M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8

ava i l ab le a t www.sc ienced i rec t . com

www.elsevier .com/locate /molonc

Development and validation of a microRNA-based

diagnostic assay for classification of renal cell carcinomas

Yael Spectora,1, Eddie Fridmanb,c,1, Shai Rosenwalda, Sofia Zilberd,Yajue Huange, Iris Barshackb,c, Orit Ziona, Heather Mitchellf, Mats Sandenf,Eti Meiria,*aRosetta Genomics Ltd., Rehovot, IsraelbSheba Medical Center, Tel-Hashomer, IsraelcSackler School of Medicine, Tel Aviv University, Tel Aviv, IsraeldRabin Medical Center, Petah Tikva, IsraeleTemple University Hospital, Philadelphia, PA, USAfRosetta Genomics Laboratories, Philadelphia, PA, USA

A R T I C L E I N F O

Article history:

Received 6 February 2013

Received in revised form

14 March 2013

Accepted 17 March 2013

Available online 26 March 2013

Keywords:

MicroRNA

Renal cell carcinoma

Molecular diagnostics

Kidney cancer

* Corresponding author.E-mail address: meirie@rosettagenomics.

1 Equal contribution.1574-7891/$ e see front matter ª 2013 Federhttp://dx.doi.org/10.1016/j.molonc.2013.03.00

A B S T R A C T

Renal cancers account for more than 3% of adult malignancies and cause more than 13,000

deaths per year in the US alone. The four most common types of kidney tumors include the

malignant renal cell carcinomas; clear cell, papillary, chromophobe and the benign onco-

cytoma. These histological subtypes vary in their clinical course and prognosis, and

different clinical strategies have been developed for their management. In some kidney tu-

mor cases it can be very difficult for the pathologist to distinguish between tumor types on

the basis of morphology and immunohistochemistry (IHC). In this publication we present

the development and validation of a microRNA-based assay for classifying primary kidney

tumors. The assay, which classifies the four main kidney tumor types, was developed

based on the expression of a set of 24 microRNAs. A validation set of 201 independent sam-

ples was classified using the assay and analyzed blindly. The assay produced results for

92% of the samples with an accuracy of 95%.

ª 2013 Federation of European Biochemical Societies.

Published by Elsevier B.V. All rights reserved.

1. Introduction (Lopez-Beltran et al., 2009), as well as 4 types of benign renal

Renal cell carcinomas (RCC) are a family of carcinomas that

arise from the epithelium of the renal tubules. Renal cancers

account for more than 3% of adult malignancies. The esti-

mated number of new cases for 2013 is w65,000 and the esti-

mated number of deaths per year is over 13,000 in the US

alone.

There are 11 subtypes of RCC according to the WHO classi-

fication of 2004 (Lopez-Beltran et al., 2006) and the 2009 update

com (E. Meiri).

ation of European Bioche2

tumors. The four most common types of kidney tumors are

Clear cell RCC (most common subtype), Papillary RCC, Chro-

mophobe RCC and Oncocytoma. These histological subtypes

vary in their clinical courses and in their prognosis, and

different clinical strategies have been developed for their

management.

The differential diagnosis between the different subtypes

of kidney tumors based on morphology can be challenging

and is subject to intra-observer variability (Ficarra et al.,

mical Societies. Published by Elsevier B.V. All rights reserved.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8 733

2006). Even when utilizing immunohistochemistry (IHC)

markers, the ability to differentiate between the different

subtypes can be challenging, especially in the setting of un-

commonmorphology and small biopsies. Allory et al. recently

described a subset of 12 antibodies as a basis for classification

of renal cell carcinomas. In this report AMACR, CK7, and CD10

were themost powerful classifierswith 78e87% of carcinomas

correctly classified (Allory et al., 2008).

Historically, the kidney cancers were characterized by a

lack of early warning signs, diverse clinical manifestations,

and resistance to radiation and chemotherapy. However

when the disease is diagnosed at earlier stages, prognosis is

often improved. Since conventional chemotherapy is not

highly effective, targeted treatments open up a new direction

in the treatment of RCC (Vasudev et al., 2012).

