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ORIGINAL ARTICLE

Expression Profiling-Based Subtyping Identifies NovelNon-small Cell Lung Cancer Subgroups and Implicates

Putative Resistance to Pemetrexed Therapy

Jun Hou, PhD,*† Margaretha Lambers, MSc,‡ Bianca den Hamer, MSc,*† Michael A. den Bakker, PhD,§Henk C. Hoogsteden, PhD,‡ Frank Grosveld, PhD,*†� Joost Hegmans, PhD,‡ Joachim Aerts, PhD,‡

and Sjaak Philipsen, PhD*†�

Introduction: A challenge of cancer therapy is to optimize thera-peutical options to individual patients. Cancers with similar histol-ogy may show dramatically different responses to therapy, indicat-ing that a refined approach needs to be developed to classify tumorsby intrinsic characteristics that may predict response to chemother-apy. Global expression profile-based classification has the potentialto identify such tumor-intrinsic subclasses. Pemetrexed effective-ness has been related to the expression of its target thymidylatesynthase. The relatively frequent resistance of squamous cell carci-noma to Pemetrexed is correlated with high levels of thymidylatesynthase expression.Methods: A global expression profile-based molecular classificationof non-small cell lung cancer (NSCLC) was performed. Geneexpression was used to predict Pemetrexed responsiveness. Thedistinct molecular attributes of NSCLCs predicted likely to beresistant to Pemetrexed were bioinformatically characterized. Wetested if routine immunohistochemical markers can be used todistinguish putative Pemetrexed responders, predicted by gene sig-natures, from nonresponders.Results: Ninety NSCLCs were divided into six subclasses by geneexpression signatures. The relevance of this novel phenotyping waslinked to other tumor characteristics. Two of the subclasses corre-lated to putative Pemetrexed resistance. In addition, the identifiedsignature genes characterizing putative Pemetrexed responsivenesspredicted therapeutic benefit in a subset of squamous cell carcinoma.Conclusions: Gene expression signatures can be used to identifyNSCLC subgroups and have potential to predict resistance to Pem-etrexed therapy. We suggest that a combination of classical patho-logical markers can be used to identify molecular tumor subclassesassociated with predicted Pemetrexed response.

Key Words: Carcinoma, Non-small-cell lung cancer, Expressionprofiling, Pemetrexed.

(J Thorac Oncol. 2012;7: 105–114)

Currently, non-small cell lung cancer (NSCLC) is classi-fied based on microscopic analysis of specific histolog-

ical features, resulting in morphological subtyping and grad-ing. This histopathological classification correlates poorlywith patient prognosis and clinical outcome. Alternatively,genome-wide expression studies have revealed that NSCLCsmay be classified beyond classical histopathological criteria,and the resultant subgroups might better indicate the diver-gence of tumor progression and recurrence.1–4 However, theclinical potential of such refined grouping of NSCLCs needsfurther exploration.

Pemetrexed is one of the most effective drugs for thetreatment of NSCLC. Pemetrexed is a folate antimetaboliteand targets multiple enzymes essential for nucleotide biosyn-thesis.5 It was established that it has possibly superior activitycompared with commonly used agents for treatment of ade-nocarcinoma (ADC) and large-cell carcinoma (LCC) but isthought to be less effective for the treatment of squamous cellcarcinoma (SCC). By using real-time-polymerase chain reac-tion, a study identified that the efficacy of Pemetrexed treat-ment was related to messenger RNA (mRNA) expressionlevel of thymidylate synthase (TYMS), a key molecule in thethymidine synthesis pathway.6 It was also demonstrated thathigh expression of TYMS is associated with resistance toPemetrexed in NSCLC (p � 0.006). Furthermore, a higherexpression of TYMS is more often seen in SCC than in ADCand LCC.7–9 On the basis of those observations, Pemetrexedwas approved as first-line treatment for advanced non-SCCNSCLC patients.8

Among ADC and LCC patients, the response rate toPemetrexed varies between 28 and 61%.10–12 Intriguingly, asignificant number of ADC and LCC cases with high level ofTYMS mRNA expression were observed,13,14 suggesting thatPemetrexed treatment would not be effective in those cases.It also indicates that to reach a higher response rate forPemetrexed treatment, it is vital to improve the criteria forpersonalized patient selection. Determination of TYMS ex-

From the *Department of Cell Biology, †Netherlands Consortium for Sys-tems Biology, ‡Department of Pulmonary Diseases, §Department ofPathology, and �Cancer Genomics Center, Erasmus University MedicalCenter, Rotterdam, The Netherlands.

Disclosure: The authors declare no conflicts of interest.Address for correspondence: Sjaak Philipsen, PhD, Erasmus MC—Cell

Biology, Room Ee720, P.O. box 2040, 3000 CA Rotterdam, The Neth-erlands. E-mail [email protected]

Copyright © 2011 by the International Association for the Study of LungCancerISSN: 1556-0864/12/0701-0105

Journal of Thoracic Oncology • Volume 7, Number 1, January 2012 105

pression levels by immunohistochemistry (IHC) lacks thesensitivity required for this purpose.14

In this study, we show that early-stage NSCLCs can bepartitioned into six subgroups based on global gene expres-sion profiles. In particular, a subset of ADC and LCC wasclustered in a novel subgroup. The potential clinical rele-vance of these novel groups was explored by linking thisrefined phenotyping to the predicted sensitivity to Pem-etrexed. Analysis of the expression levels of relevant genespredicted that tumors in this novel subgroup are highly likelyto be resistant to Pemetrexed therapy. Conversely, a subset ofSCCs are putative responders (Rs) to Pemetrexed treatment.The identification of these distinct subgroups of NSCLCsuggests that biological characteristics assessed by gene ex-pression profiling may aid in reliably stratifying patients withrespect to the choice of therapeutic agents.

