- Email: [email protected]
Metabolic fingerprinting in breast cancer stages through 1H NMR spectroscopy-based metabolomic analysis of plasma
Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Metabolic fingerprinting in breast cancer stages through 1 H NMR spectroscopy-based metabolomic analysis of plasma Shankar Suman a,c,1 , Raj Kumar Sharma b,1 , Vijay Kumar d , Neeraj Sinha b , Yogeshwer Shukla a,c,∗ a Proteomics and Environmental Carcinogenesis Laboratory, Food, Drug and Chemical Toxicology Group, 31 Vishvigyan Bhawan, CSIR-Indian Institute of Toxicology Research, Mahatma Gandhi Marg, Post Box 80, Lucknow, 226001, India b Center of Biomedical Research, SGPGIMS-campus, Raibareilly Road, Lucknow, U.P., 226014, India c Academy of Scientific and Innovative Research (AcSIR), CSIR-IITR Campus, Lucknow, India d Department of Surgical Oncology, King George’s Medical University, Chowk, Lucknow, 226003, India
a r t i c l e
i n f o
Article history: Received 22 May 2018 Received in revised form 15 July 2018 Accepted 16 July 2018 Available online 18 July 2018 Keywords: Breast cancer 1 H NMR spectroscopy Metabolomics Glutamate Lactate N-acetyl glycoprotein
a b s t r a c t Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted 1 H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches. © 2018 Published by Elsevier B.V.
1. Introduction Breast cancer (BC) is highly diagnosed and the most cancerassociated death amongst women worldwide [1]. Despite the development of several sophisticated techniques, the diagnosis and therapeutics of BC are still a challenging job for the medical and scientific communities. One of the major facts of BC progression is the complexity of molecular and biochemical pathways involved in the cellular heterogeneity of BC [2–5]. These complexities may
∗ Corresponding author at: Proteomics and Environmental Carcinogenesis Laboratory, Food, Drug and Chemical Toxicology Group, 31 Vishvigyan Bhawan, CSIR-Indian Institute of Toxicology Research, Mahatma Gandhi Marg, Post Box 80, Lucknow, 226001, India. E-mail address: [email protected] (Y. Shukla). 1 Authors contributed equally. https://doi.org/10.1016/j.jpba.2018.07.024 0731-7085/© 2018 Published by Elsevier B.V.
render the pathological mystery of BC progression. In the present, biomarker research is adding a promising tool for diagnosis and prognosis of the disease manifestation. Metabolomics is one of the established research fields, which assists to evaluate the disease risk of the causative agents by detecting altered metabolite levels in pathological samples [6–9]. Metabolism is also highly accorded to external stimuli involved in disease pathogenesis, as compared to gene or protein expressions [10–12]. The growing developments in metabolomics are providing a very good platform to elucidate the biochemical pathways of different pathologies. In BC, metabolomics also provided an important avenue to uncover diagnostic, prognostic and therapeutic markers [8,13–15]. Cancer cells are capable of reprogramming metabolism, mainly through exploiting aerobic glycolysis to promote cell proliferation as revealed by Warburg effect [11]. Literature based on the recent studies have also added more evidences of metabolic reprogram-
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
ming in the disease stratifications of cancer [14,16,17]. Proton-NMR spectroscopy based metabolomics has gained a huge momentum for discovering a panel of metabolic signatures for BC [8,14,18–21]. Clinically, modulated expressions of hormonal receptors in BC are widely used for the prognosis and making therapeutic strategy for BC patients. However, hormonal therapy has also many limitations for the successful treatment of BC patients. In the context metabolomics based investigations are gaining the advantages to understand the of tumor metabolism in highly expressed hormone receptors in BC cases [14,22,23]. Previous studies showed that in estrogen receptor positive (ER+) BC; beta alanine and glutamine metabolism are highly implicated as compared to ER negative BC [22]. In cancer, oxidative stress is one of the key factors associated with neoplastic transformation, and is involved through the elevation of ketones and lactate levels, which lead to the emergence of BC stem cells [4,24]. The abnormal accumulated metabolites in cancer are also known as oncometabolites; for example, D-2-hydroxyglutarate, fumarate and succinate are most common oncometabolites accumulated due to epigenetic changes [25]. In the context of heterogenicity of BC, current NMR spectroscopy based metabolomics have assisted to evaluate the metabolic differences in the stratified BC classes including intratumoral heterogeneity [26] and BC subtypes [27]. Furthermore, it has also assisted to reveal the predictive plasma signature for long term risk of developing BC [28] and predication of clinical end points in BC [29]. Literature from metabolomics have widely revealed the deregulated metabolic pathways in BC [3,30–32] and ongoing metabolomics studies have given more emphasis to decipher BC heterogeneity by evaluating metabolic alterations in BC patients [33]. Seeking the extent of NMR spectroscopy based metabolomics; our goal in the present study was to reveal the changes in the metabolic profiles during BC progression. We focused on the detection of early stage (EBC) and late stage (LBC) altered metabolic pattern in BC, which could facilitate in revealing the metabolite fingerprints in the BC pathogenesis.
39
trimethylsilyl-2,2,3,3-tetradeuteropropionicacid) was used for the deuterium lock as well as an external reference. 2.3. NMR experiments All one-dimensional 1 H NMR spectra were acquired using a Bruker Avance III 800 MHz NMR spectrometer operating at a proton frequency of 800.21 MHz, equipped with cryoprobe at 298 K. The one dimensional NMR experiments were performed using Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence with water pre-saturation to remove broad signals of molecules (lipid and proteins). CPMG spectra were recorded by 64 k time domain data points, 20.55 ppm spectral width, 128 scans, and relaxation delay of 5 s with constant receiver gain value of 90 and 400 ms echo time. All data were manually phase and base line corrected. All resonances of metabolites present in 1D spectra were confirmed using biological magnetic resonance data bank (BMRB), human metabolome data bank (HMDB), published research articles and by comparing with standard data. Furthermore, identification of resonances of metabolites were performed using two-dimensional homonuclear (1 H-1 H COSY and 1 H-1 H TOCSY) and hetero-nuclear (1 H-13 C HSQC) experiments. 2D experiments increase the dimensionality of spectra that helps in reducing the overlapping problems. For the purpose COSY spectra were recorded in the magnitude mode with 2 K data point in t2 domain, where spectral width was 13 ppm in both the dimensions and 256 increments in t1 dimension were acquired with 16 Scans and relaxation delay of 2 s. For TOCSY experiment total 2 K data points were collected in t2 domain with 13 ppm spectral width and 256 t1 increments for 16 scans, where 80 ms mixing time was used for the experiment. 1H-13C HSQC spectra were acquired with 2 k data points in t2 dimension. Recycle delay of 2 s was used for the experiment. The total 512 increments were acquired with 32 scans with spectral width of 16 ppm and 165 ppm in 1 H and 13 C dimension, respectively. 2.4. Data processing and analysis
2. Materials and methods 2.1. Clinical samples and processing of plasma Blood samples of breast cancer patients (n = 72) collected from department of surgical oncology with prior ethical approval by the Institutional Ethics Committee, King George’s Medical University (KGMU), Lucknow, India. Further fifty blood samples were collected from age matched healthy volunteers (n = 50). All samples were collected in around the same daytime prior to meal intake conditions with their written consent in agreement with the Declaration of Helsinki. In addition, diabetic and unhealthy control subjects were not considered under the criteria of sample collection. Clinical details of all study subjects is given in the Table S1 (Supplementary material). BC with stages 0 to II patients were included in EBC and those of stages III to IV patients in LBC grouped under TNM protocol, given by American joint committee on cancer (AJCC) [34]. All samples were collected in EDTA coated blood collection vial, centrifuged at 1200×g at 4 ◦ C for 20 min for the separation of blood plasma, and stored at -80 ◦ C till further experimentation.