For the purpose of targeted therapy it is especially impor-

tant to classify the different subtypes of RCC. The histological

types arise from: different cells of origin in the kidney,

different constellations of genetic alterations (Lopez-Beltran

et al., 2008), and expression ormutation in different oncogenic

pathways. Therefore, different subtypes offer differentmolec-

ular candidates for targeted therapy, such as Tyrosine Kinase

Inhibitors, Sorafenib & Sunitinib, mTOR inhibitors, Everoli-

mus & Tersirolimus etc. There is growing evidence that vari-

ability in response rates may be linked to sub-classification

(Tazi el et al., 2011).

Therefore, new biomarkers are needed in order to improve

the identification and diagnosis of renal tumor subtypes. The

tissue-specificity of microRNA expression opens a window to

new types of molecular diagnostic assays. MicroRNAs are

small non-coding RNA molecules with an important role in

the regulation of gene expression (Calin and Croce, 2006;

Farazi et al., 2011; Lu et al., 2005), that have been established

as strong molecular biomarkers, especially defining tissue

origin, differentiation status of cells, and histological types

(Gilad et al., 2012; Barshack et al., 2010; Lebanony et al., 2009;

Rosenfeld et al., 2008; Bentwich, 2005). The clinical utility of

microRNA in kidney cancer classification and diagnosis was

explored by us (Fridman et al., 2010) and others (White and

Yousef, 2010) and it was shown that microRNAs have a great

potential in diagnosis and therapeutics of RCC. We have pre-

viously shown that microRNAs can differentiate between

the four main types of kidney tumors (Fridman et al., 2010).

Here we present the development and validation of a 24-

genemicroRNA-based assay (miRview� kidney, Rosetta Geno-

mics Inc. Philadelphia, PA) for classification of subtypes of pri-

mary kidney tumors.

2. Methods

2.1. Patients and samples

Formalin Fixed Paraffin Embedded (FFPE) samples of primary

kidney tumors were collected from several sources (Sheba

Medical Center, Tel-Hashomer, Israel; Beilinson Hospital,

Rabin Medical Center, Petah-Tikva, Israel; ABS Inc., Wilming-

ton, DE, USA; Temple University, Philadelphia, PA, USA; Bio-

Serve, Beltsville, MD, USA; Soroka University Medical Center,

Beer Sheva, Israel; ProteoGenex, Culver City, CA, USA). Institu-

tional review board approvals were obtained for all samples in

accordance with each institute’s guidelines. The patient’s

original diagnosis was one of the 4 subtypes (clear cell, papil-

lary, chromophobe and oncocytoma) and was set as a refer-

ence diagnosis for each sample. The diagnosis was based on

all available data at the time of patients’ diagnosis and IHC

and/or colloidal iron stains were performed for some of the

cases in the different institutions as decided by the patholo-

gists at the time of diagnosis. Prior to its inclusion in the study,

the specimens were reviewed again by pathologists from the

different institutes (E.F, S.Z and Y.H) according to morphology

and available IHC data and only samples for which the pathol-

ogist agreed with the reference diagnosis were included in the

study. 181 samples were used for training of the assay.

A blinded validation of the assay was performed on 201 in-

dependent samples. Sampleswere collected in thedifferent in-

stitutes as described above andwere sent to Rosetta Genomics

together with accompanied data. An additional pathologist, (a

medical director in Rosetta’s CLIA certified and CAP accredited

laboratory-M.S or TBE) reviewed theH&E slide of all validation

cases (except 2 cases for which H&E slides were not available)

together with IHC and colloidal iron data that accompanied 55

of the validation set samples. Only samples for which the sec-

ond pathologist agreed with the reference diagnosis were

included in the study. Micro-dissection was performed, on 6

samples, to reach >50% tumor cell content.

Samples were classified and analyzed using the

microRNA-based assay and the results were compared to the

reference diagnosis. Finally, a set of 38 samples was used for

inter-laboratory reproducibility testing.

2.2. RNA extraction

High-quality total RNA, including the well-preserved micro-

RNA fraction, was extracted from the FFPE samples using a

proprietary protocol, as previously described (Rosenfeld

et al., 2008; Nass et al., 2009).

