Liquid chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers

Journal of Chromatography A, 1283 (2013) 122–131

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Journal of Chromatography A journal homepage: www.elsevier.com/locate/chroma

Liquid chromatography tandem mass spectrometry study of urinary nucleosides as potential cancer markers夽 Wiktoria Struck a , Danuta Siluk a , Arlette Yumba-Mpanga a , Marcin Markuszewski b , Roman Kaliszan a , Michał Jan Markuszewski a,∗ a b

Department of Biopharmaceutics and Pharmacodynamics, Medical University of Gda´ nsk, Al. Gen. Hallera 107, 80-416 Gda´ nsk, Poland Department of Urology, Medical University of Gda´ nsk, ul. Mariana Smoluchowskiego 17, 80-214 Gda´ nsk, Poland

a r t i c l e

i n f o

Article history: Received 29 October 2012 Received in revised form 28 January 2013 Accepted 30 January 2013 Available online 6 February 2013 Keywords: Metabolomics Mass spectrometry Biomarkers Multivariate statistical analysis Urinary nucleosides

a b s t r a c t The aim of this work was a comprehensive analysis of metabolite profiles of nucleosides from biological samples obtained from patients with urogenital cancer disease as well as an interpretation of cancer-related patterns of the analyzed profiles. In our study we proposed a targeted approach that was focused on the simultaneous determination of twelve nucleosides from over a hundred urine samples. For analytes’ quantification high performance liquid chromatography technique hyphenated with tripple quadrupole mass spectrometer was applied. The developed method was validated in terms of linearity, precision, accuracy as well as in terms of limit of quantification and limit of detection. The obtained, normalized data set was analyzed using univariate statistical analysis. The achieved results revealed statistically significant (p < 0.05) differences between levels of five nucleosides determined in urine samples from cancer patients and healthy volunteers, namely: 6-methyladenosine, inosine, N2-methylguanosine, 3-methyluridine and N,N-dimethylguanosine. Basing on the putative markers we built the discrimination models using partial least squares discriminant analysis as well as K-nearest neighbor method. The sensitivity and specificity of the markers calculated from the obtained models were in the range of 61.9–88.89% and 27.78–50%, respectively. The proposed procedure can be considered as a holistic approach for metabolites’ analysis and includes clinical, analytical and bioinformatics sections. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Over the last decade, systems biology has developed into a new research platform, which currently occupies a high position in biomedical research. As a consequence of reading the complete sequence of the genetic code, other branches of systems biology like transcriptomics, proteomics and metabolomics have gained interest as well. All of them can help to describe complex information about human’s health status. While the human body is composed of 30–50 thousands of genes, 150–300 thousands of transcriptomes, about 1 million of proteins, the amount of metabolites is in the range from 3.5 to 10 thousands [1,2]. Compared to the transcriptome and proteome, the number of metabolites in the body is relatively small, but the number of dependencies that affect the metabolic profiles has multidimensional interpretation. However, thanks to the rapid development of analytical techniques as well as

夽 Presented at the 29th International Symposium on Chromatography, Torun, Poland, 9–12 September 2012. ∗ Corresponding author. Tel.: +48 58 349 1493; fax: +48 58 349 1962. E-mail address: [email protected] (M.J. Markuszewski). 0021-9673/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chroma.2013.01.111

advanced bioinformatic methods it is possible to reasonably interpret the metabolomics data and explain some biological processes occurring in the body [3]. Metabolomics as a powerful domain has been used in toxicological studies [4], in diagnosis of inborn errors of metabolism [5], in diagnosis of amyotrophic lateral sclerosis [6] as well as in identification of plants’ metabolites [7,8]. Moreover, metabolomics is increasingly becoming a tool used for cancer diagnosis [9–11] and for prediction of its progression in response to the applied therapy [12,13]. According to the literature, it has been noticed that RNA’s metabolites, namely nucleosides, can play a significant role in carcinogenesis. Due to higher RNA turnover in cancer patients, nucleosides’ levels excreted into urine are found elevated in comparison to the healthy ones [14]. Normal nucleosides (adenosine, uridine, guanosine and cytidine) undergo reutilization or degradation to uric acid, ␤-alanine and ␤-aminoisobutyrate. However, modified nucleosides (e.g. methylated nucleosides like 6-methyladenosine or N2-methylguanosine) are excreted intact into urine. Therefore, their levels are directly correlated with the degree of degradation of RNA which reflects the pathological or physiological state of the body. It has been noticed that nucleosides determined in biological fluids seem to play an important

