SEARCH

SEARCH BY CITATION

Keywords:

  • prostate cancer;
  • high-resolution magic angle spinning (HRMAS);
  • MRS;
  • intact tissue

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men worldwide, largely as a result of the increased use of the annual serum prostate-specific antigen (PSA) screening test for detection. PSA screening has saved lives, but it has also resulted in the overtreatment of many patients with PCa because of a limited ability to accurately localize and characterize PCa lesions through imaging. High-resolution magic angle spinning (HRMAS) 1H MRS has proven to be a strong potential clinical tool for PCa diagnosis and prognosis. The HRMAS technique allows valuable metabolic information to be obtained from ex vivo intact tissue samples and also enables the performance of histopathology on the same tissue specimens. Studies have found that the quantification of individual metabolite levels and metabolite ratios, as well as metabolomic profiles, shows strong potential to improve accuracy in PCa detection, diagnosis and monitoring. Ex vivo HRMAS is also a valuable tool for the interpretation of in vivo results, including the localization of tumors, and thus has the potential to improve in vivo diagnostic tests used in the clinic. Here, we primarily review publications of HRMAS 1H MRS and its use for the study of intact human prostate tissue. Copyright © 2013 John Wiley & Sons, Ltd.


Abbreviations used
AMD1

S-adenosylmethionine decarboxylase

BPH

benign prostatic hyperplasia

DANTE

delay alternating with nutation for tailored excitation

ERETIC

electronic reference to access in vivo concentrations

GS

Gleason score

HRMAS

high-resolution magic angle spinning

ODC1

ornithine decarboxylase

PCa

prostate cancer

PSA

prostate-specific antigen

PUFA

polyunsaturated omega-6-fatty acid

TOCSY

total correlation spectroscopy

TRAMP

transgenic adenocarcinoma of the mouse prostate

INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

Prostate cancer (PCa) is the most frequently diagnosed malignancy in men worldwide, and the second leading cause of cancer death for men in the USA [1]. Currently, most cases of PCa are detected by the annual serum prostate-specific antigen (PSA) screening test, the use of which increased rapidly in the early 1990s. Serum PSA testing has proven to be a great asset to the PCa clinic as it has increased the number of PCa diagnoses and shifted the diagnosed population towards earlier stages [2, 3]. Although PSA screening has saved lives, it has also created controversies for PCa management because of a limited ability to accurately localize and characterize PCa lesions through imaging.

With the confirmation of elevated PSA levels in patients, biopsies are taken from the prostate and evaluated by histopathology. However, as radiological imaging is currently unable to visualize suspicious areas for targeted biopsy, in general, these randomly conducted biopsies often result in false negatives for patients with early-stage PCa as the method is ineffective in detecting small, heterogeneously distributed lesions [4]. In addition, even with the presence of cancer glands in a biopsy core, current histology is often unable to characterize PCa and to distinguish aggressive from indolent disease. Both of these issues present complications in the attempt to balance the benefits of therapies with the morbidities of aggressive treatments in the selection of the most appropriate treatment option for patients with PCa. Possible treatments for biopsy-proven PCa range from active surveillance without immediate intervention to a number of radical procedures, including prostatectomy, radiotherapy and chemotherapy, with the procedures often resulting in serious side-effects to the patient. At present, without the ability to appropriately characterize PCa, these radical procedures are often elected in cases of indolent PCa in order to ensure that the few aggressive cases are not missed, and result in unnecessary overtreatments that cause adverse effects and impair the quality of life for many [5]. These dilemmas could be greatly minimized if new PCa biomarkers and their imaging applications could differentiate PCa from benign tissue, predict tumor stage and location, and estimate malignant potential before the implementation of radical procedures.

High-resolution magic angle spinning (HRMAS) 1H MRS, introduced to intact biological tissue analysis in 1996 [6, 7], has proven to be a strong candidate to rectify these challenges seen in the PCa clinic. This method applies mechanical rotation of the sample at an angle of 54.7° to the magnetic field, and produces high-resolution spectra, allowing for the observation of individual metabolites without sacrificing tissue architectures. Thus, the HRMAS method allows for subsequent histopathological analysis, the current gold standard for PCa diagnosis and monitoring, of the same tissue specimen. Since the initial application of the HRMAS method, studies have found that the quantification of individual metabolite levels and metabolite ratios, as well as metabolomic profiles, i.e. collective evaluations of the entire measurable metabolome, shows strong potential to improve accuracy in PCa detection, diagnosis and monitoring. Here, we review the research studies on PCa using HRMAS 1H MRS, as summarized in Table 1; the use of HRMAS in the studies of other cancers, including lung, breast, brain, colorectal and cervical cancer, has been reviewed recently [8].

Table 1. Summary of all ex vivo intact human prostate tissue studies using high-resolution magic angle spinning (HRMAS) MRS
YearResultsSamplesReference
  1. BPH, benign prostatic hyperplasia; DANTE, delay alternating with nutation for tailored excitation; ERETIC, electronic reference to access in vivo concentrations; HR-QUEST, high-resolution quantum estimation; OAZ1, ornithine decarboxylase antizyme 1; ODC1, ornithine decarboxylase; PCa, prostate cancer; PSA, prostate-specific antigen; PUFA, polyunsaturated omega-6-fatty acid; TOCSY, total correlation spectroscopy.

