an aggressive subtype of stage i lung adenocarcinoma with...

Personalized Medicine and Imaging

An Aggressive Subtype of Stage I LungAdenocarcinoma with Molecular andPrognostic Characteristics Typical ofAdvanced Lung CancersElisa Dama1,2, Valentina Melocchi1, Fabio Dezi1, Stefania Pirroni1, Rose Mary Carletti1,2,Daniela Brambilla3, Giovanni Bertalot1, Monica Casiraghi3, Patrick Maisonneuve4,Massimo Barberis5, Giuseppe Viale5,6, Manuela Vecchi1,2, Lorenzo Spaggiari3,6,Fabrizio Bianchi1,7, and Pier Paolo Di Fiore1,2,6

Abstract

Purpose: The National Lung Cancer Screening Trial has con-firmed that lung cancer mortality can be reduced if tumors arediagnosed early, that is, at stage I. However, a substantial fractionof stage I lung cancer patients still develop metastatic diseasewithin 5 years from surgery. Prognostic biomarkers are thereforeneeded to identify patients at risk of an adverse outcome, whomight benefit from multimodality treatment.

Experimental Design: We extensively validated a 10-geneprognostic signature in a cohort of 507 lung adenocarcinomapatients using formalin-fixed paraffin-embedded samples. Fur-thermore,weperformed an integrated analysis of gene expression,methylation, somatic mutations, copy number variations, andproteomic profiles on an independent cohort of 468patients fromThe Cancer Genome Atlas (TCGA).

Results: Stage I lung cancer patients (N¼ 351) identified as high-risk by the 10-gene signature displayed a 4-fold increased risk ofdeath [HR¼ 3.98; 95% confidence interval (CI), 1.73–9.14], with a3-yearoverall survivalof84.2%(95%CI,78.7–89.7) comparedwith95.6%(92.4–98.8) in low-riskpatients. The analysisofTCGAcohortrevealed that the 10-gene signature identifies a subgroup of stage Ilung adenocarcinomas displaying distinct molecular characteristicsand associated with aggressive behavior and poor outcome.

Conclusions: We validated a 10-gene prognostic signaturecapable of identifying a molecular subtype of stage I lung ade-nocarcinoma with characteristics remarkably similar to those ofadvanced lung cancer. We propose that our signature might aidthe identification of stage I patients who would benefit frommultimodality treatment. Clin Cancer Res; 23(1); 62–72.�2016 AACR.

IntroductionLung cancer is the primary cause of cancer-related death world-

wide (1). Survival of patients with non–small cell lung cancer

(NSCLC), the predominant type of lung cancer, accounting forapproximately 85% of all lung cancer cases, largely depends ontumor stage at diagnosis; only approximately 15% of all patientswith advanced disease (stage III–IV) are alive after 5 years, whilesurvival increases to approximately 60% in patients diagnosedwith stage I disease (1). Thus, efforts have been devoted to thedevelopment of strategies for early lung cancer detection. Inparticular, annual low-dose CT (LDCT) screening in high-riskindividuals (>55 years and smokers, >30 pack/year) was shown tobe effective in diagnostic anticipation, resulting in a reduction inmortality (2–5).

As our ability to detect NSCLC in its early stage improves, theissue of the clinical management of stage I patients is becomingincreasingly relevant. As of today, a sizable fraction of stage INSCLCpatients (up to�40%) develops disease recurrencewithin5 years from surgery. Stage I NSCLC is treated preferentially bysurgery, as the benefit of adjuvant chemotherapy in these patientsremains controversial (6–9). However, prognostic biomarkerscould change this scenario by allowing the stratification ofstage I patients according to risk of disease recurrence and theselection of those patients whomight benefit frommultimodalitytreatment.

We previously described a 10-gene signature able to predictprognosis of patientswith stage I lung adenocarcinoma, themajorsubtype of NSCLC (10). Subsequently, other prognostic gene

1Molecular Medicine Program, European Institute of Oncology, Milan, Italy.2IFOM, The FIRC Institute for Molecular Oncology Foundation, Milan, Italy.3Division of Thoracic Surgery, European Institute of Oncology, Milan, Italy.4Division of Epidemiology and Biostatistics, European Institute of Oncology,Milan, Italy. 5Division of Pathology, European Institute of Oncology, Milan, Italy.6DIPO, Department of Oncology and Hemato-Oncology, University of Milan,Milan, Italy. 7Institute for Stem-cell Biology, Regenerative Medicine and Inno-vative Therapies (ISBReMIT), Casa Sollievo della Sofferenza - IRCCS, SanGiovanni Rotondo, Italy.

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

E. Dama and V. Melocchi contributed equally to this article.

L. Spaggiari, F. Bianchi, and P.P. Di Fiore share last authorship.

Corresponding Authors: Fabrizio Bianchi, IRCCS Casa Sollievo della Sofferenza,Via Cappuccini 1, San Giovanni Rotondo 71013, Italy. Phone: 3908-8241-0954;Fax: 3908-8220-4004; E-mail: [email protected]; and Pier Paolo DiFiore, European Institute of Oncology, Via Ripamonti 435, Milan 20141, Italy.Phone: 3902-9437-5198; Fax: 3902-9437-5991; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-15-3005

�2016 American Association for Cancer Research.

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expression signatures in lung cancer have been proposed,although few of them were validated in large and independentcohorts of patients (11, 12). We now developed and optimized aPCR-based method for assessing our 10-gene signature in forma-lin-fixed paraffin-embedded (FFPE) tissue samples. Using thisoptimized protocol, we validated the prognostic accuracy of the10-gene signature in an independent [obtained from a differenthospital with respect to that of our original report (10)] and largecohort of 507 lung adenocarcinoma patients, including 351 stageI lung patients (AJCC, 7th edition). Furthermore, we performedan integrated analysis of gene expression, methylation, somaticmutations, copy number variations (CNV), and proteomic pro-files on a second independent cohort of 468 lung tumors profiledby The Cancer Genome Atlas (TCGA; ref. 13) consortium. Thislatter "multiomics" analysis revealed that the 10-gene signatureidentifies an aggressive subtype of stage I lung adenocarcinomawith molecular characteristics similar to more advanced cancers.Notably, we found a prevalence of KEAP1/Nrf2 pathway altera-tions, which is particularly relevant in light of ongoing effortsaimed at the identification of small-molecule inhibitors targetingNrf2 (14, 15).

