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4. RESULTATS
1 :
ARTICLE
1
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régulation de 7 d’entre eux a été confirmée par qRT‐PCR. Aucune différence d’expression ne
fut détectée par contre entre les tumeurs exposées et non exposées.
Le programme d’analyse « David database » a permis d’observer, dans cette signature de
403 gènes différentiant les tissus normaux, que les voies de signalisation les plus dérégulées
étaient relatives au cancer et à la prolifération cellulaire.
Une analyse GSEA nous a permis de mettre en évidence un enrichissement statistiquement
significatif de notre ensemble de 793 sondes dans le transcriptome d’autres tissus
thyroïdiens normaux exposés à Tchernobyl comparés à d’autres sporadiques, ayant été
hybridés dans un laboratoire polonais collaborateur. Ceci constitue une validation externe,
imparfaite puisqu’il s’agit de la même cohorte issue de la collection de la CTB, mais
probablement la seule possible au moment de l’étude puisque les accidents nucléaires sont
heureusement limités.
Full Paper
A gene expression signature distinguishes normal tissues of
sporadic and radiation-induced papillary thyroid carcinomas
G Dom*,1, M Tarabichi1, K Unger2, G Thomas2, M Oczko-Wojciechowska3, T Bogdanova4, B Jarzab3,JE Dumont1, V Detours1and C Maenhaut1,5
1
IRIBHM, Universite´ Libre de Bruxelles, Campus Erasme, School of Medicine, Route de Lennik 808, Brussels B-1070, Belgium;2Human Cancer Studies Group, Department of Surgery and Cancer, Imperial College London, London W12 OHS, UK;3Department of Nuclear Medicine and Endocrine Oncology, Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology, Wybrzeze AK 15, Gliwice 44-101, Poland;4Laboratory of Morphology of Endocrine System, V.P. Komisarenko Institute of Endocrinology and Metabolism of National Academy of Medical Sciences of Ukraine, Vyshgorodska Street 69, Kyiv 04114, Ukraine;5Welbio, Universite´ Libre de Bruxelles, Campus Erasme, CP602, Brussels B-1070, Belgium
BACKGROUND:Papillary thyroid cancer (PTC) incidence increased dramatically in children after the Chernobyl accident, providing a unique opportunity to investigate the molecular features of radiation-induced thyroid cancer. In contrast to the previous studies that included age-related confounding factors, we investigated mRNA expression in PTC and in the normal contralateral tissues of patients exposed and non-exposed to the Chernobyl fallout, using age- and ethnicity-matched non-irradiated cohorts.
METHODS: Forty-five patients were analysed by full-genome mRNA microarrays. Twenty-two patients have been exposed to the
Chernobyl fallout; 23 others were age-matched and resident in the same regions of Ukraine, but were born after 1 March 1987, that is, were not exposed to131I.
RESULTS:A gene expression signature of 793 probes corresponding to 403 genes that permitted differentiation between normal tissues from patients exposed and from those who were not exposed to radiation was identified. The differences were confirmed by quantitative RT-PCR. Many deregulated pathways in the exposed normal tissues are related to cell proliferation.
CONCLUSION:Our results suggest that a higher proliferation rate in normal thyroid could be related to radiation-induced cancer either
as a predisposition or as a consequence of radiation. The signature allows the identification of radiation-induced thyroid cancers.
British Journal of Cancer advance online publication, 24 July 2012; doi:10.1038/bjc.2012.302 www.bjcancer.com &2012 Cancer Research UK
Keywords: thyroid; radiation; gene expression; Chernobyl
Thyroid cancer is the most common form of solid neoplasm associated with radiation exposure. There has been a considerable increase in occurrence of papillary thyroid carcinomas (PTCs) after the Chernobyl power plant explosion, particularly in children and adolescents (Baverstock et al, 1992). This increase in incidence (up to a 100-fold) is present only in the areas of Belarus, Ukraine and Russia that lie closest to the site of the Chernobyl nuclear power plant. The incidence of thyroid cancer in these age groups is very low in unexposed populations, which provides some evidence that the majority of thyroid cancers occurring in this population is a direct result of exposure to radiation (Malone et al, 1991). In radiation-induced PTC, the histology and disease stage are related to the young age of patients rather than to the triggering event (Williams et al, 2004; Jarzab et al, 2005a). Spontaneous and post-Chernobyl PTC are characterised by the constitutive activation of effectors along the RAS-RAF-MAP kinase signalling pathway: in adult PTC, BRAF somatic mutations (frequency: 36–69%) and RET/PTC rearrangements (frequency:o30%) represent the most common genetic alterations (Cohen et al, 2003; Kimura et al, 2003; Soares et al, 2003). In paediatric PTC (spontaneous and radio-induced), RET/PTC rearrangements are the most prevalent
alteration (60–80%), while BRAF point mutation is only observed in about 4% of the cases (Nikiforov, 2002; Xing, 2005).
A number of different studies have been undertaken that set out to identify a radiation signature by comparing sporadic PTC, whose ethiology is unknown, and radiation-induced PTC. So far, four transcriptomic studies comparing radiation-induced and spontaneous thyroid cancer have been reported. We have shown that post-Chernobyl PTC had the same global molecular pheno-type as spontaneous PTC (Detours et al, 2005; Detours et al, 2007). However, they were distinguishable with molecular signatures of responses to g-radiation and H2O2, and with genes involved in
homologous recombination (Detours et al, 2007). In another study, Port et al (2007) reported seven genes that discriminated post-Chernobyl from German spontaneous PTC. Recently, by investi-gating copy number and gene expression alterations in post-Chernobyl PTC, Stein et al (2010) identified 141 gene expression changes presented as potential biomarkers of radiation exposure to the thyroid. As mentioned by the authors themselves, these studies harbour potential confounding factors, namely the age and the ethnicity of the patients, because young post-Chernobyl patients were compared with adult Western European patients. Hence, besides age, differences in iodine supply, heterogeneity of stage and pathological variant-related factors may explain the reported differences in gene expression. Moreover, the overlap between those studies in term of radiation-specific signatures is quite low.
*Correspondence: Dr G Dom; E-mail: genedom@ulb.ac.be
Received 20 April 2012; revised 15 June 2012; accepted 15 June 2012
British Journal of Cancer (2012), 1–7
&2012 Cancer Research UK All rights reserved 0007 – 0920/12
The prospective collection of thyroid tumours from patients who were born after the Chernobyl accident by the Chernobyl Tissue Bank (CTB) (www.chernobyltissuebank.com) provides a unique opportunity to compare exposed and non-exposed cases, but this time with age- and ethnicity-matched cohorts. This approach, trying to minimise variability linked to age and ethnicity, has resulted in the identification of a gain of chromo-some band 7q11 associated with radiation exposure (Hess et al, 2011). In the study reported here, we compared the gene expression profiles of the normal contralateral tissues of PTC patients exposed and not exposed to radioiodine in the fallout from Chernobyl. This analysis provides the opportunity to assess the existence of a susceptibility to radiation that could be responsible for tumour development. We report the identification of a gene expression signature that permits discrimination between exposed and non-exposed normal thyroid tissues.