1998High-quality MAS spectra reveal more intense lipid signals in PCa versus BPH12[9]
2001Correlations between concentrations of spermine and citrate, and volume percentage of benign epithelia16[10]
2003Slow spinning preserves tissue pathological structures and the DANTE pulse sequence suppresses spinning sidebands22[41]
2003HRMAS identifies distinctive metabolic patterns and helps combined MRI/three-dimensional MRSI to accurately identify and locate PCa54[14]
2003Storage conditions affect metabolite intensities for absolute concentrations, but not relative intensities12[39]
2005Metabolites can be quantified when spinning sidebands are suppressed with a simple minimum function, Min(A,B)31[42]
2005Metabolomic profiles can differentiate PCa from benign samples, correlate with PSA and delineate a subset of less aggressive cancer199[23]
2005Rotor-synchronized adiabatic TOCSY allows for the identification of choline- and ethanolamine-containing metabolites10[34]
2006PCa has higher concentrations of lactate and alanine, and lower concentrations of citrate and polyamines60[15]
2007Storage-induced metabolite changes are not significant for HRMAS tissue analysis15[40]
2008Technique can be used to quantify choline- and ethanolamine-containing metabolites47[16]
2008Significant increases in lactate and alanine concentrations in PCa tissue130[17]
2008Significant metabolic differences between cancerous and benign tissue, and correlations with tumor Gleason score48[18]
2009It is possible to detect PUFAs in prostate tissue with HRMAS NMR81[21]
2009Histopathology and genetic analysis can be performed on prostate tissue samples following HRMAS40[46]
2009Quantification of metabolic products of [3-13C]pyruvate in PCa cells5[37]
2009ERETIC method provides improved quantification accuracy for HRMAS spectroscopy of prostate tissue60[36]
2010New quantification method, HR-QUEST, reliably quantifies 16 metabolite and reference signals11[35]
2010Metabolomic profiles can differentiate cancer from benign and locate malignancy to direct biopsy199[24]
2010Metabolomic profiles can predict PCa recurrence with an accuracy of 78%79[25]
2010Correlations between PSA velocity, and ODC1 and OAZ1 mRNA expression levels18[49]
2011High-grade PCa shows greater cellular proliferation and higher concentrations of choline- and ethanolamine-containing metabolites49[19]
2011Potential to determine whether PCa may be present near benign prostate tissue biopsies149[20]
2011RNA extracted after HRMAS is still intact with high integrity, allowing for comparisons with metabolomic profiles53[48]
2012RNA quality is high after HRMAS and reveals mechanisms underlying low citrate and high choline levels in PCa133[47]
2012Significant correlations between PSA velocity, density and percentage free PSA, and citrate concentrations, in benign epithelia27[13]

EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

Perhaps the earliest study employing HRMAS MRS for the analysis of intact human prostate tissue was reported by Tomlins et al. [9] in 1998, shortly after the proposal of the HRMAS MRS concept. Whole pieces of human prostate tissue were obtained from patients with benign prostatic hyperplasia (BPH) and PCa, and HRMAS spectra were compared with spectra acquired from solutions obtained from a conventional tissue extraction procedure (Fig. 1). The results showed that the HRMAS method produced high-resolution spectra capable of revealing important biochemical information regarding prostate biochemistry, whilst also preserving tissue components. In addition, they found more intense lipid signals in PCa tissue when compared with BPH tissue [9]. This early investigation showed the utility of HRMAS in the study of PCa, and led to a focus on individual metabolites or metabolite ratios that could be used as specific biomarkers of PCa.

image

Figure 1. A series of 500-MHz high-resolution magic angle spinning (HRMAS) and high-resolution 1H NMR spectra of benign prostatic hyperplasia (BPH) tissue samples (a–c) and cancer tissue samples (d–f), comparing the differences between one-dimensional presaturated MAS spectra (a, d), the Carr–Purcell–Meiboom–Gill (CPMG) spectra (b, e) of intact tissue samples and the corresponding normal presaturated spectra from an acetonitrile–H2O extraction (c, f). The specific assignments for lipid protons are shown in spectrum d. Metabolite key: 1, isoleucine; 2, leucine; 3, valine; 4, lysine; 5, glutamate; 6, glutamine; 7, acetate; 8, alanine; 9, lactate; 10, N-acetylglycoproteins; 11, creatine; 12, choline; 13, tyrosine; 14, phosphatidylcholine; 15, hypoxanthine; 16, formate; 17, phenylalanine; 18, α-glucose; 19, guanidine diphosphate. [Reproduced from Tomlins et al. [9]: fig. 1.]

Download figure to PowerPoint

In 2001, a study used HRMAS on a 9.4-T (400 MHz) MR spectrometer and quantitative histopathology to evaluate tissue specimens from the removed prostates of patients with PCa [10]. For the first time, positive linear correlations of the volume percentage of benign prostatic epithelial cells with concentrations of spermine, a polyamine abundant in the prostate and a proposed endogenous inhibitor of PCa growth [11], and citrate, a critical prostate metabolite related to zinc depletion in malignant prostate tissue [12], were reported. These results are critical for an improved understanding of the biochemical characteristics of prostate tissue. In addition, by successfully showing that metabolite concentrations could be measured accurately in intact tissue with HRMAS, and also that quantitative histopathology could follow HRMAS analysis on the same specimens, future studies began to look into the functions of these PCa metabolites. Most recently, citrate concentrations measured by HRMAS from benign epithelial glands in the peripheral zone of patients with PCa were found to be correlated with PSA velocity, PSA density and serum percentage free PSA, such that low citrate concentrations in unit benign epithelial glands represent rapidly increasing PSA values, and probably fast-growing cancer (Fig. 2) [13]. These results advance previous findings that citrate and polyamine levels are reduced in higher grade, or more aggressive, cancers [14], by presenting the potential of using citrate to estimate PCa growth rates. These studies contribute not only to our knowledge of tumor biology, but may aid in the avoidance of the overtreatment of patients with PCa by differentiating fast- from slow-growing PCa. Thus, PCa growth rates may be predicted if prostate biopsies can be evaluated for their benign epithelial citrate concentrations by either HRMAS or another method.

image

Figure 2. Relationships between levels of citrate in histo-benign epithelia versus prostate-specific antigen (PSA) velocity (a), density (b) and percentage free PSA (c). The ratios of the concentrations of citrate and the volume percentages of histology-quantified benign epithelia decrease with an increase in PSA velocity and density, and with a decrease in the percentage of blood free PSA. The points in the plot represent the average value measured for each individual case, where multiple samples were analyzed. The vertical error bars are the standard errors of these multiple measurements. DPSA, PSA density; VPSA, PSA velocity. [Reproduced from Dittrich et al. [13]: fig. 3.]

