A HRMAS NMR-Based Metabolomics Case of Study - American

Article pubs.acs.org/jpr

Toward the Reliable Diagnosis of Indeterminate Thyroid Lesions: A HRMAS NMR-Based Metabolomics Case of Study Liborio Torregrossa,*,† Laetitia Shintu,*,‡ Jima Nambiath Chandran,‡ Aura Tintaru,§ Clara Ugolini,† Alviclér Magalhaẽ s,∥ Fulvio Basolo,† Paolo Miccoli,† and Stefano Caldarelli‡,⊥ †

Department of Surgery, University of Pisa, 56100 Pisa, Italy Institut des Sciences Moléculaires de Marseille, Aix-Marseille Université, iSm2-UMR CNRS 7313, Campus scientifique de Saint Jérôme-case 512, 13397 Marseille Cedex 20, France § Institut de Chimie Radicalaire UMR 7273, Equipe SACS, Aix-Marseille Université - CNRS, Campus scientifique de Saint Jérôme-case 512, 13397 Marseille Cedex 20, France ∥ Instituto de Química, Departamento de Química Inorgânica and INCT de Bioanalítica, Universidade Estadual de Campinas, 13083-970, Post Box 6154 Campinas-SP, Brazil ⊥ Institut de Chimie des Substances Naturelles, UPR 2301 CNRS, 1 avenue de la Terrasse, 91198 Gif-sur-Yvette cedex, France ‡

S Supporting Information *

ABSTRACT: Cytological analysis of thyroid nodules detected using ultrasound-guided fine-needle aspiration technique is an efficient method for the diagnosis of well-differenciated tumors such as papillary thyroid carcinoma. However, for between 10 to 30% of all the nodules, the cytological analysis based on fineneedle aspiration biopsies leads to an “indeterminated” identification. Consequently, a surgical excision is then necessary for a definite histological diagnosis of the lesions, resulting in 85% of the patient with indeterminated nodules undergoing unnecessary surgery since their tumor is finally diagnosed as benign. In this work, we discuss how HRMAS 1H NMR-based metabolomics could be a complementary tool for the diagnosis of these elusive cases. We first showed that our approach was able to discriminate clearly any types of thyroid lesions from healthy tissues. Then we proceeded to demonstrate that the information produced by 1H HRMAS NMR spectra differentiate tumors according to their malignancy grade, even when they belong to the “indeterminate” category. Analysis of the discriminating spectral area in this last case points out toward a possible increase of phenylalanine, taurine, and lactate and a decrease of choline and choline derivatives, myo- and scyllo-inositol in the malignant tumors compared to the benign ones. KEYWORDS: metabolomics, thyroid cancer, HRMAS NMR, indeterminate

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INTRODUCTION Thyroid nodules are a common clinical problem, and according to the method of detection and the age of the patients, their prevalence may approach 20−50% of the general population.1,2 The clinical importance of thyroid nodules rests with the need to exclude thyroid cancer, which occurs in 5−15% depending on age, sex, radiation exposure history, family history, and other factors. A systematic approach to their evaluation is important for the clinicians to identify patients at risk without over investigating the vast majority of benign lesions.3 Fine-needle aspiration biopsy (FNAB) is currently the most common and efficient method used for the preoperative diagnosis of thyroid nodules. This method plays a critical role in the differentiation of the thyroid nodules that can be managed conservatively from those that require surgical resection.3 It enables the classification of about 60% of all specimens as benign, with an accuracy of more than 90%, and 5−10% as overt malignant and necessitating surgery. However, © 2012 American Chemical Society