2.3. Microarray platform

Expression levels of >700 known microRNAs (corresponding

to the Sanger miRBase, version 13), as well as >260 predicted

microRNAs sequences (MIDs) were measured using custom-

designed arrays from Agilent Technologies (Santa Clara, CA)

which harbor 8 identical sub-arrays each. RNA was labeled

and hybridized as previously described (Meiri et al., 2012). Ar-

rays are scanned using the Agilent DNA Microarray Scanner

Bundle and signals were analyzed as previously described

(Meiri et al., 2012).

2.4. 24-Gene microRNA-based kidney assay protocol

An assay that classifies kidney tumorswas developed based on

the expression of different microRNAs in the four renal tumor

types. Following extraction, seven RNA samples together with

a positive control (PC) undergo labeling and hybridization to

one array. The PC is an RNA sample that was set as a reference

and shouldmeet definedQA criteria: Pearson correlation to the

reference hybridization, median of differences from reference

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8734

and the number of the expressed microRNAs in the dynamic

range (expression above 300). QA for each sample is based on

severalparameters suchas thenumberofmicroRNAs in thedy-

namic range, the 98th percentile expression level of themicro-

RNA, the Pearson correlation between the hybridization spikes

and the reference, the expression of the negative control

probes, and the number of microRNAs with consistent tripli-

cate signals. The signal values of the 24 test microRNAs for

each sample are obtained following normalization, and used

as input to the test classifier. The assay uses a K-Nearest-

Neighbor (KNN) classifier with k ¼ 5, that searches for the 5

samples in the training database (181 samples used for assay

development) that are most similar to the tested sample. Sim-

ilarity between the tested sample and the samples in the

training database is defined by the pearson correlation coeffi-

cient over the expression of the 24 microRNAs. The result for

the tested sample is determined by the subtype that appears

most often among these 5 closest neighbors. Unless there are

four or five neighbors sharing a common subtype, the assay re-

sults in no classification and reports that the miR expression

patternof thesampledoesnotmatchanyof theexpressionpat-

terns in the panel closely enough and does not generate a

result. PPV calculation: Positive predictive value (PPV) was

calculated in two ways. First, as is widely done, it was calcu-

lated based on the validation set by taking the ratio of true pos-

itives (e.g. true calls of oncocytoma) to true positives plus false

positives (e.g. all cases the test gave oncocytoma as a result).

Second, since unlike sensitivity and specificity, the PPV is

very sensitive to the prevalence of the different classes, and

since the class distribution in the validation set does not accu-

rately reflect the distribution in the patient population ex-

pected to use the test, we also calculated the PPV of each

class by taking into account the determined sensitivity and

specificity values of the classes along with their relative preva-

lence in the patient population expected to use the test. More

precisely, let C be the counting matrix of the validation set re-

sults, where Cij is the number of cases that the test gave the

answer class j to a case with reference diagnosis of class i. We

define the normalized counting matrix, ~C, to take into account

the prevalence expected in the patient population of the test,

i.e., ~Cij ¼ Cij � Pi=Ni, where Pi is the expected prevalence of class

i and Ni is the number of cases in the validation set with refer-

ence diagnosis of class i. The PPV of class k is then defined as

PPVk ¼ ~Ckk=P

i

~Cik. Since the test is assumed to be used only

upon diagnosis (once per patient), we used incidence rates as

estimates for the prevalence of the classes in the test

population.

3. Results

This study describes the classification of the 4 most common

kidney tumor types (clear cell, papillary, chromophobe and

oncocytoma) based on microRNA expression. In the past we

have shown the ability to classify renal tumor types using six

microRNAs by two microRNA quantification platforms; micro-

array and qRT-PCR (Fridman et al., 2010). In this study we

demonstrate the robustness of using custom-designed Agilent

microarrays to develop a 24-gene microRNA-based assay, and

KNN algorithm that classifies kidney tumor subtypes.

3.1. Assay development

Identification of markers and training of the assay was done

on 181 samples of themain histological subtypes of RCC: clear

cell (51 samples), papillary (51 samples), chromophobe (40

samples) and oncocytoma (39 samples). After comparing the

expression of all microRNAs on the Agilent microarray plat-

form, 22 microRNAs were found to be differential between at

least one of: 1) oncocytoma and chromophobe vs clear cell

and papillary, 2) oncocytoma vs chromophobe, and 3) clear

cell vs papillary. In order to improve upon this list, other

microRNAs were added in a forward stepwise algorithm.

