Metabolomics (2010) 6:156–163 DOI 10.1007/s11306-009-0187-x
ORIGINAL ARTICLE
Metabolite profiling of blood plasma of patients with prostate cancer Petr G. Lokhov • Maxim I. Dashtiev • Sergey A. Moshkovskii • Alexander I. Archakov
Received: 2 August 2009 / Accepted: 13 October 2009 / Published online: 25 October 2009 Ó Springer Science+Business Media, LLC 2009
Abstract Prostate cancer is one of the most common types of cancer in men. It is though extremely important to search for specific markers including metabolites, which concentration in blood could be a diagnostic measure. In this regard, the metabolite profiling of blood plasma was performed with two groups of people: healthy volunteers (n = 30) and patients with prostate cancer, second stage (n = 40). The profiling protocol included proteins removal from blood plasma with methanol and direct analysis of metabolite fractions by mass spectrometry. Identification of the most abundant metabolites in samples was performed using an accurate mass tag and an isotope pattern methods. Cancer-specific metabolites were revealed by statistical analysis of metabolite intensities in the mass spectra. Six different metabolites were found to be cancer-specific. Two metabolites, acylcarnitine and arachidonoyl amine, have the AUC 0.97 and 0.86, respectively, which are higher than those from PSA test, 0.59. Keywords Mass spectrometry Prostate cancer Blood plasma metabolites
1 Introduction Prostate cancer is one of the most common types of cancer in men. Rates of prostate cancer vary widely across the
P. G. Lokhov (&) S. A. Moshkovskii A. I. Archakov Institute of Biomedical Chemistry RAMS, Pogodinskaya Street, 10, Moscow 119121, Russia e-mail:
[email protected] M. I. Dashtiev Bruker Ltd., Moscow, Russia
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world and found to be the highest in the US (IARC 2001). The major problem is that many men who develop prostate cancer never have symptoms, undergo no therapy, and eventually die of other causes. A key challenge in cancer medicine thus is to detect cancer at earlier stage as possible. The survival rate of patiences with prostate cancer changes from 33% when it is detected at the advanced stage to about 100% when is detected at earlier stage (Jemal et al. 2004). For early and accurate diagnosis of the disease and to monitor its progression molecular biomarkers are often used (Carini 2007). Recent advances in biomarker discovery provide some high-throughput technologies, such as: genomics, proteomics and metabolomics. Many studies employ the first two strategies (Rajcevic et al. 2009; Ahmed 2009; He and Chiu 2003; Seo and Ginsburg 2005; Emilien et al. 2000) here we will focus on the metabolomics, namely metabolic profiling. Metabolomics refers to a global analysis of the low molecular weight molecules, known as metabolites, in cells, tissues or fluids. Changes in metabolite concentrations due to a disease will add valuable information for biomarker discovery. Mass spectrometry-based metabolic profiling is nowadays a traditional method in metabolomics allowing identification of major metabolites in biosamples with high efficiency and sensitivity (Beecher 2003). In contrast to classical metabolite studies that are focused only on a few metabolites, metabolic profiling is aimed to analyze all metabolites in sample of a given biological system. Metabolic profiling has already found a wide application in practical medicine, in particular, in screening newborns for the presence of congenital failures of metabolism of amino acids, fatty acids, etc. (Piraud et al. 2003; Schulze et al. 2003; Chace et al. 2002; 2003; Chace and Kalas 2005; German et al. 2003). Up to date, there is a clear evidence of the correlation of prostate cancer with a concentration in the blood of
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metabolites, for example lysophospholipids, androgens, serotonin, amino acids, including aspartic acid, ornithine and sarcosine (Osl et al. 2008; Siddiqui et al. 2006; Dizeyi et al. 2004; Lai et al. 2005; Taylor et al. 2008; Isbarn et al. 2009; Raynaud 2009; Sreekumar et al. 2009). Thus, the prostate cancer progression is reflected in the low mass molecules of blood plasma, and further investigations in this direction such as metabolic profiling can provide new insights to the cancer diagnostics. Various protocols of the metabolic analysis of blood plasma based on mass spectrometry coupled with liquid (LC–MS) and gas chromatography (GC–MS) are known (Osl et al. 2008; Gowda et al. 2008; Xue et al. 2008). A distinct feature of our approach is a direct mass spectrometric analysis of metabolite fraction of blood plasma. The main advantage of this approach is that there is no separation prior to mass spectrometry thus reaching maximum reproducibility for mass spectrometry data. Thus, reliable changes in concentration of metabolites can be detected more clearly (Dettmer et al. 2007).