an important limitation of FNAB is the lack of sensitivity in the evaluation of “follicular-patterned lesions”, due to its inability to differentiate benign (follicular adenomas, FA) from malignant (follicular thyroid carcinomas, FTC) lesions, leading to a diagnosis of “indeterminate” in 10−30% of all FNABs. Guidelines for management of patients with thyroid nodules suggest that in these cases, a surgical excision should be necessary for a definitive histological diagnosis.4,5 In fact, the definitive discrimination between FA and FTC is specifically assigned to the histological demonstration of capsular and/or vascular invasion by the neoplastic cells. The rate of malignancy among the ”indeterminate” group ranges between 14 and 20%, indicating that the majority of patients may undergo an unnecessary surgery. Received: February 2, 2012 Published: April 17, 2012 3317

dx.doi.org/10.1021/pr300105e | J. Proteome Res. 2012, 11, 3317−3325

Journal of Proteome Research

Article

Table 1. Diagnostic Performance of Previously Investigated Molecular Markers author

statistical analysisa

study 7

Nikiforov et al. 2009 Nikiforov et al. 201112 Bartolazzi et al.15 Franco et al.16 Keutgen et al.18

Molecular analysis and cytology Molecular analysis Galectin-3 expression Galectin-3 and HBME-1 espression miRNA expression

SN SN SN SN SN

80.0%, SP 99.7%, PPV 97.6%, NPV 95.1%, ACC 95.2% 57−68%, SP 96−99%, PPV 87−95%, NPV 72−94%, ACC 81−94%b 78%, SP 93%, PPV 82%, NPV 91% 94.74%, SP 75.81%, PPV 82.76%, NPV 92.16% 100%, SP 86%, ACC 90%

sample size 470 1,056c 465 418d 29 ex vivo +72 in vivo

a

SN, sensitivity; SP, specificity; PPV, positive predictive value; NPV, negative predictive value; ACC, accuracy. bFor each statistical value, it has been indicated the range of percentage according to the specific categories of “indeterminate” FNAB used in the report (i.e., atypia of undetermined significance/follicular lesion of undetermined significance, follicular neoplasm/suspicious for a follicular neoplasm, and suspicious for malignant cells). cOnly 513 with histological diagnosis. dOnly 138 with histological diagnosis.

By use of this method, 381 of the follicular thyroid nodules referred for surgery were classified correctly preoperatively, but 29 of 130 cancers were missed by the galectin-3 test.15 Combining the expression of other markers, such as HMBE-1, might further improve the diagnostic accuracy of immunocytochemistry in such tumors. For example, with the combined use of galectin-3 and HBME-1 in “indeterminate” FNABs, Franco et al.16 have obtained a 10% increase in sensitivity than with the use of a single marker. Recent studies suggest microRNA (miRNA) could be putative markers, able to distinguish benign from malignant thyroid neoplasms in the category of “indeterminate” FNABs.17,18 In the study of Keutgen et al.,18 differential expression of four miRNAs (miR-222, miR-328, miR-197, and miR-21) using RT-PCR was first observed using 29 ex vivo “indeterminate” FNABs and then validated on an independent set of 72 in vivo collected FNABs. When applied to the set of independent in vivo samples, performance was better than predicted in differentiating malignant from benign “indeterminate” lesions.18 In Table 1, diagnostic performances of molecular analysis, immunohistochemical staining, and miRNAs expression applied to “indeterminate” FNABs are shown. A few laboratories are seeking biomarkers of thyroid tumor malignancy by 2D-gel and mass spectrometry-based proteomics.19,20 Other research groups have preferred to investigate surrogate materials such as serum in their search for potential cancer biomarkers.21,22 Of interest here, Mountford’s group was able to demonstrate that proton magnetic resonance spectroscopy (1H MRS) of resected surgical specimens could be used to differentiate normal thyroid tissue from carcinoma.23 Moreover, they pointed out that 60% of the analyzed follicular lesions, regarded as benign by histological criteria, have spectral profiles similar to the malignant ones, the remaining 40% having profiles similar to normal tissue. Similar results were obtained using FNABs.24 In this work, we used an NMR-based metabolomics approach for the diagnosis of thyroid cancer, and more specifically, for the diagnosis of “indeterminate” lesions. We first identified metabolic differences between thyroid lesions (benign and malignant) and healthy adjacent thyroid tissue on surgical biopsies. In a second step, we tested the efficiency of our metabolomics approach for the discrimination between benign and malignant tumors. Stating on the premise that metabolic variations pre-empt the development of morphologic modifications associated with malignancy, we assessed our metabolomics approach for its ability to evaluate metabolic criteria that would enable the classification of the different thyroid lesions with better accuracy than currently available methods.

