New magnetic resonance (MR) molecular imaging techniques offer the potential for noninvasive, simultaneous quantification of metabolic and perfusion parameters in tumors. This study applied a three-dimensional dynamic dual-agent hyperpolarized 13C magnetic resonance spectroscopic imaging approach with 13C-pyruvate and 13C-urea to investigate differences in perfusion and metabolism between low- and high-grade tumors in the transgenic adenocarcinoma of mouse prostate (TRAMP) transgenic mouse model of prostate cancer. Dynamic MR data were corrected for T1 relaxation and RF excitation and modeled to provide quantitative measures of pyruvate to lactate flux (kPL) and urea perfusion (urea AUC) that correlated with TRAMP tumor histologic grade. kPL values were relatively higher for high-grade TRAMP tumors. The increase in kPL flux correlated significantly with higher lactate dehydrogenase activity and mRNA expression of Ldha, Mct1, and Mct4 as well as with more proliferative disease. There was a significant reduction in perfusion in high-grade tumors that associated with increased hypoxia and mRNA expression of Hif1α and Vegf and increased ktrans, attributed to increased blood vessel permeability. In 90% of the high-grade TRAMP tumors, a mismatch in perfusion and metabolism measurements was observed, with low perfusion being associated with increased kPL. This perfusion-metabolism mismatch was also associated with metastasis. The molecular imaging approach we developed could be translated to investigate these imaging biomarkers for their diagnostic and prognostic power in future prostate cancer clinical trials. Cancer Res; 77(12); 3207–16. ©2017 AACR.

A pressing need facing the clinical management of prostate cancer patients is an accurate method for distinguishing aggressive, potentially lethal prostate cancer from indolent disease. Prostate cancer is the second most prevalent cancer in American men, with 1 in 6 being diagnosed, but is fatal in only 12% of these cases (1). Active surveillance has emerged as an appropriate management technique for patients in whom disease is likely to be indolent (tumors ≤ 0.5 cc and Gleason grade ≤ 3+3; ref. 2). New focal therapy approaches are also being considered for men with defined regions of localized intermediate risk prostate cancer (moderate size cancers with secondary Gleason 4 pattern) that can be clearly identified on imaging (3, 4), whereas patients with more aggressive but localized disease are treated with surgical or radiotherapy. Therefore, the ability to localize and provide a noninvasive imaging assessment of cancer aggressiveness has become critically important for clinical management of men with prostate cancer. The current state-of-the-art for imaging localized prostate cancer, multiparametric 1H MRI, has demonstrated the ability to localize tumors for subsequent biopsy and treatment, but cannot consistently grade tumor aggressiveness accurately in individual patients (4).

Increasing evidence points to prostate cancer being a disease strongly linked to abnormal metabolism due to changes in key metabolic enzymes (5). Also, tumor microenvironment factors such as perfusion (6) have been associated with the presence and aggressiveness of prostate cancer. In this study, a new dual-agent hyperpolarized (HP) 13C MRI approach was investigated to characterize aggressive cancers based on their metabolic and perfusion abnormalities and applied to a preclinical mouse model of prostate cancer. The development and progression of prostate cancer in the transgenic adenocarcinoma of mouse prostate (TRAMP) model mimics many aspects of human prostate cancer (7). Specifically, cancer development in the TRAMP model is targeted to the prostate and tumors progress from primary androgen dependent to large androgen-independent tumors with metastases. In addition, most male TRAMP mice develop tumors that are pathologically similar to human prostate cancer and are large enough for MRI studies (8). Metabolically, the TRAMP model has demonstrated changes in TCA metabolism and glycolysis associated with the evolution and progression of prostate cancer in patients (7).

Hyperpolarized 13C MRI is a powerful new metabolic imaging method that uses specialized instrumentation to provide signal enhancements of over 10,000-fold for 13C enriched, safe, endogenous, nonradioactive compounds (9). Although prostate cancer is often inadequately evaluated using FDG-PET (which assesses glucose uptake and phosphorylation; refs. 10, 11), HP 13C MR detects downstream metabolism, specifically the metabolic flux of HP 13C-pyruvate to lactate (kPL) catalyzed by lactate dehydrogenase (LDH). This method has shown great potential for not only detecting prostate cancer, but for also assessing the aggressiveness (pathologic grade) of the cancer (12). The “Warburg effect,” an elevation in glycolysis and lactate production in the presence of oxygen, occurs in prostate cancer and is due to multiple factors, including oncogenic modulations, mitochondrial dysfunction, as well as an adaptive response to the tumor microenvironment to promote proliferation (13). A prior single time-point 13C magnetic resonance spectroscopic imaging (MRSI) study of hyperpolarized 13C-pyruvate metabolism in the TRAMP model demonstrated a significant increase in hyperpolarized 13C-lactate in high- versus low-grade prostate tumors (12). The unprecedented gain in sensitivity provided by hyperpolarization can be combined with fast spectroscopic 13C MRSI techniques to provide spatially resolved dynamic data of hyperpolarized pyruvate metabolism (14). These dynamic data can be fit to kinetic models (15) to obtain the flux of HP 13C-pyruvate to lactate catalyzed by LDH rather than a single time-point measurement of hyperpolarized 13C-lactate, which is very sensitive to differences in the timing of probe delivery, and when the imaging data are acquired. Another important feature of HP 13C MRSI is that it encodes chemical as well as spatial information, thereby providing the potential for using multiple hyperpolarized 13C-labeled agents to detect several metabolic and/or physiologic processes simultaneously after the injection of a single bolus (16). HP 13C-urea is not taken up and metabolized by most tissues, and prior publications have demonstrated that hyperpolarized 13C-urea provides a reproducible and accurate assessment of blood perfusion in animal cancer models (16, 17). Methods for copolarizing 13C-pyruvate and 13C-urea have been developed, successfully polarized, and injected in preclinical models to simultaneously measure perfusion and metabolism (16).

The goal of this study was to use a three-dimensional (3D) dynamic, dual-agent, 13C-pyruvate and 13C-urea, HP 13C MRSI approach to investigate differences in perfusion and metabolism metrics between high- and low-grade tumors in the TRAMP model.

Animal protocol and handling

All animal studies were conducted in accordance with the policies of Institutional Animal Care and Use Committee at University of California, San Francisco. The TRAMP model, supplied by Roswell Park Cancer Institute, was generated in a C57BL/6 background utilizing a transgene consisting of a 7426/28 bp fragment of the rat probasin (rPB) gene directing the tissue-specific expression of simian virus 40 (SV40) early genes (T/t antigens; Tag) to the mouse prostate epithelium to abrogate the activity of the p53 and Rb tumor suppressors (8). The 19 TRAMP mice utilized in this study were cannulated using a 32-gauge IV catheter in the lateral tail vein and anesthetized with 1% to 1.5% isoflurane/100% oxygen at a rate of 1 L/min on a heated water bed to maintain physiological body temperature.

