Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

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1 EUROPEAN UROLOGY 59 (2011) available at journal homepage: Platinum Priority Kidney Cancer Editorial by Maria J. Ribal on pp of this issue Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature Youssef M. Youssef a,b, Nicole M.A. White a,b,jörg Grigull c, Adriana Krizova a,b, Christina Samy a,b, Salvador Mejia-Guerrero a,b, Andrew Evans a,d, George M. Yousef a,b, * a Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada b Department of Laboratory Medicine and the Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael s Hospital, Toronto, Canada c Department of Mathematics and Statistics, York University, Toronto, Canada d Department of Pathology, Toronto General Hospital, Toronto, Canada Article info Article history: Accepted January 3, 2011 Published online ahead of print on January 13, 2011 Keywords: Biomarker Cancer Clear cell renal cell carcinoma Chromophobe Diagnosis Kidney cancer MicroRNA mirna Oncocytoma Papillary Pathology Profiling Statistical classifier Prognosis RCC Renal cancer Subtypes Tumour markers Unclassified Abstract Background: Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. Objective: Our aim was to identify microrna (mirna) signatures that can distinguish the different RCC subtypes accurately. Design, setting, and participants: A total of 94 different subtype cases were analysed. mirna microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qrt-pcr) analysis with mirnaspecific primers. Measurements: Microarray data were analysed by standard approaches. Relative expression for qrt-pcr was determined using the DDC T method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log 2 value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation. Results and limitations: We developed a classification system that can distinguish the different RCC subtypes using unique mirna signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccrcc) subtype, 97% for papillary RCC (prcc) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chrcc) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccrcc is more closely related to prcc, whereas chrcc is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes. Conclusions: MiRNA expression patterns can distinguish between RCC subtypes. # 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved. * Corresponding author. Department of Laboratory Medicine, St. Michael s Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada. Tel x6129; Fax: address: yousefg@smh.ca (G.M. Yousef) /$ see back matter # 2011 European Association of Urology. Published by Elsevier B.V. All rights reserved. doi: /j.eururo

2 722 EUROPEAN UROLOGY 59 (2011) Introduction Renal cell carcinoma (RCC) represents a spectrum of histologic subtypes that are morphologically and cytogenetically distinct [1]. The most common is conventional or clear cell RCC (ccrcc), which accounts for about 75 80% of cases. Other subtypes include papillary RCC (prcc) (10 15%), chromophobe RCC (chrcc) (5%), and collecting duct carcinoma. Oncocytoma is one of the main benign neoplasms of the kidney [1]. Distinguishing RCC subtypes is of clinical importance because they have different prognoses and subsequently different management plans. This distinction relies on morphology. In a significant number of cases, however, the morphology is not conclusive. One study showed a significant interobserver variability in diagnosing subtypes among pathologists [2]. Some subtypes may have overlapping or related morphologic features (eg, oncocytoma and chrcc). Added to this complexity is the recent recognition of collision tumours and hybrid tumours with mixed features. Furthermore, some of the newly recognised entities, such as translocation carcinomas, have multiple patterns that overlap with other subtypes. The clear cell papillary RCC has a mixed morphology of clear cytoplasm and papillary architecture and lacks the cytogenetic abnormalities of either. Although it may be attempted to support diagnosis, immunohistochemical staining lacks both sensitivity and specificity [3] and thus required the creation of an unclassified category of RCC. Attempts have been made to classify RCC subtypes more accurately based on molecular signature, using mrna and protein profiling analysis [4], but none has crossed the bar to clinical use [5]. MicroRNAs (mirnas) play significant roles in many important biologic events such as cell differentiation and proliferation. They are dysregulated in many tumours, including kidney cancer [6 8]. Additionally, many cancerdysregulated mirnas are located in cancer chromosomal regions that are frequently altered in cancer. One report demonstrated that fewer mirnas can be used to distinguish human cancers with accuracy as compared with mrna [7]. Evidence has also shown that mirnas are actively involved in kidney cancer pathogenesis [9,10]. The potential scenarios of mirna involvement in RCC have been recently reviewed [11]. In this study, we identify mirna signatures that can distinguish between kidney cancer subtypes ccrcc, prcc, and chrcc, and also oncocytoma with high accuracy. We also provide a stepwise decision tree to distinguish between normal and each of the subtypes in a maximum of four steps, as well as a binary classification system to distinguish between pairs of subtypes. 