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Research Article

Solid Tumors of ChildhoodDisplay Specific SerummicroRNA ProfilesMatthew J. Murray1,2,3, Katie L. Raby2, Harpreet K. Saini4, Shivani Bailey1,2, Sophie V.Wool1,Jane M. Tunnacliffe1, Anton J. Enright4, James C. Nicholson1, and Nicholas Coleman2,5

Abstract

Background: Serum biomarkers for diagnosis and risk strati-fication of childhood solid tumors would improve the accuracy/timeliness of diagnosis and reduce the need for invasive biopsies.We hypothesized that differential expression and/or release ofmicroRNAs (miRNAs) by such tumors may be detected as alteredserum miRNA profiles.

Methods: We undertook global quantitative reverse transcrip-tion PCR (qRT-PCR) miRNA profiling (n¼ 741) on RNA from 53serum samples, representing 33 diagnostic cases of commonchildhood cancers plus 20 controls. Technical confirmation wasperformed in a subset of 21 cases, plus four independent samples.

Results:We incorporated robust quality control steps for RNAextraction, qRT-PCR efficiency and hemolysis quantification. Weevaluated multiple methods to normalize global profiling dataand identified the 'global mean' approach as optimal. We gener-ated a panel of six miRNAs that were most stable in pediatricserum samples and therefore most suitable for normalization of

targeted miRNA qRT-PCR data. Tumor-specific serum miRNAprofiles were identified for each tumor type and selected miRNAsunderwent confirmatory testing.We identified a panel ofmiRNAs(miR-124-3p/miR-9-3p/miR-218-5p/miR-490-5p/miR-1538) ofpotential importance in the clinical management of neuroblas-toma, as they were consistently highly overexpressed in MYCN-amplified high-risk cases (MYCN-NB). We also derived candidatemiRNA panels for noninvasive differential diagnosis of a livermass (hepatoblastoma vs. combinedMYCN-NB/NB), an abdom-inal mass (Wilms tumor vs. combined MYCN-NB/NB), andsarcoma subtypes.

Conclusions: This study describes a pipeline for robust diag-nostic serummiRNAprofiling in childhood solid tumors, and hasidentified candidate miRNA profiles for prospective testing.

Impact: We propose a new noninvasive method with thepotential to diagnose childhood solid tumors. Cancer EpidemiolBiomarkers Prev; 24(2); 350–60. �2014 AACR.

IntroductionCurrent challenges in the clinical management of pediatric

solid tumors include a requirement for tissue biopsy at initialdiagnosis and difficulties in detecting minimal residual disease.The identification of novel body fluid tumor markers that mayallow noninvasive diagnosis, risk stratification, or follow-up insolid tumors of childhood would be an important advance. Onepromising approach is measurement of serum microRNA(miRNA) levels.

miRNAs are short, non–protein-coding RNAs that posttran-scriptionally regulate gene expression (1). Importantly, globalmiRNAprofiles have been shown to classify human cancer tissues,including pediatric solid tumors (1, 2). In pediatric malignantgerm cell tumors (GCT), for example, the miR-371–373 andmiR-302/367 clusters are overexpressed in all cases, regardlessof patient age, histologic subtype, or anatomic site (1), thusrepresenting universal biomarkers. Packaging of specific miR-NAs in membrane-bound exosome particles by tumor cellsbefore release into the bloodstream allows for their detectionin the serum (3). Indeed, we showed that miRNAs from themiR-371–373 and miR-302/367 clusters are detectable at highlevels in the serum of patients at the time of malignant GCTdiagnosis, and that levels fall with treatment (3–5), findingsrecently replicated by others (6–9). Importantly, these profilescan detect disease at low tumor volumes, demonstrating thesensitivity of this approach (5). miRNAs have many qualitiesthat make them suitable tumor markers for translation intoclinical practice, including inherent stability and resistance todegradation, even if the samples are left at room temperature orsubjected to multiple freeze-thaw cycles (10). We thereforehypothesized that other childhood tumors would be charac-terized by the release of specific miRNAs into the bloodstream,which would be detectable as an altered serum profile com-pared with control serum samples.

Here, we report thefindings of a proof-of-principle study testingthis hypothesis at the time of diagnosis of common childhoodcancers, using a quantitative reverse transcription PCR (qRT-PCR)

1Department of Paediatric Haematology and Oncology, Adden-brooke's Hospital, Hills Road, Cambridge, United Kingdom. 2Depart-ment of Pathology, University of Cambridge,Tennis Court Road, Cam-bridge, United Kingdom. 3University of Cambridge Department ofPaediatrics, Addenbrooke's Hospital, Cambridge, United Kingdom.4European Molecular Biology Laboratory European BioinformaticsInstitute (EMBL-EBI), Hinxton, Cambridge, United Kingdom. 5Depart-ment of Histopathology, Addenbrooke's Hospital, Hills Road, Cam-bridge, United Kingdom.

