MiRNA profiles in lymphoblastoid cell lines of Finnish prostate cancer families

RESEARCH ARTICLE MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families Daniel Fischer 1 , Tiina Wahlfors 2 , Henna Mattila 2 , Hannu Oja 3 , Teuvo L. J. Tammela 4 , Johanna Schleutker 5 * 1School of Health Sciences, University of Tampere, 33014 Tampere, Finland, 2BioMediTech, University of Tampere, and Fimlab Laboratories, Tampere, Finland, 3Department of Mathematics and Statistics, University of Turku, 20014 Turku, Finland, 4Department of Urology, Tampere University Hospital and Medical School, University of Tampere, Tampere, Finland, 5Medical Biochemistry and Genetics, Institute of Biomedicine, University of Turku, Turku, Finland *[email protected] Abstract Background Heritable factors are evidently involved in prostate cancer (PrCa) carcinogenesis, but currently, genetic markers are not routinely used in screening or diagnostics of the disease. More precise information is needed for making treatment decisions to distinguish aggressive cases from indolent disease, for which heritable factors could be a useful tool. The genetic makeup of PrCa has only recently begun to be unravelled through large-scale genome-wide association studies (GWAS). The thus far identified Single Nucleotide Polymorphisms (SNPs) explain, however, only a fraction of familial clustering. Moreover, the known risk SNPs are not associated with the clinical outcome of the disease, such as aggressive or metastasised disease, and therefore cannot be used to predict the prognosis. Annotating the SNPs with deep clinical data together with miRNA expression profiles can improve the understanding of the underlying mechanisms of different phenotypes of prostate cancer. Results In this study microRNA (miRNA) profiles were studied as potential biomarkers to predict the disease outcome. The study subjects were from Finnish high risk prostate cancer families. To identify potential biomarkers we combined a novel non-parametrical test with an importance measure provided from a Random Forest classifier. This combination delivered a set of nine miRNAs that was able to separate cases from controls. The detected miRNA expression profiles could predict the development of the disease years before the actual PrCa diagnosis or detect the existence of other cancers in the studied individuals. Furthermore, using an expression Quantitative Trait Loci (eQTL) analysis, regulatory SNPs for miRNA miR-483-3p that were also directly associated with PrCa were found. PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 1/17 a11111 OPEN ACCESS Citation: Fischer D, Wahlfors T, Mattila H, Oja H, Tammela TLJ, Schleutker J (2015) MiRNA Profiles in Lymphoblastoid Cell Lines of Finnish Prostate Cancer Families. PLoS ONE 10(5): e0127427. doi:10.1371/ journal.pone.0127427 Academic Editor: Xin-Yuan Guan, The University of Hong Kong, CHINA Received: December 19, 2014 Accepted: April 15, 2015 Published: May 28, 2015 Copyright: © 2015 Fischer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are available from EBI (accession number E-MTAB3397). Funding: This work was supported by Medical Research Fund of Tampere University Hospital (9L091, 9M094, and 9N069), the Finnish Cancer Organizations, the Sigrid Juselius Foundation, and the Academy of Finland (grants 116437 and 251074) for JS. This work was also supported by The Finnish Doctoral Programme in Stochastics and Statistics for DF. Conclusion Based on our findings, we suggest that blood-based miRNA expression profiling can be used in the diagnosis and maybe even prognosis of the disease. In the future, miRNA profiling could possibly be used in targeted screening, together with Prostate Specific Antigene (PSA) testing, to identify men with an elevated PrCa risk. Introduction Prostate cancer (PrCa) is the most common noncutaneous malignancy and the second leading cause of cancer-related deaths among men in industrialised countries [1]. In Finland, 4604 new prostate cancer cases were diagnosed in 2012 (Finnish Cancer Registry, http://www.cancer.fi/ syoparekisteri/). Aging and PSA testing may be the most evident reasons for the increased number of new cases. The growing incidence creates pressure on the health care system as the concern regarding overtreatment is considerable. Therefore, one of the major challenges is to improve the diagnostic and prognostic tools to be able to distinguish lethal from indolent disease at a curable state of the disease. The contribution of genetic variants has been studied widely in association with prostate cancer predisposition. Both linkage and GWAS together with the few