Materials and MethodsStudy population and data sourcesIEO stage I cohort. Between July 1998 and December 2012, 425patients with stage I (IA and IB, according to the 7th edition of theTNMClassification ofMalignant Tumors) adenocarcinoma of thelung underwent surgery at the European Institute of Oncology(IEO, Milan, Italy). None of these patients received preoperativechemotherapy or had been previously diagnosed with cancer.Clinical information was obtained through review of medicalrecords. Vital status was assessed through the Vital RecordsOfficesof the patients' towns of residence or by contacting directly thepatients or their families. Total mRNA was successfully extractedfrom 359 FFPE blocks. To avoid any possible bias in the estimateof patients' survival due to the inclusion of perioperative deaths, 3patients who died within 30 days of resection were excluded from

the analysis. Five samples with spurious qRT-PCR results, due topoor quality mRNA, were also excluded from analysis. In total,therefore, specimens from 351 patients with stage I lung adeno-carcinoma were analyzed and divided into a training (N ¼ 189)and a validation (N ¼ 162) set. The clinical–pathologic informa-tion of the 351 patients is reported in Table 1.

IEO stage II–III cohort. We randomly identified a second cohortof 156 patients, the "advanced cancer set," from the cohort ofpatients with stage II and III adenocarcinomas, operated at IEOduring the same period as the patients of the stage I cohort, andusing the same inclusion criteria.

RNA extraction, qRT-PCR, and optimization of the protocol inFFPE samples

We initially compared the efficiency of detection of the targetgenes of interest inRNAextracted from fresh-frozen andFFPE lungspecimens, by comparing paired FFPEand fresh-frozen tumor andnormal samples (3 each) by qRT-PCR analysis (SupplementaryFig. S1A–S1D). Up to 5 tissue sections (5 mmthick) were preparedfrom each specimen according to tumor size and/or adequatetumor cellularity (>60%). Total RNA was extracted from fresh-frozen tissues using TRIzol, followed by RNeasy Mini Spin Col-umns (RNeasy FFPE Kit, Qiagen) and from FFPE tissues using theAllPrep DNA/RNA FFPE Kit (Qiagen). Total RNA (200 ng),measured using the NanoDrop ND-1000 Spectrophotometer,was reverse transcribed (SuperScript VILO cDNA Synthesis Kit,

Translational Relevance

We validated a 10-gene prognostic signature capable ofidentifying an aggressive molecular subtype of stage I lungadenocarcinoma, with genetic characteristics remarkably sim-ilar to advanced lung cancer. Although the overall prognosticoutcome of stage I NSCLC is favorable, up to 40% of thesepatients will eventually relapse with metastatic disease. Thisissue of high-risk stage I patients is becoming increasinglyrelevant to the clinical management of non–small cell lungcancer (NSCLC) as our ability to detect the disease in its earlyphases improves. Risk stratification tools may guide clinicaldecisionmaking in patientswith stage I lung cancer at high riskof disease recurrence, whowouldbe eligible formultimodalitytreatment. In this study, we moved toward the clinical appli-cation of this signature by (i) testing it in large independentcohorts of NSCLC patients; (ii) assessing the robustness of itsprognostic power across different technological platforms;(iii) reducing it into practice in a closer-to-the-clinic setting,represented by FFPE specimens.

Table 1. Patient and tumor characteristics

Trainingset(stage I)

Validationseta

(stage I)

Advancedcancer setb

(stage II–III)

N 189 162 156Age at surgery (years)Median (Q1;Q3) 65 (59;71) 65 (59;71) 65 (60;70)Min–max 42–84 42–83 39–81

Gender (female) 48 (25.4%) 59 (36.4%) 43 (27.6%)Smoking historyCurrent/former 144 (76.2%) 126 (77.8%) 128 (82.1%)Never 21 (11.1%) 23 (14.2%) 15 (9.6%)Unknown 24 (12.7%) 13 (8.0%) 13 (8.3%)

StageI 189 162 0IA 90 (47.6%) 78 (48.1%) —

IB 99 (52.4%) 84 (51.9%) —

II 0 0 105 (67.3%)IIA — — 64 (41.0%)IIB — — 41 (26.3%)

III 0 0 51 (32.7%)IIIA — — 47 (30.1%)IIIB — — 4 (2.6%)

Follow-upDeaths within 3 years 19 (10.1%) 15 (9.3%) 51 (32.7%)Survivors length of follow-up<1 yr 4 (2.1%) 6 (3.7%) 10 (6.4%)1–2 yrs 0 7 (4.3%) 5 (3.2%)2–3 yrs 1 (0.5%) 30 (18.5%) 5 (3.2%)>3 yrs 165 (87.3%) 104 (64.2%) 85 (54.5%)

Total pt-yrs 527 424 357aComparisonof distributionsbetween training andvalidation sets.Wilcoxon testfor age: P ¼ 0.69. Fisher exact test for gender: P ¼ 0.03; smoking history:P ¼ 0.29.bComparison of distributions between training and advanced cancer sets.Wilcoxon test for age: P ¼ 0.80. Fisher exact test for gender: P¼ 0.71; smokinghistory: P ¼ 0.36.

A Novel Molecular Subtype of Stage I Lung Adenocarcinoma

www.aacrjournals.org Clin Cancer Res; 23(1) January 1, 2017 63




Thermo Fisher Scientific) and preamplified using the PreAmpMaster Mix Kit (Thermo Fisher Scientific) for 10 cycles, followingthe manufacturer's instructions. The resulting cDNA was thendiluted 1:10 prior to PCR analysis (2 ng of cDNA/reaction). Incomparison, 1.0 mg of total RNA extracted from fresh-frozen andFFPE samples was reverse transcribed and directly analyzed byquantitative PCR without preamplification (5 ng of cDNA/reac-tion for fresh-frozen samples and 20 ng/reaction for FFPE sam-ples). qRT-PCR was performed using hydrolysis probe (ThermoFisher Scientific) with the SsoAdvanced Universal Probes Super-mix (Bio-Rad Laboratories) in a LightCycler 480 real-time PCRinstrument (Roche). Thermal cycling amplification was per-formed with an initial incubation at 95�C for 30 seconds, fol-lowed by 45 cycles of 95�C for 5 seconds and 60�C for 30 seconds.For the design of custom TaqMan assays (listed in SupplementaryTable S1), we employed Primer Express Software V3.0 (ThermoScientific). For accurate sample normalization, we included the 3reference genes (TBP, HPRT1, and GUSB), used for the identifi-cation of the original 10-gene signature (10) from fresh-frozensamples, and one additional reference gene (ESD).

For the expression analysis of the 10-gene signature in theretrospective NSCLC study cohorts (IEO-stage I and IEO-stageII–III), total RNA was extracted from one tissue core (1.5 mmin diameter) taken from FFPE blocks in representative tumorareas with adequate tumor cellularity (>60%), selected by apathologist. We employed the AllPrep DNA/RNA FFPE Kitautomated on QIAcube, following the manufacturer's instruc-tions (Qiagen).