Note: Supplementary data for this article are available at Cancer Epidemiology,Biomarkers & Prevention Online (http://cebp.aacrjournals.org/).

Corresponding Authors: Matthew Murray and Nicholas Coleman, Departmentof Pathology, University of Cambridge, Tennis Court Road, Cambridge CB2 1QP,United Kingdom. Phone: 4401-2237-65066; Fax: 4401-2233-33346; E-mail:[email protected], [email protected]

doi: 10.1158/1055-9965.EPI-14-0669

�2014 American Association for Cancer Research.

CancerEpidemiology,Biomarkers& Prevention

Cancer Epidemiol Biomarkers Prev; 24(2) February 2015350

on February 11, 2020. © 2015 American Association for Cancer Research. cebp.aacrjournals.org Downloaded from

Published OnlineFirst November 21, 2014; DOI: 10.1158/1055-9965.EPI-14-0669

http://cebp.aacrjournals.org/

approach. We addressed three important questions: (i) whether itwas feasible to extract RNA from the serumof children in sufficientamounts to undertake globalmiRNAprofiling; (ii) whether serumhousekeeping miRNAs in children would be similar to those

described for adults; and (iii) whether lists of candidate biomar-kers could be generated for validation in larger, prospective stud-ies. We identify serum miRNA profiles specific for each of the 11types of pediatric solid tumor investigated. In particular, we report

Table 1. Clinico-pathological characteristics of the patients in the tumor and control groups

Sample Cancer type Code Gender Age, mo Additional comments Confirmatory study

1 MYCN-amplified HR neuroblastoma MYCN-NB-1 M 98 Undifferentiated �2 MYCN-amplified HR neuroblastoma MYCN-NB-2 M 6 Undifferentiated �3 LR neuroblastoma NB-1 M 67 Ganglioneuroblastoma �4 LR neuroblastoma NB-2 F 21 Differentiating �5 Hepatoblastoma HB-1 M 14 Mixed fetal and embryonal type histology �6 Hepatoblastoma HB-2 M 85 Mixed fetal and embryonal type histology �7 Hepatoblastoma HB-3 F 25 Fetal type histology8 Hepatoblastoma HB-4 F 14 Fetal type histology �9 Wilms tumor WT-1 F 54 Biopsy—anaplastic histology �10 Wilms tumor WT-2 M 22 Biopsy—stromal type histology; WAGR �11 Wilms tumor WT-3 F 12 Biopsy—mixed type histology12 Wilms tumor WT-4 F 25 Biopsy—triphasic histology. Failed QC.13 Wilms tumor WT-5 M 41 Bilateral tumors14 Wilms tumor WT-6 F 16 Biopsy—stromal type histology15 Wilms tumor WT-7 F 97 Biopsy—triphasic histology16 Rhabdomyosarcoma RMS-1 M 121 Abdominal �17 Rhabdomyosarcoma RMS-2 M 52 Pancreatic site; jaundiced; embryonal type18 Rhabdomyosarcoma RMS-3 F 104 Nasopharyngeal site; embryonal type �19 Ewings sarcoma ES-1 M 152 Bone and soft tissue of cervical vertebrae20 Ewings sarcoma ES-2 M 34 Soft tissue of lumbo-sacral region21 Osteosarcoma OS-1 F 103 Previous RMS/Li Fraumeni syndrome22 B-cell non-Hodgkin lymphoma NHL-1 M 176 Burkitt lymphoma �23 B-cell non-Hodgkin lymphoma NHL-2 M 110 Burkitt lymphoma �24 Hodgkin disease HD-1 F 186 Classical, nodular sclerosing subtype25 Hodgkin disease HD-2 M 156 Classical, nodular sclerosing subtype �26 Hodgkin disease HD-3 M 176 Classical, nodular sclerosing subtype �27 Hodgkin disease HD-4 F 159 Classical, nodular sclerosing subtype28 Hodgkin disease HD-5 F 172 Classical, nodular sclerosing subtype29 Pleuropulmonary blastoma PPB-1 F 133 Germline DICER1 mutation30 Central nervous system glioma GL-1 M 53 Pilocytic astrocytoma WHO 2007 grade 1 �31 Central nervous system glioma GL-2 F 68 Pilocytic astrocytoma WHO 2007 grade 132 Central nervous system glioma GL-3 M 172 Gliomatosis cerebri, WHO 2007 grade 333 Central nervous system glioma GL-4 F 17 Oligodendroglioma WHO 2007 grade 234 Central nervous system glioma GL-5 M 59 Pilocytic astrocytoma WHO 2007 grade 1 �35 MYCN-amplified HR neuroblastoma MYCN-NB-3 F 17 Undifferentiated; abdominal �36 MYCN-amplified HR neuroblastoma MYCN-NB-4 M 32 Undifferentiated; abdominal �37 LR neuroblastoma NB-3 M 5 Poorly differentiated �38 LR neuroblastoma NB-4 M 3 Poorly differentiated �39 Control C-1 F 38 N/A40 Control C-2 M 159 N/A41 Control C-3 F 34 N/A �42 Control C-4 F 142 N/A �43 Control C-5 F 129 N/A44 Control C-6 M 23 N/A45 Control C-7 F 21 N/A46 Control C-8 M 57 N/A47 Control C-9 M 34 N/A48 Control C-10 F 38 N/A �49 Control C-11 M 134 N/A50 Control C-12 M 48 N/A51 Control C-13 M 74 N/A52 Control C-14 F 140 N/A53 Control C-15 M 181 N/A54 Control C-16 M 29 N/A55 Control C-17 M 26 N/A56 Control C-18 F 50 N/A57 Control C-19 M 36 N/A �58 Control C-20 F 86 N/A