Download figure to PowerPoint

Other studies by Swanson et al. [14-16] found a number of differing metabolite levels in cancerous and benign prostate tissue. Phosphocholine, taurine, myo-inositol, scyllo-inositol, lactate and alanine concentrations were significantly higher in cancer compared with benign tissue, whereas citrate and ethanolamine concentrations were lower in PCa tissue. The use of lactate and alanine as metabolic biomarkers of PCa was investigated further in a 2008 study using HRMAS of prostate ‘snap-frozen’ needle biopsy tissues, where very low concentrations of lactate and alanine were reported in benign prostate biopsy tissues, whereas their levels were increased significantly in biopsies containing cancer (Fig. 3) [17]. Although the use of alanine and lactate as ex vivo biomarkers of PCa has been disputed because of issues of metabolic degradation for postsurgical samples [15], biopsy tissues that were frozen within seconds of collection and analyzed using the electronic reference to access in vivo concentrations (ERETIC) method, provided a more accurate representation of metabolite levels in vivo [17].

image

Figure 3. Representative 1H high-resolution magic angle spinning (HRMAS) spectra and corresponding histopathological sections of benign predominantly glandular (40% glandular, 60% stroma) (a) and prostate cancer (70% Gleason 3 + 3) (b) biopsy tissues. The major metabolite, trimethylsilyl propanoic acid (TSP) and electronic reference to access in vivo concentrations (ERETIC) resonances are shown. Lactate (Lac) to alanine (Ala) regions from 144-ms Carr–Purcell–Meiboom–Gill (CPMG) spectra are magnified above the spectra. Cho, choline; GPC, glycerophosphocholine; PA, polyamines; PC, phosphocholine. [Reproduced from Tessem et al. [17]: fig. 1.]

Download figure to PowerPoint

van Asten et al. [18] studied metabolite concentrations and metabolite ratios in prostate needle biopsies, and found significant differences between cancerous and benign tissues, together with significant correlations between the Gleason score (GS) and metabolite ratios involving choline, creatine and citrate. Similar correlations with GS were reported by other studies showing that high-grade PCas (GS ≥ 4 + 3) have higher concentrations of choline- and ethanolamine-containing metabolites than do lower grade cancers (GS ≤ 3 + 4); in addition, a number of metabolite ratios correlated with the distance of tissue samples to the nearest tumor, the fraction of tumor cells present in the sample and the amount of cell proliferation as measured by Ki-67 staining [19, 20]. HRMAS has also been used to detect the presence of polyunsaturated omega-6-fatty acids (PUFAs), known promoters of PCa, in malignant prostate tissues using two-dimensional 1H–13C correlation spectra [21]. These results indicate that the analysis of individual metabolites by HRMAS can provide valuable information regarding the metabolic changes in PCa that may be relevant in the clinical setting. A summary of all individual metabolite and metabolite ratio findings from studies of intact human prostate tissue using HRMAS is listed in Table 2.

Table 2. Metabolite level changes and correlations observed in human prostate cancer using high-resolution magic angle spinning (HRMAS) MRS
MetabolitesObservationReference
  1. GS, Gleason score; PCa, prostate cancer; PSA, prostate-specific antigen.

AlanineIncreases[15, 17]
CitrateDecreases; correlates with PSA velocity, PSA density and percentage free PSA[13-15]
Citrate/creatineDecreases; correlates with GS[18]
Choline/creatineIncreases; correlates with GS and tumor fraction[18, 20]
Ethanolamine (ETA)Decreases[16]
Glycerophosphocholine (GPC)Increases, correlates with GS[14, 16, 19]
Glycerophosphoethanolamine (GPEA)Increases[16]
GPEA/ETAIncreases[16]
(GPC + PC)Increases[15]
(GPC + PC)/creatineIncreases; correlates with tumor fraction, rate of cell proliferation, distance to nearest tumor and GS[18, 20]
LactateIncreases[15, 17]
Lactate/alanineIncreases[18]
LipidsIncrease[9]
myo-InositolIncreases[14]
myo-Inositol/scyllo-inositolCorrelates with GS, tumor fraction and distance to nearest tumor[20]
Phosphocholine (PC)Increases; correlates with GS[14, 16, 19]
Phosphoethanolamine (PE)Increases[16]
PC/GPCIncreases[16]
PC/PEIncreases[16]
PE/ETAIncreases[16]
PolyaminesDecrease; lower levels in more aggressive cancers[14, 15]
Polyunsaturated omega-6 fatty acidsAccumulates in PCa tissue, related to higher GS[21]
scyllo-InositolIncreases[14]
scyllo-Inositol/creatineCorrelates with tumor fraction[20]
SpermineCorrelates with volume percent of normal epithelia[10]
TaurineIncreases[14]
Total cholineIncreases[15]
Total choline/citrateIncreases, correlates with GS[18]

PCA METABOLOMICS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

Studies focusing on individual metabolites in prostate tissue are valuable in advancing our understanding of PCa; however, metabolomic profiles have shown even greater promise for the improvement of PCa diagnosis and monitoring of disease progression. Metabolomics evaluates physiological or pathological conditions by profiling the entire measurable metabolome, instead of focusing only on certain metabolites or isolated metabolic pathways [22]. Using PCa metabolomic profiles through different combinations of metabolite spectral intensities obtained from a single set of HRMAS data, different PCa characteristics can be probed. For instance, results show that metabolomic profiles can improve sensitivity in PCa detection, when compared with indications from single metabolites, and in PCa characterization, when compared with histopathological evaluations for the predictions of PCa stage and recurrence potential. A 2005 study showed that different metabolomic profiles, obtained through HRMAS from 199 samples from 82 patients with PCa, could differentiate between cancerous and benign samples from the same patients with 98.2% accuracy, and correlated with serum PSA levels [23]. Furthermore, the metabolomic profiles could delineate a subset of less aggressive tumors and predict tumor perineural invasion within the subset, all achievable using concentration-based metabolite intensities measured from a single set of tissue spectra [23]. Later, it was shown that metabolomic profiles obtained from relative spectral intensities (normalized by the spectral region of 0.5–4.5 ppm) of the same patient population could also differentiate between cancerous and histologically benign samples with 93% accuracy (Fig. 4) [24].

image

Figure 4. Prostate cancer metabolomic profiles of relative metabolic intensities at 14 T. To compensate for the lack of an established in vivo concentration reference standard, we re-analyzed tissue metabolomic profiles according to relative metabolite intensities (normalized by the metabolite spectral region of 0.5–4.5 ppm) for 42 samples from 13 patients [19]. (a) The overall loading factors (combined coefficients from principal component analysis and canonical analysis) for the 36 metabolites and regions included, which provide examples of phosphorylcholine (PCh; 3.22 ppm), spermine (Spm; 3.05–3.15 ppm) and creatine (Cr; 3.03 ppm), are labeled. (b) Metabolomic profile expressed as canonical score 1 distinguishes cancer (filled circles) from histo-benign (open circles) samples (overall accuracy of 93%, indicated by the receiver operating characteristic curve, not shown here) with statistical significance (p < 0.0001). Median (M) and standard deviation (SD) values were calculated for all samples. [Reproduced from Wu et al. [24]: fig. 2.]