2. Material and methods 2.1. Specimen collection A total of 94 cases from fresh frozen tissues collected between 2007 and 2010 were included in this study. For the discovery phase, 70 specimens were included (20 ccrccs and 20 normal matched tissues from the same patients, 10 prccs, 10 chrccs, and 10 oncocytomas). An additional 24 cases were included for the validation phase. Specimens were obtained from patients undergoing nephrectomy at St. Michael s Hospital in Toronto, Canada. Tissues were collected following research ethics board approval from St. Michael s Hospital. All specimens were immediately snap-frozen in liquid nitrogen (within 5 min of resection) and stored at 80 8C after frozen-section confirmation of the diagnosis from the same submitted specimen. Two independent pathologists confirmed the diagnosis RNA extraction and microarray analysis Total RNA was extracted from fresh tissues with the mirneasy Kit (Qiagen, Mississauga, Canada), as described in our previous publications [12,13]. The quality of extracted RNA was assessed by electropherogram and gel analysis. Microarray analysis was performed as described in our previous work [6], using the mparaflo microfluidic technology as per the manufacturer s protocol (LC Sciences, Houston, TX, USA) and the release 13 update of the Sanger Institute Database of mirnas Quantitative reverse transcription polymerase chain reaction Quantitative reverse transcription polymerase chain reaction (qrt-pcr) was performed using TaqMan mirna assays according to the supplier s protocol (Applied Biosystems, Foster City, CA, USA) for samples used for the microarray, in addition to a second independent set of samples. Expressions of these eight mirnas were measured: hsa-mir-10a, hsamir-15b, hsa-mir-99a, hsa-mir-191, hsa-mir-194, hsa-mir-195, hsamir-200b, and hsa-mir-221. Assays were run using the StepOnePlus Real-Time PCR System (Applied Biosystems). Relative expression was determined using the DDC T method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44), which has been shown to be a suitable reference gene [6]. Experiments were done in triplicate and an average was calculated. Fold change was expressed as a log 2 value Clustering and statistical analysis Microarray data from 70 experiments were quantitated and normalised using the locally weighted scatterplot smoothing normalisation method [14]. Data were collated and filtered using BRB-ArrayTools software v [15]. To test for differential expression between different subtypes and/or normals, a one-way analysis of variance analysis was conducted using MATLAB. The Eisen Lab Cluster and Treeview software [16], which is embedded in BRB-ArrayTools, was used for hierarchic clustering Top-Scoring Pairs Classifier The top-scoring pairs classifier was chosen for its efficacy, parsimony, and transparency in identifying transcripts among gene expression data that discriminate between probes of different oncologic status. Applying the algorithm in pathologic/clinical practice requires the practitioner to proceed in a maximum sequence of four decision steps that are defined in terms of binary decision rules over sets of between three and nine transcript pairs. This avoids the opacity of classificatory algorithms with a similar purpose that work in high-dimensional parametric space, and we anticipate that the top-scoring pairs classifier, combined with the results extracted in the present data, will become an operational benchto-bedside tool for the clinical pathologist. Categories of RCC subtypes were defined using the following abbreviations: T 1 : normal, U 1 : (clear cell, papillary, oncocytoma, chromophobe); T 2 : clear cell, U 2 : (papillary, oncocytoma, chromophobe);

3 EUROPEAN UROLOGY 59 (2011) Table 1 The 15 most statistically significant differentially expressed micrornas among different renal cell carcinoma subtypes, oncocytoma, and normal kidney tissue mirna p value * hsa-mir-126 hsa-mir-192 hsa-mir-194 hsa-mir-200b hsa-mir-221 hsa-mir-222 hsa-mir-182 hsa-mir-548m hsa-mir-183 hsa-mir-663 hsa-mir-22 hsa-mir-498 hsa-mir-25 hsa-mir-200c hsa-mir-21 mirna = microrna. * One-way analysis of variance. [()TD$FIG] T 3 : papillary, U 3 : (oncocytoma, chromophobe); T 4 : oncocytoma, U 4 : (chromophobe). The decision tree classifier screens all pairs in each node from the filtered list of 216 mirna expression data for maximally discriminatory pairs, predicts malignancy against normal, (ie, T 1 over U 1 ), and compares any RCC subtype T i against the remaining group U i of different subtypes in four or fewer steps. For a complete set of maximally discriminatory mirna pairs, a decision rule for node h i based on the expression values is formulated. Each mirna pair from the set identified by the algorithm casts a vote on the origin of the tissue, and a