MR imaging

The imaging studies were performed on a 3T MR scanner (MR750, GE Healthcare) using a custom-built dual-tuned 13C-proton quadrature murine coil. Dynamic HP 13C spectral data were acquired after a tail vein injection of 350 μL of copolarized 80 mmol/L [1-13C]pyruvate and 13C urea (details in Supplementary Materials and Methods) using a 3D HP 13C compressed sensing (CS) EPSI sequence (14) with a field of view (FOV) of 4 cm x 4 cm x 8.6 cm, a spectral BW of 581 Hz, with a spatial resolution of 3.3 mm x 3.3 mm x 5.4 mm, and a temporal resolution of 2 seconds. Eighteen time-points were acquired starting immediately after the 15-second injection. Anatomic reference images were acquired using a T2-weighted Fast Spin Echo (FSE) sequence (spatial resolution: 0.23 mm, FOV: 6 cm, TE/TR = 102/5,821 ms).

Histopathologic analysis

TRAMP mice were euthanized and dissected within 6 hours after the MRI study. To measure tumor hypoxia, Pimonidazole (PIM) solution was injected approximately 45 minutes prior to euthanization. Dissection was performed by an experienced uro-oncologist, and digital images were taken as a reference for localization and registration of tumor specimens. The excised tissue was aliquoted for histochemical processing, gene expression, and activity analyses (details in Supplementary Materials and Methods). The histologic index, as defined in Albers and colleagues, was used to pathologically define high- versus low-grade TRAMP tumors in this study (12). A histologic index was calculated based on the weighted percentages of tumor differentiation (normal, well-differentiated, moderately well differentiated, and poorly differentiated) from standard hematoxylin and eosin (H&E) staining. The histologic index ranged between 0 and 3, where 0 indicated that 100% of the tissue was normal and 3 indicated that 100% of the tissue was poorly differentiated. In the Albers' publication as in this one, the tumors were dichotomized to be either low grade (index ≤ 1) or high grade (index ≥ 2) in a manner that reflects the clinical pathologic situation in which patients with Gleason score ≤ 3+3 are considered to have low versus Gleason ≥ 3 + 4 high-grade tumors. In all cases, the histologic assessments were determined from what were considered globally representative sections of the tumor masses.

Metastases were detected and enumerated at the time of primary tumor dissection, by careful, systematic, visual inspection, and concomitant photography of the abdominal and thoracic cavity contents, starting in the pelvic area with identification/inspection of pelvic and para-aortic lymph nodes, then moving to the mid-abdomen with inspection of the kidneys, perirenal lymph nodes, and mesentery, then moving to the upper abdomen, removing the liver and inspecting the surfaces of all its lobes, and lastly inspecting the thoracic cavity, particularly the lungs for any parenchymal metastases and the mediastinum area for any adenopathy. All visually identified metastatic lesions were confirmed by subsequent histologic examination.

Data processing

The HP MRSI data were reconstructed using a CS approach (details in Supplementary Materials and Methods; ref. 18). As shown in Fig 1, this processing resulted in 3D arrays (Fig. 1A) of dynamic HP 13C spectra (Fig. 1B), demonstrating resonances due to 13C-urea, [1-13C]alanine, [1-13C]pyruvate, and [1-13C]lactate. Coregistration of the histologic sections and HP 13C MRI data was achieved by cutting multiple sections of tumors in the same axial plane as the MRI images and taking care to mark various surfaces of primary tumors in situ with surgical dyes. Where possible, anatomic landmarks, such as urethra and bladder, were also utilized to help register histologic cross-sections with the MRI sections. Although data were zero filled for display purposes, quantitation of the HP 13C MR data was performed using the native acquisition resolution of the data. The ROIs incorporating the tumor were defined using the anatomic T2w images as reference, and only voxels that were > 85% in the tumor were quantified. Necrotic regions could be also visualized on the T2w images, and these voxels were found to have low S/N, so the spectral S/N filter (SNR < 4) employed removed necrotic voxels from the analysis. All preprocessing of 13C data, including reorganizing the k-space and l1-minimization and signal filtering, were performed using MATLAB (Mathworks), and the data were displayed using the open-source SIVIC package (19).

Figure 1.

A,In vivo 3D 13C MR spectral arrays shown at a single time point taken 20 seconds after injection of copolarized HP 13C-pyruvate in the axial, coronal, and sagittal planes overlaid on T2-weighted anatomic references. The spectra have an anterior–posterior and right–left resolution of 3.3 mm and a superior–inferior resolution of 5.4 mm. The primary tumor is outlined in green. B, Dynamic data (2s temporal resolution) shown for a representative 0.059 cm3 voxel in the center of the TRAMP tumor (red arrow) demonstrating resonances of 13C-urea, [1-13C]pyruvate, and the metabolic products [1-13C]lactate, [1-13C]alanine. C, Corresponding axial pyruvate-to-lactate conversion rate kPL and urea AUC images overlaid on T2-FSE anatomic references, respectively.

Figure 1.

A,In vivo 3D 13C MR spectral arrays shown at a single time point taken 20 seconds after injection of copolarized HP 13C-pyruvate in the axial, coronal, and sagittal planes overlaid on T2-weighted anatomic references. The spectra have an anterior–posterior and right–left resolution of 3.3 mm and a superior–inferior resolution of 5.4 mm. The primary tumor is outlined in green. B, Dynamic data (2s temporal resolution) shown for a representative 0.059 cm3 voxel in the center of the TRAMP tumor (red arrow) demonstrating resonances of 13C-urea, [1-13C]pyruvate, and the metabolic products [1-13C]lactate, [1-13C]alanine. C, Corresponding axial pyruvate-to-lactate conversion rate kPL and urea AUC images overlaid on T2-FSE anatomic references, respectively.

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Modeling of kPL and ktrans

HP [1-13C]pyruvate to [1-13C]lactate flux (kPL) was modeled using a two-compartment model as shown in Fig. 2C, described in the following equations:

formula
formula
formula
formula
Figure 2.

A, Dynamic hyperpolarized spectral data acquired after injection of HP [1-13C]pyruvate. B and C, Plot showing the raw signal intensities of pyruvate, lactate, alanine versus time (solid line), and the corresponding modeled metabolic data (dash line) used to determine kPL (B) as shown in the flow diagram (C) and described in Materials and Methods. D, HP 13C urea signals were extracted from the tumor and from nearby vessels to define an AIF. E, Fitting dynamic models to urea signal curves, with shaded region showing urea AUC. F, Two-compartment model for calculating the rate constant ktrans. AU, arbitrary units.

Figure 2.

A, Dynamic hyperpolarized spectral data acquired after injection of HP [1-13C]pyruvate. B and C, Plot showing the raw signal intensities of pyruvate, lactate, alanine versus time (solid line), and the corresponding modeled metabolic data (dash line) used to determine kPL (B) as shown in the flow diagram (C) and described in Materials and Methods. D, HP 13C urea signals were extracted from the tumor and from nearby vessels to define an AIF. E, Fitting dynamic models to urea signal curves, with shaded region showing urea AUC. F, Two-compartment model for calculating the rate constant ktrans. AU, arbitrary units.