Molecular studies have provided information about the pathogenesis of thyroid cancer, enhancing the diagnosis of thyroid lesions, both in cytological and histological material.6−10 BRAF mutation is the most extensively studied genetic alteration in thyroid tumors: it has never been reported in benign lesions and represents the most accurate marker of papillary thyroid carcinoma (PTC), with a specificity nearly to 100% and a prevalence ranging from 29 to 83% of cases. Rearranged in transformation/papillary thyroid carcinoma (RET/PTC) rearrangements are also specific to PTC, with prevalence from 13 to 43%. Neverthless, the diagnosis of PTC is mostly feasible by routine cytology, detecting the nuclear alterations typical of this cancer (e.g., nuclear grooves, pseudoinclusions, nuclear clarification), so the diagnostic contribution of mutation analysis in this group of FNAB samples is still under discussion. RAS mutations are uncommon in conventional PTC, more frequently found in FTC (40− 53%), and reported also in benign lesions (20−40%), mostly in FAs. PAX8/peroxisome proliferator-activated receptor (PPAR)-γ rearrengments are found only in FTC, with a prevalence ranging from 25 to 63%, but have been reported also in 2−10% of benign lesions. Taken together, these data suggest that a positive mutation analysis is associated with malignancy in the majority of the cases, while a negative mutation analysis does not exclude malignancy at all. Recently, Filicori et al.11 have reported that in the absence of mutation, the overall risk of malignancy in “indeterminate” lesions is about 22%, which is similar to the malignancy rate in “indeterminate” lesions (14−20%) using only routine cytology. In another report, Nikiforov et al.12 have tested 1056 consecutive thyroid FNAB samples with “indeterminate” cytology for a panel of mutations consisted of all the genetic markers above-reported. The detection of any mutation conferred the risk of histologic malignancy of about 90%, while the risk of cancer in mutation-negative nodules was about 16%. So, mutation analysis alone seems unable to definitely classify the majority of “indeterminate” lesions: still 37 of 52 “indeterminate” samples investigated for BRAF, RAS, RET/ PTC, and PAX8/PPARγ, 7 88 of 89 follicular lesions investigated for BRAF,13 and 19 of 21 “indeterminate” FNAB samples investigated for BRAF, TRK, and RET/PTC14 could not be clarified by mutation testing. Immunohistochemistry represents another “ancillary” technique extensively used in the past few years in order to improve the yield of “indeterminate” FNABs. Among the various immunomarkers of malignancy used in thyroid pathology, galectin-3 and Hector Battifora mesothelial cell (HBME)-1 have shown the most consistent results.15,16 Bartolazzi and colleagues have used a standardized method of galectin-3 staining on 465 thyroid nodules with “indeterminate” cytology. 3318



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Article

MATERIALS AND METHODS

Patients and Preoperative FNAB Analysis

A cohort of 72 patients (47 females and 25 males) was studied, with solitary thyroid nodule ranging from 1 to 8.5 cm (mean size, 3.27 ± 1.7 cm. Mean age was 42.8 ± 17.9 yr (range 9−88 yr). All patients had undergone surgical total thyroidectomy at the Department of Surgery of the University of Pisa. Participation in the study required informed consent. The indication for surgery was an FNAB diagnostic of malignancy in 28 (39%) patients, follicular lesion in 40 (55.5%) patients, and presence of compressive symptoms in 4 (5.5%) patients with nodular goiter with non-neoplastic cytology. Cytological classification was performed according to the Proposal of the SIAPEC-IAP Italian Consensus Working Group,25 which is in line with the second edition of the guidelines for the thyroid cancer management published by the British Thyroid Association.26 Specimen Collection

At the time of surgery, the surgical specimen was bisected, and a sample (weight ∼10−25 mg) of nodule tissue and controlateral healthy thyroid tissue (control specimen) were excised for 28 out of 72 patients. For the remaining 44 patients, only nodule tissues were taken. All tissue samples collected were placed in 1.5 mL tubes marked with an identification number, immediately frozen in liquid nitrogen, and stored at −80 °C until NMR analysis.