2.2. Sample processing for NMR spectroscopy For the NMR data collection, plasma samples were thawed and centrifuged to remove debris prior to the recording the spectra. Further, 300 l of supernatant and 100 ul deuterium oxide (D2 O) were mixed and was taken in 5 mm NMR tubes (Wilmad Glass, USA). A co-axial insert containing TSP solution (Sodium salt of
NMR spectra of all samples were recorded with constant parameters to ensure results accuracy. All NMR spectra were phased and baseline-corrected using Bruker Biospin TOPSPIN software (version 2.1). The total of 122 samples (n = 72; BC and n = 50; controls) were used for multivariate analysis. All NMR spectral data were exported to AMIX software (Version 3.8.7, Bruker Biospin, Germany) for binning of 0.03 ppm width chemical shift with a range of 0.5–9 ppm. Region between 4.2 to 5.2 ppm were excluded from binning to avoid water suppression variability. Bins were normalized to the total spectral area and, subsequently scaled to remove the possible bias from sample variability and preparation. The final data set was reduced to ASCII files, converted into excel file, which were exported to the Unscramble X software package (Version 10.0.1, Camo ASA, Norway) for multivariate analysis. Principal Component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were performed on data matrix to differentiate the groups based on metabolic profiles. Further, the quantitative analysis of metabolites was performed by integrating the buckets area (binned spectral regions) after normalizing these spectra with TSP signal. The significance and relative levels of metabolites and receiver operative curve analyses were performed using SPSS statistical package. Non-parametric test was used to calculate significance of altered level of metabolites in early and late stages from healthy subjects. The selected metabolites based on their significance were used for the evaluation of clinical diagnostic accuracy in BC from healthy subjects by performing receiver operating characteristic (ROC) curve analysis. Metaboanalyst 3.0 web server was also used to view the expression pattern by heat-map, power and metabolic pathway analysis in the selected data matrix of assigned spectral
40
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
Table 1 Characteristics R2 Y and Q2 values of PLS DA models. Groups
Healthy-EBC-LBC
Healthy-EBC
Healthy-LBC
EBC-LBC
R2 Y Q2
0.74 0.60
0.77 0.61
0.75 0.61
0.52 0.36
regions of metabolites (www.metaboanalyst.ca/). The association of differential metabolites in EBC and LBC was determined by pathway impact, which was calculated by using the sum of importance measures of metabolites of interest normalized with the total sum of the importance measures of all metabolites associated with metabolic pathway [35]. 3. Results 3.1. 1 H NMR metabolic profiles of blood plasma from healthy, EBC and LBC subjects One-dimensional 1 H CPMG NMR spectra were recorded from the blood plasma of healthy control and BC patients to profile and analyze altered level of metabolites. Twenty-six endogenous metabolites were assigned by analyzing NMR spectra. The assignment of resonances of metabolites was done by using biological magnetic resonance data bank (www.bmrb.wisc.edu/) and previously reported literature [36,37]. Assignments of different resonances have been given with their chemical shifts in Table S2 (Supplementary material). A representative class of 1 H CPMG spectra of both healthy and BC patients were shown in Fig.1. On the visual inspection on each of the spectra of healthy, EBC and LBC subjects there were found a prominent change in many peaks. These peaks were further assigned as lipoprotein, isoleucine, leucine, valine, 2-hydroxybutyrate, N-acetyl glycoprotein (NAG), lipid 1, lactate, threonine, alanine, citrulline, leucine, arginine, lysine, acetate, proline, glutamic acid, glutamine, choline, glycine, lactate,  glucose, ␣ glucose, tyrosine, histidine and formate (Fig. 1). 3.2. Multivariate analysis In order to reduce complexities of NMR spectral data and interpret the comparative metabolic changes in early and late stage of BC, multivariate data analyses were performed. Initially, the natural group separation, including outlier detection and clustering in all data sets was performed by PCA analysis. The NMR data matrix of healthy and BC subjects were primarily studied with multivariate analysis and results showed a clear separation in both groups (Fig. S1, Supplementary material). Subsequently, the associations of metabolites in early and late stages of BC from healthy subjects, PLS-DA models were generated taking NMR data matrix. A separate pair-wise PLS-DA models were analyzed in early stage, late stage with control sample using non-linear iterative partial least squares algorithm (NIPALS) and validated with 7 fold cross validation. The score plot of PLS-DA analysis indicated good separation in healthy control with early and late stages of BC, in which goodness of fit and predictability was accessed by cumulated R2 and cumulated Q2 values. Each of PLS-DA models showed satisfactory fit with good accuracy of prediction in healthy-EBC, healthy-LBC and HealthyEBC-LBC (Table 1). The PLS-DA model revealed the segregation of groups by R2 Y (cum) = 0.74 and Q2 (cum) = 0.60 among healthy, EBC and LBC cohorts. The pair wise PLS-DA in between healthy- EBC and healthy-LBC revealed clear separation with R2 Y (cum) = 0.77, Q2 (cum) = 0.61 and R2 Y (cum) = 0.75, Q2 (cum) = 0.61, respectively (Fig. 2). However, in between EBC and LBC group, PLS-DA statistical model revealed R2 Y (cum) = 0.52 and Q2 (cum) = 0.36 (Fig. 2). Further to avoid over-fitting and enhance robustness of data; we also verified PLS-DA model by taking 25% of random samples as test set
and remaining 75% as original training sets of all samples (Fig. S2, Supplementary material). The results revealed the R2 Y (cum) = 0.70, Q2 (cum) = 0.58 in healthy-EBC-LBC group, however, in between healthy-EBC, healthy-LBC and EBC-LBC, R2 Y (cum) and Q2 (cum) values were found to be (0.77, 0.60), (0.81, 0.56) and (0.46, 0.26), respectively. 3.3. Characterization of metabolic level alteration in EBC and LBC The characterization of the altered metabolite level in blood plasma of EBC and LBC was performed, and the analysis indicated the 16 metabolites altered in healthy-EBC, healthy-LBC and EBCLBC cases. Further, heat-map of assigned metabolites by Euclidean correlation analysis in each group was performed to study the different patterns of metabolites level (Fig. 3). We also evaluated the significance of altered metabolites level in the early or late stage BC as compared to healthy subjects. By performing two-sample Kolmogorov-Smirnov and Mann-Whitney test together in same data sets, sixteen metabolites were found to be significantly altered (p < 0.05) in EBC or LBC as compared to healthy subjects. Amongst these metabolites; the level of lactate, glutamate, lysine, NAG,  glucose, 2-hydroxybutyrate, lipid and ␣ glucose were increased in EBC and formate and glutamine level were decreased in LBC patients as compared to healthy subjects (p < 0.05) (Table 2, Fig. S3, Supplementary material). The significant modulation of  glucose, glycine, glutamic acid and NAG in between EBC and LBC subjects was also noted (Table 2, Fig S3, Supplementary material). 3.4. Clinical diagnostic accuracy of altered levels of metabolites in BC and their associated metabolic pathways To evaluate the performance of each altered metabolite in BC stages from healthy subjects and from EBC to LBC; we analyzed the diagnostic accuracy through receiver operating characteristic (ROC) curves analysis. The diagnostic accuracy in the form of area under the ROC curve (AUC) was evaluated in the datasets of healthy vs. EBC, healthy vs. LBC and healthy vs. BC patients and EBC vs. LBC (Fig. 3, Table 3). The metabolites were also used to construct an independent model and we found that hydroxybutrate, lysine, glutamate,  glucose, ␣ glucose, Lactate, NAG performed good diagnostic potential with the AUC score more than 0.65 in overall BC patients compared with healthy subjects. In classified BC subjects, the significance of all these metabolites were similarly found in LBC patients, however in EBC patients hydroxybutrate,  glucose, ␣ glucose and lactate only gave AUC score ≥0.70 (Fig. 3 and Table 3). Further, our interest was to analyses ROC curve in between EBC and LBC, which showed that glutamate, lactate and NAG levels of metabolic alterations separate the both the group with AUC value of ≈0.7 (Table 3). The pathway analysis of modulated metabolites by metaboanalyst 3.0 showed their association with different metabolic pathways and the five major pathways were associated with the pathway impact of more than 0.2. These five major pathways are d-Glutamine and d-glutamate metabolism, arginine and proline metabolism, pyruvate metabolism, alanine, aspartate and glutamate metabolism and glycine, serine and threonine metabolism (Fig. 4, Table S3, Supplementary material). 4. Discussion 1H
NMR spectroscopy based metabolomics has gained a huge momentum for the detection of metabolic biomarkers of various pathologies including BC. Our study revealed the discriminated metabolic patterns in EBC and LBC as compared to healthy subjects and apparently good predictive power of study with samples size per group (Fig. S4, Supplementary material). The data obtained
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
41
Fig. 1. Representative spectral assignments of metabolites using 800 MHz NMR recorded spectra of blood plasma of Control healthy, early stage BC and late stage BC subjects.
Fig. 2. Score plot of PLS-DA model of profiled metabolites in healthy and BC subjects in different model indicated (A). Healthy, EBC and LBC, (B). Healthy-EBC, (C). Healthy-LBC, and (D). EBC-LBC.
42
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
Fig. 3. ROC curves of metabolites with high AUC scores. (A). Healthy vs. BC, (B). Healthy vs. EBC, (C). Healthy vs. LBC, (D). EBC vs. LBC.
Table 2 Metabolic alterations in early and late stages of BC. Serial Chemical no. shift (ppm)
Metabolites
Relative Significance abundance (EBC vs. healthy)
1 2 3 4 5 6 7 8 9 10 11 12 13
8.45 6.8 5.28 5.21 4.62 4.09 3.58 2.42 2.33 1.87 1.70 1.18 1.15
0.6 1.32 1.76 1.36 1.03 1.29 1.0 0.81 1.11 1.1 1.07 0.83 1.22
14 15 16
0.96 0.93 0.84
Formate Tyrosine Lipid ␣ Glucose  Glucose Lactate Glycine Glutamine Glutamate Lysine Arginine NAG Hydroxybutyrate Valine Isoleucine Lipoprotein
0.85 0.84 1.18
Relative abundance (LBC vs. healthy)
Significance
Relative abundance (LBC vs. EBC)
Significance
p = 0.001a , p = 0.001b p = 0.090 a , p = 0.002 b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b # p = 0.969 a , # p = 0.812 b p < 0.001a , p = 0.001b # p = 0.064 a , # p = 0.100 b # p = 0.272 a , # p = 0.338 b # p = 0.583 a , # p = 0.618 b # p = 0.256 a , # p = 0.141 b p = 0.004a ,p = 0.024 b
0.64 1.19 1.8 1.6 1.32 1.58 0.76 0.8 1.25 1.19 1.15 1.97 1.33
p = 0.001a ,p < 0.001b # p = 0.698 a , # p = 0.385 b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p = 0.005 a , p = 0.012b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p = 0.002 a , p = 0.012b p = 0.018 a , p = 0.02 b p < 0.001a , p < 0.001b p = 0.001a ,p = 0.001 b
1 0.9 1.02 1.17 1.27 1.22 0.77 0.98 1.13 1.08 1.06 2.33 1.09
#
p = 0.011a ,p = 0.033 b p = 0.017a ,p = 0.076 b p = 0.007a ,p = 0.066 b
0.89 0.87 1.03
#
p = 0.268 a , # p = 0.113b 1.04 p = 0.264 a , # p = 0.220 b 1.03 # p = 0.586 a , # p = 0.683b 0.88
#
#
#
p = 0.947a , # p = 0.828 b p = 0.211 a , # p = 0.254 b # p = 0.592 a , # p = 0.558 b # p = 0.279 a , # p = 0.356 b p = 0.012 a , p = 0.055 b # p = 0.841a , # p = 0.889 b p = 0.012a , p = 0.018b # p = 0.991a , # p = 0.828b p = 0.012 a , p = 0.053b # p = 0.099 a , # p = 0.070b # p = 0.141 a , # p = 0.116b p < 0.001a , p = 0.007b # p = 0.525 a , # p = 0.373b #
p = 0.264a , p = 0.033 p = 0.237a , # p = 0.100b # p = 0.086a , # p = 0.211b
Note: Altered level of metabolites in EBC and LBC from healthy subjects and in between EBC and LBC were analyzed by fold change (average) and P values were obtained by Two−Sample Kolmogorov−Smirnov and Mann−Whitney non−parametric test, denoted by superscript a and b, respectively. # Non-significant changes.
from 1 H NMR spectra of the samples were taken for the multivariate analysis by both PCA and PLS-DA and these analysis showed a distinct separation of groups in healthy-BC, healthy-EBC-LBC,
healthy-EBC and healthy-LBC. Furthermore, PLS-DA model predicts good accuracy with R2 > 0.6 and Q2 > 0.5 (Fig. 2, Table 1 & Fig. S2, Supplementary material). However, in between EBC and LBC sub-
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
43
Table 3 Details of ROC curves of all significantly altered metabolites in different groups. Healthy vs BC
Healthy vs EBC
Healthy vs LBC
EBC vs LBC
AUC
95% CI (p value)
AUC
95% CI (p value)
AUC
95% CI (p value)
AUC
95% CI (p value)
Hydroxybutyrate
0.697
0.687
Glutamate
0.681
 glucose
0.818
␣ Glucose
0.78
NAG
0.652
Lactate
0.78
0.608–0.842 (0.001) 0.590–0.825 (0.002) 0.664–0.885 (0.000) 0.746–0.929 (0.000) 0.672–0.880 (0.000) 0.659–0.869 (0.000) 0.721–0.914 (0.000)
0.544
0.627
0.571–0.8043 (0.004) 0.429–0.681 (0.395# ) 0.484–0.732 (0.092) 0.760–0.960 (0.000) 0.737–0.921 (0.000) 0.440–0.698 (0.285# ) 0.675–0.892 (0.000)
0.725
Lysine
0.604–0.790 (0.017 0.528–0.725 (0.001) 0.587–0.775 (0.000) 0.726–0.91 (0.000) 0.694–0.867 (0.004 0.55–0.754 (0.000) 0.689–0.871 (0.000)
0.410–0.679 (0.525# ) 0.484–0.743 (0.099# ) 0.546–0.780 (0.012) 0.443–0.706 (0.279# ) 0.350–0.623 (0.841# ) 0.644–0.867 (0.000) 0.544–0.801 (0.012)
Name of Metabolites
#
0.555 0.609 0.860 0.829 0.569 0.784
0.708 0.774 0.837 0.776 0.764 0.817
0.614 0.673 0.575 0.486 0.756 0.672
Non-significant changes.
