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Tumour-microenvironmental blood flow determines a metabolomic signature identifying lysophospholipids and resolvin D as biomarkers in endometrial cancer patients

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Oncotarget. 2017; 8:109018-109026. https://doi.org/10.18632/oncotarget.22558

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Núria Eritja, Mariona Jové, Kristine Eldevik Fasmer, Sònia Gatius, Manuel Portero-Otin, Jone Trovik, Camilla Krakstad, Joaquim Sol, Reinald Pamplona, Ingfrid S. Haldorsen _ and Xavier Matias-Guiu

Abstract

Núria Eritja1,2,*, Mariona Jové3,*, Kristine Eldevik Fasmer4,5,*, Sònia Gatius1,2, Manuel Portero-Otin3, Jone Trovik6,7, Camilla Krakstad6,7, Joaquim Sol3, Reinald Pamplona3,**, Ingfrid S. Haldorsen4,5,** and Xavier Matias-Guiu1,2,8,**

1Department of Pathology and Molecular Genetics/Oncologic Pathology Group, Arnau de Vilanova University Hospital, University of Lleida, IRBLleida, Lleida, Spain

2Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Madrid, Spain

3Department of Experimental Medicine, University of Lleida, IRBLleida, Lleida, Spain

4Department of Radiology, Haukeland University Hospital, Bergen, Norway

5Department of Clinical Medicine, University of Bergen, Bergen, Norway

6Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway

7Department of Clinical Science, Center for Cancer Biomarkers, University of Bergen, Bergen, Norway

8Department of Pathology, University Hospital of Bellvitge, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet de Llobregat, Catalonia, Spain

*These authors have contributed equally to this work

**Senior co-authors

Correspondence to:

Ingfrid S. Haldorsen, email: ingfrid.haldorsen@uib.no

Xavier Matias-Guiu, email: fjmatiasguiu.lleida.ics@gencat.cat

Keywords: endometrial cancer; DCE-MRI; blood flow; metabolomic analysis

Received: July 07, 2017    Accepted: October 30, 2017    Published: November 20, 2017

ABSTRACT

Purpose: We aimed to study the potential influence of tumour blood flow –obtained from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)- in the metabolomic profiles of endometrial tumours.

Methods: Liquid chromatography coupled to mass spectrometry established the metabolomic profile of endometrial cancer lesions exhibiting high (n=12) or low (n=14) tumour blood flow at DCE-MRI. Univariate and multivariate statistics (ortho-PLS-DA, a random forest (RF) classifier and hierarchical clustering) and receiver operating characteristic (ROC) curves were used to establish a panel for potentially discriminating tumours with high versus low blood flow.

Results: Tumour blood flow is associated with specific metabolomic signatures. Ortho-PLS-DA and RF classifier resulted in well-defined clusters with an out-of-bag error lower than 8%. We found 28 statistically significant molecules (False Discovery Rate corrected p<0.05). Based on exact mass, retention time and isotopic distribution we identified 9 molecules including resolvin D and specific lysophospholipids associated with blood flow, and hence with a potentially regulatory role relevant in endometrial cancer.

Conclusions: Tumour flow parameters at DCE-MRI quantifying vascular tumour characteristics are reflected in corresponding metabolomics signatures and highlight disease mechanisms that may be targetable by novel therapies.


Tumour-microenvironmental blood flow determines a metabolomic signature identifying lysophospholipids and resolvin D as biomarkers in endometrial cancer patients | Eritja | Oncotarget

INTRODUCTION

Endometrial cancer (EC) is the most common gynaecologic malignancy in the high-income countries, and its incidence has increased by 21% since 2008 [1]. Presently, the treatment and prognosis are influenced by a subset of parameters based on molecular markers and the microscopic appearance of the tumours. Although approximately 75% of patients are primary curable by surgery and adjuvant treatment, 15-20% of these tumours will recur, with an aggressive and therapy-resistant behavior [2]. Several events (including genetic alterations and tumour microenvironment factors) have been reported to be activated or co-existing in aggressive endometrial cancer [3].

Angiogenesis is an essential mechanism for tumour growth, invasion and metastatic spread in many cancers types [4]. Moreover, tumour-associated vessels are frequently structurally and functionally abnormal, exhibiting delayed maturation and increased permeability [5].