MATERIALS AND METHODS
Tissue samples
Paired RNA samples of tumoural and non-tumoural thyroid
tissues were obtained from Ukraine via the CTB (n ¼B150
www.chernobyltissuebank.com). Diagnoses were confirmed by the members of the International Pathology Panel of the CTB. The CTB is an established research tissue bank and is approved by both the Institutional ethics committees of the contributing organisa-tions (in the case of this study, the Institute of Endocrinology and Metabolism, Kiev and Imperial College London), and by the Institutional Review Board of the National Cancer Institute of the United States. The available patient information, clinical and gene alteration data relative to these samples are presented in Supplementary Table 1. RNA quality was assessed using an automated gel electrophoresis system (Experion, Bio-Rad Laboratories, Nazareth Eke, Belgium). The presence of RET/PTC rearrangement or BRAF mutation in tumours was based on real-time quantitative RT-PCR (qRT-PCR) (Taqman) analyses, and genomic DNA sequencing after PCR amplification of exon 15, respectively (Powell et al, 2005).
Microarray experiments
The quality of RNA was assessed using an automated electrophor-esis system (Experion, Bio-Rad). Only samples with RNA Quality Indicator (RQI) 47.5 were kept for the microarray analyses (for most samples: RQI 48.5/9).
RNA amplification, and cDNA synthesis and labelling were performed following Affymetrix (Santa Clara, CA, USA) protocol. Two micrograms of RNA from 22 paired RNA samples from exposed thyroid tissues (tumour and adjacent tissue) and from 23 paired RNA samples from non-exposed thyroid tissues, together with five additional non-exposed tumour samples were hybridised on Affymetrix Human Genome U133 Plus 2.0 Arrays.
Analysis of expression data
CEL file data were subjected to normalisation by GCRMA. Hierarchical clustering and principal component analysis (PCA) were conducted with GenePattern (http://www.broad.mit.edu/ cancer/software/genepattern/) (Reich et al, 2006). Significance Analysis Of Microarray (SAM) (Tusher et al, 2001) was used to search for single gene expression differences (1000 permutations), and GSEA (GenePattern, MsigDB) to search for multigene signatures allowing to distinguish classes (Subramanian et al, 2005). Class prediction based on leave-one-out cross-validation
was performed with the k-nearest neighbours algorithm
(KNNXValidation, GenePattern), and two supervised classification algorithms were also used to search for the best classifiers, in R
version 2.11.1: Support Vector Machine (SVM, packages e1071 1.5–24) (Meyer, 2011) and Random Forest (RF, package random-Forest 4.6-2) (Liaw, 2011). They were used in an inner/outer cross-validation as implemented in the MCRestimate (2.4.0) package (Ruschhaupt, 2004) with parameters of partition ci ¼ 5 and co ¼ 10, and repeats cr ¼ 10. Different ranges of parameters for each algorithm were tuned in the inner cross-validation loop: VAR numbers in {23, 25, 27), SVM cost equal to 0.01 or 0.1), RF node size equal to 5 or 7. As a negative control the entire inner/outer cross-validation loop was repeated with 100 permutations of the sample labels, which gave an approximation of the P-value for the correct classification rate. As a positive control, the entire cross-validation loop was used to classify the samples regarding the sex of the patients, a classification task for which there should exist a perfect linear separation in the normal.
Covariate adjustment
The expression of each gene was decorrelated with respect to the age at operation by taking the residuals of a robust linear fitting model with respect to the age at operation for each gene (package MASS: function lqs method lqs).
Real-time qRT-PCR
Validation of microarray results was performed by real-time qRT-PCR (SYBR green method) (Eurogentec, Liege, Belgium). The primers were designed with the Primer-3 software (http:// frodo.wi.mit.edu/primer3/) and are listed in Supplementary Table 2. All PCR efficiencies, obtained with four or five serial dilutions points (ranging from 20 ng to 20 or 200 pg), were above 90% and real-time qRT-PCR was performed in duplicate for each gene. NEDD8 and TTC1 expressions were used to normalise the data, as described previously (Delys et al, 2007).
RESULTS
Exposed and non-exposed tumours and normal adjacent tissues have similar global expression profiles
About 150 thyroid tissues samples were received from the CTB. Samples showing RQI below 7.5 were excluded from the study and 95 samples were kept for further analysis: 45 tumour/normal paired tissues (22 exposed, 23 non-exposed) and 5 tumours from non-exposed tissues for which the normal counterpart was not available. The samples were hybridised onto Affymetrix Human Genome U133 Plus 2.0 Arrays.
We first searched for global expression differences between exposed and non-exposed normal and tumour tissues, that is, extensive differences detectable when all the genes present on our arrays were considered. To search for biologically relevant subgroups among the samples, unsupervised analyses, including hierarchical clustering and PCA, were conducted. Both analyses showed a perfect separation between normal and tumour tissues (Figure 1). To look for consistent upregulated or downregulated genes across tumour and normal tissues, we used supervised methods such as SAM, which revealed 22 289 probes that significantly differentiated tumour and normal tissues. Thus, a large fraction of the transcriptome was significantly differently regulated in PTCs compared with normal thyroid tissues
(FDRo5%).
To validate our microarray data, the modulation of the following eight genes, four upregulated and four downregulated in tumours compared with normal tissues, was investigated by qRT-PCR: carbonic anhydrase 12, BH3-interacting domain death agonist, clusterin, cyclin D2, trefoil factor 3, low-density lipoprotein receptor-related protein 1B, dual specificity phosphatase 1 (DUSP1) and thrombospondin, type I, domain-containing 7A.
Gene expression in irradiated thyroid G Dom et al
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These genes were selected because they were already identified as being important in carcinogenesis. Expressions were normalised with TTC1 and NEDD8, which were identified in a previous work as being the best normalisation genes for PTC, resulting from their very stable, non-regulated, expression across the samples (Delys et al, 2007). Similar modulation patterns were found for the expression of the eight genes comparing microarray analyses with qRT-PCR (Supplementary Figure 1).
When considering all probes, hierarchical clustering and PCA did not separate exposed and non-exposed samples (Figure 1). Exposed and non-exposed samples did not separate either when the analysis was performed with only the normal samples or only the tumour samples. Similarly, pairing the tumour and normal samples from the same patient and considering the tumour/normal gene expression ratios led to the same result (data not shown).
However, this does not exclude that a subset of genes might distinguish them. We investigated this hypothesis by conducting supervised analyses.
SAM analyses revealed differences between exposed and non-exposed normal tissues
Before the supervised analyses, we looked for the presence of potential confounding factors that may bias the results if they were unequally distributed within the two considered groups, that is, give a gene expression signature unrelated to the exposed/ non-exposed conditions. We performed a systematic study of the following data (Supplementary Table 1): sex (25% males for exposed and 20% males for non-exposed), age at operation (median age at operation: 17 for exposed and 16.5 for non-exposed), date of operation, geographical origin (oblast) of the patients, PTC morphological subtype, TNM classification, presence of BRAF mutation or RET/PTC rearrangement, tumour size, percentage of epithelial cells in the samples, percentage of lymphocytic infiltration, localisation of the surgical pieces in the thyroid gland, RNA quality (small differences in RQI between exposed and non-exposed samples), hybridisation series (five different batches) and freezing time of the frozen tissue samples before RNA extraction. Only two factors were significantly associated with the radiation exposure status: the length of storage of frozen tissue samples before RNA extraction and the age of the patients at operation. The freezing time was for obvious reasons longer for the exposed samples, but there was no significant correlation between the storage length of the frozen tissue samples before RNA extraction and their quality (data not shown). Regarding age at operation, there was a small but significant difference (median: 6 months, P ¼ 0.006) between the groups of exposed and non-exposed samples (Supplementary Figure 2). Significance Analysis of Microarray analysis identified genes with expression significantly associated with age. Data were adjusted in order to remove age-related signals from the expression data (Materials and Methods). A hierarchical clustering on the age-adjusted data showed a perfect distinction between normal and tumour tissues, but still no distinction between exposed and non-exposed tissues (Supplementary Figure 3).