call on either T i or U i is determined by the majority of the counted votes. This algorithm is adapted from Geman and others [17,18] Classifier cross-validation analysis To test for robustness against overfitting, we performed a crossvalidation analysis by dividing the set of 70 samples randomly 10 times into training and test sets of size 63 and 7 each, respectively. Feature selection (ie, selection of discriminating mirna pairs for the decision tree h 1 h 4 ) was based on the expression data of the test set only and, accordingly, resulted in somewhat varying sets of those mirnas that are selected for the classifier in each round. Fig. 1 A hierarchic cluster heat map showing differential microrna (mirna) expression in normal kidney, oncocytoma, and different renal cell carcinoma (RCC) subtypes. The bars on the right indicate mirna clusters among subtypes that appear highly dysregulated across the samples. MiRNA expression profiles can distinguish between the subtypes with high accuracy. ccrcc = clear cell renal cell carcinoma; chrcc = chromophobe renal cell carcinoma; prcc = papillary renal cell carcinoma.

4 724 EUROPEAN UROLOGY 59 (2011) Correlation between micrornas and genomic changes in renal cell carcinoma subtypes We compiled the literature for 11 reports that identified chromosomal aberrations in RCC subtypes (Appendix). Chromosomal locations of the dysregulated mirnas were compared with reported genomic alterations in the different subtypes. 3. Results 3.1. MicroRNA expression can accurately classify renal cell carcinoma subtypes Using mirna microarray analysis, 91 mirnas were identified as statistically significantly differentially expressed among RCC subtypes, oncocytoma, and normal kidney (Supplementary Table S1). Table 1 lists the 15 most statistically significant mirnas among the subtypes. Unsupervised clustering (Fig. 1) shows that with minimal exceptions, species of each RCC subtype can be clustered together in a separate arm with the existence of two separate biologic groups of roughly highly specific mirnas, which are co-overexpressed or counderexpressed relative to normal. Hierarchic clustering shows thatccrcc is more closely related to prcc and that both are distinct from oncocytoma and chrcc (data not shown), which are more closely related Microarray validation by qrt-pcr analysis We validated our mirna microarray results by qrt-pcr analysis using mirna-specific probes. We also validated an independent set. The average fold change of relative expression of cancer to normal for each subtype is shown in Table 2 for all mirnas. The results are statistically significant ( p < 0.05) between the subtype groups. Table 2 Validation of microrna dysregulation between renal cell carcinoma subtypes as assessed by quantitative reverse transcription-polymerase chain reaction * mirna ccrcc prcc chrcc Oncocytoma p value y mir mir-10a mir mir-15b mir mir mir-99a mir-200b ccrcc = clear cell renal cell carcinoma; chrcc = chromophobe renal cell carcinoma; mirna = micro RNA; prcc = papillary renal cell carcinoma. * Relative expression values are shown as fold changes compared with normal. Shaded cells mark the highest expression level for the mirna across the subtypes. y One-way analysis of variance. The qrt-pcr results were quite comparable with the microarray results (Fig. 2A). The frequencies of differential expression (ie, the number of cases that show the same pattern compared with the total number of cases in the subtype) were mostly consistent across the same subtype (data not shown). Similar to microarray findings, some mirnas showed a similar expression pattern between the subtypes. As shown in Figure 2B, mir-221 showed similar downregulated expression levels in ccrcc and prcc while showing an upregulated pattern in both chrcc and oncocytoma. We also validated our results on an independent set of formalin-fixed paraffin-embedded tissues from the different subtypes. Results were comparable to that of fresh frozen tissue, although values for fresh tissues were slightly higher (data not shown). [()TD$FIG] Fig. 2 (A) Average expression levels (fold change) of mir-194 and mir-195 in the four renal cell carcinoma (RCC) subtypes compared with a normal pool, as determined by both quantitative reverse transcription polymerase chain reaction (qrt-pcr) (normalised against RNU44 endogenous control) and microarray analysis. R 2 coefficients indicate the level of correlation between microarray and qrt-pcr results for each of mir-194 and mir-195. As shown in the figure, and as represented by the strong R 2 coefficients, microarray results are comparable with the qrt-pcr results for both mirnas. (B) A representative graph for qrt-pcr validated mir-221 and its relative expression (log fold change) across the RCC subtypes. Bars marked with an asterisk (*) denote the average value for mir-221 in each subtype group. As shown, mir-221 is upregulated in both chromophobe renal cell carcinoma (chrcc) and oncocytoma and downregulated in the clear cell renal cell carcinoma (ccrcc) and papillary renal cell carcinoma (prcc) subtypes.