W. Struck et al. / J. Chromatogr. A 1283 (2013) 122–131

role in various types of cancer like breast cancer [13,15,16], ovarian [17], kidney and urinary bladder cancer [18,19], thyroid [20], lung [21] and colorectal cancer [22–24]. In addition, it seems that nucleosides would be universal indicators of carcinogenesis since they are not specific to a particular type of tumor but for its various types. Great interest in metabolomics and the role of nucleosides as potential cancer markers result in the use of modern analytical techniques for determination of metabolites. The first article concerning determination of nucleosides was published 34 years ago (in 1978) by Gehrke et al. [25] and described the method for determination of six nucleosides from urine samples from healthy volunteers and cancer patients using high-performance liquid chromatography with spectrophotometric detection. Since then nucleosides have been successfully determined using chromatographic and electrophoretic techniques with different types of detection methods which was described in the reviews written by our group [26,27]. Nowadays, separation techniques combined with mass spectrometry are the method of choice for metabolomics in regard of sensitivity and accuracy. The choice of a particular technique depends mainly on a chosen analytical approach, namely on untargeted or on targeted study. In untargeted assay one is focused on all metabolites present in biological samples. This approach is mainly based on searching for changes between the so-called metabolite fingerprints and on identification of the newly discovered metabolites. This approach is directed on qualitative study therefore there is a need for an accurate mass spectrometry analyzer like, for instance, time-of-flight analyzer [28]. The second approach is related to the determination of the exact group of metabolites, e.g. nucleosides, and generally is associated with a quantitative analysis. Hence, the applied method should be sensitive enough in order to measure changes between determined analytes’ concentrations. So far, there are a lot of publications published concerning analysis of nucleosides with the use of mass spectrometry detection. Some of them are focused on all cis-diol metabolites with particular attention aimed at their fragmentation pattern and their identification [29–35]. Thus, the nucleosides can be determined by applying constant neutral loss mode using the mass of ribose as a neutral loss entity (−132 amu) [29,30], or with the use of solid phase extraction with phenylboronic acid gel which is selective to cis-diol groups present in their structure [31–34]. As these procedures are mainly focused on qualitative analysis, usually no statistical exploration targeted at the evaluation of possible biomarkers, like discrimination model, is proposed in the study. Contrary to that approach, we are focused on measuring concentrations of determined nucleosides from urine samples in order to evaluate differences between their levels in cancer patients and healthy volunteers. Taking into account that one metabolite can be insufficient for cancer prediction, we analyzed 12 nucleoside metabolites and tested the relations between them. To our best knowledge the proposed study is unique in terms of the used analytical approach (mass spectrometry detection, sample extraction procedure) as similar studies were also performed but with different types of detection as well as extraction procedures [23,35–39]. In this study, we evaluated the potential of HPLC–ESI-MS/MS to determine changing levels of twelve nucleosides: adenosine (A), 6-methyladenosine (6 mA), cytidine (C), 3-methylcytidine (3mC), uridine (U), 3-methyluridine (3mU), 5-methyluridine (5mU), pseudouridine (Pseu), xanthosine (X), N-2-methylguanosine (N2mG), NN-dimethylguanosine (NNdmG) and inosine (I) in urine from urogenital tract cancer and healthy volunteers. We also proposed univariate and multivariate statistical approach for the analysis of the obtained data set in order to assess the nucleosides’ role as cancer markers.

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2. Materials and methods 2.1. Chemicals Reference standards for 12 nucleosides (pseudouridine (Pseu), uridine (U), 3-methyluridine (3mU), 5-methyluridine (5mU), adenosine (A), 6-methyladenosine (6 mA), inosine (I), 3-methylcytidine (3mC), cytidine (C), N-2-methylguanosine (N2mG), N,N-dimethylguanosine (NNdmG), xanthosine (X)) and internal standard (8-bromoguanosine, (IS)) were purchased from Sigma–Aldrich (St. Louis, MO, USA). HPLC-grade methanol for LC–MS was obtained from J.T. Baker (Griesheim, Germany), formic acid 98% was purchased from Lancaster Synthesis (Lancaster, UK), ammonium acetate was from Sigma–Aldrich (St. Louis, MO, USA) and ammonia was from POCH (Gliwice, Poland). Deionised water from a Milli-Q water system (Millpore Inc., Bedford, MA, USA) was used in the preparation of the samples and buffer solutions. The stock solution of each standard at the concentration of 10 mM was prepared in deionized water and kept frozen at −80 ◦ C. The working standard solutions were prepared by dilution of the stock solutions with deionized water to concentration in the range of 1–1000 ␮M. 2.2. Urine collection and storage Urine samples from 61 healthy adults and 68 cancer patients ´ from the Department of Urology, the Medical University of Gdansk, Poland, were collected after signing their informed consents. The studies were performed in accordance with the principles embodied in the Declaration of Helsinki and executed according to the Ethical Committee of the Medical University of Gdansk (number of consent: NKEBN/660/2003). The group of healthy volunteers (39 women, 22 men; average age 54.85 ± 14.25) consisted of people who were not undergoing medication at the time of sample collection and who declared the healthy status. The cancer group (24 women, 44 men; average age 65.98 ± 12.09) consisted of kidney, urinary bladder and prostate cancer patients. The clinical characteristics of the patients were presented in supplementary data table (Table S1). After urine collection the samples were frozen immediately and stored at −80 ◦ C. Directly before analysis the samples were thawed at room temperature. 2.3. Instrumentation The experiments were carried out using liquid chromatography–tandem mass spectrometry (LC–MS/MS) system from Agilent Technology (Waldbronn, Germany) 1200 series composed of a binary pump, a membrane degasser, a thermostated autosampler and a 6430 triple quadrupole (QqQ) mass spectrometer. The QqQ mass spectrometer was equipped with an electrospray ionization source (ESI). The analyzes were performed in positive polarity in multiple reaction monitoring mode (MRM). The nebulizer pressure and capillary voltage were set at 30 psi and 3500 V, respectively. Nitrogen was used as a drying (10 L/min, 300 ◦ C) and as a collision gas. The fragmentor voltages and collision energy voltages were selected individually for each analyte and listed in Table 1. For separation of the analytes a Zorbax Extend-C18 column (2.1 mm × 50 mm, 1.8 ␮m) from Agilent Technologies (Waldbronn, Germany) was used. Besides, low dispersion in-line filter with 2 ␮m frits (Agilent Technologies, Waldbronn, Germany) was placed directly before the analytical column. The chromatographic separation was performed using 0.05% formic acid in water (mobile phase A) and 0.05% formic acid in methanol (mobile phase B) under gradient conditions. The gradient started from 1% to 1.2% of B from 0 till 2 min, then increased to 3.2% of B from 2 to 3 min and

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Table 1 LC/MS/MS parameters obtained after collision-induced dissociation in MRM mode. Compound

tR [min]

[M+H]+

Quantifier/qualifier. MRM transitions

Collision potential [V]

Fragmentor voltage [V]

Cytidine (C) Pseudouridine (Pseu) 3-Methylcytidine (3mC) Uridine (U) Adenosine (A) Inosine (I) 5-Methyluridine (5 mU) 3-Methyluridine (3 mU) Xanthosine (X) 6-Methyladenosine (6 mA) N2 -Methylguanosine (N2mG) NN-Dimethylguanosine (NNdmG) 8-Bromoguanosine (IS)