Prin. Comp. 2 Prin. Comp. 1 Prin. Comp.3 Normal exposed Tumours non-exposed Normal non-exposed Tumours exposed
Figure 1 Global gene expression profiles of exposed and non-exposed normal and tumour tissues: PCA of the microarray data plotted with respect to first, second and third principal components. All probes were considered for the analysis. Tumour samples are shown in green (exposed) and in yellow (non-exposed), and normal samples are shown in red (exposed) and in blue (non-exposed). Abbreviation: Prin. Comp.¼ principal component.
qRT-PCR Microarray Exposed 4 2 0 –2 –4 Non-exposed Log r atio (log 2)
SERPINE1 DUSP1 TRIB1 S100A10 RDH12 ANXA1 GNAL
Figure 2 Comparison of differential gene expression data obtained by microarrays and qRT-PCR on exposed and non-exposed normal tissues. The upper and lower limits of each box stand for the upper and the lower quartiles, respectively; bold lines represent medians; and whiskers represent the 10–90 percentiles. Regulation of serpine peptidase inhibitor clade E (SERPINE1), DUSP1, tribbles homologue 1 (TRIB1), calcium-binding protein A10 (S100A10), retinol dehydrogenase 12 (RDH12), annexin A1 (ANXA1) and guanine nucleotide-binding protein G(olf) subunit alpha (GNAL) was confirmed on 13 exposed normal contralateral tissues and 20 non-exposed normal contralateral tissues.
Gene expression in irradiated thyroid G Dom et al
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Significance Analysis of Microarray was used to compare exposed vs non-exposed tissues, on the age-adjusted data and identified differentially expressed genes between the normal tissues. Indeed, 793 probes, representing 403 genes, for the age-adjusted data (500 probes for the non-age-adjusted data) were found to be significantly upregulated in the exposed normal tissues (qo0.05, q is a multiple-testing-adjusted confidence measure) (Supplementary Table 3, shows the 50 most regulated genes). Twenty-eight of these genes had a fold change higher than 2 (overall mean fold change: 1.53). No probe was found to be downregulated in the exposed normal tissues.
Quantitative RT-PCR analysis confirmed the expression differ-ences for seven genes, that is, serpine peptidase inhibitor clade E, DUSP1, tribbles homologue 1, S100 calcium-binding protein A10, annexin A1, guanine nucleotide-binding protein G(olf) subunit alpha and retinol dehydrogenase 12, with similar modulation patterns for mRNA expression comparing microarray with qRT-PCR analyses (Figure 2).
When SAM was performed to compare age-adjusted expression values of exposed and non-exposed tumour samples, no significantly upregulated or downregulated probes were detected.
Validation with an external data set
The reliability of our signature was supported by similar results obtained with an external data set. Data sets for validation are very limited; however, in the context of a European Union-coordinated consortium, GENRISK-T, gene expression analyses on exposed and non-exposed samples were also carried out in the laboratory of B Jarzab (Poland). Owing to technical study/lab differences, microarrays profiles could not be meaningfully compared at the level of individual genes (Tamayo et al, 2007). Consequently, we used our 793 probes as a gene set and evaluated their collective expression with GSEA in the Polish data set. Collectively, these genes were regulated in the same direction in a significant manner (P ¼ 0.004, NES ¼ 1.773) (Supplementary Figure 4). These results showed that our signature was not restricted to our data set.
Biological meaning of the 793 probe signature
Investigations of the biological meaning of these 793 probes differentially expressed between normal non-exposed and exposed tissues were conducted with the DAVID (Database for Annotation, Visualisation and Integrated Discover) software (Dennis et al, 2003), which finds the most represented pathways or functions according to gene annotation databases such as KEGG and Gene Ontology. The most significantly altered KEGG pathways were related to cancer or proliferation, and included MAPK, insulin and mTOR signalling pathways, as well as cell adhesion, suggesting the presence of a proliferation signal in the trans-criptome of the exposed normal tissues (Table 1).
The main global molecular functions that were significantly enriched in exposed normal samples were linked to nucleic acid processing, also suggesting a proliferative activity (Supplementary Table 4).
Supervised machine-learning classifiers distinguished exposed and non-exposed normal tissues with 30% error
Supervised machine-learning algorithms were used to search for a gene expression signature that predicts class membership for exposed and non-exposed normal tissues. K-nearest neighbours classification with leave-one-out cross-validation was chosen in a first approach. The classification was run with the whole set of probes. Sixty-seven percentage of our samples were correctly classified. Furthermore, accuracies of 69% and 71% were obtained using two other linear classification algorithms, respectively, SVM and RF, and an inner/outer cross-validation protocol designed to
prevent parameter and feature selection biases (Table 2). To control whether chance alone could explain these accuracies, the entire SVM and RF cross-validation loops were repeated with 100 random permutations of the sample labels. Equal or better accuracies were obtained for zero and three permutations, respectively. As a positive control, we used the exact same procedure to classify patients according to sex and obtained a 100% accuracy bringing perspective on the limits of the radiation-related transcriptional signal present in normal tissues.
DISCUSSION
The aim of this study was to investigate gene expression profiles in thyroid tumours that have arisen in the population exposed to the radioactive fallout from the Chernobyl accident (i.e., born before 26 April 1986), and to compare them with profiles of tumours of similar pathology, arising in an age-matched population, residing in the same geographical area, and born after 1 March 1987. Thus, contrary to previous studies, this work included a carefully matched control group and investigated a larger number of patients. Both tumours and their contralateral normal tissues were analysed in order to reveal a radiation signature. Although we may not exclude that our exposed cohort might contain some spontaneous PTC, they have been estimated to be o15% of the cases (Hess et al, 2011).
The microarray expression data confirmed previous results showing that a very large fraction of the transcriptome was dysregulated in the tumours (Huang et al, 2001; Jarzab et al, 2005b; Delys et al, 2007; Maenhaut et al, 2011). On a global scale, whereas unsupervised analyses clearly distinguished normal and tumour tissues, no distinction between the transcriptomes of exposed and non-exposed samples was observed. However, when using a supervised approach, SAM, differentially expressed genes between exposed and non-exposed normal tissues were detected, that is, a gene expression signature that permits discrimination between
Table 1 KEGG pathways enriched in exposed normal tissues and statistical significance following the analysis of the 793 probes signature with DAVID software
Term PVal FDR
hsa05220: Chronic myeloid leukaemia 8.89E 06 0.010354846 hsa04722: Neutrophin signalling pathway 2.28E 05 0.02649372 hsa04010: MAPK signalling pathway 1.32E 04 0.153409419 hsa 04910: Insulin signalling pathway 2.32E 04 0.270422672 hsa05211: Renal cell carcinoma 6.64E 04 0.770791067 hsa05212: Pancreatic cancer 8.19E 04 0.949136812 hsa04810: Regulation of actin cytoskeleton 0.00126321 1.461223451 hsa03040: Spliceosome 0.00139603 1.613718043 hsa04150: mTOR signalling pathway 0.00201986 2.327103458 hsa04210: Apoptosis 0.00314594 3.602869581 hsa04510: Focal adhesion 0.00424634 4.834818104 Abbreviations: DAVID¼ Database for Annotation, Visualisaion and Integrated Discover; FDR¼ false discovery rate; MAPK ¼ mitogen-activated protein kinase; mTOR¼ mammalian target of rapamycin.
