Biology of Human Tumors

A Novel Epigenetic Signature for EarlyDiagnosis in Lung CancerAngel Diaz-Lagares1, Jesus Mendez-Gonzalez1, David Hervas2, Maria Saigi3,Maria J. Pajares4,5, Diana Garcia1, Ana B. Crujerias6, Ruben Pio4,7,Luis M. Montuenga4,5, Javier Zulueta8, Ernest Nadal3, Antoni Rosell9,Manel Esteller1,10, and Juan Sandoval1

Abstract

Purpose: Lung cancer remains as the leading cause of cancer-related deathworldwide,mainly due to late diagnosis. Cytology isthe gold-standardmethod for lung cancer diagnosis inminimallyinvasive respiratory samples, despite its low sensitivity. We aimedto identify epigenetic biomarkers with clinical utility for cancerdiagnosis in minimally/noninvasive specimens to improve accu-racy of current technologies.

Experimental Design: The identification of novel epigeneticbiomarkers in stage I lung tumors was accomplished using anintegrative genome-wide restrictive analysis of two different largepublic databases. DNA methylation levels for the selected bio-markers were validated by pyrosequencing in paraffin-embeddedtissues and minimally invasive and noninvasive respiratory sam-ples in independent cohorts.

Results: We identified nine cancer-specific hypermethylatedgenes in early-stage lung primary tumors. Four of these genespresented consistent CpG island hypermethylation compared

with nonmalignant lung and were associated with transcrip-tional silencing. A diagnostic signature was built using multi-variate logistic regression model based on the combination offour genes: BCAT1, CDO1, TRIM58, and ZNF177. Clinicaldiagnostic value was also validated in multiple independentcohorts and yielded a remarkable diagnostic accuracy in allcohorts tested. Calibrated and cross-validated epigenetic modelpredicts with high accuracy the probability to detect cancer inminimally and noninvasive samples. We demonstrated thatthis epigenetic signature achieved higher diagnostic efficacy inbronchial fluids as compared with conventional cytology forlung cancer diagnosis.

Conclusions: Minimally invasive epigenetic biomarkers haveemerged as promising tools for cancer diagnosis. The hereinobtained epigenetic model in combination with current diagnos-tic protocols may improve early diagnosis and outcome of lungcancer patients. Clin Cancer Res; 22(13); 3361–71. �2016 AACR.

IntroductionLung cancer is the main cause of death from cancer worldwide

(1). Several factors are associated with the poor outcome of lungcancer patients. One of them is, despite recent advances, thescarcity of effective therapies achieving durable responses. Anoth-er, and evenmore important, factor is late diagnosis, as most lungtumors are detected at advanced stages of the disease (2). This is

crucial, taking into account that survival rates drop substantiallyfrom early to late stages.

In this context, the data reported by early observational studiesand by the randomized National Lung Screening Trial (NLST)have shown that lung cancer screening with low-dose helicalcomputed tomography (LDCT) is able to reduce lung cancermortality, as significantly more cases can be detected in earlierstages (3). Last year, the United States Preventive Services Task

1Cancer Epigenetics and Biology Program (PEBC), Bellvitge Bio-medical Research Institute (IDIBELL), L'Hospitalet, Catalonia,Spain. 2Biostatistics Unit, Medical Research Institute La Fe,Valencia,Spain. 3Department of Medical Oncology, Catalan Institute ofOncology, L'Hospitalet, Barcelona, Spain. 4IDISNA and Program inSolid Tumors and Biomarkers, Centre for Applied Medical Research(CIMA), University of Navarra, Pamplona, Spain. 5Department ofHistology and Pathology, School of Medicine, University of Navarra,Pamplona, Spain. 6Laboratory of Molecular and Cellular Endocri-nology, Health Research Institute of Santiago (IDIS), UniversityHospital of Santiago (XXIS/SERGAS), Santiago de CompostelaUniversity (USC), Travesia da Choupana S/N, Santiago de Compos-tela and CIBER of Physiopathology of Obesity and Nutrition(CIBERobn), Madrid, Spain. 7Department of Biochemistry andGenetics, School of Science, University of Navarra, Pamplona,Spain. 8Pulmonary Department, Clínica Universidad de Navarra,Pamplona, Spain. 9Pneumology Department, Hospital UniversityBellvitge, IDIBELL, L'Hospitalet, Barcelona and CIBER of Respira-tory diseases (CIBERes), Madrid, Spain. 10Catalan Institution forResearch and Advanced Studies (ICREA) and Passeig de LluísCompanys, Barcelona, Catalonia, Spain.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Current address for D. Garcia and J. Sandoval: Laboratory of PersonalizedMedicine, Epigenomics Unit, Medical Research Institute La Fe, Av. FernandoAbril Martorell 106, Valencia, Spain.

A. Diaz-Lagares and J. Mendez-Gonzalez share first authorship.

M. Esteller and J. Sandoval share last authorship.

Corresponding Authors: Juan Sandoval, Laboratory of Personalized Medicine,Epigenomics Unit, Medical Research Institute La Fe, Av. Fernando Abril Martorell106, Valencia 46026, Spain. Phone: 349-6124-6709; Fax: 349-6124-6620; E-mail:juan.sandoval@uv.es; and Manel Esteller, Epigenetics and Biology Program,(PEBC), Bellvitge Biomedical Research Institute (IDIBELL), 3rd Floor, HospitalDuran i Reynals, Av.Gran Via de L'Hospitalet 199, L'Hospitalet de Llobregat,Barcelona 08908, Spain. Phone: 932-607-140; E-mail: mesteller@idibell.cat

doi: 10.1158/1078-0432.CCR-15-2346

�2016 American Association for Cancer Research.

ClinicalCancerResearch

www.aacrjournals.org 3361

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

Force (USPSTF) issued the recommendation to implement annu-al lung cancer screening for smokers with the inclusion criteria oftheNLST.Nevertheless, there are still a significant number of openquestions and areas for optimization of the different aspectsrelated to this screening strategy. For example, there is a need forrisk models and markers to improve the screening cost-benefitratio by better selecting the screened population. Moreover,although the CT-based imaging is a very sensitive technique, itsspecificity is low, and it yields a large proportion of cases withindeterminate nodules, which may require further follow up orinvasive procedures, which may turn out to be futile in thefrequent case of these nodules being benign. Biomarkers for thecorrect classification of the indeterminate nodules and as anadjunct to the diagnostic procedure are a clear unmet clinicalneed (4, 5).

Epigenetic biomarkers, mainly DNA methylation, haveemerged as one of the most promising approaches to improvecancer diagnosis and present several advantages as comparedwith other markers, such as gene expression or genetic signa-tures. DNA methylation alterations are covalent modificationsthat are remarkably stable and often occur early during carci-nogenesis. In addition, DNA methylation can be detected by awide range of sensitive and cost-efficient techniques even insamples with low tumor purity. This epigenetic modificationcan also be detected in different biologic fluids which repre-sents a promising tool for minimally and noninvasive cancerdetection (6). In recent years, different epigenetic candidateshave been proposed, but none has reached the clinic yet,mainly due to the lack of large validation studies or the useof analytical methods difficult to standardize. In addition, moststudies were performed by single candidate-gene hypothesis-driven (7–11), although incipient genome-wide approaches arealso appearing (12). Nowadays, high-throughput epigenomicstudies, that permit an unbiased data-driven research, havebecome a great tool for systematically dissecting the role ofepigenetic variation in cancer with the potential of identifyingnovel and more robust biomarkers (13).