0.89 0.92 1.41 2.46 5.11 5.01 6.46 8.91 9.46 10.61 10.55 11.99 15.57

244.1 245.1 258.1 245.1 268.1 269.1 259.1 259.1 285.1 282.1 298.1 312.1 362.1

112/95 155/– 126/109 113/133 136/119 137/– 127/– 127/96 153/– 150/94 166/149 180/110 229/–

4/44 8 8/40 12/16 12/48 4 4 4/40 4 12/44 4/36 8/40 12

84 84 84 84 84 84 84 84 84 108 84 84 108

tR – retention time.

subsequently, to 12% of B to 7 min and held at 12% of B from 7 to 12 min. After 12 min the gradient increased to 15% of B till 13 min. From 13 to 17 min the mobile phase composition was set unchanged. After the analysis, the 8 min post-run program was started, keeping the system at initial conditions in order to equilibrate the analytical column for the next analysis. The injection volume was 2 ␮L, the flow rate and the column temperature were set at 0.3 mL/min and 8 ◦ C, respectively. Solid phase extractions were performed on a vacuum manifold column processor (J.T. Baker, Griesheim, Germany). Eluates obtained during the extraction procedure were centrifuged and evaporated with the use of GeneVac (Quatro MiVac Concentration, UK). 2.4. Sample preparation The sample preparation procedure was composed of several steps among which solid phase extraction was the main stage. For SPE method Varian PBA columns (100 mg, 1 mL) were used from Agilent Technologies (Waldbronn, Germany). They are ready-touse so they are a new alternative to the commonly applied Affi-gel 601 from Bio-Rad (Hercules, CA, USA) which is distributed as a powder for preparing extraction cartridges in laboratory before the SPE. Similarly to Affi-gel 601 powder, the extraction Varian PBA columns contain phenylboronic acid as an extraction bed. This phase is selective for nucleosides thanks to the vicinal hydroxyl groups in their structure. Under basic conditions the vicinal hydroxyl groups of nucleosides bind to the phenylboronic acid and, thanks to that, all interferences occurred in the sample can be eluted. Subsequently, by changing pH from basic to acidic, the vicinal hydroxyl groups of nucleosides are being released and eluted afterwards. Before the extraction, 25% ammonia was added to urine samples to adjust pH to the range from 8.2 to 8.6. Subsequently, urine samples were mixed and centrifuged. After centrifugation 1 mL of the supernatant was loaded on a preconditioned PBA column, together with a constant volume of the internal standard (50 ␮L of 150 ␮M internal standard solution). For the extraction purposes the procedure proposed by Gehrke et al. [25] was modified and applied to urine samples. The PBA columns were first equilibrated by washing sequentially with 0.1 M formic acid in 50% methanol, and 0.25 M ammonium acetate at pH 8.6. After loading the sample, PBA columns were washed with 0.5 mL of 0.25 M ammonium acetate (pH 8.6). 10 min after washing, the columns were rinsed with 2 mL of ammonium acetate (pH 8.6) and 0.3 mL 50% methanol in water. Then 0.15 mL of 0.1 M formic acid in 50% methanol was introduced to the columns to replace the 50% methanol in water. After that 3 min interval was applied in order to prepare columns for the elution of nucleosides. The last extraction step was the elution of nucleosides using 1.5 mL of 0.1 M formic acid in 50% methanol.

The eluate was evaporated under vacuum with the use of vacuum centrifuge (4 h at 37 ◦ C). The residue was dissolved in 100 ␮L of deionized water, vortex-mixed and injected on to the LC column. Thanks to the applied SPE extraction, the obtained aliquots were cleaned of possible interferences and 10-times concentrated.

2.5. Validation of analytical methods The developed method was validated in terms of linearity, range, intra-day, inter-day precision, accuracy, limit of quantification and limit of detection. The calibration curves were performed with the use of calibration solutions that were prepared by diluting the aqueous standard solutions of nucleosides. Moreover, in assessment of the intra and inter-day precision, quality control samples at three concentration levels were used, namely 4 ␮M (low quality control, LQC), 10 ␮M (middle quality control, MQC) and 100 ␮M (high quality control, HQC). For intra-day precision three different concentration levels were prepared three times simultaneously and each sample was measured in three replicates. The total number of analyses for one concentration level was nine. For inter-day precision the same procedure as for intra-day was performed for three consecutive days. The accuracy of the method was measured by determining the mean concentration of LQC, MQC and HQC samples and was calculated as percentage error of theoretical versus measured concentration. The detection limit was calculated with the use of graphical method. This method was based on analysis of six replicates of each sample at three concentration levels that were close to the hypothetical value of the detection limit. From the obtained data the standard deviation was calculated and the new function SD = f(c) was obtained, where “SD” is a standard deviation and “c” is a concentration close to the detection limit. The limit of detection was three times higher than the intercept of the function SD = f(c) [40].

2.6. Data extraction The analytes were determined using Mass Hunter Acquisition software (Agilent Technologies, Waldbronn, Germany). The quantitative analysis was performed using Mass Hunter Quantitative Analysis (Agilent Technologies, Waldbronn, Germany). The levels of 12 nucleosides were determined on the basis of the ratio of the peak areas of metabolite and the internal standard. All determined nucleosides’ concentrations were normalized against creatinine an expressed as ␮M analyte/mM creatinine ratios. Urinary creatinine levels were determined with the Jaffe method, based on the reaction between creatinine and picric acid, using colorimetric detection [41].