Table 2 Error rates for supervised classification (based on all genes)
Classification algorithm Exposed error Non-exposed error Global error KNNXValidation 27 39 33 SVM 27 35 31 RF 31 26 29
Abbreviations: KNNXValidation¼ K-nearest neighbours classification with leave-one-out cross-validation; SVM¼ Support Vector Machine; RF ¼ Random Forest. Gene expression in irradiated thyroid
G Dom et al
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both types of samples. Such a difference was not observed among the tumours, probably because the latter have evolved into very diversified phenotypes, depending on the initial mutation, the local environment and other factors, and accordingly, are more heterogeneous (Figure 1). Thus, we may not exclude that differentially expressed genes might be revealed if a larger set of tumour samples was investigated.
Although age-matched patients were used in this study, contrary to the previous transcriptomic studies on radiation-induced thyroid cancer (Detours et al, 2005; Detours et al, 2007; Port et al, 2007; Stein et al, 2010), age was still a potential confounder with a median age difference of 6 months between the exposed and non-exposed patients. Consequently, the data were age-adjusted, that is, corrected for the correlation between age and gene expression. Age matching and age adjustment are important for several reasons. First, it has been observed that the incidence of thyroid cancer varies with age and is uncommon among children in normal conditions. Second, the risk of developing thyroid cancer after radiation exposure is higher during childhood, that is, the effects of radiation exposure on thyroid cancer development are age-dependent (Cardis et al, 2005). This is consistent with the decrease of thyroid cell proliferation with age (Coclet et al, 1989; Saad et al, 2006). Third, genetic alterations present in PTC vary with age, RET/PTC rearrangements being the most common abnormalities described in paediatric sporadic and radiation-induced PTC.
Seven hundred and ninety-three probes, corresponding to 403 genes, were shown to be differentially expressed between normal exposed and non-exposed samples in the age-adjusted data set. Although the overall differences in gene expression between the two groups were rather small, they were statistically significant (qo0.05) and were confirmed by qRT-PCR for seven genes. In the field of carcinogenesis, Bozic et al (2010) showed that tumour development could result from the accumulation of multiple driver and passenger mutations, while each mutation on its own only has a little contribution to the process of cancer development. Similarly, multiple small gene expression differences could be the basis of susceptibility in our study of radiation-related PTC, and this signature may allow the identification of radiation-sensitive individuals.
There are so far no clear arguments that demonstrate that radiation affects everyone equally, and the propensity to develop cancer following exposure is likely to be variable. A genetic analysis of radiation-induced gene expression changes in immor-talised human lymphocytes showed an extensive individual variation for several genes (Smirnov et al, 2009). Differences in genetic background underlie variation in the susceptibility to the effects of radiation in normal tissues (Chuang et al, 2006; Barnett et al, 2009).
A recent genome-wide association study (Takahashi et al, 2010) compared 500 000 polymorphisms in patients with PTC and in healthy Belarusian and Ukrainian subjects, all exposed to radiation from the Chernobyl fallout. An association between PTC and a polymorphism near the FOXE1 gene (TTF2) was found, but this polymorphism had also been reported previously in a non-irradiated Icelandic population (Gudmundsson et al, 2009). This study, however, had no demographically and ethnically matched control group of non-irradiated PTC patients. Thus, while it pointed out that radiation-induced and spontaneous PTC share the FOXE1 suceptibility loci, this study design could not unambigu-ously conclude on radiation-specific cancer predisposition loci.
Investigations about the biological meaning of our 793 probes signature highlighted significantly altered proliferation pathways, suggesting that the exposed normal tissues exhibit a proliferation signal in their transcriptome. This suggests that a higher proliferation rate would predispose to cancer after irradiation. Evidence of an association of the proliferative activity in thyroid cells with a risk of cancer after radiation exposure has been
reported and may explain the higher risks of radiation-related thyroid cancer in children compared with adults (Saad et al, 2006). The levels of radiation observed after the Chernobyl accident were low to moderate, but no precise and individual radiation doses are available.
This signature might reflect radiation susceptibility, but various alternative interpretations should be considered. First, this signature might be a consequence of radiation. Radiation has potential DNA damaging and carcinogenic effects, and causes single- and double-strand breaks. Double-strand breaks are thought to be particularly important for cancer development, and represent the major effect of b-radiation, for example, 131I, although the radiation effects are complex and numerous (Bourguignon et al, 2005; Harper and Elledge, 2007; Riley et al, 2008). However, DNA damage is repaired within the few hours or days after radiation exposure, and our signature would then reflect the long-lasting consequences of incorrectly repaired DNA damage. This damage would still be present in the normal tissues, but not severe enough to have induced tumourigenesis without (an) additional mutation(s) that generated the initial cancer cells. To investigate whether radiation-related signatures could be detected in our samples, we constructed gene sets with radiation signatures published previously following analyses of post-Chernobyl PTC (Detours et al, 2007; Port et al, 2007; Stein et al, 2010; Ugolin et al, 2011) and used them in a GSEA-type analysis. None of them was found to be enriched in exposed or non-exposed normal tissues (Supplementary Table 5).
Second, this signal could be owing to the presence of microcarcinomas in the exposed normal tissues, as a result of radiation (Hayashi et al, 2010). This hypothesis is, however, unlikely, as analysis of many cases of the irradiated cohort by a panel of internationally recognised pathologists showed no evidence of an increase in microcarcinomas in this group. Moreover, to be detectable in whole-tissue gene expression analyses, such a presence should involve a significant part of the cell population.
Third, differences in iodine intake between the two cohorts might explain the signature. This proliferation signal could indeed be related to iodine deficiency or differential gland stimulation. It was proposed by some authors (Malone et al, 1991; Williams et al, 2008) that the morphological characteristics of Chernobyl-related childhood PTC were related to iodine intake and independent of radiation exposure. However, the existence of a difference in iodine dietary between the two studied Ukrainian cohorts is unlikely: Ukraine was iodine deficient before the Chernobyl disaster and is still deficient today, according to ICCIDD (International Council for Control of Iodine Deficiency Disorders) (www.iodinenetwork.net/documents/scorecard-2010.pdf) and to UNICEF. In addition, the majority of our exposed cases were between 0 and 2 years old at exposure, that is, born between 1984 and 1986, while most non-exposed cases were born between 1987 and 1990. The median date at operation was end 2001 for the exposed group and mid-2006 for the non-exposed group. Thus, the two groups had widely overlapping lifespan before surgery, and were therefore raised mostly in comparable historical context regarding iodine availability. Of course, these are global observa-tions, and we cannot exclude individual differences in iodine intake.
Furthermore, as iodine deficiency results in an increase in TSH levels and in thyrocyte proliferation, we compared our signature with reported transcriptional signatures characterising stimulated thyroid tissues such as autonomous adenomas and familial non-autoimmune hyperthyroidism (Hebrant et al, 2009), or thyroid disorders linked to iodine deficiency such as follicular thyroid cancers. None of them was enriched in our exposed versus non-exposed normal tissues, again suggesting that our signature does not reflect differences in iodine dietary (data not shown).
In conclusion, by comparing the transcriptomes of normal contralateral tissues of PTC occurring in children exposed and
Gene expression in irradiated thyroid G Dom et al
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non-exposed to the Chernobyl fallout, we have identified a gene expression signature that permits discrimination between both cohorts. This signature suggests the existence of a higher proliferation rate in the exposed normal thyroid tissues, which might predispose to cancer after radiation. Whether the signature reflects a susceptibility to radiation or a late effect of radiation, it gives, for a given tissue, an indication that the carcinoma was sporadic or caused by irradiation. It also suggests that decreasing the already slow renewal rate of thyroid cells (Coclet et al, 1989) by a preventive thyroxine treatment, suppressing the major trophic stimulus TSH, could prevent radiation-induced thyroid cancer. Of course, this hypothesis deserves to be tested.