2 Materials and methods 2.1 Samples of blood plasma Blood for measurement of prostate-specific antigen (PSA) concentration in men was provided by ‘‘New Medical Technologies’’ Ltd., Russia. ELISA (test kit ‘‘oncoELISAtotal PSA’’, AlcorBio, Russia) was chosen as a measure of the PSA concentration. All patients signed an informed consent to provide their blood samples for research purposes. They were then examined in Clinical Cancer Center (Voronezh, Russia) by palpation and ultrasound investigation, needle biopsy and then the diagnosis was specified. For mass spectrometry analysis 40 samples from patients with prostate cancer stage II, T2NxMO and 30 samples from healthy patients were selected. All patients were 55–80 years old. The following protocol for blood sampling was used: 10 ml of blood from cubital vein were collected in glass tubes, containing sodium citrate. In 15 min a 3.8% citrate blood was centrifuged for 15 min and 16009g at the room temperature. After that, 2.5 ml of plasma were aliquoted in 4 eppendorf-like tubes and then frozen and stored at 80°C. Samples for analysis were frozen-thawed not more than once. 2.2 Sample preparation of blood plasma for mass spectrometry For the deproteinization of blood plasma 100 ll of them was mixed with 100 ll of water (LiChrosolv, Merck, USA)
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and 800 ll of methanol (Fluka, Germany) and then incubated for 10 min at 4°C. Samples with precipitated proteins were centrifuged for 10 min at 13000 rpm (MiniSpin plus, Eppendorf, Germany), and then supernatant was transferred to clean eppendorf tubes (Eppendorf, Germany) and the solvent was evaporated during 3 h at 45°C in the SpeedVac (Eppendorf, Germany). The resulting dry residue was dissolved in 100 ll of 95% acetonitrile (Acros Organics, USA) containing 0.1% formic acid (Fluka, Germany). Samples were sonicated for better dissolution in the Bandelin RM 40UH ultrasonic bath (Sonorex Technik, Germany) twice for 30 s. Then, the samples were centrifuged again for 10 min at 13000 rpm (MiniSpin plus) and the resulting supernatant was used for mass spectrometry analysis. 2.3 Mass spectrometry and sample processing Mass spectrometry analysis was carried out on an electrospray hybrid quadrupole time-of-flight mass spectrometer MicrOTOF-Q (Bruker Daltonik GmbH, Germany). Mass spectrometer was tuned for a mass range of 250– 1500 Da for optimal signal intensity. Both positive and negative ions were measured. Samples were delivered into the mass spectrometer by a direct infusion with a syringe pump (Hamilton Bonaduz, Switzerland). Accumulation time of one measurement was 1 min at a flow rate of 3 ll/min. Threshold for peak selection was set to S/N: 10. Mass spectra were then processed in Data Analysis 3.4 (Bruker Daltonik GmbH, Germany). For the 25 most intense peaks having clear isotopic distribution of positively and negatively charged metabolites the difference between healthy and diseased patients was defined by twosided Wilcoxon rank sum test (Matlab, MathWorks, USA). Two peaks were considered to relate to the same metabolite if the mass difference does not exceed 0.01 Da. For the metabolite peaks, whose intensity statistically changing (1 - p [ 0.95) in the event of illness, the average values and standard deviations for the groups corresponding to the sick and healthy patients were calculated. To determine the parameters of laboratory diagnosis, such as specificity and sensitivity, as well as the construction of the ROC (Receiver Operating Characteristic)-curve and calculating the area under the ROC-curve (AUC) ‘‘Statistical Package for the Social Sciences (SPSS)’’ version of 10.0 (SPSS Inc., USA) was used. These specificity and sensitivity of diagnosis based on the intensities of the metabolites were compared with the sensitivity and specificity of diagnosis, based on the concentration of PSA in the blood of patients. Given the age of patients with prostate cancer, the standard values of the PSA concentration were taken from 0 to 5.36 ng/ml (0–0.16 9 10-9 M).
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2.4 Metabolite identification For mass spectrometric peaks having the highest intensity and having clear isotopic distribution, the correspondence to the specific metabolites from the database ‘‘Human Metabolome Database’’ (http://www.hmdb.ca) (Wishart et al. 2007) and/or Metlin (Scripps Center for Mass Spectrometry, USA; http://metlin.scripps.edu) (Smith et al. 2005) was established. An example of the identification of metabolite mass, chemical formula and isotopic distribution is demonstrated in Fig. 1.
