A Multicenter, Double-Blinded Validation Study of Methylation Biomarkers for Progression Prediction in Barrett's Esophagus

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A Multicenter, Double-Blinded Validation Study of Methylation Biomarkers for Progression Prediction in Barrett's Esophagus
  A multicenter, double-blinded validation study of methylationbiomarkers for progression prediction in Barrett’s esophagus Zhe Jin 1, Yulan Cheng 1, Wen Gu 2, Yingye Zheng 2, Fumiaki Sato 1, Yuriko Mori 1, AlexandruV. Olaru 1, Bogdan C. Paun 1, Jian Yang 1, Takatsugu Kan 1, Tetsuo Ito 1, James P.Hamilton 1, Florin M. Selaru 1, Rachana Agarwal 1, Stefan David 1, John M. Abraham 1, HerbertC. Wolfsen 3, Michael B. Wallace 3, Nicholas J. Shaheen 4, Kay Washington 5, Jean Wang 1, Marcia Irene Canto 1, Achyut Bhattacharyya 6, Mark A. Nelson 6, Paul D. Wagner 7, YvonneRomero 8, Kenneth K. Wang 8, Ziding Feng 2, Richard E. Sampliner 9,†, and Stephen J.Meltzer 1,† 1 Gastroenterology Division, Johns Hopkins University, Baltimore 2 Biostatistics Department, Fred Hutchinson Cancer Center, Seattle 3 Gastroenterology Division, Mayo Clinic, Jacksonville 4 Gastroenterology Division, University of North Carolina, Chapel Hill 5 Pathology Department, Vanderbilt University, Nashville 6 Pathology Department, University of Arizona-SAVAHCS, Tucson 7 Early Detection Research Network, Bethesda 8 Gastroenterology Division, Mayo Clinic, Rochester 9 Gastroenterology Division, University of Arizona-SAVAHCS, Tucson Abstract Esophageal adenocarcinoma risk in Barrett’s esophagus (BE) is increased 30- to 125-fold versus  thegeneral population. Among all BE patients, however, neoplastic progression occurs only once per200 patient-years. Molecular biomarkers are therefore needed to risk-stratify patients for moreefficient surveillance endoscopy and to improve the early detection of progression. We thereforeperformed a retrospective, multicenter, double-blinded validation study of 8 BE progressionprediction methylation biomarkers. Progression or nonprogression were determined at 2 years (tier1) and 4 years (tier 2). Methylation was assayed in 145 nonprogressors (NPs) and 50 progressors(Ps) using real-time quantitative methylation-specific PCR. Ps were significantly older than NPs(70.6 vs . 62.5 years, p < 0.001). We evaluated a linear combination of the 8 markers, using coefficientsfrom a multivariate logistic regression analysis. Areas under the ROC curve (AUCs) were high inthe 2-, 4-year and combined data models (0.843, 0.829 and 0.840; p<0.001, p<0.001 and p<0.001,respectively). In addition, even after rigorous overfitting correction, the incremental AUCscontributed by panels based on the 8 markers plus age vs . age alone were substantial ( Δ -AUC = 0.152,0.114 and 0.118, respectively) in all three models. A methylation biomarker-based panel to predictneoplastic progression in BE has potential clinical value in improving both the efficiency of surveillance endoscopy and the early detection of neoplasia. †Correspondence to: Stephen J. Meltzer, Gastroenterology Division, Johns Hopkins University 1503 E. Jefferson Street, Baltimore, MD21287; 410-502-6071; smeltzer@jhmi.edu. †Correspondence to: Richard E. Sampliner, Gastroenterology Division, Southern ArizonaVA HealthCare System, Tucson, AZ 85723-0001; 520-792-1450; samplinr@email.arizona.edu. NIH Public Access Author Manuscript Cancer Res . Author manuscript; available in PMC 2010 May 15. Published in final edited form as: Cancer Res . 2009 May 15; 69(10): 41124115. doi:10.1158/0008-5472.CAN-09-0028. N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t    Barrett’s esophagus (BE) is a metaplastic condition where the normal squamous epithelium of the lower esophagus is replaced by a small intestinal-like columnar lining(1). Esophagealadenocarcinoma (EAC) risk in BE is increased 30- to 125-fold relative to the general population(2), and endoscopic surveillance in BE patients is recommended at intervals of two to threeyears(1,3). EACs detected in surveillance programs occur at earlier stages and have betterprognoses(4,5), but endoscopic surveillance suffers from high cost, inconvenience, patientanxiety, low yield, and procedure-related risks. In addition, the current marker of EAC risk inBE, dysplasia, is plagued by high inter-observer variability and limited predictive accuracy(6-8). Because neoplastic progression is infrequent in BE, the merits of and appropriate intervalfor endoscopic surveillance in BE have led to frequent debate(3,5). This process would benefitgreatly from effective biomarkers to stratify patients according to their level of neoplasticprogression risk.In 2005, we reported that hypermethylation of  p16  ,  RUNX3 , and  HPP1  occurs early in BE-associated neoplastic progression and predicts progression