Download figure to PowerPoint

Metabolomic profiles calculated from HRMAS have also been used to assess PCa biochemical recurrence potential, defined as the detection of serum PSA elevations after radical prostatectomy, for which there is currently no biological test to accurately predict the likelihood at the time of surgery. A retrospective study in 2010 analyzed clinical and pathological stage-matched groups with and without biochemical recurrence as training and testing cohorts [25]. By applying the metabolomic profiles obtained for the differentiation of groups with and without biochemical recurrence for the training cohort, to the testing cohort, the results revealed the ability of the calculated metabolomic profiles of the testing cohort to predict biochemical recurrence potential with an overall accuracy of 78% [25], much improved over the 50–50 prediction that can be reached in the current PCa clinic for these matched cases. If such tests became applicable in the PCa clinic, PCa aggressiveness in terms of cancer recurrence could be determined on a personal basis and could help to guide the most appropriate course of treatment, for at least a subgroup of patients with PCa.

ADVANCES IN THE METHODOLOGY

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

In the study of PCa, ex vivo NMR measurements have evolved from the use of conventional solution methods, starting in the mid-1980s, applied on either intact tissue samples [26, 27] or solutions of tissue extracts [28, 29], to the analysis of intact prostate tissue samples using the HRMAS method [30]. Prior to the introduction of HRMAS, prostate tissue samples were either packed in standard NMR tubes, immersed in phosphate-buffered solutions and analyzed on an NMR spectrometer with limited spectral resolution, or analyzed at high spectral resolution with solutions chemically extracted from tissue. Although intact tissue studies revealed certain biochemical information, including the presence of spermine in tissue [31], and could be correlated with pathology of the same tissue samples, the spectral resolution was limited and prevented the identification of many individual metabolites. However, while extract solutions could provide high spectral resolution, the extraction process destroyed tissue architecture and prevented subsequent histopathological evaluation of the same tissue. In the study of early-stage PCa, a disease known for its heterogeneously distributed, small lesions, the verification of tissue pathology is critical because of the statistical eventuality that the majority of prostate tissue samples obtained from these patients would in fact be histologically benign specimens from cancerous prostates [32]. Combining the strengths of these two analytical approaches, the HRMAS method offers a solution to obtain high spectral resolution without destructive tissue chemical extraction procedures, so that tissue specimens can be histologically evaluated after HRMAS analysis. In addition, HRMAS analysis of intact tissue requires a smaller sample volume (~10 mg) and shorter preparation time than solution extraction methods, making it suitable for the analysis of both PCa biopsies and larger specimens [33].

Since the advent of HRMAS for intact prostate tissue analysis, many studies have invested in improvements of the methodology to advance its utility in PCa investigations. Much of this research has focused on producing better resolved spectra to optimize metabolite identification and quantification. Two-dimensional total correlation spectroscopy (TOCSY) experiments provide an enhanced way to identify metabolites that are difficult to resolve in one-dimensional HRMAS spectra. The combination of rotor-synchronized adiabatic pulse sequences and two-dimensional HRMAS TOCSY allowed for the identification of cross-peaks associated with choline- and ethanolamine-containing metabolites [34], and for their quantification [16]. Studies have also used different T1 and T2 relaxation times to optimize the quantification of major metabolites, and have developed new semi-parametric time-domain quantification methods [15, 35]. In addition, the ERETIC method, mentioned previously for the quantification of alanine and lactate, which uses a synthesized radiofrequency pulse to produce a signal, presents a possible substitute for chemical reference standards in HRMAS MRS [36]. Other studies have used HRMAS 13C MRS to study PCa cells grown in culture to further characterize PCa metabolism using 13C-labeled substrates as metabolic probes, and have found indications that citric acid cycle metabolism is responsible for much of the consumption of pyruvate in PCa cells [37]. Although the use of HRMAS 31P MRS in the study of intact human prostate tissue has not been presented, to the best of our knowledge, it has proven to be a useful method for the measurement of phospholipid metabolites in tumor samples [38].

The quantification of metabolite concentrations also raises concerns regarding the state of prostate tissue before HRMAS analysis, and as to whether storage conditions, especially freezing, affect metabolite concentrations. One study found freezing to affect absolute metabolite concentrations, but not relative intensities [39], and another study found that long-term frozen storage of prostate tissue might only alter metabolic concentrations to a degree below HRMAS detection limits [40]. A study of prostate needle biopsies found that, although the echo gel used for ultrasound guidance could potentially contaminate tissue HRMAS spectra, preventing accurate analysis of metabolic concentrations, it could be resolved by avoiding contact of the biopsy needle with the gel [18]. With respect to this concern, our laboratory has established a protocol of transporting fresh, unfrozen prostate biopsy cores for HRMAS analysis on the same day as the biopsy procedure, adoptable in the clinic, such that HRMAS may constitute an integrated step in routine PCa biopsy evaluation without interference with histological analysis. In this protocol, the biopsy core is briefly rinsed in deuterium oxide (D2O) to rid it of contaminants, and then stored on ice in an apparatus specifically designed to maintain hydration of the core whilst preserving metabolite concentrations (Fig. 5). Rinsing of the biopsy cores with D2O to eliminate contaminants introduced during the biopsy procedure may result in a certain degree of metabolite loss. However, by rinsing for a standardized minimal time (3–5 s) without soaking the core, the potential metabolite losses would be slight and would probably only appear at the superficial surface of the biopsy cores.

image

Figure 5. A hydrated apparatus used for the transport of fresh prostate needle biopsy cores. A small piece of Kimwipe is placed inside a 1.5-mL microcentrifuge tube and dampened with deuterium oxide (D2O). A 0.2-mL polymerase chain reaction tube is then placed inside the microcentrifuge tube, resting on the Kimwipe and moisturized by D2O vapor. During the collection of a prostate biopsy core, the core is first rinsed briefly in a 1.5-mL tube filled with D2O and then placed inside the PCR tube before the closure of the microcentrifuge tube; the apparatus is then placed on ice until high-resolution magic angle spinning (HRMAS) analysis. This design provides a moist environment for the tissue, but protects it from the possible loss of metabolites if completely immersed in liquid.