NOTE: Samples 3 to 38 were independent samples used for the technical confirmation study only.Abbreviations: MYCN-NB, MYCN-amplified high-risk (HR) neuroblastoma; NB, non–MYCN-amplified low-risk (LR) neuroblastoma; HB, hepatoblastoma; WT, Wilmstumor; RMS, rhabdomyosarcoma; ES, Ewing sarcoma; OS, osteosarcoma; B-NHL, B-cell non-Hodgkin lymphoma; HD, Hodgkin disease; PPB, pleuropulmonaryblastoma; GL, central nervous system glioma; C, control samples; WAGR, Wilms tumor, aniridia, genitourinary abnormalities, and retardation syndrome.

Serum microRNAs in Childhood Tumors

www.aacrjournals.org Cancer Epidemiol Biomarkers Prev; 24(2) February 2015 351




Mean of four housekeeping miRNAs in QC and full qRT-PCR run

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a panel of serum miRNAs that segregate MYCN-amplified Inter-national Neuroblastoma Risk Group (INRG; ref. 11) high-riskneuroblastoma from non–MYCN-amplified INRG low-risk neu-roblastoma and other tumors.

Materials and MethodsThe study received approval from the Multicenter Research

Ethics Committee (reference 02/4/71) and Local Research EthicsCommittee (reference 01/128) and was performed with fullinformedparental consent. All experimental stepswere compliantwith the Minimum Information for Publication of QuantitativeReal-time PCR Experiments (12).

Patient demographics and tumor types analyzedFor themaindiscovery-phaseof theproject,we initially recruited

34 patients ages 0 to 16 years at the time of malignant tumordiagnosis (between April 2010 and June 2013), together with afurther 20 age- and gender-matched anonymized samples from acontrol group of patients without malignant disease. Clinicopath-ologic details are listed in Table 1. The 34 tumors were from 11different tumor types. They comprised (i) four neuroblastomas[two MYCN-amplified INRG high-risk tumors (MYCN-NB); twonon-MYCN-amplified INRG low-risk tumors (NB)]; (ii) four hepa-toblastomas (HB); (iii) seven Wilms tumors (WT); (iv) sevenlymphomas [five cases of classical nodular-sclerosing Hodgkin'sdisease (HD) and two cases of B-cell non-Hodgkin lymphoma (B-NHL)]; (v) six sarcomas [three rhabdomyosarcoma (RMS), twoEwing sarcoma (ES), one osteosarcoma (OS)]; (vi) one DICER1-mutated pleuropulmonary blastoma (PPB; ref. 13), and (vii) fivecentral nervous system tumors (gliomas; threeWHO 2007 grade I,one grade II, and one grade III) (Table 1).

For the technical confirmation phase of the study, we used 25samples. These comprised 17 representative tumor and fourcontrol serum samples from the discovery-phase test set (Table1), plus four independent neuroblastoma serum samples fromtwo MYCN-NB and two NB cases. In total, the confirmatory setcomprised four MYCN-NB samples, four NB, three HB, two WT,two RMS, two B-NHL, two HD, two gliomas, and four controlsamples (Table 1). There was no difference in tumor volumebetween theMYCN-NB andNB cases.While all four of theMYCN-NB cases were undifferentiated, the grade of the NB cases variedfrom poorly differentiated to differentiating and ganglioneuro-blastoma (Table 1).