Figure 1. Representative hematoxylin- and eosin-stained sections of follicular thyroid carcinoma and follicular adenoma. (a) Microscopic image (4) demonstrating capsular invasion (arrows) in follicular thyroid carcinoma. (b) Follicular adenoma with a thin capsule (asterisk *) showing no sign of neoplastic invasion (4×). (c−d) High-power views (40×) of neoplastic cells organized in a microfollicular pattern both in follicular thyroid carcinoma (c) and in follicular adenoma (d).

Histological Analysis

The corresponding surgical samples of all patients were formalin-fixed, paraffin-embebbed, sectioned at 7 μm, and stained with hematoxylin and eosin for routine histopathological diagnosis according to standard protocols.27 For all the 72 cases included in the study, histopathological review and classification of the corresponding surgical samples were performed, according to the current World Health Organization thyroid tumor classification system.28 From 3 to 10 hematoxylin and eosin-stained slides were available for histopathological review. Since the diagnosis of follicular carcinoma needs the histological demonstration of capsular and/or vascular invasion, in all tumors with a follicular growth pattern, a minimum of eight blocks of tumor and capsule were processed and analyzed (see Figure 1).

For both sequences, the FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0.3 Hz and zero-filled prior to Fourier transformation. Subsequently, the spectra were phased and baseline corrected manually and referenced to the alanine signal (δ = 1.47 ppm). Assignments of the metabolite signals were performed using 1H−1H TOCSY spectrum,29 1H−13C HSQC spectrum,30 in-house databases, and literature.31,32 The 1H NMR spectra were directly exported in iNMR (http://www.inmr.net) and divided into 0.005 ppm-width buckets leading to 2500 variables. In order to remove the effects of possible variations in the water suppression efficiency, the region between 4.82 and 5.27 ppm was discarded. The obtained data set (X matrix) was then normalized to the total spectrum intensity and scaled to unit variance. The X matrix was then exported to the software Simca-P 12 (Umetrics, Umeå, Sweden) for statistical analysis.

1 H HRMAS NMR Spectroscopy and Data Processing of Thyroid Biopsies

Between 10 and 25 mg of intact thyroid tissues were placed into a 4 mm ZrO2 HRMAS rotor (50 μL of volume) with 5 μL of D2O. All NMR experiments were carried out on a Bruker Avance spectrometer operating at 400 MHz for the 1H frequency equipped with a 1H/13C/31P HRMAS probe. Spectra were acquired at 288 K with a spin rate of 4 kHz. A Carr− Purcell−Meiboom−Gill (CPMG) NMR spin echo sequence with an effective spin echo time of 80 ms, preceded by a water presaturation pulse during a relaxation time of 1.2 s ([presat90°-(τ-180°-τ)n]), was employed to reduce signal intensities of lipids and macromolecules. For each sample, 256 free induction decays (FID) of 8000 data points were collected using a spectral width of 8000 Hz. In addition, a typical 90° sequence with water signal presaturation during a relaxation delay of 1.2 s was recorded on 56 samples (healthy tissues and their tumoral counterparts). Two hundred fifty-six transients and 8000 data points were collected using a spectral width of 8000 Hz.