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where |{M_x}( t )$| is the longitudinal magnetization of metabolite x, kPL is the pyruvate-to-lactate flux, kPA is the pyruvate-to-alanine flux (Alanine was included in modeling), and T1,x is the spin-lattice relaxation time. Mx+[n] and Mx[n] represent the net HP 13C magnetization before and after the nth RF excitation (with flip angle |{\theta _n}$|⁠), and |{S_x}[ n ]$| is the observed metabolite signal.

Quantitatively, perfusion, and permeability can be reflected by the dynamic bio-distribution of HP 13C-urea between blood and tissue in vivo similar to the above equation (Fig. 2F).

formula

where Cblood(t) represents the arterial input function (AIF), Ctissue(t) is the time-resolved concentration in tissue, and ktrans (20) and k2 are modified forward and reverse perfusion coefficients, respectively. In the nonlinear numerical fitting, the reverse perfusion coefficient kep and the relaxation T1 are highly linearly-dependent variables. Therefore, we combined kep and T1 into a generalized term k2. The coefficient k2 reflects the combined effect of physiology and longitudinal relaxation. The summed HP 13C-urea peak area under dynamic curve (AUC) was also calculated and normalized to kidney urea AUC. Normalized urea AUC provides a measure of the tracer distribution in tissue, whereas ktrans provides combined measure of perfusion and permeability (20). For the AUC metric, although a rigorous definition of timing requires an offset t0 in the parameter models, the definition used should be reasonable given the relatively consistent urea peak position (±3 seconds) observed (Fig. 7).

Numerical fitting of the in-vivo dynamic data to the two-compartment model was performed using nonlinear least squares algorithm for both dynamic HP [1-13C]pyruvate (Fig. 2B) and HP 13C-urea signals (Fig. 2E). The multiband variable flip excitation scheme (θ) and spin lattice (T1) relaxation times were taken into account as sources of signal loss using a hybrid continuous-discrete time dynamical system (15). The reverse flux of HP [1-13C]lactate to HP [1-13C]pyruvate was excluded from the models because it has been determined to be negligible in TRAMP tumors (21), and a lower number of free variables improves fitting stabilization. The MATLAB-based fitting iterates until a local minimum in object function is arrived from the initial values.

Statistical analyses

Average values of kPL, urea AUC, and urea ktrans were calculated from the entire TRAMP tumor, excluding regions of necrosis, using the T2-weighted anatomic reference image to identify the tumor. Ki-67 and PIM immunohistochemical staining were reported as the mean fraction of cells (average ± stdev) staining positive. mRNA expression data are reported as relative changes to housekeeping genes. Significance was reported using the standard Mann–Whitney–Wilcoxon test comparing all measured parameters in low- versus high-grade TRAMP tumors (MATLAB) and at 3 significance levels, i.e., P values of <0.05, P < 0.01, P < 0.001.

Hyperpolarized 13C dynamic MRSI data were acquired on a total of 19 TRAMP mice, 10 with high- and 9 with low-grade tumors. The FOV of the dynamic CS-EPSI sequence (FOV—4 × 4 × 8.6 cm) enabled three-dimensional detection of hyperpolarized 13C spectra throughout the primary tumor and from the majority of mouse abdomen. Figure 1A shows representative HP 13C MR spectra in the axial, coronal, and sagittal planes acquired 21 seconds after the injection of HP [1-13C]pyruvate. Figures 1B, 2A, and 2D show representative dynamic HP 13C spectra (2-second temporal resolution) taken from a single voxel from the center of the TRAMP tumor shown in Fig. 1A (red arrow). The dynamic HP 13C spectral acquisition started at approximately 15 seconds from the start of injection of the hyperpolarized solution, at which time the HP [1-13C]pyruvate signal was near maximum (t ≈ 15–20 seconds). Also at this time-point, the metabolic products, HP [1-13C]lactate and [1-13C]alanine, were already observed in the TRAMP tumor and reached maximum signal intensity at approximately 25 to 30 seconds and 30 to 35 seconds, respectively. Similar to HP [1-13C]pyruvate, HP-13C urea had a maximal signal intensity at approximately 15 to 20 seconds. The timing of the dynamic data acquired for HP [1-13C]pyruvate, [1-13C]lactate, [1-13C]alanine, and 13C-urea was not different between low- and high-grade tumors.

Figure 2 depicts the analysis of the dynamic spectral data including correction for T1 relaxation and flip-angle and modeling to provide quantitative measures of pyruvate-to-lactate rate constants (kPL) and ktrans. Representative fits of the dynamic HP [1-13C]pyruvate and HP 13C-urea data to obtain kPL and ktrans are shown in Fig. 2B and E. Spatially interpolated maps of kPL and urea AUC were overlaid on the corresponding anatomic reference image (Fig. 1C). The kPL and urea overlays were restricted to the region of the tumor as identified on the T2-weighted anatomic reference image. In Fig. 1C, the mismatch between urea AUC and metabolism is clearly seen in comparing the kPL and urea AUC images of the high-grade tumor. Areas of this TRAMP tumor that demonstrated the lowest urea AUC (≈75–100 units) also showed the highest kPL (0.055–0.075 sec−1). This mismatch between metabolism and urea AUC was observed in 9 of 10 of the high-grade TRAMP tumors studied, and this mismatch was not observed in any of the low-grade tumors. In addition, 50% of the high-grade tumors demonstrated either lymph node or liver metastases, with mice having low-grade tumors not demonstrating any metastases.

Representative immunochemical-stained tissue sections from low- and high-grade TRAMP tumors are shown in Fig. 3. Similar to the human prostate, normal murine prostate histology is highly glandular with secretory epithelial cells lining glands and stromal tissue supporting the glands (not shown). The H&E histologic sections from the low- and high-grade tumors (Fig. 3A) depict the gradual replacement of the secretory epithelial cells by less differentiated epithelial cells until the glands were completely eliminated and only sheets of pleomorphic cells with irregular nuclei remained in the high-grade tumors. High-grade TRAMP tumors also demonstrated higher Ki-67 (Fig. 3B) and PIM (Fig. 3C) staining as compared with low-grade TRAMP tumors.

Figure 3.

Immunochemical staining of excised representative low- and high-grade TRAMP tumors; H&E section (A), Ki-67 staining (B), and PIM staining (C). The micrographs in A–C were taken under ×200 magnification and PIM staining (D) under ×40 magnification.

Figure 3.

Immunochemical staining of excised representative low- and high-grade TRAMP tumors; H&E section (A), Ki-67 staining (B), and PIM staining (C). The micrographs in A–C were taken under ×200 magnification and PIM staining (D) under ×40 magnification.