In EC, increased microvascular proliferation correlates with a more aggressive phenotype [6-8]. Furthermore, high microvascular proliferation is associated with lower tumour blood flow (Fb) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), and low tumour Fb is observed in EC patients with reduced recurrence /progression-free survival [6, 9]. Altogether, is consistent with the hypothesis that vascular proliferation with coexisting disorganized angiogenesis promotes tumour progression and metastatic spread in EC [6].

Cancer was primarily considered a proliferative disorder; however, altered cell metabolism has emerged as a “hallmark of cancer”, since growing tumour cells rewire their metabolic program to meet the biosynthetic and bioenergetic demands of constant cell growth and phenotype changes. Metabolomics analysis provides direct evidences of biochemical context-dependent changes that can be correlated with the pathologic state of a cell, tissue or organ [10]. Therefore, metabolomics has been applied to reveal new mechanisms in cancer development and has identified specific biomarkers relevant for diagnosis and prognosis in various cancers.

The present study explores the different metabolomic tumour signatures potentially characterizing EC lesions exhibiting high versus low tumour blood flow at DCE-MRI. The identification of high versus low Fb metabolic biomarkers may highlight preoperative functional tumour characteristics with potential relevance for preoperative risk assessment and for identification of EC patients with expected poor outcome.

RESULTS

The main objective of this work was to determine metabolomics differences based on tumour Fb using a non-targeted metabolomics approach. We employed multivariate statistics to study changes in whole sample metabolome both in unsupervised-principal component (PCA) and hierarchical clustering analysis- and in supervised approaches-partial least discriminate analysis (PLS-DA), ortho-PLS-DA and Random Forest (RF) [11].

Patients had a mean age of 68 years and 20/26 (77%) of tumours had endometrioid histology (Table 1). In total 17/26 (66%) were classified as early stage (FIGO 1: Federation International of Gynecology and Obstetrics), and received no adjuvant therapy in accordance with national guidelines. 4/6 (67%) of non endometrioid histological subtype patients received adjuvant chemotherapy.

Table 1: Clinicopathological patient characteristics (n = 26) of women treated for endometrial cancer and evaluated by enhanced contrast MRI and metabolomics

Age; mean (range) years

68 (41-93)

Postmenopausal, n (%)

23 (89)

Lymph node sampling performed, n (%)

21 (81)

Adjuvant therapy received, n (%)

9 (35)

FIGO stagea, n (%)

 

 IA

10 (39)

 IB

7 (27)

 II

4 (15)

 III

4 (15)

 IV

1 (4)

Histologic differentiation, n (%)

 

 Grade 1

5 (19)

 Grade 2

5 (19)

 Grade 3

16 (62)

Histologic subtype, n (%)

 

 Endometrioid

20 (77)

 Clear cell

1 (4)

 Serous

3 (11)

 Carcinosarcoma

2 (8)

aAccording to International Federation of Gynaecology and Obstetrics (FIGO) 2009 staging criteria (ref)

In the present work PCA analysis was chosen as an unsupervised method (Figure 1). This analysis showed that lesions with high tumour Fb clustered better than lesions exhibiting low tumour blood flow. Once we discarded the presence of outliers and checked the technical quality of the technique we applied ortho-PLSDA as a specific tool for clustering (Figure 2A). This analysis resulted in two well-defined clusters (Q2=0.656 and R2Y =0.985). RF analysis reinforced these results showing an out of bag error of 0.07 (Figure 2B). Because of endometrioid and non-endometrioid tumours are different EC subtypes we also performed statistics excluding non-endometrioid EC patients obtaining similar results (Supplementary Figure 1A-1C). Specifically multivariate statistics show a good culsterization according to high and low tumour Fb (ortho-PLSDA parameters: Q2 =0.553 and R2Y =0.979, random forest out of bag error = 0.1).

PCA demonstrate a metabolomic-specific signature depending on tumour blood flow in endometrial cancer patients.

Figure 1: PCA demonstrate a metabolomic-specific signature depending on tumour blood flow in endometrial cancer patients. Red spots represent high tumour blood flow and green spots represent low tumour blood flow as determined by DCE-MRI.

Figure 2:

Figure 2: (A) Ortho-PLSDA algorithm using all metabolites found in samples from endometrial cancer tumours is able to discriminate groups with an observed Q2 of 0. 656 and observed R2Y of 0.985. Red spots represent high tumour blood flow patients and green spots represent low tumour blood flow as determined by DCE- MRI. (B) RF classification shows and overall classification error of 0.07.