examples arising from candidate gene approaches have led to the identification of about 100 genetic loci that explain only approximately 30% of the genetic risk for the disease [2][3][4][5]. However, there is no obvious molecular or functional evidence indicating how the variations in these candidate sites or their co-inherited neighbouring variants could cause PrCa. In fact, most of the single nucleotide variants (SNPs) found by GWAS are unlikely to affect the coding sequence of any gene but rather reside in intergenic regions. These findings suggest that they have a regulatory role, such as in transcription, splicing or mRNA stability, instead of a direct effect on the function of the gene product [6]. In recent years, the importance of the non-protein coding genome in the functional regulation of normal development and disease development has become evident. MiRNAs are short non-coding RNAs that regulate their target gene expression typically by binding to the 3’untranslated region (UTR) of the target mRNA [7]. Individual variation of the miRNA expression levels can influence the expression of the mRNA target gene, causing phenotypic differences. Several studies have shown that miRNA expression levels are predictive for the outcome of solid tumours and leukaemias, but the contribution of altered miRNA expression levels to genetic cancer susceptibility is not known. The transcriptional activity of protein coding genes is inherited as a quantitative trait, and regulatory polymorphisms associated with the variability in the levels of mRNA are considered to be eQTL. Despite the demonstrated importance, knowledge of the genetic regulation of miRNA expression is still in its infancy. In a recent publication, over one hundred eQTLs in primary fibroblasts were described, indicating at least a partial role for genetic variation in altered miRNA expression [8]. Combined analyses of common SNPs and variations in miRNA expression profiles might serve as one way to elucidate the biological functions of SNPs identified from GWAS in common diseases. The objective of this study was to evaluate the miRNA expression profiles of lymphoblastoid cell lines (LCL) derived from members of high risk PrCa families. Altered miRNA expression in patient LCLs compared with those from healthy family members provided an opportunity to identify germline variants in promoter or other regulatory regions of protein coding genes as a considerable amount of miRNA expression is correlated to host and target gene expression MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 2/17 Competing Interests: The authors have declared that no competing interests exist. [9]. The large amount of significant miRNA-wise test results within the data also required the development of a new type of differentially expression analysis pipeline. To develop such a pipeline, differentially expression testing has been combined with the importance measures of the machine learning algorithm, Random Forest [10]. Materials and Methods Ethics Statement This study has been approved by the respective IRB boards of The Ministry of Social Affairs and Health (SMT), National Supervisory Authority for Welfare and Health (Valvira) and Ethics Committee of Tampere University Hospital. Every individual participating in the study has given written informed consent. Study population All samples are of Finnish origin and the collection of the families has been reported previously [11]. For the miRNA microarray study, 115 cases from 70 PrCa families were used. The selected families had at least two first-degree relatives diagnosed with prostate cancer at any age. Healthy (= no diagnosed prostate cancer) individuals (n = 78) from 47 families were used as the controls. The median age at diagnosis for the cases was 65 (44–86.2) years and the controls had a median age of 57.5 (35.2–83.3) years at the time the samples were obtained. A subset of individuals (n = 54) from the microarray experiment were genotyped with Illumina’s HumanOmniExpress array for another experiment, and the results are published elsewhere [12]. Hence, those 54 samples could be used here for an eQTL analysis (39 PrCa cases and 15 controls). Additional 83 individuals could be used for validation purposes. Altogether, there were 137 genotyped persons from 33 families (20 overlapping families with the microarray part of the study). The clinical outcome of prostate cancer can roughly be classified into aggressive and nonaggressive cancer, based on PSA, Gleason score and other clinical evaluations [13]. Based on these guidelines, the prostate cancer patients from the two experiments were