Fig. 4. (A). Pathways impact of metabolic pathways in BC. (B). Altered level metabolites associated with metabolic pathways involved in BC pathogenesis.
jects, these values did not go with mentioned cutoff, which might be because of fewer differences of the metabolic patterns due to close resemblances of clinical stages like stage IIB and stage IIIA BC patients that were included in EBC and LBC patients, respectively. After the analysis of spectral data, 26 distinct metabolites were assigned based on resonance variability in blood plasma samples of healthy, EBC and LBC samples (Fig. 1). The statistical analysis showed the significant modulation of lactate, glutamic acid, lysine, NAG,  glucose, 2-hydroxybutyrate, ␣ glucose, formate, lipid and glutamine level in EBC and LBC patients from healthy subjects (Table 2). Furthermore, tyrosine, valine, isoleucine, and lipoprotein in EBC and arginine and glycine in LBC was found to be significantly altered from healthy subjects, however,  glucose, glycine, glutamic acid and NAG were significantly altered in between EBC and LBC subjects (p ≤ 0.05, Table 2). The higher level of glutamic acid and lower level of glutamine in EBC and LBC compared to healthy subject showed the probable association of glutamic acid-glutamine metabolism in BC progression. Previous reports also evidenced that the glutaminolysis metabolism is also highly involved in many cancer types and attracts as new target for cancer [38–41]. Glycine level in blood plasma of LBC was found to be lower than healthy subjects (p < 0.05), it might be because of high glycine consumption in high-grade tumors [42,43]. Previous studies showed that glycine is
involved in HER-2 positive tumors and acts as a potential marker for tumor aggressiveness [22,42]. Apart from these, glycine and sarcosine, a methyl derivative of glycine are the potent diagnostic markers of prostate cancer [44]. Study has also showed that glycine promotes BC proliferation through metabolic reprogramming [42]. Hypoxia and reactive oxygen species (ROS) mediated response is well recognized in cancer, which is executed by lactate, folate and pyruvate metabolism [31,45]. Among several metabolites associated with tumor cell survival in low oxygen condition, glutamate is observed as one of the key metabolic marker for hypoxia in cancer [46]. Glutamate, glycine and formate may also involve in ROS generation and oxidative stress via different metabolic process, including folate pathways [43,47,48]. Folate cycle is a myriad of processes of the metabolic pathways associated with cancer [49]. Lactate is notably involved in BC pathogenesis and aggressiveness through a variety of mechanisms involved in cancer [4,50-51]. Therefore, lactate concentration in blood plasma was found to be rationally increased in EBC and LBC patients comparatively healthy subjects. Glutamate-glutamine cycle is known to involve in presence of NH4+ ion released by some of amino acid like valine and isoleucine. Our data showed that the levels of both amino acids were down regulated, which might possibly due to enhanced consumption of both these amino acids in the glutamine synthesis. Extracellular arginine is reported as major discriminants of neo-
44
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
plastic and normal hepatocytes [51]. We noted that arginine level was higher in LBC patients (p < 0.05). Further, the modulated lipid level was also observed in both early and late BC stages (p ≤ 0.05). Previous reports greatly emphasized on the fact that the modulated level of lipid is essentially play an important role in cancer progression [52]. Furthermore, our study also revealed that  glucose and ␣ glucose level were elevated in both EBC and LBC, which could predict their role in BC pathogenesis. High glucose levels are reported to promote the proliferation of BC cells through GTPases [53] and a study revealed that impaired glucose homeostasis in premenopausal obese or overweight patients is associated with BC [54]. The previous study showed that histidine, acetoacetate, pyruvate, NAG, phenylalanine, glycerol, glutamate and mannose were highly discriminated in metastatic BC as compared to localized cancer [19]. Our data also correspond to many of these altered metabolites, which were significantly associated with the BC progression even from early stage (Fig. 3B & Table 3). Furthermore, a key discriminatory metabolites including hydroxybutyrate, lysine, glutamate,  glucose, ␣ glucose, NAG, and lactate showed good diagnostic accuracy and these may act as metabolic fingerprints of BC patients as well as it also pinpoints various metabolic pathways involve in BC progression. Among them glutamate, NAG and lactate have shown their probable association in LBC from EBC and link the deregulations of metabolic pathways in the BC progression. However, further investigations are recommended to advance the study in a very large patients cohort to assess and validate these metabolites with biomarker potential identified in our study. 5. Conclusion Our investigation reinforces the significance of metabolic signatures for BC. Our data advocate that several altered metabolic contents in early and late stages of BC may involve in the deregulations of carcinogenic events via switching multiple biochemical pathways (Fig. 4) and their differences in the levels in BC stages, may their possible association in the BC progression. Hydroxybutyrate, lysine, glutamate, glucose, NAG, and Lactate metabolites showed a marked shift in the BC population with a good diagnostic potential for BC. However, the differences in the metabolite level appeared by genetic or environmental contaminant in some of the control side population cannot be neglected, and these may possibly help in the risk assessment for BC. Acknowledgements The authors are thankful to Council of Scientific and Industrial Research, New Delhi, for funding this work from INDEPTH (BSC0111) and providing research fellowship to Shankar Suman. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jpba.2018.07. 024. References [1] J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, F. Bray, GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet], International Agency for Research on Cancer, Lyon, France, 2015, 2013. [2] J.D. Brenton, L.A. Carey, A.A. Ahmed, C. Caldas, Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J. Clin. Oncol. 23 (29) (2005) 7350–7360. [3] G. Schramm, E.-M. Surmann, S. Wiesberg, M. Oswald, G. Reinelt, R. Eils, R. König, Analyzing the regulation of metabolic pathways in human breast cancer, BMC Med. Genomics 3 (2010), 39–39.
[4] F. Kallinowski, P. Vaupel, S. Runkel, G. Berg, H.P. Fortmeyer, K.H. Baessler, K. Wagner, W. Mueller-Klieser, S. Walenta, Glucose uptake, lactate release, ketone body turnover, metabolic micromilieu, and pH distributions in human breast cancer xenografts in nude rats, Cancer Res. 48 (24 Pt. 1) (1988) 7264–7272. [5] S. Suman, P.K. Sharma, G. Rai, S. Mishra, D. Arora, P. Gupta, Y. Shukla, Current perspectives of molecular pathways involved in chronic inflammation-mediated breast cancer, Biochem. Biophys. Res. Commun. 472 (3) (2016) 401–409. [6] M. Mamas, W.B. Dunn, L. Neyses, R. Goodacre, The role of metabolites and metabolomics in clinically applicable biomarkers of disease, Arch. Toxicol. 85 (1) (2011) 5–17. [7] E. Acar, G. Gurdeniz, F. Savorani, L. Hansen, A. Olsen, A. Tjonneland, L.O. Dragsted, R. Bro, Forecasting chronic diseases using data fusion, J. Proteome Res. 16 (7) (2017) 2435–2444. [8] L. Lecuyer, A. Victor Bala, M. Deschasaux, N. Bouchemal, M. Nawfal Triba, M.P. Vasson, A. Rossary, A. Demidem, P. Galan, S. Hercberg, V. Partula, L. Le Moyec, B. Srour, T. Fiolet, P. Latino-Martel, E. Kesse-Guyot, P. Savarin, M. Touvier, NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer, Int. J. Epidemiol. 47 (2) (2018) 484–494. [9] R.K. Rai, A. Azim, N. Sinha, J.N. Sahoo, C. Singh, A. Ahmed, S. Saigal, A.K. Baronia, D. Gupta, M. Gurjar, B. Poddar, R.K. Singh, Metabolic profiling in human lung injuries by high-resolution nuclear magnetic resonance spectroscopy of bronchoalveolar lavage fluid (BALF), Metabolomics 9 (3) (2013) 667–676. [10] A. Koulman, G.A. Lane, S.J. Harrison, D.A. Volmer, From differentiating metabolites to biomarkers, Anal. Bioanal. Chem. 394 (3) (2009) 663–670. [11] D. Jia, J.H. Park, K.H. Jung, H. Levine, B.A. Kaipparettu, Elucidating the metabolic plasticity of cancer: mitochondrial reprogramming and hybrid metabolic states, Cells 7 (3) (2018) 21. [12] K. Hollywood, D.R. Brison, R. Goodacre, Metabolomics: current technologies and future trends, Proteomics 6 (17) (2006) 4716–4723. [13] M.P. Cala, J. Aldana, J. Medina, J. Sanchez, J. Guio, J. Wist, R.J.W. Meesters, Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: a pilot control-case study in Colombian Hispanic Women, PLoS One 13 (2) (2018), e0190958. [14] C.D. Hart, A. Vignoli, L. Tenori, G.L. Uy, T. Van To, C. Adebamowo, S.M. Hossain, L. Biganzoli, E. Risi, R.R. Love, C. Luchinat, A. Di Leo, Serum metabolomic profiles identify ER-positive early breast cancer patients at increased risk of disease recurrence in a multicenter population, Clin. Cancer Res. 23 (6) (2017) 1422–1431. [15] K.A. Bertrand, H.J. Baer, E.J. Orav, C. Klifa, A. Kumar, N.M. Hylton, E.S. LeBlanc, L.G. Snetselaar, L. Van Horn, J.F. Dorgan, Early life body fatness, serum anti-mullerian hormone, and breast density in young adult women, Cancer Epidemiol. Biomarkers Prev. 25 (7) (2016) 1151–1157. [16] S. Venneti, P. Mischel, Metabolic reprogramming in cancer – a coordinated effort, Brain Pathol. (Zurich, Switzerland) 25 (6) (2015) 753–754. [17] K.E. Allison, B.L. Coomber, B.W. Bridle, Metabolic reprogramming in the tumour microenvironment: a hallmark shared by cancer cells and T lymphocytes, Immunology 152 (2) (2017) 175–184. [18] A.M. Weljie, A. Bondareva, P. Zang, F.R. Jirik, (1)H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system, J. Biomol. NMR 49 (3–4) (2011) 185–193. [19] E. Jobard, C. Pontoizeau, B.J. Blaise, T. Bachelot, B. Elena-Herrmann, O. Tredan, A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer, Cancer Lett. 343 (1) (2014) 33–41. [20] B. Mamtimin, M. Hizbulla, N. Kurbantay, L. You, X. Yan, H. Upur, An magnetic resonance-based plasma metabonomic investigation on abnormal Savda in different complicated diseases, J. Tradit. Chin. Med. 34 (2) (2014) 166–172. [21] J.M. Pearce, M.C. Mahoney, J.H. Lee, W.J. Chu, K.M. Cecil, S.M. Strakowski, R.A. Komoroski, 1H NMR analysis of choline metabolites in fine-needle-aspirate biopsies of breast cancer, MAGMA 26 (3) (2013) 337–343. [22] J. Budczies, S.F. Brockmoller, B.M. Muller, D.K. Barupal, C. Richter-Ehrenstein, A. Kleine-Tebbe, J.L. Griffin, M. Oresic, M. Dietel, C. Denkert, O. Fiehn, Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism, J. Proteom. 94 (2013) 279–288. [23] M.D. Cao, S. Lamichhane, S. Lundgren, A. Bofin, H. Fjosne, G.F. Giskeodegard, T.F. Bathen, Metabolic characterization of triple negative breast cancer, BMC Cancer 14 (2014) 941. [24] U.E. Martinez-Outschoorn, M. Prisco, A. Ertel, A. Tsirigos, Z. Lin, S. Pavlides, C. Wang, N. Flomenberg, E.S. Knudsen, A. Howell, R.G. Pestell, F. Sotgia, M.P. Lisanti, Ketones and lactate increase cancer cell “stemness,” driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics, Cell Cycle 10 (8) (2011) 1271–1286. [25] M. Yang, T. Soga, P.J. Pollard, Oncometabolites: linking altered metabolism with cancer, J. Clin. Invest. 123 (9) (2013) 3652–3658. [26] M. Gogiashvili, S. Horsch, R. Marchan, K. Gianmoena, C. Cadenas, B. Tanner, S. Naumann, D. Ersova, F. Lippek, J. Rahnenfuhrer, J.T. Andersson, R. Hergenroder, J. Lambert, J.G. Hengstler, K. Edlund, Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning (1) H NMR spectroscopy, NMR Biomed. 31 (2) (2018) 5.