Bronchoscopic examination and pathologic assessment ofcytologic specimen is the most currently used diagnostic method.However, almost half of the cases remain occult, especially inperipherally located tumors (14). This leads to additional invasive

procedures, such as surgical lung biopsy or transthoracic needlebiopsy associatedwith significantmorbidity (15). The implemen-tation of molecular biomarkers, including epigenetic and geneexpression classifiers, in bronchial aspirates or sputum representsa promising approach to improve the accuracy of minimally andnoninvasive neoplasmdiagnosis (16,17). This kindof biomarkerscan also be used to develop clinical tools such as nomograms,which allow calculating the probability of a clinical event. Thesepredictive models can increase the individualized risk assessmentcompared with risk groups leading to a more personalized med-icine (18).

Here, we have identified and validated a signature of DNAmethylation biomarkers already present in early-stage lung cancerand globally absent in normal tissue. For this purpose, we usedtwo different datasets: the CURELUNG FP7 Consortium and theCancer Genome Atlas (TCGA). Subsequently, we tested by pyr-osequencing the selectedbiomarkers in several independent case–control datasets (formalin-fixed paraffin-embedded tissues, bron-chial aspirates, bronchioalveolar lavages, and sputum samples).This study provides a novel epigenetic predictive model that mayhelp to improve lung cancer diagnosis.

Materials and MethodsStudy design and participants

This is a collaborative and retrospective study including datafrom publicly available datasets, formalin-fixed paraffin-embedded (FFPE) tissues, bronchial aspirates/lavages, andsputum samples obtained from lung cancer patients and can-cer-free individuals, as they arrived to the laboratory and passedthe technical quality checks. Genome-wide DNA methylationdata for the discovery cohort (Infinium 450K array) was down-loaded from our previous published lung cancer dataset depos-ited at the Gene expression omnibus (GSE39279; ref. 19) orfrom the TCGA data repository (lung adenocarcinoma LUAD orLung squamous cell carcinoma LUSC). The Biologic validationof the selected methylation biomarkers was conducted bypyrosequencing in four independent cohorts. Lung validatingcohorts were obtained from different institutions in Spain. (i) Atotal of 201 FFPE samples were obtained from Health InstituteCarlos III (ISCIII), Madrid and Centre for Applied MedicalResearch/Hospital of the University of Navarre, (CIMA/CUN)Pamplona. Regarding minimally invasive samples, (ii) 80bronchial aspirates and (iii) 98 sputums were obtained fromCatalan Institute of Oncology and Bellvitge University Hospi-tal, Barcelona. (iv) 111 Bronchioalveolar lavages came fromCIMA, Pamplona and Hospital of Talavera de la Reina, Talaverade la Reina. All DNA extractions form different specimens weredeveloped and run by the same technicians to avoid interla-boratory variation.

The study was approved by the corresponding Institutionalreview board and patients signed up the informed consent toparticipate. The main clinical characteristics of the differentcohorts are described in Table 1 or have been also describedpreviously, as is the case for the discovery cohort (Table 1).

ProceduresPreparation of lung specimens.DNAwas extracted fromminimallyand noninvasive specimens using a standard phenol chloroformextraction method. DNA from FFPE tissue blocks was extractedfrom two sequential unstained sections, each 10-mm thick. For

Translational Relevance

Lung cancer is the leading cause of cancer mortality world-wide. Patient outcome is closely linked to tumor stage atdiagnosis and unfortunately, most lung cancer patients arediagnosed at late stages when a curative treatment is no longerpossible. Using an integrative genome-wide experimentalmethod whereby hundreds of stage I patients from two inde-pendent lung cancer datasets were examined, we identified anepigenetic four-genemodel with diagnostic value for detectinglung cancer. This DNA methylation signature was validatedwith a gene locus–specific technique in three minimally andnoninvasive independent cohorts. The combination of thishighly sensitive and specific epigenetic model with standardclinical markers may help to improve lung cancer diagnosisand therefore decrease current mortality rates.

Diaz-Lagares et al.

Clin Cancer Res; 22(13) July 1, 2016 Clinical Cancer Research3362

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

each sample of tumor tissue, subsequent sections were stainedwith hematoxylin and eosin for histologic confirmation of thepresence (>50%) of tumor cells. Unstained tissue sections weredeparaffinized, andDNAwas extracted using the sameprotocol asforminimally invasive specimens. ExtractedDNAwas checked forintegrity and quantity with 1.3% agarose gel electrophoresis andpicogreen quantification, respectively. Bisulfite conversion of 500ng of DNA for each sample was performed according to themanufacturer's recommendation.

Data prefiltering.DNAmethylation status of 450,000CpGsites byusing the Infinium 450K Methylation array was available at (19).Methylation score of each CpG was represented as beta (b) valueand were previously normalized for color bias adjustment, back-ground level adjustment and quantile normalization acrossarrays. Probes and sample filtering involved a two-step processfor removing SNPs and unreliable betas with high detection PvalueP>0.001. Sex chromosomeprobeswere also removed. After

the filtering, the remaining 409,219 CpGs were considered validfor the study. Stage I patients were selected coming up to 237 lungtumor patients and 25 histologically nontumor lung tissue sam-ples (Fig. 1).

Data filtering for hypomethylated CpGs in nontumoral tissues. Thechoice of region to be studied is one of the critical challenges toestablishing a DNA methylation biomarker that is clinicallyuseful. The investigated region should ideally fulfill the followingcriteria: first, the region should be unmethylated in nontumorcases and methylated in lung cancer cases; and second, themethylation levels of this region should clearly allow the classi-ficationof a sample as non-cancerous or cancerous (20,21).We setthresholds to select homogeneous unmethylated CpGs in non-tumor cases; (i) Average and median of b values lower than 0.1and (ii) the percentile 90 for beta values of control donors lowerthan 0.2. Using these restrictive thresholds, we obtained 133,444filtered CpGs.

Table 1. Clinical characteristics of the invasive [Discovery (A), Validation (B) and Paraffin (C)] and minimally invasive [BAS (D), BALs (E) and Sputum (F)] cohorts

A. Discovery cohort B. TCGA validation cohort C. Paraffin cohort

PatientsTumor(n ¼ 237)

Nontumoral(n ¼ 25)

Tumor(n ¼ 350)

Nontumoral(n ¼ 62)

Tumor(n ¼ 122)

Nontumoral(n ¼ 79)

Age (years) 68 (38–90) 63.5 (39–86) 66.9 (33–90) 66.9 (40–86) 63.8 (40–80) 62.5 (42–85)GenderMale 131 (55.3%) 20 (80%) 190 (54.1%) 36 (58.0%) 108 (88.5%) 66 (83.5%)Female 106 (44.7%) 5 (20%) 160 (45.9%) 26 (42.0%) 14 (11.5%) 13 (16.5%)

Smoking historyCurrent or former smoker 190 (80.1%) 24 (96%) 313 (89.4%) 55 (89.4%) 70 (57.4%) 71 (89.9%)Nonsmoker 25 (10.5%) 1 (4%) 32 (9.1%) 2 (9.1%) 36 (29.5%) 8 (10.1%)Unknown 22 (9.4%) 0 (0%) 5 (1.5%) 5 (1.5%) 16 (13.1%) 0 (0%)