risk(9). Later, we developed a tieredrisk stratification model to predict progression in BE using epigenetic and clinical features(10). We also studied methylation levels and frequencies of individual genes using real-timequantitative methylation-specific PCR (qMSP) in 259 endoscopic esophageal biopsyspecimens of differing histologies. Among 10 genes evaluated, five, namely nel-like 1 (  NELL1 ), tachykinin-1  ( TAC1 ), somatostatin (SST) ,  AKAP12 , and CDH13 , were methylatedearly and often in BE-associated neoplastic progression(11-15). In the above studies,methylation status and levels correlated inversely with mRNA expression levels (9-15). In lightof these findings, we performed a retrospective, multicenter, double-blinded validation studyof these 8 methylation biomarkers ( i.e., p16, RUNX3, HPP1, NELL1, TAC1, SST, AKAP12 ,and CDH13 ) for their accuracy in predicting neoplastic progression in BE. Materials and Methods Definition of Barrett’s esophagus progressor and nonprogressor patients and samplecollection Progressors (Ps) and nonprogressors (NPs) were defined as described previously.(10) Ps wereconsidered both as a single combined group, and in two tiers: progression within 2 years (tier1) or 4 years (tier 2). 195 BE biopsies (145 NPs and 50 Ps) were obtained from 5 participatingcenters: the Mayo Clinic at Rochester/Jacksonville, the University of Arizona, the Universityof North Carolina, and Johns Hopkins University. All patients provided written informedconsent under a protocol approved by Institutional Review Boards at their institutions. Biopsieswere taken using a standardized biopsy protocol. (9,10) Clinicopathologic features aresummarized in Supplementary Table 1. Bisulfite Treatment and Real-Time Quantitative Methylation-Specific PCR (qMSP) Bisulfite treatment was performed as described.(11) Promoter methylation levels of 8 genes(  p16, HPP1, RUNX3, CDH13, TAC1, NELL1, AKAP12  and SST  ) were determined by qMSPon an ABI 7900 Sequence Detection (Taqman) System .(11)  β  -actin  was used fornormalization. Primers and probes for qMSP are described in Supplementary Table 2. Astandard curve was generated using serial dilutions of CpGenome Universal Methylated DNA(CHEMICON, Temecula, CA). A normalized methylation value (NMV) for each gene of interest was defined as described.(11) Wetlab analysts (ZJ and YC) and all SJM laboratorypersonnel were blinded to specimen P or NP status. Jin et al.Page 2 Cancer Res . Author manuscript; available in PMC 2010 May 15. N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t    Data Analysis and Statistics Associations between progression status and patient characteristics were tested using Student’st-test or Chi-squared testing. Relationships between biomarkers and patient progression statuswere examined using Wilcoxon rank-sum testing.To evaluate the predictive utility of the markers, we constructed receiver operatingcharacteristic (ROC) curves. ROC curve analyses were first conducted on individual markers,then in combination to determine whether a panel performed better than any single marker.Our algorithm rendered a single composite score, using the linear predictor from a binaryregression model justified under the linearity assumption(16). The predictive accuracy of composite scores was evaluated based on a resampling algorithm: we randomly split data intoa learning set containing 2/3 and a test set including 1/3 of observations. The combination rulederived from the learning set produced two ROC curves, from the learning and test sets,respectively. Vertical differences between these two ROC curves yielded the overestimationof sensitivities at given specificities. This procedure was repeated 200 times, and these 200differences were averaged to estimate the expected overfitting.We also utilized predictiveness curves(17) to display risk distribution as a function of thecombined marker in the population. This curve represents a plot of risk associated with the v th  quantile of the marker, P{D=1|Y =F -1 ( v )} vs . v , with F(·) the cumulative distribution of themarker. These plots display population proportions at different risk levels more clearly thando other metrics (like ROC curves). Since a case-control sample was studied, we used anexternal progression prevalence rate to calculate risk in the targeted screening population. Tocalibrate for future samples, a shrinkage coefficient estimated from the logistic regressionmodel was applied to the linear predictors from which risk was calculated(18).All analyses were performed in R (http://www.r-project.org). Statistical data analysts (Y.Z.,W.G., and Z.F.) were blinded to the identities of the 8 biomarkers. RESULTS Clinical characteristics: Ps vs . NPs did not differ significantly by gender, body mass index,BE segment length, LGD patient percentage,family history of BE, LGD, HGD or EAC,cigarette smoking, or alcohol use; however, Ps were significantly older than NPs (70.6 vs . 