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Pyruvate to lactate flux images overlaid on corresponding T2-FSE anatomic reference images from a representative low- and a high-grade TRAMP tumor (Fig. 4A) demonstrated a heterogeneous but higher kPL flux in the high-grade tumor. Also in this high-grade tumor, there was a region of necrosis observed at pathology in the anterior aspect of the tumor associated with undetectable kPL (red arrow). Pyruvate to lactate flux (kPL) values (Fig. 4B) were significantly (P < 0.001) higher (0.056 ± 0.005 sec−1 vs. 0.019 ± 0.001 sec−1) for high- versus low-grade TRAMP tumors with no overlap of individual kPL values between the 2 groups in this study. The increase in kPL flux significantly correlated with higher LDH activity (0.85 ± 0.06 vs. low-grade: 0.43 ± 0.03, mmol/L/mg protein/min, P < 0.001) in high- versus low-grade tumors (Fig. 4D). Also, the high-grade TRAMP tumors were found to be more proliferative with a significantly (P < 0.001) larger portion of the tumor staining positive for Ki-67 than for low-grade tumors (95 ± 3% vs. 30 ± 7%). The alanine conversion was 1 to 2 orders of magnitude smaller than kPL, and there was no difference between high- and low- grade tumors (kPA low-grade: 0.002 ± 0.001 sec−1, high-grade: 0.004 ± 0.001 sec−1, P > 0.4).

Figure 4.

A, Representative calculated pyruvate-to-lactate flux (kPL) images overlaid on corresponding T2-FSE reference images from a low-grade (left) and a high-grade (right) TRAMP tumor. At pathology, a region of necrosis was observed in the anterior aspect of the tumor (red arrow). B, Box plots showing individual (diamonds), median, and standard deviation kPL flux measurements in the 9 low-grade and 10 high-grade TRAMP tumors. C, A bar plot showing the fraction of cells staining positive for Ki-67 (mean ± SE). D, LDH activities for the same TRAMP tumors. *, significantly different; P < 0.001.

Figure 4.

A, Representative calculated pyruvate-to-lactate flux (kPL) images overlaid on corresponding T2-FSE reference images from a low-grade (left) and a high-grade (right) TRAMP tumor. At pathology, a region of necrosis was observed in the anterior aspect of the tumor (red arrow). B, Box plots showing individual (diamonds), median, and standard deviation kPL flux measurements in the 9 low-grade and 10 high-grade TRAMP tumors. C, A bar plot showing the fraction of cells staining positive for Ki-67 (mean ± SE). D, LDH activities for the same TRAMP tumors. *, significantly different; P < 0.001.

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Figure 5A shows urea AUC images overlaid on reference anatomic images of the same animals shown in Fig. 4A, visually demonstrating heterogeneous but higher urea AUC in the low- versus high-grade tumor. A quantitative comparison of urea AUC and ktrans for the low- and high-grade tumors is shown in Fig. 5B. Urea AUC was significantly reduced (P < 0.01, 640 ± 94 as compared with the 1,407 ± 221 AU), whereas ktrans significantly increased (P < 0.01, 358 ± 38 as compared with 180 ± 24 mL/dL/min) in high- versus low- grade tumors. The hypoxia was also significantly (P < 0.05) increased in high- relative to low-grade prostate cancer (27 ± 6% vs. 14 ± 4% of the tumor staining positive for PIM, respectively), as measured by PIM immunohistochemical staining (Fig. 5D). Interestingly, no significant difference was found in microvessel density between high- and low-grade TRAMP tumors (high-grade = 14.3 ± 1.7, low-grade = 14.7 ± 2.1 vessels/hpf 200x, P > 0.5).

Figure 5.

A, Representative calculated urea AUC images overlaid on the corresponding T2-FSE anatomic reference images from a low-grade (right) and a high-grade TRAMP tumor. B, Bar plots showing average ± SE for urea AUC and ktrans. P < 0.01. AU, arbitrary units. C, Fraction of cells staining positive for PIM in the 9 low-grade and 10 high-grade TRAMP tumors studied. P < 0.05. D, No difference found in representative microvascular density (MVD) assessed by CD31 IHC staining (3 high-grade vs. 3 low-grade). *, significantly different.

Figure 5.

A, Representative calculated urea AUC images overlaid on the corresponding T2-FSE anatomic reference images from a low-grade (right) and a high-grade TRAMP tumor. B, Bar plots showing average ± SE for urea AUC and ktrans. P < 0.01. AU, arbitrary units. C, Fraction of cells staining positive for PIM in the 9 low-grade and 10 high-grade TRAMP tumors studied. P < 0.05. D, No difference found in representative microvascular density (MVD) assessed by CD31 IHC staining (3 high-grade vs. 3 low-grade). *, significantly different.

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Figure 6 summarizes the expression of key transporters and enzymes associated with pyruvate transport and metabolism (Mct1 and Mct4, Ldha, and Ldhb) and of factors affected by the hypoxic tumor microenvironment (Hif1α and Vegf). Expression of the monocarboxylate transporters Mct1 and Mct4 was significantly upregulated (Mct1: 2.75 ± 0.72-fold, P < 0.05, Mct4: 20.7 ± 7.12-fold, P < 0.01) in high- versus low-grade TRAMP tumors. Ldha was significantly increased 1.98 ± 0.22-fold (P < 0.01) and Ldhb significantly decreased to 0.13 ± 0.05- fold (P < 0.01), leading to a dramatic 15.0 ± 4.8-fold increase (P < 0.001) in the Ldha/Ldhb ratio in high- versus low-grade TRAMP tumors. Due to increased hypoxia in the tumor microenvironment (higher PIM staining), there was a significant 3.64 ± 0.55-fold (P < 0.01) and 6.40 ± 1.82-fold (P < 0.01) increase in Hif1α and Vegf expression, respectively, in high- versus low-grade TRAMP tumors.

Figure 6.

A, mRNA expression level ± SE of Hif1α (P < 0.01), Ldha (P < 0.01), Ldhb (P < 0.01), Vegf (P < 0.01), Mct1 (P < 0.05), and Mct4 (P < 0.01) for the 9 low-grade and 10 high-grade TRAMP tumors studied; values given are relative percent expression normalized to m.Hprt. B,Ldha/Ldhb ratio (P < 0.01) for the same TRAMP tumors. *, significantly different.

Figure 6.

A, mRNA expression level ± SE of Hif1α (P < 0.01), Ldha (P < 0.01), Ldhb (P < 0.01), Vegf (P < 0.01), Mct1 (P < 0.05), and Mct4 (P < 0.01) for the 9 low-grade and 10 high-grade TRAMP tumors studied; values given are relative percent expression normalized to m.Hprt. B,Ldha/Ldhb ratio (P < 0.01) for the same TRAMP tumors. *, significantly different.