This resulted in the addition of three microRNAs (miR-146a-

5p, miR-192-5p, MID-00536) to the 22 differential microRNAs.

Each of these 25 microRNAs was tested to see whether its

removal improved or did not change classification within

the training set. Following this analysis, miR-125b-5p was

removed, resulting in the final list of 24 microRNAs that

were chosen as features in the KNN algorithm. Figure 1 shows

an unsupervised clustering of the 181 samples based on the

expression of the 24 microRNAs. As seen in the figure there

is a very distinct pattern of microRNAs differentiating the

groups of clear cell RCC and papillary RCC from the groups

of chromophobe RCC and oncocytoma. Specific microRNAs

can also distinguish between clear cell and papillary RCC

(miR-31-5p, miR-126-3p, miR-195-5p, miR-200a-3p, miR-

200b-3p, and miR-122-5p) and between chromophobe and

oncocytoma (miR-200a-3p, miR-200b-3p, miR-200c-3p, and

miR-141-3p). Not surprisingly, themicroRNAs that were found

to be differentially expressed in a previous cohort that we

studied (Fridman et al., 2010), and in another study (Youssef

et al., 2011) were also shown to be differential in this study.

Previously we demonstrated the ability to differentiate the 4

types using a binary tree with 2 microRNAs in each node,

differentiating first the more resembling groups (clear cell

and papillary from chromophobe and oncocytoma) and then

differentiating the 4 subtypes. Based on these initial results,

we developed an assay that utilizes a microarray platform

and can use the optimal number of microRNAs needed for

the classification. In the initial steps, microRNAs were chosen

based on their ability to separate between histological types in

terms of p-value, fold-change, and classification performance.

KNN was chosen as the classification algorithm, based on its

performance on the training set as evaluated by Leave-One-

Out Cross Validation.

3.2. Validation

A blinded independent validation set was used to study the

assay performance. The validation set included only samples

that met the Gold Standard criteria: over 50% tumor cell in the

sample, and concordance in diagnosis between two patholo-

gists. The validation set included a balanced number of sam-

ples from each type (see Table 1). Out of the 201 samples

used for validation, one sample failed QA due to insufficient

RNA and additional 16 samples completed processing but

did not result in classification, as explained in methods. For

184 samples (92%) the assay was able to produce results. 174

samples out of the 184 samples (95%) were classified accu-

rately. Figure 2 is a confusion matrix showing all validation

Figure 1 e MicroRNA expression in main renal tumor subtypes e unsupervised clustering of 181 samples used for the training of the assay using

the expression of the 24 assay microRNAs. Expression was measured using the Agilent microRNA microarray platform. The different tumor

subtypes are color coded and are marked on the top bar. The expression profile clearly shows the differential sub-classification to the 4 subtypes

tested.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8 735

results and Table 1 presents the overall performance of the

assay. Sensitivity and specificity are shown per tumor type

indicating the very high accuracy of the assay. Out of the 10

misclassified samples, 7 were due to confusion between chro-

mophobe RCC and oncocytoma (Figure 2).

3.3. Inter-laboratory reproducibility

For anassay to be robust it is important to showthat the results

can be reproduced in other laboratories. For this purpose a

Table 1e Validation performance per tumor typee the table shows,for each histological class, the total number of samples (“N”), andnumber of samples with results (in parentheses), the sensitivity, thespecificity and the positive predictive value (“PPV”).

N # Sensitivity % Specificity % PPV % a

Clear cell 55 (54) 98 99 98 (99.8)

Papillary 55 (51) 98 99 98 (92)

Chromophobe 48 (42) 93 97 89 (83)

Oncocytoma 42 (37) 86 98 91 (93)

a PPV was calculated using the validation set distribution as well

as using the estimated prevalence in the test population (in paren-

theses). See Materials and Methods for explanations of PPV

calculation.

