10), 60 mg/kg pimonidazole (Hypoxyprobe) was injected intraperitoneally before imaging. For imaging of mice with subcutaneous FaDu tumors, a 26-mm diameter volume transmit-receive coil was used, with mice positioned supine. Insulation was placed around the mice to prevent excessive heat loss and provide a small amount of immobilization.

The CEST MRI pulse sequence used for rat imaging comprised a pulsed saturation scheme of 50 saturation pulses, with each pulse comprising a 20 ms Gaussian radiofrequency (RF) pulse with flip angle 184° followed by a 20 ms crusher gradient, for a total saturation duration of 2 s with equivalent continuous wave RF power of 0.55 μT. This saturation scheme was preferentially sensitive to the exchange of amide protons at pH 7.02 in vivo (⁠${\omega _1} \! = \! \gamma {B_1} = 42.58\ {\rm{MHz\ }}{{\rm{T}}^{{\rm{ - 1}}}}\! \times 0.55\, {{μ {\curr T}}} = 23.4 \ { {\rm H}} z, \ {{{\rm pH} = }}\hskip4pt 6.4 \hskip4pt +${{\rm lo}} {{{\rm g}}_{10}} ( {\frac{{23.4}}{{5.57}}} ) = 7.02$⁠). The use of a pulsed saturation scheme was motivated by matching saturation parameters of previous human APT MRI studies at lower field strengths, which are more restricted in terms of hardware and specific absorption rate considerations (15, 25). Following saturation, a spin-echo echo planar imaging (SE-EPI) readout with field of view = 32 mm x 32 mm measured the Z-magnetization. Other sequence parameters were TR = 5 s, TE = 27 ms, 1 average, 10 slices, slice thickness = 1 mm, in-plane resolution 500 x 500 μm. A full Z-spectrum was measured following saturation at 49 saturation frequencies unevenly sampled between −4.1 and 5.0 ppm, with a further two measurements following saturation at ±300 ppm for normalization.

CEST MRI data were acquired from mice using a multi-slice gradient echo sequence (TR = 195 ms, TE = 1.4 ms) with constant TR respiration gating using a SPLICER acquisition scheme (26). Briefly, within each TR a CEST saturation pulse (Gaussian shape, duration 20 ms, flip angle 180°) was applied followed by a 1 ms crusher gradient and gradient echo readout of one k-space line. Data acquisition that was corrupted by a breath was reacquired. Data were acquired linearly through k-space to ensure that CEST saturation was in the steady state when acquiring the center of k-space (27). Other sequence parameters were slice thickness = 2 mm, in-plane resolution 469 x 469 μm. A full Z-spectrum was measured following saturation at 35 saturation frequencies evenly sampled between −5.1 and 5.1 ppm, with a further two measurements at ±300 ppm for normalization.

Quantitative maps of the T1 and T2 relaxation times were acquired to correct for the concomitant change in T1 and T2 in tumors when analyzing CEST MRI data (see MRI Data Analysis). For rats, T1 and T2 relaxation times were determined using inversion recovery [TR = 10 s, TE = 8.22 ms, inversion time (TI) varied in 9 steps from 13.14 to 8,000 ms, signals fitted to MZ = M0(1-2exp(−TI/T1)] and spin echo [TR = 10 s, TE varied in 10 steps from 30 to 160 ms, signals fitted to MZ = M0exp(−TE/T2)] experiments, respectively. In both cases the same SE-EPI readout used for CEST imaging was used to acquire images. The slice plan for T1 and T2 mapping was identical to CEST MRI to enable co-registration of the images. T1 and T2 times were quantified post-mortem immediately after in vivo CEST MRI in a subset of mice used for the hypoxia alleviation experiment (n = 5 Atovaquone treated, n = 5 control) using the same method. Mice were euthanized with an overdose of pentobarbital and replaced in the MRI, with their temperature continually maintained at 37°C to minimize variations in relaxation times owing to reduced thermal regulation post-mortem. The relaxation times were measured post-mortem because no respiration-gated relaxation time mapping sequence was available on the spectrometer used in this study.

For rats, T1-weighted gradient echo anatomical imaging was performed pre- and post-injection of gadolinium contrast agent (Omniscan, GE Healthcare) to elucidate the extent of blood-brain barrier breakdown (TR = 500 ms, TE = 20 ms, same slice plan as CEST MRI but with in-plane resolution 125 × 125 μm).

MRI data analysis

The APT effect from the measured CEST MRI data was quantified using the APTR* metric as described previously (25, 28–30). Briefly, data were fit to the Bloch-McConell equations using BayCEST in the FMRIB Software Library (FSL; https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/baycest) assuming a 3-pool exchange model comprising water, amide protons at 3.5 ppm, and a combined NOE+MT pool at -2.41 ppm. Correction for water T1 and T2 effects was implemented by allowing BayCEST to fit water T1 and T2 from the data, with prior values set based on the average T1 and T2 time within the whole rat brain or whole tumor for mice, yielding an APTR* value that is sensitive only to amide proton concentration and exchange rate (which is itself proportional to 10pH). A previous study (30) has shown that APTR* quantified using this analysis approach is specific to changes in protein concentration and pH in biologically relevant phantoms. The exchange rate and concentration estimated by BayCEST were used to generate an idealized two-pool Z-spectrum, and APTR* calculated using Eq. A. The calculation compares the signal at the amide proton frequency from this two-pool Z-spectrum [Sw+a(3.5ppm)) to the signal from an idealized one-pool Z-spectrum (Sw(3.5ppm)), normalized by the unsaturated signal (Mw0). The T1 and T2 relaxation times in the idealized simulations were set as 1.8s and 50ms for water, and as 1.8s and 1ms for amide protons.