Close modal

An accurate assessment of the aggressiveness of prostate cancer requires the complete coverage of the prostate at high spatial resolution due to the often small, multifocal, and biologically diverse nature of this disease. In this study, a volumetric dynamic dual-agent hyperpolarized 13C spectroscopic imaging approach using 13C-pyruvate and 13C-urea to simultaneously image changes in urea AUC and pyruvate metabolism with prostate cancer progression was performed for the first time in a transgenic murine model of prostate cancer. The 20,000- to 40,000-fold enhancement in signal-to-noise relative to the respective thermal MR signals at 3T provided by the copolarization of 13C-pyruvate and 13C-urea combined with fast volumetric 13C spectroscopic imaging allowed for both high spatial (0.06 cm3) and temporal (2 seconds) resolution imaging data to be acquired throughout the primary prostate tumor and the body of the mouse. This dynamic metabolic and perfusion data were fit to kinetic models (15) to obtain the flux of HP 13C-pyruvate to lactate (kPL) catalyzed by LDH and ktrans, a measure of both perfusion and vascular permeability. In addition, an estimate of tumor urea tissue distribution was calculated using the area under curve of hyperpolarized 13C-urea signal (16, 17). Moreover, the ability to dynamically measure urea delivery, tissue distribution, and pyruvate metabolism allows for differences in the timing of hyperpolarized probe delivery and metabolism that can occur between individual subjects. Although these differences in dynamics were small (2–5 seconds), they can add substantially to the variability of single time-point hyperpolarized 13C-lactate to 13C-pyruvate ratio measurements, because the 13C-pyruvate signal is dramatically decreasing due to T1 relaxation and metabolism during the spectral acquisition (12).

There is growing evidence that the upregulation of aerobic glycolysis and increased lactate production and efflux is an adaptation of cancer cells that aids in survival, growth, and metastasis and often leads to poor response to therapy (22, 23). The results of this study indicate that increased hyperpolarized 13C-pyruvate to lactate flux (kPL) is associated with more aggressive prostate cancer. Specifically, the kPL was 3-fold higher in high- relative to low-pathologic grade prostate tumors, with no overlap between kPL values from individual high- and low-grade tumors. High-grade TRAMP tumors were also more proliferative, having 3-fold higher Ki-67 staining than low-grade tumors. In concordance with the HP 13C-pyruvate-to-lactate metabolism data, there was a 2-fold increase in tissue LDH activity, as measured ex vivo, in the same high- relative to low-grade tumors. Because the measurement of HP lactate signal generation is in non–steady-state dynamics, increased LDH activity is a major factor contributing to the increase in the observed HP lactate signal in high-grade TRAMP tumors. The increased HP 13C-lactate signal observed in high- versus low-grade prostate cancer was also associated with increased mRNA expression of lactate dehydrogenase-A (Ldha), decreased expression of Ldhb, as well as increased monocarboxylate transporters (Mct1 and Mct4) that have a role in both pyruvate uptake and lactate export (5). LDHA and B are responsible for encoding the M and H subunits of LDH, and the high Ldha/Ldhb expression ratio observed in high-grade TRAMP tumors leads to the production of a predominance of the LDH5 isoenzyme (5M subunits) favoring increased lactate production. In patient studies, a high-serum LDH level is associated with aggressive disease and a poor survival for a variety of cancers (24, 25) including prostate cancer (26), and inhibition of the LDH-catalyzed production of lactate has become a therapeutic target (27, 28). Studies using patient biopsies and prostate tissue slices removed at surgery have demonstrated increased steady-state pools of lactate, HP 13C-lactate production, increased LDH activity, and increased mRNA expression of LDHA and MCT1 and 4 in prostate cancer tissues similar to what has been observed in the TRAMP model used in this study (29). Moreover, the kPL fluxes observed in this preclinical study were in line with what was calculated from 2D dynamic HP 13C MRSI studies in a phase 1 clinical trial of hyperpolarized [1-13C]pyruvate in patients with prostate cancer (0.045 ± 0.025 s−1; ref. 30). However, no correlation of kPL with tumor grade was performed in these prior preclinical and clinical studies.

The 6-fold increase in Mct4 observed in high-grade TRAMP tumors results in increased export of lactate out of the cells, which is important to sustaining a high glycolytic flux (31). Tumor excretion of lactic acid, combined with poor tumor perfusion, results in an acidic extracellular pH in tumors compared with normal tissue (16, 32), and this acidification of the tumor microenvironment has also been shown to occur in the TRAMP model (33). The resulting acidic environment promotes cancer aggressiveness and metastasis by facilitating a degradation of the extracellular matrix by proteinases (34, 35), increasing angiogenesis through the release of VEGF (36), and inhibiting the immune response to tumor antigens (37). Extracellular acidification also may render tumors chemo-resistant (37). Taken together, these observations suggest that not only increased lactic acid production, but also its efflux are important parameters associated with aggressive prostate cancer (38, 39). Moreover, tumor-specific metabolic shifts, such as increased production and efflux of lactate, can potentially be exploited for cancer therapy with minimal impact on normal tissues (32).

Hypoxia is a feature of many human cancers and has been implicated as an important biologically modulator of aggressiveness, clinical behavior, and treatment response in prostate cancer (40, 41). This study found a significant reduction in urea AUC in high- relative to low-grade TRAMP tumors, and an associated significant increase in hypoxia as measured by increased PIM staining. The oxygen-sensitive HIF1α transcription factor has been found to be upregulated in regions of tumor hypoxia and increases the expression of angiogenesis factors such as VEGF to increase oxygen delivery as well as increasing aerobic glycolysis through increasing expression and activity of key enzymes in the glycolytic pathway, including LDH (38). Consistent with this scenario, a significant increase was also observed in the mRNA expression of Hif1α, Vegf, and Ldha, as well as an increase in LDH activity in high- versus low-grade TRAMP tumors. Hypoxic prostate cancers, which induce HIF1α and glycolysis most strongly, tend to be of higher Gleason grade, are more invasive and metastatic, and less responsive to therapy than those with normal oxygen levels (41, 42).

Fitting the dynamic HP 13C-urea to a Tofts-like 2–compartment model (20) demonstrated that ktrans was significantly increased in high- versus low-grade prostate cancer. The parameter ktrans represents the transfer of the contrast agent, hyperpolarized 13C-urea, from the vasculature into the extravascular space and is therefore a function of both perfusion and permeability. Therefore, the observed increase in ktrans is presumably due to vascular hyperpermeability in high-grade TRAMP tumors, which is also consistent with increased expression of Vegf in high-grade tumors. The urea ktrans data are also consistent with prior gadolinium dynamic contrast-enhanced MRI studies of prostate cancer patients, in which high-grade prostate tumors demonstrated the earliest and greatest rate of enhancement and ktrans (43, 44). Although ktrans significantly increases, urea AUC significantly decreases in high-grade TRAMP prostate cancer tumors. The explanation for the decrease in urea AUC is the higher clearance of urea from high- versus low-grade tumors as observed in Fig. 7A and B. The increased rate of urea clearance (kep) was most likely due to a dramatic increase in the cellularity of high-grade TRAMP tumors and a decrease in extravascular extracellular space (EES) and associated urea tissue distribution volume VT (⁠|{V_T} = \ {k_{trans}}/{k_{ep}})$|⁠. The measurement of kep was not directly possible in this study due to the fact that k2 in our modeling was a lumped coefficient, that included urea clearance (kep) but was also heavily affected by urea T1 relaxation and other signal loss mechanisms (Fig. 7; ref. 17).