StageI 237 (100%) 350 (100%) 122 (100%)

HistologyAdenocarcinoma 181 (76.4%) 217 (62.1%) 62 (50.8%)Squamous cell carcinoma 56 (23.6%) 133 (37.9%) 60 (49.2%)

Pack-years 40 (0–180) 54.4 (0–184) 46.7 (1–94.5) 46.4 (5–192) 35.9 (0–130) 46.7 (0–141)

Data are average (range) or number (%)D. BAS cohort E. BALs cohort F. Sputum cohort

PatientsLung cancerpatient (n ¼ 51)

Cancer-freedonor (n ¼ 29)

Lung cancerpatient (n ¼ 82)

Cancer-freedonor (n ¼ 29)

Lung cancerpatient (n ¼ 72)

Cancer-freedonor (n ¼ 26)

Age (years) 65.6 (47–85) 64.0 (35–87) 62.1 (38–83) 57.5 (30–82) 65.1 (40–83) 52.7 (29–69)GenderMale 46 (90.2%) 16 (55.2%) 66 (80.4%) 19 (65.5%) 62 (86.1%) 17 (65.4%)Female 5 (9.8%) 10 (34.5%) 16 (19.6%) 9 (31.0%) 7 (9.7%) 9 (34.6%)Unknown 0 (0%) 3 (10.3%) 0 (0%) 1 (3.5%) 3 (4.2%)

Smoking historyCurrent or former smoker 45 (88.2%) 16 (55.2%) 42 (51.2%) 15 (51.7%) 62 (86.1%) 20 (76.9%)Nonsmoker 4 (7.8%) 8 (27.6%) 39 (47.5%) 12 (41.3%) 7 (9.7%) 6 (23.1%)Unknown 2 (4.0%) 5 (17.2%) 1 (1.3%) 2 (7%) 3 (4.2%) 0 (0%)

StageI 5 (9.8%) 17 (20.7%) 12 (16.7%)II 6 (11.8%) 8 (9.8%) 13 (18.0%)III 21 (41.2%) 20 (24.4%) 23 (32.0%)IV 18 (35.3%) 18 (22.0%) 19 (26.4%)Unknown 1 (1.9%) 19 (23.1%) 5 (6.9%)

HistologyAdenocarcinoma 17 (33.3%) 25 (30.5%) 38 (52.7%)Squamous cell carcinoma 19 (37.3%) 40 (48.8%) 24 (33.3%)Large cell carcinoma 2 (4.0%) 2 (2.4%) 2 (3%)Small cell carcinoma 2 (4.0%) 12 (14.6%) 5 (7%)NSCLC (NOS) 11 (21.4%) 3 (3.7%) 3 (4%)

Pack-years 49.6 (0–120) 32.4 (0–100) 45.5 (0–120) 26.3 (0–90) 49.1 (0–120) 24.1 (0–114)

NOTE: Data are average (range) or number (%).Abbreviations: NSCLC (NOS), not otherwise specified.

Epigenetic Signature for Lung Cancer Diagnosis

www.aacrjournals.org Clin Cancer Res; 22(13) July 1, 2016 3363

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

Differentially methylated CpG identification between tumor andnontumor samples. Differentially methylated CpGs betweentumor and nontumor groups were identified using the follow-ing procedure: for each probe/CpG, the sets of methylation bvalues T (belonging to the tumor samples: first group) and NT(belonging to the nontumor lung tissue samples: secondgroup) were compared. The following three measures werecalculated:

1. Differences in average b values between groups higher than aset threshold. (MD ¼ |mT � mNT| > 0.20)

2. Multiple testing correction P value with 95% of confidence toassign significant differentially methylated sites.False discovery rate (FDR) with adjusted P value < 0.05.

3. To maximize differences between tumor and nontumorgroup. Difference between quartile 1 in tumor group andquartile 3 in nontumor should be higher that a selected cut-off. P25 (tumor)-P75 (nontumor) > 0.1

To identify early-stage cancer-related epigenetic markers withdiagnostic value for both subtypes together, these three criteria(1,2,3) were evaluated in three distinct comparisons based onhistologic subtypes.

a) Comparison: Adenocarcinoma (ADC) group (n¼ 181) versusnontumor group (n ¼ 25). Identified 29 differentially meth-ylated CpGs (DMCpG) specific for ADC (SupplementaryTable S1A).

b) Comparison: Squamous cell carcinoma (SCC) group (n¼ 56)versus nontumor group (n ¼ 25). Identified 78 DMCpGsspecific for SCC (Supplementary Table S1B).

c) Comparison: Lung cancer (ADC and SCC; n ¼ 237) versusnontumor group (n¼ 25). Identified 24 significant DMCpGswhen both groups were analyzed together (SupplementaryTable S1C).

Differentially methylated CpGs were selected using an integra-tive approach to rank the Infinium probes based on their meth-ylation status and the fulfillment of all the criteria (1,2,3) andin the three different comparisons (a, b, c). Finally, 12 CpGscorresponding to 9 genes were common in the three comparisonsand ranked by averaged z-score (Supplementary Fig. S1; Supple-mentary Table S2).

PyrosequencingPyrosequencing analyses to determine CpGmethylation status

were developed as described previously (19) to validate the resultsobtained from the arrays. Briefly, a set of primers for PCR ampli-fication and sequencing were designed using a specific softwarepack (PyroMark assay design version 2.0.01.15). Primersequences were designed to hybridize with CpG-free sites toensure methylation-independent amplification (SupplementaryTable S2A). DNA was converted using the EZ DNA MethylationGold (ZYMO RESEARCH) bisulfite conversion kit following themanufacturer's recommendations and used as a template forsubsequent PCR step. PCR was performed under standard

Figure 1.Epigenetic signature in lung primary tumor patients using genome-wide DNA methylation datasets. A, DNA methylation levels of selected genes (BranchedChain Aminoacid Transaminase 1 -BCAT1-, Cysteine Dioxygenase type 1 -CDO1-, Tripartite Motif Containing 58 -TRIM58-, zinc finger protein 177 -ZNF177- andCrystallin, Gamma D -CRYGD-) in primary tumor samples from patients with lung cancer and nontumoral specimens using our FP7 Curelung dataset. B,validation of DNAmethylation values using publicly available dataset fromTCGA. C, expression values for the gene candidates using the TCGAdatabase. P values forall the analyses were calculated using the two-sided Mann–Whitney U test. NT (light gray circle dots) stands for nontumoral and T (dark gray square dots)for tumor. ��� , P < 0.001.

Diaz-Lagares et al.

Clin Cancer Res; 22(13) July 1, 2016 Clinical Cancer Research3364

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

conditions with primers biotinylated to convert the PCR productto single-strandedDNA templates.Weused theVacuumPrepTool(Biotage) to prepare single-stranded PCR products according tomanufacturer's instructions. PCR products were observed at 2%agarose gels before pyrosequencing. Pyrosequencing reactionsand methylation quantification were performed in a PyroMarkQ24 System version 2.0.6 (Qiagen) using appropriate reagentsand protocols, and the methylation value was obtained from theaverage of the CpG dinucleotides included in the sequenceanalyzed, with a minimum of 3 valid CpGs per primer. Onlythose averagemethylation values within the region analyzed withcoefficient of variation lower than 1 were accepted as valid.Controls to assess correct bisulfite conversion of the DNA wereincluded in each run, as well as sequencing controls to ensure thefidelity of the measurements.