Data normalizationSamples were obtained and processed as described (3).

miRNAs were quantified as described (13) using miRNAReady-to-Use PCR Human Panels I and II (Exiqon). We per-formed extensive quality control steps to measure hemolysisand the efficiencies of RNA extraction, reverse transcription andqRT-PCR. A detailed account of this assay pipeline is available

in the Supplementary Materials and Methods. Only a singlesample (WT-4) failed initial quality control; 53 samples (98%)therefore underwent full qRT-PCR profiling. In initial work, wecompared a number of different data normalization methods(14) to define the optimal approach to our global serummiRNA profiling study. We assessed:

i the global mean method, which used the average Ct value ofmiRNAs expressed in at least one of the 53 samples analyzed,as described (14);

ii the modified global mean method, which did not take intoaccount other samples in the dataset and instead used asample-specific normalization factor plotted on a linear scale(14);

iii themodified global meanmethod of commonmiRNAs (14),i.e., those miRNAs expressed in all 53 samples analyzed;

iv the 10 top-ranking miRNAs identified as most stablyexpressed in the study by geNorm (15);

v the 10 top-ranking miRNAs identified as most stablyexpressed in the study by NormFinder (16);

vi the six top-ranking overlapping housekeeping miRNAs iden-tified in both (iv) and (v) above;

vii the four housekeepingmiRNAs used for initial quality controlpurposes (hkgsQC), namely miR-23a-3p, miR-30c-5p, miR-103a-3p, and miR-191-5p;

viii the two small nucleolar RNAs (snoRNA), RNU38B andRNU49A, present on the Exiqon platform;

ix the single small nuclear RNA (snRNA) RNU6, present on theplatform. Both snoRNAs and snRNAs are commonly used formiRNA normalization purposes in tissue samples (14).

Statistical analyses for discovery-phase serum miRNAquantification

Following normalization, we removedmiRPlus sequences andmiRNAs listed as obsolete according to miRBase (www.mirbase.org/). miRNA levels were then quantified using the delta Ct meth-od, with fold change ¼ 2(Tumor-C

t� mean "other tumor" samples-C

t)

and 2(Tumor-Ct�mean control samples-C

t). miRNAs that had a�2.0 fold

change in expression in the tumor type under considerationcompared with the mean expression value from (i) the controlgroup and (ii) the "other tumor" group (comprising all othertumor samples except those under consideration) were calledas overexpressed and ranked according to fold change, as describ-ed previously (13).

Additional analyses were also performed to increase the strin-gency of our findings further. First, it was necessary for amiRNA tobe detected at least 2 Ct values lower in the test group comparedwith the 'no template control' (NTC) sample to be included in thesubsequent data analysis. Second, a coefficient of variation (CV)was calculated for each overexpressed miRNA, using the formulaCV ¼ SD for that miRNA/n, where n was the mean expressionvalue of all miRNAs within that tumor type. This step identified

Figure 1.Discovery-phase global qRT-PCRprofiling. A, comparison of serumhousekeepingmiRNAexpression (miR-23a-3p,miR-30c-5p,miR-103a-3p, andmiR-191-5p) in theinitial quality control and full discovery-phase qRT-PCR analyses. QC, quality control; HK, housekeeping. For tumor abbreviations please refer to Table 1. B,assessment of normalization approaches for global miRNA profiling in serum samples from pediatric cancer patients and controls using a cumulative distributiongraph. C, heatmap showing overall expression levels of selected serum miRNAs identified by global qRT-PCR profiling. NOTE: selected serum miRNAsignature for panel 1 ¼ MYCN-NB vs. NB; 2 ¼ HB vs. MYCN-NB/NB; 3 ¼ WT; 4 ¼ MYCN-NB/NB vs. WT; 5/6/7 ¼ RMS, ES, and OS, respectively, vs. theother two sarcoma subtypes; 8 ¼ HD vs. B-NHL; 9 ¼ PPB; and 10 ¼ glioma. Italicized miRNAs were those used for the technical confirmation study. Red, miRNAoverexpression; blue, miRNA underexpression.






Differen�al diagnosis plot; MYCN-NB vs. NB; miR-124-3p

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the level of variation in miRNA expression within the specifictumor type and between the control and 'other tumor' group.Third, the Robust Rank Aggregate (RRA) method was employed,as described (ref. 17; Supplementary Materials and Methods).Adjusted RRA scores of P < 0.01 were considered significant.Importantly, the RRA method has been used by others in meta-analysis of cancer datasets (18). Differences in serum miRNAexpression levels between experimental groups were assessedusing a two-tailed t test (P < 0.05 significant).

Serum miRNA qRT-PCR for technical confirmationTo maximise sensitivity in the technical confirmation study,

cDNA was diluted 1:7.5, rather than the standard 1:50 dilution.For this targeted work, following quality control analysis (Sup-plementary Materials and Methods), data were normalized usingthe six top-ranking overlapping housekeeping miRNAs identifiedin the overlap between the geNorm and NormFinder methods inthe full discovery-phase qRT-PCR profiling study. Differences inserum miRNA expression levels between experimental groupswere assessed using a two-tailed t test (P < 0.05 significant).