Figure 7.

Mean urea dynamic data (solid lines: blue, AIF; gold, tumor) for low-grade (A) and high-grade (B) TRAMP prostate tumors were fitted and ktrans and k2 parameters calculated (high-grade ktrans = 344, k2 = 4,380; low-grade ktrans = 163, k2 = 819). The fit curves were extrapolated to the bolus arrival time at t = 5(s). In high-grade tumors, higher cellularity contributes to more rapid clearance of urea and low AUC (high-grade urea AUC = 1,407 ± 221; low-grade = 640 ± 94).

Figure 7.

Mean urea dynamic data (solid lines: blue, AIF; gold, tumor) for low-grade (A) and high-grade (B) TRAMP prostate tumors were fitted and ktrans and k2 parameters calculated (high-grade ktrans = 344, k2 = 4,380; low-grade ktrans = 163, k2 = 819). The fit curves were extrapolated to the bolus arrival time at t = 5(s). In high-grade tumors, higher cellularity contributes to more rapid clearance of urea and low AUC (high-grade urea AUC = 1,407 ± 221; low-grade = 640 ± 94).

Close modal

Another important finding of this study was that there was significant heterogeneity (>2-fold) of kPL and urea AUC in individual TRAMP tumors, with 90% of the high-grade TRAMP tumors demonstrating regions that had areas of the lowest urea AUC having the highest kPL. Moreover, 50% of the high-grade TRAMP tumors demonstrated either lymph node or liver metastases, whereas none of the low-grade tumors demonstrated a urea AUC–kPL mismatch nor any metastases. Although perfusion and metabolism are tightly coupled in most normal tissues, mismatches have been observed in a variety of tumors including lung, breast, liver, colon, and head and neck cancers, and this mismatch was associated with more aggressive disease (45, 46). A perfusion–metabolism mismatch, specifically, a high glycolytic rate relative to low perfusion, has been imaged in locally advanced breast cancer by 15O-water and 18F-FDG PET; this was associated with poor response to treatment and early relapse or disease progression (45, 46). This study demonstrated that the urea AUC–kPL mismatch was similarly associated with aggressive prostate cancer in the TRAMP model. However, the relationship between urea AUC–kPL mismatch and aggressive prostate cancer needs to be validated, and the relationship between urea AUC and more conventional measurement of perfusion or blood flow needs to be determined in future patient studies.

The TRAMP murine model used in this preclinical study, like all preclinical models, has its limitations. Most importantly, disease progression in this murine model is faster than what is observed in prostate cancer patients, with prostate cancer progressing from early-stage to late-stage disease in a matter of weeks instead of years. Although the pathologic progression of disease in this model mimics the human situation, it progresses from an admixture of normal glandular tissue and cancer to large areas of densely packed malignant cells in a much shorter period of time. Early- and late-stage disease is more homogenous in the TRAMP model than in the human situation due to a lower incidence of coexisting benign prostate tissue, early and late stage cancer. In addition, although there are significant lymph node metastases in the TRAMP model and some liver metastases, similar to the human situation, there are virtually no bone metastases, unlike the human situation. The data acquisition scheme and the compartmental modeling approach used in this study represented a trade-off between several factors. The dynamic data acquisition scheme utilized double spin-echo refocusing (47) in order to provide improved SNR for individual images, but at the expense of saturating signals at the edges of the RF coil, requiring the acquisition to start at the end, and causing loss of the earliest points of the dynamic data. With this acquisition approach, we found that a two-site unidirectional pyruvate-to-lactate model provided the most robust and reproducible fits. Our approach also did not use information from the AIF. There is potential to improve the kinetic modeling of metabolism and perfusion with acquisitions capturing the bolus input signal, incorporating the AIF, and by using advanced modeling methods such as those presented in Kazan and colleagues (48), Khegai and colleagues (49), and Bankson and colleagues (50).

In summary, the pathologic grade dependence of hyperpolarized pyruvate-to-lactate flux, urea ktrans, and AUC were measured in a single imaging acquisition after administration of hyperpolarized 13C-pyruvate and 13C-urea in a transgenic mouse model of prostate cancer. High HP 13C-pyruvate to 13C-lactate flux, low 13C urea AUC, and high forward perfusion coefficient (ktrans) were found to be biomarkers of high-grade prostate cancer in this preclinical study. In addition, a substantial mismatch in urea AUC and a high pyruvate to lactate flux observed in this study were associated with highly proliferative disease with increased metastases. These hyperpolarized imaging biomarkers of aggressive prostate cancer, and their relationship to the perfusion–metabolism mismatch observed in prior studies, will clearly need to be better understood and validated in future patient studies. The likelihood of translating this hyperpolarized dual-agent MR approach is high because HP 13C-pyruvate is already FDA IND-approved for ongoing clinical trials, and 13C-urea has an excellent safety profile and is administered clinically at doses even higher than would be used for hyperpolarized MRI.

D.B. Vigneron reports receiving commercial research grant from GE Healthcare. No potential conflicts of interest were disclosed by the other authors.

Conception and design: H.-Y. Chen, P.E.Z. Larson, J. Kurhanewicz, D.B. Vigneron

Development of methodology: H.-Y. Chen, P.E.Z. Larson, R.A. Bok, C. von Morze, R. Delos Santos, N. Bahrami, M. Ferrone, J. Kurhanewicz, D.B. Vigneron

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): H.-Y. Chen, R.A. Bok, R. Sriram, R. Delos Santos, J. Delos Santos, M. Ferrone, J. Kurhanewicz, D.B. Vigneron

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): H.-Y. Chen, P.E.Z. Larson, C. von Morze, R. Sriram, J. Delos Santos, J.W. Gordon, J. Kurhanewicz, D.B. Vigneron

Writing, review, and/or revision of the manuscript: H.-Y. Chen, P.E.Z. Larson, R.A. Bok, C. von Morze, R. Sriram, J.W. Gordon, M. Ferrone, J. Kurhanewicz, D.B. Vigneron

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): R. Delos Santos, J. Delos Santos

Study supervision: R. Delos Santos, J. Kurhanewicz, D.B. Vigneron

This study was supported by grants from the Department of Defense (PC120148) and NIH (R01EB017449, R01EB013427, and P41EB013598, all to D.B. Vigneron; R01CA166655 to J. Kurhanewicz).