We also assessed the effect of long-term storage of FFPEsamples on RNA quality, using the cohort of lung cancerpatients profiled in this study. The qRT-PCR analysis revealeda trend toward an increase of Cq values as a function of the ageof the FFPE samples, which is consistent with augmenteddegradation of RNA, as described previously (16). With theexception of TBP gene, which seemed not to be affected by timeof storage, all the other reference genes shared a similar profilethat was maintained in almost all the genes of the 10-genesignature (Supplementary Fig. S1E–S1F). For this reason, theTBP gene was replaced by ESD as reference gene, together withGUSB and HRPT1, for the assessment of the prognostic signif-icance of the 10-gene signature.

We defined Cq ¼ 35 as our limit of detection; Cq values abovethis limit were set to 35 for further calculations. For each genein each sample, the expression level was measured in triplicate,and the average Cq was calculated (the average was calculatedfrom triplicate values when the SD of replicates was <0.4 andfrom the best duplicate values when SD was �0.4). Data(average Cq) were normalized using the 3 genes (HPRT1, ESD,GUSB) as reference. The normalized CqðCq

normalizedÞ of each

target gene (E2F1, E2F4, HOXB7, HSPG2, MCM6, NUDCD1,RRM2, SCGB3A1, SERPINB5, SF3B1) was calculated using thefollowing formula:

Cqnormalized

¼ average Cq þ SF

where SF is the difference between a constant reference value Kand the average Cq value of the reference genes; K represents themean of the average Cq of the 3 reference genes calculated acrossall samples (K ¼ 23.66). This normalization strategy allows theretention of information about the abundance of the original

transcripts, as measured by PCR (i.e., in CT scale), which isconversely lost when using the more classical DCt method. Nor-malized data were then processed for statistical analysis.

Survival analysisCox regression model to calculate a continuous risk score. The ridge-penalizedCox regressionmodelwas implemented on the trainingset considering the normalized gene expression of the 10 genes inthe prognostic signature as continuous covariates with log-lineareffect. Cross-validated (10-fold) log-likelihood (CVL) with opti-mization of the tuning penalty parameter was applied. Tuning ofthe penalty parameter was repeated 500 times using a differentfolding at each simulation, and the model associated with thehighest CVL was selected (17–19).

A continuous risk score was assigned to each patient based on:

Risk score ¼Xi

bi�average Cq

normalized

� �

where i is the summation index for E2F1, E2F4, HOXB7, HSPG2,MCM6, NUDCD1, RRM2, SCGB3A1, SERPINB5, and SF3B1; b isthe ridge-penalized Cox model coefficient for each target gene;and average Cq

normalizedis the normalized average Cq for each

target gene.Minimum andmaximum risk scores from the training set were

used to scale risk scores in a 0 to 1 range. Themedian of scaled riskscores from the training set was used as a cutoff to categorizepatients into high- and low-risk groups (Supplementary Tables S2and S3).

Sample size calculation for stage II–III cohortThe sample size was calculated to detect a minimum of 15%

difference in the 3-year overall survival for high- and low-riskgroups (6–9). We applied the formula proposed by Schoenfeld(20) assuming 80% power, one-sided 5% significance level, 50%of cases assigned to the high-risk group, and a 3-year survival forstage II–III lung adenocarcinomas of 55% to 65% (based on ourprevious experience).We assumed a 20%drop in the total samplesize due to inadequate tissue blocks, poor quality mRNA, andexclusion of perioperative deaths. Two hundred patients wereselected and, after tissue blocks and qRT-PCR data quality controland exclusion of perioperative deaths, a total of 156 patients werefinally included in the analysis. Clinical and pathologic charac-teristics of these 156 patients and of the original cohort are shownin Supplementary Table S4.

In silico analysis of the TCGA datasetDatabase. We downloaded genomics data for a cohort of 468patients with lung adenocarcinoma available in the TCGA dataportal (http://cancergenome.nih.gov; see Supplementary TableS5 for patient information). In addition, for further validation ofthe 10-gene signature, we downloaded gene expression data for acohort of 442 patients with lung adenocarcinoma available inGEO database (DCC dataset; GSE68465; http://www.ncbi.nlm.nih.gov/geo/). Overall survival was defined as the time from thedate of tumor resection until death from any cause and estimatedby the Kaplan–Meier method. Follow-up was truncated at 3 yearsto reduce the potential overestimation of overall mortality withrespect to lung cancer–specific mortality.

Dama et al.

Clin Cancer Res; 23(1) January 1, 2017 Clinical Cancer Research64




Gene expression analysis.We used "reads per kilobase of transcriptper million reads mapped" (RPKM) data from the TCGA patientcohort or MAS5 signal (as it was originally performed by authors)from the DCC patient cohort to perform hierarchical clusteringanalysis. Data were log2 transformed and median centered. Toreduce the complexity of the two datasets and extract the mostinformative data, we analyzed the SD of gene expression dataacross samples and selected genes belonging to the upper 25%(SD >Q3) of the SD distribution for a total of 5,126 genes of the20,501 annotated genes (TCGA; IDs are available in Supplemen-tary Table S6A) and 4,238 genes of the 13,513 annotated genes(DCC). We then used the significance analysis of microarray(SAM) to identify differentially expressed genes (delta ¼1.03578 and fudge factor ¼ 0.03554, TCGA; delta ¼ 0.76071and fudge factor¼ 0.11973, DCC; FDR (q-value) < 0.05 was usedas cutoff). The uncentered correlation and the centroid linkagemethods were used to cluster gene expression data in Cluster 3.0for Mac OS X (http://bonsai.hgc.jp/�mdehoon/software/cluster/software.htm). Hierarchical clustering structures and heatmapswere generated using Java TreeView 1.1.1 (http://jtreeview.sour-ceforge.net). JMP 10 (SAS) generated Kaplan–Meier survival plotsbased on clustering analysis together with calculation of P valuesbased on the log-rank test. The Ingenuity Pathway Analysis (IPA)and Upstream Regulator Analysis were performed using theonline available web tool (http://www.ingenuity.com/).

Somatic mutation analysis. The BI Mutation Calling Level 2 DNA-seqdata available for 445of 468patients (TCGA)were used in ouranalysis. Silent mutations and redundant mutations, due toreplicate sequencing, were discarded. The gene mutation rate("MutGene rate") of patients in every stage/cluster was computedas follows:

MutGene rate ¼ Mi

Pi

whereMi is the number ofmutated genes in stage/cluster i;Pi is thetotal number of patients in each stage/cluster i.

The number ofmutated genes in each stage/clusterwas countedusing a binary annotation: for each patient–gene pair, a score of 1was assigned when one or more mutations were present and 0 inthe absence of any mutations. See Supplementary Table S5 forpatient–cluster correspondence.

Frequency of mutation of each gene was calculated by dividingthe total number of patients found with a given mutated gene bythe total number of patients.