ResultsQuality control and normalization

For full details of the quality control steps and the assay pipelinedeveloped, see Supplementary Results and Supplementary Figs.S1–S5. In the discovery-phase global qRT-PCR profiling study,expression of the four housekeepingmiRNAs (19) and their meanrawCt values for all sampleswere very similar to the levelsobtainedin our initial quality control qRT-PCR (Fig. 1A), and were con-firmed by linear regression analysis (P < 0.0001). The global meanmethod, using the averageCt value ofmiRNAs expressed in at leastone of the 53 samples analyzed (n ¼ 568), was the optimalnormalization approach (Fig. 1B) and was therefore used in theglobal profiling study. No additional benefit was identified byusing either the modified global mean method or the modifiedglobal mean method of common miRNAs (Fig. 1B). Use of thesingle snRNA RNU6 (U6), or the two snoRNAs RNU38B andRNU49A, while often used for normalizing microRNA expressionin tissue samples (14), were not appropriate methods for normal-izing serumdata, as they resulted in increased technical variationofthe data compared with other normalization methods (Fig. 1B).Assessment of the 10 top-ranking miRNAs using the geNorm andNormFinder algorithms showed only marginal inferiority to theglobal mean method as a normalization approach.

Normalization using the six top-ranking housekeeping miR-NAs common to the top-10 lists generated usingNormFinder andgeNorm [namely miR-140-3p (chromosomal locus 16q22.1),miR-30b-5p (8q24.22), miR-26a-5p (3p22.2), miR-15b-5p(3q25.33),miR-30c-5p (6q13), andmiR-191-5p (3p21.31); Sup-plementary Fig. S6] also performed well in our analysis (Fig. 1B).Consequently, these six miRNAs were used for normalizationpurposes for the technical confirmation phase of the study. Five ofthese miRNAs are also abundant and stably expressed in serum/

plasma in adult patients (19). The four housekeeping miRNAsused in the initial QC checks (hkgsQC) also performed well fornormalizationpurposes in the full discovery-phaseprofiling study(Fig. 1B), indicating their suitability for screening serum samples.

Serum miRNAs in different tumor typesNo consistent profile of miRNA overexpression common to all

tumor groups was identified when compared with the controlgroup. However, for each of the 11 tumor types studied, weidentified miRNAs that were overexpressed compared with boththe cohort of other childhood tumors and the control group. Thenumber of such miRNAs ranged from 22 to 49 (mean 34),depending on tumor type (Supplementary Tables S1 and S2).The RRA method refined this to 16 to 26 (mean 21) serummiRNAs in each tumor group (Supplementary Table S1). As theMYCN-NB group had the largest coefficient of variance for thedelta Ct (miR-23a-3p-miR-451a) hemolysis levels (Supplemen-tary Fig. S1E), we only includedmiRNAs that were�2-fold greaterthan the 'other tumor' group and control group in both of theMYCN-NB samples being interrogated. This reduced the numberof miRNAs called as overexpressed in MYCN-NB from 96 to 35(Supplementary Table S1), avoiding identification of serummicroRNAs that were released from red blood cells rather thanbeing disease associated.

The lists of overexpressed serummiRNAs for each tumor groupare shown in Supplementary Tables S3 to S13. Selected findingsfor the individual tumor groups were used to generate an expres-sion heatmap (Fig. 1C). miRNAs for the heatmap were chosenbased on their overall abundance and potential value in differ-ential diagnosis. For two miRNAs (miR-122 and miR-877), the-5p strands were selected rather than the -3p strands identified inthe global profiling study, due to the greater abundance of the -5pstrands (20) (Supplementary Fig. S7). Ten miRNAs from theheatmap were chosen for subsequent validation in the technicalconfirmation study (italics in Fig. 1C; Supplementary Table S2),along with the six top-ranking housekeeping miRNAs that over-lapped in the NormFinder and geNorm lists. In the technicalconfirmation study, the 1:7.5 cDNA dilution increased sensitivityand did not inhibit the PCR reaction. As expected, the 16miRNAswere detected at approximately 3 Ct values lower in the 1:7.5dilutions compared with the 1:50 dilutions (Supplementary Fig.S8). The suitability of all 25 samples for the technical confirma-tion study was verified by assessment of triplicate UniSp6 values(Supplementary Fig. S9A). Expression levels of the six housekeep-ingmiRNAs were assessed in technical triplicate and showed highconsistency (Supplementary Fig. S9B).

Taken together, our data strongly suggest important clinicalapplications based on serum miRNA quantification. The mostpromising are described in the following sections.