62.5years; p< 0.001, Student’s t test; Supplementary Table 1). Samples consisted of one biopsyfrom each of 50 Ps and 145 NPs (195 patients) in the combined model. In the 2-year model,we redefined progressors whose interval from index to final procedure exceeded 2 years asnonprogressors, yielding 36 Ps and 159 NPs. In the 4-year model, we redefined progressorswhose interval from index to final procedure exceeded 4 years as nonprogressors, yielding 47Ps and 148 NPs. Univariate analyses: NMVs of  HPP1 ,  p16   and  RUNX3  were significantly higher in Ps vs .NPs by Wilcoxon test (0.456, 0.138, and 0.104 vs . 0.273, 0.069 and 0.063; p = 0.0025, 0.0066and 0.0002, respectively). The remaining 5 markers did not differ significantly in Ps vs . NPs(Supplementary Table 3). We further assessed the classification accuracy of single markersusing ROC curve analyses. Areas under ROC curve (AUCs) for  HPP1 ,  p16   and  RUNX3  wereall significantly greater than 0.50, (Supplementary Table 4). Logistic regression analyses of the 8-marker panel: We then combined all 8 markers byperforming logistic regression and treating them as linear predictors (Table 1, SupplementaryFigure 1). All models exhibited high AUCs (0.843, 0.829 and 0.840, respectively; Table 1,Supplementary Figures 1A-1C). We performed overfitting correction based on 3-fold cross-validation and 200 bootstraps. The overfitting-corrected AUCs remained high (0.745, 0.720 Jin et al.Page 3 Cancer Res . Author manuscript; available in PMC 2010 May 15. N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t    and 0.732, respectively), while shrinkages from overfitting correction were modest (0.098,0.109 and 0.108, respectively) in the three models (Table 1, Supplementary Figures 1A-1C).We also explored the incremental AUC value contributed by an 8-marker-plus-age panel tothat of age alone (Table 2, Supplementary Figure 1). The AUCs of the 8-marker-plus-age panelsin the three models (0.858, 0.850 and 0.855, respectively) were higher than those of age alone(0.604, 0.630 and 0.635, respectively; Table 2, Supplementary Figures 1D-1F). Overfitting-corrected AUCs remained high (0.756, 0.744 and 0.753, respectively), and incrementscontributed by the age-plus-biomarker panel vs . age were substantial (0.152, 0.114 and 0.118,respectively) in the three models (Table 2, Supplementary Figures 1D-1F). Sensitivity and specificity of the 8-marker panel: While maintaining high specificity tominimize false-positive results, our model still predicted a number of new early diagnoses, i.e ., diagnoses that would not have occurred earlier without the panel (Table 3). Whilemaintaining specificity at 0.9 or 0.8, sensitivities (0.443 and 0.629 for the combined model,0.607 and 0.721 for the 2-year model, and 0.465 and 0.606 for the 4-year model, respectively)were above or approached 50% in all three models based on the 8-marker panel alone.Furthermore, at 0.9 or 0.8 specificities, sensitivities (0.457 and 0.757 for the combined model,0.536 and 0.786 for the 2-year model, and 0.450 and 0.724 for the 4-year model, respectively)exceeded or approached 50% in all models based on the 8-marker-plus-age panel. Risk stratification of BE patients: ROC curves derived from these marker-based models wereused to establish thresholds to stratify patients into risk categories. This procedure wasperformed to identify high-risk (HR) individuals for more frequent endoscopic screening. Thethreshold above which patients were classified as HR was chosen at specificity  = 90%, tominimize false-positive, unnecessary endoscopies (type II error). A second threshold wasestablished to identify low-risk (LR) individuals for less frequent endoscopic screening. Thethreshold below which patients were classified as LR was chosen at sensitivity  = 90%, tominimize false-negative, missed HR individuals (type I error). Based on the combined P andNP classification, we classified patients as LR with a threshold that corresponded to 90% true-positives and 43% false-positives; the HR group was defined using a threshold that yielded43% true-positives and 10% false-positives. Assuming a cumulative progression rate to HGDand/or EAC of 7.5% over 5 years(19), the corresponding negative predictive value relating toour LR threshold was 98.7% ( i.e ., progression risk in the LR group was 1.3%) and the positivepredictive value relating to HR was 27% ( i.e ., progression risk in the HR group was 27%). Predictiveness curve analyses: We used predictiveness curves (also known as risk plots) toassess the clinical utility of the combined classification rules in stratifying patients accordingto risk levels in the target population. To