The analysis revealed 28 statistically significant molecules (Table 2). The annotation process (using exact mass, retention time and isotopic distribution) disclosed several lipids belonging to phospholipid, glycerolypid and sphinogolipid families. Specifically, four lysophosphatidylcholines were up-regulated in tumours with high Fb. Moreover, resolvin D was identified as a potential tumour Fb biomarker. The ROC curve of resolvin D (AUC= 0.855, specificity= 0.9 and sensitivity = 0.7) confirmed the robustness of this metabolite to predict high tumour Fb (Figure 3A). To further study the predictive value of the metabolites found, we applied ROC curves using groups of metabolites (5, 10, 15, 25, 50 and 100) with lowest p-value obtained from T-Test between EC tumours with high versus low Fb observing a higher sensitivity and specificity when only the statistical significant metabolites (T-Test, Benjamini Hochberg False Discovery Rate, p<0.05) with a potential identity were used (Supplementary Figure 2). Using these metabolites we could reach a higher predictive value than using only Resolvin D1. Finally, we applied hierarchical clustering analyses using the 25 most statistically significant metabolites and an almost perfect separation between groups was observed (Figure 3B).

Figure 3:

Figure 3: (A) ROC curve of tumour resolvin D levels. (B) Hierarchical clustering analyses using 25 most statistical significant metabolites significantly different between endometrial cancer tumours with high versus low blood flow (Student’s T-Test, Benjamini Hochberg False Discovery Rate, p<0.05). Unknown identities are represented as exact mass@retention time.

Table 2: Metabolites from endometrial cancersignificantly different expressed in tumors with high versus low blood flow (Fb) determined by dynamic contrast enhanced (DCE)-MRI blood flow (Student’s T-Test, p<0.05)

Potential ID (exact mass)

p (Corr)

Regulation (H vs L)

FC

Mass

Retention Time

Unknown

0.001717

down

-3140.98

645.2509

12.776092

Unknown

0.002955

down

-1.55451

529.339

10.614731

Unknown

0.002955

down

-2.36942

640.2945

12.776231

LysoPS(14:0)

0.002955

up

1313.153

469.2301

11.289364

Unknown

0.002955

up

2939.315

644.3455

12.713154

MG(20:3)

0.005209

up

2786.49

380.2894

11.154073

LysoPI(20:0)

0.00663

up

451.2966

628.3694

12.711375

LysoPC(18:1)

0.0079

up

7360.114

521.3482

11.1267

Unknown

0.0079

up

744.7858

675.3582

12.709785

Unknown

0.0079

up

784.6694

380.7346

13.018824

Unknown

0.008335

up

3.061549

588.4521

12.71631

Unknown

0.008335

up

1489.749

395.3035

10.927358

Unknown

0.009024

up

4.129054

530.4012

12.716731

Unknown

0.010229

up

584.1685

294.2502

11.812234

Unknown

0.017847

down

-1.76119

497.3935

5.910731

LysoPC(20:3)

0.027094

up

1097.7

545.3473

10.9397335

Unknown

0.029734

down

-1.53841

185.2144

4.237846

Resolvin D

0.03647

up

480.5512

376.2332

10.606182

Unknown

0.03647

down

-135.16

639.5252

12.372849

PC(42:2)

0.03647

down

-251.465

869.6867

13.405749

Unknown

0.03647

down

-175.365

915.7157

13.490999

Unknown

0.03647

up

319.0776

563.4208

12.652739

Unknown

0.03647

up

275.0069

344.2021

12.714

Unknown

0.03647

down

-297.762

890.7981

14.891124

NeuAcalpha2-3Galbeta-Cer(d34:1)

0.03647

up

274.8682

990.6635

10.921999

Unknown

0.03647

up

383.5934

410.2134

11.286181

Unknown

0.04131

up

861.9869

901.5447

13.720384

PI-Cer(t46:0(2OH))

0.044655

up

223.0672

981.7524

12.743363

DISCUSSION

Advanced DCE-MRI techniques enable visualization and quantification of functional tumour characteristics in vivo. Previous studies revealed that in EC, low tumour Fb accompanied by increased capillary leakage and high microvascular proliferation is associated with reduced recurrence/progression free survival [6, 9]. These associations are somewhat puzzling, but may be related to the fact that tumoural angiogenesis generates vessels that in spite of being abundant, are heterogenous and rough making them altogether dysfunctional [12]. Further, the negative impact of low tumour Fb and disorganized angiogenesis have suggested tumour hypoxia promotes tumour progression and metastatic spread in endometrial carcinomas [13].