ACKNOWLEDGEMENTS
The authors gratefully acknowledge the confirmation of diagnosis provided by the International Pathology Panel of the CTB— AAbrosimov, TI Bogdanova, M Ito, V LiVolsi, J Rosai and ED Williams. This work was supported by the European Union GENRISK-T project (FP6-36495), Fonds de la Recherche Scienti-fique Me´dicale, Fondation contre le Cancer and Fondation Van Buuren.
Supplementary Information accompanies the paper on British Journal of Cancer website (http://www.nature.com/bjc)
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Gene expression in irradiated thyroid G Dom et al
7
Supplementary data Fig 1: Comparison of differential mRNA expression data
obtained by qRT-PCR and microarray (relative expression in tumors compared to
normal tissues).
normal tissues).
The upper and lower limits of each box stand for the upper and the lower quartiles,
respectively; bold lines represent medians; whiskers represent extreme
measurements. Regulation of CA12, BID, CLU, CCND2, TFF3, LRP1B, DUSP1,
THSD7A was confirmed on 11 tumor/non tumor pairs of PTC
Supplementary data Figure 3
Hierarchical Clustering (pearson correlation, pairwise average-linkage) (Gene Pattern) on the
basis of all probes after age-adjustment;
Supplementary Table 1: Patient information, clinical data and gene alterations for the PTC samples used for microarray analyses.
Exposed Paired
samples oblast sex
Age at operation dominant subtype TNM Ret_exp_stat us ret/ptc_type BRAF mutation Lymphocytic infiltration % epithelial.c ells (tumour) % epithelial.c ells (normal) lesion_size(c m) UA147 Zhytomyr F 16.5 P T2N0M0 NRE neg pos 0% 60-70 50-60 2.30
UA242 Ki F 17 7 P T3N1 M0 b l d TK t d fi d 0% 30 40 40 50 4 50
UA242 Kiev F 17.7 P T3N1aM0 unbalanced TK not-defined neg 0% 30-40 40-50 4.50
UA144 Chernigov F 14.7 F T3N1aM0 unbalanced TK RET/PTC1 neg 0% 50-60 50-60 1.20
UA343 Kiev F 18.5 S T2N1aM0 unbalanced TK NA neg 30-40% 70-80 40-50 2.80
UA145 Zhytomyr M 15.8 Mixed (SF) T1N1aM0 balanced neg neg 0% 70-80 50-60 1.10
UA103 Zhytomyr F 15.0 Mixed (FS) T3N1abM1 unbalanced TK RET/PTC3 neg 1-10% 60-70 40-50 2.70
UA130 Pripyat F 17.7 Mixed (SF) T1N1aM0 N/A neg N/A 0% 60-70 50-60 0.80
UA243 Kiev M 17.0 F T3N1aM0 NRE neg neg 0% 50-60 30-40 2.50
UA366 Sumy M 15.6 P T1N0M0 NRE neg neg 0% 40-50 40-50 0.90
UA446 Kiev M 17 1 P T1N0M0 NRE neg neg 0% 60 70 30 40 1 40
UA446 Kiev M 17.1 P T1N0M0 NRE neg neg 0% 60-70 30-40 1.40
UA502 Zhytomyr F 16.9 Mixed (PS) T3N1abM1 unbalanced TK RET/PTC3 neg 0% 60-70 40-50 3.00
UA165 Chernigov F 16.7 F T1N0M0 NRE neg neg 0% 40-50 40-50 1.50
UA686 Chernigov M 18.1 Mixed (PF) T3N1aM0 NRE neg neg 0% 40-50 40-50 3.20
UA601 Kiev M 16.9 F T3N1abM0 NRE RET/PTC3 pos 1-10% 50-60 40-50 5.00
UA758 Zhytomyr F 18.8 S (F areas) T2N1aM0 unbalanced TK RET/PTC1 neg 1-10% 60-70 40-50 1.70
UA771 Kiev F 20.0 FP T1N0M0 unbalanced EC RET/PTC1 neg 1-10% 60-70 40-50 1.70
UA886 Chercassy F 24.5 SP T3N0M0 NRE RET/PTC1 neg 40-50% 60-70 30-40 1.00
UA905 Sumy F 21.2 SP T3N0M0 NRE neg pos 1-10% 40-50 30-40 0.90
UA905 Sumy F 21.2 SP T3N0M0 NRE neg pos 1 10% 40 50 30 40 0.90
UA954 Kiev F 19.8 F (S areas) T3N1aM0 unbalanced TK neg neg 1-2% 20-30 40-50 0.50
UA249 Rovno F 16.8 F T2N1aM0 NRE RET/PTC3 (weak neg 0% 50-60 40-50 3.50
UA515 Chernigov F 17.7 P T1N0M0 NRE neg pos 0% 50-60 40-50 0.70
UA501 Chernigov F 16.6 P T3N1aM0 N/A NA N/A 0% 30-40 50-60 1.60
Non exposed Paired samplesp
UA1030 Sumy M 12.8 S T3N1aM0 unbalanced TK RET/PTC3 neg 0% 80-90 50-60 0.80
UA939 Sumy F 17.7 Mixed (PF) T1N0M0 NRE neg pos 0% 20-30 30-40 0.60
UA710 Chernigov F 16.7 P T1N1aM0 NRE neg neg 10-20% 40-50 40-50 1.30
UA615 Sumy F 15.4 Mixed (FS) T3N1abM0 unbalanced TK RET/PTC3 neg 1-10% 20-30 40-50 3.50
UA691 Chercassy F 7.7 P T3N0M0 NRE neg neg 0% 40-50 40-50 1.80
UA964 Sumy F 15.2 P T3N1aM0 NRE neg pos 0% 60-70 40-50 4.50
UA307 Zhytomyr F 13.1 F T1N0M0 unbalanced TK RET/PTC1 neg 30-40% 30-40 30-40 1.00
UA1144 Sumy M 16.3 P T1N0M0 NRE neg neg 0% 20-30 50-60 1.30
UA1175 Kiev M 16.6 F T1N0M0 NRE neg neg 0% 60-70 50-60 2.50
UA1502 Kiev M 19.3 FS T3N0M0 NRE neg NA 0% 30-40 50-60 4.30
UA1190 Kiev F 16.6 PS T1N0M0 unbalanced TK neg neg 1-10% 40-50 50-60 1.50
UA1208 Kiev F 15.2 P T2N1aM0 unbalanced TK RET/PTC1 neg 1-10% 40-50 40-50 2.40
UA1224 Chernigov F 17.0 PF T3N0M0 NRE neg NA 1-10% 60-70 50-60 0.80
UA1243 Chernigov F 18.2 S(P areas) T3N1a,bM0 NRE neg neg 1-10% 70-80 40-50 2.80
UA1247 Zhytomyr F 15.6 P T1N0M0 NRE neg pos 0% 60-70 60-70 1.70
UA1328 Sumy F 18.0 SP T3N1a,bM0 NRE neg neg 40-50% 40-50 30-40 3.80
UA1337 S F 18 3 P (S ) T3N0M0 b l d TK 0% 80 90 40 50 5 20
UA1337 Sumy F 18.3 P (S areas) T3N0M0 unbalanced TK neg neg 0% 80-90 40-50 5.20
UA1367 Kiev F 17.7 F (S areas) T1N0M0 NRE neg neg 50-60% 50-60 40-50 1.50
UA1426 Kiev F 16.6 PF T2N0M0 NRE neg neg 0% 70-80 40-50 3.00
UA1486 Chercassy F 20.7 F T1N1aM0 NRE NA pos 1-2% 50-60 40-50 1.50
UA1319 Zhytomyr F 12.4 PS T3N1a,bM0 unbalanced TK RET/PTC1 neg 10-20% 40-50 40-50 3.00
UA574 Kiev F 13.6 Mixed (SP) T2N1aM0 balanced neg neg 20-30% 70-80 40-50 2.50
UA1053 Sumy F 16.1 F T1N0M0 unbalanced TK RET/PTC1 neg 0% 70-80 50-60 1.20
Tumors
UA465T Chernigov M 13.9 S T3N0M0 unbalanced TK RET/PTC3 neg 80-90 2.10
UA411T Kiev F 13.2 S T1N0M0 N/A neg N/A 70-80 1.30
UA752T Kiev M 16.5 Mixed (FS) T1N0M0 N/A neg N/A 40-50 1.10
UA607T Kiev F 14.0 Mixed (PFS) T3N1abM0 NRE neg neg 40-50 4.50
Suppl. Table 1 (continued):
The PTCs were characterized for the presence of RET/PTC rearrangements and for the most frequent BRAF
mutation, V600E, via DNA direct sequencing (details in ref. 3). Expression of the RET gene was classified as
balanced (BAL) when expression of the extracellular domain (EC) and tyrosine kinase domain (TK) were the same,
as unbalanced TK when the expression of the TK was significantly higher than expression of the EC domain, as
b l
d EC
h
th EC d
i
hi hl
d
d
ith
i
f th TK d
i
d
pp
(
)
unbalanced EC when the EC domain was more highly expressed compared with expression of the TK domain, and
as nonRET expressor (nre) when no expression, or very low expression, of both domains was
detectable.