CNV analysisWe used the cBIO data portal (21) to download SNP array data

(GISTIC-preanalyzed data; http://gdac.broadinstitute.org), whichwere available for 466 of 468 patients (TCGA). We used theCGDS-R package to retrieve GISTIC calls of CNVs and annotatedthe dataset with tumor stage and cluster information (see Sup-plementary Table S5 for patient–cluster correspondence). Wecomputed a CNV rate considering amplifications (GISTICvalue ¼ 2) and deletions (GISTIC value ¼ �2) together, as ageneral index for CNVs, and then calculated:

CNV rate ¼ VSiPi

where VSi is the number of altered genes (amplified/deleted) instage/cluster i; Pi is the total number of patients in stage/cluster i.

Methylation analysisIllumina Infinium HumanMethylation450 BeadChip data

were available for 402 of 468 samples (TCGA). Patients withunknown tumor stage (13 of 402) were removed from furtheranalyses. Quality control, preprocessing, and normalization wereperformed using RnBeads R package (http://rnbeads.mpi-inf.mpg.de). One sample was marked as "unreliable" during qualitycontrol step and removed; the final dataset resulted in 388samples that were used in our analysis (Supplementary TableS5). b values, representing the percentage of methylation for eachCpG site in a sample, were extracted for promoter regions. Toreduce the complexity of the dataset and extract the most infor-mative data, we first analyzed the SD of b values calculated acrosssamples. We then selected the most informative (i.e., those withthe highest variability) 7,134 promoters (out of 28,534 uniquepromoters; IDs are available in Supplementary Table S6B),belonging to the upper 25% (SD >Q3) of the SD distribution.SAM was then used to identify differentially methylated genesusing this set of 7,134 promoter regions (delta value ¼ 1.19456;fudge factor¼ 0.02526; FDR (q-value) < 0.05 was used as cutoff).

Hierarchical clustering analysis was performed using GeneCluster 3.0 for Mac OSX (http://bonsai.hgc.jp/�mdehoon/soft-ware/cluster/software.htm). Methylation data were clusteredusing the uncentered correlation and centroid linkage methods.Tree pictures were generated using Java TreeView software (http://jtreeview.sourceforge.net).

Protein expression analysisReverse-phase protein array data relative to 190 proteins (Sup-

plementary Table S6C) were available for 234 of 468 patients(TCGA). Data were downloaded from The Cancer Protein Atlas(22). SAM was used to identify the differentially expressed pro-teins (delta value ¼ 1.01291; fudge factor ¼ 0.02792; FDR(q-value) < 0.05 was used as cutoff). Normalized protein expres-sion data (level 3) were used in clustering and statistical analyses.Hierarchical clustering analysiswas performedusingGeneCluster3.0 forMacOSX (http://bonsai.hgc.jp/�mdehoon/software/clus-ter/software.htm). Expression data were clustered using uncen-tered correlation and centroid linkagemethods. Tree picturesweregenerated using Java TreeView software (http://jtreeview.source-forge.net).

ResultsOptimization of qRT-PCR conditions for the analysis of the10-gene signature in FFPE samples

The 10-gene prognostic signature was originally derived usingRNAextracted from frozen lung tissue specimens from lung cancerpatients (10). To apply this signature to FFPE samples, we firstoptimized the procedure for measuring target gene expressionusing lower quality, partially degraded, RNA, as is typicallyobtained from FFPE samples. First, we designed qRT-PCR probesand primers targeting short regions of each transcript (<100 bp),preferably in the 3'UTR region of the gene (Supplementary TableS1).We also introduced a gene-specific preamplification reaction,which allowed us to increase the number of qRT-PCR reactionswecould perform on each sample and to improve the signal-to-noiseratio.






We then profiled paired fresh-frozen and FFPE samples (3normal lung and 3 lung tumors) to compare the gene detectionefficiency under the two conditions. We observed that the averageCq in FFPE sampleswithout the preamplification stepwas approx-imately 5 Cq higher than that of the matching fresh-frozensamples. This is equivalent to approximately 32 times fewercopies of detected mRNAs in the FFPE samples compared withthe fresh-frozen samples (Supplementary Fig. S1A). Importantly,adding the preamplification step before the qRT-PCR in the FFPEsamples resulted in a decrease of approximately 7-8 Cq (Supple-mentary Fig. S1B) and improved the correlation between FFPEand fresh-frozen samples (0.63 vs. 0.43; R2 of bivariate linear fit;P < 0.0001; Supplementary Fig. S1C–S1D).

Validation of the 10-gene signature in a cohort of FFPE lungadenocarcinomas

We applied the optimized protocol to profile a consecutivecohort of 351 patients with stage I lung adenocarcinoma and 156patients with more advanced lung adenocarcinoma (stage II–III,i.e., the "IEO cohort"; Fig. 1A; Table 1). Overall survival in theentire groupof 507patientswasdependent, as expected, on tumorstage, and no differences were observed between stage IA and IBpatients (Fig. 1B).

Stage I patients were divided into training (N ¼ 189) andvalidation (N ¼ 162) sets. The two sets were balanced for overallsurvival (P ¼ 0.94; Supplementary Fig. S2A). Using the trainingset, we fitted a ridge-penalized Cox regressionmodel to generate acontinuous risk score from the expression data of the 10-genesignature that was then assigned to each patient (SupplementaryTable S2). Patients were classified at high- or low-risk of death iftheir risk scores were above, or below, the median risk (i.e., thecutoff; Supplementary Table S3) calculated in the training set(Supplementary Fig. S2B and S2C).

In the validation set, 83 patients were classified at high risk and79 patients at low risk according to the 10-gene signature (Table2). The 3-year overall survival was 84.0% [95% confidence inter-val (CI), 76.6–91.4] for patients at high risk and 95.6% (91.4–99.8) for patients at low risk (Fig. 1C). The risk of death wassignificantly higher for high-risk compared with low-risk patientsboth in univariate andmultivariate analyses (HR¼ 4.04; 95%CI,1.14–14.31; P ¼ 0.03; univariate, HR ¼ 4.20; 1.18–14.99; P ¼0.03, multivariate; Table 2). Of note, the risk model improvedwhen the "10-gene risk" was added to traditional clinical andpathologic parameters (age, sex, smoking status, and tumor stage;P ¼ 0.01, nested likelihood ratio test; Table 2). No significantimprovement was observed in model fit adding clinical andpathologic parameters to the 10-gene signature (P¼ 0.43, nestedlikelihood ratio test; Table 2).