formula

Regions of interest (ROI) were defined to measure the mean ± standard deviation APTR* from areas of biologically similar tissue. For rats, an initial abnormality volume was defined manually based on the T1-weighted post-Gd anatomical images, and down-sampled to the resolution of the CEST images. Because the model of brain metastasis used here is known to reflect the heterogeneity present in human disease (23), with a characteristic rim of infiltrating tumor cells surrounding a core of necrotic tissue and cellular debris, the water T1 time was used within the abnormality region as a tissue classifier: voxels with T1 < 1.6 s were classified as normal tissue, voxels with 1.6 s < T1 < 2.2 s as tumor, and tissue with T1 > 2.2s as necrotic (see Supplementary Fig. S1 for example T1 and T2 maps). The automatic segmentation was implemented using MATLAB (MathWorks Inc.) and adjusted manually to ensure an accurate “rim-core” pattern was present in the full-tumor volume. An ROI of approximately equal size to the tumor volume was defined in an equivalent anatomical location in the contralateral hemisphere to represent normal tissue, ensuring that the contralateral ROI contained the same composition of tissue (i.e., striatal) as the region encompassing the tumor ROI.

Regions of interest for infiltrating tumor rim, necrotic tumor core, and contralateral hemisphere were then used to calculate the relative APTR* as rAPTR* = APTR*(Tumor)/APTR*(Normal). The relative APTR* metric calculated this way normalizes differences in raw APTR* values between animals, thereby allowing group-wise statistics to be calculated. rAPTR* measurements from all animals and all ROIs were combined using a random effects model to generate a weighted mean that accounts for the size of the ROI in each animal as well as the variability of APTR* within each ROI (see Supplementary Methods for further details). Group-wise rAPTR* values are reported as a mean ± 95% CI, and were compared with a hypothetical mean of 1 (which would suggest no difference from normal tissue) using a one-sample t test; statistical significance was defined as P < 0.05.

For analysis of the subcutaneous tumors in mice, the whole-tumor region was manually segmented. Because no normal control tissue was available to permit calculation of rAPTR*, the absolute APTR* values for all voxels in tumors from both Atovaquone-treated and control groups were calculated and the cumulative frequency of APTR* values compared using the Kolmogorov–Smirnov test, with statistical significance defined as P < 0.05.

Ex vivo protein concentration measurements and histology

Immediately following MRI experiments, rats were split into two groups. One group (n = 10) were sacrificed under terminal anesthesia by transcardial perfusion-fixation with 0.9% heparinized saline, followed by periodate-lysine-paraformaldehyde containing 0.025% glutaraldehyde. The brains of these animals were collected for histology. The remaining rats (n = 5) were sacrificed under terminal anesthesia by transcardial perfusion with 0.9% heparinized saline. The brains of these rats were extracted and sliced into 2-mm slices using an ice-cold rat brain matrix (Braintree Scientific). Tissue biopsies of the tumor and contralateral brain tissue were taken for subcellular fractionation and protein concentration quantification. The biopsies were snap-frozen in liquid nitrogen and stored at −80°C until further processing.

Mice in the hypoxia alleviation experiment were also split into two groups following MRI. One group (n = 5 Atovaquone-treated animals and n = 6 control animals) were injected intraperitoneally with 0.01 mL/g EF5 (2-(2-Nitro-1H-imidazol-1-yl)-N-(2,2,3,3,3-pentafluoropropyl) acetamide) and sacrificed under terminal anesthesia by transcardial perfusion-fixation with 0.9% heparinized saline, followed by periodate-lysine-paraformaldehyde containing 0.025% glutaraldehyde. The tumors of these animals were excised for histology. The remaining animals were sacrificed following MRI under terminal anesthesia without perfusion, and their tumors excised and stored at −80°C until further processing.

The snap-frozen biopsies from both mice and rats were homogenized in Cytoplasmic Extraction Buffer (from the Subcellular Protein Fractionation Kit for Tissues, Thermo Scientific), sedimented by centrifugation (500 × g for 5 minutes at 4°C), and the supernatant recovered; this sample contained the cytoplasmic protein from the tissue biopsy. The pellet was resuspended in PBS and mixed until homogenous; this sample contained the remaining (non-cytoplasmic) protein from the biopsy. The protein concentration of these fractions was quantified by bicinchoninic acid (BCA) assay (Pierce BCA Assay Kit, Thermo Scientific). Mean and standard deviation protein concentrations were taken from triplicate measurements, and were combined across animals and cellular locations to give group-wise protein concentration estimates. The total protein concentration was derived as the sum of the two fractions. The protein concentrations were compared using an unpaired two-tailed t test, with statistical significance defined as P < 0.05.