Multivariate Pattern Recognition of 1H NMR Spectra

Principal component analysis (PCA) and the orthogonal partial least-squares algorithm (OPLS) were applied to the NMR data (X matrix) in order to discriminate the samples according to tissue types.33 PCA was used to detect intrinsic clusters and outliers within the data sets. When discrimination was not achieved using PCA, the data were analyzed with orthogonalized partial least-squares discriminant analysis (OPLS-DA). The OPLS algorithm derives from basic partial least-squares (PLS) regression and allows a more effective use of the relevant discriminating variables by removing information orthogonal to the Y matrix (matrix containing the sample classes), i.e., not relevant for this particular discrimination. The resulting scores 3319



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the full thickness of the capsule, with mushroom-like tumor extension into pericapsular thyroid parenchyma. However, the follicular adenoma shown in Figure 1b has a thin capsule (asterisk *) that shows no sign of neoplastic invasion (4×). With a high-power view (40×) of the tissues, we are able to visualize the organization in a microfollicular pattern of the neoplastic cells both in follicular thyroid carcinoma (Figure 1c) and in follicular adenoma (Figure 1d) and then to differentiate both lesions. After histological examination, 27 out of 28 cases with an FNAB diagnostic of malignancy were diagnosed as papillary thyroid carcinoma (PTC), and the remaining case was classified as anaplastic carcinoma (AC); among the FNAB follicular lesions, 30 out of 40 (75%) were diagnosed as FA, and 10 (25%) were classified as FTC; all the cases with non-neoplastic FNAB (n = 4) were confirmed as goiter nodules (GN) (Table 2).

and loadings plots were used to visualize respectively the samples and the NMR frequency signals (variables) in the predictive and orthogonal reduced component frame. OPLS-DA model validation was performed by resampling the model 999 times under the null hypothesis, that is, generating models with a randomly permuted Y matrix interest. The quality of the model was assessed by monitoring changes in goodness-of-fit and predictive statistics, R2 and Q2, between the permuted and the original Y matrix. Receiver Operating Characteristic (ROC) Curve. The performance of the prediction derived from O-PLS modeling of the 1H NMR spectra was evaluated by computing the area under the receiver operating characteristic (ROC) curve (AUC), using the performance curve algorithm from Matlab statistics toolbox (Matlab v.7.4, Mathwork Inc.).

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HRMAS NMR Discrimination between Thyroid Lesions and Healthy Thyroid Tissues

RESULTS AND DISCUSSION

Cytological and Histological Analysis of the Samples

Mean 1H CPMG HRMAS NMR spectra (normalized data) from healthy tissues and thyroid lesions are represented in Figure 2a,b, respectively (see the Supporting Information, Figure S1a,b, for the representation of the mean 1H HRMAS NMR spectra (normalized data) from tumoral and healthy tissues, respectively). The assignments of all detected metabolites are reported in Table 3. Profile differences are easily observable by visual comparison; in particular, a higher level of fatty acid signals as well as a decrease in those of amino acids and choline are observed in the healthy sample spectra compared to their tumoral counterparts. However, taking into account the biological variability among patients, the statistical analysis of all the spectra is necessary in order to identify the statistically significant metabolites responsible for the discrimination between both tissue types. Consequently, PCA based on the correlation matrix was first performed on tumor tissues and their healthy counterparts taken from 28 different patients (n(PTC) = 20, n(GN) = 2, n(FA) = 6). Two outliers in the tumor group (one of which was suspected to be mislabeled) were discarded with their healthy counterpart. A second PCA

The results of the FNAB were matched with the thyroidectomy biopsy report (gold standard). In Figure 1 are presented microscopic images of hematoxylin- and eosin-stained sections of follicular thyroid carcinoma and follicular adenoma used for histological analysis. Figure 1a shows the microscopic image (4×) of capsular invasion (arrows) in follicular thyroid carcinoma. We observe neoplastic cells that penetrate through Table 2. Final Histological Diagnosisa histology benign (n = 34) cytology FNAB non-neoplastic (n = 4) follicular lesion (n = 40) malignant (n = 28) total cases (n = 72)

FA

GN

malignant (n = 38) PTC

FTC

AC

4 30 30

10 4

27 27

10

1 1

a

FA, follicular adenoma; GN, goiter nodule; PTC, papillary thyroid carcinoma; FTC, follicular thyroid carcinoma; AC, anaplastic carcinoma.