Download figure to PowerPoint

Although the quantification of metabolites with HRMAS is essential, subsequent histopathology is the key to the characterization of metabolic profiles according to cancer characteristics, especially given that the majority of analyzed prostate tissues are often eventually defined by histology as benign tissues from patients with PCa [23]. Therefore, multiple studies have investigated the best procedure for preserving tissue architecture for subsequent pathology evaluation and additional biomolecular analyses. The use of slow spinning rates with HRMAS has shown greater preservation than faster spinning rates of tissue morphological structures (Fig. 6) [41]. Although beneficial for subsequent pathology, slow spinning rates also present a challenge for metabolite quantification with the presence of spinning sidebands. These spinning sidebands need to be suppressed in order to prevent them from interfering with and confounding the spectral regions of interest. Various rotor-synchronized spinning sideband suppression methods have been investigated, and both the rotor-synchronized DANTE (delay alternating with nutation for tailored excitation) pulse sequence and Min(A,B) post-spectral editing scheme have been reported as simple and efficient sideband suppression methods for the accurate quantification of metabolic concentrations (Fig. 7) [41, 42]. The ability to perform additional prostate tissue analyses after HRMAS has also been studied, including the quantification of tissue pathological features with computer-aided image analysis [43].

image

Figure 6. The effects of high-resolution magic angle spinning (HRMAS) stress on tissue morphology. (a) A human prostate tissue sample taken directly from a tissue bank without HRMAS testing. The tissue shows highly organized ductal cellular structures with well-defined epithelial layers. (b) Another sample from the same patient, but after an HRMAS experiment, which involved spinning at 600 Hz for 45 min and then at 700 Hz for 15 min. The tissue still exhibits a normal ductal structure that cannot be differentiated from the original sample (a). (c) A sample, again from the same clinical case, after MAS analysis at higher spinning frequency: 3.0 kHz for 1 h. The tissue ductal structures are visibly distorted compared with the natural specimens. Images presented at the same magnification. [Reproduced from Taylor et al. [41]: fig. 2; color version of figure is available online].

Download figure to PowerPoint

image

Figure 7. Human prostate continuous-wave water presaturated spectra at spinning rates of (a) 600 Hz (A) and (b) 700 Hz (B). (c) A spectrum that was edited, using A and B, with Min(A, B) to be visually compared with (d), a spectrum obtained at a spinning rate of 3.0 kHz, plotted with a different vertical scale. (e) A digital analysis presents the differences between spectra (d) and (c). *Spinning sidebands from tissue water and rubber standard signals. [Reproduced from Burns et al. [42]: fig. 6.]

Download figure to PowerPoint

Although most PCa investigations with HRMAS have used human prostate tissues, HRMAS analyses of PCa animal models have also been reported. Specifically, two recent studies have used the HRMAS method as a way to determine the characteristics of mouse and rat PCa models, including their differences and common features with human PCa. In 2008, Teichert et al. [44] compared tissue from the mouse model ‘transgenic adenocarcinoma of the mouse prostate’ (TRAMP) with wild-type mice. They found that, although citrate trends were similar to those observed in human PCa, changes in choline species did not agree with human PCa, as the levels were decreased in TRAMP tumors [44]. A more recent study by Stenman et al. [45] found significant metabolic differences between human and rat PCa, where many of the key metabolic markers for human PCa, such as citrate, aspartate, lysine, taurine, glutamate, glutamine, creatine, inositol and choline compounds, did not translate to the rat model. These studies presented HRMAS as a promising method to help improve future interpretations in PCa studies using animal models.

FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

An exciting area of intact prostate tissue study is the combination of genetic analysis with HRMAS evaluation. In an early study, RNA was extracted from tissues after their analysis with HRMAS [46]. Although the RNA integrity was significantly lower with biopsy samples after HRMAS analyses, in comparison with control biopsy samples without HRMAS measurements, the integrity of HRMAS-analyzed prostatectomy samples was not significantly different from the control samples and, indeed, the mean integrity values for the control biopsy samples were very similar to all surgical samples (with and without HRMAS). In addition, there were no significant differences in histopathological integrity for surgical and biopsy samples versus control samples [46]. Not only did this show that histopathology and genetic analysis could be successfully performed on prostate tissue after HRMAS analysis, but it contributed an expanded understanding of the genetic mechanisms underlying the different concentrations of certain metabolites in cancerous and benign prostate tissue, specifically lower levels of citrate in aggressive PCa cases [47]. Furthermore, a standardized method for fresh tissue harvesting from radical prostatectomy specimens has been developed to ensure that the collected fresh frozen tissue would be suitable for HRMAS analysis and subsequent gene expression profiling [48].

Additional investigations include the evaluation of mRNA expression levels of rate-limiting enzymes in the spermine metabolic pathway, following the reported function of spermine as an endogenous inhibitor of PCa growth [11]. In contrast with the measured concentrations of spermine from HRMAS spectra, expression levels of the spermine anabolic enzymes ornithine decarboxylase (ODC1) and S-adenosylmethionine decarboxylase (AMD1) were reduced in the benign epithelia surrounding cancer glands with an increase in PSA velocity, and expression levels of ornithine decarboxylase antizyme (OAZ1) were increased with increased PSA velocity [49]. If PSA velocity is used as a measure for fast-growing PCa, and therefore aggressive PCa, the evaluation of spermine levels with HRMAS and the expression levels of these enzymes could be used as clinical measurements to determine cancer aggressiveness for patients with newly diagnosed PCa.