3 Results and discussion On the average 1900 metabolite’s ions were detected in samples both from healthy and diseased patients. Typical mass spectra are shown in Fig. 2. The spectra were then queried against the database and the list of identified metabolites was retrieved (Table 1). It can be seen that the area of the most intense peaks both in positive and negative modes correspond to the fraction of phospholipids. High number of peaks is observed due to differences in hydrocarbon chains of fatty acids that present in phospholipids. The mass range from 450–600 Da corresponds mainly to lysoforms of phospholipids. Low molecular weight region (\400 Da) corresponds to metabolites of different Fig. 1 Metabolite identification using accurate mass-tag method in combination with isotope pattern. The measured molecular mass of the metabolite is queried against a metabolite database (Human Metabolome database) (‘1’) that gives a list of matched compounds with chemical formula. Using True Isotopic Pattern (‘2’) with Smart Formula 3D (Bruker Daltonik GmbH, Germany) the theoretical isotopic distributions were calculated for chemical formulas of given candidates and through the comparing them with experimental isotopic pattern the exact molecular composition and name of candidate is defined (‘3’,’4’)
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chemical classes that are responsible for various physiological functions. The metabolites, whose intensity changes significantly in the event of illness were identified by statistical analysis. All of the identified differences were in the area of low molecular metabolites, namely: 302.2442, 304.2602, 377.2680 m/z for the positively charged metabolites and 307.0452, 367.1486, 369.1631 m/z for the negatively charged metabolites. For intensity data of metabolites mean values and standard deviations were calculated (Fig. 3). Results of the identification for these masses are given in Table 1. It should be noted that for masses 367.1486 and 369.1632 m/z there are two candidates for each mass. This is related to the fact that candidates have the same chemical formula and, consequently, identical isotope distribution that does not allow to differentiate them with the used in this study a method of metabolite identification. The cancer-specific reliable changes in metabolite intensities allow considering them as potential markers of disease. In order to determine the possible effectiveness of such diagnostic data the ROC-curve was plotted using intensities of metabolite’s mass peaks (Fig. 4). The area under the ROC-curve is a direct indication of the efficiency and clinical applicability of the diagnostic method. It is widely accepted that diagnostic methods are considered to be clinically applicable when the value for the area under
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Fig. 2 Mass spectrum of negatively charged metabolites of human blood plasma
ROC-curve (AUC) is not less than 0.6, and to have a good diagnostic measure when the value is greater than 0.8 (Metz 1978). Our suggested models of the diagnostic system based on the metabolites with masses of 302.2442 m/z and 304.2602 m/z can be attributed to the ‘‘good’’ because they have a values higher than 0.8. Metabolites with masses 377.2680, 367.1486, and 369.1632 m/z are not suitable for the diagnosing of prostate cancer, because the value for the AUC is less then 0.6 (Table 2). For the mass 307.0452 m/z the ROC-curve was not plotted because of no data in the group of healthy volunteers for this mass (see Fig. 3). It should be noted that the PSA test for the same group of patients had AUC 0.59, which is consistent with data from other sources (0.51 and 0.54 for the test kits of Roche and Bayer, respectively; Lein et al. 2003), and this fact illustrate that the PSA test for the diagnosis of prostate cancer 2nd stage is questionable. High death rates from prostate cancer are usually due to a long-term asymptomatic course of the disease that causes delays in diagnosis. More than 60% of patients treated by a physician doctor already have metastases in organs. This risk group mostly consists of men older than 50–55 years (Hankey et al. 1999). The metabolic profiling of blood plasma for this target group of patients has been done and several cancer-specific changes have been revealed. Identified changes in the concentration of androgens in the blood of patients with advanced prostate cancer are not accidental. A number of studies have reported worrisome associations between low serum testosterone and prostate cancer. Morgentaler et al. assessed the prostate cancer prevalence in hypogonadal men in two studies (Morgentaler et al. 1996; Morgentaler and Rhoden 2006). The risk
