Oncogene (2014), 1–9 © 2014 Macmillan Publishers Limited All rights reserved 0950-9232/14 www.nature.com/onc

ORIGINAL ARTICLE

CLIP2 as radiation biomarker in papillary thyroid carcinoma M Selmansberger1, A Feuchtinger2, L Zurnadzhy3, A Michna1, JC Kaiser4, M Abend5, A Brenner6, T Bogdanova3, A Walch2, K Unger1,7, H Zitzelsberger1,7 and J Hess1,7 A substantial increase in papillary thyroid carcinoma (PTC) among children exposed to the radioiodine fallout has been one of the main consequences of the Chernobyl reactor accident. Recently, the investigation of PTCs from a cohort of young patients exposed to the post-Chernobyl radioiodine fallout at very young age and a matched nonexposed control group revealed a radiation-specific DNA copy number gain on chromosomal band 7q11.23 and the radiation-associated mRNA overexpression of CLIP2. In this study, we investigated the potential role of CLIP2 as a radiation marker to be used for the individual classification of PTCs into CLIP2positive and -negative cases—a prerequisite for the integration of CLIP2 into epidemiological modelling of the risk of radiationinduced PTC. We were able to validate the radiation-associated CLIP2 overexpression at the protein level by immunohistochemistry (IHC) followed by relative quantification using digital image analysis software (P = 0.0149). Furthermore, we developed a standardized workflow for the determination of CLIP2-positive and -negative cases that combines visual CLIP2 IHC scoring and CLIP2 genomic copy number status. In addition to the discovery cohort (n = 33), two independent validation cohorts of PTCs (n = 115) were investigated. High sensitivity and specificity rates for all three investigated cohorts were obtained, demonstrating robustness of the developed workflow. To analyse the function of CLIP2 in radiation-associated PTC, the CLIP2 gene regulatory network was reconstructed using global mRNA expression data from PTC patient samples. The genes comprising the first neighbourhood of CLIP2 (BAG2, CHST3, KIF3C, NEURL1, PPIL3 and RGS4) suggest the involvement of CLIP2 in the fundamental carcinogenic processes including apoptosis, mitogen-activated protein kinase signalling and genomic instability. In our study, we successfully developed and independently validated a workflow for the typing of PTC clinical samples into CLIP2-positive and CLIP2-negative and provided first insights into the CLIP2 interactome in the context of radiation-associated PTC. Oncogene advance online publication, 6 October 2014; doi:10.1038/onc.2014.311

INTRODUCTION One of the major consequences of the Chernobyl nuclear accident in 1986 has been a significant increase in the incidence of papillary thyroid carcinomas (PTCs) among children exposed to the radioiodine fallout, particularly to iodine-131.1 To date, PTC has developed in 44000 individuals who were children or adolescents at the time of exposure.2 Thus, young age at exposure is a significant risk factor for the development of radiationinduced PTC.3 In order to delineate radiation-associated effects, it is crucial that the tumour cohorts of exposed and nonexposed cases are matched on age and other factors as closely as possible.4 This approach was enabled by the Chernobyl Tissue Bank (CTB, www.chernobyltissuebank.com) that systematically collects thyroid tumour tissue samples from residents who lived in the contaminated regions of Ukraine and the Russian Federation at the time of the accident. The CTB collection also includes a substantial number of thyroid tumours from nonexposed patients. Several studies evidence that radiation exposure can induce copy number alterations and deregulation of gene expressions with the potential of triggering carcinogenic processes, both of which was observed even at low doses of radiation.5–7 Common genetic

alterations in PTC are point mutations of the BRAF gene (V600E) and various variants of RET gene rearrangements, all of which lead to a constitutive activation of the mitogen-activated protein kinase (MAPK) pathway.8 The frequency of RET/PTC rearrangements (that is, RET/PTC1 and RET/PTC3) was associated with radiation exposure levels in a study of the atomic bomb survivors and some but not all studies of post-Chernobyl PTC.9–11 However, RET/PTC3 rearrangements have also been observed with similar frequencies in sporadic PTCs from young patients, indicating a relation with young age of PTC onset.11,12 A more recent publication by Ricarte-Filho et al.13 reported a higher frequency of fusion oncogenes in radiation-induced PTCs, including rare TRK and BRAF rearrangements and the recently discovered ETV6– NTRK3 kinase fusion oncogene. These promising findings require further validation in independent cohorts. We recently reported a radiation-specific DNA copy number gain on the chromosomal band 7q11.23 and a radiation-associated mRNA overexpression of the gene CLIP2, located on chromosome 7q11.23.4 Based on these findings, we aimed to investigate CLIP2 as a radiation biomarker in PTC. However, one of the crucial requirements for a biomarker to be used in epidemiological studies and risk modelling is its validity in terms of sensitivity, specificity, reproducibility and biological

1 Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany; 2Research Unit Analytical Pathology, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany; 3Institute of Endocrinology and Metabolism, National Academy of Medical Sciences of the Ukraine, Kiev, Ukraine; 4Institute of Radiation Protection, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany; 5Bundeswehr Institute of Radiobiology, Munich, Germany and 6Radiation Epidemiology Branch, Division of Cancer Epidemiology and Genetics, NCI, Bethesda, MD, USA. Correspondence: Dr J Hess, Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstr. 1, Neuherberg 85764, Germany. E-mail: [email protected] 7 Clinical Cooperation Group ‘Personalized Radiotherapy of Head and Neck Cancer’, Helmholtz Zentrum München, German Research Center for Environmental Health GmbH, Neuherberg, Germany. Received 21 March 2014; revised 16 July 2014; accepted 9 August 2014