Possibly the most important research focus for the study of PCa with HRMAS concerns the great potential for the translation of PCa metabolomics to in vivo MRI and MRS to aid in the development of noninvasive clinical protocols for the detection, monitoring and diagnosis of patients with PCa. A study by Swanson et al. [14] found that in vivo MRSI reveals typical HRMAS patterns. The higher resolution achieved with HRMAS allows for the elucidation of spectral patterns associated with different prostate tissue types and cancer grades, which may improve significantly the interpretation of in vivo MRSI data [14]. Addressing the limitation of current clinical radiology in localizing PCa lesions in vivo, the study also showed the ability of combined MRI/three-dimensional MRSI to guide the selection of PCa tissue for ex vivo HRMAS analysis with 71% accuracy (Fig. 8) [14]. Furthermore, to demonstrate the improved ability of PCa metabolomics in differentiating PCa from histologically benign tissues, a study published in 2010 revealed a 93–97% overall accuracy for the detection of the presence of cancer using PCa metabolomic profiles, when whole prostates removed by prostatectomy from patients with biopsy-proven PCa were evaluated on a 7-T human whole-body MR scanner [24]. Localized, multi-cross-sectional, multi-voxel spectra were used to generate PCa metabolomic imaging according to intact tissue HRMAS MRS analyses, which was then evaluated against whole-mount prostate histology [24]. Therefore, by using HRMAS analysis in tandem with in vivo imaging modalities, we can reveal spectral patterns that can improve the clinical interpretations of in vivo MRI/MRSI and, more importantly, work towards the development of clinically implementable PCa metabolomic imaging.

image

Figure 8. (a) T2-weighted MR image from the prostatic apex of a 56-year-old patient with prostate cancer. (b) Three-dimensional MRSI spectrum taken from the 0.24-cm3 voxel shown in (a). (c) 1H high-resolution magic angle spinning (HRMAS) spectrum of excised tissue section showing high levels of choline (Cho) and GPC + PC, and low levels of citrate and polyamines, relative to creatine (Cr). Gln, glutamine; Glu, glutamate; GPC, glycerophosphocholine; PA, polyamines; PC, phosphocholine. (d) Hematoxylin and eosin stain of a tissue sample indicating the presence of Gleason 3 + 4 prostate cancer. (Note the acetone impurity was introduced during tissue inking.) [Reproduced from Swanson et al. [14]: fig. 2.]

Download figure to PowerPoint

Presently, the major concern in the PCa clinic is represented by the urgent need for noninvasive imaging of cancer foci to perform targeted biopsies and to predict PCa aggressiveness prior to prostatectomy. The study of the individual metabolite concentrations and metabolomics of PCa using HRMAS has shown reliable potential to remedy the absence of these in vivo diagnostic tools [14]. Although these analyses are not meant to replace histopathological evaluations, they have the potential to provide additional biological information which may be able to better detect, characterize and subcategorize cases according to tumor biochemical potentials, currently unachievable with existing PCa clinical and pathological criteria. Although the utility of HRMAS MRS remains limited to studies of ex vivo prostate tissue samples, the metabolic markers thus obtained can ultimately assist in the translation of these findings into imaging paradigms in order to provide a noninvasive technique for the diagnosis and monitoring of PCa.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES

This work was supported by Public Health Service/National Institutes of Health (PHS/NIH) grants CA115746, CA115746S2, CA162959 and CA141139 (LLC), and the Massachusetts General Hospital (MGH) A. A. Martinos Center for Biomedical Imaging.