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45 [27] F. Tayyari, G.A.N. Gowda, O.F. Olopade, R. Berg, H.H. Yang, M.P. Lee, W.F. Ngwa, S.K. Mittal, D. Raftery, S.I. Mohammed, Metabolic profiles of triple-negative and luminal A breast cancer subtypes in African-American identify key metabolic differences, Oncotarget 9 (14) (2018) 11677–11690. [28] L. Lecuyer, A. Victor Bala, M. Deschasaux, N. Bouchemal, M. Nawfal Triba, M.P. Vasson, A. Rossary, A. Demidem, P. Galan, S. Hercberg, V. Partula, L. Le Moyec, B. Srour, T. Fiolet, P. Latino-Martel, E. Kesse-Guyot, P. Savarin, M. Touvier, NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer, Int. J. Epidemiol. 47 (2) (2018) 484–494. [29] L.R. Euceda, T.H. Haukaas, T.F. Bathen, G.F. Giskeodegard, Prediction of clinical endpoints in breast cancer using NMR metabolic profiles, Methods Mol. Biol. (2018), 7493–1 9. [30] E. Martineau, I. Tea, S. Akoka, P. Giraudeau, Absolute quantification of metabolites in breast cancer cell extracts by quantitative 2D (1) H INADEQUATE NMR, NMR Biomed. 25 (8) (2012) 985–992. [31] J.A. Hess, M.K. Khasawneh, Cancer metabolism and oxidative stress: insights into carcinogenesis and chemotherapy via the non-dihydrofolate reductase effects of methotrexate, BBA Clin. 3 (2015) 152–161. [32] M. Jang, S.S. Kim, J. Lee, Cancer cell metabolism: implications for therapeutic targets, Exp. Mol. Med. 45 (2013), e45. [33] A. McCartney, A. Vignoli, L. Biganzoli, R. Love, L. Tenori, C. Luchinat, A. Di Leo, Metabolomics in breast cancer: a decade in review, Cancer Treat. Rev. 67 (2018) 88–96. [34] S.E. Singletary, J.L. Connolly, Breast cancer staging: working with the sixth edition of the AJCC cancer staging manual, CA: Cancer J. Clin. 56 (1) (2006) 37–47. [35] J. Xia, D.S. Wishart, MetPA: a web-based metabolomics tool for pathway analysis and visualization, Bioinformatics 26 (18) (2010) 2342–2344. [36] C.M. Rocha, J. Carrola, A.S. Barros, A.M. Gil, B.J. Goodfellow, I.M. Carreira, J. Bernardo, A. Gomes, V. Sousa, L. Carvalho, I.F. Duarte, Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma, J. Proteome Res. 10 (9) (2011) 4314–4324. [37] P. Zheng, H.C. Gao, Q. Li, W.H. Shao, M.L. Zhang, K. Cheng, Y. Yang de, S.H. Fan, L. Chen, L. Fang, P. Xie, Plasma metabonomics as a novel diagnostic approach for major depressive disorder, J. Proteome Res. 11 (3) (2012) 1741–1748. [38] A. Le, Andrew N. Lane, M. Hamaker, S. Bose, A. Gouw, J. Barbi, T. Tsukamoto, Camilio J. Rojas, Barbara S. Slusher, H. Zhang, Lisa J. Zimmerman, Daniel C. Liebler, Robbert J.C. Slebos, Pawel K. Lorkiewicz, Richard M. Higashi, Teresa W.M. Fan, Chi V. Dang, Glucose-independent glutamine metabolism via TCA cycling for proliferation and survival in B cells, Cell Metab. 15 (1) (2012) 110–121. [39] C.T. Hensley, A.T. Wasti, R.J. DeBerardinis, Glutamine and cancer: cell biology, physiology, and clinical opportunities, J. Clin. Invest. 123 (9) (2013) 3678–3684. [40] A. Mohamed, X. Deng, F.R. Khuri, T.K. Owonikoko, Altered glutamine metabolism and therapeutic opportunities for lung cancer, Clin. Lung Cancer 15 (1) (2014) 7–15.
45
´ ˛ ´ [41] K.P. Michalak, A. Mackowska-K edziora, B. Sobolewski, P. Wozniak, Key roles of glutamine pathways in reprogramming the cancer metabolism, Oxid. Med. Cell. Longev. 2015 (2015) 964321. [42] M. Jain, R. Nilsson, S. Sharma, N. Madhusudhan, T. Kitami, A.L. Souza, R. Kafri, M.W. Kirschner, C.B. Clish, V.K. Mootha, Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation, Science 336 (6084) (2012) 1040–1044. [43] I. Amelio, F. Cutruzzola, A. Antonov, M. Agostini, G. Melino, Serine and glycine metabolism in cancer, Trends Biochem. Sci. 39 (4) (2014) 191–198. [44] G. Lucarelli, M. Rutigliano, V. Galleggiante, A. Giglio, S. Palazzo, M. Ferro, C. Simone, C. Bettocchi, M. Battaglia, P. Ditonno, Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer, Expert Rev. Mol. Diagn. 15 (9) (2015) 1211–1224. [45] C.S. Ahn, C.M. Metallo, Mitochondria as biosynthetic factories for cancer proliferation, Cancer Metab. 3 (1) (2015) 1. [46] H.L. Kotze, E.G. Armitage, K.J. Sharkey, J.W. Allwood, W.B. Dunn, K.J. Williams, R. Goodacre, A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions, BMC Syst. Biol. 7 (2013) 107. [47] T. Forges, P. Monnier-Barbarino, J.M. Alberto, R.M. Gueant-Rodriguez, J.L. Daval, J.L. Gueant, Impact of folate and homocysteine metabolism on human reproductive health, Hum. Reprod. Update 13 (3) (2007) 225–238. [48] J.R. Bertino, Cancer research: from folate antagonism to molecular targets, Best Pract. Res. Clin. Haematol. 22 (4) (2009) 577–582. [49] S.W. Choi, J.B. Mason, Folate and carcinogenesis: an integrated scheme, J. Nutr. 130 (2) (2000) 129–132. [50] R.E. Burke, S.C. Harris, W.L. McGuire, Lactate dehydrogenase in estrogen-responsive human breast cancer cells, Cancer Res. 38 (9) (1978) 2773–2776. [51] K. Dettmer, F.C. Vogl, A.P. Ritter, W. Zhu, N. Nurnberger, M. Kreutz, P.J. Oefner, W. Gronwald, E. Gottfried, Distinct metabolic differences between various human cancer and primary cells, Electrophoresis 34 (19) (2013) 2836–2847. [52] G. Corona, J. Polesel, L. Fratino, G. Miolo, F. Rizzolio, D. Crivellari, R. Addobbati, S. Cervo, G. Toffoli, Metabolomics biomarkers of frailty in elderly breast cancer patients, J. Cell. Physiol. 229 (7) (2014) 898–902. [53] Y. Hou, M. Zhou, J. Xie, P. Chao, Q. Feng, J. Wu, High glucose levels promote the proliferation of breast cancer cells through GTPases, Breast Cancer 9 (2017) 429–436. [54] R.M. Luque, L.M. López-Sánchez, A. Villa-Osaba, I.M. Luque, A.L. Santos-Romero, E.M. Yubero-Serrano, M. Cara-García, M. Álvarez-Benito, J. ˜ Breast cancer is associated to López-Mirand, M.D. Gahete, J.P. Castano, impaired glucose/insulin homeostasis in premenopausal obese/overweight patients, Oncotarget 8 (46) (2017) 81462–81474.