Figure 2. Representation of the mean 1H HRMAS CPMG spectra from (a) healthy thyroid tissues and (b) thyroid lesions. Signal assignment: 1, fatty acids; 2, leucine and isoleucine; 3, valine; 4, lactate; 5, alanine; 6, arginine and lysine; 7, N-acytelated compound; 8, glutamine; 9, glutamate; 10, choline; 11, phosphocholine and glycerophosphocholine; 12, scyllo-inositol; 13, taurine; 14, myo-inositol; 15, unknown; 16, tyrosine; 17, phenylalanine. 3320



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Table 3. Metabolite 1H and 13C NMR Signal Assignments 1

compound Alanine Ascorbate

Arginine

Choline Citrate Ethalonamine Formate Glutamine

Glutamate

Lactate Leucine

Fatty acids

Lysine

Methionine

Myo-inositol

Phenylalanine

Phosphoryl-/ Glycerophospho-choline Proline

Scyllo-inositol Serine Threonine

H chemical shift (in ppm) 3.77 1.47 4.51 4.02 3.74 3.78 3.23 1.91 1.68 3.19 2.68 2.53 3.82 3.14 8.44 3.77 2.44 2.13 3.76 2.34 2.07 4.12 1.32 3.73 2.06

multiplicity d m m

m m s d d dd m s

C chemical shift (in ppm) 51.4 17.0 78.6 69.9 63.6 55.1 28.2 25.0 54.8

57.4 42.1

m

55.1 31.6 27.3 55.1 34 23.6 69.1 20.8 54.6 23.6

0.96 2.25 2.03 1.58

d broad broad broad

22.3 34.2 27.2 25.4

1.29

broad

30.3

0.89 3.78 3.03 1.90 1.72 1.46 3.86 2.63 2.15 4.06 3.62 3.54 3.27 7.40 7.37 7.31 3.21

broad

15.0 55.4 40 30.8 27.3 22.4 57.4 29.7 30.4 73.2 73.4 72 75.3 130.2 130.3 130.3 54.6

4.13 3.41 3.34 2.35 2.00 3.34 3.97 3.84 4.25

dd

m m dt m q d

m m m m dd m m m m m t m m m s

s

dd

Table 3. continued

13

69.1 47.1 47.1 29.9 23.1 74.5 61 57.7 66.7

1

assignment compound

CH CH3 CH CH 2xCH2 CH deltaCH2 betaCH2 gammaCH2 CH3 CH2(i) CH2(ii) CH2OH CH2NH2 CH alphaCH gammaCH2 betaCH2 alphaCH gammaCH2 betaCH2 CH3 CH alphaCH CH2 and gammaCH terminal-CH3 CH2CO CH2C = C CH3CH2 (CH2) CH3CH2 (CH2) CH3CH2 AlphaCH epsilonCH3 betaCH2 deltaCH2 gammaCH2 alphaCH SCH2 betaCH2 H2 H4, H6 H1, H3 H5 H3, H5 H4 H2, H6 CH3