CLIP2 as radiation marker in PTC M Selmansberger et al

2 plausibility. A further aspect for the integration into molecular epidemiology is the suitability of both a highly specific marker and an appropriate assay for its detection.14 In addition, a biomarker may provide insights into the mechanisms of the disease it is a surrogate for. Therefore, this study also aimed to test whether a CLIP2 radiation-specific mRNA overexpression persists at the protein level and to establish a reproducible workflow that allows a classification of PTCs from patients with unknown radiation history. Moreover, we intended to reconstruct the CLIP2 interactome from global transcriptome data that were derived from radiation-induced PTCs in order to clarify the role of CLIP2 in radiation-associated PTC carcinogenesis.15 RESULTS CLIP2 protein expression is elevated in PTCs from patients exposed to radioiodine fallout compared with a nonexposed control group In order to evaluate the expression of CLIP2 at the protein level in exposed and nonexposed cases of the Genrisk-T cohort, immunohistochemical staining with an antibody against CLIP2 was performed in a highly standardized manner, followed by digital image analysis using the Definiens software (Definiens AG, Munich, Germany). The obtained average marker staining intensities within the analysed tumour regions (regions of interest) are listed in Supplementary Table 1. Statistical testing revealed significantly increased staining intensities (Mann–Whitney test, P-value = 0.0149) in PTC tissues from the exposed group as compared with the nonexposed group (Figure 1). Representative immunohistochemically stained formalin-fixed, paraffin-embedded (FFPE) tumour sections from nonexposed and exposed patients are shown in Figure 1, illustrating a radiation-associated overexpression of the CLIP2 protein. Establishment of a workflow for an individual classification of the CLIP2 expression We simplified and standardized the whole procedure of CLIP2 protein expression measurement and established an approach for scoring and classification of individual cases by visual inspection of digital immunohistochemistry (IHC) images. Subsequently, we compared the results with the above-mentioned software-based approach. For this purpose, the automated stained FFPE tissue

sections were scanned and the generated images imported into the image viewer software Panoramic Viewer (3DHISTECH, Budapest, Hungary). The staining intensities were visually scored as follows: negative staining (score 0), weak staining (score 1), intermediate staining (score 2) and strong staining (score 3), as demonstrated in Figure 2. Detailed scoring criteria are outlined in the Materials and methods section. The entire classification workflow is illustrated in Figure 3. The resulting visual scores for the 33 cases of the Genrisk-T cohort are shown in Table 1. A statistically significant difference (two-sided Fisher’s exact test, P-value = 0.005) between the exposed and nonexposed group was also revealed using the visual scoring approach. Correlation analysis of the data from both evaluation approaches showed a strong correlation between digital analysis and visual scoring (Spearman’s correlation coefficients: 0.83, P-value: 1.63 × 10 − 6, Figure 4). In order to obtain an individual biomarker classification for each case, cases with a visual score of 0 or 1 were considered as CLIP2 biomarker negative, whereas cases with visual score 3 were classified as CLIP2 biomarker positive. For a classification of an intermediate CLIP2 staining (score 2), the genomic copy number status (gained or not gained) of chromosomal band 7q11.23 (localization of CLIP2) was taken into account. Preferably, data from array comparative genomic hybridization (array CGH) were used. However, if array CGH data were not available, interphase fluorescence in situ hybridization analysis with a probe specific for 7q11.23 was performed. Cases with a visual score of 2 and a DNA gain of chromosomal band 7q11.23 were finally classified as CLIP2-positive, and those showing normal copy number of chromosomal band 7q11.23 were classified as CLIP2negative (Figure 3). Analysis of CLIP2 as a radiation biomarker in three independent tumour cohorts In order to validate CLIP2 as a radiation biomarker in PTCs of young patients, two independent tumour cohorts (Genrisk-T-PLUS and UkrAm) comprising in total 115 Ukrainian PTC cases were analysed in addition to the discovery set Genrisk-T. Genrisk-TPLUS, which is an extension of the initial Genrisk-T cohort, included exposed and nonexposed cases, whereas the set of PTC cases from the UkrAm study included PTCs from exposed patients only.16 The investigated cohorts are summarized in Table 2. All PTC cases were individually scored for CLIP2 expression using our

Figure 1. Immunohistochemical staining of FFPE tumor sections from patients not exposed and exposed to radioiodine fallout using an antibody against CLIP2. A significantly increased CLIP2 expression (P = 0.0149) in exposed (n = 16, green boxplot) compared with nonexposed cases (n = 17, red boxplot) is shown at the protein level by IHC (middle). The marker staining intensities were evaluated by relative quantification using the Definiens software. The P-value was calculated using the Mann–Whitney test. Representative immunohistochemically stained FFPE sections from PTCs from nonexposed (left side; cases from top to bottom: UA0312, UA1328 and UA1208) and exposed (right side; cases from top to bottom: UA0905, UA0648 and UA0501) patients are shown. Oncogene (2014), 1 – 9

© 2014 Macmillan Publishers Limited

CLIP2 as radiation marker in PTC M Selmansberger et al

3

Figure 2. Visual scoring of immunohistochemically stained FFPE tumour sections using an antibody against CLIP2. The marker staining intensities were evaluated by visual scoring 0–3. Two representative immunohistochemically stained papillary thyroid carcinoma cases with scores of 0, 1, 2 and 3, respectively, are shown from the left to the right. Image details of A-a and B-a (black frames) are shown below in A-b and B-b, respectively.

standardized visual scoring approach. Figure 5 summarizes the resulting CLIP2 scores and the final classification into CLIP2positive and -negative cases for all three independent tumour cohorts. The results of single cases are listed in Supplementary Table 1. Gain of the chromosomal band 7q11.23 in score-2 cases was present in 0/9 Genrisk-T cases (0%), 4/11 Genrisk-T PLUS cases (36%) and 7/18 UkrAm cases (39%). In Supplementary Figure 1, array CGH profiles representing chromosome 7 from six UkrAm cases are exemplarily shown. Following the inclusion of the genomic copy number status of chromosomal band 7q11.23, we obtained sensitivity rates of 75%, 75% and 72.4% for the Genrisk-T, Genrisk-T-PLUS and UkrAm cohorts, respectively. The specificity rates were 82.4 and 57.1% for the Genrisk-T and Genrisk-T-PLUS cohorts (Figures 5d–f). Association of CLIP2 protein expression with patient and histological data The CLIP2 protein expression (IHC CLIP2 visual scores 0–3) was not associated with age at exposure, sex, histological dominant pattern, presence of RET/PTC rearrangements or BRAF V600E mutations. CLIP2 protein expression was associated with exposure to radioiodine (P = 0.0015). Testing was applied to the merged clinical data on all three cohorts. CLIP2 gene regulatory network reconstruction The global gene regulatory network based on global gene expression data from 31 UkrAm cases consisted of 4746 nodes (genes) and 29 842 edges (interactions between genes).15 The first neighbourhood network of CLIP2, which was extracted from the global gene regulatory network, consisted of the seven genes BAG2, CHST3, GOLM1, KIF3C, NEURL1, PPIL3 and RGS4 (Figure 6). Furthermore, four of the CLIP2 first neighbourhood genes were associated with each other (KIF3C with BAG2 and RGS4, BAG2 with RGS4 and NEURL1 with RGS4). All but one (interaction of GOLM1 © 2014 Macmillan Publishers Limited