0.32/0.08 0.42/0.1 0.75/0.18 0.48/0.12 0.43/0.11 0.14/0.04 0.29/0.08 0.55/0.15 0.38/0.11 0.41/0.12 0.46/0.14 0.42/0.13

0.96/0.24 1.26/0.3 2.25/0.54 1.44/0.36 1.29/0.33 0.42/0.12 0.87/0.24 1.65/0.45 1.14/0.33 1.23/0.36 1.38/0.42 1.26/0.39

The normalized data set was statistically analyzed. Data distribution was evaluated with the use of W Shapiro–Wilk test. Due to lack of normal distribution the latter data were analyzed with U Mann–Whitney test. These statistical tests were done with the use of STATISTICA 10.0 software (Statsoft Inc., Tulsa, OK, USA). After univariate statistical analysis the multivariate statistical analyses were applied using Matlab environment (Matlab, 2011, Mathworks, Natick, MA, USA). Here, after normalization we scaled our data set using (i) autoscaling and (ii) level scaling in order to emphasize different aspects of the data. Classification of the data was performed with the use of principal component analysis (PCA) whereas data discrimination was analyzed using partial least squares discriminant analysis (PLS-DA) as well as K-nearest neighbor method (K-nn). For classification the data matrix composed of statistically significant nucleosides versus number of samples was applied. For discrimination, two randomly chosen data sets were used: training set and test set. Besides, for sample selection two another algorithms were applied, namely the Kennard–Stone algorithm [42] and duplex algorithm [43], however the obtained models did not give any better impact on further results. The test set contained 70% of samples (n = 90) from the total data set (n = 129). Besides, the discrimination models were validated using “leave one out cross validation” (LOOCV) and the model with both the lowest root mean square error of cross validation as well as the lowest number of variables was chosen.

0.723 0.068 0.024 0.607 0.069 0.121 0.374 0.032 1.004 0.176 0.759 1.188

3. Results and discussion 3.1. HPLC–MS/MS analysis

SD: standard deviation; Sxy : standard error; LOD: detection limit; LOQ: quantification limit.

1–200 2.5–200 2.5–200 2.5–200 2.5–200 1–200 1–200 2.5–400 2.5–200 2.5–200 2.5–400 2.5–400 C Pseu U 3mC 3 mU 5 mU A I 6 mA X N2mG NNdmG

0.0566 0.0057 0.0053 0.0394 0.0129 0.0200 0.0717 0.0033 0.1712 0.0191 0.0518 0.1097

± ± ± ± ± ± ± ± ± ± ± ±

0.0038 0.0003 0.0001 0.0032 0.0003 0.0006 0.0019 0.00006 0.0053 0.0009 0.0015 0.0024

0.0472–0.0660 0.0048–0.0065 0.0050–0.0056 0.0315–0.0473 0.0120–0.0138 0.0183–0.0217 0.0669–0.0766 0.0032–0.0035 0.1581–0.1842 0.0166–0.0216 0.0482–0.0554 0.1040–0.1153

0.6446 0.0615 0.0454 0.5069 0.0344 0.1285 0.3929 0.0363 0.7227 0.1205 0.7036 0.8955

± ± ± ± ± ± ± ± ± ± ± ±

0.3116 0.0295 0.0107 0.2618 0.0301 0.0577 0.1612 0.0128 0.4328 0.0840 0.3022 0.4725

−0.1179–1.407 −0.0106–0.1337 0.0191–0.0717 −0.1338–1.148 −0.0393–0.1080 −0.0197–0.2768 −0.0016–0.7875 0.0067–0.0658 −0.3363–1.782 −0.0955–0.3366 0.0066–1.4011 −0.1940–1.985

0.9864 0.9880 0.9981 0.9805 0.9975 0.9972 0.9977 0.9984 0.9971 0.9936 0.9963 0.9980

LOQ [␮M]/[␮g/mL] LOD [␮M]/[␮g/mL] Correlation coefficient Sxy Confidence interval (95%) for intercept Intercept ± SD Confidence interval (95%) for slope Linear range [␮M]

Slope ± SD

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2.7. Statistical analysis

Analyte

Table 2 Linearity parameters: linear range, slope, limit of confidence for slope, intercept, limit of confidence for intercept, standard error, correlation coefficient, limit of detection and quantification for nucleosides.

W. Struck et al. / J. Chromatogr. A 1283 (2013) 122–131

In order to separate twelve nucleosides two chromatographic columns were tested in a SCAN mode: Poroshell EC-C18 (2.1 mm × 50 mm, 2.7 ␮m) and Zorbax Extend C18 (2.1 mm × 50 mm, 1.8 ␮m). For the further analyses Zorbax Extend C18 column was chosen as to the observed coelutions of analytes with the use of the Poroshell EC-C18 column (data not shown). Because the applied columns were short and narrow the single analysis of 12 nucleosides lasted only 17 min plus 8 min for equilibration. That made our method competitive to others [23,36]. Although the main analyses were performed in MRM mode that selectively determines the exact mass of pseudomolecular ion and its daughter ions, the peaks’ coelution could cause ions’ suppression and could decrease the determination sensitivity. Besides, other parameters were tested like column temperature and flow rate of mobile phase as well as the temperature and flow rate of a drying gas, nebulizer pressure and capillary voltage. The fragmentor voltage and collision energy were chosen individually for each nucleoside with the use of Mass Hunter Optimizer from Agilent Technology (Waldbronn, Germany). In the MS/MS experiments, we monitored the separated nucleosides in positive ionization tandem mass spectrometry in multiple reaction monitoring mode (MRM) which is more sensitive than constant neutral loss mode and allow for quantification only these nucleosides that were taken into consideration basing on the available reference substances. The protonated precursor ion [M+H]+ was selected as to be the most abundant ion of each nucleoside. After collision induced dissociation the most abundant ion was the protonated base ion [BH2 ]+ which is a consequence of breakdown of the glycosidic bond that links the ribose moiety (132 amu) and the base moiety. The only exception was in case of pseudouridine for the sake of different type of bonding. Most of nucleosides have a bonding between a nitrogen atom of pyridine or purine and a carbon of ribose whereas in case of pseudouridine, a carbon atom of

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Fig. 1. Mass chromatogram of 12 nucleosides and internal standard (8-bromoguanosine) with the use of MRM mode for (A) quality control sample contained reference substances and (B) urine sample.