5 EUROPEAN UROLOGY 59 (2011) Table 3 MicroRNA (mirna) members of the decision tree hierarchic mirna classifier system to distinguish between normal kidney and different renal cell carcinoma subtypes h 1 h 2 h 3 h 4 mir-200c > mir-222 mir-194 > mir-548m mir-331-3p > mir-139-5p mir-99a > mir-200b mir-194 > mir-15b mir-192 > mir-221 mir-191 > mir-221 mir-22 > mir-183 mir-324-5p > mir-34a mir-424 > mir-183 mir-106a > mir-663 mir-625* > mir-1300 mir-500* > mir-425 mir-181b > mir-663 mir-10b* > mir-28-3p mir-100 > mir-182 mir-532-5p > mir-93 mir-15a > mir-222 mir-195 > mir-10a mir-26b > let-7g h 1 step distinguishes class 1, normal from class 2, kidney tumour. h 2 step distinguishes class 1, ccrcc from class 2, other subtypes (prcc + chrcc + oncocytoma). h 3 step distinguishes class 1, prcc from class 2, remaining other subtypes (chrcc + oncocytoma). h 4 step distinguishes class 1, chrcc from class 2, oncocytoma. The * sign following a mirna name denotes the less predominant form of the mirna arising from the opposite arm of a shared pre-mirna hairpin. For example, mir-500* is expressed at low levels relative to mir-500 in the cell, and both share the same hairpin A multistep decision tree to distinguish renal cell carcinoma subtypes accurately The decision tree classification between normal and kidney tumour, in addition to between RCC subtypes proceeds in a maximum of four steps (Table 3 and Fig. 3A). Twelve mirnas grouped in six pairs are selected for discrimination between normal and tumour tissue (Fig. 3B). The distinction relies on comparing the expression levels between pairs of mirnas, and majority vote is used for classification. For instance, in this node we have six votes, and the majority of them will determine if the specimen is normal or tumour [()TD$FIG] (Fig. 3B). To distinguish ccrcc from other subtypes, 8 pairs (16 mirnas) are used. Six more mirnas can distinguish prcc from chrcc and oncocytoma, and an additional six mirnas can distinguish chrcc from oncocytoma. The application of the top-scoring pairs classifier to each of the 70 cases analysed discriminates correctly between normal and tumour (decision node h 1 ) in 68 cases (97.1%), with two incidences of false normal predictions for ccrcc probes. In decision node h 2, 48 of 48 cases (100%) were correctly classified as either ccrcc or one of prcc, chrcc, or oncocytoma. In decision node h 3, 29 of 30 cases (96.7%) were classified correctly as either prcc or one of Fig. 3 (A) An overview of the decision tree hierarchic microrna (mirna) classifier system. The decision tree has a total of four steps. Each step consists of a group of mirna pairs, which based on their differential expression can classify a sample as belonging to one of the two possible outcomes in a single step. (B) A representative figure showing the method in which votes in step 1 (h 1 ) for the expression-based mirna classifier for two samples were cast. Each mirna pair in the classifier votes for the identity of the sample based on the relative expression between each mirna pair. In this case, the majority vote classified sample 1 as normal and sample 2 as malignant. ccrcc = clear cell renal cell carcinoma; chrcc = chromophobe renal cell carcinoma; prcc = papillary renal cell carcinoma.