Contents lists available at ScienceDirect
Journal of Pharmaceutical and Biomedical Analysis journal homepage: www.elsevier.com/locate/jpba
Metabolic fingerprinting in breast cancer stages through 1 H NMR spectroscopy-based metabolomic analysis of plasma Shankar Suman a,c,1 , Raj Kumar Sharma b,1 , Vijay Kumar d , Neeraj Sinha b , Yogeshwer Shukla a,c,∗ a Proteomics and Environmental Carcinogenesis Laboratory, Food, Drug and Chemical Toxicology Group, 31 Vishvigyan Bhawan, CSIR-Indian Institute of Toxicology Research, Mahatma Gandhi Marg, Post Box 80, Lucknow, 226001, India b Center of Biomedical Research, SGPGIMS-campus, Raibareilly Road, Lucknow, U.P., 226014, India c Academy of Scientific and Innovative Research (AcSIR), CSIR-IITR Campus, Lucknow, India d Department of Surgical Oncology, King George’s Medical University, Chowk, Lucknow, 226003, India
a r t i c l e
i n f o
Article history: Received 22 May 2018 Received in revised form 15 July 2018 Accepted 16 July 2018 Available online 18 July 2018 Keywords: Breast cancer 1 H NMR spectroscopy Metabolomics Glutamate Lactate N-acetyl glycoprotein
a b s t r a c t Breast cancer (BC) is one of the most common malignancies among women worldwide, which is indeed associated with metabolic reprogramming. However, BC is a very complex and heterogeneous disease, which can relate with the changes in metabolic profiles during BC progression. Hence, investigating the metabolic alterations during BC stage progression may reveal the deregulated pathways and useful metabolic signatures of BC. To demonstrate the metabolic insights, we opted 1 H NMR spectroscopy based metabolomics of blood plasma of early and late stage BC (N = 72) with age and gender matched healthy subjects (N = 50). Further, the metabolic profiles were analyzed to delineate the potential signatures of BC by performing multivariate and nonparametric statistical analysis in early and late stages of BC in comparison with healthy subjects. Sixteen metabolites levels were differentially changed (p < 0.05) in the early and late stages of BC from healthy subjects. Among them, the levels of hydroxybutyrate, lysine, glutamate, glucose, N-acetyl glycoprotein, Lactate were highly distinguished in BC stages and showed a good biomarker potential using receiver-operating curves based diagnostic models. Furthermore, the significant modulation and good diagnostic performances of glutamate, N-acetyl glycoprotein and Lactate in LBC as compared to EBC give their significance in the BC progression. In general, our observations demonstrate that these panels of metabolites may act as vital component of the metabolism of early to late stage BC progression. Our results also open new avenue towards early and late stage BC diagnosis and intervention implying metabolomics approaches. © 2018 Published by Elsevier B.V.
1. Introduction Breast cancer (BC) is highly diagnosed and the most cancerassociated death amongst women worldwide [1]. Despite the development of several sophisticated techniques, the diagnosis and therapeutics of BC are still a challenging job for the medical and scientific communities. One of the major facts of BC progression is the complexity of molecular and biochemical pathways involved in the cellular heterogeneity of BC [2–5]. These complexities may
∗ Corresponding author at: Proteomics and Environmental Carcinogenesis Laboratory, Food, Drug and Chemical Toxicology Group, 31 Vishvigyan Bhawan, CSIR-Indian Institute of Toxicology Research, Mahatma Gandhi Marg, Post Box 80, Lucknow, 226001, India. E-mail address: [email protected] (Y. Shukla). 1 Authors contributed equally. https://doi.org/10.1016/j.jpba.2018.07.024 0731-7085/© 2018 Published by Elsevier B.V.
render the pathological mystery of BC progression. In the present, biomarker research is adding a promising tool for diagnosis and prognosis of the disease manifestation. Metabolomics is one of the established research fields, which assists to evaluate the disease risk of the causative agents by detecting altered metabolite levels in pathological samples [6–9]. Metabolism is also highly accorded to external stimuli involved in disease pathogenesis, as compared to gene or protein expressions [10–12]. The growing developments in metabolomics are providing a very good platform to elucidate the biochemical pathways of different pathologies. In BC, metabolomics also provided an important avenue to uncover diagnostic, prognostic and therapeutic markers [8,13–15]. Cancer cells are capable of reprogramming metabolism, mainly through exploiting aerobic glycolysis to promote cell proliferation as revealed by Warburg effect [11]. Literature based on the recent studies have also added more evidences of metabolic reprogram-
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
ming in the disease stratifications of cancer [14,16,17]. Proton-NMR spectroscopy based metabolomics has gained a huge momentum for discovering a panel of metabolic signatures for BC [8,14,18–21]. Clinically, modulated expressions of hormonal receptors in BC are widely used for the prognosis and making therapeutic strategy for BC patients. However, hormonal therapy has also many limitations for the successful treatment of BC patients. In the context metabolomics based investigations are gaining the advantages to understand the of tumor metabolism in highly expressed hormone receptors in BC cases [14,22,23]. Previous studies showed that in estrogen receptor positive (ER+) BC; beta alanine and glutamine metabolism are highly implicated as compared to ER negative BC [22]. In cancer, oxidative stress is one of the key factors associated with neoplastic transformation, and is involved through the elevation of ketones and lactate levels, which lead to the emergence of BC stem cells [4,24]. The abnormal accumulated metabolites in cancer are also known as oncometabolites; for example, D-2-hydroxyglutarate, fumarate and succinate are most common oncometabolites accumulated due to epigenetic changes [25]. In the context of heterogenicity of BC, current NMR spectroscopy based metabolomics have assisted to evaluate the metabolic differences in the stratified BC classes including intratumoral heterogeneity [26] and BC subtypes [27]. Furthermore, it has also assisted to reveal the predictive plasma signature for long term risk of developing BC [28] and predication of clinical end points in BC [29]. Literature from metabolomics have widely revealed the deregulated metabolic pathways in BC [3,30–32] and ongoing metabolomics studies have given more emphasis to decipher BC heterogeneity by evaluating metabolic alterations in BC patients [33]. Seeking the extent of NMR spectroscopy based metabolomics; our goal in the present study was to reveal the changes in the metabolic profiles during BC progression. We focused on the detection of early stage (EBC) and late stage (LBC) altered metabolic pattern in BC, which could facilitate in revealing the metabolite fingerprints in the BC pathogenesis.
39
trimethylsilyl-2,2,3,3-tetradeuteropropionicacid) was used for the deuterium lock as well as an external reference. 2.3. NMR experiments All one-dimensional 1 H NMR spectra were acquired using a Bruker Avance III 800 MHz NMR spectrometer operating at a proton frequency of 800.21 MHz, equipped with cryoprobe at 298 K. The one dimensional NMR experiments were performed using Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence with water pre-saturation to remove broad signals of molecules (lipid and proteins). CPMG spectra were recorded by 64 k time domain data points, 20.55 ppm spectral width, 128 scans, and relaxation delay of 5 s with constant receiver gain value of 90 and 400 ms echo time. All data were manually phase and base line corrected. All resonances of metabolites present in 1D spectra were confirmed using biological magnetic resonance data bank (BMRB), human metabolome data bank (HMDB), published research articles and by comparing with standard data. Furthermore, identification of resonances of metabolites were performed using two-dimensional homonuclear (1 H-1 H COSY and 1 H-1 H TOCSY) and hetero-nuclear (1 H-13 C HSQC) experiments. 2D experiments increase the dimensionality of spectra that helps in reducing the overlapping problems. For the purpose COSY spectra were recorded in the magnitude mode with 2 K data point in t2 domain, where spectral width was 13 ppm in both the dimensions and 256 increments in t1 dimension were acquired with 16 Scans and relaxation delay of 2 s. For TOCSY experiment total 2 K data points were collected in t2 domain with 13 ppm spectral width and 256 t1 increments for 16 scans, where 80 ms mixing time was used for the experiment. 1H-13C HSQC spectra were acquired with 2 k data points in t2 dimension. Recycle delay of 2 s was used for the experiment. The total 512 increments were acquired with 32 scans with spectral width of 16 ppm and 165 ppm in 1 H and 13 C dimension, respectively. 2.4. Data processing and analysis
2. Materials and methods 2.1. Clinical samples and processing of plasma Blood samples of breast cancer patients (n = 72) collected from department of surgical oncology with prior ethical approval by the Institutional Ethics Committee, King George’s Medical University (KGMU), Lucknow, India. Further fifty blood samples were collected from age matched healthy volunteers (n = 50). All samples were collected in around the same daytime prior to meal intake conditions with their written consent in agreement with the Declaration of Helsinki. In addition, diabetic and unhealthy control subjects were not considered under the criteria of sample collection. Clinical details of all study subjects is given in the Table S1 (Supplementary material). BC with stages 0 to II patients were included in EBC and those of stages III to IV patients in LBC grouped under TNM protocol, given by American joint committee on cancer (AJCC) [34]. All samples were collected in EDTA coated blood collection vial, centrifuged at 1200×g at 4 ◦ C for 20 min for the separation of blood plasma, and stored at -80 ◦ C till further experimentation.