with CLIP2) association of the network could be validated by quantitative reverse transcriptase–PCR (qRT–PCR; Supplementary Table 2). The second neighbourhood (including the first neighbourhood, Supplementary Figure 2 and Supplementary Table 3) of CLIP2 contained 218 nodes and 1304 edges, including the gene LMO3 that was shown to be associated with radiation dose in the study by Abend et al.15 Pathway enrichment analysis of the second neighbourhood revealed the significantly enriched pathways signalling by Nodal, transport of mature transcript to cytoplasm, Ras activation upon Ca2 influx through NMDA receptor and immune system. DISCUSSION We continued and extended our previous study describing the genomic radiation marker 7q11.23 that was exclusively detected in PTCs from patients exposed to the Chernobyl radioiodine fallout and the radiation-associated CLIP2 overexpression at the mRNA level.4 Here, we confirmed CLIP2 overexpression in PTCs from radiation-exposed patients at the protein level. Moreover, we developed a generally applicable workflow for the classification of PTCs into CLIP2-positive or -negative cases and proposed CLIP2 as a surrogate biomarker for radiation exposure in PTCs, allowing for subsequent integration of CLIP2 into epidemiological studies and thyroid cancer risk models. Furthermore, we reconstructed the CLIP2 interactome from published global mRNA expression data of the UkrAm cohort.15 We were able to confirm the previously demonstrated radiation-associated CLIP2 mRNA expression at the protein level in the same discovery cohort of cases (Figure 1). Within this cohort, the CLIP2 protein overexpression was present in 7q11.23 gained cases. The observed variation of CLIP2 protein expression in cases with normal copy number on chromosome 7q11.23 can be explained by epigenetic regulations of CLIP2 expression, for example, by miRNA miR-16-5p (http://mirtarbase.mbc.nctu.edu. Oncogene (2014), 1 – 9

CLIP2 as radiation marker in PTC M Selmansberger et al

4 Table 1. IHC CLIP2 visual scoring for PTC cases of the exposed and nonexposed groups (Genrisk-T cohort)

Exposed (n = 16) Nonexposed (n = 17) Abbreviations: carcinoma.

IHC,

Score 1

Score 2

Score 3

2 7

2 7

12 3

immunohistochemistry;

PTC,

papillary

thyroid

Figure 4. Correlation between relative marker staining intensity (Definiens software) and visual scores. Good correlation (Spearman’s correlation coefficient rho = 0.83, P-value = 1.63 × 10 − 9) of computerbased analysis (Definiens software) and visual scores of IHC CLIP2 stainings (scores 0–3; no case with score 0 within the Genrisk-T cohort) indicates consistency of analysis methods and justifies the transition from the analysis by image processing software to visual scoring evaluation. Table 2. Number of radiation-exposed and nonexposed papillary thyroid carcinoma (PTC) cases in the three investigated cohorts Number of cases in cohorts Exposed Nonexposed

Figure 3.

Schematic workflow for CLIP2 biomarker classification.

tw), or other so far unknown epigenetic mechanisms like DNA methylation or histone modifications. In order to perform CLIP2 typing of individual cases, we subsequently established a simplified and standardized procedure. The motivation behind this effort was based on the fact that the continuous intensity values generated with a digital image analysis software strongly depend on the used digital slide scanner, the applied quantification algorithm and the regions of interest defined within the Oncogene (2014), 1 – 9

Genrisk-T

Genrisk-T-PLUS

UkrAm

16 17

32 7

76 —

digital image. Digital IHC slide images as generated by different slide scanner models might exhibit varying colour spectra because of different implementations in their hardware and software.17 Another variable is the manufacturer-dependent image processing software that might use diverging colour recognition algorithms, resulting in different relative staining intensities and intensity ranges within one data set. Therefore, because of the influences of the above-mentioned parameters, the calculated average staining intensities generated by one lab are hardly reproducible by another. However, in order to use the CLIP2 biomarker in molecular epidemiology, major requirements include its reproducibility and the possibility to classify individual cases.14 For this purpose, we established a generally applicable approach for CLIP2 typing without the use of a digital analysis software. The proposed approach for classifying individual cases in one out of four categories is comparable to the procedures used in clinical routine diagnostics, for example, HER2neu typing in breast © 2014 Macmillan Publishers Limited

CLIP2 as radiation marker in PTC M Selmansberger et al

5

Figure 5. Stacked bar plots a, b, and c show the percentages of scores 0, 1, 2 and 3 of IHC staining intensities for the three different cohorts and their subgroups of exposed and nonexposed cases. Stacked bar plots d, e, and f show the classification into biomarker-positive and biomarker-negative cases for the three different cohorts after inclusion of the genomic-level information (presence of gain on chromosomal band 7q11.23).

Figure 6. First neighbourhood network of CLIP2. De novo gene regulatory network reconstruction was performed using the GeneNet method. All edges were independently validated by qRT–PCR (high correlation between the expressions of connected genes), except the association between GOLM1 and CLIP2.