pyridine is bonded to the carbon of ribose. The bonding existing in pseudouridine is characterized by higher energy (80 kcal/mol) than in other nucleosides (62 kcal/mol), that’s way the fragmentation pattern can be different which was also proven in other publications [33,44]. The parameters of fragmentation for each nucleoside and the multiple reaction monitoring chromatograms were presented in Table 1 and Fig. 1, respectively. 3.2. Validation results The developed method that enables determination of 12 nucleosides was validated. The calibration curves were calculated according to the standard solutions of nucleosides. The samples designated for calibration were measured every single day of analysis so each randomly selected set of extracted urine samples

determined one day had its own calibration curve. The obtained calibration curves and correlation coefficient were in the range from 1 to 400 ␮M and from 0.9805 to 0.9984, respectively. The following concentrations of nucleosides were used in the creation of calibration curves: 2.5, 5, 10, 50, 100, 200 ␮M for Pseu, U, 3mU, 6 mA, X, 3mC, 2.5, 5, 10, 50, 100, 200, 400 ␮M for I, N2mG, NNdmG, and 1, 2.5, 5, 10, 50, 100, 200 ␮M for 5mU, C and A. The regression parameters such as linearity, slope, intercept, standard error and correlation coefficient as well as other validation parameters like detection limit and quantification limit were presented in Table 2. The precision of the method was evaluated as an average of peak’s area and retention time reproducibility in three samples (LQC, MQC and HQC). The results described as correlation coefficient, CV [%] for inter-day as well as intra-day precision for retention time and peak’s area as well as accuracy were presented in Table 3. The

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Table 3 Retention time and peak area precision expressed as a coefficient of variation [%]. The accuracy was expressed as a percentage value between theoretical concentration versus measured concentration. Analyte

Level

Inter-day precision for tR n = 9 [%]

Inter-day precision for AUC n = 9 [%]

Intra-day precision for tR n = 27 [%]

Inter-day precision for AUC n = 27 [%]

Accuracy n = 9 [%]

C

LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC LQC MQC HQC

1.26 2.47 3.28 1.56 2.60 3.23 1.99 1.97 3.06 1.70 2.33 2.96 0.89 0.52 1.26 2.02 1.33 2.14 2.28 2.08 2.21 2.23 2.17 2.27 0.47 0.45 0.95 0.86 0.42 1.19 0.52 0.42 1.00 0.32 0.51 1.08

3.98 1.65 5.66 2.73 2.11 5.50 2.36 3.80 3.63 5.86 3.97 2.44 4.07 2.92 2.54 5.01 4.58 5.32 2.60 3.16 5.60 2.27 3.44 2.38 2.80 4.62 2.54 4.03 4.88 4.06 6.45 2.86 5.15 5.50 6.62 3.59

1.18 1.78 2.50 1.46 1.84 2.45 1.87 1.69 2.52 1.59 1.67 2.30 0.83 0.53 1.04 1.52 1.20 1.83 2.14 1.91 1.89 2.10 1.95 1.95 0.44 0.42 0.79 0.80 0.53 1.00 0.49 0.42 0.87 0.30 0.44 0.96

6.23 6.89 6.60 4.65 9.07 8.14 11.04 6.20 4.99 6.12 5.47 5.26 9.02 9.74 7.96 5.14 12.88 4.99 5.65 4.97 7.89 4.50 5.93 4.88 7.17 6.26 3.54 9.91 8.92 5.20 9.57 7.98 5.03 7.27 8.06 6.75

97.76 105.82 101.39 101.88 100.78 102.90 101.97 101.37 107.51 99.29 102.19 108.21 100.58 101.70 99.45 103.21 97.41 98.23 100.30 104.38 101.23 102.14 101.91 101.59 101.37 105.39 99.42 104.19 104.36 99.36 93.52 91.76 99.55 100.16 106.02 105.45

Pseu

U

3mC

3 mU

5 mU

A

I

6 mA

X

N2mG

NNdmG

inter-assay and intra-assay CVs ranged from 0.3% to 12.9% so was at a satisfactory level below 15%. The accuracy was from 91.8% to 108.2% and was in an acceptable range ±15% of the theoretical concentration. The recoveries of nucleosides were measured by determining pooled urine samples that were fortified with standard mixture at two concentration levels, 10 ␮M and 100 ␮M. The average recoveries for 12 nucleosides was 94.6%, CV = 10.7% and 83.1%, CV = 14.5% for concentration 100 and 10 ␮M, respectively.

3.3. Statistical analysis The determined concentrations of 12 nucleosides in each out of 129 urine samples were normalized versus creatinine concentration and expressed as ␮M nucleoside/mM creatinine ratios. Next, the data set were statistically analyzed using U Mann–Whitney test in order to evaluate individual differences in the nucleosides’ levels between the two groups (cancer patients and healthy volunteers). The mean and median levels for some nucleosides were found elevated in cancer patients in comparison to the healthy ones.

Table 4 Nucleoside/creatinine ratios [␮M nucleoside/mM creatinine] in the urine samples from urogenital tract cancer patients and healthy controls. Metabolite

C Pseu 3mC U A I 5 mU 3 mU X 6 mA N2mG NNdmG

Healthy volunteers (n = 61)

Cancer patients (n = 68)

Mean

SD

Median

Min

Max

Mean

SD

Median

Min

Max

0.51 0.98 5.11 10.46 7.88 10.57 2.27 5.98 5.22 0.91 16.09 46.45

0.47 0.84 3.39 13.13 5.22 10.10 2.97 5.51 25.09 1.16 13.18 33.91

0.36 0.68 4.11 6.36 6.13 7.32 1.62 4.41 0.96 0.38 10.95 33.42

0.02 0.11 1.23 0.70 1.19 2.39 0.07 1.05 0.03 0.01 1.61 4.63

2.39 4.01 18.12 80.61 27.72 59.51 13.55 40.26 196.46 4.60 55.58 195.19

0.52 1.49 6.77 8.74 10.64 17.09 3.57 7.87 2.33 1.50 22.26 64.66

0.48 2.05 5.26 9.74 13.04 17.61 5.70 6.05 3.28 2.19 14.74 39.73

0.39 0.81 5.94 6.36 7.96 10.39 1.46 6.03 0.90 0.51 20.61 55.18

0.001 0.14 0.96 0.48 0.38 0.67 0.07 0.98 0.05 0.06 0.002 11.05

2.70 14.41 30.73 50.07 99.08 79.16 31.37 38.00 16.38 10.77 90.13 217.61

ns: no significance; SD: standard deviation.