To test the performance of the 10-gene signature in a largercohort, we decided to pool together training and validation sets.We are aware of the possible bias related to present resubstitutionstatistics (11); however, as the performance of the signature wasvery similar in the training and validation sets (training set: HR¼3.93; 1.31–11.85; P ¼ 0.02; validation set: HR ¼ 4.04; 1.14–14.31; P¼ 0.03), we assumed that the possible bias introduced bythepoolingprocedurewas trivial.Whenboth setswere consideredtogether (N¼ 351), 178 patients were found to be at high risk (27deaths) and 173 at low risk (7 deaths) according to the 10-genesignature, with a 3-year overall survival of 95.6% (92.4–98.8) inlow-risk patients compared with 84.2% (78.7–89.7) in high-riskpatients (Fig. 1D). Of note, high-risk patients diagnosed with a

stage IA tumor had an approximately 4-fold increased risk ofdeath, similarly to patients diagnosedwith a stage IB tumor (stageIA: HR ¼ 4.04; 1.11–14.66; P ¼ 0.03; stage IB: HR ¼ 3.83; 1.29–11.39; P ¼ 0.02). Importantly, the 10-gene signature remainedprognostic alsowhenwe considered the disease-specificmortality(cause of death available for 23 of 34, 68%, see SupplementaryTable S7): HR ¼ 4.39; 1.48–13.06; P ¼ 0.01.

Finally, we assessed whether the 10-genemodel was prognosticin more advanced lung tumors (stage II–III; 156 patients). Weobserved an increase in survival at 3 years of more than 7% forlow-risk compared with high-risk patients, although the prognos-tic statistical significance of the 10-gene signature could not beconfirmed (HR ¼ 1.40; 0.80–2.45; P ¼ 0.23; Fig. 1E).

In silico analysis of the 10-gene signature using TCGA dataWe probed into the molecular characteristics of another large

and independent cohort of lung cancer patients using the 10-genesignature (see Supplementary Table S5 for patient information).We took advantage of the TCGA (13) lung cancer databasecontaining whole -omic data (i.e., transcriptional, mutational,CNV, DNA methylation, and proteomic profiles) of 468 lungadenocarcinoma patients (247 stage I; Fig. 2). As these tumorswere profiled byRNA sequencing (RNA-seq) andnot by qRT-PCR,we could not apply directly our risk model to the TCGA data dueto the different dynamic ranges of the two methodologies. How-ever, we performed a hierarchical clustering analysis of RNA-seqdata corresponding to the 10-gene signature, which highlightedfour main gene expression clusters (Fig. 2A). Of these, cluster C1displayed a significant increase in the expression of 8 of the 9geneswhose upregulation in the 10-gene signature associateswithpoor prognosis (10) and a decrease in the expression of SCGB3A1,which is downregulated in the 10-gene signature (SupplementaryFig. S2D–S2E; ref. 10). Consistently, survival analysis revealedthat patients (all stages) in cluster C1 displayed a more adverseprognosis (35% overall survival at 3 years vs. �70% in the otherclusters; Fig. 2B). Results were comparable when we restricted theanalysis to stage I lung cancer patients (N¼ 247; C1 patients: 55%overall survival at 3 years vs.�85% in the other clusters; Fig. 2B) orwhen we used another independent cohort of lung adenocarci-noma profiled by Affymetrix [N ¼ 442; the DCC dataset (23);Supplementary Fig. S3A], despite the absence of the expressionprofile of two genes of the 10-gene signature (SCGB3A1 andNUDCD1) that were not present in the Affymetrix Chip (HG-U133A). These results further confirmed the prognostic signifi-cance of the 10-gene signature in independent cohorts of lungcancer patients, screened by RNA-seq or Affymetrix, both for stageI and stage II–IV disease.

Next, we analyzed, in the four identified clusters, the muta-tional profile of 18 genes, previously identified as frequentlymutated in lung adenocarcinoma (13).We observed an increasedmutation rate in cluster C1, which was independent to smokingstatus (Supplementary Table S8; Supplementary Fig. S2F), bothwhen considering all stages or stage I alone (Fig. 2C). Importantly,stage I tumors clustered in C1 (C1-stage I) had a mutation ratecomparable to more advanced stage tumors (stage II–IV) and aCNV rate higher than all other groups (Fig. 2D). Consistently,prognosis ofC1-stage I patientswas similar to that of patientswithmore advanced lung cancer (P¼ 0.10; Fig. 2E). These results arguethat C1-stage I tumors, identified using the 10-gene signature,represent a distinct subtype of stage I tumors that shares geneticand clinical characteristics withmore advanced lung tumors. This

Dama et al.





A

Stage IN = 351

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isk

168 159 149 133183 174 161 136105 91 80 65 51 39 30 20

IEO - All stagesB C

D E

IEO Cohort

Training setN = 189

Training

10-geneCox model

Validation

Validation setN = 162

IEO CohortStage II−III

0 6

0 6

0 6

Advanced cancerset

N = 156

Ove

rall

surv

ival

0

20

40

60

80

100

12 18 24 30 36

Low riskHigh risk

173 168 159 138 178 165 151 131

Months

IEO Training + validation - stage I

P = 0.0004

0 6

Pts

. at r

isk

Pts

. at r

isk

Pts

. at r

isk

Figure 1.

The 10-gene prognostic model. A, Overall study design. B, Kaplan–Meier survival curves of all IEO lung cancer patients included in the study stratified bystage (IA, IB, II, III); IEO cohort stage I plus stage II–III (N¼ 507). C, Kaplan–Meier survival curves of all IEO patients with stage I disease in the validation set stratifiedusing the 10-gene model (N ¼ 162). D, Kaplan–Meier survival curves of all IEO patients with stage I disease (i.e., the training set plus the validation set, N ¼ 351)stratified using the 10-gene model. E, Kaplan–Meier survival curves of the "advanced cancer set" (N ¼ 156) of IEO patients with stage II–III lung stratifiedusing the 10-gene model. B–E, Numbers of patients at risk at 0, 12, 24, and 36 months are reported underneath the graphs. Log-rank test P values are reported.






notion is further supported by the large difference in gene expres-sion (2, 349 genes, q<0.05),DNAmethylation (1,199promoters,q < 0.05), and protein (25 proteins, q < 0.05) expression profilesobserved when we compared C1-stage I tumors with other stage Itumors (Fig. 3A–C; Supplementary Table S6A–S6C). Conversely,the differencewas smaller whenC1-stage I tumorswere comparedwith more advanced lung cancer (Supplementary Fig. S4).

Finally, we explored the mechanisms that could potentiallyexplain the enhanced aggressiveness of C1-stage I tumors byanalyzing the "C1-stage I" gene expression signature (2,349genes) with the Ingenuity (IPA) Upstream Regulator Analysis,which predicts the activation/inhibition of specific modulatorsof gene expression (e.g., transcription factors, miRNAs). Weidentified 183 potential upstream modulators predicted to beregulated in C1-stage I tumors (|z-score| > 2; see SupplementaryTable S9). These modulators display activity on the majority ofgenes present in the C1-stage I signature (N ¼ 1,568, 66.8%),which span a multitude of cancer-relevant mechanisms (Sup-plementary Table S10).