cancer.18 For CLIP2 typing we particularly aimed at a simplification while ensuring crucial aspects of biomarker detection such as reproducibility, sensitivity and specificity. The visual scoring results almost perfectly reflected the initial software-derived results, justifying the proposed simplification (Figure 4). Beyond this methodological optimization, we validated a radiation-associated CLIP2 overexpression in two additional independent tumour cohorts (Genrisk-T-PLUS and UkrAm). The very similar sensitivity rates between 72.4 and 75.0% in all three cohorts demonstrate both the robustness of the approach and the ability to detect a high number of true positive cases (Figure 5). The specificity in the Genrisk-T cohort of 82.4% is particularly high and, in conjunction © 2014 Macmillan Publishers Limited

with the sensitivity of 75.0%, points to a high quality of the CLIP2 biomarker. The Genrisk-T-PLUS cohort was classified with the same sensitivity of 75.0% but a lower specificity of 57.1%, probably because of the low number of nonexposed cases in this cohort. However, because of the very low rate of sporadically occurring PTCs diagnosed at young age, the number of nonexposed cases within this cohort cannot be increased within a reasonable timeframe.19 The sensitivity rate of 72.4% within the UkrAm cohort of exposed cases is similar to the rates determined in the GenriskT and Genrisk-T-PLUS cohort. Moreover, the sensitivity rates for Genrisk-T, Genrisk-T-PLUS and UkrAm are in good accordance with the estimated proportion of patients with radiation-induced PTC in the exposed cohorts. Hess et al.4 estimated that ∼ 85% of PTCs from the exposed group of the Genrisk-T cohort were indeed radiation induced. Compared with the Genrisk-T cohort (mean age at exposure 2 years; mean age at operation 16 years), patients from the UkrAm cohort were in average older at the time of exposure (8 years) and at the time of operation (25 years). The proportion of radiation-induced cases in the UkrAm cohort was determined using the estimated average thyroid dose of 0.65 Gy and an excess relative risk of 5 Gy − 1 for the screening prevalence cohort and 1.91 Gy − 1 for the incidence cases (second to fourth screening).20,21 Consequently, the estimated proportion of patients with radiation induced PTC in the UkrAm cohort was 55–75%. The sensitivity rates for the CLIP2 marker of 75% and 72% for Genrisk-T and UkrAm, respectively, are likely to reflect the estimated frequencies of radiation-induced PTCs and suggest a very sensitive detection of radiation-induced cases. The radiation-specific overexpression of CLIP2 in PTCs on both the mRNA and the protein levels strongly suggests an important Oncogene (2014), 1 – 9

CLIP2 as radiation marker in PTC M Selmansberger et al

6 role of CLIP2 in radiation-induced carcinogenesis of PTC. In the published literature, CLIP2 is mainly known to be associated with the Williams–Beuren syndrome because of its genomic localization within the Williams–Beuren syndrome hemizygous deletion.22,23 Amplifications of CLIP2 have been detected in glioblastomas and colorectal carcinomas.24,25 CLIPs are cytoplasmatic linker proteins binding to the ends of growing microtubules and are thereby involved in the dynamics regulation of the cytoskeletal network including the localization of the dynein– dynactin complex.26,27 The latter, in turn, is involved in various processes such as spindle organization, chromosome alignment and chromosome segregation in cell division.28 Moreover, CLIP2 shows high structural similarities to the protein CLIP1 and shares conserved protein domains like cytoskeleton-associated protein glycine rich and structural maintenance of chromosomes that is linked to chromosome segregation and cell division.29,30 CLIP1 is essential in the G2/M transition, facilitates the formation of kinetochore–microtubule attachments during mitosis and interacts directly with the dynein–dynactin complex.31–34 For CLIP2 an indirect connection to the dynein–dynactin complex via BICD2 is proposed.35,36 Despite these published links, a detailed knowledge about the function of CLIP2 is quite limited. To gain knowledge about the functional role of CLIP2 in radiation-associated PTC, we used the reconstructed gene regulatory network inferred from microarray transcriptome data and extracted the putative CLIP2 interaction partners from this network.15 The global mRNA expression data used for GRN reconstruction were previously published by Abend et al.15 and were generated from PTC tissue samples from 31 patients of the UkrAm cohort.15 A subgroup (n = 15) of UkrAm cases of the transcriptome data set was also subjected to CLIP2 typing in this study. Thus, the reconstructed GRN was partly conducted on the same cases that were used for CLIP2 biomarker typing. We assume that this network represents the CLIP2 interactome, that is, its direct or indirect interaction partners. The identified six first network neighbours of CLIP2 (BAG2, CHST3, KIF3C, NEURL1, RGS4 and PPIL3), which were validated by qRT–PCR on RNA from FFPE sections from the same cohort, are known to be involved in fundamental carcinogenic processes, indicating a functional role of CLIP2 in the carcinogenesis of radiation-associated PTC. The published literature reveals that these genes are likely to be linked to the hallmarks of cancer resisting cell death, sustaining proliferative signalling and genome instability and mutation.37 BAG2 and NEURL1 are involved in apoptosis and might therefore play a role in evading cell death by epithelial thyroid cancer cells.37–39 BAG2 as well as RGS4 are known to play a role in thyroid cancer and are also known to be involved in the MAPK signalling pathway.40,41 The importance of the MAPK pathway has been well established in thyroid carcinogenesis. The MAPK pathway is frequently constitutively activated in PTCs by genetic alterations such as rearrangements of the RET gene (RET/PTC), TRK or BRAF or by point mutations of the BRAF and RAS genes as well as by the recently discovered kinase fusion oncogenes ETV6–NTRK3.13,42,43 The aforementioned NEURL1 is known to be involved in the Notch pathway that in turn is activated by MAPK signalling in PTC.38,44 Furthermore, the subsequent pathway enrichment analysis including all first and second CLIP2 neighbourhood genes revealed the significantly enriched pathways Ras activation upon Ca2 influx through NMDA receptor and signalling by Nodal. Both are also connected to the MAPK pathway, the latter of which by a molecular cross-talk between the aforementioned Notch pathway and Nodal signalling.45–47 Activation of the MAPK pathway plays a fundamental role in the regulation of cell proliferation, linking the function of the assumed CLIP2 interacting genes to the cancer hallmark sustaining proliferative signalling. Moreover, the CLIP2 first neighbour KIF3C suggests a connection to the cancer hallmark genome instability & mutation. The motor protein KIF3C belongs to the kinesin family and is part of a microtubule-associated Oncogene (2014), 1 – 9