Based on these premises we designed an experiment to evaluate potential changes in tissue metabolome according to whether the tumours preoperatively exhibited high- or low Fb.

Previous studies have suggested a specific metabolomics signature of EC compared to normal tissue [14], but the present results indicate that there is a specific metabolome associated with tumour Fb, suggesting important changes in tumour metabolism which could modulate EC progression and angiogenesis related resistance to treatment [15]. Among all statistically different molecules found, we detected lipid species belonging to different families including phospholipids and, specifically, lysophospholipids (LPL). LPL were first discovered as constituents of cell membranes; however, today we know that they can also act as signalling molecules, playing important roles in central processes in cancer: stimulating angiogenesis, inhibiting apoptosis or modulating immune response [16]. In our study we identified several LPL species increased in high Fb EC patient tissues. Specifically, in this work we describe, for the first time, the potential role of lysophophatydilcholine (18:1), lysophosphatydilserine (14:0), lysophosphatydilinositol (20:0) and lysophosphatydilcholine (20:3) in EC tumour progression. These results reinforce the idea of LPL metabolism as a disease modifier in EC progression. Furthermore, this study has revealed that tissue resolvin D could be a good predictor of high tumour Fb (generally correlated to better patient survival). Resolvin D is known to be involved in the resolution of inflammation processes and shows potent effects in protecting against neovascularization and hypoxia in some pathological diseases [17]. Thus, our findings suggest that loss of the protective and inflammatory-resolution effect induced by tumour LPL and Resolvin D coexists with vascular proliferation leading to disorganized angiogenesis and hypoxia, relevant mechanisms in tumour progression and metastatic spread in EC [13].

All in all, the present work delves into the different signalling molecules involved in tumour progression. These achievements can serve to better understand EC metabolism helping to develop, in a near future, novel and more personalized therapies. Moreover, our results support the promising role of DCE-MRI as preoperative non-invasive imaging tool depicting tumour vascular features relevant for the corresponding metabolomic tumour signature that may be targetable in EC therapy.

MATERIALS AND METHODS

Patients and tissue samples

Endometrial tissue from hysterectomy specimens was collected from 26 patients surgically treated for endometrial carcinoma. All patients were diagnosed and treated at the same university hospital (Haukeland University Hospital, Bergen, Norway) written informed consent was obtained from all patients for the collection of imaging data and specimens for biomarker studies included in an institutional review board-approved protocol (Rek Vest 2009/2315).

Dynamic contrast enhanced (DCE)-MRI protocol and blood flow (Fb) analysis

Preoperative pelvic magnetic resonance imaging (MRI) was performed in all patients on a 1.5 Tesla MRI scanner (Siemens Avanto running Syngo v. B17, Germany) using a six-channel body coil and a standardized imaging protocol [6, 9] 20 mg butyl-scopolamine bromide (Buscopan; Boehringer, Germany) was administered intravenously prior to scanning in order to reduce motion artefacts. Contrast agent was administrated intravenously (Dotarem, Guerbet: 0.1 mmol gadolinium/kg, 3 ml/s injection speed). Pelvic sagittal and axial oblique (perpendicular to the long axis of the uterus) T2-weighted images were acquired together with axial oblique T1-weighted gradient-echo images prior to contrast agent administration. Pelvic DCE-MRI was obtained for 12 axial oblique slices applying a 3D spoiled gradient echo (FLASH) sequence (echo time/repetition time (TE/TR) = 1.05/2.64 ms, flip angle (FA) = 12°, matrix = 256x256, field of view (FOV) = 300 x 300 mm2, slice thickness = 5 mm, number of averages (NA) = 1). The temporal resolution was 2.49 s. and sequential images were acquired from 30 s before i.v. administration of contrast medium to 6.3 min after contrast injection. At 2 min post contrast medium administration, a pause of 33 s was utilized to acquire axial oblique T1-weighted gradient-echo contrast-enhanced images.