grouped into 36 (36) aggressive and 79 (66) non-aggressive prostate cancers. The maximum number of aggressive cases per family was 3, and the minimum was 1. A detailed overview of the individuals in the study is given in Fig 1. RNA extraction from lymphoblastoid cell lines LCLs were derived by the Epstein-Barr virus transformation of peripheral mononuclear leukocytes from patients and their healthy relatives. The lymphoblastoid cell lines were grown in RPMI-1640 medium (Lonza, Walkersville, MD, USA) supplemented with 10% fetal bovine serum (Sigma-Aldrich, St. Louis, MO, USA) and antibiotics at 37°C, 5% CO2 and 95% humidity. The cell pellets were snap-frozen, and total RNA was extracted with Trizol according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA, USA). The RNA yields were quantified using an ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). MicroRNA microarray analysis The microRNA expression levels in LCLs were detected using Agilent Human miRNA V2 Oligo Microarray Kit (Agilent Technologies). First, 100 ng of total RNA was used as the starting material, and miRNAs were labeled using the Agilent miRNA Labelling Kit. Labelled RNA was hybridised to Agilent miRNA microarrays that have eight identical arrays per slide, with MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 3/17 each array containing probes directed against 817 miRNAs (719 human, 76 non-human viral miRNAs and 22 control miRNAs). In total, 26 slides were used, and the data were extracted using Agilent’s Feature Extraction software (FES), version 10.7.1.1 with the grid layout D_F_20091030. For the data analysis, low quality samples were first removed, resulting in 193 individuals. Each individual Agilent microarray V2 measures 13,737 features, and the FES then used these features to calculate the expression values for 2,466 (2,125 human) probes; based on those probes the 817 miRNA expression values were calculated. The data can be accessed via ArrayExpress accession E-MTAB-3397. The miRNA expression values are typically calculated with the algorithm gTotalGeneSignal as implemented in FES, but in this study, however, probe-wise, background subtracted median values were used instead. The analysis of different probes of the same miRNA as a single miRNA expression value did not appear to be reliable enough, and an analysis at the probe level was more feasible. After calculating the expression values at the probe level, all nonhuman probes and those not detected by the FES were removed. Only those probes that were detected for at least 50% of the samples in at least one health status group were used for further analysis. Additionally, non-human control features were removed before the analysis. In total, 547 probes, representing 211 miRNAs, fulfilled these criteria. The technical variability of the data was reduced by applying a quantile normalisation [14]. Genotyping Data Analysis The single nucleotide polymorphism (SNP) genotype data were generated using Illumina’s HumanOmniExpress array in collaboration with the Institute of Molecular Medicine Finland (FIMM). The chosen array enabled the genotyping of approximately 700k SNPs. To produce the genotype data, the raw data were analysed with Genome Studio according to the manufacturer’s instructions (Illumina, San Diego, USA). In total, the genotype information for 137 individuals was available, with the miRNA expression levels also measured in 54 of these individuals. Hence, the eQTL analysis was based on these 54 persons. The remaining 83 individuals were used for validation of the results. Fig 1. upper: Population quantities, visualisation of how the 277 individuals in this study are distributed among the three health-status groups. For each health group, the number of individuals from the different experiments is shown. The overall number from an experiment is then indicated by the respective coloured box plus the red box (overlap). lower: Visualisation of the familial background. The three options ‘PrCa only’,‘Healthy only’or ‘PrCa/ Healthy’are shown and grouped accordingly. Additionally, involvement of different families in the two experiments is shown. Ordering is according to an internal family code. doi:10.1371/journal.pone.0127427.g001 MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 4/17 Identification of differentially expressed probes using directional testing PrCa patients were divided into aggressive (A) and non-aggressive/mild (M) PrCa groups and compared with healthy controls (H). A new generalisation of Mann-Whitney type tests was applied to identify differentially expressed probes in the three-group comparison. The same