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.
American Cancer Society.
Cancer facts & figures: The American Cancer Society
; 
2015
.
Available from
: http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2015/.
2.
Eggener
SE
,
Mueller
A
,
Berglund
RK
,
Ayyathurai
R
,
Soloway
C
,
Soloway
MS
, et al
A multi-institutional evaluation of active surveillance for low risk prostate cancer
.
J Urol
2013
;
189
:
S19
25
.
3.
Chang
ST
,
Westphalen
AC
,
Jha
P
,
Jung
AJ
,
Carroll
PR
,
Kurhanewicz
J
, et al
Endorectal MRI and MR spectroscopic imaging of prostate cancer: Developing selection criteria for MR-guided focal therapy
.
J Magn Reson Imaging
2014
;
39
:
519
25
.
4.
Muller
BG
,
Futterer
JJ
,
Gupta
RT
,
Katz
A
,
Kirkham
A
,
Kurhanewicz
J
, et al
The role of magnetic resonance imaging (MRI) in focal therapy for prostate cancer: Recommendations from a consensus panel
.
BJU Int
2014
;
113
:
218
27
.
5.
Keshari
KR
,
Sriram
R
,
Van Criekinge
M
,
Wilson
DM
,
Wang
ZJ
,
Vigneron
DB
, et al
Metabolic reprogramming and validation of hyperpolarized 13C lactate as a prostate cancer biomarker using a human prostate tissue slice culture bioreactor
.
Prostate
2013
;
73
:
1171
81
.
6.
Wilson
DM
,
Kurhanewicz
J
. 
Hyperpolarized 13C MR for molecular imaging of prostate cancer
.
J Nucl Med
2014
;
55
:
1567
72
.
7.
Costello
LC
,
Franklin
RB
,
Zou
J
,
Feng
P
,
Bok
R
,
Swanson
MG
, et al
Human prostate cancer ZIP1/zinc/citrate genetic/metabolic relationship in the TRAMP prostate cancer animal model
.
Cancer Biol Ther
2011
;
12
:
1078
84
.
8.
Gingrich
JR
,
Barrios
RJ
,
Foster
BA
,
Greenberg
NM
. 
Pathologic progression of autochthonous prostate cancer in the TRAMP model
.
Prostate Cancer Prostatic Dis
1999
;
2
:
70
5
.
9.
Kurhanewicz
J
,
Vigneron
DB
,
Brindle
K
,
Chekmenev
EY
,
Comment
A
,
Cunningham
CH
, et al
Analysis of cancer metabolism by imaging hyperpolarized nuclei: Prospects for translation to clinical research
.
Neoplasia
2011
;
13
:
81
97
.
10.
Beauregard
JM
,
Williams
SG
,
Degrado
TR
,
Roselt
P
,
Hicks
RJ
. 
Pilot comparison of F-fluorocholine and F-fluorodeoxyglucose PET/CT with conventional imaging in prostate cancer
.
J Med Imaging Radiat Oncol
2010
;
54
:
325
32
.
11.
Farsad
M
,
Schiavina
R
,
Franceschelli
A
,
Sanguedolce
F
,
Castellucci
P
,
Bertaccini
A
, et al
Positron-emission tomography in imaging and staging prostate cancer
.
Cancer Biomark
2008
;
4
:
277
84
.
12.
Albers
MJ
,
Bok
R
,
Chen
AP
,
Cunningham
CH
,
Zierhut
ML
,
Zhang
VY
, et al
Hyperpolarized 13C lactate, pyruvate, and alanine: Noninvasive biomarkers for prostate cancer detection and grading
.
Cancer Res
2008
;
68
:
8607
15
.
13.
Kim
J-w
,
Gardner
LB
,
Dang
CV
. 
Oncogenic alterations of metabolism and the Warburg effect
.
Drug Discov Today
2005
;
2
:
233
8
.
14.
Larson
PE
,
Hu
S
,
Lustig
M
,
Kerr
AB
,
Nelson
SJ
,
Kurhanewicz
J
, et al
Fast dynamic 3D MR spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13C studies
.
Magn Reson Med
2011
;
65
:
610
9
.
15.
Bahrami
N
,
Swisher
CL
,
Von Morze
C
,
Vigneron
DB
,
Larson
PE
. 
Kinetic and perfusion modeling of hyperpolarized (13)C pyruvate and urea in cancer with arbitrary RF flip angles
.
Quant Imaging Med Surg
2014
;
4
:
24
32
.
16.
Wilson
DM
,
Keshari
KR
,
Larson
PE
,
Chen
AP
,
Hu
S
,
Van Criekinge
M
, et al
Multi-compound polarization by DNP allows simultaneous assessment of multiple enzymatic activities in vivo
.
J Magn Reson
2010
;
205
:
141
7
.
17.
von Morze
C
,
Larson
PEZ
,
Hu
S
,
Keshari
K
,
Wilson
DM
,
Ardenkjaer-Larsen
JH
, et al
Imaging of blood flow using hyperpolarized [13C]Urea in preclinical cancer models
.
J Magn Reson Imag
2011
;
33
:
692
7
.
18.
Hu
S
,
Lustig
M
,
Balakrishnan
A
,
Larson
PE
,
Bok
R
,
Kurhanewicz
J
, et al
3D compressed sensing for highly accelerated hyperpolarized (13)C MRSI with in vivo applications to transgenic mouse models of cancer
.
Magn Reson Med
2010
;
63
:
312
21
.
19.
Crane
JC
,
Olson
MP
,
Nelson
SJ
. 
SIVIC: Open-source, standards-based software for DICOM MR spectroscopy workflows
.
Int J Biomed Imaging
2013
;
2013
:
169526
.
20.
Tofts
PS
,
Brix
G
,
Buckley
DL
,
Evelhoch
JL
,
Henderson
E
,
Knopp
MV
, et al
Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: Standardized quantities and symbols
.
J Magn Reson Imaging
1999
;
10
:
223
32
.
21.
Swisher
CL
,
Larson
PEZ
,
Kruttwig
K
,
Kerr
AB
,
Hu
S
,
Bok
RA
, et al
Quantitative measurement of cancer metabolism using stimulated echo hyperpolarized carbon-13 MRS
.
Magn Reson Med
2014
;
71
:
1
11
.
22.
Feron
O.
Pyruvate into lactate and back: From the Warburg effect to symbiotic energy fuel exchange in cancer cells
.
Radiother Oncol
2009
;
92
:
329
33
.
23.
Hirschhaeuser
F
,
Sattler
UG
,
Mueller-Klieser
W
. 
Lactate: A metabolic key player in cancer
.
Cancer Res
2011
;
71
:
6921
5
.
24.
Petrelli
F
,
Cabiddu
M
,
Coinu
A
,
Borgonovo
K
,
Ghilardi
M
,
Lonati
V
, et al
Prognostic role of lactate dehydrogenase in solid tumors: A systematic review and meta-analysis of 76 studies
.
Acta Oncol
2015
;
54
:
961
70
.
25.
Wulaningsih
W
,
Holmberg
L
,
Garmo
H
,
Malmstrom
H
,
Lambe
M
,
Hammar
N
, et al