The top hit in the list of modulators was the redox-sensitivetranscription factor, nuclear factor (erythroid-derived 2)-like 2,also known as NFE2L2 or Nrf2, (z-score ¼ 8.4; SupplementaryTable S9). Nrf2 is involved in the oxidative stress response throughthe transcriptional activation of genes encoding antioxidants,xenobiotic detoxification enzymes, and drug efflux pumps (Sup-plementary Fig. S5; ref. 24). Nuclear accumulation of Nrf2 and thetranscriptional activation of target genes have been observed inlung cancer, concomitantlywith thedecreased activity of theKelch-like ECH-associated protein 1 [KEAP1 (25)]. Indeed, KEAP1 func-tions as an adaptor protein for the Cul3-based E3 ligase, which, inturn, targets NRF2 to 26S proteasomal degradation (26, 27).Importantly and consistently, we found KEAP1 among the "inhib-ited"modulators in theTCGAdataset by the IPA (z-score¼�2; Fig.3D; Supplementary Table S9), and also when we used the DCCdataset (z-score¼�1.7; Supplementary Table S11; SupplementaryFig. S6A). We also observed a 2-fold increase in the mutationfrequency of KEAP1 in C1 patients compared with other non-C1patients (27% vs. 13%, all stages; 25% vs. 13%, stage I; 28% vs.12%, stage II–IV; Fig. 3E; Supplementary Table S12), while nodifferences were detected when comparing copy number or meth-ylation status (Supplementary Table S12). These mutations spantheBTB–BACKdomainsandKelchmotif ofKEAP1(Fig. 3F),whichare important regions for protein–protein interactions and candecrease KEAP1-inhibitory activity on Nrf2 (25, 28). Indeed, weobserved a significant upregulation of NRF2 target genes in stage Itumors with mutated KEAP1 gene (Supplementary Fig. S6B).Finally, high-risk patients in the IEO cohort displayed significantupregulation of the KEAP1/NRF2–regulated genes in the TCGAdataset (Supplementary Fig. S6C; Supplementary Table S14).

DiscussionAlthough the overall prognostic outcome of stage I NSCLC is

favorable, up to 40%of these patients will eventually relapse withmetastatic disease. This issue of high-risk stage I patients isbecoming increasingly relevant to the clinical management ofNSCLC as our ability to detect the disease in its early phasesimproves (29). Risk stratification toolsmay guide clinical decisionmaking in patients with stage I lung cancer at high risk of diseaserecurrence, who would be eligible for multimodality treatment.However, this approach is hampered by the high inter- andintratumoral genetic heterogeneity of lung cancer (13, 30, 31).Genomics studiesmight help in identifyingmolecular subtypes ofstage I NSCLC associated with poor prognosis, as exemplified byour previous discovery of a 10-gene prognostic signature (10).

In this study, we moved toward the clinical application of thissignature by (i) testing it in large independent cohorts of NSCLCpatients; (ii) assessing the robustness of its prognostic poweracross different technological platforms; and (iii) reducing it intopractice in a closer-to-the-clinic setting, represented by FFPEspecimens. Combining our current and past results, the 10-genesignature has now been analyzed in 1,487 lung adenocarcinomapatients from three different case collections comprising patientsfrom Italy or from other countries, and across different platforms(507 by qRT-PCR on FFPE, 468 by RNA-seq, and 442 by Affyme-trix in the current work, and 70 by RT-qPCR on fresh frozen inref. 10). In all instances, the signature identified poor prognosisstage I patients. Although the strength of our results may besomehow limited by the lack of a multicentric validation of the10-gene risk model, it is worth noting that the 10-gene signaturewas capable of identifying groups of patients with an adverseprognosis in independent cohorts of patients, analyzed withdifferent screening platforms (RNA-seq and Affymetrix). Eventu-ally, however, a final assessment of clinical utility of this 10-genesignature will only derive from randomized prospective studies toevaluate mortality reduction in patients identified at high risk byour 10-gene signature treated with multimodality therapy.

The availability of TCGA multiomics dataset allowed furthermolecular insights into the subgroup of stage I patients withadverse prognosis (the C1 cluster) identified by the 10-genesignature. In particular, it allowed the identification of a distinctmutational, gene expression, DNA methylation, and proteinexpression profiles arguing that they represent a molecular sub-group of stage I tumors. Notably, we identified in stage I-C1tumors a 2-fold increase in the mutation rate of the KEAP1 geneversus non-C1 stage I tumors. KEAP1 is a known suppressor ofthe Nrf2 transcription factor, which is involved in the transcrip-tional activation of genes encoding antioxidants, xenobioticdetoxification enzymes, and drug efflux pumps, which protect

Table 2. Cox proportional hazards models in the stage I validation set (N ¼ 162; N deaths ¼ 15)

UnivariateMultivariate without

risk classa Multivariatewith risk classa

N N deaths HR (95% CI) Pb HR (95% CI) Pb HR (95% CI) Pb

10-gene risk high (vs. low) 83 12 4.04 (1.14–14.31) 0.03 — 4.20 (1.18–14.99) 0.03Age �65 (vs. <65) 86 10 1.78 (0.61–5.22) 0.29 1.72 (0.57–5.14) 0.33 1.96 (0.66–5.87) 0.23Male (vs. female) 103 11 1.54 (0.49–4.84) 0.46 1.24 (0.38–4.07) 0.72 1.18 (0.37–3.81) 0.78Never smoker (vs. smoker/unknown) 23 1 0.46 (0.06–3.50) 0.45 0.44 (0.05–3.54) 0.44 0.49 (0.06–3.93) 0.50Stage IB (vs. IA) 84 10 1.95 (0.67–5.71) 0.22 1.76 (0.59–5.28) 0.31 1.83 (0.62–5.42) 0.27aNested likelihood ratio test to compare themodelwith 10-gene risk class added to themodelwith clinical andpathologic parameters:P¼0.01; nested likelihood ratiotest to compare the model with clinical and pathologic parameters added to the model with 10-gene risk class: P ¼ 0.43.bWald test P value.

Dama et al.





normal cells from oxidative stress (32, 33). In cancer, geneticalterations of KEAP1 lead to its inactivation and, consequently,to the nuclear accumulation of Nrf2, which was shown tocontribute to oxidative stress- and chemotherapy resistance(34–36). The potential implications of our findings are 2-fold.First, stage I lung tumors classified as high risk by the 10-genesignature might be (or might become) more resistant to micro-environmental stresses, such as reactive oxidative species, pro-duced during hypoxia, a frequently occurring condition in

cancer tissues (37–39). Second, patients with more advancedcancer classified as high risk could become resistant to adjuvantchemotherapy (platin-based). Thus, alternative therapies, suchas molecularly targeted ones, might be more appropriate forthese patients. This latter possibility is particularly interesting inlight of current efforts aimed at the identification of small-molecule inhibitors targeting Nrf2 (14, 15).