cohort of 38 samples from RCC patients were studied; 20 of

the samples were divided to the two testing laboratories for

RNAextraction (extractionwas done in each lab fromdifferent

sections of the same tissue blocks, and the assay was per-

formed in the same laboratory that extracted the RNA), and

for an additional 18 samples, RNAwas extracted in one labora-

tory and the assay was performed in both laboratories using

that RNA. The first study measured the concordance of the

entireprocessof the24-genemicroRNA-basedassay (including

RNA extraction), and the second study measured the concor-

dance of the RNA labeling and array hybridization in the two

laboratories starting from the same 18 RNA samples. 37 out

of the 38 samples passed QA criteria of the assay in both labo-

ratories. One of the 20 samples extracted in both laboratories

failed QA in one of the sites. Two cases completed processing

but resulted in no classification (see Methods) in both labora-

tories. The remaining 35 samples got the same classification

in both laboratories (100% inter-laboratory concordance) and

in agreement with the reference diagnosis.

4. Discussion

In this study, a set of 24 differential microRNAs was identified

for the accurate classification of kidney tumors. This set was

Figure 2 e Validation confusion matrix e on the y-axis, the “reference diagnosis” represents the classification given by the two independent

pathologists and on the x-axis, the “classifier answer” is the classification given by the test. Values in the matrix represent the number of samples

with a specific “reference diagnosis” that were given a specific “classifier answer” by the assay.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8736

the basis for the development and validation of a standardized

diagnostic assay for the classification of renal cell tumors

from FFPE resections or biopsy samples. The validation results

showed 95% accuracy and demonstrated again the diagnostic

power of microRNAs.

The 4 most common subtypes of renal tumor are charac-

terized by different genetic alteration and different chromo-

somal material loss (Youssef et al., 2011; Bodmer et al., 2002;

Steiner and Sidransky, 1996), generating different protein

expression and histological markers (Cheng et al., 2009). How-

ever those characteristics cannot solely differentiate well be-

tween the subtypes. Since histological evaluation by regular

light microscopy cannot always distinguish between the

various renal tumor types, panels of dozens of biomarkers

were chosen for immunohistochemical staining (Shen et al.,

2012; Truong and Shen, 2011). For example clear cell RCC is

typically positive for vimentin, AE1/AE3 keratins, CD10, RCC

marker, and carbonic anhydrase IX (G250), and it has usually

diffused immune-reactivity for CK7, CD117, kidney specific

cadherin and parvalbumin. Kidney specific cadherin can

show a complex pattern of positive staining or negative stain-

ing in RCCs. Papillary RCC is often uniformly positive for

vimentin, AE1/AE3 keratins, CK7, AMACR, and RCC marker,

and it is usually negative for CD117, kidney-specific cadherin,

and parvalbumin. Chromophobe is positive for kidney-

specific cadherin, parvalbumin, CD117, epithelial membrane

antigen, AE1/AE3 keratin, and CK7, and it is usually negative

for vimentin, carbonic anhydrase IX, and AMACR. Oncocy-

toma shares a similar immunoprofile with chromophobe

RCC, but in some studies vimentin, S100A1, and CD82, were

shown to be helpful in some situations (Truong and Shen,

2011). Hale’s colloidal iron stain can be helpful in some situa-

tions since it is confluently positive for oncocytoma and

diffusely positive for chromophobe RCC.

As can be seen from the list above the similarity between

the IHC markers of clear cell RCC and papillary RCC makes

IHC a limited tool in differentiating the subtypes. This is

also true for differentiation between chromophobe and

oncocytoma, which is almost impossible in needle biopsy

(Blumenfeld et al., 2010). Moreover, the increasing use of

core needle biopsy in preoperative diagnosis limits the

amount of material available for a large panel of immuno-

histochemical markers to be tested.

Understanding the molecular mechanism underlying the

development of renal carcinomas allowed the development

of novel targeted therapies like sorafenib, bevacizumab

(with IFN-a), sunitinib, temsirolimus and more (Najjar and

Rini, 2012). In the past, most clinical trials included patients

with clear cell or predominantly clear cell histology, but

recently more data is accumulating pointing to different mo-

lecular pathophysiology of the different subtypes and

different responses to targeted therapies. Therefore the ability

to correctly classify the different renal tumor subtypes is of

great importance. Moreover although FNA can provide useful

information in RCC patients the biopsy has poor sensitivity

and specificity when assigning Fuhrman nuclear grade, and

physicians should be cautious when assigning grade or sarco-

matoid elements from biopsy data (Abel et al., 2010).