Statistical analysisData were summarized by mean, SD, median, and first and

third quartiles in the case of continuous variables and by relativeand absolute frequencies in the case of categorical variables.Differences in expression values and methylation levels amonggroupswere assessed using thenonparametricWilcoxon rank sumtest. Receiver Operating Characteristic (ROC) curves were used toassess the predictive capacity of eachmarker. Area under the curve(AUC) was computed for each ROC curve, and 95% confidenceintervals (CI) were also estimated by bootstrapping with 1,000iterations. A predictive model for each sample type was builtincluding all selected markers in a multivariable logistic regres-sion model. ROC curves and AUC were also computed for thepredictive models. Calibration of the models was assessed byplotting predicted versus observed values obtained by bootstrapresampling of the original data. Internal validation of the modelswas performed using 10-fold crossvalidation. The final predictivemodels were represented in nomograms to facilitate their use byclinicians. Sensitivity and specificitywere estimated at the optimalcut-off point according to Youden criterion. In addition, thesensitivity and specificity curves were estimated for the wholerange of predictions of the model to allow for personalizeddecisions in different clinical scenarios. Globally, a two-tailed Pvalue of less than 0.05 was considered to indicate statisticalsignificance. P values were adjusted for multiple comparisonsusing the FDR procedure by Benjamini and Hochberg. All statis-tical analyseswere performedusingR software (version 3.2.0) andthe pROC R-package (version 1.7.3).

ResultsIdentification and validation of cancer-related methylatedgenes

The discovery cohort consisted of 237 stage I non–small celllung primary tumors (NSCLC) and 25 nontumoral matched lungtissues from the CURELUNG FP7 publicly available dataset (19).DMCpGs were identified by genome-wide DNA methylationanalysis. In this cohort (Table 1A), lung ADC (n ¼ 181,76.3%) was the most frequent histologic subtype, followed bySCC (n ¼ 56, 23.6%). To obtain highly cancer-specific biomar-kers, we focused our analysis in those regions deeply hypomethy-lated in nontumoral tissues. After data filtering and analysis withrestrictive criteria (Supplementary Fig. S1; Supplementary TablesS1 and S2), we obtained 12 significant DMCpGs common to bothsubtypes of NSCLC corresponding to 9 different genes. In cancer

cells, hypermethylation in CpG islands (CGI) is a principalepigenetic mechanism for gene regulation that has been pro-posed as a relevant biomarker with diagnostic value (22).Therefore, the top 5 hypermethylated CGI-containing geneswere selected as candidate biomarkers for further validation inNSCLC: BCAT1, CDO1, TRIM58, ZNF177, and CRYGD (forextended explanation, see Materials and Methods, Fig. 1A, andSupplementary Table S2B).

To confirm these results, we evaluated the DMCpGs of the 5selected biomarkers in an independent cohort (350 stage INSCLCpatients; 62 nontumoral lung samples) from TCGA public data-base. The clinical characteristics of this cohort (Table 1B) resem-bled the previous discovery cohort, including 217 (62.1%) ADCsand 133 (37.9%) SCCs. As expected, themethylation levels of the5 selected genes were similar to the discovery cohort with differ-ence in median values for each gene (DBCAT1: 59%; DCDO1:40%; DTRIM58: 50%; DZNF177: 46%; DCRYGD: 40%) and allwith P values lower than 0.001 (Fig. 1B). In addition, no signif-icant differences were found between ADCs and SCCs (Supple-mentary Fig. S2A). These data confirmed our previous results,suggesting that the methylation of the 5 selected biomarkers is acommon feature for both NSCLC subtypes despite their differ-ences at histologic and molecular level.

Epigenetic silencing of the cancer-specific hypermethylatedgenes in lung cancer primary tumors

Gene expression analysis from the TCGA cohort samplesshowed a significantly decreased expression in BCAT1, CDO1,TRIM58, and ZNF177 (Fig. 1C). However, no expression valueswere detected for CRYGD and this gene was discarded for futureanalysis. Interestingly, expression results were also obtained forADCs and SCCs separately (Supplementary Fig. S2B). Moreover,promoter hypermethylation of multiple consecutive CpGs isrecognized as an important mechanism by which genes may besilenced in both physiologically and pathologic conditions (23).This mechanism for gene silencing has also been shown to play arelevant functional role in the development and progression ofmany common human tumors (24). In this regard, analyzing theCURELUNG and TCGA datasets, we observed a similar methyl-ation pattern between the significant DMCpGs of the selectedbiomarkers and their surrounding CpGs (Supplementary Fig. S3).These results reinforced the role of DNA methylation in thefunctional regulation of BCAT1, CDO1, TRIM58, and ZNF177.Importantly, the data obtained suggest that the methylationvalues of these four genes represent an epigenetic signature thatmay be relevant in early steps of lung carcinogenesis.

Diagnostic utility of the epigenetic signature to detect lungcancer in primary tumors

Once the epigenetic signature was established (BCAT1, CDO1,TRIM58, andZNF177), we evaluated the ability of each individualbiomarker of the four-gene panel to detect lung cancer in primarytumors by using pyrosequencing. This technique is a suitableapproach in a clinical setting because it represents a quantitativeand reproducible method able to detect multiple CpGs not onlyin FFPE tissues but also inminimally and noninvasive samples asbiologic fluids. Therefore, an independent cohort of FFPE primarytumors (122 stage I NSCLC and 79 nonmalignant lung samples)was recruited and DNA methylation levels for all selected geneswere determined by pyrosequencing. Clinical characteristics for

Epigenetic Signature for Lung Cancer Diagnosis

www.aacrjournals.org Clin Cancer Res; 22(13) July 1, 2016 3365

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

this cohort are described in Table 1C. The four biomarkers hadsignificantly higher levels of DNA methylation in tumor samplesas compared with nontumoral controls (Fig. 2A). Next, ROCanalysis was performed to assess the diagnostic value of eachindividual biomarker to detect lung cancer. Importantly, all thegenes of the signature showed significant areas under the ROCcurve (AUC) greater than 0.8 (AUCBCAT1 ¼ 0.94, AUCCDO1 ¼0.84, AUCTRIM58¼ 0.97 and AUCZNF177¼ 0.94), suggesting agreat accuracy of these biomarkers for NSCLC diagnosis (Fig. 2B).Similarly, when samples were classified on the basis of histologicsubtypes (ADC and SCC), we observed for all the biomarkerssignificant differences in methylation status (Supplementary Fig.S4A) and AUCs close to 1.0 (Supplementary Fig. S4B and S4C).These results confirmed the diagnostic value of evaluating DNAmethylation levels by locus-specific PCR based techniques, suchas pyrosequencing.

Validation of the epigenetic signature for lung cancer diagnosisusing minimally invasive respiratory samples: bronchialaspirates and bronchioalveolar lavages

One of the most important aspects for early diagnostics is toidentify markers associated with cancer using minimally invasivemethods for sample collection (25). In line, we collected anindependent cohort of BAS from patients diagnosed with lungcancer (n ¼ 51) and cancer-free patients (n ¼ 29; Table 1D). Thiscohort included different lung cancer subtypes, especially ADCand SCC. We compared by pyrosequencing the median methyl-ation levels and generated ROC curves to assess the performanceof each marker independently. Airways fluids from lung cancer

patients presented significant differences in DNA methylationlevels and high AUCs for all four genes (Fig. 3A and B). Combi-nation of BCAT1, CDO1, TRIM58, and ZNF177 in a logisticregression model yielded a significant AUC of 0.91 [95% CI(0.83–0.98) P < 0.001, Fig. 3C]. Calibration of themodel showedno evident deviations from the ideal identity slope (data notshown). Internal validation of the AUC estimate for this modelyielded optimism corrected AUC of 0.90, showing high general-ization of the predictive capacity of the model for future samples.There were also no evident differences in prediction accuracyamong early and late tumor stages.