complex.48 Beside their function in microtubule-dependent transport, kinesin motor proteins also contribute to the organization and movement of spindle poles and chromosomes during mitosis.49–51 Therefore, KIF3C might represent an essential mitotic component required for accurate cell division including accurate chromosome segregation—prerequisites for genomic stability. An interaction of KIF3C with CLIP2 would indicate a potential role of CLIP2 in chromosome segregation and cell division as it has already been shown and discussed above for CLIP1. Genomic instability is a well-established phenotype after irradiation and might be a critical step in the radiation-associated carcinogenesis.52–55 An effect of CLIP2 on genomic instability could therefore contribute to radiation-induced carcinogenesis of PTC. Moreover, the identification of CLIP2 interacting genes opens the possibility to investigate their role as additional biomarkers in radiation-induced thyroid carcinogenesis. In conclusion, this study provides the validation of the recently published radiation marker CLIP2 at the protein level in radiationassociated PTCs from independent cohorts. We established a standardized procedure for CLIP2 typing, an essential step in integrating a molecular marker into epidemiological studies. Finally, analysis of the CLIP2 interactome suggests the involvement of CLIP2 in the fundamental carcinogenic processes apoptosis, MAPK signalling and genomic instability, indicating a functional role of CLIP2 in the carcinogenesis of radiationassociated PTC. MATERIALS AND METHODS Patient data and tumour tissues Tissue samples from 124 PTCs that developed in young patients (0.1–17 years of age at exposure, born before 26 April 1986) after exposure to radioiodine fallout as a consequence of the Chernobyl reactor accident as well as 24 sporadic PTCs were obtained from the CTB. The patients were residents in one of the following oblasts of Ukraine: Cherkassy, Chernigov, Kiev (including Pripyat city), Rovno, Sumy or Zhytomyr. Sporadic PTC cases from patients born after 1 January 1987, and therefore not exposed to radioiodine fallout from the Chernobyl accident, were matched on residency, age at operation and sex to the exposed PTC cases. Pathological diagnosis was performed at the Laboratory of Morphology of Endocrine System (IEM, Kiev, Ukraine) by two pathologists (LZ/TB) and reviewed by the CTB Pathology Panel.56 All tumours were diagnosed as PTCs. The dominant histological patterns (follicular/papillary/solid) of the studied sections were determined (by LZ/TB). An overview of the investigated tumour cohorts is given in Table 2. The patients’ individual data are listed in Supplementary Table 1. We analysed a discovery cohort consisting of 33 PTC cases (16 exposed and 17 nonexposed cases, the so-called ‘Genrisk-T’) and two validation cohorts consisting of 39 PTCs (32 exposed and 7 nonexposed cases, so-called ‘Genrisk-T-PLUS’) and 76 PTCs (exposed cases only, so-called ‘UkrAm’), respectively. FFPE tumour tissue sections of all cases and tumour DNA isolated from fresh-frozen tissue of the Genrisk-T and Genrisk-T-PLUS cases were provided by the CTB. Tumour DNA from the UkrAm cases was isolated from FFPE tissue sections using the Qiagen AllPrep DNA/RNA FFPE Kit (Qiagen, Hilden, Gemany). Tumour RNA was isolated using the Qiagen RNeasy FFPE Kit. RET/PTC1 and RET/PTC3 rearrangements, as well as BRAFV600E mutation status, were determined as described previously.4

Immunohistochemistry Immunohistochemical staining of FFPE tumour tissue sections was performed using a primary antibody against CLIP2 (HPA020430; Sigma Prestige Antibodies, St Louis, MO, USA). The antibody was selected from the ‘The Human Protein Atlas’ database that comprises information about the antibody specificity and staining patterns.57,58 Antibody specificity was validated by western blot analysis with protein lysates from a CLIP2-expressing cell line and a CLIP2 small interfering RNA (Ambion, Carlsbad, CA, USA, Silencer Select ID: s14847) knockdown cell line (Supplementary Figure 3). Primary antibody was used in a dilution of 1:100 in the automated staining instrument Discovery XT (Roche, Ventana, Tucson, AZ, USA) and Discovery-Universal (Ventana) was used as secondary antibody. Signal detection was performed using peroxidase-DAB© 2014 Macmillan Publishers Limited

CLIP2 as radiation marker in PTC M Selmansberger et al

7 (diaminobenzidine)-MAP chemistry (Roche, Ventana). The stained tissue sections were fixed in an ethanol series and coated by a coverslip before scanning at × 20 objective magnification with a digital slide scanning system (Mirax Desk, Carl Zeiss MicroImaging, Jena, Germany). The resulting staining was confirmed by a pathologist (AW).

Image analysis of immunohistochemistry Digital image analysis. The marker staining intensities were evaluated by relative quantification using digital image analysis platform DefiniensTissueStudio (Definiens AG). For this purpose, the digital slide images were imported into the image analysis software using the tissue portal (DefiniensTissueStudio). In the first step, regions of interest, that is, tumour areas, were defined. In order to detect and quantify stained tissue areas, a continuous spectrum of brown staining intensity in relative units (0.00–3.00) was obtained using predefined algorithm and optimized settings. Finally, the developed image analysis was automatically applied to all digital images in a batch process analysing the relevant regions of interest. Visual scoring classification. The visual scoring classification was carried out by two independent observers (JH/MS) in a blinded manner with respect to the PTC exposure status. Only epithelial tumour cells were evaluated. Tumour stroma and infiltrations, such as lymphocytes, were not considered. The tumour area with the most pronounced IHC staining was used for scoring and classified into one of the four staining categories: negative staining (score 0), weak staining (score 1), intermediate staining (score 2) and strong staining (score 3). Each case was independently evaluated three times in a blinded scenario by each of the two observers. Thereby, six scores were obtained for each individual case. Subsequently, the modus (that is, the most frequent score) of all six scores was taken as a consensus result. In case of a bimodal distribution (that is, two different scores with the same frequencies), the higher value was taken. If the six scores differed more than one scoring level, this particular case was excluded from further analysis. Statistical analysis. The average marker staining intensities (obtained by the Definiens software) of the FFPE PTC tissue sections from the exposed and nonexposed tumour groups were tested for statistical significant differences using the Mann–Whitney test (R base function wilxox.test with ‘paired’ option set to false). Correlation of the continuous values (Definiens) with the visually assessed scores (0/1/2/3) was tested using Spearman’s correlation method (R base function cor.test). Possible associations of the obtained CLIP2 visual scores with clinicopathological and patient data were statistically tested using Fisher’s exact test (R base function fisher. test). P-values of o0.05 were considered statistically significant.