6 726 [()TD$FIG] EUROPEAN UROLOGY 59 (2011) Fig. 4 Expression profiles of the binary classification system for the six different pairwise subtype combinations. These follow the similar set of rules based on majority votes that classify a given sample as one of two subtypes that are paired for comparison using a microrna pair. In these figures, each classifier attempts to distinguish (A) clear cell renal cell carcinoma (ccrcc) from papillary renal cell carcinoma (prcc), (B) ccrcc from oncocytoma, (C) ccrcc from chromophobe renal cell carcinoma (chrcc), (D) prcc from oncocytoma, (E) prcc from chrcc, and (F) oncocytoma from chrcc.

7 EUROPEAN UROLOGY 59 (2011) Table 4 A list of 65 biologically significant differentially regulated micrornas in the different renal cell carcinoma subtypes and the subtype classifiers they represent mirna hsa-let-7a, hsa-let-7b, hsa-let-7c, hsa-let-7d, hsa-mir-30d, hsa-let-7f, hsa-mir-1915, hsa-mir-21, hsa-mir-155, hsa-mir-192, hsa-mir-181b, hsa-mir-663, hsa-mir-194, hsa-mir-196a, hsa-mir-221 hsa-mir-222, hsa-mir-106a, hsa-mir-146a, hsa-mir-150, hsa-mir-574-5p, hsa-mir-149* hsa-mir-1280, hsa-mir-145, hsa-mir-195, hsa-mir-16, hsa-mir-548m, hsa-mir-26b, hsa-mir-182, hsa-mir-720, hsa-mir-451, hsa-mir-215, hsa-mir-342-3p, hsa-mir-122 hsa-mir-10b, hsa-mir-191, hsa-mir-1979, hsa-mir-1975, hsa-mir-92a, hsa-mir-423-5p, hsa-mir-1277, hsa-mir-1289, hsa-mir-186*, hsa-mir-223, hsa-mir-126, hsa-mir-548h, hsa-mir-31 hsa-mir-125b, hsa-mir-1826, hsa-mir-23a, hsa-mir-23b, hsa-mir-638, hsa-mir-204, hsa-mir-200c, hsa-mir-888, hsa-mir-891a, hsa-mir-148a, hsa-mir-30b hsa-mir-1974, hsa-mir-1977, hsa-mir-1978, hsa-mir-200b, hsa-mir-139-5p, hsa-mir-22 hsa-mir-498, hsa-mir-140-3p Subtype classifier ccrcc + prcc chrcc + oncocytoma ccrcc prcc chrcc Oncocytoma prcc + chrcc The * sign following a mirna name denotes the less predominant form of the mirna arising from the opposite arm of a shared pre-mirna hairpin. oncocytoma or chrcc. In decision node h 4, 19 of 19 cases (100%) were classified correctly as either oncocytoma or chrcc. In cross-validation analysis, each of the 70 cases was classified by a decision tree that was trained on a set of 90% of the data, which did not include the cases of an independent test set (see Methods section for details). In this analysis, the class/subtype was predicted correctly for 61 of 70 probes; 3 normals were classified as malignant, 2 of the 50 malignant tissues were predicted incorrectly as normals, and in 4 other cases tumour probes were predicted to be tumours but the RCC subtype was predicted incorrectly. Thus we conservatively estimate the accuracy of the top-scoring pairs classifier to be 65 of 70 (92.9%) for discrimination between normal and malignant probes, and 61 of 70 (87.1%) for correct classification into one of the categories (normal, clear cell, papillary, oncocytoma, chromophobe) (data not shown). Higher classification accuracy was obtained when comparing individual pairs of subtypes. Given a probe of unknown status/rcc subtype, the decision tree classifier with the identified sets of mirna pairs in each step h i is used as follows (referring to Table 3 legend for notation): Each cell entry under h 1 h 4 in Table 3 contains an inequality in normalised expression values for two mirnas that guides the probe classification by applying this rule: If the given inequality is fulfilled, add one vote to the diagnosis, The probe is of class 1 ; if the reverse inequality holds, add one vote to the alternative diagnosis, The probe is of class 2. In each step h i, the votes thus accumulated are counted: In case the majority vote is for the first diagnosis, stop; otherwise, proceed to step h i + 1 (i =1,2,3) Binary classifier system for pairwise classification between two subtypes Based on the relative expression values of pairs of mirnas, we also established a binary classifier system for pairwise classification between any two different subtypes of RCC based on mirna microarray analysis. Fig. 4 shows heat maps of the different