2.2. Sample processing for NMR spectroscopy For the NMR data collection, plasma samples were thawed and centrifuged to remove debris prior to the recording the spectra. Further, 300 l of supernatant and 100 ul deuterium oxide (D2 O) were mixed and was taken in 5 mm NMR tubes (Wilmad Glass, USA). A co-axial insert containing TSP solution (Sodium salt of
NMR spectra of all samples were recorded with constant parameters to ensure results accuracy. All NMR spectra were phased and baseline-corrected using Bruker Biospin TOPSPIN software (version 2.1). The total of 122 samples (n = 72; BC and n = 50; controls) were used for multivariate analysis. All NMR spectral data were exported to AMIX software (Version 3.8.7, Bruker Biospin, Germany) for binning of 0.03 ppm width chemical shift with a range of 0.5–9 ppm. Region between 4.2 to 5.2 ppm were excluded from binning to avoid water suppression variability. Bins were normalized to the total spectral area and, subsequently scaled to remove the possible bias from sample variability and preparation. The final data set was reduced to ASCII files, converted into excel file, which were exported to the Unscramble X software package (Version 10.0.1, Camo ASA, Norway) for multivariate analysis. Principal Component analysis (PCA) and partial least squares discriminant analysis (PLSDA) were performed on data matrix to differentiate the groups based on metabolic profiles. Further, the quantitative analysis of metabolites was performed by integrating the buckets area (binned spectral regions) after normalizing these spectra with TSP signal. The significance and relative levels of metabolites and receiver operative curve analyses were performed using SPSS statistical package. Non-parametric test was used to calculate significance of altered level of metabolites in early and late stages from healthy subjects. The selected metabolites based on their significance were used for the evaluation of clinical diagnostic accuracy in BC from healthy subjects by performing receiver operating characteristic (ROC) curve analysis. Metaboanalyst 3.0 web server was also used to view the expression pattern by heat-map, power and metabolic pathway analysis in the selected data matrix of assigned spectral
40
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
Table 1 Characteristics R2 Y and Q2 values of PLS DA models. Groups
Healthy-EBC-LBC
Healthy-EBC
Healthy-LBC
EBC-LBC
R2 Y Q2
0.74 0.60
0.77 0.61
0.75 0.61
0.52 0.36
regions of metabolites (www.metaboanalyst.ca/). The association of differential metabolites in EBC and LBC was determined by pathway impact, which was calculated by using the sum of importance measures of metabolites of interest normalized with the total sum of the importance measures of all metabolites associated with metabolic pathway [35]. 3. Results 3.1. 1 H NMR metabolic profiles of blood plasma from healthy, EBC and LBC subjects One-dimensional 1 H CPMG NMR spectra were recorded from the blood plasma of healthy control and BC patients to profile and analyze altered level of metabolites. Twenty-six endogenous metabolites were assigned by analyzing NMR spectra. The assignment of resonances of metabolites was done by using biological magnetic resonance data bank (www.bmrb.wisc.edu/) and previously reported literature [36,37]. Assignments of different resonances have been given with their chemical shifts in Table S2 (Supplementary material). A representative class of 1 H CPMG spectra of both healthy and BC patients were shown in Fig.1. On the visual inspection on each of the spectra of healthy, EBC and LBC subjects there were found a prominent change in many peaks. These peaks were further assigned as lipoprotein, isoleucine, leucine, valine, 2-hydroxybutyrate, N-acetyl glycoprotein (NAG), lipid 1, lactate, threonine, alanine, citrulline, leucine, arginine, lysine, acetate, proline, glutamic acid, glutamine, choline, glycine, lactate,  glucose, ␣ glucose, tyrosine, histidine and formate (Fig. 1). 3.2. Multivariate analysis In order to reduce complexities of NMR spectral data and interpret the comparative metabolic changes in early and late stage of BC, multivariate data analyses were performed. Initially, the natural group separation, including outlier detection and clustering in all data sets was performed by PCA analysis. The NMR data matrix of healthy and BC subjects were primarily studied with multivariate analysis and results showed a clear separation in both groups (Fig. S1, Supplementary material). Subsequently, the associations of metabolites in early and late stages of BC from healthy subjects, PLS-DA models were generated taking NMR data matrix. A separate pair-wise PLS-DA models were analyzed in early stage, late stage with control sample using non-linear iterative partial least squares algorithm (NIPALS) and validated with 7 fold cross validation. The score plot of PLS-DA analysis indicated good separation in healthy control with early and late stages of BC, in which goodness of fit and predictability was accessed by cumulated R2 and cumulated Q2 values. Each of PLS-DA models showed satisfactory fit with good accuracy of prediction in healthy-EBC, healthy-LBC and HealthyEBC-LBC (Table 1). The PLS-DA model revealed the segregation of groups by R2 Y (cum) = 0.74 and Q2 (cum) = 0.60 among healthy, EBC and LBC cohorts. The pair wise PLS-DA in between healthy- EBC and healthy-LBC revealed clear separation with R2 Y (cum) = 0.77, Q2 (cum) = 0.61 and R2 Y (cum) = 0.75, Q2 (cum) = 0.61, respectively (Fig. 2). However, in between EBC and LBC group, PLS-DA statistical model revealed R2 Y (cum) = 0.52 and Q2 (cum) = 0.36 (Fig. 2). Further to avoid over-fitting and enhance robustness of data; we also verified PLS-DA model by taking 25% of random samples as test set
and remaining 75% as original training sets of all samples (Fig. S2, Supplementary material). The results revealed the R2 Y (cum) = 0.70, Q2 (cum) = 0.58 in healthy-EBC-LBC group, however, in between healthy-EBC, healthy-LBC and EBC-LBC, R2 Y (cum) and Q2 (cum) values were found to be (0.77, 0.60), (0.81, 0.56) and (0.46, 0.26), respectively. 3.3. Characterization of metabolic level alteration in EBC and LBC The characterization of the altered metabolite level in blood plasma of EBC and LBC was performed, and the analysis indicated the 16 metabolites altered in healthy-EBC, healthy-LBC and EBCLBC cases. Further, heat-map of assigned metabolites by Euclidean correlation analysis in each group was performed to study the different patterns of metabolites level (Fig. 3). We also evaluated the significance of altered metabolites level in the early or late stage BC as compared to healthy subjects. By performing two-sample Kolmogorov-Smirnov and Mann-Whitney test together in same data sets, sixteen metabolites were found to be significantly altered (p < 0.05) in EBC or LBC as compared to healthy subjects. Amongst these metabolites; the level of lactate, glutamate, lysine, NAG,  glucose, 2-hydroxybutyrate, lipid and ␣ glucose were increased in EBC and formate and glutamine level were decreased in LBC patients as compared to healthy subjects (p < 0.05) (Table 2, Fig. S3, Supplementary material). The significant modulation of  glucose, glycine, glutamic acid and NAG in between EBC and LBC subjects was also noted (Table 2, Fig S3, Supplementary material). 3.4. Clinical diagnostic accuracy of altered levels of metabolites in BC and their associated metabolic pathways To evaluate the performance of each altered metabolite in BC stages from healthy subjects and from EBC to LBC; we analyzed the diagnostic accuracy through receiver operating characteristic (ROC) curves analysis. The diagnostic accuracy in the form of area under the ROC curve (AUC) was evaluated in the datasets of healthy vs. EBC, healthy vs. LBC and healthy vs. BC patients and EBC vs. LBC (Fig. 3, Table 3). The metabolites were also used to construct an independent model and we found that hydroxybutrate, lysine, glutamate,  glucose, ␣ glucose, Lactate, NAG performed good diagnostic potential with the AUC score more than 0.65 in overall BC patients compared with healthy subjects. In classified BC subjects, the significance of all these metabolites were similarly found in LBC patients, however in EBC patients hydroxybutrate,  glucose, ␣ glucose and lactate only gave AUC score ≥0.70 (Fig. 3 and Table 3). Further, our interest was to analyses ROC curve in between EBC and LBC, which showed that glutamate, lactate and NAG levels of metabolic alterations separate the both the group with AUC value of ≈0.7 (Table 3). The pathway analysis of modulated metabolites by metaboanalyst 3.0 showed their association with different metabolic pathways and the five major pathways were associated with the pathway impact of more than 0.2. These five major pathways are d-Glutamine and d-glutamate metabolism, arginine and proline metabolism, pyruvate metabolism, alanine, aspartate and glutamate metabolism and glycine, serine and threonine metabolism (Fig. 4, Table S3, Supplementary material). 4. Discussion 1H
NMR spectroscopy based metabolomics has gained a huge momentum for the detection of metabolic biomarkers of various pathologies including BC. Our study revealed the discriminated metabolic patterns in EBC and LBC as compared to healthy subjects and apparently good predictive power of study with samples size per group (Fig. S4, Supplementary material). The data obtained
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
41
Fig. 1. Representative spectral assignments of metabolites using 800 MHz NMR recorded spectra of blood plasma of Control healthy, early stage BC and late stage BC subjects.
Fig. 2. Score plot of PLS-DA model of profiled metabolites in healthy and BC subjects in different model indicated (A). Healthy, EBC and LBC, (B). Healthy-EBC, (C). Healthy-LBC, and (D). EBC-LBC.
42
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
Fig. 3. ROC curves of metabolites with high AUC scores. (A). Healthy vs. BC, (B). Healthy vs. EBC, (C). Healthy vs. LBC, (D). EBC vs. LBC.
Table 2 Metabolic alterations in early and late stages of BC. Serial Chemical no. shift (ppm)
Metabolites
Relative Significance abundance (EBC vs. healthy)
1 2 3 4 5 6 7 8 9 10 11 12 13
8.45 6.8 5.28 5.21 4.62 4.09 3.58 2.42 2.33 1.87 1.70 1.18 1.15
0.6 1.32 1.76 1.36 1.03 1.29 1.0 0.81 1.11 1.1 1.07 0.83 1.22
14 15 16
0.96 0.93 0.84
Formate Tyrosine Lipid ␣ Glucose  Glucose Lactate Glycine Glutamine Glutamate Lysine Arginine NAG Hydroxybutyrate Valine Isoleucine Lipoprotein
0.85 0.84 1.18
Relative abundance (LBC vs. healthy)
Significance
Relative abundance (LBC vs. EBC)
Significance
p = 0.001a , p = 0.001b p = 0.090 a , p = 0.002 b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b # p = 0.969 a , # p = 0.812 b p < 0.001a , p = 0.001b # p = 0.064 a , # p = 0.100 b # p = 0.272 a , # p = 0.338 b # p = 0.583 a , # p = 0.618 b # p = 0.256 a , # p = 0.141 b p = 0.004a ,p = 0.024 b
0.64 1.19 1.8 1.6 1.32 1.58 0.76 0.8 1.25 1.19 1.15 1.97 1.33
p = 0.001a ,p < 0.001b # p = 0.698 a , # p = 0.385 b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p = 0.005 a , p = 0.012b p < 0.001a , p < 0.001b p < 0.001a , p < 0.001b p = 0.002 a , p = 0.012b p = 0.018 a , p = 0.02 b p < 0.001a , p < 0.001b p = 0.001a ,p = 0.001 b
1 0.9 1.02 1.17 1.27 1.22 0.77 0.98 1.13 1.08 1.06 2.33 1.09
#
p = 0.011a ,p = 0.033 b p = 0.017a ,p = 0.076 b p = 0.007a ,p = 0.066 b
0.89 0.87 1.03
#
p = 0.268 a , # p = 0.113b 1.04 p = 0.264 a , # p = 0.220 b 1.03 # p = 0.586 a , # p = 0.683b 0.88
#
#
#
p = 0.947a , # p = 0.828 b p = 0.211 a , # p = 0.254 b # p = 0.592 a , # p = 0.558 b # p = 0.279 a , # p = 0.356 b p = 0.012 a , p = 0.055 b # p = 0.841a , # p = 0.889 b p = 0.012a , p = 0.018b # p = 0.991a , # p = 0.828b p = 0.012 a , p = 0.053b # p = 0.099 a , # p = 0.070b # p = 0.141 a , # p = 0.116b p < 0.001a , p = 0.007b # p = 0.525 a , # p = 0.373b #
p = 0.264a , p = 0.033 p = 0.237a , # p = 0.100b # p = 0.086a , # p = 0.211b
Note: Altered level of metabolites in EBC and LBC from healthy subjects and in between EBC and LBC were analyzed by fold change (average) and P values were obtained by Two−Sample Kolmogorov−Smirnov and Mann−Whitney non−parametric test, denoted by superscript a and b, respectively. # Non-significant changes.
from 1 H NMR spectra of the samples were taken for the multivariate analysis by both PCA and PLS-DA and these analysis showed a distinct separation of groups in healthy-BC, healthy-EBC-LBC,
healthy-EBC and healthy-LBC. Furthermore, PLS-DA model predicts good accuracy with R2 > 0.6 and Q2 > 0.5 (Fig. 2, Table 1 & Fig. S2, Supplementary material). However, in between EBC and LBC sub-
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
43
Table 3 Details of ROC curves of all significantly altered metabolites in different groups. Healthy vs BC
Healthy vs EBC
Healthy vs LBC
EBC vs LBC
AUC
95% CI (p value)
AUC
95% CI (p value)
AUC
95% CI (p value)
AUC
95% CI (p value)
Hydroxybutyrate
0.697
0.687
Glutamate
0.681
 glucose
0.818
␣ Glucose
0.78
NAG
0.652
Lactate
0.78
0.608–0.842 (0.001) 0.590–0.825 (0.002) 0.664–0.885 (0.000) 0.746–0.929 (0.000) 0.672–0.880 (0.000) 0.659–0.869 (0.000) 0.721–0.914 (0.000)
0.544
0.627
0.571–0.8043 (0.004) 0.429–0.681 (0.395# ) 0.484–0.732 (0.092) 0.760–0.960 (0.000) 0.737–0.921 (0.000) 0.440–0.698 (0.285# ) 0.675–0.892 (0.000)
0.725
Lysine
0.604–0.790 (0.017 0.528–0.725 (0.001) 0.587–0.775 (0.000) 0.726–0.91 (0.000) 0.694–0.867 (0.004 0.55–0.754 (0.000) 0.689–0.871 (0.000)
0.410–0.679 (0.525# ) 0.484–0.743 (0.099# ) 0.546–0.780 (0.012) 0.443–0.706 (0.279# ) 0.350–0.623 (0.841# ) 0.644–0.867 (0.000) 0.544–0.801 (0.012)
Name of Metabolites
#
0.555 0.609 0.860 0.829 0.569 0.784
0.708 0.774 0.837 0.776 0.764 0.817
0.614 0.673 0.575 0.486 0.756 0.672
Non-significant changes.