Genomic copy number typing In order to detect copy number aberrations on chromosome 7q11, array CGH analysis was performed using Agilent (Santa Clara, CA, USA) 60k or 180k (AMADID 252192/252206) CGH microarrays as described previously.4 Alternatively, interphase fluorescence in situ hybridization analysis was performed on FFPE tissue sections as described previously.4

De novo reconstruction of the CLIP2 gene regulatory network using mRNA expression microarray data We used published global mRNA expression data from 31 PTC cases of the UkrAm cohort for the reconstruction of the CLIP2 GRN.15 Raw data import, filtering and normalization (preprocessing) of the data were carried out within the statistical programming framework R using the Bioconductor package limma.59–61 In order to solely consider genes that are likely to play a role within the GRN, only probes binding to transcripts with curated RefSeq records were considered for the analysis. Replicated expressions were summarized using the Limma function ‘summarize.probes’. The resulting matrix with log2-normalized expression values (13 662 genes and 31 samples) was subjected to de novo network reconstruction using the Bioconductor package GeneNet.62 It is generally assumed that genes with a strong linear dependency in their expression patterns, and thus a high correlation between them, do either directly or indirectly interact with each other. The GeneNet package allows the reconstruction of GRNs based on pairwise partial correlation of gene expressions. Partial correlation is a measure of the degree of association between two gene expression vectors after removing the influence of all other genes in the data set and © 2014 Macmillan Publishers Limited

thereby providing a much more accurate estimate of pair-wise correlations compared with traditional correlation approaches. The approach implemented in GeneNet is particularly tailored for ‘large-p small-n’ situations in which the number of measurements is much higher than the number of samples analysed. For the definition of edges (that is, connections) between two genes we used a considerably stringent probability cutoff of 0.988. From the resulting global network, the CLIP2-centred first neighbourhood network (direct neighbours) and second neighbourhood (neighbours of first neighbours of CLIP2) were extracted. For pathway enrichment analysis, Reactome pathway gene sets (674) were downloaded from GSEA.63 The gene list building the first and second neighbourhood was matched to the gene sets and tested for overrepresentation by twosided Fisher’s exact test with an assumed number of total human genes of 19 104 (all HUGO Gene Nomenclature Committee (HGNC) annotated genes).64 The P-values were corrected for multiple testing errors using the Benjamini–Hochberg correction.65 False discovery rates of o 0.3 were considered statistically significant.

Quantitative RT–PCR Reverse transcription of RNA was performed using the VILO SuperScript Reverse Transcription Kit (Life Technologies, Carlsbad, CA, USA). The qRT–PCR reactions (10 μl) were carried out in duplicates in a ViiA 7 Real Time PCR System in combination with the ViiA 7 Software v1.2.2 (Life Technologies). TaqMan gene expression assays (Life Technologies) detecting the following genes were used to validate the first neighbourhood network (see previous section) of CLIP2: CLIP2 (Hs00185593_m1), BAG2 (Hs00188716_m1), CHST3 (Hs00427946_m1), RGS4 (Hs01111690_g1), NEURL1 (Hs00907830_m1), KIF3C (Hs01547426_m1), PPIL3 (Hs00368985_m1) and GOLM1 (Hs00895845_m1). Assays detecting the genes RPL30 (Hs01066167_g1) and PGK1 (Hs99999906_m1) were used for the purpose of endogenous normalization. Relative expression levels were calculated using the ΔΔCt method.66 The expression levels of all eight genes (including CLIP2) from the CLIP2 first neighbourhood network were determined in five PTC samples of the UkrAm cohort. Correlations of the genes were calculated based on the obtained expression values using Spearman’s correlation method. Correlations were considered as ‘high’ and thus the association between the network genes was validated if the Spearman’s correlation coefficient was 40.6.

ABBREVIATIONS CGH, comparative genomic hybridization; CTB, Chernobyl tissue bank; FFPE, formalin-fixed, paraffin-embedded; GRN, gene regulatory network; IHC, immunohistochemistry; PTC, papillary thyroid carcinoma. CONFLICT OF INTEREST The authors declare no conflict of interest.

ACKNOWLEDGEMENTS We thank the International Pathology Panel of the Chernobyl Tissue Bank for confirmation of diagnosis: Professors A Abrosimov, TI Bogdanova, G Fadda, G Hant, V LiVolsi, J Rosai and ED Williams; The Chernobyl Tissue Bank for collection of thyroid tissue samples; Professor G Thomas for establishing the matched Genrisk-T cohort; Dr Peter Jacob for discussion and determination of the proportion of radiationinduced tumours among the exposed cases in the UkrAm cohort; U Buchholz, C Innerlohinger, E Konhäuser, CM Pflüger and A Selmaier for technical support; and H Braselmann for mathematical/statistical support. This study was supported by the European Commission, EpiRadBio project, FP7 Grant No. 269553 and in part by the European Commission, DoReMi project, Grant No. 249689.

REFERENCES 1 Cardis E, Hatch M. The Chernobyl accident-an epidemiological perspective. Clin Oncol 2011; 23: 251–260. 2 Tuttle RM, Vaisman F, Tronko MD. Clinical presentation and clinical outcomes in Chernobyl-related paediatric thyroid cancers: what do we know now? What can we expect in the future? Clin Oncol 2011; 23: 268–275.