NA= not available, F = female, M = male, tbno = Chernobyl Tissue Bank number, age op = age at operation;
Subtypes: P = papillary, F = follicular variant, S = solid.
Subtypes: P papillary, F follicular variant, S solid.
Supplementary data Table 2: Sequences of the primers used
for real-time RT-PCR (fwd: forward, rev: reverse)
Supplementary data Table 3: Top of the 50 most upregulated genes in the normal exposed tissues compared to the normal non exposed tissues (SAM Stanford)pp y p p g g p p p ( )
Gene symbol Gene Name Fold Change q-value(%)
TMEM49 transmembrane protein 49 2.99 4.51
RBM47 RNA binding motif protein 47 2.55 0.64
IL6ST interleukin 6 signal transducer (gp130, oncostatin M receptor) 2.51 0.00
DUSP1 dual specificity phosphatase 1 2.32 1.31
SFRS6 splicing factor, arginine/serine-rich 6 2.28 0.87
GNAL guanine nucleotide binding protein (G protein), alpha activating activity polypeptide, olfactory type 2.28 1.31 MALAT1 metastasis associated lung adenocarcinoma transcript 1 (non-protein coding) 2 27 4 95 MALAT1 metastasis associated lung adenocarcinoma transcript 1 (non protein coding) 2.27 4.95
S100A10 S100 calcium binding protein A10 2.26 4.17
SFRS4 splicing factor, arginine/serine-rich-4 2.24 4.17
CALD1 caldesmon 1 2.22 3.13
SAR1A SAR1 homolog A (S. cerevisiae) 2.19 1.31
CHP calcium binding protein P22 2.18 0.87
DLG1 discs, large homolog 1 (Drosophila) 2.17 0.00
SLC39A14 solute carrier family 39 (zinc transporter), member 14 2.17 0.64
MBNL2 muscleblind-like 2 (Drosophila) 2.17 0.00
CTDSPL2 CTD (carboxy-terminal domain RNA polymerase II polypeptide A) small phosphatase like 2 2 15 0 64 CTDSPL2 CTD (carboxy-terminal domain, RNA polymerase II, polypeptide A) small phosphatase like 2 2.15 0.64
AFF4 AF4/FMR2 family, member 4 2.15 0.87
RHOB ras homolog gene family, member B 2.13 0.64
TFRC transferrin receptor (p90, CD71) 2.11 0.87
MALAT1 metastasis associated lung adenocarcinoma transcript 1 (non-protein coding) 2.07 3.85
EIF2AK2 eukaryotic translation initiation factor 2 2.06 3.48
RBBP9 retinoblastoma binding protein 9 2.03 1.31
TRIB1 tribbles homolog 1 (Drosophila) 1.99 0.87
C5orf22 chromosome 5 open reading frame 22 1.99 0.00
FAM62B family with sequence similarity 62 (C2 domain containing) member B 1 98 0 00
FAM62B family with sequence similarity 62 (C2 domain containing) member B 1.98 0.00
EIF1 eukaryotic translation initiation factor 1 1.97 3.85
BCL2L2 BCL2-like 2 1.96 0.64
SSFA2 sperm specific antigen 2 1.95 0.64
AFF4 AF4/FMR2 family, member 4 1.94 2.14
GNAL guanine nucleotide binding protein (G protein), alpha activating activity polypeptide, olfactory type 1.93 3.48
PURB purine-rich element binding protein B 1.93 4.95
SUPT16H suppressor of Ty 16 homolog (S. cerevisiae) 1.91 1.31
NCRNA00084 non-protein coding RNA 84 1.91 4.51
RBBP9 ti bl t bi di t i 9 1 91 4 95
RBBP9 retinoblastoma binding protein 9 1.91 4.95
GALC galactosylceramidase 1.91 4.17
AKT2 v-akt murine thymoma viral oncogene homolog 2 1.90 4.95
AP1S3 adaptor-related protein complex 1, sigma 3 subunit 1.90 1.57
FUS fusion (involved in t(12;16) in malignant liposarcoma) 1.90 4.17
FLT1 fms-related tyrosine kinase 1 (vascular endothelial growth factor/vascular permeability factor receptor) 1.89 4.95
TTC26 tetratricopeptide repeat domain 26 1.89 0.64
RNF38 ring finger protein 38 1.88 0.87
CXADR coxsackie virus and adenovirus receptor 1.88 3.85
AHCYL2 S d lh t i h d l lik 2 1 87 1 62
AHCYL2 S-adenosylhomocysteine hydrolase-like 2 1.87 1.62
C10orf18 chromosome 10 open reading frame 18 1.87 0.64
SCAMP1 secretory carrier membrane protein 1 1.86 2.14
EXOC4 exocyst complex component 4 1.86 1.57
TOB1 transducer of ERBB2, 1 1.85 0.64
OGDH oxoglutarate (alpha-ketoglutarate) dehydrogenase (lipoamide) 1.85 0.00
NCAM1 neural cell adhesion molecule 1 1.85 4.17
Supplementary data Table 4: molecular functions of gene ontology categories
enriched in exposed normal tissues and statistical
signifiance following the analysis of the 793 probes discriminating normal non exposed/exposed tissues with DAVID software
T
PV l
FDR
Term
PValue
FDR
GO:0000166: nucleotide binding
1.27E-08
1.91E-05
GO:0003723: RNA binding
4.44E-08
6.67E-05
GO:0019787: small conjugating protein ligase activity
6.47E-05
0.0971797
GO:0017076: purine nucleotide binding
9 45E-05
0 1418328
GO:0017076: purine nucleotide binding
9.45E-05
0.1418328
GO:0032553: ribonucleotide binding
1.14E-04
0.170456
GO:0032555: purine ribonucleotide binding
1.14E-04
0.170456
GO:0019901: protein kinase binding
1.56E-04
0.2341273
GO:0019900: kinase binding
g
1.71E-04
0.2565218
GO:0016881: acid-amino acid ligase activity
2.55E-04
0.3824837
GO:0003924: GTPase activity
4.70E-04
0.7041631
GO:0019899: enzyme binding
9.11E-04
1.3600265
GO:0016879: ligase activity, forming carbon-nitrogen bonds
0.001401
2.0840762
GO:0030554: adenyl nucleotide binding
0.0030592
4.4983173
GO:0001883: purine nucleoside binding
0.0031182
4.5831353
GO:0032561: guanyl ribonucleotide binding
0.0032155
4.7229929
GO:0019001: guanyl nucleotide binding
0.0032155
4.7229929
GO:0019887: protein kinase regulator activity
0 0036581
5 3565547
Supplementary data Table 5: GSEA analysis with previously published radiation signatures (11-13,40): class phenotypes: normal non
exposed versus normal exposed tissues (Metric for ranking genes: signal to noise).