generalisation was used for the eQTL analysis (for details see [15] and [16]). For a general definition, let the sample sizes of the three groups be N H ,N M and N A which results in a total sample size of N H +N M +N A =N. The generalised Mann-Whitney test is based on probabilistic indices calculated with triple sums of corresponding indicator functions. Let x p;H =(x 1,p;H ,x 2,p;H ,...,x N H ,p;H ) T ,x p;M =(x 1,p;M ,x 2,p;M ,...,x N M ,p;M ) T and x p;A =(x 1,p;A ,x 2,p;A , ...,x N A ,p;A ) T be the expression values for a probe pin each health group with underlying cdf’s F p;H ,F p;M and F p;A . The probabilistic index ^ PH;M;A;pfor probe pused in this approach can then be calculated by ^ PH;M;A;p¼1 NHNMNA X NH i¼1 X NM j¼1 X NA k¼1 Iðxi;p;H<xj;p;M<xk;p;AÞ; and I() is the indicator function that is 1 if condition () is true and 0 if not. Please notice that the order in the index of ^ PH;M;A;prefers to the order used in the indicator function. Furthermore, the probabilistic index ^ PH;M;A;pcan then be used to test the directional hypothesis H0:Fp;H¼Fp;M¼Fp;Avs:H1:Fp;HFp;MFp;A; where refers to the stochastic ordering of cdf’s. Naturally, different orders in the condition () of the indicator function can be used to test for different alternatives. In addition, when expression values are assigned to genotype groups instead of health status, this test procedure is ideal for eQTL testing as it tests for the directional alternatives that are clearly present in the context of an eQTL analysis. The two probabilistic indices ^ PH;M;A;pand ^ PA;M;H;pwere used for testing probes p=1,..., 547, and p-values for the permutation test version were calculated based on 5000 permutations. Test results with p-value less than 0.01 were considered to be significant. The test method is implemented in the R-package gMWT [16], and the package GeneticTools exploits this test method for eQTL testing. Both packages are freely available from the Comprehensive R Archive Network (CRAN). The Benjamini-Hochberg multiple testing procedure to control the false discovery rate is visualised using rejection plots and lines. The ratio of expected rejections under the null hypothesis is plotted against the observed ratio of rejections. If this curve is above the (0, 1)-line, we have more rejections than expected under the null hypothesis. The rejections for a fixed test size can be visualised with a vertical line, and the rejections for different multiple testing adjustments can be visualised by lines with a certain slope. The number of rejected null hypotheses is then determined by the crossing point of the curve and the line. For details, see [15]. Classification, Importance Measure and Clustering The machine learning classifier Random Forest [10], as implemented in the R-package randomForest [17], was applied to the expression data, such that the dataset was split into the training (75%) and test (25%) data. The training data were used to create an ensemble of 2500 decision trees, and these trees were then used to classify the test data. The division between the training and validation data was then repeated 2000 times, and afterwards the classification MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 5/17 results of all test data runs were evaluated. The Gini importance measure was also extracted for every single Random Forest, and the average importance of each probe was combined with the corresponding p-value from the directional test. Probes that had a p-value less than 0.01 and that belonged to the 10% most important probes over all Random Forest runs were considered to be of high interest (HI probes) and were then used in the clustering step and in the eQTL analysis. The Random Forests were trained for the three possible outcome classes healthy (H), mild PrCa (M) and aggressive PrCa (A). Let L i,r;H ,L i,r;M and L i,r;A be the class likelihoods provided by the Random Forest classifier run rfor individual iwith L i,r;H +L i,r;M +L i,r;A = 1. These likelihoods were then combined into a single PrCa severeness value Si;r¼1 2Li;r;MþLi;r;A. The severness value S i,r was chosen in such a way that S i,r = 0 in case that L i,r;H =1,S i,r = 0.5 for L i,r;M =1 and S i,r =1ifL i,r;A =1. In a 2-way Random Forest run, the classification was performed only between the healthy and PrCa classes, with same setup as that for the 3-way Random Forest described above. To calculate the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve in the Random Forest case, two different approaches were chosen. First, the two likelihoods L i,r;M and L i,r;A were added to evaluate the Random Forest’s capability to classify PrCa in