A visual representation of the methylation profile for the genesincluded in the model is provided as a heatmap (SupplementaryFig. S5A). A nomogram based on the results of this model isproposed as a predictive tool for clinical diagnostic use. Results ofthe nomogramprovide an individual probability (0%–100%) forsuffering lung cancer for each patient (Supplementary Fig. S5Band Materials and Methods). Evaluation of the full range ofpredictions of the model shows that shifting the cutoff to POC¼ 30% would yield a sensitivity of 100% and a specificity of65.4% and shifting the cutoff to POC ¼ 80% would yield asensitivity of 71.4% and a specificity of 92.3%. Sensitivity andspecificity at the optimal cutoff (probability of cancer; POC ¼63%) were 84.6% and 81.0%, respectively (Fig. 3D). It is impor-tant to point out that current protocols for lung cancer diagnosisare based mainly in bronchioalveolar cytology and further lungbiopsy. There are cases where the cytology is doubtful or incon-clusive. Moreover, there are a notable number of cases wherecytology and biopsy are negative for cancer cells, but there is high

Figure 2.Epigenetic signature in paraffin samples using pyrosequencing. A, DNA methylation levels of candidate genes in paraffin-embedded sections from patients withlung cancer and control donors. P values for all the analyses were calculated using the two-sided Mann–Whitney U test. NT (light gray circle dots) stands fornontumoral and T (dark gray square dots) for tumor. ��� , P < 0.001. B, ROC curves and area under the curve (AUC) with 95% confidence intervals for thecandidate genes.

Diaz-Lagares et al.

Clin Cancer Res; 22(13) July 1, 2016 Clinical Cancer Research3366

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

suspicion of cancer. Our results not only improve the overallprediction accuracy of BAS cytology in this cohort (sensitivity ¼43.8%, specificity ¼ 100%), but also permit a flexible and per-sonalized approach for the clinicians in every possible scenario bysimply adapting the cut-off value of the probabilistic model. Inthis sense, in our cohort 24 of 51 tumor samples were misinter-preted as nontumoral by the cytology test. However, using ourpredictive epigenetic model, 19 out of the 24 false negativecytologies (79%) would have been considered as positive settingour threshold at 50% probability of cancer (Supplementary TableS3). Of note, the majority of them (16/24) with a predictedprobability of cancer higher than 80%. Also three of them wereclassified as borderline nontumor, with a predicted probability ofcancer between 40% and 50%. In these three doubtful cases,clinical patient manage would require a closer follow up. This ledus to propose our epigenetic signature as a useful clinical diag-nostic tool in BAS specimens, especially in doubtful cases.

In addition, we evaluated DNAmethylation levels in BAL frompatients with lung cancer (n ¼ 82) as compared with nonmalig-nant lung diseases (n ¼ 29; Table 1E). The methylationlevels of those four markers were significantly higher in BAL fluidfrom cancer patients than noncancer patients (Fig. 4A). AUCswere significant for all four genes with the following valuesAUCBCAT1 ¼ 0.80, AUCCDO1 ¼ 0.65, AUCTRIM58 ¼ 0.72,andAUCZNF177¼ 0.66 (Fig. 4B). Combination of the four genesin a logistic regression model achieved a significant AUC of 0.85[95% CI (0.78–0.93) P < 0.001], with an optimism-corrected

value of 0.83 (Fig. 4C). Evaluation of the full range of predictionsof the model is also shown (Fig. 4D). As in the case with BASspecimens, our epigenetic signature with diagnostic value may behighly valuable for doubtful patients with negative cytology.

Validation of epigenetic biomarkers in noninvasive sputumsamples

Finally, the methylation level of these 4markers was examinedin additional noninvasive samples. Sputums samples from 72lung cancer patients and 26 cancer-free individuals were consid-ered for evaluation (Table 1F). Methylation levels were signifi-cantly higher in individuals with lung cancer for all the genestested, except for CDO1 (Fig. 5A). Individual AUC values wereAUCBCAT1¼ 0.92, AUCCDO1¼ 0.67, AUCTRIM58¼ 0.67, andAUCZNF177 ¼ 0.69 (Fig. 5B). The multivariable logistic regres-sionmodel yielded an AUC value of 0.93 [95%CI (0.86–1.0), P <0.001; Fig. 5C]. Sensitivity and specificity for the different thresh-old values of themodel are depicted (Fig. 5D). This result suggeststhat ourmarkersmaybe of high value to detect lung cancer even innoninvasive specimens as sputum.

DiscussionLung cancer is the leading cause of cancer-related death world-

wide with 1.3 million deaths annually, following data from theWorld Health Organization (WHO) in 2011. Late diagnosis inlung cancer is one of the main reasons that explain the extremely

Figure 3.Epigenetic signature in bronchial aspirates using pyrosequencing. A, DNA methylation levels in bronchial aspirates from patients with lung cancer and controldonors. NT (light gray circle dots) stands for nontumoral and T (dark gray square dots) for tumor. P values for all the analyses were calculated using the two-sidedMann–Whitney U test. ��� , P < 0.001. B, ROC curves and areas under the curve (AUC) for the selected genes. C, The AUC for the combined signature using alogistic regression model D, sensitivity and specificity profiles for the different possible cut-off values of the results from the logistic regression model.

Epigenetic Signature for Lung Cancer Diagnosis

www.aacrjournals.org Clin Cancer Res; 22(13) July 1, 2016 3367

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

highmortality of this disease.Ononehand, screeningbymeansoflow-dose helical CT (LDCT) has shown to reduce mortality in alarge randomized trial (26); however, the positive predictive valueis still low. On the other hand, low sensitivity associated withminimally invasive cytologies is also a current hurdle for theaccurate diagnosis of lung cancer. Thus, lung cancer diagnosisusingminimally andnoninvasive strategies is amajor challenge toimprove survival and its refinement is urgently needed to ame-liorate the overall mortality figures for lung cancer worldwide.Here, we have searched for powerful biomarkers by using the twolargest publicly available databases (FP7 Curelung and TCGA;ref. 19) with high-throughput data coming from Infinium 450karrays. Only stage I cancer cases were selected to identify themolecular changes associated to earlier steps of cancer evolution.We developed an integrative approach to identify the mostdiscriminative marks leading to a final epigenetic signature con-sisting of top four selected genes: CDO1, BCAT1, TRIM58, andZNF177. We conducted several validation steps using minimallyand noninvasive cohorts to define a consistent epigenetic modeluseful for early lung cancer diagnosis valid for both major histo-logic subtypes. This signature yielded a notably high specificity,one of the Achilles heels of LDCT and other methylation bio-markers (27,28) and also improved sensitivity, which is generallylimited when using cytology for early lung cancer diagnosis.