Oncogene (2014), 1 – 9

CLIP2 as radiation marker in PTC M Selmansberger et al

8 3 Cardis E, Howe G, Ron E, Bebeshko V, Bogdanova T, Bouville A et al. Cancer consequences of the Chernobyl accident: 20 years on. J Radiol Prot 2006; 26: 127–140. 4 Hess J, Thomas G, Braselmann H, Bauer V, Bogdanova T, Wienberg J et al. Gain of chromosome band 7q11 in papillary thyroid carcinomas of young patients is associated with exposure to low-dose irradiation. Proc Natl Acad Sci USA 2011; 108: 9595–9600. 5 Iizuka D, Imaoka T, Takabatake T, Nishimura M, Kakinuma S, Nishimura Y et al. DNA copy number aberrations and disruption of the p16Ink4a/Rb pathway in radiation-induced and spontaneous rat mammary carcinomas. Radiat Res 2010; 174: 206–215. 6 Ishida Y, Takabatake T, Kakinuma S, Doi K, Yamauchi K, Kaminishi M et al. Genomic and gene expression signatures of radiation in medulloblastomas after low-dose irradiation in Ptch1 heterozygous mice. Carcinogenesis 2010; 31: 1694–1701. 7 Mullenders L, Atkinson M, Paretzke H, Sabatier L, Bouffler S. Assessing cancer risks of low-dose radiation. Nat Rev Cancer 2009; 9: 596–604. 8 Klugbauer S, Lengfelder E, Demidchik EP, Rabes HM. High prevalence of RET rearrangement in thyroid tumors of children from Belarus after the Chernobyl reactor accident. Oncogene 1995; 11: 2459–2467. 9 Hamatani K, Eguchi H, Ito R, Mukai M, Takahashi K, Taga M et al. RET/PTC rearrangements preferentially occurred in papillary thyroid cancer among atomic bomb survivors exposed to high radiation dose. Cancer Res 2008; 68: 7176–7182. 10 Leeman-Neill RJ, Brenner AV, Little MP, Bogdanova TI, Hatch M, Zurnadzy LY et al. RET/PTC and PAX8/PPARgamma chromosomal rearrangements in post-Chernobyl thyroid cancer and their association with iodine-131 radiation dose and other characteristics. Cancer 2013; 119: 1792–1799. 11 Tuttle RM, Lukes Y, Onstad L, Lushnikov E, Abrosimov A, Troshin V et al. ret/PTC activation is not associated with individual radiation dose estimates in a pilot study of neoplastic thyroid nodules arising in Russian children and adults exposed to Chernobyl fallout. Thyroid 2008; 18: 839–846. 12 Nikiforov YE, Rowland JM, Bove KE, Monforte-Munoz H, Fagin JA. Distinct pattern of ret oncogene rearrangements in morphological variants of radiation-induced and sporadic thyroid papillary carcinomas in children. Cancer Res 1997; 57: 1690–1694. 13 Ricarte-Filho JC, Li S, Garcia-Rendueles ME, Montero-Conde C, Voza F, Knauf JA et al. Identification of kinase fusion oncogenes in post-Chernobyl radiationinduced thyroid cancers. J Clin Invest 2013; 123: 4935–4944. 14 Pernot E, Hall J, Baatout S, Benotmane MA, Blanchardon E, Bouffler S et al. Ionizing radiation biomarkers for potential use in epidemiological studies. Mutat Res 2012; 751: 258–286. 15 Abend M, Pfeiffer RM, Ruf C, Hatch M, Bogdanova TI, Tronko MD et al. Iodine-131 dose dependent gene expression in thyroid cancers and corresponding normal tissues following the Chernobyl accident. PLoS ONE 2012; 7: e39103. 16 Tronko M, Mabuchi K, Bogdanova T, Hatch M, Likhtarev I, Bouville A et al. Thyroid cancer in Ukraine after the Chernobyl accident (in the framework of the UkraineUS Thyroid Project). J Radiol Prot 2012; 32: N65–N69. 17 Rojo MG, Bueno G, Slodkowska J. Review of imaging solutions for integrated quantitative immunohistochemistry in the Pathology daily practice. Folia Histochem Cytobiol 2009; 47: 349–354. 18 Varga Z, Noske A, Ramach C, Padberg B, Moch H. Assessment of HER2 status in breast cancer: overall positivity rate and accuracy by fluorescence in situ hybridization and immunohistochemistry in a single institution over 12 years: a quality control study. BMC Cancer 2013; 13: 615. 19 Williams D. Cancer after nuclear fallout: lessons from the Chernobyl accident. Nat Rev Cancer 2002; 2: 543–549. 20 Tronko MD, Howe GR, Bogdanova TI, Bouville AC, Epstein OV, Brill AB et al. A cohort study of thyroid cancer and other thyroid diseases after the chornobyl accident: thyroid cancer in Ukraine detected during first screening. J Natl Cancer Inst 2006; 98: 897–903. 21 Brenner AV, Tronko MD, Hatch M, Bogdanova TI, Oliynik VA, Lubin JH et al. I-131 dose response for incident thyroid cancers in Ukraine related to the Chornobyl accident. Environ Health Perspect 2011; 119: 933–939. 22 Hoogenraad CC, Koekkoek B, Akhmanova A, Krugers H, Dortland B, Miedema M et al. Targeted mutation of Cyln2 in the Williams syndrome critical region links CLIP-115 haploinsufficiency to neurodevelopmental abnormalities in mice. Nat Genet 2002; 32: 116–127. 23 Ferrero GB, Howald C, Micale L, Biamino E, Augello B, Fusco C et al. An atypical 7q11.23 deletion in a normal IQ Williams-Beuren syndrome patient. Eur J Hum Genet 2010; 18: 33–38. 24 Suzuki T, Maruno M, Wada K, Kagawa N, Fujimoto Y, Hashimoto N et al. Genetic analysis of human glioblastomas using a genomic microarray system. Brain Tumor Pathol 2004; 21: 27–34. 25 Lassmann S, Weis R, Makowiec F, Roth J, Danciu M, Hopt U et al. Array CGH identifies distinct DNA copy number profiles of oncogenes and tumor suppressor