p-Value

Significance

0.986 0.131 0.053 0.356 0.248 0.019 0.407 0.005 0.285 0.039 0.001 0.0002

ns ns ns ns ns p < 0.05 ns p < 0.05 ns p < 0.05 p < 0.05 p < 0.05

ns ns p < 0.05 ns p < 0.005 p < 0.01 ns p < 0.01 ns p < 0.001 p < 0.05 p < 0.005 0.41 0.53 1.98 5.99 2.51 4.89 2.71 1.64 41.67 1.01 6.57 12.73 ± ± ± ± ± ± ± ± ± ± ± ± 0.43 0.69 3.56 7.28 5.02 6.71 2.02 3.94 10.05 0.52 10.73 31.61 0.51 0.95 3.72 15.59 5.68 11.59 3.14 6.54 3.72 1.19 14.98 39.08 ± ± ± ± ± ± ± ± ± ± ± ± ns ns ns ns ns ns ns ns ns ns ns ns

0.55 1.14 5.98 12.26 9.50 12.75 2.42 7.12 2.49 1.13 19.12 54.81

Mean ± SD

Mean ± SD

0.303 0.100 0.006 0.256 0.0002 0.002 0.657 0.005 0.310 0.0003 0.018 0.003

p-Value

0.151 0.537 0.147 0.649 0.817 0.618 0.772 0.577 0.106 0.155 0.547 0.237 0.39 0.69 2.67 7.43 4.36 7.98 2.75 2.72 31.72 1.21 12.52 24.77 ± ± ± ± ± ± ± ± ± ± ± ± 0.44 0.86 4.52 8.69 7.68 10.29 2.21 5.19 6.68 0.845 15.30 40.91 0.57 1.04 4.21 19.05 6.48 13.06 3.36 8.22 3.93 1.10 14.39 44.32 ± ± ± ± ± ± ± ± ± ± ± ± 0.62 1.18 6.09 13.39 8.23 11.03 2.37 7.27 2.79 1.02 17.40 55.59 ns ns ns ns p < 0.01 ns ns p < 0.01 ns ns ns p < 0.01 0.341 0.161 0.068 0.305 0.004 0.075 0.499 0.009 0.596 0.606 0.125 0.009 0.39 0.81 2.65 6.62 2.22 6.48 2.08 7.16 2.52 0.80 7.06 18.54 SD: standard deviation; BMI: body mass index.

± ± ± ± ± ± ± ± ± ± ± ± 0.44 0.84 4.20 7.87 5.59 8.28 2.14 5.40 1.64 0.67 11.69 33.80

Mean ± SD

0.53 0.87 3.76 16.41 6.17 12.03 3.55 3.78 33.48 1.37 15.75 39.82 ± ± ± ± ± ± ± ± ± ± ± ±

Mean ± SD

0.56 1.09 5.83 12.52 9.70 12.39 2.37 6.43 8.06 1.08 19.59 56.49 C Pseu 3mC U A I 5mU 3mU X 6 mA N2mG NNdmG

Men (n = 22) Women (n = 39) Significance p-Value

25 ≤ BMI < 18.5 kg/m2 (n = 38) Mean ± SD =18.5 ≤ BMI < 25 kg/m2 (n = 23) Mean ± SD Significance p-Value Age <50 years old (n = 27)

Healthy volunteers (n = 61)

In order to study the relationship between nucleosides’ profiles and the presence of cancer disease the principal component analysis (PCA) was applied. This method is defined as unsupervised one because all analyses are performed without any prior information about samples’ classification. The PCA was performed only on statistically significant nucleosides (5 (variables) × 129 (subjects)) after two types of normalization, namely (1) after autoscaling and (2) after level scaling. A scaling factor in autoscaling is a measure of data dispersion (spread measure) and uses the standard deviation of variables’ concentrations. Autoscaling approach enables treating all metabolites as equally important regardless their intensity and compares metabolites based on correlations. As far as level scaling is concerned, a scaling factor is a mean value (size measure). The changes in metabolite concentrations are converted into changes relative to the average concentration of each analyte. Hence level scaling is useful in comparing metabolites according to the relative changes in their levels and is used in case of a presence of large changes in metabolites’ concentrations as a response to e.g. disease, stress or enzymatic dysfunctions [47]. The principal component analyses for analyzed matrix after autoscaling and level scaling were presented in Fig. 2. As it was shown in Fig. 2, there were some differences in the location of samples from healthy volunteers and cancer patients for both data sets. Above all, the two groups did not create separate clusters. The samples from healthy volunteers were more homogenous. Besides, cancer samples were more dispersed in the space limited by three principal components which was related to their higher diversity. It could be noticed especially in the case of level scaled data set. Similar results in the distribution of samples derived from healthy volunteers and patients with cancer in the analysis of nucleosides ´ from urine samples were observed in the work of Szymanska et al. [19] and Zheng et al. [13]. In order to study the relation between variables, a biplot was created (Fig. 3). The measure of similarity between the two variables is the angle formed by the vectors between them. When the angle between two variables amounts to 180◦ or 0◦ then the

>50 years old (n = 34)

3.4. Classification method

Metabolite

The observed differences between the groups were statistically significant (p < 0.05) for 5 out of 12 nucleosides, namely inosine, 3-methyluridine, 6-methyladenosine, N-2-methylguanosine and NN-dimethylguanosine. In addition, some of these metabolites like inosine, NN-dimethylguanosine and 6-methyladenosine revealed to be statistically significant also in the work of other groups [35,37,38,45]. Besides, the concentration ranges of nucleosides levels differed between two groups. Ranges obtained for cancer patients were in most cases wider and enclosed the ranges for healthy controls. This could be related to the greater diversity of cancer groups for the sake of different type and stage of tumor. The results were described in Table 4. We also assessed if gender, age and body mass index (BMI) had the statistically significant impact on the differences between nucleosides’ levels (Table 5). By applying the U Mann–Whitney test only for the non-cancer controls (n = 61) it was shown that body mass index did not have any statistically significant impact on the nucleosides’ levels. However, three nucleosides (adenosine, 3-methyluridine and NN-dimethylguanosine) were statistically significant between two age groups (below and over 50 years old) selected from the non-cancer group. Moreover, lower levels of seven nucleosides were noticed in men in comparison to women. Similar results were observed in the other studies [19,46]. The observed differences may be due to the different hormones’ levels between men and women. So other normalization criteria should be applied that minimizes existing differences.