Serum lactate dehydrogenase and survival following cancer diagnosis
.
Br J Cancer
2015
;
113
:
1389
96
.
26.
Ryan
CJ
,
Smith
MR
,
Fizazi
K
,
Saad
F
,
Mulders
PF
,
Sternberg
CN
, et al
Abiraterone acetate plus prednisone versus placebo plus prednisone in chemotherapy-naive men with metastatic castration-resistant prostate cancer (COU-AA-302): Final overall survival analysis of a randomised, double-blind, placebo-controlled phase 3 study
.
Lancet Oncol
2015
;
16
:
152
60
.
27.
DeBerardinis
RJ
,
Thompson
CB
. 
Cellular metabolism and disease: What do metabolic outliers teach us?
Cell
2012
;
148
:
1132
44
.
28.
Kroemer
G
,
Pouyssegur
J
. 
Tumor cell metabolism: Cancer's Achilles' heel
.
Cancer Cell
2008
;
13
:
472
82
.
29.
Tessem
MB
,
Swanson
MG
,
Keshari
KR
,
Albers
MJ
,
Joun
D
,
Tabatabai
ZL
, et al
Evaluation of lactate and alanine as metabolic biomarkers of prostate cancer using H-1 HR-MAS spectroscopy of biopsy tissues
.
Magn Reson Med
2008
;
60
:
510
6
.
30.
Nelson
SJ
,
Kurhanewicz
J
,
Vigneron
DB
,
Larson
PEZ
,
Harzstark
AL
,
Ferrone
M
, et al
Metabolic imaging of patients with prostate cancer using hyperpolarized [1-C-13]pyruvate
.
Sci Transl Med
2013
;
5
:
198ra108
. doi: 10.1126/scitranslmed.3006070.
31.
Dimmer
KS
,
Friedrich
B
,
Lang
F
,
Deitmer
JW
,
Broer
S
. 
The low-affinity monocarboxylate transporter MCT4 is adapted to the export of lactate in highly glycolytic cells
.
Biochem J
2000
;
350 Pt 1
:
219
27
.
32.
Parks
SK
,
Chiche
J
,
Pouyssegur
J
. 
Disrupting proton dynamics and energy metabolism for cancer therapy
.
Nat Rev Cancer
2013
;
13
:
611
23
.
33.
Korenchan
D
,
Flavell
R
,
Baligand
C
,
Sriram
R
,
Neumann
K
,
Sukumar
S
, et al
Dynamic nuclear polarization of biocompatible 13 C-enriched carbonates for in vivo pH imaging
.
Chem Commun
2016
;
52
:
3030
3
.
34.
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
.
35.
Gatenby
RA
,
Gawlinski
ET
,
Gmitro
AF
,
Kaylor
B
,
Gillies
RJ
. 
Acid-mediated tumor invasion: A multidisciplinary study
.
Cancer Res
2006
;
66
:
5216
23
.
36.
Fukumura
D
,
Xu
L
,
Chen
Y
,
Gohongi
T
,
Seed
B
,
Jain
RK
. 
Hypoxia and acidosis independently up-regulate vascular endothelial growth factor transcription in brain tumors in vivo
.
Cancer Res
2001
;
61
:
6020
4
.
37.
Lardner
A.
The effects of extracellular pH on immune function
.
J Leukoc Biol
2001
;
69
:
522
30
.
38.
Coller
HA
. 
Is cancer a metabolic disease?
Am J Pathol
2014
;
184
:
4
17
.
39.
Dhup
S
,
Dadhich
RK
,
Porporato
PE
,
Sonveaux
P
. 
Multiple biological activities of lactic acid in cancer: Influences on tumor growth, angiogenesis and metastasis
.
Curr Pharm Des
2012
;
18
:
1319
30
.
40.
Milosevic
M
,
Warde
P
,
Menard
C
,
Chung
P
,
Toi
A
,
Ishkanian
A
, et al
Tumor hypoxia predicts biochemical failure following radiotherapy for clinically localized prostate cancer
.
Clin Cancer Res
2012
;
18
:
2108
14
.
41.
Wang
L
,
Chen
Z
,
Wang
Q
,
Cao
W
,
Jian
Y
,
Wang
S
, et al
Expression of hypoxia-inducible factor 1 alpha and vascular endothelial growth factor in prostate cancer and its significance
.
Zhonghua Nan Ke Xue
2006
;
12
:
57
9
.
42.
Matsumoto
S
,
Batra
S
,
Saito
K
,
Yasui
H
,
Choudhuri
R
,
Gadisetti
C
, et al
Antiangiogenic agent sunitinib transiently increases tumor oxygenation and suppresses cycling hypoxia
.
Cancer Res
2011
;
71
:
6350
9
.
43.
Oto
A
,
Yang
C
,
Kayhan
A
,
Tretiakova
M
,
Antic
T
,
Schmid-Tannwald
C
, et al
Diffusion-weighted and dynamic contrast-enhanced mri of prostate cancer: Correlation of quantitative MR parameters with Gleason score and tumor angiogenesis
.
Am J Roentgenol
2011
;
197
:
1382
90
.
44.
Peng
YH
,
Jiang
YL
,
Yang
C
,
Brown
JB
,
Antic
T
,
Sethi
I
, et al
Quantitative analysis of multiparametric prostate MR images: Differentiation between prostate cancer and normal tissue and correlation with Gleason score-A computer-aided diagnosis development study
.
Radiology
2013
;
267
:
787
96
.
45.
Mankoff
DA
,
Dunnwald
LK
,
Partridge
SC
,
Specht
JM
. 
Blood Flow-metabolism mismatch: Good for the tumor, bad for the patient
.
Clin Cancer Res
2009
;
15
:
5294
6
.
46.
Mankoff
DA
,
Dunnwald
LK
,
Gralow
JR
,
Ellis
GK
,
Charlop
A
,
Lawton
TJ
, et al
Blood flow and metabolism in locally advanced breast cancer: Relationship to response to therapy
.
J Nucl Med
2002
;
43
:
500
9
.
47.
Cunningham
CH
,
Chen
AP
,
Albers
MJ
,
Kurhanewicz
J
,
Hurd
RE
,
Yen
YF
, et al
Double spin-echo sequence for rapid spectroscopic imaging of hyperpolarized 13C
.
J Magn Reson
2007
;
187
:
357
62
.
48.
Kazan
SM
,
Reynolds
S
,
Kennerley
A
,
Wholey
E
,
Bluff
JE
,
Berwick
J
, et al
Kinetic modeling of hyperpolarized c-13 pyruvate metabolism in tumors using a measured arterial input function
.
Magn Resonance Med
2013
;
70
:
943
53
.
49.
Khegai
O
,
Schulte
RF
,
Janich
MA
,
Menzel
MI
,
Farrell
E
,
Otto
AM
, et al
Apparent rate constant mapping using hyperpolarized [1-C-13]pyruvate
.
NMR Biomed
2014
;
27
:
1256
65
.
50.
Bankson
JA
,
Walker
CM
,
Ramirez
MS
,
Stefan
W
,
Fuentes
D
,
Merritt
ME
, et al
Kinetic modeling and constrained reconstruction of hyperpolarized [1-C-13]-Pyruvate offers improved metabolic imaging of tumors
.
Cancer Res
2015
;
75
:
4708
17
.