Tissue from both rats and mice that had been perfusion-fixed was sectioned at 10-μm thickness. Tissue sections were stained nonspecifically for all proteins using 1.25% w/v Coomassie Brilliant Blue G-250 dye (Thermo Scientific) in 0.9 % NaCl with 0.5 % Tween for 30 minutes. The addition of Tween to the staining solution ensures that cellular membranes are permeated and that Coomassie also stains intracellular proteins. Subsequently, sections were washed in PBS for 3 × 15 minutes or until the destain solution ran clear, and imaged at 200 x magnification using a Leica Biosystem ScanScope CS2 scanner (Aperio, ePathology Solutions, Milton Keynes). To convert the blue stain from Coomassie into protein concentration measurements, homogenized samples of naïve rat brain tissue were processed in a similar way to experimental tissue and serially diluted, as described previously (31). The protein concentration of each dilution was measured by both BCA assay and Coomassie staining to produce a standard curve (see Supplementary Fig. S2 for more details). For rat tissue, tissue sections adjacent to those stained for Coomassie Blue were stained for hypoxia (pimonidazole; Hypoxyprobe) and blood vessel (CD31; AF3628, R&D Systems) markers. For mouse tissue, adjacent tissue sections to those stained with Coomassie Blue were stained for the hypoxia marker EF5 and cellular nucleus marker DAPI, visualized using immunofluorescence, to evaluate the effect of Atovaquone treatment on tumor hypoxia.

Estimation of contribution of protein concentration and pH to APT signals using isoAPTR*

An isoAPTR* analysis (32) was performed to determine the contribution of protein concentration to measured APTR* differences between tumor and normal tissue. Briefly, a library of over 500,000 theoretical APTR* values was generated for a range of amide proton concentrations (relative concentration to water 0–4 × 10−3, n = 501) and pH values (5.00–7.65, n = 1,001, pH dependence of exchange rate defined by kamide = 5.57 × 10pH - 6.4) using a two-pool model of the Bloch-McConnell equations. The T1 and T2 relaxation times for water were set as 1.8 s and 50 ms, respectively, and for amide protons as 1.8 s and 1 ms, respectively. Lines of constant APTR* (isoAPTR* lines) were defined in pH-amide concentration space corresponding to those measured from tumor and normal appearing tissue in vivo. The biophysical source of the difference in APTR* between these two tissue areas can then be determined by (i) assuming a normal tissue pH value and (ii) inferring a change in amide proton concentration from some independent measurement. Using a normal brain pH of 7.1 (15) and the APTR* measured in normal appearing brain in vivo, the normal brain amide proton concentration was estimated. Subsequently, the change in amide proton concentration between tumor and normal tissue was assumed to be the same as the protein concentration change measured by Coomassie staining. Combining the tumor amide proton concentration estimate with the tumor measured APTR*, an estimate of the tumor pH may be found. By comparing the measured tumor rim APTR* with the APTR* expected to be measured if there was no pH change in the tumor, the proportion of APTR* signal change attributable to pH and protein concentration changes, respectively, was determined.

Because the tumor pH estimated using isoAPTR* depends on the initial value of the normal tissue pH assumed, the steps of the isoAPTR* method were repeated but using different assumptions of normal tissue pH within a realistic physiological range (7.0–7.3) and the final tumor pH change taken as the average of all of these repeated calculations.

APTR* is elevated in the tumor rim and tumor core of brain metastases

Post-Gd T1-weighted imaging showed the heterogeneity typical of human disease, with a contrast-enhancing rim and hypo- to isointense central area; taken to reflect an infiltrating rim and necrotic core, respectively (Fig. 1A and B). APTR* was visually hyperintense in tumor regions (Fig. 1C). Conventional MTRasym measurements were elevated in the tumor, and concomitant changes in the T1 and T2 relaxation times were also evident (see Supplementary Figs. S2 and S3 and Supplementary Table S1). rAPTR* in both rim and core ROIs were significantly greater than 1, indicating increased APTR* values in tumor areas [Fig. 2, rAPTR*(Rim) = 1.10 ± 0.09, rAPTR*(Core) = 1.14 ± 0.01, mean ± 95% CI].

Figure 1.

Representative post-gadolinium image (A) with tumor rim (red) and core (green) regions of interest overlaid (B), and APTR* map (C) from preclinical model of brain metastasis. The contrast-enhancing tumor rim and hypo- to isointense tumor core resemble images obtained from human metastatic foci and illustrate the intratumoral heterogeneity of an infiltrating rim region and necrotic core. Enhancement in the APTR* map in the region of the tumor is evident. No tumor burden in the contralateral hemisphere is apparent.

Figure 1.

Representative post-gadolinium image (A) with tumor rim (red) and core (green) regions of interest overlaid (B), and APTR* map (C) from preclinical model of brain metastasis. The contrast-enhancing tumor rim and hypo- to isointense tumor core resemble images obtained from human metastatic foci and illustrate the intratumoral heterogeneity of an infiltrating rim region and necrotic core. Enhancement in the APTR* map in the region of the tumor is evident. No tumor burden in the contralateral hemisphere is apparent.

Close modal
Figure 2.

APTR* is elevated in both tumor rim and tumor core. P values show significance level of a one-sample t test compared with a hypothetical mean of rAPTR* = 1 (which would indicate no difference from normal tissue). Error bars, 95% CI. The number of voxels for each tissue type is 1,347 for tumor rim and 1,475 for tumor core.

Figure 2.

APTR* is elevated in both tumor rim and tumor core. P values show significance level of a one-sample t test compared with a hypothetical mean of rAPTR* = 1 (which would indicate no difference from normal tissue). Error bars, 95% CI. The number of voxels for each tissue type is 1,347 for tumor rim and 1,475 for tumor core.