The current results highlight the relevance of DNAmethylationchanges in the natural history of lung cancer. CpG island hyper-methylation of MGMT and GSTP1 has already proven useful forthe chemotherapy response prediction in gliomas (29–31) and

the screening of prostate cancer, respectively (32,33). DNAmeth-ylation biomarkers have been proposed as promising candidatesfor early diagnosis (20,21) for several reasons: they are covalentand stable marks and they occur as early events in carcinogenesis,even in pretumoral stages such as adenomatous hyperplasia of thelung (34). Great efforts have been undertaken in identifyingsuitable DNA methylation markers to improve lung cancer diag-nosis. However, only one biomarker, SHOX2 methylation, hasbeen commercialized to date (10,35), although is not routinelyused in the clinic.

It is noteworthy to explain that cancer-specific DNA methyla-tion in our selected biomarkers correlated with gene silencing inlung primary tumors. This fact suggests a potential functional rolewith biologic implications in early stages of this pathologicprocess (36). To our knowledge, there is a recent study addressingthis issue with a different approach, taking benefit of the TCGAdatabase: Wrangle and colleagues recently identified a three-genepanel (CDO1, HOXA9, and TAC1) for detecting NSCLC (12).They focused on reexpressed genes after treatment with demethy-lating agents and used TCGA as the only database incorporatingall-stage tumors, not only stage I, among other differences. Inter-estingly, despite using different strategies,CDO1methylationwascommon for both studies. On the other hand, a study combiningmiRNA and gene expression arrays in three lung squamous cellcarcinoma patients has also identified methylation-deregulatedCDO1 (37). CDO1, cysteine dioxygenase type 1, has been pos-tulated as a tumor suppressor gene silenced by promoter meth-ylation in multiple human cancers, including breast, esophagus,

Figure 4.Epigenetic signature in bronchioalveolar lavages using pyrosequencing. A, DNA methylation levels in bronchioalveolar lavages from patients with lung cancerand control donors. NT (light gray circle dots) stands for nontumoral and T (dark gray square dots) for tumor. P values for all the analyses were calculated using thetwo-sided Mann–Whitney U test. ��� , P < 0.001; � , P < 0.05. B, ROC curves and AUC for the selected genes. C, the AUC for the combined signature using alogistic regression model D, sensitivity and specificity profiles for the different possible cut-off values of the results from the logistic regression model.

Diaz-Lagares et al.

Clin Cancer Res; 22(13) July 1, 2016 Clinical Cancer Research3368

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

lung, bladder, and stomach (38). For the other genes, BCAT1(branched chain amino-acid transaminase 1) is a cytosolicenzyme that promotes cell proliferation though amino acidcatabolism (39) and high frequency of methylation on BCAT1promoter in colorectal cancer has been reported (40).ZNF177 is azinc finger transcription factor that has been reported to bemethylation-silenced in gastric cancer cell lines (41). TRIM58,tripartite motif containing 58, is an E3 ubiquitin ligase super-family member that has already been patented as a potentialepigenetic marker for detecting neoplastic cells originating fromlung tissue of NSCLC patients (PCT/EP2012/061852). Moreover,it has alsobeen reportedhypermethylationof Trim58promoter inhepatocytes derived from hepatitis B virus–related hepatocellularcarcinoma (42). It is also worthy to indicate that we were verystringent in the selection of those genes, so alternative analysesfrom the same dataset may identify new DNA methylation bio-markers for lung cancer diagnosis in the future.

The diagnostic value of the epigenetic signature was first val-idated by pyrosequencing in FFPE samples from non–small celllung primary tumors. Results from our four-gene methylationsignature presented high diagnostic accuracy and were extremelysimilar to those obtained from public databases. Importantly, weanalyzed a total of 79 nontumoral control tissues, and DNAmethylation was almost negligible in the vast amount of samples,thus confirming previous results and encouraging the potential ofthe selected markers. Of note, in the study by Wrangle andcolleagues, the methylation status was assessed by using the MSPtechnique, in a smaller number of FFPE nontumoral samples(12). We chose pyrosequencing, as targeted-region validation

technique, because is an affordable and quantitative method thatcounterbalances some weaknesses of previous and extensivelyused methods, due to its easy standardization and lower falsepositive rate (43). Moreover, it is a robust and quantitativemethod able to detect multiple CpGs not only in FFPE tissuesbut also in minimally and noninvasive samples as biologic fluidswith potential use in daily basis clinical settings.

The performance of the epigenetic model in these types ofspecimens, such as BAS, BAL, and sputum was outstandingdespite the limited number of tumoral cells compared with FFPEsamples. The improvement of the diagnosis of lung cancerpatients represents a major challenge. Our epigenetic modelprovides a balanced and flexible approach able to cater to bothextreme scenarios: the high sensitivity and low specificity of lowdose CT in screening programs and the high specificity and lowsensitivity of cytology (44,45) in respiratory specimens routinelyused for lung cancer diagnosis. Our signature improves the pre-dictions of cytology by providing a method for continuous pre-dictions. Cytology is a useful dichotomized classifier producingtwo types of predictions: 100 % positive or 0% positive (100%negative). Therefore, the final output will be either a completesuccess or a total failure. In contrast, our signature based in alogistic regressionmodel, represented by a nomogram, thus beingable to produce a continuous range of predictions between 100%positive and 0% positive (46). That way, not all predictions are acomplete success or a total failure, uncertainty can be measuredfor each prediction and errors are almost always lower (47). Aclinician could take different actions according to the (un)cer-tainty of the predictions, maybe performing additional tests in

Figure 5.Epigenetic signature in sputum samples using pyrosequencing. A, DNA methylation levels in sputums from patients with lung cancer and control donors. NT(light gray circle dots) stands for nontumoral and T (dark gray square dots) for tumor. P values for all the analyses were calculated using the two-sided Mann–Whitney U test. ��� , P < 0.001; �� , P < 0.01; � , P < 0.05. B, ROC curves and AUC for the selected genes. C, the AUC for the combined signature using a logisticregression model. D, sensitivity and specificity profiles for the different possible cut-off values of the results from the logistic regression model.

Epigenetic Signature for Lung Cancer Diagnosis

www.aacrjournals.org Clin Cancer Res; 22(13) July 1, 2016 3369

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

borderline cases. In a virtual situation where our model predictstwo negative samples with different probability of being positive:such as 5% and 49%, the bimodal classifier predictor (cytology)would have output only absolute responses: negative and nega-tive. Therefore, no information about uncertainty and chances ofbeing positive for patient 1 (very low) andpatient 2 (almost 50%)would have been delivered. The combination of current diagnos-tic protocol with new epigenetic nomograms will be of great helpfor diagnosis of lung cancer and consequently improving theoutcome of lung cancer patients (48).

In summary, we have identified and independently validateda powerful epigenetic signature diagnosis of lung cancer inminimally and noninvasive samples. Genome-wide DNAmethylation analyses led us to identify 4 candidates that havebeen tested not only in publicly available datasets, but also inextensive and independent cohorts of respiratory samples. Theherein identified epigenetic model, once it will be validated inintended of use samples such as in patients with suspiciousindeterminate lung nodules, might be extremely helpful tosolve these clinical issues with current diagnostic protocolsand define more precise screening programs for lung cancer.In addition, novel and more sensitive methods, currently indevelopment, such as Methyl-Beaming or droplet digital PCR(49) could enhance their diagnostic value for the managementof suspicious lung nodules in the clinic or within a program oflung cancer screening.