general. Then, in the second comparison, the likelihoods L i,r;H and L i,r;M were added to evaluate its aptitude to identify aggressive PrCa. Eventually, to plot the ROC a continuous cutoff value in [0, 1] was applied onto the likelihood to classify individuals into true/ false positives. For the clustering in the heatmap, the Kendall tau correlation matrix Samong all samples was calculated based on the expression values of the HI probes. Kendall’tau between two variables is a measure of positive/negative dependence and is invariant under any strictly increasing transformation to the marginal variables. The corresponding distance between the variables is then defined as D=(1−S)/2. Let then Dbe the matrix of distances used for the hierachical clustering. eQTL Analysis The genotype information from the 700k array was combined with the expression values of the HI probes using an eQTL analysis. The chromosomal locations of the miRNA probes were identified and all SNPs within a window of 1Mb around the probe’s central location were linked to this probe. The probe expression values were then assigned to the genotype groups of every linked SNP (Fig 2 shows a systematic sketch of this step). In an eQTL approach, three cases are possible, depending on whether the expression values have been assigned to one, two or all three possible genotype groups. Monomorphic variants were not further considered in the analysis, and in the two-group case, a two-sided MannWhitney test was applied. In the three-group case, the generalised Mann-Whitney test for directional alternatives was used for the two different alternatives whether the higher expression values were linked to the wild-type or the homozygous mutation. This type of directional test was used in the three-group case as an order for the expression values with respect to the genotype groups is clearly expected. Comparative Analysis The here used two-stage approach was compared with two other commonly used methods. The first method was a classical Analysis of Variance (ANOVA), testing the alternative hypothesis that there is a difference between at least two out of the three groups. Let μ p,H ,μ p,M and μ p,A be the average expression values of probe pfor the three groups, then is the probe-wise MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 6/17 hypothesis for the one-way ANOVA H0:mp;H¼mp;M¼mp;Avs:H1:Not all mp;: are equal Resulting p-values were then adjusted for multiple testing using a bonferroni correction. The second method that was used as comparison was a two-staged logistic regression with lasso (LRL). First, LRL was applied onto the full dataset with the two classes healthy/diseased. The tuning parameter λwas chosen such that the amount of selected variables were in the same level of magnitude as the here proposed method identifies. The second LRL run was then applied onto the cancer cases only and aimed for the separation of mild and aggressive PrCa. Finally the resulting probes were merged to one result matrix from the LRL analysis. To compare the results of the ANOVA and the LRL with the here proposed approach, a hierarchical clustering was applied onto the identified probes using also a Kendall’s tau based distance matrix. Then, the adjusted Rand Index was calculated between the classification of the three different clusterings and the true cancer status of the individuals to determine the level of agreement. Results Using the directional testing procedure, 146 (87 with higher expression in aggressive PrCa and 59 with higher expression in controls) out of a total of 547 probes were identified having different expression profiles. The chromosomal location of the significant probes and the type of testing alternative are visualised in Fig 3. To identify HI probes from this unexpectedly large amount of differentially expressed probes, a Random Forest classifier was also applied to the expression data. Significant probes that were within 10% of the most important probes in the Random Forest, measured as Gini Index, were called HI probes and are highlighted in Fig 3. The 13 identified probes represent eight different miRNAs and one spliceosomal RNA. More details about the 13 identified probes are listed in Table 1. The overall classification result based on the severeness values S i,r of the Random Forest is visualised in Fig 4. Healthy individuals (green) clearly tended to be in the lower risk area, but Fig 2. Each line represents an individual, having a certain expression value for miRNA X. Independent of the health status of each individual, the expression values are grouped according to the genotype groups of the