mirnas used for this classification. Expression levels of six pairs of mirnas were used to distinguish ccrcc from prcc (Fig. 4A), five pairs to distinguish prcc from chrcc (Fig. 4B), nine pairs to distinguish ccrcc from chrcc (Fig. 4C), five pairs to distinguish prcc from oncocytoma (Fig. 4D), five pairs to distinguish prcc from chromophobe (Fig. 4E), and three pairs to distinguish between oncocytoma and chrcc (Fig. 4F). These six binary classifiers were also validated using a leave-one-out cross-validation analysis. The classification system correctly identified 147 of the total 150 decisions (98%), thus providing accurate classification between any given subtype pair Dysregulated micrornas are located in regions of chromosomal aberrations in renal cell carcinoma subtypes We identified the top 65 differentially expressed mirnas among the four subtypes from our microarray analysis. The criterion for selection was the ability of the mirna to have a distinct signal intensity that was unique to a certain subtype or subtype pair. Table 4 lists these mirnas and the classifiers they predict. Table 5 lists a summary of the reported chromosomal abnormalities associated with all the subtypes, as well as some of the well-studied genes located within those aberrated regions that are involved in the pathogenesis of the tumour subtypes. Almost all of the biologically significant 65 mirnas are located within cytogenetic regions that are significantly aberrated in the different subtypes (Table 5). 4. Discussion The present study delineates a more accurate distinction between RCC subtypes and oncocytoma in cases where the morphologic assessment is not sufficient for diagnosis. To diagnose resection specimens, our system is also valuable in small biopsy specimens before resection. MiRNA testing can be performed on paraffin-embedded tissues [19]. A growing attempt has been made to diagnose small renal masses by biopsy [20]. On average, a needle core biopsy is diagnostic in only about 70% of cases [21]. Distinguishing chrcc from oncocytoma may be virtually impossible. Studies have

8 728 EUROPEAN UROLOGY 59 (2011) Table 5 Chromosomal aberrations associated with each of the subtypes of renal cell carcinoma and the tumour-associated genes in those locations RCC subtype Frequency Reported chromosomal MiRNAs within regions Reported associated aberrations * genes ccrcc 60 70% 3p25-26, mutation, or mir-16-2 VHL, OGG1 hypermethylation 3p14.2-p25 continuous VHL, FHIT, HRCA1, OGG1 deletion 3p14.1 3p14.2 FHIT 3p12 mir-720 NRC1 3p mir q (14q32.32-qter) 8p23.3 mir-30b 9q21.13-qter mir-126, mir-181b-2, mir-204 9p- 5q33.1-qter + mir q11.22-q35 + mir p12.3-p mir-1826 Loss of heterozygosity PTEN/MAC on chromosome 10q prcc 10 15% 7q31 (c-met mutation) MET 7 +/trisomy mir-148a, mir-182, mir mir-30b, mir-30d, mir-548h-4 12q + 16q /trisomy mir-21, mir-22, mir-195, mir-196a-1, Fumarate hydratase mir-423, mir-451, mir-548h-3 20q + mir-663, mir p mir-181b-1, mir-186, mir mir-200b, mir-215 4q mir-574, mir q 9p let-7a-1, let-7d, let-7f-1, mir-23b 11p let-7a-2, mir-125b-1, mir-139, mir-192, mir q mir-16-1, mir-92a-1 14q mir-342, mir-548h-1 18 mir q let-7c, mir-125b-2, mir-155 X let-7f-2, mir-92a-2, mir-106a, mir-221, mir-222, mir-223, mir-548m mir-888, mir-891a, mir-1277 Y chrcc 5% Multiple losses of whole chromosomes: Y, 1, 2, 6, 10, 13, 17, and 21 17p11.2 (clinical manifestations of the BHD syndrome) Oncocytoma 10% Not many genetic changes; chromosome 14 loss mir-342, mir-548h-1, mir-181b-1, mir-186, p53 mutation mir-194-1, mir-200b, mir-215, mir-10b, mir-26b, mir-149, mir-1915, mir-16-1, mir-92a-1, mir-21, mir-22, mir-195, mir-196a-1, mir-548h-3, let-7c, mir-125b-2, mir-155 mir-342, mir-548h-1 T(11q13) 7 + mir-148a, mir-182, mir mir-30b 5q + mir mir-16-1, mir-92a-1 10q + BHD = Birt-Hogg-Dubé; ccrcc = clear cell renal cell carcinoma; chrcc = chromophobe renal cell carcinoma; mirna = microrna; prcc = papillary renal cell carcinoma. * The minus sign denotes a loss and the plus sign denotes a gain in chromosomal material. shown that a significant fraction of small solitary renal masses are benign despite suspicious radiology [22], and thus patients will have unnecessary nephrectomies. Also, our decision tree enables a more accurate diagnosis of tumours with mixed subtypes. Our findings show that the ccrcc and prcc subtypes are more closely related when compared with chrcc and oncocytoma, which are in turn related. These results agree with previous reports [23,24]. Previous studies identified gene expression changes that are common to both chrcc