Significance

W. Struck et al. / J. Chromatogr. A 1283 (2013) 122–131

Table 5 Differences in nucleoside/creatinine ratios [␮M nucleoside/mM creatinine] in urine of healthy volunteers in groups according to age, gender and body mass index with the use of U Mann–Whitney test.

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A

B 1.5

3

1

2

0.5 PC 3 (11,78 %)

2.5

1.5 PC 3 (9,95 %)

129

1 0.5 0

0 −0.5 −1 −1.5

−0.5

−2

−1

−2.5

−1.5

−3

−2

−3.5 4

2

6

2

0

4

0 2

PC 2 (22,55 %) −2

0

−4

PC 2 (14,69 %) −2

PC 1 (60,47 %)

−4 −4

−2

0

4

2

6

8

PC 1 (62,70 %)

Fig. 2. A principal component analysis for data (A) after level scaling and (B) after autoscaling. Blue triangles relate to healthy volunteers (n = 61), while the red circles are defined as cancer patients (n = 68). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

variables are strongly correlated (positively or negatively). However, if the angle between the variables is close to 90◦ then the variables are orthogonal. As it was noticed in Fig. 3 all variables (metabolites) had a positive impact on first principal component (PC 1). The largest impact on second principal component had 6 mA and inosine, although these metabolites were not correlated to each other. On the other hand, NNdmG and N2mG were strongly

A

1

positively correlated. Similar situation existed in case of pair of metabolites like 3 mU and N2mG or 3 mU and NNdmG. In practice, when in the study group NNdmG’s level is increased, also other positively correlated metabolites will be increased like e.g. N2mG. Besides comparing two data sets, the positive correlation between NNdmG, N2mG and 3 mU for data after level scaling was higher (Fig. 3A) than in case of autoscaled data (Fig. 3B).

B

1 6MA

0.8

0.8 I

0.6

0.6

0.4 N2MG NNDMG 3MU

0.2

PC 2 (14,69 %)

PC 2 (22,55 %)

0.4

0

−0.2

0.2 3MU 0 NNDMG −0.2 N2MG

−0.4

−0.6

−0.6

6MA

−0.8

−1 −1

I

−0.4

−0.8

−0.5

0

PC 1 (60,47 %)

0.5

1

−1 −1

−0.5

0

0.5

1

PC 1 (62,70 %)

Fig. 3. A principal component analysis that presents the distribution of objects and variables on the so-called biplot graph in a space defined by two principal components. Blue and red circles represent healthy volunteers (n = 61) and cancer patients (n = 69), respectively. (A) Data after level scaling and (B) data after autoscaling. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Table 6 The calculated parameters of the discrimination model: ccr cal means the percentage of correctly classified samples in the calibration set (n = 90), ccr test is defined as the percentage of correctly classified samples in the test set (n = 39), the sensitivity is the percentage of correctly classified cancer patients and the specificity is the percentage of correctly classified healthy controls. Parameter

Correct classification rate for training set Correct classification rate for test set Sensitivity Specificity

Discrimination model K-nn after autoscaling, K=5

K-nn after level scaling, K=5

PLS-DA after autoscaling, LV = 4

PLS-DA after level scaling, LV = 4

65.56% 61.54% 71.43% 50.00%

67.78% 46.15% 61.90% 27.78%

68.89% 64.10% 88.89% 42.86%

67.78% 58.97% 83.33% 38.09%

K: number of nearest neighbors, LV: number of latent variables.

3.5. Discrimination models After the unsupervised multivariate analysis, the data sets were analyzed using supervised methods. The main difference between supervised and unsupervised methods is that the supervised analysis is performed on data matrix together with information about class membership. Hence, one can adopt the data set to build discrimination model and evaluate its prediction power. It is especially important in future diagnostics role of studied metabolites. Before the analysis, the data set after autoscaling and level scaling was randomly divided into two other data sets: test set and training set [48]. The training set was used for building the discrimination model which was used for classification of test set samples. The both data sets were chosen using random selection but also sample subset selection was performed with the use of Kennard–Stone as well as duplex algorithm. In this study two discrimination methods were used: non parametric K-nearest neighbor (K-nn) method and parametric partial least squares discriminant analysis (PLS-DA). In Table 6 the results of correct classification rate as well as sensitivity and specificity of both models were presented. The K-nearest neighbor method classifies the samples to the group (healthy or cancer) on the basis of an assumption that in the nearest neighborhood exist samples from the same group. This method does not require any assumption about data distribution. The samples are assigned to a specific group only on a basis of group membership of one or “K”-nearest neighbor. In a case of situation, when in K-nearest neighbors are samples from both groups, the test sample is classified by a majority of classes of its neighbors. That’s why it is recommended to use only odd numbers of K-nearest neighbor. However, if there are differences between number of samples in each group there is a possibility of false classification. In our study we classified the samples according to the 5-nearest neighbors using the Euclidean distance. Our data set contained a greater number of samples from cancer patients (n = 68) than from healthy volunteers (n = 61), which explained poorer prediction of samples from a group of healthy volunteers. Specificity which is described as correctly classified healthy controls was 50% for data after autoscalation and 27.78% for data after level scaling. Differences between two data sets (after autoscalation and level scaling) were also noticed which could be related with the differences in the samples dispersion and various distance between them. The partial least squares discriminant analysis is a parametric method. It uses the several orthogonal variables called latent variables that describe the highest covariance between the data matrix (X) and the class membership (Y). This allows the reduction of variables and their exploration. Contrary to K-nearest neighbor method, PLS-DA allows to create a hyperplane in the space of latent variables and then it is possible to distinguish groups between tested samples. As it was presented in Table 6 the model obtained after PLS-DA discriminates test samples to specific groups in 64.1% for data after autoscalation and ca. 59% for data after level scaling.