Oncogene (2014), 1 – 9

26

27 28 29 30

31

32

33 34

35

36

37 38

39

40 41 42 43 44

45

46 47

48

49 50 51 52

53 54

genes in chromosomal- and microsatellite-unstable sporadic colorectal carcinomas. J Mol Med 2007; 85: 293–304. Hoogenraad CC, Akhmanova A, Grosveld F, De Zeeuw CI, Galjart N. Functional analysis of CLIP-115 and its binding to microtubules. J Cell Sci 2000; 113(Pt 12): 2285–2297. Galjart N. CLIPs and CLASPs and cellular dynamics. Nat Rev Mol Cell Biol 2005; 6: 487–498. Karki S, Holzbaur EL. Cytoplasmic dynein and dynactin in cell division and intracellular transport. Curr Opin Cell Biol 1999; 11: 45–53. Hoogenraad CC, Akhmanova A, Galjart N, De Zeeuw CI. LIMK1 and CLIP-115: linking cytoskeletal defects to Williams syndrome. Bioessays 2004; 26: 141–150. Marchler-Bauer A, Anderson JB, Chitsaz F, Derbyshire MK, DeWeese-Scott C, Fong JH et al. CDD: specific functional annotation with the Conserved Domain Database. Nucleic Acids Res 2009; 37: D205–D210. Yang X, Li H, Liu XS, Deng A, Liu X. Cdc2-mediated phosphorylation of CLIP-170 is essential for its inhibition of centrosome reduplication. J Biol Chem 2009; 284: 28775–28782. Wieland G, Orthaus S, Ohndorf S, Diekmann S, Hemmerich P. Functional complementation of human centromere protein A (CENP-A) by Cse4p from Saccharomyces cerevisiae. Mol Cell Biol 2004; 24: 6620–6630. Tanenbaum ME, Galjart N, van Vugt MA, Medema RH. CLIP-170 facilitates the formation of kinetochore-microtubule attachments. EMBO J 2006; 25: 45–57. Lansbergen G, Komarova Y, Modesti M, Wyman C, Hoogenraad CC, Goodson HV et al. Conformational changes in CLIP-170 regulate its binding to microtubules and dynactin localization. J Cell Biol 2004; 166: 1003–1014. Hoogenraad CC, Wulf P, Schiefermeier N, Stepanova T, Galjart N, Small JV et al. Bicaudal D induces selective dynein-mediated microtubule minus end-directed transport. EMBO J 2003; 22: 6004–6015. Fumoto K, Hoogenraad CC, Kikuchi A. GSK-3beta-regulated interaction of BICD with dynein is involved in microtubule anchorage at centrosome. EMBO J 2006; 25: 5670–5682. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144: 646–674. Teider N, Scott DK, Neiss A, Weeraratne SD, Amani VM, Wang Y et al. Neuralized1 causes apoptosis and downregulates Notch target genes in medulloblastoma. Neuro Oncol 2010; 12: 1244–1256. Wang HQ, Zhang HY, Hao FJ, Meng X, Guan Y, Du ZX. Induction of BAG2 protein during proteasome inhibitor-induced apoptosis in thyroid carcinoma cells. Br J Pharmacol 2008; 155: 655–660. Arndt V, Daniel C, Nastainczyk W, Alberti S, Hohfeld J. BAG-2 acts as an inhibitor of the chaperone-associated ubiquitin ligase CHIP. Mol Biol Cell 2005; 16: 5891–5900. Druey KM, Blumer KJ, Kang VH, Kehrl JH. Inhibition of G-protein-mediated MAP kinase activation by a new mammalian gene family. Nature 1996; 379: 742–746. Nikiforov YE. Thyroid carcinoma: molecular pathways and therapeutic targets. Mod Pathol 2008; 21(Suppl 2): S37–S43. Xing M. Molecular pathogenesis and mechanisms of thyroid cancer. Nat Rev Cancer 2013; 13: 184–199. Yamashita AS, Geraldo MV, Fuziwara CS, Kulcsar MA, Friguglietti CU, da Costa RB et al. Notch pathway is activated by MAPK signaling and influences papillary thyroid cancer proliferation. Transl Oncol 2013; 6: 197–205. Strizzi L, Hardy KM, Seftor EA, Costa FF, Kirschmann DA, Seftor RE et al. Development and cancer: at the crossroads of Nodal and Notch signaling. Cancer Res 2009; 69: 7131–7134. Grewal T, Koese M, Tebar F, Enrich C. Differential regulation of RasGAPs in cancer. Genes Cancer 2011; 2: 288–297. Xia Z, Dudek H, Miranti CK, Greenberg ME. Calcium influx via the NMDA receptor induces immediate early gene transcription by a MAP kinase/ERK-dependent mechanism. J Neurosci 1996; 16: 5425–5436. Muresan V, Abramson T, Lyass A, Winter D, Porro E, Hong F et al. KIF3C and KIF3A form a novel neuronal heteromeric kinesin that associates with membrane vesicles. Mol Biol Cell 1998; 9: 637–652. Goldstein LS. Kinesin molecular motors: transport pathways, receptors, and human disease. Proc Natl Acad Sci USA 2001; 98: 6999–7003. Mandelkow E, Mandelkow EM. Kinesin motors and disease. Trends Cell Biol 2002; 12: 585–591. De S, Cipriano R, Jackson MW, Stark GR. Overexpression of kinesins mediates docetaxel resistance in breast cancer cells. Cancer Res 2009; 69: 8035–8042. Little MP, Heidenreich WF, Moolgavkar SH, Schollnberger H, Thomas DC. Systems biological and mechanistic modelling of radiation-induced cancer. Radiat Environ Biophys 2008; 47: 39–47. Huang L, Snyder AR, Morgan WF. Radiation-induced genomic instability and its implications for radiation carcinogenesis. Oncogene 2003; 22: 5848–5854. Morgan WF. Non-targeted and delayed effects of exposure to ionizing radiation: I. Radiation-induced genomic instability and bystander effects in vitro. Radiat Res 2003; 159: 567–580.

© 2014 Macmillan Publishers Limited

CLIP2 as radiation marker in PTC M Selmansberger et al

9 55 Morgan WF. Non-targeted and delayed effects of exposure to ionizing radiation: II. Radiation-induced genomic instability and bystander effects in vivo, clastogenic factors and transgenerational effects. Radiat Res 2003; 159: 581–596. 56 Williams ED. Guest Editorial: two proposals regarding the terminology of thyroid tumors. Int J Surg Pathol 2000; 8: 181–183. 57 Uhlen M, Bjorling E, Agaton C, Szigyarto CA, Amini B, Andersen E et al. A human protein atlas for normal and cancer tissues based on antibody proteomics. Mol Cell Proteom 2005; 4: 1920–1932. 58 Uhlen M, Oksvold P, Fagerberg L, Lundberg E, Jonasson K, Forsberg M et al. Towards a knowledge-based Human Protein Atlas. Nat Biotechnol 2010; 28: 1248–1250. 59 Team RDC. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, 2013. 60 Smyth GK. Limma: linear models for microarray data. In: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W (eds) Bioinformatics and Computational Biology Solutions Using {R} and Bioconductor. Springer: New York, 2005, pp 397–420.

61 Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S et al. Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 2004; 5: R80. 62 Opgen-Rhein R, Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst Biol 2007; 1: 37. 63 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102: 15545–15550. 64 Gray KA, Daugherty LC, Gordon SM, Seal RL, Wright MW, Bruford EA. Genenames.org: the HGNC resources in 2013. Nucleic Acids Res 2013; 41: D545–D552. 65 Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat So 1995; 57: 289–300. 66 Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods 2001; 25: 402–408.

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