Close modal

Protein concentration is elevated in tumor rim assessed histologically

No significant difference in cytoplasmic, non-cytoplasmic or total protein concentration was evident between the biopsied tumor and contralateral brain tissue (Fig. 3). However, the protein concentration measurements are likely biased owing to tumor heterogeneity, meaning mixed tissue types (necrotic tumor core and infiltrating tumor rim) were likely assayed in the same sample. Consequently, quantitative protein concentration measurements were made using Coomassie staining of tissue sections in a second cohort of animals, yielding spatial information on protein concentration heterogeneity. Example sections through a tumor volume of a representative rat are shown in Fig. 4A, as well as zoomed regions of the tumor rim, necrotic core and contralateral hemisphere from a single section (Fig. 4B–D). These sections show a clear pattern of lighter blue staining (corresponding to lower protein concentration) in the core areas, darker blue staining in the tumor rim areas, and an intermediate level of blue staining in the contralateral hemisphere. The same spatial distribution was maintained over all sections and all animals (18 sections per animal) with a small but significant increase in protein concentration in the tumor rim evident (normal 8% ± 2% w/w, tumor 9% ± 2% w/w, mean ± S.D., P < 0.05, Fig. 4E). Pimonidazole and CD31 vessel staining confirmed that core regions were largely necrotic, whereas the rim regions maintained vessel structure but also showed a degree of hypoxia (Supplementary Fig. S4).

Figure 3.

Cytoplasmic, non-cytoplasmic, and total protein concentration measured by BCA assay of tissue biopsies (n = 5) is not significantly different between tumor (gray) and normal (black) tissue. Statistical comparison was unpaired t test comparing contralateral and tumor protein concentration in each fraction. n.s., nonsignificant.

Figure 3.

Cytoplasmic, non-cytoplasmic, and total protein concentration measured by BCA assay of tissue biopsies (n = 5) is not significantly different between tumor (gray) and normal (black) tissue. Statistical comparison was unpaired t test comparing contralateral and tumor protein concentration in each fraction. n.s., nonsignificant.

Close modal
Figure 4.

Coomassie staining reveals the spatial heterogeneity of protein concentration in tumors. A. Examples of Coomassie-stained tissue sections through a tumor of a representative rat. B–D, Magnified (×200) regions of tumor rim, tumor core, and contralateral tissue, respectively. E, Group-wise protein concentration measurements show that tumor rim has a significantly higher protein concentration than contralateral tissue, whereas tumor core has significantly lower protein concentration than contralateral tissue. *, P < 0.05; ****, P < 0.0001 (one-way repeated measures ANOVA, followed by Tukey's multiple comparison test).

Figure 4.

Coomassie staining reveals the spatial heterogeneity of protein concentration in tumors. A. Examples of Coomassie-stained tissue sections through a tumor of a representative rat. B–D, Magnified (×200) regions of tumor rim, tumor core, and contralateral tissue, respectively. E, Group-wise protein concentration measurements show that tumor rim has a significantly higher protein concentration than contralateral tissue, whereas tumor core has significantly lower protein concentration than contralateral tissue. *, P < 0.05; ****, P < 0.0001 (one-way repeated measures ANOVA, followed by Tukey's multiple comparison test).

Close modal

Protein concentration changes account for 66% of measured APTR* signal change

Using the isoAPTR* method with the normal and tumor tissue measured APTR* (normal 3.43% ± 0.10% M0, tumor 3.77% ± 0.09% M0, mean ± 95% CI) and the measured protein concentration increase in the tumor rim (from Coomassie measurements), it was found that approximately 66 % of the APTR* change was caused by protein concentration (α in Fig. 5). Thus, the remaining 34% signal change (β in Fig. 5) reflects an increase in tumor pH to 7.14 ± 0.01. The contralateral hemisphere was used as a measurement of normal tissue because no evidence of tumor burden was observed using the post-Gd T1-weighted imaging (Fig. 1A). In addition, previous studies have confirmed histologically that there is no tumor present in the contralateral hemisphere in this model (23).

Figure 5.

Demonstration of the isoAPTR* method to measure the pH of ENU tumors. Black and red lines indicate the isoAPTR* lines for the APTR* values measured in the contralateral and tumor rim ROIs, respectively, with 95% CI shown in gray for contralateral and pink for tumor rim. Arrows show the isoAPTR* methodology, where the contralateral tissue pH is assumed to be 7.11 and used with the measured APTR* to estimate the amide proton concentration. Using the relative increase in protein concentration in the tumor rim tissue measured by Coomassie staining, a tumor pH of 7.14 ± 0.01 was measured. The blue “x” shows the APTR* value that would be expected with no pH change in the tumor, indicating that approximately 66% of the observed APTR* change reflects protein concentration changes (α), with the remaining 34% a result of pH changes in the tumor (β).

Figure 5.

Demonstration of the isoAPTR* method to measure the pH of ENU tumors. Black and red lines indicate the isoAPTR* lines for the APTR* values measured in the contralateral and tumor rim ROIs, respectively, with 95% CI shown in gray for contralateral and pink for tumor rim. Arrows show the isoAPTR* methodology, where the contralateral tissue pH is assumed to be 7.11 and used with the measured APTR* to estimate the amide proton concentration. Using the relative increase in protein concentration in the tumor rim tissue measured by Coomassie staining, a tumor pH of 7.14 ± 0.01 was measured. The blue “x” shows the APTR* value that would be expected with no pH change in the tumor, indicating that approximately 66% of the observed APTR* change reflects protein concentration changes (α), with the remaining 34% a result of pH changes in the tumor (β).