Fig. 4. (A). Pathways impact of metabolic pathways in BC. (B). Altered level metabolites associated with metabolic pathways involved in BC pathogenesis.
jects, these values did not go with mentioned cutoff, which might be because of fewer differences of the metabolic patterns due to close resemblances of clinical stages like stage IIB and stage IIIA BC patients that were included in EBC and LBC patients, respectively. After the analysis of spectral data, 26 distinct metabolites were assigned based on resonance variability in blood plasma samples of healthy, EBC and LBC samples (Fig. 1). The statistical analysis showed the significant modulation of lactate, glutamic acid, lysine, NAG,  glucose, 2-hydroxybutyrate, ␣ glucose, formate, lipid and glutamine level in EBC and LBC patients from healthy subjects (Table 2). Furthermore, tyrosine, valine, isoleucine, and lipoprotein in EBC and arginine and glycine in LBC was found to be significantly altered from healthy subjects, however,  glucose, glycine, glutamic acid and NAG were significantly altered in between EBC and LBC subjects (p ≤ 0.05, Table 2). The higher level of glutamic acid and lower level of glutamine in EBC and LBC compared to healthy subject showed the probable association of glutamic acid-glutamine metabolism in BC progression. Previous reports also evidenced that the glutaminolysis metabolism is also highly involved in many cancer types and attracts as new target for cancer [38–41]. Glycine level in blood plasma of LBC was found to be lower than healthy subjects (p < 0.05), it might be because of high glycine consumption in high-grade tumors [42,43]. Previous studies showed that glycine is
involved in HER-2 positive tumors and acts as a potential marker for tumor aggressiveness [22,42]. Apart from these, glycine and sarcosine, a methyl derivative of glycine are the potent diagnostic markers of prostate cancer [44]. Study has also showed that glycine promotes BC proliferation through metabolic reprogramming [42]. Hypoxia and reactive oxygen species (ROS) mediated response is well recognized in cancer, which is executed by lactate, folate and pyruvate metabolism [31,45]. Among several metabolites associated with tumor cell survival in low oxygen condition, glutamate is observed as one of the key metabolic marker for hypoxia in cancer [46]. Glutamate, glycine and formate may also involve in ROS generation and oxidative stress via different metabolic process, including folate pathways [43,47,48]. Folate cycle is a myriad of processes of the metabolic pathways associated with cancer [49]. Lactate is notably involved in BC pathogenesis and aggressiveness through a variety of mechanisms involved in cancer [4,50-51]. Therefore, lactate concentration in blood plasma was found to be rationally increased in EBC and LBC patients comparatively healthy subjects. Glutamate-glutamine cycle is known to involve in presence of NH4+ ion released by some of amino acid like valine and isoleucine. Our data showed that the levels of both amino acids were down regulated, which might possibly due to enhanced consumption of both these amino acids in the glutamine synthesis. Extracellular arginine is reported as major discriminants of neo-
44
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45
plastic and normal hepatocytes [51]. We noted that arginine level was higher in LBC patients (p < 0.05). Further, the modulated lipid level was also observed in both early and late BC stages (p ≤ 0.05). Previous reports greatly emphasized on the fact that the modulated level of lipid is essentially play an important role in cancer progression [52]. Furthermore, our study also revealed that  glucose and ␣ glucose level were elevated in both EBC and LBC, which could predict their role in BC pathogenesis. High glucose levels are reported to promote the proliferation of BC cells through GTPases [53] and a study revealed that impaired glucose homeostasis in premenopausal obese or overweight patients is associated with BC [54]. The previous study showed that histidine, acetoacetate, pyruvate, NAG, phenylalanine, glycerol, glutamate and mannose were highly discriminated in metastatic BC as compared to localized cancer [19]. Our data also correspond to many of these altered metabolites, which were significantly associated with the BC progression even from early stage (Fig. 3B & Table 3). Furthermore, a key discriminatory metabolites including hydroxybutyrate, lysine, glutamate,  glucose, ␣ glucose, NAG, and lactate showed good diagnostic accuracy and these may act as metabolic fingerprints of BC patients as well as it also pinpoints various metabolic pathways involve in BC progression. Among them glutamate, NAG and lactate have shown their probable association in LBC from EBC and link the deregulations of metabolic pathways in the BC progression. However, further investigations are recommended to advance the study in a very large patients cohort to assess and validate these metabolites with biomarker potential identified in our study. 5. Conclusion Our investigation reinforces the significance of metabolic signatures for BC. Our data advocate that several altered metabolic contents in early and late stages of BC may involve in the deregulations of carcinogenic events via switching multiple biochemical pathways (Fig. 4) and their differences in the levels in BC stages, may their possible association in the BC progression. Hydroxybutyrate, lysine, glutamate, glucose, NAG, and Lactate metabolites showed a marked shift in the BC population with a good diagnostic potential for BC. However, the differences in the metabolite level appeared by genetic or environmental contaminant in some of the control side population cannot be neglected, and these may possibly help in the risk assessment for BC. Acknowledgements The authors are thankful to Council of Scientific and Industrial Research, New Delhi, for funding this work from INDEPTH (BSC0111) and providing research fellowship to Shankar Suman. Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jpba.2018.07. 024. References [1] J. Ferlay, I. Soerjomataram, M. Ervik, R. Dikshit, S. Eser, C. Mathers, M. Rebelo, D.M. Parkin, D. Forman, F. Bray, GLOBOCAN 2012 v1.0, Cancer Incidence and Mortality Worldwide: IARC CancerBase No. 11 [Internet], International Agency for Research on Cancer, Lyon, France, 2015, 2013. [2] J.D. Brenton, L.A. Carey, A.A. Ahmed, C. Caldas, Molecular classification and molecular forecasting of breast cancer: ready for clinical application? J. Clin. Oncol. 23 (29) (2005) 7350–7360. [3] G. Schramm, E.-M. Surmann, S. Wiesberg, M. Oswald, G. Reinelt, R. Eils, R. König, Analyzing the regulation of metabolic pathways in human breast cancer, BMC Med. Genomics 3 (2010), 39–39.
[4] F. Kallinowski, P. Vaupel, S. Runkel, G. Berg, H.P. Fortmeyer, K.H. Baessler, K. Wagner, W. Mueller-Klieser, S. Walenta, Glucose uptake, lactate release, ketone body turnover, metabolic micromilieu, and pH distributions in human breast cancer xenografts in nude rats, Cancer Res. 48 (24 Pt. 1) (1988) 7264–7272. [5] S. Suman, P.K. Sharma, G. Rai, S. Mishra, D. Arora, P. Gupta, Y. Shukla, Current perspectives of molecular pathways involved in chronic inflammation-mediated breast cancer, Biochem. Biophys. Res. Commun. 472 (3) (2016) 401–409. [6] M. Mamas, W.B. Dunn, L. Neyses, R. Goodacre, The role of metabolites and metabolomics in clinically applicable biomarkers of disease, Arch. Toxicol. 85 (1) (2011) 5–17. [7] E. Acar, G. Gurdeniz, F. Savorani, L. Hansen, A. Olsen, A. Tjonneland, L.O. Dragsted, R. Bro, Forecasting chronic diseases using data fusion, J. Proteome Res. 16 (7) (2017) 2435–2444. [8] L. Lecuyer, A. Victor Bala, M. Deschasaux, N. Bouchemal, M. Nawfal Triba, M.P. Vasson, A. Rossary, A. Demidem, P. Galan, S. Hercberg, V. Partula, L. Le Moyec, B. Srour, T. Fiolet, P. Latino-Martel, E. Kesse-Guyot, P. Savarin, M. Touvier, NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer, Int. J. Epidemiol. 47 (2) (2018) 484–494. [9] R.K. Rai, A. Azim, N. Sinha, J.N. Sahoo, C. Singh, A. Ahmed, S. Saigal, A.K. Baronia, D. Gupta, M. Gurjar, B. Poddar, R.K. Singh, Metabolic profiling in human lung injuries by high-resolution nuclear magnetic resonance spectroscopy of bronchoalveolar lavage fluid (BALF), Metabolomics 9 (3) (2013) 667–676. [10] A. Koulman, G.A. Lane, S.J. Harrison, D.A. Volmer, From differentiating metabolites to biomarkers, Anal. Bioanal. Chem. 394 (3) (2009) 663–670. [11] D. Jia, J.H. Park, K.H. Jung, H. Levine, B.A. Kaipparettu, Elucidating the metabolic plasticity of cancer: mitochondrial reprogramming and hybrid metabolic states, Cells 7 (3) (2018) 21. [12] K. Hollywood, D.R. Brison, R. Goodacre, Metabolomics: current technologies and future trends, Proteomics 6 (17) (2006) 4716–4723. [13] M.P. Cala, J. Aldana, J. Medina, J. Sanchez, J. Guio, J. Wist, R.J.W. Meesters, Multiplatform plasma metabolic and lipid fingerprinting of breast cancer: a pilot control-case study in Colombian Hispanic Women, PLoS One 13 (2) (2018), e0190958. [14] C.D. Hart, A. Vignoli, L. Tenori, G.L. Uy, T. Van To, C. Adebamowo, S.M. Hossain, L. Biganzoli, E. Risi, R.R. Love, C. Luchinat, A. Di Leo, Serum metabolomic profiles identify ER-positive early breast cancer patients at increased risk of disease recurrence in a multicenter population, Clin. Cancer Res. 23 (6) (2017) 1422–1431. [15] K.A. Bertrand, H.J. Baer, E.J. Orav, C. Klifa, A. Kumar, N.M. Hylton, E.S. LeBlanc, L.G. Snetselaar, L. Van Horn, J.F. Dorgan, Early life body fatness, serum anti-mullerian hormone, and breast density in young adult women, Cancer Epidemiol. Biomarkers Prev. 25 (7) (2016) 1151–1157. [16] S. Venneti, P. Mischel, Metabolic reprogramming in cancer – a coordinated effort, Brain Pathol. (Zurich, Switzerland) 25 (6) (2015) 753–754. [17] K.E. Allison, B.L. Coomber, B.W. Bridle, Metabolic reprogramming in the tumour microenvironment: a hallmark shared by cancer cells and T lymphocytes, Immunology 152 (2) (2017) 175–184. [18] A.M. Weljie, A. Bondareva, P. Zang, F.R. Jirik, (1)H NMR metabolomics identification of markers of hypoxia-induced metabolic shifts in a breast cancer model system, J. Biomol. NMR 49 (3–4) (2011) 185–193. [19] E. Jobard, C. Pontoizeau, B.J. Blaise, T. Bachelot, B. Elena-Herrmann, O. Tredan, A serum nuclear magnetic resonance-based metabolomic signature of advanced metastatic human breast cancer, Cancer Lett. 343 (1) (2014) 33–41. [20] B. Mamtimin, M. Hizbulla, N. Kurbantay, L. You, X. Yan, H. Upur, An magnetic resonance-based plasma metabonomic investigation on abnormal Savda in different complicated diseases, J. Tradit. Chin. Med. 34 (2) (2014) 166–172. [21] J.M. Pearce, M.C. Mahoney, J.H. Lee, W.J. Chu, K.M. Cecil, S.M. Strakowski, R.A. Komoroski, 1H NMR analysis of choline metabolites in fine-needle-aspirate biopsies of breast cancer, MAGMA 26 (3) (2013) 337–343. [22] J. Budczies, S.F. Brockmoller, B.M. Muller, D.K. Barupal, C. Richter-Ehrenstein, A. Kleine-Tebbe, J.L. Griffin, M. Oresic, M. Dietel, C. Denkert, O. Fiehn, Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism, J. Proteom. 94 (2013) 279–288. [23] M.D. Cao, S. Lamichhane, S. Lundgren, A. Bofin, H. Fjosne, G.F. Giskeodegard, T.F. Bathen, Metabolic characterization of triple negative breast cancer, BMC Cancer 14 (2014) 941. [24] U.E. Martinez-Outschoorn, M. Prisco, A. Ertel, A. Tsirigos, Z. Lin, S. Pavlides, C. Wang, N. Flomenberg, E.S. Knudsen, A. Howell, R.G. Pestell, F. Sotgia, M.P. Lisanti, Ketones and lactate increase cancer cell “stemness,” driving recurrence, metastasis and poor clinical outcome in breast cancer: achieving personalized medicine via Metabolo-Genomics, Cell Cycle 10 (8) (2011) 1271–1286. [25] M. Yang, T. Soga, P.J. Pollard, Oncometabolites: linking altered metabolism with cancer, J. Clin. Invest. 123 (9) (2013) 3652–3658. [26] M. Gogiashvili, S. Horsch, R. Marchan, K. Gianmoena, C. Cadenas, B. Tanner, S. Naumann, D. Ersova, F. Lippek, J. Rahnenfuhrer, J.T. Andersson, R. Hergenroder, J. Lambert, J.G. Hengstler, K. Edlund, Impact of intratumoral heterogeneity of breast cancer tissue on quantitative metabolomics using high-resolution magic angle spinning (1) H NMR spectroscopy, NMR Biomed. 31 (2) (2018) 5.