Recently, two other signatures were proposed for the stratifica-tion of early-stage NSCLC (12, 40, 41). The characteristics of these

E2F4 E2F1 MCM6 RRM2 NUDCD1 HOXB7 SERPINB5 SF3B1 HSPG2 SCGB3A1

EGFR

KRAS TP53

NF1

CDKN2A

KEAP1 STK11

SMARCA4

SETD2

ARID1A BRAF

PIK3CA

MGA

MET

RBM10

RIT1

RB1

U2AF1

2.51.91.9 2.1

TP53KRAS

KEAP1NF1

STK11EGFR

ARID1ABRAFMGA

RBM10SMARCA4

PIK3CARB1MET

SETD2CDKN2A

U2AF1RIT1

5228161413121088766665521

1.9 2.1 1.9 2.3

1.51.71.92.12.32.5

100125150175200225

II−IV

II−IV II−IV

Log2 ratio

Survival

C1C2C3C4

Cluster ID

A

B

C

Stage

3210

−1−2−3

Stage I All stages

Ove

rall

surv

ival

P = 0.01

Smokers/ex-

Never-smokers

Female

Male

NA

All stages

SmokingGender

5430

15

12998887765422

17

14 Frequency (%

)

Stage I

SmokingGender

Frequency (%

)

MutGene rate

MutGene rate

II−IV

Mut

Gen

e ra

te

D

Stage

Mutated

Wild type

Stage

CN

V r

ate

Ove

rall

surv

ival

P < 0.0001

I-otherI-C1

StageE

0

20

40

60

80

100

0

20

40

60

80

100

Months0 6 12 18 24 30 36

Months0 6 12 18 24 30 36

Months0 6 12 18 24 30 36

C1

C2

C3

C4

Cluster ID

I-oth

erI-C

1

I-oth

erI-C

1

P = 0.02

P = 0.9

P < 0.001

P < 0.001

Months0 6 12 18 24 30 36

Ove

rall

surv

ival

0

20

40

60

80

100Stage II−IV

DeadStage IStage II−IV

P = 0.002

P = 0.10 P < 0.0001

Months0 6 12 18 24 30 36

Ove

rall

surv

ival

020406080

100

Ove

rall

surv

ival

020406080

100 Stage

Figure 2.

Multiomics analysis of the 468 TCGAlung adenocarcinoma patientsstratified by the 10-gene signature.A, Hierarchical cluster analysis of theentire cohort of 468 patients usingRNA-seq data corresponding to the 10genes of the signature. Yellow,increased expression; blue, decreasedexpression. The four main identifiedclusters are indicated by a color code(red, cluster 1-C1; green, cluster 2-C2;orange, cluster 3-C3; blue, cluster4-C4). The "survival" bar below theheatmap indicates death events inblack, while the "stage" bar indicatesstage I disease in gray and moreadvanced stage disease (stages II–IV)in violet. B, Kaplan–Meier survivalcurves of all patients in the TCGAcohort (left), or limited to stage I(N ¼ 247) or to stage II–IV (N ¼ 221)lung cancer patients (middle and right,respectively), using the "Cluster IDs"as grouping parameters defined bythe hierarchical clustering analysis asdescribed in A. P values werecomputed by using the log-rank test.C, Mutational analysis of the 18 mostfrequently mutated genes in lungadenocarcinoma in all the TCGApatients with available information(445 patients, left) or limited to stage Ilung cancer patients (238 patients,right). Columns represent the patientsordered by the hierarchical clusteringanalysis and labeledwith cluster IDs asin A. Rows represent the mutationalprofile of each of the 18 genes acrosspatients. The overall gene mutationrate is reported for each cluster (topbar, "MutGene rate"). Frequency ofmutation of each gene is reported tothe right of the diagram. Smokingstatus and gender of patients areshown by colored bars in the lowerpart of the diagram. D, MutGene rateand CNV rate in lung cancer patientsstratified by stage. Stage I patientswere subdivided into those belongingto C1 (I-C1; red) or not (I-other; violet).P values were computed using theexact Poisson test. E, Kaplan–Meiersurvival curves of patients grouped asin D. P values were computed usingthe log-rank test.






BTB BACK K1 K1 K1 K1 K1 K1

Log2 β

I-otherI-C1Stage

BA

Log2ratio

1.0

0.50.0

−0.5−1.0

I-otherI-C1Stage

I-otherI-C1Stage

C

Smokers/ex-

Never-smokers

Female

Male

NA

Smokers/ex-

Never-smokers

Female

Male

NA

Smokers/ex-CP: NRF2-mediated oxidative stress response

Never-smokers

Female

Male

NA

Log2Ratio

SmokingGender

SmokingGender

SmokingGender

1.0

0.50.0

−0.5−1.0

1.191.61

0.822.09

D

Mut

atio

n fr

eque

ncy

(%) P = 0.056P < 0.001

Inhibited

Modulator Target

Upregulated

E F

Missense Nonsense/Indel/Splice

P = 0.009

−2.0−1.0 0.0 1.0 2.0

0 100 200 300 400 500 624 aa

0

5

# M

utat

ions

KEAP1

C1Oth

er

Stage I

KEAP1

All stages

05

10

1520

2530

C1Oth

er

Stage II−IV

C1Oth

er

Figure 3.

Multiomics analysis of C1-stage I patients and of the other stage I lung adenocarcinoma patients of the TCGA cohort.A,Hierarchical cluster analysis using the 2,349 genelist found to be transcriptionally regulated by SAM analysis (q < 0.05). Yellow, increased expression; blue, decreased expression. B, Hierarchical clustering analysisusing the methylation profile of 1,199 promoters found to be regulated by SAM analysis (q < 0.05). b values, percentage of methylation for each CpG site ina sample. Red, increased methylation; blue, decreased methylation. C, Hierarchical cluster analysis using the 25 proteins found regulated by SAM analysis (q < 0.05).Red, increased protein expression; light blue, decreased protein expression. In clusters, C1-stage I patients were labeled in red (I-C1), whereas the other stage Ipatients are labeled in violet (I-other).A–C,Smoking status and gender of patients are shownby coloredbars in the lower part of the diagrams. NA, not available.D,Genenetwork of KEAP1-regulated genes present in the "C1-stage I" gene expression signature (2,349 genes; q < 0.05). Blue, IPA-predicted inhibited upstream modulator,KEAP1. Lines connect the modulator to direct targets, and orange color indicates consistency between the predicted activity and the expression change observedinC1-stage I versusother-stage I patients (i.e., target expression). Numbers indicateexpression change (log2) of targets inC1-stage I patientsversusother-stage I patients.E, Mutation frequency of KEAP1 in lung adenocarcinoma patients by tumor stage. Patients were subdivided in those belonging to C1 (C1) or not (other). P valueswere computed using the c2 test for difference in proportion. F, Mutation plot of the KEAP1 protein in lung adenocarcinoma patients. Protein domains are indicatedby colored sections in the cartoon. BTB, the BTB domain (Broad-Complex, Tramtrack, and Bric-a-brac); BACK, the BTB/Kelch-associated domain; K1, the Kelchrepeat type 1 motif. Lines with colored ellipses indicate the positions of mutations in the gene, whereas the length of the lines indicates the number of mutations(# mutations) observed in the same position across tumor samples. Green, missense mutations; red, nonsense, indel, or splice site mutations.