Regions of interests (ROIs) were drawn within the endometrial tumour by a radiologist (ISH with > 7 years of experience with pelvic MRI) who was blinded for tumour stage, histological diagnosis and patient outcome. Care was taken to avoid including necrotic and haemorrhagic areas of the tumours in the ROIs. The dynamic image series were analysed using an in-house implementation of the adiabatic approximation model of Johnson and Wilson [18]. Representative diagnostic MRI images and parametric DCE-MRI maps illustrating endometrial tumours with high and low blood flow (Fb) are shown in Figure 4.

Figure 4:

Figure 4: Axial oblique T2-weighted MRI (left panel), contrast-enhanced T1-weighted MRI (middle panel) and parametric blood flow (Fb, right panel) map from a 68 year-old patient with FIGO stage 1B, grade 3 and high tumour Fb (upper panel) and a 52 year-old patient with FIGO stage 1B, grade 1 and low tumour Fb (lower panel). At conventional diagnostic MRI the endometrial carcinomas (arrows) are typically depicted as hyper-intense relative to the surrounding myometrium on T2-weigthed image while hypo-intense on contrast-enhanced T1-weighed image. On the corresponding parametric maps high blood flow is depicted as white/red while low blood flow is depicted as dark blue.

Metabolomic analyses

Tissue samples (50–70 mg) were homogenized using 20 volumes of cold methanol as previously described [14]. Metabolite extracts were subjected to mass spectrometry using an HPLC 1290 series coupled to an ESI-Q-TOF MS/MS 6520 (Agilent Technologies, Santa Clara, CA, USA) as previously described [14]. Multivariate statistic analyses were performed using Metaboanalyst platform [19]. The preliminary identification of differential metabolites (Student’s T-Test, Benjamini Hochberg False Discovery Rate, p<0.05) was performed using the PCDL database from Agilent (Agilent Technologies, Barcelona, Spain), which accounts retention times in a standardized chromatographic system, exact mass and isotope distribution as an orthogonal searchable parameters to complement accurate mass data (AMRT approach) according to previously published works [14]. The version of the PCDL database used had retention times and accurate mass data for 679 compounds. To complete the identification process we searched for unidentified metabolites in Metlin Database (https://metlin.scripps.edu/index.php) which includes accurate masses and MS/MS spectrum for 961.829 molecules.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written informed consent was obtained from all patients for the collection of imaging data and specimens for biomarker studies; included in an institutional review board-approved protocol (Rek Vest 2009/2315). This article does not contain any studies with animals performed by any of the authors.

Abbreviations

EC: Endometrial Cancer; DCE-MRI: Dynamic Contrast-Enhanced Magnetic Resonance Imaging; Fb: Blood Flow; FIGO: Federation International of Gynecology and Obstetrics; LPL: Lysophospholipids; RF: Random Forest; ROC: Receiver operating characteristics; ROI: Region of Interest; PCA: Principal Component Analysis; PLS-DA: Partial Least Discriminate Analysis

Author contributions

Conceive experiments: NE, MJ, MPO, RP, ISH and XMG

Carry out experiments: NE, MJ, SG, KEF, JT and CK

Analyze data: MJ, JS, KEF, JT, CK, ISH and XMG

Write the paper: NE, MJ, MPO, RP, CK, ISH and XMG

Generation of the figures: MJ, KEF, SG, JT, CK, KEF and ISH

Approve the submitted version: All authors

CONFLICTS OF INTEREST

None.

FUNDING

Supported by grants, PI13/01701 and PI16/00692 from Fondo de Investigaciones Sanitarias del Instituto de Salud Carlos III cofinanciado por Fondo Europeo de Desarrollo Regional (FEDER) (“Una manera de hacer Europa”), Red Temática de investigación en Cáncer RD12/0036/0013 and Red de Oncología (CIBERONC-ONCG33/2017) and the Department of Health from the Generalitat of Catalonia (SLT002/16/00250). Grups consolidats de la Generalitat de Catalunya (2009SGR794 and 2014SGR168), Fundació La Marató de TV3 (2/C2013), Grupos estables AECC, Plataforma de Biobancos ISCIII (PT13/0010/0014) and ENITEC NETWORK. NE holds a postdoctoral fellowship from Generalitat de Catalunya (SLT002/16/00274).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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