of prostate cancer detection was correlated with the severity of androgen deficiency. Also, in a subset analysis of 184 men, a low testosterone-to-PSA ratio was an independent predictor of prostate cancer after adjustment for age and PSA level (Rhoden et al. 2008). Additionally, other studies have reported that a low serum testosterone level is associated with pathologic stage and high Gleason score (Imamoto et al. 2005; Schatzl et al. 2001; Yano et al. 2007). Thus, recorded in this study, the reduction of androgens in the blood of patients with cancer is a supplement previously revealed prostate cancer connection with a low concentration of androgens in the blood. The observed fall in steroid-like substance such as isolithocholic acid in the blood of patients with advanced prostate cancer was not shown before. The nature of this phenomenon is difficult to interpret clearly. Perhaps the fall of isolithocholic acid is a consequence of low levels of androgens in the blood of a patient, that directly affects the secretion of biliary lipids (Ohshima et al. 1996) to which isolithocholic acid relates. Numerous disorders have been described that lead to disturbances in energy production and in intermediary metabolism of the body that are characterized by the production and excretion of unusual acylcarnitines. Determination of the qualitative pattern of acylcarnitines can be of diagnostic and therapeutic importance. Changes in the concentration of acylcarnitine including those observed in cancer patients are described in Vinci et al. (2005), Sachan and Dodson (1987), and Malaguarnera et al. (2006). This phenomenon was studied for advanced pathologic stages when there was a malignant cachexia. In our case, we have an early stage of cancer and, more likely, the increase in concentration of acylcarnitine, namely dimethylheptanoyl
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Table 1 Results of metabolite identification
Measured Matching MW from Chemical adduct MW (m/z) database (Da) formula
Metabolite common name
Wilcoxon test (1 - p)
Positively charged metabolites 1
288.2906
287.2824
C17H37NO2
Sphingosine
0.51
2
302.2442
301.2253
C16H31NO4
Dimethylheptanoyl carnitine
1
3
304.2602
303.2562
C20H33NO
Arachidonoyl amine
1
4
360.3225
359.3188
C24H41NO
R-arachidonoyl amine
0.44
5
377.2680
376.2978
C24H40O3
Isolithocholic acid
6
437.1928
436.2590
C21H41O7P
0.94
7
518.3269
517.3168
1-Oleoyl-lysophosphatidic acid C26H48NO7P Linolenoyl lysolecithin
8
542.3208
541.3168
C28H48NO7P LysoPC
0.73
9
1
0.22
544.3353
543.3325
C28H50NO7P LysoPC
0.58
10 780.5537
779.5464
C44H78NO8P PC
0.26
11 804.5503
803.5465
C46H78NO8P PC
0.57
12 806.5628
805.5621
C46H80NO8P PC
0.16
13 808.5793
807.5778
C46H82NO8P PC
0.38
14 828.5477
827.5465
C48H78NO8P PC
0.11
15 830.5617
829.5621
C48H80NO8P PC
0.13
16 832.5798
831.5778
C48H82NO8P PC
0.35
Negatively charged metabolites
PC phosphatidylcholine; PE phosphatidylethanolamine; R N-butyl or N-propyl alphamethyl; MW molecular weight
17 303.2238
304.2402
C20H32O2
18 307.0452
308.0410
C9H13N2O8P Deoxyuridine monophosphate
0.96
19 367.1486
368.1657
C19H28O5S
Testosterone sulfate or Dehydroepiandrosterone sulfate
0.99
20 369.1632
370.1814
C19H30O5S
Androsterone sulfate or 5a-Dihydrotestosterone sulfate
0.95
21 397.1911
398.1938
C16H33NO8P Glycerophosphocholine
22 445.3184
446.3190
C28H43FO3
23 480.2977
481.3168
C23H48NO7P LysoPC
0.47
24 530.2886
531.3319
C27H50NO7P LysoPE
0.08
25 532.2867
533.3481
C27H52NO7P LysoPE
0.12 0.72
Dihydroxyvitamin D2
0.10
0.59 0.86
26 557.4414
558.4284
C35H58O5
Diglyceride
27 593.4630
594.5223
C37H70O5
Diglyceride
28 792.5106
793.5985
C46H84NO7P PC
0.54
29 794.5182
795.5778
C45H82NO8P PC
0.05
30 816.5114
817.5621
C47H80NO8P PE
0.66
31 818.5194
819.5778
C47H82NO8P PE
0.81
32 820.5393
821.5929
C47H84NO8P PE
0.52
33 840.4984
841.5621
C49H80NO8P PE
0.62
34 842.5192 35 844.5391
843.5778 845.5934
C49H82NO8P PE C49H84NO8P PE
0.92 0.84
36 850.4731
851.5465
C50H78NO8P PC
0.92
37 852.4776
853.5621
C50H80NO8P PC
0.91
carnitine, in blood of patients with prostate cancer is associated with the above changes in hormonal background, determined by the low level of androgens. The correlation of testosterone concentration in blood with the carnitine and acylcarnitine concentrations was previously
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Arachidonic acid
0.94
described (Marquis and Fritz 1965; Carter et al. 1980). Moreover, Carter et al. (1980) found significant negative correlation between blood plasma testosterone concentrations and blood plasma carnitine in animals, the fact, which is consistent with our data.