MYCN-amplified high-risk neuroblastoma versus non–MYCN-amplified low-risk neuroblastoma

Treatment schedules for neuroblastoma rely on distinguishingtumors by the presence or absence of INRG high-risk molecular

Figure 2.SerummiRNAexpression inMYCN-NB.A, rawCt values (y-axis) offivehighly overexpressedmiRNAs inMYCN-NB (miR-124-3p/miR-9-3p/miR-218-5p/miR-490-5p/miR-1538) relative to other tumors and controls in the discovery-phase test set. B, mean normalized relative expression values for the miRNAs listed inA above, in MYCN-NB samples versus 'other tumor' and control samples, in the technical confirmation qRT-PCR study. C, mean normalized relative expressionvalues for miR-124-3p and miR-9-3p in MYCN-NB samples versus NB samples, in the technical confirmation qRT-PCR study. In addition, an equally weightedoverall mean-adjusted relative expression value for the specific five miRNA panel was calculated. Error bars, SEM. For tumor abbreviations, see Table 1.






abnormalities, such as MYCN amplification (11). Interestingly,our most striking findings were found in the MYCN-NB group.The five overexpressed serum miRNAs (miR-124-3p/miR-9-3p/miR-218-5p/miR-490-5p/miR-1538) that were top-ranking com-pared with both the 'other tumor' group (including NB samples)and the controls (Supplementary Table S3) are shown in theheatmap (Fig. 1C, panel 1). Levels of these five miRNAs inindividual tumor types in the discovery-phase test set arehighlighted in boxplot analysis (Fig. 2A). Significant overexpres-sion of all fivemiRNAs in theMYCN-NB samples versus the 'othertumor' group was shown in the subsequent confirmatory qRT-PCRexperiments, performed in technical triplicate (P<0.05 for allcomparisons; Fig. 2B). In a focussed 'differential diagnosis' anal-ysis of the MYCN-NB versus the NB group in the confirmatorystudy, there was a significant difference in expression levels formiR-124-3p and miR-9-3p individually (P < 0.05; Fig. 2C). Inaddition, in the technical confirmation set, the panel of all fivemiRNAs successfully distinguished MYCN-NB from NB samples(P ¼ 0.031; Fig. 2C).

Hepatoblastoma versus all neuroblastomas (MYCN-NB/NB)In children presenting with a liver tumor/enlargement, it

may be important to distinguish a primary lesion, such as HB,from involvement by a tumor from elsewhere, for example,neuroblastoma. For HB, of the 49 overexpressed serum miR-NAs, miR-483-3p, miR-122-3p, and miR-205-5p were rankedin the top 10 by fold change (Supplementary Table S5), withsimilar fold changes versus the controls and 'other tumor'group. Because of its greater abundance (Supplementary Fig.S7; ref. 20), miR-122-5p, rather than miR-122-3p, was selectedfor confirmatory testing along with miR-483-3p and miR-205-5p (Fig. 1C, panel 2). The findings for these three miRNAs inindividual tumor types in the discovery-phase test set arehighlighted by boxplot analysis (Fig. 3A). Significant overex-pression of each of the three miRNAs in the HB group wasverified in the technical confirmation study, versus both the'other tumor' group (which included all eight MYCN-NB/NBsamples, two of which showed liver involvement) and thecontrol group (P < 0.05 for all comparisons; Fig. 3B). For theneuroblastomas, there was no association between liverinvolvement and greater abundance of the HB-associated miR-NAs. It was however noted that levels of the liver-specific miR-122-5p (21) were occasionally increased in non-HB samples,for example, in a case of pancreatic RMS presenting withobstructive jaundice (RMS-2; Fig. 3B). Accordingly, miR-122-5p needed to be a part of a larger panel to ensure sufficientspecificity for HB. In focussed 'differential diagnosis' plots forthe HB group versus the combined MYCN-NB/NB group, allthree miRNAs were individually significantly elevated in HB (P< 0.05 for all comparisons; Fig. 3C). Furthermore, the threemiRNA panel also distinguished HB from MYCN-NB/NB (P ¼0.0001; Fig. 3C).

Wilms tumor versus all neuroblastomas (MYCN-NB/NB)Because of their anatomical proximity, it may be clinically and

radiologically difficult to distinguish WT from neuroblastoma;indeed, someneuroblastomasmaybe intrarenal (22, 23). Becauseof the different management and prognosis of these two tumortypes, it is important to differentiate themdiagnostically (23).Wescreened our profiling data from the discovery-phase test set for

miRNAs that might be informative in this differential diagnosissetting. We identified miR-129-5p (overexpressed in both theMYCN-NB and NB lists; Supplementary Tables S3 and S4, respec-tively) and miR-143-3p (10.7 greater fold change in levels in WTcompared with the combined MYCN-NB/NB samples; data notshown) for subsequent testing (Fig. 1C, panel 4 and Fig. 4A). Thetechnical confirmation study established that miR-143-3p wasincreased in WT compared with the 'other tumor' group (P ¼0.003), with miR-129-5p being overexpressed in the majority ofMYCN-NB/NB cases (Fig. 4B). In 'differential diagnosis' plots,miR-143-3p distinguishedWT fromMYCN-NB/NB (P¼ 0.0005),with miR-129-5p being overexpressed in six of eight MYCN-NB/NB cases versus WT (Fig. 4C).