REFERENCES

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. EVALUATIONS OF MAJOR METABOLITES IN NORMAL PROSTATE AND PCa
  5. PCA METABOLOMICS
  6. ADVANCES IN THE METHODOLOGY
  7. FUTURE DIRECTIONS AND IN VIVO TRANSLATIONS
  8. Acknowledgements
  9. REFERENCES
  • 1
    Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J. Clin. 2011; 61(2): 6990.
  • 2
    Andriole GL, Crawford ED, Grubb RL 3rd, Buys SS, Chia D, Church TR, Fouad MN, Gelmann EP, Kvale PA, Reding DJ, Weissfeld JL, Yokochi LA, O'Brien B, Clapp JD, Rathmell JM, Riley TL, Hayes RB, Kramer BS, Izmirlian G, Miller AB, Pinsky PF, Prorok PC, Gohagan JK, Berg CD. Mortality results from a randomized prostate-cancer screening trial. N. Engl. J. Med. 2009; 360(13): 13101319.
  • 3
    Welch HG, Albertsen PC. Prostate cancer diagnosis and treatment after the introduction of prostate-specific antigen screening: 1986–2005. J. Natl. Cancer Inst. 2009; 101(19): 13251329.
  • 4
    Andriole GL, Crawford ED, Grubb RL 3rd, Buys SS, Chia D, Church TR, Fouad MN, Isaacs C, Kvale PA, Reding DJ, Weissfeld JL, Yokochi LA, O'Brien B, Ragard LR, Clapp JD, Rathmell JM, Riley TL, Hsing AW, Izmirlian G, Pinsky PF, Kramer BS, Miller AB, Gohagan JK, Prorok PC. Prostate cancer screening in the randomized Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial: mortality results after 13 years of follow-up. J. Natl. Cancer Inst. 2011; 104(2): 125132.
  • 5
    Schroder FH, Hugosson J, Roobol MJ, Tammela TL, Ciatto S, Nelen V, Kwiatkowski M, Lujan M, Lilja H, Zappa M, Denis LJ, Recker F, Berenguer A, Maattanen L, Bangma CH, Aus G, Villers A, Rebillard X, van der Kwast T, Blijenberg BG, Moss SM, de Koning HJ, Auvinen A. Screening and prostate-cancer mortality in a randomized European study. N. Engl. J. Med. 2009; 360(13): 13201328.
  • 6
    Cheng LL, Lean CL, Bogdanova A, Wright SC Jr, Ackerman JL, Brady TJ, Garrido L. Enhanced resolution of proton NMR spectra of malignant lymph nodes using magic-angle spinning. Magn. Reson. Med. 1996; 36(5): 653658.
  • 7
    Cheng LL, Ma MJ, Becerra L, Ptak T, Tracey I, Lackner A, Gonzalez RG. Quantitative neuropathology by high resolution magic angle spinning proton magnetic resonance spectroscopy. Proc. Natl. Acad. Sci. USA 1997; 94(12): 64086413.
  • 8
    DeFeo EM, Cheng LL. Characterizing human cancer metabolomics with ex vivo 1H HRMAS MRS. Technol. Cancer Res. Treat. 2010; 9(4): 381391.
  • 9
    Tomlins A, Foxall P, Lindon J, Lynch M, Spraul M, Everett J, Nicholson J. High resolution magic angle spinning 1H nuclear magnetic resonance analysis of intact prostatic hyperplastic and tumor tissue. Anal. Commun. 1998; 35: 113115.
  • 10
    Cheng LL, Wu C, Smith MR, Gonzalez RG. Non-destructive quantitation of spermine in human prostate tissue samples using HRMAS 1H NMR spectroscopy at 9.4 T. FEBS Lett. 2001; 494(1–2): 112116.
  • 11
    Smith R, Litwin M, Lu Y, Zetter B. Identification of an endogenous inhibitor of prostate carcinoma cell growth. Nat. Med. 1995; 1(10): 10401045.
  • 12
    Franklin RB, Feng P, Milon B, Desouki MM, Singh KK, Kajdacsy-Balla A, Bagasra O, Costello LC. hZIP1 zinc uptake transporter down regulation and zinc depletion in prostate cancer. Mol. Cancer, 2005; 4: 32.
  • 13
    Dittrich R, Kurth J, Decelle EA, DeFeo EM, Taupitz M, Wu S, Wu CL, McDougal WS, Cheng LL. Assessing prostate cancer growth with citrate measured by intact tissue proton magnetic resonance spectroscopy. Prostate Cancer Prostatic Dis. 2012; 15(3): 278282.
  • 14
    Swanson MG, Vigneron DB, Tabatabai ZL, Males RG, Schmitt L, Carroll PR, James JK, Hurd RE, Kurhanewicz J. Proton HR-MAS spectroscopy and quantitative pathologic analysis of MRI/3D-MRSI-targeted postsurgical prostate tissues. Magn. Reson. Med. 2003; 50(5): 944954.
  • 15
    Swanson MG, Zektzer AS, Tabatabai ZL, Simko J, Jarso S, Keshari KR, Schmitt L, Carroll PR, Shinohara K, Vigneron DB, Kurhanewicz J. Quantitative analysis of prostate metabolites using 1H HR-MAS spectroscopy. Magn. Reson. Med. 2006; 55(6): 12571264.
  • 16
    Swanson MG, Keshari KR, Tabatabai ZL, Simko JP, Shinohara K, Carroll PR, Zektzer AS, Kurhanewicz J. Quantification of choline- and ethanolamine-containing metabolites in human prostate tissues using 1H HR-MAS total correlation spectroscopy. Magn. Reson. Med. 2008; 60(1): 3340.
  • 17
    Tessem MB, Swanson MG, Keshari KR, Albers MJ, Joun D, Tabatabai ZL, Simko JP, Shinohara K, Nelson SJ, Vigneron DB, Gribbestad IS, Kurhanewicz J. Evaluation of lactate and alanine as metabolic biomarkers of prostate cancer using 1H HR-MAS spectroscopy of biopsy tissues. Magn. Reson. Med. 2008; 60(3): 510516.
  • 18
    van Asten JJ, Cuijpers V, Hulsbergen-van de Kaa C, Soede-Huijbregts C, Witjes JA, Verhofstad A, Heerschap A. High resolution magic angle spinning NMR spectroscopy for metabolic assessment of cancer presence and Gleason score in human prostate needle biopsies. MAGMA 2008; 21(6): 435442.
  • 19
    Keshari KR, Tsachres H, Iman R, Delos Santos L, Tabatabai ZL, Shinohara K, Vigneron DB, Kurhanewicz J. Correlation of phospholipid metabolites with prostate cancer pathologic grade, proliferative status and surgical stage – impact of tissue environment. NMR Biomed. 2011; 24(6): 691699.
  • 20
    Stenman K, Stattin P, Stenlund H, Riklund K, Grobner G, Bergh A. 1H HRMAS NMR derived bio-markers related to tumor grade, tumor cell fraction, and cell proliferation in prostate tissue samples. Biomark. Insights 2011; 6: 3947.
  • 21
    Stenman K, Hauksson JB, Grobner G, Stattin P, Bergh A, Riklund K. Detection of polyunsaturated omega-6 fatty acid in human malignant prostate tissue by 1D and 2D high-resolution magic angle spinning NMR spectroscopy. MAGMA, 2009; 22(6): 327331.
  • 22
    Cheng LL, Pohl U. The role of NMR-based metabolomics in cancer. In: Lindon JC, Nicholls JK, Holmes E (eds). The Handbook of Metabonomics and Metabolomics. Elsevier: Amsterdam; 2007, pp. 345374.
  • 23
    Cheng LL, Burns MA, Taylor JL, He W, Halpern EF, McDougal WS, Wu C-L. Metabolic characterization of human prostate cancer with tissue magnetic resonance spectroscopy. Cancer Res. 2005; 65(8): 30303034.
  • 24