create predictiveness curves, we ordered and plottedrisks from lowest to highest value. A progression rate to HGD and/or EAC of 7.5% over 5years(19) was assumed in adjusting estimates from the case-control sample to reflectpopulation risk and its distribution. Results are shown in Table 4 and Supplementary Figure2. After overfitting correction, by age alone, nearly 90% of BE patients were classified asintermediate-risk (IR), whereas patients were well-stratified into low-risk (LR), IR, or high-risk (HR) categories by both the 8-marker alone and age plus 8-marker panels in all threemodels (Table 4, Supplementary Figure 2). Discussion In the current study, with specificity at 0.9, sensitivities of progression prediction approached50% based on both the 8-marker panel alone and 8-marker-plus-age panel in all three models.These findings indicate that even while performing at high specificity, these biomarker models Jin et al.Page 4 Cancer Res . Author manuscript; available in PMC 2010 May 15. N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t    predicted half of progressors to HGD and EAC that would not have been diagnosed earlierwithout using these biomarkers.Based on age alone, with specificity at 90%, only 17.6%, 23.2% and 22.1% of progressorswere predicted in the three models. However, with panels based on age plus biomarkers or onbiomarkers alone, approximately 60%, 50% and 50% of progressors were accurately predictedin these three models. Predicted progressors represent patients in whom we can intercedeearlier, resulting in higher cure rates. Finally, our combined risk model outperformed knownrisk in the general BE population (7.5% progression risk over 5 years), both in terms of negativepredictive value (1.3% progression risk over 5 years for the LR group) and positive predictivevalue (27% progression risk over 5 years for the HR group).Age is a common risk factor for many cancers, including EAC(20). In the current study, Pswere significantly older than NPs, and the AUCs of age alone were 0.604, 0.630 and 0.635,respectively in the three models, suggesting that age  per se  predicts neoplastic progression inBE. However, methylation of tissues increases with aging, even in the absence of neoplasticprogression (21-22). Thus, aging may exert risk on progression either independently, orthrough its influence on methylation. Nevertheless, the incremental prediction accuracy (aboveage) contributed by the 8-marker panel was substantial in all three models.Thus, the current findings suggest that this 8-marker panel is more objective and quantifiableand possesses higher predictive sensitivity and specificity than do clinical features, includingage. Furthermore, although age was a good classifier for disease progression, predictivenesscurves revealed that age did not successfully stratify BE patients according to their progressionrisk. Moreover, age  per se  is not an accepted risk marker on which to base clinical decisionsregarding surveillance interval or neoplastic progression risk in BE. In contrast, models basedon both the 8-marker panel and the age-plus-8-marker panel provided estimated progressionrisks either close to 0 ( i.e ., LR) or between 0.1 and 0.5 ( i.e ., IR) in the majority of individuals,suggesting that these markers exerted a substantial impact on risk category. This finding alsosuggests that in clinical practice, separate thresholds can be chosen to define high, intermediate,and low risk, based on predictiveness curves.In conclusion, we have developed a risk stratification strategy to predict neoplastic progressionin BE patients based on an 8-marker tissue methylation panel. At high specificity levels, thismodel accurately predicted approximately half of HGDs and EACs that would not haveotherwise been predicted. This model is expected to reduce endoscopic procedures performedin BE surveillance while simultaneously increasing detection at earlier stages. Future studiesshould explore additional potentially predictive methylation targets, along with alternativemeans of assessing methylation biomarkers (such as immunohistochemical staining forreduced biomarker expression). Thus, these findings suggest that a methylation biomarkerpanel offers promise as a clinically useful tool in the risk stratification of BE patients. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Acknowledgements We recognize the key contributions of Ms. Kim Nicolini to the design and execution of this study.Supported by CA085069, CA001808, CA106763, CA95060, CA062924, CA106991, and the Early DetectionResearch Network Jin et al.Page 5 Cancer Res . Author manuscript; available in PMC 2010 May 15. N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  N I  H -P A A  u t  h  or M an u s  c r i   p t  
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