Close modal

Atovaquone treatment alleviates tumor hypoxia with no concomitant alteration of protein concentration

Representative sections of tumors from mice in the control (DMSO) or Atovaquone-treated groups are shown in Fig. 6A stained for DAPI (cell nuclei), EF5 (hypoxia) and Coomassie (protein concentration). The alleviation of tumor hypoxia in the Atovaquone-treated tumor is evident as a reduction in the intensity of the fluorescent signal in the image. The relative fluorescence intensity over all animals showed the significant effect of Atovaquone in reducing tumor hypoxia (Fig. 6B). Importantly, no concomitant alteration in cytoplasmic protein concentration was observed, as shown by similar intensities in the Coomassie-stained images in Fig. 6A, the nonsignificant difference between protein concentration quantified from these images in Fig. 6C, and the quantitation of protein concentration in various subcellular fractions by BCA assay in Fig. 6D.

Figure 6.

A, Representative tissue sections from tumors in the DMSO or Atovaquone treatment groups stained for DAPI, EF5, and Coomassie show reduction in tumor hypoxia due to Atovaquone with no concomitant alteration of protein concentration. B, The reduction in tumor hypoxia is evident as a significant reduction in EF5 fluorescence intensity across all animals (**, P < 0.01, unpaired t test). C and D, Cytoplasmic protein concentration as measured by Coomassie staining (C) or BCA assay (D) was not significantly different between Atovaquone and DMSO groups (*, P > 0.05, unpaired t test). n.s., nonsignificant.

Figure 6.

A, Representative tissue sections from tumors in the DMSO or Atovaquone treatment groups stained for DAPI, EF5, and Coomassie show reduction in tumor hypoxia due to Atovaquone with no concomitant alteration of protein concentration. B, The reduction in tumor hypoxia is evident as a significant reduction in EF5 fluorescence intensity across all animals (**, P < 0.01, unpaired t test). C and D, Cytoplasmic protein concentration as measured by Coomassie staining (C) or BCA assay (D) was not significantly different between Atovaquone and DMSO groups (*, P > 0.05, unpaired t test). n.s., nonsignificant.

Close modal

APTR* measures pH change associated with alleviation of hypoxia due to Atovaquone treatment

The histogram of APTR* measurements from Atovaquone treated tumors was significantly different from the histogram of APTR* measured from control animals, with the median APTR* being lower in Atovaquone-treated animals (Fig. 7A). The spatial heterogeneity of the APTR* maps in tumors, necessitating the measurement of effect size by cumulative frequency distributions, is shown in Fig. 7B for two representative animals, clearly showing a higher APTR* in control (DMSO)-treated animals compared with Atovaquone treated. Crucially, because no significant change in protein concentration was measured between the two groups using BCA assay and Coomassie staining techniques, it is likely that this APTR* difference is a result of a change in tumor pH associated with the reduction in tumor hypoxia. The size of this pH change was estimated as −0.07 pH units by isoAPTR* (Fig. 7C), reducing tumor pH from 7.14 ± 0.01 to 7.07 ± 0.01. The tumor pH of 7.14 ± 0.01 in the DMSO group was assumed from the previous estimate of tumor pH in the rat model of brain metastasis.

Figure 7.

A, Histogram of APTR* values measured from tumors on mice treated with Atovaquone to decrease tumor hypoxia or with DMSO as control. The median APTR* was significantly lower in Atovaquone-treated animals (P < 0.001, Mann–Whitney test). B, The heterogeneity of APTR* values within single tumors is shown in representative animals from each group to demonstrate the necessity of the histogram analysis. C, isoAPTR* analysis measured a tumor pH reduction of 0.07 pH units, consistent with the decrease in tumor pH expected with a reduction of tumor hypoxia.

Figure 7.

A, Histogram of APTR* values measured from tumors on mice treated with Atovaquone to decrease tumor hypoxia or with DMSO as control. The median APTR* was significantly lower in Atovaquone-treated animals (P < 0.001, Mann–Whitney test). B, The heterogeneity of APTR* values within single tumors is shown in representative animals from each group to demonstrate the necessity of the histogram analysis. C, isoAPTR* analysis measured a tumor pH reduction of 0.07 pH units, consistent with the decrease in tumor pH expected with a reduction of tumor hypoxia.

Close modal

In this study, we sought to better understand the contribution of protein concentration and pH changes in tumors to APT signals. We performed in vivo APT MRI and ex vivo protein concentration measurements, and combined information from both modalities to determine that approximately 66% of the measured APT signal change was explained by a protein concentration increase. The remaining 34% was assumed to be a result of an alkalosis of tumor intracellular pH (compared with normal tissue) to 7.14 ± 0.01, in agreement with previous studies showing that the intracellular pH in tumor cells is slightly alkalotic compared with normal cells (4, 21). Elucidation of the contribution of pH effects to altered APT signals in tumors leads to improved understanding of APT measurements in the clinic, and may enable inferences regarding the pH of individual patients' tumors in clinical settings. In addition, we used the anti-malarial drug Atovaquone to reduce tumor hypoxia and measured the associated alteration in tumor pH using APT MRI, which opens the possibility of using APT MRI for assessing therapeutic response. The observed shift in intracellular pH of Atovaquone-treated tumors, to values typically seen in normal cells, is consistent with expectations based on the link between hypoxia and tumor pH (6, 33).