    Wu CL, Jordan KW, Ratai EM, Shen J, Adkins CB, DeFeo EM, Jenkins BG, Ying L, McDougal WS, Cheng LL. Metabolomic imaging for human prostate cancer detection. Sci. Transl. Med. 2010; 2: 16ra18.
  • 25
    Maxeiner A, Adkins CB, Zhang Y, Taupitz M, Halpern EF, McDougal WS, Wu CL, Cheng LL. Retrospective analysis of prostate cancer recurrence potential with tissue metabolomic profiles. Prostate, 2010; 70(7): 710717.
  • 26
    Mountford CE, Saunders JK, May GL, Holmes KT, Williams PG, Fox RM, Tattersall MH, Barr JR, Russell P, Smith IC. Classification of human tumours by high-resolution magnetic resonance spectroscopy. Lancet, 1986; 1(8482): 651653.
  • 27
    Hahn P, Smith IC, Leboldus L, Littman C, Somorjai RL, Bezabeh T. The classification of benign and malignant human prostate tissue by multivariate analysis of 1H magnetic resonance spectra. Cancer Res. 1997; 57(16): 33983401.
  • 28
    Schiebler M, Miyamoto K, White M, Maygarden S, Mohler J. In vitro high resolution 1H-spectroscopy of the human prostate: benign prostatic hyperplasia, normal peripheral zone and adenocarcinoma. Magn. Reson. Med. 1993; 29(3): 285291.
  • 29
    Fowler A, Pappas A, Holder J, Finkbeiner A, Dalrymple G, Mullins M, Sprigg J, Komoroski R. Differentiation of human prostate cancer from benign hypertrophy by in vitro 1H NMR. Magn. Reson. Med. 1992; 25(1): 140147.
  • 30
    Fossel ET, Carr JM, McDonagh J. Detection of malignant tumors. Water-suppressed proton nuclear magnetic resonance spectroscopy of plasma. N. Engl. J. Med. 1986; 315(22): 13691376.
  • 31
    van der Graaf M, Schipper RG, Oosterhof GO, Schalken JA, Verhofstad AA, Heerschap A. Proton MR spectroscopy of prostatic tissue focused on the detection of spermine, a possible biomarker of malignant behavior in prostate cancer. MAGMA 2000; 10(3): 153159.
  • 32
    Swindle P, McCredie S, Russell P, Himmelreich U, Khadra M, Lean C, Mountford C. Pathologic characterization of human prostate tissue with proton MR spectroscopy. Radiology, 2003; 228(1): 144151.
  • 33
    Cheng LL, Chang IW, Louis DN, Gonzalez RG. Correlation of high-resolution magic angle spinning proton magnetic resonance spectroscopy with histopathology of intact human brain tumor specimens. Cancer Res. 1998; 58(9): 18251832.
  • 34
    Zektzer AS, Swanson MG, Jarso S, Nelson SJ, Vigneron DB, Kurhanewicz J. Improved signal to noise in high-resolution magic angle spinning total correlation spectroscopy studies of prostate tissues using rotor-synchronized adiabatic pulses. Magn. Reson. Med. 2005; 53(1): 4148.
  • 35
    Ratiney H, Albers MJ, Rabeson H, Kurhanewicz J. Semi-parametric time-domain quantification of HR-MAS data from prostate tissue. NMR Biomed. 2010; 23(10): 11461157.
  • 36
    Albers MJ, Butler TN, Rahwa I, Bao N, Keshari KR, Swanson MG, Kurhanewicz J. Evaluation of the ERETIC method as an improved quantitative reference for 1H HR-MAS spectroscopy of prostate tissue. Magn. Reson. Med. 2009; 61(3): 525532.
  • 37
    Levin YS, Albers MJ, Butler TN, Spielman D, Peehl DM, Kurhanewicz J. Methods for metabolic evaluation of prostate cancer cells using proton and (13)C HR-MAS spectroscopy and [3-(13)C] pyruvate as a metabolic substrate. Magn. Reson. Med. 2009; 62(5): 10911098.
  • 38
    Payne GS, Troy H, Vaidya SJ, Griffiths JR, Leach MO, Chung YL. Evaluation of 31P high-resolution magic angle spinning of intact tissue samples. NMR Biomed. 2006; 19(5): 593598.
  • 39
    Wu CL, Taylor JL, He W, Zepeda AG, Halpern EF, Bielecki A, Gonzalez RG, Cheng LL. Proton high resolution magic angle spinning NMR analysis of fresh and previously frozen tissue of human prostate. Magn. Reson. Med. 2003; 50: 13071311.
  • 40
    Jordan K, He W, Halpern E, Wu C, Cheng L. Evaluation of tissue metabolites with high resolution magic angle spinning MR spectroscopy of human prostate samples after three-year storage at −80°C. Biomark. Insights 2007; 2: 147154.
  • 41
    Taylor JL, Wu CL, Cory D, Gonzalez RG, Bielecki A, Cheng LL. High-resolution magic angle spinning proton NMR analysis of human prostate tissue with slow spinning rates. Magn. Reson. Med. 2003; 50(3): 627632.
  • 42
    Burns MA, Taylor JL, Wu CL, Zepeda AG, Bielecki A, Cory D, Cheng LL. Reduction of spinning sidebands in proton NMR of human prostate tissue with slow high-resolution magic angle spinning. Magn. Reson. Med. 2005; 54(1): 3442.
  • 43
    Burns MA, He W, Wu CL, Cheng LL. Quantitative pathology in tissue MR spectroscopy based human prostate metabolomics. Technol. Cancer Res. Treat. 2004; 3(6): 591598.
  • 44
    Teichert F, Verschoyle RD, Greaves P, Edwards RE, Teahan O, Jones DJ, Wilson ID, Farmer PB, Steward WP, Gant TW, Gescher AJ, Keun HC. Metabolic profiling of transgenic adenocarcinoma of mouse prostate (TRAMP) tissue by 1H-NMR analysis: evidence for unusual phospholipid metabolism. Prostate, 2008; 68(10): 10351047.
  • 45
    Stenman K, Surowiec I, Antti H, Riklund K, Stattin P, Bergh A, Grobner G. Detection of local prostate metabolites by HRMAS NMR spectroscopy: a comparative study of human and rat prostate tissues. Magn. Res. Insights 2010; 4: 2741.
  • 46
    Santos CF, Kurhanewicz J, Tabatabai ZL, Simko JP, Keshari KR, Gbegnon A, Santos RD, Federman S, Shinohara K, Carroll PR, Haqq CM, Swanson MG. Metabolic, pathologic, and genetic analysis of prostate tissues: quantitative evaluation of histopathologic and mRNA integrity after HR-MAS spectroscopy. NMR Biomed. 2010; 23(4): 391398.
  • 47
    Bertilsson H, Tessem MB, Flatberg A, Viset T, Gribbestad I, Angelsen A, Halgunset J. Changes in gene transcription underlying the aberrant citrate and choline metabolism in human prostate cancer samples. Clin. Cancer Res. 2012; 18(12): 32613269.
  • 48
    Bertilsson H, Angelsen A, Viset T, Skogseth H, Tessem MB, Halgunset J. A new method to provide a fresh frozen prostate slice suitable for gene expression study and MR spectroscopy. Prostate 2011; 71(5): 461469.
  • 49
    Kaul D, Wu CL, Adkins CB, Jordan KW, Defeo EM, Habbel P, Peterson RT, McDougal WS, Pohl U, Cheng LL. Assessing prostate cancer growth with mRNA of spermine metabolic enzymes. Cancer Biol. Ther. 2010; 9(9): 736742.