In light of the above, the use of a robust biomarker like

microRNA expression that can be assessed in a single mea-

surement using a very small amount of material is of great

appeal.

Our validation shows that an assay based on the expression

of 24 microRNAs can distinguish between all 4 common sub-

types very well, with an overall accuracy of 95%. 7 out of the

10 misclassifications were confusion between chromophobe

and oncocytoma (see Figure 2), which is the most difficult dif-

ferential diagnosis. Nevertheless the sensitivity for those

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 7 3 2e7 3 8 737

classes using the 24-gene microRNA-based assay is high (93%

and 86% respectively). Inter-laboratory concordance of 100%

demonstrates the robustness of the assay. The study uses the

classical morphology and IHC diagnosis as a gold standard,

despite its limitation, but efforts were made to have an accu-

rate diagnosis by validation by a second pathologist.

Although using microRNA as biomarkers can be unrelated

to the downstream biological changes caused by their

differential expression or the upstream changes that resulted

with differential expression of microRNAs, we looked at the

differential microRNAs to see if we can find biological rele-

vance to renal carcinoma. MiR-126-3p is known to be associ-

ated with endothelial cells, and regulated angiogenesis

signaling (Wang et al., 2008). The knockdownof thismicroRNA

is associatedwith vascular integrity loss. In our assaymiR-126-

3p was found to be lower in papillary RCC, which is relatively

hypovascular in comparison to clear cell RCC (Vikram et al.,

2009).

The assay also detected the family of miR-200 and miR-

141-3p that are known to be associated with Epithelial Mesen-

chymal transition (EMT) (Mongroo and Rustgi, 2010; Korpal

and Kang, 2008) and are characterized as expressed in epithe-

lial cells. This family of microRNAs has higher expression in

the chromophobe subtype of RCC, especially miR-200c-3p

and miR-141-3p of this family are higher specifically in this

subtype and have very low expression in the clear cell RCC

and papillary RCC. Both of those microRNAs are located in

the same cluster on chromosome 12 and their transcription

is suppressed in several cancers resulting in repression of E-

cadherin and leading to EMT. The down-regulation of miR-

141-3p and miR-200c-3p in clear cell RCC was proposed to be

involved in suppression of CDH1/E-cadherin transcription

via up-regulation of ZFHX1B (Nakada et al., 2008). RCC is

known to have EMTmarkers that are expressed in correlation

to prognosis and resistance to therapy (Harada et al., 2012).

Therefore the low expression in the more aggressive subtypes

of RCC is in line with previous data.

Mir-122-5p, which is highly expressed in liver tissue and is

rarely expressed in other tissues (Landgraf et al., 2007), was

found here to be expressed (although very low expression

relative to liver tissue) specifically in the clear cell subtype

of RCC, as was already shown previously (Zhou et al., 2010),

but its role in RCC is not yet understood. MiR-21-5p that is

known to be a widespread marker of cancer and bad prog-

nosis is also higher in clear cell and papillary subtypes

compared to chromophobe and the benign oncocytoma.

This differential expression is again within the biological

rationale since this microRNA is known to be a prognostic

marker (Faragalla et al., 2012; Fu et al., 2011) and clear cell

RCC and papillary RCC are both the RCC types which are

considered more aggressive and therefore have the worst

prognosis (Cheville et al., 2003). The same is shown for

miR-210 that also has higher expression in clear cell and

papillary subtypes and is yet another microRNA that is asso-

ciated with several cancers, known to be induced by hypoxia

(Devlin et al., 2011; Huang et al., 2010), and also serves as a

powerful prognostic marker in breast cancer (Camps et al.,

2008) and other cancers.

Therefore the identification of the 24 microRNA bio-

markers seems not to be a random occurrence, but to have

biological relevance to RCC in general and to the different sub-

types in particular.

In summary, the 24-gene microRNA-based assay,

measured on a microarray platform, can accurately differen-

tiate between the four main types of primary kidney tumors.

This assay can serve as a reliable diagnostic tool to aid physi-

cians with the growing unmet need for kidney tumor

classification.

Acknowledgments

We thank Dr. Tina Bocker Edmonston for her help in perform-

ing pathological review of validation samples.

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