DNA repair signatures: NER, NER-RELATED, BER, MMR, HR, NHEJ: Wood R et al 2005: Human DNA repair genes. Mutation
Research 577: 275–283.
Normalized
i
Gene set
Description
Enriched
Score
p-val
NER
Nucleotide excision repair
-1.23
0.25
NER_RELATED Nucleotide excision repair-related
0.78
0.78
BER
Base excision repair
0 80
0 72
BER
Base excision repair
0.80
0.72
MMR
Mismatch repair
0.83
0.68
HR
Homologous recombination
-0.83
0.70
NHEJ
Non-homologous end joining
-0.66
0.84
STEIN (21)
Radiation signature
1.04
0.41
STEIN (21)
Radiation signature
1.04
0.41
DETOURS (19) Radiation signature
-0.83
0.71
UGOLIN (26)
Radiation signature
-1.07 0.35111
5. RESULTATS
2 :
ARTICLE
2
Dans le cancer de la thyroïde, le manque de réponse à des traitements spécifiques comme
l’iode radioactif est causé par une perte de différenciation des cellules tumorales.
L’hypothèse est que cette perte est due à des modifications épigénétiques. Par conséquent
des drogues épigénétiques capables de lever cette répression d’expression génique ont été
testées.
Pour ce faire, nous avons utilisé des lignées cancéreuses thyroïdiennes humaines (FTC‐133,
BCPAP, TPC‐1, WRO, 8505C) provenant des principaux types de tumeurs thyroïdiennes que
sont les FTC, PTC, et ATC.
Divers travaux du laboratoire ont montré précédemment que la plupart des lignées
thyroïdiennes les plus utilisées avaient perdu l’expression des gènes de différenciation
spécifiques de la thyroïde, mais avaient cependant conservé l’expression des facteurs de
transcription PAX8, TTF1 et TTF2.
Nous avons donc testé par RT‐PCR si l’expression des marqueurs de différenciation pouvait
être réinduite dans ces lignées (FTC‐133, BCPAP, TPC‐1 et 8505C), et mesuré sur microarrays
l’expression génique globale suite à des traitements avec des agents épigénétiques : l’agent
déméthylant 5‐Aza‐2’‐deoxycytidine (5‐AzadC), et les inhibiteurs d’histone déacétylase TSA
(trichostatine A) et SAHA (acide hydroxamique suberoylanilide). Ces derniers ont été
employés dans différentes conditions, combinés ou seuls, et en présence de quantités
variables de sérum, ainsi qu’à une gamme de concentrations de 1, 5 et 10 µM pour la 5‐
AzadC.
La plupart des marqueurs de différenciation ne furent pas ou que très peu ré‐exprimés après
traitement par la 5‐AzadC, qu’elle soit ou non combinée à d’autres agents, la TSA ou la
forskoline qui induit la différenciation dans les thyrocytes normaux. La plus forte
réexpression de NIS fut observée après un traitement combinant la 5‐AzadC avec la TSA et la
forskoline, principalement sur les FTC133. Mais le niveau d’expression restait cependant
largement inférieur à celui observé dans les cultures primaires traitées à la TSH.
112
des gènes fortement modulés dans les lignées traitées par la 5‐AzadC étaient soit non
régulés soit surexprimés dans les ATC. Les gènes modulés après traitement de lignées non
thyroïdiennes (MCF7, T98G, HeLa, HEK) sont, pour 40% d’entre eux, identiques à ceux qui
sont modulés après le même traitement dans les lignées thyroïdiennes. Les gènes
épigénétiquement réprimés dans les lignées cancéreuses reflètent surtout une adaptation
des cellules aux conditions imposées in vitro.
5-Aza-2¢-Deoxycytidine Has Minor Effects on Differentiation
in Human Thyroid Cancer Cell Lines, But Modulates
Genes That Are Involved in Adaptation In Vitro
Genevie`ve Dom,1,* Vanessa Chico Galdo,1,* Maxime Tarabichi,1Gil Toma´s,1Aline He´brant,1Guy Andry,2 Viviane De Martelar,1Fre´de´rick Libert,1Emmanuelle Leteurtre,3 Jacques E. Dumont,1Carine Maenhaut,1,4and Wilma C.G. van Staveren1
Background: In thyroid cancer, the lack of response to specific treatment, for example, radioactive iodine, can be caused by a loss of differentiation characteristics of tumor cells. It is hypothesized that this loss is due to epigenetic modifications. Therefore, drugs releasing epigenetic repression have been proposed to reverse this silencing. Methods: We investigated which genes were reinduced in dedifferentiated human thyroid cancer cell lines when treated with the demethylating agent 5-aza-2¢-deoxycytidine (5-AzadC) and the histone deacetylase inhibitors trichostatin A (TSA) and suberoylanilide hydroxamic acid, by using reverse transcriptase–polymerase chain re-action and microarrays. These results were compared to the expression patterns in in vitro human differentiated thyrocytes and in in vivo dedifferentiated thyroid cancers. In addition, the effects of 5-AzadC on DNA quantities and cell viability were investigated.
Results: Among the canonical thyroid differentiation markers, most were not, or only to a minor extent, re-expressed by 5-AzadC, whether or not combined with TSA or forskolin, an inducer of differentiation in normal thyrocytes. Furthermore, 5-AzadC–modulated overall mRNA expression profiles showed only few commonly regulated genes compared to differentiated cultured primary thyrocytes. In addition, most of the commonly strongly 5-AzadC–induced genes in cell lines were either not regulated or upregulated in anaplastic thyroid carcinomas. Further analysis of which genes were induced by 5-AzadC showed that they were involved in pathways such as apoptosis, antigen presentation, defense response, and cell migration. A number of these genes had similar expression responses in 5-AzadC–treated nonthyroid cell lines.
Conclusions: Our results suggest that 5-AzadC is not a strong inducer of differentiation in thyroid cancer cell lines. Under the studied conditions and with the model used, 5-AzadC treatment does not appear to be a potential redifferentiation treatment for dedifferentiated thyroid cancer. However, this may reflect primarily the inadequacy of the model rather than that of the treatment. Moreover, the observation that 5-AzadC negatively affected cell viability in cell lines could still suggest a therapeutic opportunity. Some of the genes that were modulated by 5-AzadC were also induced in nonthyroid cancer cell lines, which might be explained by an epigenetic modifi-cation resulting in the adaptation of the cell lines to their culture conditions.