Tyrosine

Unsaturated Fatty Acid

Unsaturated Fatty Acid Uracil (provisional) Valine

Unknown 1

Unknown 2 Unknown 3 Unknown 4

H chemical shift (in ppm) 3.60 1.32 7.17 6.88 3.99 3.22 3.07 5.32

multiplicity d d d

C chemical shift (in ppm) 61.4 20.8 131.7 116.9 56.9

broad

2.03 1.30 5.32

broad broad broad

2.79 7.52

broad

5.78 3.62 2.27 1.03 0.99 3.86 3.35 2.65 6.03 5.64 1.54 1.63

13

m d d

27.2 30.3

assignment alphaCH CH3 oCH mCH CH CH2(i) CH2(ii) CH=CH CH2-CH= CH2 CH=CH CH2 H6

61.3 29.9 18.6 17.3 61.9

H5 alphaCH2 betaCH Gamma-CH3 Gamma′-CH3

39.0

broad broad

18.3 26.1

analysis was then performed on the samples taken from the remaining 26 patients (Figure 3). A clear discrimination between both tissue types is observable along the PC1 axis (Figure 3a). Analysis of the PC1 loadings (Figure 3b) showed that the discrimination between both groups is characterized by higher relative concentrations of several amino acids (phenylalanine, tyrosine, serine, lysine, taurine, glutamine, glutamic acid, alanine, isoleucine, leucine, and valine) and lactate and a lower relative concentration of saturated and unsaturated fatty acids in the tumor samples, which is consistent with the results previously reported by Mountford and co-workers.23,34 Regarding the fatty acid signals, their intensity was attenuated by the T2 filter of the CPMG sequence. As all the samples contained the same type of fatty acids, they all suffered the same level of attenuation, making possible the comparison of the spectra. However, in order to assess the reliability of our result, we compared the normalized 1H HRMAS NMR spectra (without using a T2 filter) acquired on the same samples (Supporting Information, Figure S1). As expected, we clearly observed a higher level of fatty acids (saturated and unsaturated) in the healthy tissues as well as a lower level of the most resolved amino acid signals. A lower lipid level in the lesion phenotype could be related to a higher metabolic turnover and a demand in membrane biosynthesis for cell propagation.35 The cell proliferation would also explain the results reported by Fan et al.,21 where the expression of apolipoprotein CI and CIII, inhibitors of the lipoprotein lipase, was down-regulated, highlighting the possible activation of this enzyme to provide fatty acids as fuel for the cancerous cells.36 However, further research is

alphaCH half deltaCH2 Half deltaCH2 Half beta-CH2 gammaCH2 Beta-CH2 alphaCH betaCH 3321



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Figure 3. (a) PCA score plot showing the discrimination between thyroid lesions and their healthy counterpart tissues. (b) PC1 loadings plot showing the model coefficients for each NMR variable. Horizontal axis corresponds to the NMR chemical shift scale; vertical axis corresponds to the variable weights on PC1. The line variation corresponds to model covariance derived from the mean-centered model, whereas the color map corresponds to correlation coefficient derived from the unit-variance model. Metabolites significantly discriminant were annotated on the model coefficient plot.

significant discrimination between the two groups (benign tissues in red [A + GN] versus malignant tissue in blue [AC + FTC + PTC]) with a p-value of 4.10−4 (R2Y = 0.82, Q2 = 0.37), using 30% of the total X-variance. The robustness of this model was assessed using a 999 permutations validation model (Figure 4b), which shows that the originally observed separation was not due to a random effect, as the predictive discrimination values of the random models are all lower than that of the original model. According to the OPLSDA loadings of the predictive latent variable, an increase of lactate and taurine and a decrease of choline, phosphocholine, myo-inositol, and scylloinositol and unknown compounds were observed in malignant samples (Figure 4c). The significant increase of lactate in cancers has been reported in several previous studies.37−40 This metabolic response could indicate an increase of the glycolytic flux due to hypoxia and ischemia in the tumor tissues38 or be the consequence of the so-called “Warburg effect”.41 Accelerated cancer cell metabolism was also shown as producing more waste product such as lactate or superoxide for extrusion and neutralization.41 As suggested by Tessem et al.39 in colon cancer, the increase of taurine combined with the decrease of

required to better understand the mecanism of the degradation of lipids in thyroid carcinoma. We also noticed that, along PC2 axis, one sample belonging to the PTC group is isolated from the others. This sample, which cannot be considered an outlier according to its “distance to the model” value, presents an original metabolic profile with, in particular, a lower content in lactate and a higher content in myo-inositol (Table S1 in the Supporting Information). HRMAS NMR Discrimination between Benign and Malignant Thyroid Tissues