Disclosure of Potential Conflicts of InterestJ. Zulueta has ownership interest (including patents) in VisionGate, Inc. No

potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: A. Diaz-Lagares, J. Mendez-Gonzalez, J. SandovalDevelopment of methodology: A. Diaz-Lagares, J. Mendez-Gonzalez,A.B. Crujeiras, M. Esteller, J. Sandoval

Acquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): J. Mendez-Gonzalez, M. Saigi, M.J. Pajares, R. Pio,L.M. Montuenga, J. Zulueta, E. Nadal, A. Rosell, M. Esteller, J. SandovalAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): A. Diaz-Lagares, J. Mendez-Gonzalez, D. Hervas,D. Garcia, E. NadalWriting, review, and/or revisionof themanuscript:A.Diaz-Lagares, J.Mendez-Gonzalez, D. Hervas, L.M. Montuenga, E. Nadal, A. Rosell, J. SandovalAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases):M. Saigi, M.J. Pajares, D. Garcia, R. Pio, J. Zulueta,E. Nadal, A. RosellStudy supervision: J. Mendez-Gonzalez, J. Sandoval

AcknowledgmentsThe authors thank Ana Remírez, Ana Moreno, Carles Arribas, Laia Seto, Lola

Gonz�alez, Alejandro Fern�andez, and Cesar García for technical support.

Grant SupportThis work was supported by the "Miguel Servet" (CP00055) grant, FIS grant

(PI08/1042) from the FEDER, FSE and Carlos III Health Institute (ISCIII), andPR185/13 research grant from the Mutua Madrile~na Foundation. A. Diaz-Lagares was supported by a Río Hortega (CM14/00067). A.B. Crujeiras andJ. Mendez-Gonzalez are "Sara Borrell" researchers (C09/00365and CD13/00335 from the "ISCIII"). E. Nadal is supported by a Juan Rod�es fellowshipfrom the ISCIII (JR13/0002). L.M.Montuenga, R. Pio, andM.J. Pajares' workwassupported by the "Instituto de SaludCarlos III" projects PI13/00806 and RD12/0036/0040. M. Esteller is an ICREA Research Professor and J. Sandoval isa "Miguel Servet" researcher (CP13/00055). J. Sandoval, L.M. Montuenga,J. Zulueta, E. Nadal, and M. Esteller are supported by the "Red tem�atica deinvestigaci�on cooperativa en cancer" (RTICCS); groups: RD 12/0036/0062,RD12/0036/0040, RD12/0036/0039, and RD12/0036/0045.

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 September 29, 2015; revised December 23, 2015; accepted January16, 2016; published OnlineFirst February 3, 2016.

References1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin

2013;65:5–29.2. International Early Lung Cancer Action Program Investigators, Henschke

CI, Yankelevitz DF, Libby DM, Pasmantier MW, Smith JP, et al. Survival ofpatients with stage I lung cancer detected on CT screening. N Engl J Med2006;355:1763–71.

3. Aberle DR, Adams AM, Berg CD, BlackWC, Clapp JD, FagerstromRM, et al.Reduced lung-cancer mortality with low-dose computed tomographicscreening. N Engl J Med 2011;365:395–409.

4. Massion PP,Walker RC. Indeterminate pulmonary nodules: risk for havingor for developing lung cancer?Cancer Prev Res 2014;7:1173–8.

5. Massion PP. Biomarkers to the rescue in a lung nodule epidemic. J ClinOncol 2014;32:725–6.

6. Heyn H, Esteller M. DNA methylation profiling in the clinic: applicationsand challenges. Nat Rev Genet 2012;13:679–92.

7. Belinsky SA, Klinge DM, Dekker JD, Smith MW, Bocklage TJ, Gilliland FD,et al. Gene promoter methylation in plasma and sputum increases withlung cancer risk. Clin Cancer Res 2005;11:6505–11.

8. Topaloglu O, Hoque MO, Tokumaru Y, Lee J, Ratovitski E, Sidransky D,et al. Detection of promoter hypermethylation of multiple genes in thetumor and bronchoalveolar lavage of patients with lung cancer. ClinCancer Res 2004;10:2284–8.

9. Geng J, Sun J, Lin Q, Gu J, Zhao Y, Zhang H, et al. Methylation status ofNEYROG2 and NID2 improves the diagnosis of stage I NSCLC. Oncol Lett2012;3:901–6.

10. Schmidt B, Liebenberg V, Dietrich D, Schlegel T, Kneip C, SeegebarthA, et al. SHOX2 DNA methylation is a biomarker for the diagnosisof lung cancer based on bronchial aspirates. BMC Cancer 2010;10:600

11. Nikolaidis G, Raji OY, Markopoulou S, Gosney JR, Bryan J, Warburton C,et al. DNAmethylation biomarkers offer improved diagnostic efficiency inlung cancer. Cancer Res 2012;72:5692–701.

12. Wrangle J, Machida EO, Danilova L, Hulbert A, Franco N, Zhang W, et al.Functional identification of cancer-specificmethylation ofCDO1,HOXA9,and TAC1 for the diagnosis of lung cancer. Clin Cancer Res 2014;20:1856–64.

13. Sandoval J, Esteller M. Cancer epigenomics: beyond genomics. Curr OpinGenet Dev 2012;22:50–5.

14. Griffin JP, Zaman MK, Niell HB, Tolley EA, Cole FHJr, Weiman DS.Diagnosis of lung cancer: a bronchoscopist's perspective. J BronchologyInterv Pulmonol 2012;19:12–8.

15. KaarteenahoR. The current positionof surgical lungbiopsy in thediagnosisof idiopathic pulmonary fibrosis. Respir Res 2013;14:43.

16 Liloglou T, Bediaga NG, Brown BR, Field JK, Davies MP. Epigeneticbiomarkers in lung cancer. Cancer Lett 2014;342:200–12.

17. Silvestri GA, Vachani A,WhitneyD, ElashoffM, Porta-SmithK, Ferguson JS,et al. A bronchial genomic classifier for the diagnostic evaluation of lungcancer. N Engl J Med 2015;373:243–51.

18. LowranceWT, ScardinoPT. Predictivemodels for newlydiagnosedprostatecancer patients. Rev Urol 2009;11:117–26.

Diaz-Lagares et al.

Clin Cancer Res; 22(13) July 1, 2016 Clinical Cancer Research3370

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

19. Sandoval J, Mendez-Gonzalez J, Nadal E, Chen G, Carmona FJ, Sayols S,et al. A prognostic DNA methylation signature for stage I non-small-celllung cancer. J Clin Oncol 2013;31:4140–7.

20. Belinsky SA. Gene-promoter hypermethylation as a biomarker in lungcancer. Nat Rev Cancer 2004;4:707–17.

21. Mikeska T, Bock C, Do H, Dobrovic A. DNA methylation biomarkers incancer: progress towards clinical implementation. Expert Rev Mol Diagn2012;12:473–87.

22. Sandoval J, Peiro-Chova L, Pallardo FV, García-Gimenez JL. Epigeneticbiomarkers in laboratory diagnostics: emerging approaches and opportu-nities. Expert Rev Mol Diagn 2013;13:457–71.