Introduction
A
loss of differentiation, during which cells gradually lose the expression of their organ-specific tissue charac-teristics, is a part of the process of cancer progression. This is often accompanied by a lack of response to target-specific treatments. Thyroid neoplasms are an interesting model tostudy differentiation and dedifferentiation processes, because they include a spectrum of different morphologically recog-nizable grades of malignancy and levels of expression of dif-ferentiation markers. Although differentiated thyroid cancers offer good treatment opportunities, poorly differentiated and dedifferentiated thyroid cancers (e.g., anaplastic thyroid car-cinomas [ATC]) still remain an important clinical challenge, 1
Institute of Interdisciplinary Research (IRIBHM) and4Walloon Excellence in Life Sciences and Biotechnology (WELBIO), Universite´ libre de Bruxelles, Brussels, Belgium.
2
Department of Surgery, Jules Bordet Institute, Brussels, Belgium.
3Lille Regional University Hospital Center, Lille, France.
*These two authors contributed equally to this work. THYROID
Volume 23, Number 3, 2013 ª Mary Ann Liebert, Inc. DOI: 10.1089/thy.2012.0388
with most patients with ATC dying within 6 months (1–5). Unresponsive thyroid cancers have lost the functional ex-pression of the sodium iodide symporter (NIS or SLC5A5), the first protein involved in the synthesis of thyroid hormones (6). As radioactive iodine (RAI) uptake is mediated by this sym-porter, the absence of NIS precludes its use to detect and treat such cancers. Therefore, any (even short) treatment tran-siently inducing the expression of functional NIS protein and thus RAI uptake by the tumor would give a window of op-portunity for a potentially curative treatment.
Epigenetic alterations are a common finding in thyroid tu-mors (7), and epigenetic silencing of a number of genes has been reported, including hypermethylation of thyroid differ-entiation genes such as the thyrotropin receptor (TSHR) (7–10) and NIS (10,11). Based on these observations, re-expression of these hypermethylated genes might result after treatment with demethylating agents or other chromatin-modifying drugs such as inhibitors of histone deacetylases (HDAC). A first study investigating the effect of the DNA methylation inhibitor 5-azacytidine on NIS expression in cell lines reported an in-crease of NIS mRNA expression in four out of seven tumor cell lines after treatment, but an increased iodide transport was detected only in two of them (12). However, these cell lines were later shown to be of nonthyroid origin (13). Other studies have also investigated epigenetic treatments of thyroid cancer cell lines on differentiation, and although modulations of thyroid-specific genes have been described, this did not always lead to functional NIS expression in all of the investigated cell lines (14–23). To investigate whether culture conditions might influence the expression of thyroid-specific genes, we analyzed the effect of the DNA methylation inhibitor 5-aza-2¢-deoxycytidine (5-AzadC, decitabine), for which a dual action has been reported: it reactivates the silenced genes, and it in-duces differentiation at low doses, and it is cytotoxic at high doses (24). Previously, we showed that thyroid cancer cell lines from different origins have lost the expression of most classical thyroid differentiation markers and that, compared to in vivo thyroid tumors, regardless of their origin, their gene expression profiles were closest to ATC (25,26). Therefore, we used these cell lines as a model for dedifferentiated thyroid cancers and asked which genes could be reinduced by treatment with 5-AzadC alone or in combination with other agents. These combinations included HDAC inhibitors such as trichostatin A (TSA) and suberoylanilide hydroxamic acid (SAHA), and the adenylate cyclase activator forskolin, the latter being a stimu-lator of differentiation in normal thyrocytes. Compounds were tested using various concentrations, drug combinations, treat-ment times, and different culture conditions. The effect of drug treatments was evaluated by studying the expression of a panel of differentiation genes by quantitative and semi-quantitative reverse transcriptase–polymerase chain reaction (RT-PCR) and investigating drug-induced gene expression profiles by micro-array analysis. 5-AzadC–modulated profiles were also compared to expression levels in differentiated primary thyrocytes in vitro, in ATC in vivo, and in 5-AzadC–treated nonthyroid cell lines. In addition, the effect of 5-AzadC on cell growth was investigated. Materials and Methods
Cell lines
Human thyroid cancer cell lines were originally derived from follicular thyroid carcinomas (FTC133 and WRO), papillary
thyroid carcinomas (TPC1 and BCPAP), and an ATC (8505C). The identity of each of the cell lines was established by DNA fingerprinting as shown previously (25). Profiles were identical to the patterns published recently by Schweppe et al. (13). The mutational status of each of the thyroid cell lines has been verified. Cell lines were cultured at 37C in air with 5% CO2
under the conditions described previously (25). The nonthyroid human cell lines used were HeLa (cervix adenocarcinoma), human embryonic kidney (HEK), MCF7 (breast adenocarci-noma), and T98G (human glioma). HeLa, HEK, and T98G were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco BRL, Life Technologies), and MCF7 was cultured in 1:1 RPMI 1640 containing l-glutamine and DMEM. Cells were cultured in the presence of 10% fetal calf serum, 1% sodium pyruvate, 2% streptomycin and penicillin, and 1% fungizone.
Cells were plated, and the next day, different doses of 5-AzadC (Sigma-Aldrich) ranging from 0.5, 1, and 5 lM to 10 lM were added for 3 to 7 days. Every 24 hours, the medium was replaced with fresh 5-AzadC, derived from a 10 mM stock solution dissolved in dimethylsulfoxide (DMSO). Cells were treated with HDAC inhibitors using concentrations of 50 nM, 100 nM, 500 nM, or 1 lM of TSA (Sigma-Aldrich) de-rived from a 3.3 mM stock solution dissolved in ethanol, or with 1 lM SAHA (Sigma-Aldrich) derived from a 50 mM stock solution that was prepared with DMSO. Cells were treated with an HDAC inhibitor alone ranging from 24 to 72 hours or in combination with 5-AzadC. Forskolin (AG Sci-entific, Inc.), dissolved in ethanol, was used at 10 lM during 24 hours. All stock solutions were stored at - 20C. Total RNA was isolated using TRIzol Reagent, followed by a purification on RNeasy columns (Qiagen). RNA was used for semi- or quantitative RT-PCR and microarray analysis after verifica-tion of its quality as described previously (27).
RT-PCR
The effect of drug treatment on the mRNA expression of thyroid-specific markers, including TSHR, thyroperoxidase (TPO), thyroglobulin (Tg), NIS, dual oxidase 1 and 2 (DUOX1 and DUOX2), and paired box gene 8 (PAX8) was investigated by RT-PCR as described previously (25), but using 35 cycles. mRNA expression patterns were compared to the expression of porphobilinogen deaminase (PBGD) (25). The expression of these genes was also investigated using another protocol in which PCRs contained 0.1 lg cDNA, 1 · PCR Buffer (con-taining 15 mM MgCl2; Qiagen), 5% DMSO, 0.2 mM dNTPs,
0.2 lM of each primer, 1 · Q-Solution (Qiagen), and 0.67 lL homemade Taq polymerase in a total volume of 50 lL. PCR amplifications were performed as described previously (25), but using 35 cycles. All samples were analyzed on a 1% aga-rose gel and visualized with ethidium bromide.
Quantitative RT-PCR
NIS expression was investigated by quantitative RT-PCR using the forward primer 5¢-TGC TCT TCA TGC CCG TCT TC-3¢ and the reverse primer 5¢-AGC GCA TCT CCA GGT ACT CGT-3¢ under the conditions described by Burniat et al. (28) and using the NIS primers described by Hou et al. (23). NIS mRNA expression was normalized using neural pre-cursor cell expressed developmentally downregulated 8 (NEDD8) and tetratricopeptide-repeat domain 1 (TTC1) as described previously (27).