In order to test the efficiency of HRMAS NMR-based metabolomics for the discrimination of benign and malignant tumors, we analyzed 72 independent samples. PCA was unable in this case to produce a clear discrimination of the two sample types, and thus supervised statistics were employed. Specifically, OPLS-DA was performed in order to differentiate benign (FA and GN, n = 34) from malignant tissues (PTC, FTC, and AC, n = 38). As shown in Figure 4a, the OPLSDA score plot (built with 1 predictive and 4 orthogonal components) shows a statistically 3322



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Figure 4. (a) OPLS-DA score plot showing the discrimination between malignant and benign thyroid lesions. Benign groups: A = adenoma, GN = goiter nodule. Malignant groups: PTC = papillary thyroid carcinoma, FTC = follicular thyroid carcinoma, AC = anaplastic carcinoma. (b) Result of random permutation tests (999 random permutations of the sample malignancy) showing a decline in the model goodness-of-fit between the R2 and Q2 of the target model, on the top right corner, and the swarm of random models on the bottom left of the plot. (c) OPLS loadings plot showing the model coefficients for each NMR variable. Signals are color-coded according to their weights related to the correlation between the X and Y matrices. Metabolites significantly discriminant were annotated on the model coefficient plot. Abbreviations: Cho = choline, PC = phosphorylcholine, GPC = glycerophosphocholine, Phe = phenylalanine, U3 = unknown 3, U4 = unknown 4.

myo-inositol and scyllo-inositol could reflect an imbalance in osmolyte function in cancer cells. Furthermore, alterations in phospholipidic compounds and in particular the decrease in choline-containing compounds such as phosphatidylcholine, which is synthesized from choline and phosphocholine, were reported in thyroid carcinoma.42 In order to assess the diagnostic efficiency of our OPLSDA model, we iteratively predicted a test set in a full 7-fold crossvalidation. The initial malignancy grade (set to 0 for benign and 1 for malignant) and the ones predicted by our OPLSDA model were used to construct a receiver operating characteristic (ROC) curve.43 The area under the curve (AUC) was 0.77, showing that 77% of samples were accurately predicted (Figure 5). Our method showed lower prediction efficiency in comparison with the other techniques presented in Table 1. However, the differences in the number and the type of analyzed samples make very difficult the relevant comparison between our approach and theirs. Moreover, we introduced here a

Figure 5. ROC curve showing OPLSDA model ability to predict thyroid tumor malignancy. 3323



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preliminary investigation in which the number of samples needs to be dramatically increased in order to assess the predictivity robustness of our model with more accuracy. In conclusion, 1H HRMAS NMR on surgical biopsies appears to have a good potential to discriminate malignant and benign thyroid lesions. It is noteworthy that the significant variations in molecular compositions observed between the two classes could be used to suggest or invalidate the involvement of metabolic pathways to the insurgence of the disease. The preliminary model presented in this work, based on the analysis of a limited number of cases, shows that cytologically unclassified biopsies can be correctly diagnosed with a large degree of success. However, the produced model must be further refined by increasing the analyzed data set, and the validation of this approach as a tool for the pathologist, complementary to histological analysis, can be only assessed by exporting the analysis to fine-needle aspiration biopsies.

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ASSOCIATED CONTENT

* Supporting Information S

Supplementary table and figure. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (L.T.); [email protected] (L.S.). Phone/Fax: +39 050992481 (L.T.). Phone: +33 491288900 (L.S.). Fax: +33 491289187 (L.S.). Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS L.T. is grateful to the VINCI program of the Università Italofrancese for a scholarship. L.S., J.C., and S.C. acknowledge ANR (ANR-08-BLAN-273) and Region PACA (APO-G 2009) for financial support. L.S. holds a Young Investigator Award from ANR (ANR-2011- JS08-014-01).

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REFERENCES

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A HRMAS NMR-Based Metabolomics Case of Study - American

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