Sarcoma differential diagnosisSarcomas may present with bone lesions (ES/OS), soft tissue

masses (RMS/ES), or both (ES). Consequently, discriminatingthese tumors is important, but may be challenging. Serum miR-NAs that were overexpressed for RMS, ES, and OS are listed inSupplementary Tables S7 to S9, respectively. Overall levels ofrepresentative miRNAs specific for RMS, ES, and OS versus theother sarcoma subtypes are illustrated in Fig. 1C, (panels 5–7,respectively). In particular, overall analysis (Fig. 5A) and 'differ-ential diagnosis' plots of the global profiling data (i.e., discovery-phase test set; Fig. 5B) showed thatmiR-214-3p,miR-214-5p, andmiR-92b-3p individually segregated ES from RMS/OS (P < 0.05for all comparisons), as did the three miRNA panel (P ¼0.0098; Fig. 5B). For OS, miR-500a-5p, miR-512-5p, and miR-519a-3p showed much higher serum expression than othertumors, including ES (Supplementary Fig. S10).

DiscussionWe report a robust, quality controlled pipeline suitable for

quantifying serum miRNA levels in pediatric patient cohorts(and potentially adult samples), including the assessment ofRNA extraction, degree of sample hemolysis and qRT-PCRefficiency. The described approach minimizes technical altera-tions and maximizes true biologic variation between samples,to allow identification of lists of overexpressed serum miRNAsbetween study groups, as exemplified here for common solidtumors of childhood. We assessed multiple normalizationapproaches and identified that for high-throughput globalserum miRNA qRT-PCR data, the 'global mean' method (14)was optimal. In addition, we generated a panel of six house-keeping miRNAs that were most stable in pediatric serumsamples and therefore suitable for normalizing qRT-PCR datain more targeted low-throughput studies. Interestingly, the sixmiRNAs showed substantial overlap with findings from adultsamples (19).

The most striking tumor findings were for MYCN-amplifiedhigh-risk neuroblastoma (MYCN-NB). The blood-based miRNApanel identified here has potential clinical value, due to thecurrent requirement for surgical biopsy to confirm the diagnosisof neuroblastoma and test for INRG high-risk genomic changes,such asMYCN amplification (11). Very recently, qRT-PCR detec-tion of neuroblastoma-specific mRNAs in peripheral blood fromchildren at diagnosis of advanced stage neuroblastoma has beenreported, with high levels of TH and PHOXB2 representingclinically useful biomarkers of risk (24). However, potentialblood-based mRNA biomarkers can be subject to considerable

Murray et al.






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Figure 3.SerummiRNA expression in hepatoblastoma (HB) versus all neuroblastomas (MYCN-NB/NB). A, raw Ct values (y-axis) of three highly overexpressed miRNAs in HB(miR-483-3p, miR-205-5p, and miR-122-5p) relative to other tumors and controls in the discovery-phase test set. B, mean normalized relative expression valuesfor the miRNAs listed in A above, in HB samples versus the 'other tumors' and control samples, in the confirmatory qRT-PCR study. C, mean normalized relativeexpression values for the miRNAs listed in A above, in HB samples versus MYCN-NB/NB samples, in the technical confirmation qRT-PCR study. In addition,an equally weighted overall mean-adjusted relative expression value for the specific three miRNA panel was calculated. Error bars, SEM. For tumor abbreviations,see Table 1.






variation in levels, for technical as well as biologic reasons (25,26). In particular, mRNAs are inherently unstable at room tem-perature and rapidly degrade in blood samples that are not storedcorrectly (25, 26). In contrast, serum miRNAs offer particularadvantages as blood-based biomarkers as they are not prone tosuch technical variations (10).

The MYCN-NB–specific serum miRNAs identified here are ofbiologic relevance. It has been known for some time that MYCNamplification status of neuroblastoma tissue samples determinesglobal miRNA profiles (27) and our findings are in keeping withthis observation. miR-124-3p is the most abundant neuronal-specific miRNA and silencing of miR-124 in neuroblastoma cellsin vitro resulted in their differentiation (28). In addition, expres-sion of miR-9 is activated by MYCN protein, which directly bindsto the promoter region of this miRNA (29). In neuroblastomatissues, high miR-9 levels correlated with MYCN amplification,tumor grade, and metastatic status (29).