This study represents the first application of the APTR* method (29) to tumor imaging, with all previous in vivo studies investigating ischemic stroke. The measured increase in APTR* in the tumor rim and core in this study is in agreement with previous studies that have reported an increased APT signal in viable tumor and necrotic tissue using other nonquantitative APT MRI analysis methods. Although the BCA-derived protein assay showed no differences in protein concentration between normal brain and tumor tissue, the Coomassie histology revealed a reduced protein concentration in the necrotic core region compared with normal brain tissue. Tissue detachment during the sectioning and staining process is a common problem in acellular tissue areas, which could bias the Coomassie protein concentration measurements in the necrotic regions. For this reason, only tumor rim areas were used in the subsequent isoAPTR* analysis. Additional potential limitations to using the Coomassie stain as a protein concentration measurement technique include inaccurate measurements as a result of poor sectioning technique and difficulties with co-registration of the APT MRI data with histology resolution images. Nevertheless, Coomassie staining retains more information about tumor heterogeneity than simple biopsies of tumor tissue. The agreement between the BCA-derived protein concentration measurements in this study and previous measurements from a 9L glioma model (19), suggests that those studies may not have fully captured tumor protein heterogeneity and that future studies using other tumor models may benefit from protein concentration measurements made using the Coomassie staining procedure.

The invasive nature of histology limits the clinical translatability of the methods used in this preclinical study. The histological measurement of protein concentration, and the associated estimation of pH by isoAPTR*, is not necessarily meant for clinical translation, however, but rather to aid in the interpretation of APT MRI. Whereas prior clinical studies have interpreted the APT signal in tumors in terms of changes in intracellular protein content alone, the results of this study suggest that the APT signal change in tumors also reflects changes in pH. In addition, it may not be necessary to use ex vivo histological measurements of protein concentration to estimate tumor pH using isoAPTR* if tumor pH can be selectively modulated, as was done using Atovaquone in this study. Alternative endogenous CEST MRI methods such as AACID (34) have been used in this way to investigate the tumor pH change associated with administration of dichloroacetate (35), topiramate (36), and lonidamine (37) in preclinical experiments. In addition, exogenous diaCEST (38–41) and paraCEST (42, 43) contrast agents have been developed that successfully measure tumor extracellular pH, though these methods are limited by contrast agent availability to the tumor, which may be limited in areas of particularly poor perfusion (44) or in the brain where the blood-brain barrier may be partially intact in the early stages of tumor growth.

The isoAPTR* method used in this study assumes that the measurement of protein concentration can be used to infer a change in the amide proton concentration, which is the true biophysical origin of the APT signal. This simplification requires further assumptions, for instance that the number of amide protons available for exchange per protein molecule does not change. Protein conformation changes, proteolysis, or differences in protein size between the two tissues may also affect the validity of this assumption. Although it is unlikely that protein structure would be sufficiently different between tumor and normal tissue to affect the conclusions of this work, further studies to investigate the effect of differences in protein structure and size on the APT MRI signal in tumors are warranted.

This study combined in vivo APT MRI measurements with ex vivo histological measurements of protein concentration in a model of brain metastasis to determine that the proportion of APT signal change originating from changes in protein concentration is ca. 66%, with the remaining 34% originating from changes in tumor pH. Furthermore, a significant change in tumor pH associated with a pharmacologically induced reduction in tumor hypoxia was measured using APT MRI in a subcutaneous tumor model. This study extended our understanding of APT MRI, and may enable the use of APT MRI to infer the pH of individual patients' tumors as a biomarker either for therapy stratification or of therapeutic response in clinical settings.

Michael A. Chappell has provided expert testimony for royalties from non-academic licensing of the FMRIB Software Library. No potential conflicts of interest were disclosed by the other authors.

Conception and design: K.J. Ray, M.A. Chappell, N.R. Sibson

Development of methodology: K.J. Ray, J.R. Larkin, P. Kinchesh, S.C. Smart, M.A. Chappell

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): K.J. Ray, M.A. Simard, J.R. Larkin, J. Coates, S.C. Smart, N.R. Sibson

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): K.J. Ray, M.A. Simard, J.R. Larkin, J. Coates, G.S. Higgins, M.A. Chappell, N.R. Sibson

Writing, review, and/or revision of the manuscript: K.J. Ray, M.A. Simard, J.R. Larkin, J. Coates, P. Kinchesh, G.S. Higgins, M.A. Chappell, N.R. Sibson