23. Esteller M. Epigenetic gene silencing in cancer: the DNA hypermethylome.Hum Mol Genet 2007;16:R50–9.

24. Baxter E, Windloch K, Gannon F, Lee JS. Epigenetic regulation in cancerprogression. Cell Biosci 2014;4:45.

25. Heitzer E, Ulz P, Geigl JB. Circulating tumor DNA as a liquid biopsy forcancer. Clin Chem 2015;61:112–23.

26. National Lung Screening Trial Research Team, Church TR, Black WC,Aberle DR, Berg CD, Clingan KL, et al. Results of initial low-dosecomputed tomographic screening for lung cancer. N Engl J Med 2013;368:1980–91.

27. Palmisano WA, Divine KK, Saccomanno G, Gilliland FD, Baylin SB, Her-man JG, et al. Predicting lung cancer by detecting aberrant promotermethylation in sputum. Cancer Res 2000;60:5954–8.

28. Leng S,DoK, Yingling CM, PicchiMA,Wolf HJ, Kennedy TC, et al. Defininga gene promoter methylation signature in sputum for lung cancer riskassessment. Clin Cancer Res 2012;18:3387–95.

29. Esteller M, Garcia-Foncillas J, Andion E, Goodman SN, Hidalgo OF,Vanaclocha V, et al. Inactivation of the DNA-repair gene MGMT and theclinical response of gliomas to alkylating agents. N Engl J Med 2000;343:1350–4.

30. Hegi ME, Diserens AC, Gorlia T, HamouMF, de Tribolet N, Weller M, et al.MGMT gene silencing and benefit from temozolomide in glioblastoma. NEngl J Med 2005;352:997–1003.

31. Barault L, Amatu A, Bleeker FE, Moutinho C, Falcomat�a C, Fiano V, et al.Digital PCR quantification of MGMT methylation refines prediction ofclinical benefit from alkylating agents in glioblastoma and metastaticcolorectal cancer. Ann Oncol 2015;26:1994–9.

32. Jer�onimo C, Usadel H, Henrique R, Oliveira J, Lopes C, Nelson WG, et al.Quantitation of GSTP1methylation in non-neoplastic prostatic tissue andorgan-confined prostate adenocarcinoma. J Natl Cancer Inst 2001;93:1747–52.

33. Hoque MO, Topaloglu O, Begum S, Henrique R, Rosenbaum E, VanCriekinge W, et al. Quantitative methylation-specific polymerasechain reaction gene patterns in urine sediment distinguish prostatecancer patients from control subjects. J Clin Oncol 2005;23:6569–75.

34. Licchesi JD, Westra WH, Hooker CM, Herman JG. Promoter hypermethy-lation of hallmark cancer genes in atypical adenomatous hyperplasia of thelung. Clin Cancer Res 2008;14:2570–8.

35. Dietrich D, Kneip C, Raji O, Liloglou T, Seegebarth A, Schlegel T, et al.Performance evaluationof theDNAmethylation biomarker SHOX2 for theaid in diagnosis of lung cancer based on the analysis of bronchial aspirates.Int J Oncol 2012;40:825–32.

36. Belinsky SA. Silencing of genes bypromoter hypermethylation: key event inrodent and human lung cancer. Carcinogenesis 2005;26:1481–7.

37. Kwon YJ, Lee SJ, Koh JS, Kim SH, Lee HW, Kang MC, et al. Genome-wideanalysis of DNA methylation and the gene expression change in lungcancer. J Thorac Oncol 2012;7:20–33.

38. Brait M, Ling S, Nagpal JK, Chang X, Park HL, Lee J, et al. Cysteinedioxygenase 1 is a tumor suppressor gene silenced by promoter methyl-ation in multiple human cancers. PLoS One 2012;7:e44951.

39. T€onjes M, Barbus S, Park YJ, Wang W, Schlotter M, Lindroth AM, et al.BCAT1 promotes cell proliferation through amino acid catabolism ingliomas carrying wild-type IDH1. Nat Med 2013;19:901–8.

40. Mitchell SM, Ross JP,DrewHR,HoT, BrownGS, SaundersNF, et al. A panelof genes methylated with high frequency in colorectal cancer. BMC Cancer2014;14:54.

41. Yamashita S, Tsujino Y, Moriguchi K, Tatematsu M, Ushijima T. Chemicalgenomic screening for methylation-silenced genes in gastric cancer celllines using 5-aza-20-deoxycytidine treatment and oligonucleotide micro-array. Cancer Sci 2006;97:64–71.

42. Tao R, Li J, Xin J, Wu J, Guo J, Zhang L, et al. Methylation profile of singlehepatocytes derived from hepatitis B virus-related hepatocellular carcino-ma. PLoS One 2011;6:e19862.

43. Doyle B, O'Riain C, Appleton K. Pyrosequencing of DNA extracted fromformalin-fixed paraffin-embedded tissue. Methods Mol Biol 2011;724:181–90

44. Dobler CC, Crawford AB. Bronchoscopic diagnosis of endoscopicallyvisible lung malignancies: should cytological examinations be carried outroutinely?Intern Med J 2009;39:806–11.

45. Van't Westeinde SC, van Klaveren RJ. Screening and early detection of lungcancer. Cancer J 2011;17:3–10.

46. Harrell FEJr. Regression modeling strategies 2nd edition. New York:Springer; 2015. p.4–6.

47. Dawid AP. Statistical Theory: The Prequential Approach. Journal of theRoyal Statistical Society. Series A (General). The 150th Anniversary of theRoyal Statistical Society; 1984;147:278–92.

48. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms inoncology: more than meets the eye. Lancet Oncol 2015;16:e173–80.

49. Wiencke JK, Bracci PM,HsuangG, Zheng S,HansenH,WrenschMR, et al. Acomparison of DNAmethylation specific droplet digital PCR (ddPCR) andreal time qPCR with flow cytometry in characterizing human T cells inperipheral blood. Epigenetics 2014;9:1360–5.

www.aacrjournals.org Clin Cancer Res; 22(13) July 1, 2016 3371

Epigenetic Signature for Lung Cancer Diagnosis

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346

2016;22:3361-3371. Published OnlineFirst February 3, 2016.Clin Cancer Res   Angel Diaz-Lagares, Jesus Mendez-Gonzalez, David Hervas, et al.   A Novel Epigenetic Signature for Early Diagnosis in Lung Cancer

  Updated version

  10.1158/1078-0432.CCR-15-2346doi:

Access the most recent version of this article at:

  Material

Supplementary

  http://clincancerres.aacrjournals.org/content/suppl/2016/02/03/1078-0432.CCR-15-2346.DC1

Access the most recent supplemental material at:

   

   

  Cited articles

  http://clincancerres.aacrjournals.org/content/22/13/3361.full#ref-list-1

This article cites 47 articles, 12 of which you can access for free at:

  Citing articles

  http://clincancerres.aacrjournals.org/content/22/13/3361.full#related-urls

This article has been cited by 3 HighWire-hosted articles. Access the articles at:

   

  E-mail alerts related to this article or journal.Sign up to receive free email-alerts

  Subscriptions

Reprints and

  .pubs@aacr.org

To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department at

  Permissions

  Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)

.http://clincancerres.aacrjournals.org/content/22/13/3361To request permission to re-use all or part of this article, use this link

on October 17, 2020. © 2016 American Association for Cancer Research. clincancerres.aacrjournals.org Downloaded from

Published OnlineFirst February 3, 2016; DOI: 10.1158/1078-0432.CCR-15-2346