9 EUROPEAN UROLOGY 59 (2011) and oncocytoma [23,24]. Few studies have identified distinct gene expression changes for each of these subtypes. As shown in Table 5, our results indicate the presence of a partial matching pattern between the cytogenetic changes and mirna dysregulation in the different subtypes. This suggests that chromosomal aberrations can control dysregulation of mirnas. While this study was being completed, mirna signatures for each RCC subtype were reported [25]. The results of this report overlap with our findings (eg, mir-200b had increased expression in chrcc when compared with oncocytoma). Limitations of this study are the relatively small number of cases used and a lack of independent validation. A very recent report by Fridman et al. [26] showed the ability of mirnas to distinguish between RCC subtypes. Some of their findings overlap with ours (eg, both studies showed that mir-221 has higher expression in chrcc and oncocytoma than ccrcc and prcc). Both studies also showed that mir-126 can be used to distinguish prcc from ccrcc. Our study had the advantage of using fresh frozen tissue. Our approach ensures more accuracy by using a larger mirna set, direct comparison between levels of expression of paired mirnas, and cross-validation. 5. Conclusions Knowing the specific mirna signature for each subtype provides the basis of a specific targeted therapy for the particular subtype by seeking the mirnas [27]. We provide mirna signatures that enable the distinction between the RCC subtypes, in addition to classifying between them accurately using a stepwise decision tree and pairwise binary classifiers. Author contributions: George M. Yousef had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: Youssef, White, Grigull, Yousef. Acquisition of data: Youssef, White, Samy. Analysis and interpretation of data: Youssef, White, Krizova, Mejia-Guerrero, Samy. Drafting of the manuscript: Youssef, Yousef. Critical revision of the manuscript for important intellectual content: Youssef, Grigull, Evans, Yousef. Statistical analysis: Grigull. Obtaining funding: Yousef. Administrative, technical, or material support: None. Supervision: White, Yousef. Other (specify): None. Financial disclosures: I certify that all conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript (eg, employment/affiliation, grants or funding, consultancies, honoraria, stock ownership or options, expert testimony, royalties, or patents filed, received, or pending), are the following: None. Funding/Support and role of the sponsor: This work was supported by grants from the Canadian Institute of Health Research (CIHR grant No ), Canadian Cancer Society (CCS grant No ), the Ministry of Research and Innovation, Government of Ontario, and the Kidney Foundation of Canada. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi: /j.eururo References [1] Meloni-Ehrig AM. Renal cancer: cytogenetic and molecular genetic aspects. Am J Med Genet 2002;115: [2] Ficarra V, Martignoni G, Galfano A, et al. Prognostic role of the histologic subtypes of renal cell carcinoma after slide revision. Eur Urol 2006;50: [3] Srigley JR, Delahunt B. Uncommon and recently described renal carcinomas. Mod Pathol 2009;22(Suppl 2):S2 23. [4] Arsanious A, Bjarnason GA, Yousef GM. From bench to bedside: current and future applications of molecular profiling in renal cell carcinoma. Mol Cancer 2009;8:20. [5] Young AN, Master VA, Amin MB. 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