surrounding SNPs and then tested for differential expression between those groups. (Figure taken from [16]) doi:10.1371/journal.pone.0127427.g002 MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 7/17 aggressive PrCa patients (red) did not tend to have larger values than non-aggressive PrCa patients (yellow). In addition, an average classification rate over all classification runs was determined separately for the comparisons between healthy and PrCa and between aggressive PrCa and combined healthy and non-aggressive PrCa. The Random Forest was able to classify PrCa with an average AUC of the ROC of approximately 0.89 and aggressive PrCa versus the combined samples of non-aggressive PrCa and controls of 0.68 (Fig 5). The classification results at the individual level are visualised in the supporting information (S1 and S2 Figs). A hierarchical clustering shows the importance of the HI probes. Clustering the dataset based on all probes resulted in only a slightly better classification than the clustering based on Fig 3. Location of the directional test results for the two probabilistic indicies ^ PH;M;Aand ^ PA;M;Hdenoted by H<M<Arespective A<M<H. Significant test results that also belong to the 10% most important (Gini Index) miRNAs in the Random Forest run are denoted as HI probes. doi:10.1371/journal.pone.0127427.g003 Table 1. Overview of the HI Probes, their target miRNAs with corresponding median expression values and chromosomal position. ProbeID TargetID ChromosomalLocation ~ xH~ xM~ xA A_25_P00010263 mir|hsa-miR-328 Chr16:67,236,292—67,236,276 20.81 27.15 25.75 A_25_P00011068 mir|hsa-miR-107 Chr10:91,352,575—91,352,557 405.20 483.35 483.35 A_25_P00011440 mir|hsa-miR-801_v10.1 Chr1:28,847,749—28,847,763 50.77 41.10 34.29 A_25_P00011476 mir|hsa-miR-770-5p Chr14:101,318,754—101,318,768 28.59 24.52 24.13 A_25_P00011477 mir|hsa-miR-770-5p Chr14:101,318,755—101,318,768 24.59 20.52 20.53 A_25_P00011979 mir|hsa-miR-770-5p Chr14:101,318,752—101,318,768 31.16 26.31 26.08 A_25_P00012461 mir|hsa-miR-483-3p Chr11:2,155,431—2,155,415 24.72 29.56 29.94 A_25_P00012462 mir|hsa-miR-483-3p Chr11:2,155,431—2,155,414 23.46 29.28 29.35 A_25_P00012991 mir|hsa-miR-885-5p Chr3:10,436,204—10,436,189 22.43 33.34 32.14 A_25_P00013086 mir|hsa-miR-939 Chr8:145,619,401—145,619,390 152.93 71.98 72.08 A_25_P00013207 mir|hsa-miR-29a*Chr7:13,0561,530—130,561,511 18.89 21.36 21.52 A_25_P00014864 mir|hsa-miR-202 Chr10:135,061,097—135,061,083 25.92 21.29 20.68 A_25_P00014914 mir|hsa-miR-885-5p Chr3:10,436,204—10,436,188 21.25 30.92 29.61 doi:10.1371/journal.pone.0127427.t001 MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 8/17 the 13 HI probes. The dendrogram for clustering individuals based on the 13 HI probes together with the corresponding heatmap is shown in Fig 6. Here, the ability to separate clearly between aggressive and non-aggressive PrCa was limited, but interestingly only five of the 78 healthy individuals were clustered closely together with PrCa individuals. In contrast, 46 of 115 PrCa cases were inside the cluster that contained most of the healthy individuals. In addition, a cis-eQTL (0.5Mb up/downstream window) for the HI probes was performed. In total, 3863 SNP-miRNA associations were tested, and 79 had a p-value of 0.01, (S3 Fig in the supporting information). All SNPs that were found to have a possible regulatory effect on an HI probe were then tested for a direct PrCa association by applying a Fisher-test on the 2 × 3 table between genotype and health status groups. For four SNPs, a significant association was found for the 53 genotypes of the eQTL samples (test size 0.05). Fig 4. Overall classification results of the Random Forest classifier using the severeness measure S i,r . doi:10.1371/journal.pone.0127427.g004 MiRNA Profiles in Lymphoblastoid Cell Lines PLOS ONE | DOI:10.1371/journal.pone.0127427 May 28, 2015 9/17 References 1. Andriole GL, Crawford ED, Grubb RL, Buys SS, Chia D, Church TR, et al. Mortality results from a randomized prostate-cancer screening trial. N Engl J Med. 2009; 360(13): 1310–1319. doi: 10.1056/ NEJMoa0810696 PMID: 19297565 2. Varghese JS, Easton DF. Genome-wide association studies in common cancers—what have we learnt? Curr Opin Genet Dev. 2010; 20(3): 201–209. PMID: 20418093 3. Eeles RA, Olama AA, Benlloch S, Saunders EJ, Leongamornlert DA, Tymrakiewicz M, et al. Identification of 23 new prostate cancer susceptibility loci using the iCOGS custom genotyping array. Nat Genet. 2013; 45(4): 385–391. doi: 10.1038/ng.2560 PMID: 23535732 4. Kim ST, Cheng Y, Hsu FC, Jin T, Kader AK, Zheng SL, et al. 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