However, the sensitivity of the model that is explained as correctly classified cancer patients was set to be 88.9% for data after autoscalation and 83.3% for data after level scaling. These results suggest to verify the healthy volunteers group in advance as they were correctly classified in much lower level than cancer patients (specificity was 42.9% for data after autoscaling and 38.1% for data after level scaling). In order to investigate why the specificity of both models (PLS-DA and K-NN) was so poor, we checked if the sample subset selection had an influence on the improperly diagnosed healthy volunteers. Instead of random selection, we adopted sample selection with the use of Kennard–Stone algorithm and duplex algorithm which are based on Euclidean distance between samples. The newly built models revealed similar results to the model based on random sample selection algorithm. Another cause of poor specificity could be a not enough high number of healthy volunteers. A large number of healthy volunteers would be particularly important for the results obtained from K-NN method. The problem could also exist with the type of methods used. The methods applied in the study are used to predict, on the basis of statistically significant descriptors, the presence or absence of cancer disease in tested samples. Thus, samples are classified into one out of two groups with a certain probability (0 – healthy, 1 – cancer). Some of the tested samples can lie in a range between 0 (healthy) and 1(cancer) which might mean a benign or malignant type of tumor. Hence, it seems that non-linear methods could be successfully used in this type of studies rather than methods based on linear regression like PLS-DA. According to the literature, support vector machine with nonlinear kernel function could also be appropriate for samples’ prediction [45,49,50]. Therefore, in further studies, we intend to apply this method in order to assess sensitivity and specificity of the obtained potential biomarkers. 4. Conclusions This work presented a comprehensive analysis of metabolic profiles of nucleosides from urine samples. The total metabolomic approach included sample preparation procedure basing on solid phase extraction with new ready-to-use SPE cartridges competitive to commonly used extraction powder Affi-gel, LC–MS/MS separation technique as well as univariate and multivariate statistical analyses. The developed method allowed for determination of 12 nucleosides in only 17 min which is an advantage over other quantitative methods [23,35–37,39]. Moreover, thanks to the short analytical column (5 cm length) the equilibration time was much shorter than in case of longer columns (8 min). The last step was construction of a prediction model that could discriminate samples to one of two groups: healthy volunteers or cancer patients. The used analytical technique (LC–MS/MS) is considered the most relevant analytical tool for quantitative analysis of metabolites, mainly due to high sensitivity of determinations. The multiple reaction monitoring mode allowed simultaneous determination of twelve nucleosides within 17 min of a chromatographic

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run with the sufficient accuracy and precision. The obtained data were statistically analyzed. The achieved results revealed statistically significant (p < 0.05) differences between levels of five urinary nucleosides found in samples from cancer patients and healthy volunteers. Some of them, namely inosine, 6-methyladenosine and N,N-dimethylguanosine have been also confirmed to be statistically significant by another groups [35,37,38,45]. To evaluate the relationship between nucleosides’ profiles and the human health status the classification and discrimination methods were applied like principal component analysis as well as K-nearest neighbor method and partial least squares discriminant analysis. Classification method revealed the higher diversity among samples from cancer patients than from healthy controls which is related to samples with a different types of cancer. Besides the relation between potential markers was also investigated with the biplots creation. Among proposed discrimination models, PLS-DA method gave better results than K-nn method. Sensitivity (correct classification of cancer patients) of the PLS-DA model was found to be more than 83% for both data sets (after autoscalation or level scaling). In terms of specificity (correct classification of non-cancer controls) healthy volunteers were not well classified properly (42.9% for data after autoscaling and 38.1% for data after level scaling). Although the model’s sensitivity was reasonable and comparable with models presented in other publications [19,45], the obtained specificity was not satisfactory. Therefore, continuation of the study is necessary with a broader statistical approach including other discrimination methods like e.g. support vector machine and logistic regression. Nevertheless, these results indicate the potential of urinary metabolites and particularly nucleosides as tumor markers that need to be confirmed on a much larger data set. Acknowledgments The project was supported by Ministry of Science and Higher Education, Warsaw, Poland (grant no N405 630338) as well as by the system project “InnoDoktorant – Scholarships for PhD students, IVth edition”. Project was co-financed by the European Union in the frame of the European Social Fund. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.chroma. 2013.01.111. References [1] J.O. Lay, S. Borgman, R. Liyanage, C.L. Wilkins, Trends Anal. Chem. 25 (2006) 1046. [2] D.B. Kell, Drug Discov. Today 11 (2006) 1085. [3] J.K. Nicholson, J.C. Lindon, Nature 455 (2008) 1054. [4] J.C. Lindon, J.K. Nicholson, E. Holmes, H. Antti, M.E. Bollard, H. Keun, O. Beckonert, T.M. Ebbels, M.D. Reily, D. Robertson, G.J. Stevens, P. Luke, A.P. Breau, G.H. Cantor, R.H. Bible, U. Niederhauser, H. Senn, G. Schlotterbeck, U.G. Sidelmann, S.M. Laursen, A. Tymiak, B.D. Car, L. Lehman-McKeeman, J.M. Colet, A. Loukaci, C. Thomas, Toxicol. Appl. Pharmacol. 187 (2003) 137. [5] M.A. Constantinou, E. Papakonstantinou, M. Spraul, S. Sevastiadou, C. Costalos, M.A. Koupparis, K. Shulpis, A. Tsantili-Kakoulidou, E. Mikros, Anal. Chim. Acta 542 (2005) 169. [6] A. Kumar, B. Lakshimi, J. Kalita, U.K. Misra, R.L. Singh, C.L. Khetrapal, B.G. Nagesh, Clin. Chim. Acta 411 (2010) 563.

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