Other tumor-specific serum miRNAs are also of biologicsignificance. For example, miR-122 is highly abundant in livertissues and is considered liver specific. Recently, reduced miR-122 expression has been shown in HB compared with normalliver tissue (30). Here, we found elevated serum levels of miR-122-5p in HB samples compared with other tumors and con-trols. Downregulation of miR-122 in HB (compared withnormal liver tissue; ref. 30), but detection at elevated levels inthe serum of HB patients is consistent with observations in

other tumors (13), and may reflect passive miRNA leakage fromtumor cells. It should be noted that miR-122 is a generalnonspecific marker of liver injury, and is increased in the serumin jaundiced patients, for example (31). Therefore, a widerpanel of serum miRNAs, as identified in this study, is likelyto offer increased specificity for HB compared with othertumors and disease states. Further discussion of the biologicrelevance of our findings in individual tumor types is providedin the Supplementary Discussion.

As the number of samples assessed for each of the tumorsubtypes is small in the current proof-of-principle study, it willbe important to confirm our findings in larger, prospective inves-tigations. The fact that the panel of serum miRNAs that distin-guished the initial group (n ¼ 4) of MYCN-NB from NB patientswas confirmed in a small independent panel of patient samples(n¼ 4) highlights that these changes are promising and worthy offurther testing. If confirmed in future studies, we propose that thepanels of childhood solid tumor-associated serummiRNAs iden-tified here represent useful candidate biomarkers for improvingthe accuracy of pediatric cancer diagnosis. Such serum markersmay reduce or obviate the need for diagnostic histologic biopsyand the associated risks of anaesthesia and surgery. Furthermore,compared with the labor-intensive diagnostic techniques in cur-rent clinical use, a qRT-PCR approach, based on the analysispipeline reported here, is likely to be more cost-effective, therebyoffering health economic as well as clinical benefits.

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Figure 4.Serum miRNA expression in Wilms tumor (WT) versus all neuroblastomas (MYCN-NB/NB). A, raw Ct values (y-axis) of two miRNAs (miR-129-5p and miR-143-3p)relative to other tumors and controls in the discovery-phase test set. B and C, mean normalized relative expression values in MYCN-NB/NB (miR-129-5p)orWT (miR-143-3p) samples versus (B) 'other tumors' and control samples or (C) each other, in the technical confirmation qRT-PCR study. Error bars, SEM. For tumorabbreviations see Table 1.


Murray et al.




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Figure 5.Serum microRNA expression insarcoma subtypes. A, raw Ct values(y-axis) of three selected miRNAs(miR-214-3p/miR-214-5p/miR-92b-3p) in Ewing sarcoma samplescompared with other sarcomasamples (OS/RMS) in the discovery-phase test set. B, 'Differentialdiagnosis' plots for ES versus RMS/OSin the discovery-phase test set. Inaddition, an equally weighted overallmean-adjusted relative expressionvalue for the specific three miRNApanel was calculated. Error bars, SEM.For tumor abbreviations see Table 1.






Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: M.J. Murray, N. ColemanDevelopment of methodology: M.J. Murray, K.L. Raby, H.K. SainiAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): M.J. Murray, K.L. Raby, H.K. Saini, S.V. Wool, J.M.Tunnacliffe, J.C. NicholsonAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): M.J. Murray, K.L. Raby, H.K. Saini, S. Bailey, A.J.Enright, J.C. Nicholson, N. ColemanWriting, review, and/or revision of the manuscript:M.J. Murray, K.L. Raby, H.K. Saini, S. Bailey, J.C. Nicholson, N. ColemanAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): M.J. Murray, K.L. Raby, H.K. Saini, S.V. Wool,J.M. Tunnacliffe, A.J. Enright, N. ColemanStudy supervision: M.J. Murray, N. Coleman

AcknowledgmentsThe authors thank the patients and their families for study participation. The

authors also thank Dr. David Halsall, Ms. Amy Munro, and Mr. JonathanBroomfield (Department of Biochemistry, Addenbrooke's Hospital, Cam-bridge, United Kingdom) for assistance.

Grant SupportThis work was supported by Cancer Research UK (to N. Coleman), Medical

Research Council (toM.J. Murray), SPARKS (to N. Coleman,M.J. Murray, and J.C. Nicholson), and Children with Cancer UK/Great Ormond Street HospitalChildren's Charity (toN. Coleman,M.J. Murray, K.L. Raby, and J.C. Nicholson).

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received June 12, 2014; revised November 13, 2014; accepted November 14,2014; published OnlineFirst November 21, 2014.

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2015;24:350-360. Published OnlineFirst November 21, 2014.Cancer Epidemiol Biomarkers Prev Matthew J. Murray, Katie L. Raby, Harpreet K. Saini, et al. ProfilesSolid Tumors of Childhood Display Specific Serum microRNA

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