S. Suman et al. / Journal of Pharmaceutical and Biomedical Analysis 160 (2018) 38–45 [27] F. Tayyari, G.A.N. Gowda, O.F. Olopade, R. Berg, H.H. Yang, M.P. Lee, W.F. Ngwa, S.K. Mittal, D. Raftery, S.I. Mohammed, Metabolic profiles of triple-negative and luminal A breast cancer subtypes in African-American identify key metabolic differences, Oncotarget 9 (14) (2018) 11677–11690. [28] L. Lecuyer, A. Victor Bala, M. Deschasaux, N. Bouchemal, M. Nawfal Triba, M.P. Vasson, A. Rossary, A. Demidem, P. Galan, S. Hercberg, V. Partula, L. Le Moyec, B. Srour, T. Fiolet, P. Latino-Martel, E. Kesse-Guyot, P. Savarin, M. Touvier, NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer, Int. J. Epidemiol. 47 (2) (2018) 484–494. [29] L.R. Euceda, T.H. Haukaas, T.F. Bathen, G.F. Giskeodegard, Prediction of clinical endpoints in breast cancer using NMR metabolic profiles, Methods Mol. Biol. (2018), 7493–1 9. [30] E. Martineau, I. Tea, S. Akoka, P. Giraudeau, Absolute quantification of metabolites in breast cancer cell extracts by quantitative 2D (1) H INADEQUATE NMR, NMR Biomed. 25 (8) (2012) 985–992. [31] J.A. Hess, M.K. Khasawneh, Cancer metabolism and oxidative stress: insights into carcinogenesis and chemotherapy via the non-dihydrofolate reductase effects of methotrexate, BBA Clin. 3 (2015) 152–161. [32] M. Jang, S.S. Kim, J. Lee, Cancer cell metabolism: implications for therapeutic targets, Exp. Mol. Med. 45 (2013), e45. [33] A. McCartney, A. Vignoli, L. Biganzoli, R. Love, L. Tenori, C. Luchinat, A. Di Leo, Metabolomics in breast cancer: a decade in review, Cancer Treat. Rev. 67 (2018) 88–96. [34] S.E. Singletary, J.L. Connolly, Breast cancer staging: working with the sixth edition of the AJCC cancer staging manual, CA: Cancer J. Clin. 56 (1) (2006) 37–47. [35] J. Xia, D.S. Wishart, MetPA: a web-based metabolomics tool for pathway analysis and visualization, Bioinformatics 26 (18) (2010) 2342–2344. [36] C.M. Rocha, J. Carrola, A.S. Barros, A.M. Gil, B.J. Goodfellow, I.M. Carreira, J. Bernardo, A. Gomes, V. Sousa, L. Carvalho, I.F. Duarte, Metabolic signatures of lung cancer in biofluids: NMR-based metabonomics of blood plasma, J. Proteome Res. 10 (9) (2011) 4314–4324. [37] P. Zheng, H.C. Gao, Q. Li, W.H. Shao, M.L. Zhang, K. Cheng, Y. Yang de, S.H. Fan, L. Chen, L. Fang, P. Xie, Plasma metabonomics as a novel diagnostic approach for major depressive disorder, J. Proteome Res. 11 (3) (2012) 1741–1748. [38] A. Le, Andrew N. Lane, M. Hamaker, S. Bose, A. Gouw, J. Barbi, T. Tsukamoto, Camilio J. Rojas, Barbara S. Slusher, H. Zhang, Lisa J. Zimmerman, Daniel C. Liebler, Robbert J.C. Slebos, Pawel K. Lorkiewicz, Richard M. Higashi, Teresa W.M. Fan, Chi V. Dang, Glucose-independent glutamine metabolism via TCA cycling for proliferation and survival in B cells, Cell Metab. 15 (1) (2012) 110–121. [39] C.T. Hensley, A.T. Wasti, R.J. DeBerardinis, Glutamine and cancer: cell biology, physiology, and clinical opportunities, J. Clin. Invest. 123 (9) (2013) 3678–3684. [40] A. Mohamed, X. Deng, F.R. Khuri, T.K. Owonikoko, Altered glutamine metabolism and therapeutic opportunities for lung cancer, Clin. Lung Cancer 15 (1) (2014) 7–15.
45
´ ˛ ´ [41] K.P. Michalak, A. Mackowska-K edziora, B. Sobolewski, P. Wozniak, Key roles of glutamine pathways in reprogramming the cancer metabolism, Oxid. Med. Cell. Longev. 2015 (2015) 964321. [42] M. Jain, R. Nilsson, S. Sharma, N. Madhusudhan, T. Kitami, A.L. Souza, R. Kafri, M.W. Kirschner, C.B. Clish, V.K. Mootha, Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation, Science 336 (6084) (2012) 1040–1044. [43] I. Amelio, F. Cutruzzola, A. Antonov, M. Agostini, G. Melino, Serine and glycine metabolism in cancer, Trends Biochem. Sci. 39 (4) (2014) 191–198. [44] G. Lucarelli, M. Rutigliano, V. Galleggiante, A. Giglio, S. Palazzo, M. Ferro, C. Simone, C. Bettocchi, M. Battaglia, P. Ditonno, Metabolomic profiling for the identification of novel diagnostic markers in prostate cancer, Expert Rev. Mol. Diagn. 15 (9) (2015) 1211–1224. [45] C.S. Ahn, C.M. Metallo, Mitochondria as biosynthetic factories for cancer proliferation, Cancer Metab. 3 (1) (2015) 1. [46] H.L. Kotze, E.G. Armitage, K.J. Sharkey, J.W. Allwood, W.B. Dunn, K.J. Williams, R. Goodacre, A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions, BMC Syst. Biol. 7 (2013) 107. [47] T. Forges, P. Monnier-Barbarino, J.M. Alberto, R.M. Gueant-Rodriguez, J.L. Daval, J.L. Gueant, Impact of folate and homocysteine metabolism on human reproductive health, Hum. Reprod. Update 13 (3) (2007) 225–238. [48] J.R. Bertino, Cancer research: from folate antagonism to molecular targets, Best Pract. Res. Clin. Haematol. 22 (4) (2009) 577–582. [49] S.W. Choi, J.B. Mason, Folate and carcinogenesis: an integrated scheme, J. Nutr. 130 (2) (2000) 129–132. [50] R.E. Burke, S.C. Harris, W.L. McGuire, Lactate dehydrogenase in estrogen-responsive human breast cancer cells, Cancer Res. 38 (9) (1978) 2773–2776. [51] K. Dettmer, F.C. Vogl, A.P. Ritter, W. Zhu, N. Nurnberger, M. Kreutz, P.J. Oefner, W. Gronwald, E. Gottfried, Distinct metabolic differences between various human cancer and primary cells, Electrophoresis 34 (19) (2013) 2836–2847. [52] G. Corona, J. Polesel, L. Fratino, G. Miolo, F. Rizzolio, D. Crivellari, R. Addobbati, S. Cervo, G. Toffoli, Metabolomics biomarkers of frailty in elderly breast cancer patients, J. Cell. Physiol. 229 (7) (2014) 898–902. [53] Y. Hou, M. Zhou, J. Xie, P. Chao, Q. Feng, J. Wu, High glucose levels promote the proliferation of breast cancer cells through GTPases, Breast Cancer 9 (2017) 429–436. [54] R.M. Luque, L.M. López-Sánchez, A. Villa-Osaba, I.M. Luque, A.L. Santos-Romero, E.M. Yubero-Serrano, M. Cara-García, M. Álvarez-Benito, J. ˜ Breast cancer is associated to López-Mirand, M.D. Gahete, J.P. Castano, impaired glucose/insulin homeostasis in premenopausal obese/overweight patients, Oncotarget 8 (46) (2017) 81462–81474.