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): P. Kinchesh

Study supervision: N.R. Sibson

The authors would like to thank Karla Watson, Jade Harris, and Jessica Law for assistance with animal husbandry. This work was supported by Cancer Research UK (C5255/A15935), the CRUK/EPSRC Cancer Imaging Centre in Oxford (grant number C5255/A16466), and the Medical Research Council (MC_ST_U13080, MR/K501256/1). G.S. Higgins was supported by Cancer Research UK through their Clinician Scientist Awards program (C34326/A19590 and C34326/A13092). S.C. Smart was supported by Cancer Research UK grants C5255/A12678, C2522/A10339, EPSRC grant C2522/A10339, and the MRC Unit Grant for the Oxford Institute for Radiation Oncology.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Hanahan
D
,
Weinberg
RA
. 
The hallmarks of cancer
.
Cell
2000
;
100
:
57
70
.
2.
Tannock
IF
,
Rotin
D
. 
Acid pH in tumors and its potential for therapeutic exploitation
.
Cancer Res
1989
;
49
:
4373
84
.
3.
Wojtkowiak
JW
,
Verduzco
D
,
Schramm
KJ
,
Gillies
RJ
. 
Drug resistance and cellular adaptation to tumor acidic pH microenvironment
.
Mol Pharm
2011
;
8
:
2032
8
.
4.
Webb
BA
,
Chimenti
M
,
Jacobson
MP
,
Barber
DL
. 
Dysregulated pH: a perfect storm for cancer progression
.
Nat Rev Cancer
2011
;
11
:
671
7
.
5.
Flinck
M
,
Kramer
SH
,
Pedersen
SF
. 
Roles of pH in control of cell proliferation
.
Acta Physiol
. 
2018
;
223
:
e13068
.
6.
Harris
AL
. 
Hypoxia—a key regulatory factor in tumour growth
.
Nat Rev Cancer
2002
;
2
:
38
47
.
7.
Eales
KL
,
Hollinshead
KE
,
Tennant
DA
. 
Hypoxia and metabolic adaptation of cancer cells
.
Oncogenesis
2016
;
5
:
e190
.
8.
Chiche
J
,
Brahimi-Horn
MC
,
Pouysségur
J
. 
Tumour hypoxia induces a metabolic shift causing acidosis: a common feature in cancer
.
J Cell Mol Med
2010
;
14
:
771
94
.
9.
van Zijl
PCM
,
Lam
WW
,
Xu
J
,
Knutsson
L
,
Stanisz
GJ
. 
Magnetization transfer contrast and chemical exchange saturation transfer MRI. Features and analysis of the field-dependent saturation spectrum
.
Neuroimage
2017
;
168
:
222
41
.
10.
Zhou
J
,
Lal
B
,
Wilson
DA
,
Laterra
J
,
van Zijl
PCM
. 
Amide proton transfer (APT) contrast for imaging of brain tumors
.
Magn Reson Med
2003
;
50
:
1120
6
.
11.
Zhou
J
,
Zhu
H
,
Lim
M
,
Blair
L
,
Quinones-Hinojosa
A
,
Messina
SA
, et al
Three-dimensional amide proton transfer MR imaging of gliomas: initial experience and comparison with gadolinium enhancement
.
J Magn Reson Imaging
2013
;
38
:
1119
28
.
12.
Zhou
J
,
Tryggestad
E
,
Wen
Z
,
Lal
B
,
Zhou
T
,
Grossman
R
, et al
Differentiation between glioma and radiation necrosis using molecular magnetic resonance imaging of endogenous proteins and peptides
.
Nat Med
2011
;
17
:
130
4
.
13.
Mehrabian
H
,
Desmond
KL
,
Soliman
H
,
Sahgal
A
,
Stanisz
GJ
. 
Differentiation between radiation necrosis and tumor progression using chemical exchange saturation transfer
.
Clin Cancer Res
2017
;
23
:
3667
75
.
14.
Zaiss
M
,
Windschuh
J
,
Paech
D
,
Meissner
J-E
,
Burth
S
,
Schmitt
B
, et al
Relaxation-compensated CEST-MRI of the human brain at 7T: unbiased insight into NOE and amide signal changes in human glioblastoma
.
Neuroimage
. 
2015
112
:
180
8
.
15.
Zhou
J
,
Payen
JF
,
Wilson
DA
,
Traystman
RJ
,
van Zijl
PCM
. 
Using the amide proton signals of intracellular proteins and peptides to detect pH effects in MRI
.
Nat Med
2003
;
9
:
1085
90
.
16.
Tee
YK
,
Harston
GWJ
,
Blockley
N
,
Okell
TW
,
Levman
J
,
Sheerin
F
, et al
Comparing different analysis methods for quantifying the MRI amide proton transfer (APT) effect in hyperacute stroke patients
.
NMR Biomed
2014
;
27
:
1019
29
.
17.
Sun
PZ
,
Wang
E
,
Cheung
JS
. 
Imaging acute ischemic tissue acidosis with pH-sensitive endogenous amide proton transfer (APT) MRI–correction of tissue relaxation and concomitant RF irradiation effects toward mapping quantitative cerebral tissue pH
.
Neuroimage
2012
;
60
:
1
6
.
18.
Yan
K
,
Fu
Z
,
Yang
C
,
Zhang
K
,
Jiang
S
,
Lee
D-H
, et al
Assessing amide proton transfer (APT) MRI contrast origins in 9 L gliosarcoma in the rat brain using proteomic analysis
.
Mol Imaging Biol
2015
;
17
:
479
87
.
19.
Xu
J
,
Zaiss
M
,
Zu
Z
,
Li
H
,
Xie
J
,
Gochberg
DF
, et al
On the origins of chemical exchange saturation transfer (CEST) contrast in tumors at 9.4 T
.
NMR Biomed
2014
;
27
:
406
16
.
20.
Zhang
X
,
Lin
Y
,
Gillies
RJ
. 
Tumor pH and its measurement
.
J Nucl Med
2010
;
51
:
1167
70
.
21.
Bhujwalla
ZM
,
Aboagye
EO
,
Gillies
RJ
,
Chacko
VP
,
Mendola
CE
,
Backer
JM
. 