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Fig. 3 Graphs of mean values and standard deviations of the ion intensities of the blood metabolites of patients with prostate cancer (1) and healthy volunteers (2)
Fig. 4 ROC-curve for the models of diagnostic systems of prostate cancer based on measurement of intensities of metabolite peaks in the mass spectrometric profile of blood plasma. To construct the curves of blood plasma samples, 40 prostate cancer patients and 30 healthy volunteers were used. ROC-curves for: 1, dimethylheptanoyl carnitine; 2, arachidonoyl amine; 3, isolithocholic acid; 4, testosterone sulfate/dehydroepiandrosterone sulfate; 5, androsterone sulfate/5adihydrotestosterone sulfate
Changes of concentration of various lipids in blood of patients are well known. Changes in lysophospholipids level are presumably due to binding and activation by them
the specific G-protein binding receptor followed by growth and proliferation of cancer cells. This interaction leads the decrease of concentration of lysophospholipids in blood, which can be used for diagnostic purposes (Murph et al. 2007; Osl et al. 2008). There are no statistical significant changes in phospholipids revealed in our research. Probably, it is connected with lower dynamic range of direct mass spectrometry in comparison with LC–MS used in previous works. Also, the used early stages of cancers may be reason why several detected phospholipids did not reach statistical significance accepted for biomarkers (Table 1). Required value ‘0.95’ may be reached at further progression of cancers. Statistically reliable change of deoxyuridine monophosphate is most likely associated with a known rise in the blood of nucleotides in patients with cancer as the primary degradation products of tRNA (Cho et al. 2009; Hsu et al. 2009; Zheng et al. 2005). Moreover, changes in concentrations of some nucleotides in the urine have been proposed for the diagnosis of certain types of cancer (Hsu et al. 2009; Zheng et al. 2005). Since the deoxyuridine is detected only in the blood of prostate cancer patients, the ROC was not plotted and the potential use of this metabolite as a marker of prostate cancer was not estimated as required. A statistically reliable change of arachidonoyl amine is not shown previously for patient with prostate cancer. Given the lack of literature data of arachidonoyl amine metabolism in men, it is difficult to interpret the role of this
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Table 2 Parameters of the efficiency of the prostate cancer diagnosis based on the intensities of metabolite peaks in the mass spectrometric profile Measured metabolite mass (m/z)
Metabolite common name
Sensitivity (%)
Specificity (%)
AUC
302.2442
Dimethylheptanoyl carnitine
94.6
96.4
0.97
304.2602
Arachidonoyl amine
86.5
92.9
0.86
377.2680 367.1486
Isolithocholic acid Testosterone sulfate or dehydroepiandrosterone sulfate
21.6 40.5
21.4 42.9
0.11 0.36
369.1632
Androsterone sulfate or 5a-dihydrotestosterone sulfate
PSA concentration
metabolite in prostate cancer. However, calculated AUC for the metabolite, which is 0.86, indicates the potential usefulness as a marker of early diagnosis of prostate cancer.
4 Concluding remarks In this work a mass spectrometric metabolic profiling of blood plasma of patients with prostate cancer was carried out. The suggested protocol allows observation of dominant peaks from lysophospholipids, and phospholipids in the mass spectrometric profile, which are not specific for prostate cancer. However, in the low molecular weight region two metabolites with masses 302.2442 m/z (301.2253 Da, acylcarnitine) and 304.2602 m/z (303.2562 Da, arachidonoyl amine), were identified as potentially suitable markers for early diagnostic of prostate cancer with the efficiency of significantly higher than the current PSA test. Acknowledgments The authors would like to thank ‘‘New Medical Technologies’’ Ltd. (Russia) and Clinical Cancer Center (Voronezh, Russia) for the blood samples.
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