Dama et al.




signatures, in comparison with the 10-gene signature, are reportedin Table 3. Although a direct comparison of the performances ofthe three signatures is currently impossible, we took an initial stepin this direction, testing the ability of the two published signaturesto stratify the tumors in the TGCA dataset. We found that unlikeour 10-gene signature, which was able to identify stage I NSCLCpatients with an adverse prognosis in this dataset, the other twosignatures failed to do so (Supplementary Fig. S3B and S3C). Thisfinding should not be taken at this stage as an indication of thesuperiority of our signature, but rather as evidence that additionalwork is needed to compare the various prognostic models in thesame cohorts of patients, possibly with the intent of integratingthem toward higher accuracy and prognostic power.

This latter possibility is particularly important from the perspec-tive of applying current stratificationmodels to the design of clinicaltrials for adjuvant chemotherapy inearly-stageNSCLC. In such trials,itwill be important to limit thenumberof "goodprognosis"patientsundergoing unnecessary treatment by carefully establishing thecutoff used to classify patients at high risk. This decision shouldtake into account the trade-off between the numbers of overtreatedand undertreated patients (Supplementary Fig. S2C) and might beaided by the integration of multiple prognostic models.

Disclosure of Potential Conflicts of InterestF. Bianchi and P.P. Di Fiore are listed as co-inventors of a patent, which is

owned by Instituto FIRC di Oncologia Molecolare (IFOM), related to methodsof diagnosis and prognosis of cancer, including lung cancer (NSCLC). Nopotential conflicts of interest were disclosed by the other authors.

DisclaimerThe study funders had no role in the design of the study, the collection,

analysis, and interpretation of the data, the writing of the manuscript, and thedecision to submit the manuscript for publication.

Authors' ContributionsConception and design: E. Dama, G. Bertalot, G. Viale, L. Spaggiari, F. Bianchi,P.P. Di FioreDevelopment of methodology: E. Dama, M. Vecchi, F. BianchiAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): E. Dama, S. Pirroni, R.M. Carletti, D. Brambilla,G. Bertalot, M. Casiraghi, P. Maisonneuve, M. Barberis, G. Viale, M. Vecchi,L. Spaggiari, F. BianchiAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): E. Dama, V. Melocchi, F. Dezi, P. Maisonneuve,F. BianchiWriting, review, and/or revision of the manuscript: E. Dama, V. Melocchi,F. Dezi, P. Maisonneuve, G. Viale, M. Vecchi, L. Spaggiari, F. Bianchi, P.P. DiFioreAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): E. Dama, V. Melocchi, S. Pirroni, R.M. Carletti,M. Barberis, F. BianchiStudy supervision: L. Spaggiari, F. Bianchi, P.P. Di Fiore

AcknowledgmentsWe are indebted to Francesca de Santis, Flavia Troglio, and Giovanna Jodice

for technical support. We also thank the Molecular Pathology Unit of theMolecular Medicine Program at IEO and the Division of Thoracic Surgery andthe Division of Pathology at IEO.

Grant SupportThis work was supported by grants from AIRC (Associazione Italiana per la

Ricerca sulCancro,MCO10.000 toP.P.Di Fiore, andMFAG17568 to F. Bianchi)and from the Monzino Foundation (to P.P. Di Fiore).

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 December 11, 2015; revised May 11, 2016; accepted May 31, 2016;published OnlineFirst June 29, 2016.

Table 3. Comparison of published signatures with the 10-gene signature

11-gene signatureKratz, 2012 (40)

CCP scoreWistuba, 2013 (12)

CCP scoreBueno, 2015 (41)

10-gene signaturecurrent study

N genes 11a 31b 31b 10c

N reference genes 3 15 15 3FFPE cohort Kaiserd CCTC MDACC IEO BWH RIE IEOPeriod of surgery 1998–2005 2000–2008 1997–2008 1999–2005 1998–2012N NSCLC 420 967 207 174 474 176 507N stage I NSCLC 420 471 163 174 451 89 351Signature risk classes Low-, intermediate,

high-riskLow-, intermediate,high-risk

Low-, high-risk Low-, high-risk Low-, high-risk Low-, high-risk Low-, high-risk

Signatureperformancefor stage I

5-year OS

Low: 71.4%Intermediate: 58.3%High: 49.2%

5-year OS

Low: 83.0%Intermediate: 67.7%High: 64.6%

5-year LCS

Low: 90%High: 79%

5-year LCS

Low: 90%High: 75%

5-year LCS

Low: 82%High: 72%

3-year OS

Low: 95.6%High: 84.2%

5-yeare OS

Low: 88.1%High: 77.6%

HRHigh vs. low2.04 (1.28–3.26)

HRHigh vs. lowf

2.37 (1.63–3.43)

HRHigh vs. lowg

1.92 (1.18–3.10)

HRHigh vs. low1.56 (1.12–2.18)

HRHigh vs. low3.98 (1.73–9.14)

HRHigh vs. low2.34 (1.30–4.22)

Intermediate vs. low1.66 (1.00–2.74)

Intermediate vs. lowf

1.60 (1.03–2.49)

Abbreviations: BWH, Brigham and Women's Hospital; CCTC, China Clinical Trials Consortium; LCS, lung cancer survival; MDACC, The University of Texas MDAnderson Cancer Center; OS, overall survival; RIE, Royal Infirmary of Edinburgh.aBAG1, BRCA1, CDC6, CDK2AP1, ERBB3, FUT3, IL11, LCK, RND3, SH3BGR, WNT3A.bASF1B, ASPM, BIRC5, BUB1B, CDC2, CDC20, CDCA3, CDCA8, CDKN3, CENPF, CENPM, CEP55, DLGAP5, DTL, FOXM1, KIAA0101, KIF11, KIF20A, MCM10, NUSAP1,ORC6L, PBK, PLK1, PRC1, PTTG1, RAD51, RAD54L, RRM2, SKA1, TK1, TOP2A.cE2F1, E2F4, HOXB7, HSPG2, MCM6, NUDC1, RRM2, SCGB3A1, SERPINB5, SF3B1.dKaiser Permanente Northern California system.eFollow-up extended to 5 years for direct comparison with the other studies.fMultivariable model on the complete cohort (including stage I–III tumors) with adjustment for stage.gMultivariable model on the complete cohort (including stage I–II tumors) with adjustment for stage.






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2017;23:62-72. Published OnlineFirst June 29, 2016.Clin Cancer Res Elisa Dama, Valentina Melocchi, Fabio Dezi, et al. Lung CancersMolecular and Prognostic Characteristics Typical of Advanced An Aggressive Subtype of Stage I Lung Adenocarcinoma with

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