Nm23-transfected MDA-MB-435 human breast carcinoma cells form tumors with altered phospholipid metabolism and pH: a 31P nuclear magnetic resonance study in vivo and in vitro
.
Magn Reson Med
1999
;
41
:
897
903
.
22.
Workman
P
,
Aboagye
EO
,
Balkwill
F
,
Balmain
A
,
Bruder
G
,
Chaplin
DJ
, et al
Guidelines for the welfare and use of animals in cancer research
.
Br J Cancer
2010
;
102
:
1555
77
.
23.
Serres
S
,
Martin
CJ
,
Sarmiento Soto
M
,
Bristow
C
,
O'Brien
ER
,
Connell
JJ
, et al
Structural and functional effects of metastases in rat brain determined by multimodal MRI
.
Int J Cancer
2014
;
134
:
885
96
.
24.
Ashton
TM
,
Fokas
E
,
Kunz-Schughart
LA
,
Folkes
LK
,
Anbalagan
S
,
Huether
M
, et al
The anti-malarial atovaquone increases radiosensitivity by alleviating tumour hypoxia
.
Nat Commun
2016
;
7
:
12308
.
25.
Harston
GW
,
Tee
YK
,
Blockley
N
,
Okell
TW
,
Thandeswaran
S
,
Shaya
G
, et al
Identifying the ischaemic penumbra using pH-weighted magnetic resonance imaging
.
Brain
2015
;
138
:
36
42
.
26.
Kinchesh
P
,
Allen
PD
,
Beech
JS
,
Fokas
E
,
Gilchrist
S
,
Kersemans
V
, et al
Dynamic reacquisition for respiratory gated, constant TR 2D multi-slice MRI
.
Proc 23rd Sci Meet Int Soc Magn Reson Med
. 
2015
.
p. 2574
.
27.
Walker-Samuel
S
,
Ramasawmy
R
,
Torrealdea
F
,
Rega
M
,
Rajkumar
V
,
Johnson
SP
, et al
In vivo imaging of glucose uptake and metabolism in tumors
.
Nat Med
2013
;
19
:
1067
72
.
28.
Chappell
MA
,
Groves
AR
,
Whitcher
B
,
Woolrich
MW
. 
Variational Bayesian inference for a nonlinear forward model
.
IEEE Trans Signal Process
2009
;
57
:
223
36
.
29.
Chappell
MA
,
Donahue
MJ
,
Tee
YK
,
Khrapitchev
AA
,
Sibson
NR
,
Jezzard
P
, et al
Quantitative Bayesian model-based analysis of amide proton transfer MRI
.
Magn Reson Med
2013
;
70
:
556
67
.
30.
Ray
KJ
,
Larkin
JR
,
Tee
YK
,
Khrapitchev
AA
,
Karunanithy
G
,
Barber
M
, et al
Determination of an optimally sensitive and specific chemical exchange saturation transfer MRI quantification metric in relevant biological phantoms
.
NMR Biomed
2016
;
29
:
1624
33
.
31.
Miller
JA
,
Curella
P
,
Zahniser
NR
. 
A new densitometric procedure to measure protein levels in tissue slices used in quantitative autoradiography
.
Brain Res
1988
;
447
:
60
6
.
32.
Ray
KJ
,
Larkin
JR
,
Chappell
MA
,
Sibson
NR
.
isoAPTR*: a novel method to measure tumour pHi using CEST MRI
.
Proc 25th Sci Meet Int Soc Magn Reson Med
. 
2017
.
p. 1972
.
33.
Stubbs
M
,
McSheehy
PM.
,
Griffiths
JR
,
Bashford
CL
. 
Causes and consequences of tumour acidity and implications for treatment
.
Mol Med Today
2000
;
6
:
15
9
.
34.
McVicar
N
,
Li
AX
,
Gonçalves
DF
,
Bellyou
M
,
Meakin
SO
,
Prado
MA
, et al
Quantitative tissue pH measurement during cerebral ischemia using amine and amide concentration-independent detection (AACID) with MRI
.
J Cereb Blood Flow Metab
2014
;
34
:
690
8
.
35.
Albatany
M
,
Li
A
,
Meakin
S
,
Bartha
R
. 
Dichloroacetate induced intracellular acidification in glioblastoma: in vivo detection using AACID-CEST MRI at 9.4 Tesla
.
J Neurooncol
2018
;
136
:
255
62
.
36.
Marathe
K
,
McVicar
N
,
Li
A
,
Bellyou
M
,
Meakin
S
,
Bartha
R
. 
Topiramate induces acute intracellular acidification in glioblastoma
.
J Neurooncol
2016
;
130
:
465
72
.
37.
Mcvicar
N
,
Li
AX
,
Meakin
SO
,
Bartha
R
. 
Imaging chemical exchange saturation transfer (CEST) effects following tumor-selective acidification using lonidamine
.
NMR Biomed
2015
;
28
:
566
75
.
38.
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
.
39.
Chen
M
,
Chen
C
,
Shen
Z
,
Zhang
X
,
Chen
Y
,
Lin
F
, et al
Extracellular pH is a biomarker enabling detection of breast cancer and liver cancer using CEST MRI
.
Oncotarget
2017
;
8
:
45759
67
.
40.
Longo
DL
,
Sun
PZ
,
Consolino
L
,
Michelotti
FC
,
Uggeri
F
,
Aime
S
. 
A general MRI-CEST ratiometric approach for pH imaging: demonstration of in vivo pH mapping with iobitridol
.
J Am Chem Soc
2014
;
136
:
14333
6
.
41.
Moon
BF
,
Jones
KM
,
Chen
LQ
,
Liu
P
,
Randtke
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
.
42.
Li
AX
,
Suchy
M
,
Li
C
,
Gati
JS
,
Meakin
S
,
Hudson
RH
, et al
In vivo detection of MRI-PARACEST agents in mouse brain tumors at 9.4 T
.
Magn Reson Med
2011
;
66
:
67
72
.
43.
Ferrauto
G
,
Di Gregorio
E
,
Auboiroux
V
,
Petit
M
,
Berger
F
,
Aime
S
, et al
CEST-MRI for glioma pH quantification in mouse model: validation by immunohistochemistry
.
NMR Biomed
2018
;31:
e4005
.
44.
Jones
KM
,
Randtke
EA
,
Yoshimaru
ES
,
Howison
CM
,
Chalasani
P
,
Klein
RR
, et al
Clinical translation of tumor acidosis measurements with AcidoCEST MRI
.
Mol Imaging Biol
2017
;
19
:
617
25
.