MATERIALS AND METHODS

NSCLC Tumor Samples and ValidationMicroarray Data Sets

Ninety resected tumor samples from NSCLC patientswere collected and studied under an anonymous tissue pro-tocol approved by the medical ethical committee of ErasmusUniversity Medical Center.15 Microarray data are available atthe Gene Expression Omnibus of the National Center forBiotechnology Information (GSE19188). Patient and tumorcharacteristics are summarized in Supplemental Table 1 (Sup-plemental Digital Content 1, http://links.lww.com/JTO/A168).Two additional NSCLC microarray datasets, one containing 25NSCLC cell lines and the other 96 primary NSCLC cases fromDuke University, were obtained from the Gene ExpressionOmnibus database (GSE8332 and GSE3593). The sensitivityof NSCLC cell lines to Pemetrexed was previously tested,16

and IC50 response data were downloaded from the DTPwebsite (http://dtp.nci.nih.gov).

Microarray Data AnalysisMicroarray preparation and microarray data process-

ing and normalization are as described in Ref. 15 and inSupplementary Methods (Supplemental Digital Content 2,http://links.lww.com/JTO/A169).

Unsupervised Clustering and Visualization ofGene/Sample Similarity

This was performed as described in Ref. 15 and inSupplementary Methods (Supplemental Digital Content 2,http://links.lww.com/JTO/A169).

Scoring Formula Using Internal ReferenceGenes

The detailed methodology of the predictive algorithm isdescribed in Supplementary Methods (Supplemental DigitalContent 2, http://links.lww.com/JTO/A169). The scheme pre-dicts tumor response using the expression difference betweenPemetrexed targets and the Internal Reference Genes, whichis based on the average expression level of 11 selected probesets. NSCLC patients were stratified in such a way thatapproximately 60% of cases were supposed to respond to

Pemetrexed, while the remaining approximately 40% of caseswith higher expression levels of the signature genes weredeemed to be nonresponders (NRs).

Supervised Analysis to Identify PemetrexedResistance-Associated Genes

Gene profiling with respect to predicted sensitivity toPemetrexed was performed using Significance Analysis ofMicroarray.17 Significance Analysis of Microarray discov-ered differentially expressed genes between two classes,17

e.g., predicted NRs and Rs.The obtained signatures were subjected to identify

subgroups of genes that maintain the capacity of the completesignatures in distinguishing different groups optimally.18 Theperformance of minimized signatures was validated by“leave-one-out” cross validation.19 Optimized gene signa-tures characterized a certain tumor property, such as belong-ing to a novel subgroup or putative resistance to Pemetrexedtreatment.

Pathway Enrichment AnalysisFunctional pathways overrepresented by each signature

were identified using different methodologies, and the com-mon ones were used to extract molecular characteristicsdriving the novel grouping or predicted differential sensitivityto Pemetrexed (Supplemental Methods and Results, Supple-mental Digital Content 2, http://links.lww.com/JTO/A169).

Signature genes were mapped to public databases offunctionally related gene sets, Gene Ontology and KyotoEncyclopedia of Genes and Genomes. Alternatively, a globalgene set enrichment analysis was performed using expressionof all informative genes on the microarrays.20

The enrichment of certain gene sets was statisticallydetermined (enrichment p value �0.05) by comparing theco-occurrence of gene members associated with a certainfunction in the generated gene list to that in a referencebackground of the human genome.

In addition, the relationships between the identified genesets were explored. The related gene sets were hierarchicallylinked on the basis of interrelationships in a network context.Subordinate gene sets were combined to ancestors because ofthe inheritance of genes in a hierarchical ontologism.

Tissue Microarray Analysis/IHCRoutine IHC was performed as previously described.15

Tissue microarrays (TMAs) containing 0.6 mm cores offormalin-fixed paraffin-embedded tumors were used. TheTMA is composed of 68 of the 90 tumor tissues and 2 normallung tissues, in 3 replicates, from the Erasmus MC patientcohort used for the expression microarray analyses. TMAblocks were cut into 6-�m slices, and antigen retrieval wasperformed by a 20-minute incubation at 95°C using Tris-ethylenediaminetetraacetic acid buffer (Klinipath, Duiven,The Netherlands). Slides were cooled down to room temperatureand stained with the primary antihuman antibodies (TYMS,epidermal growth factor receptor [EGFR], tumor protein p53[TP53], TP63, thyroid transcription factor 1 [TTF1], SYG,neural cell adhesion molecule 1 [NCAM1], chromogranin A[CHGA], or KRT5). The sources of the antibodies and dilutions

Hou et al. Journal of Thoracic Oncology • Volume 7, Number 1, January 2012

Copyright © 2011 by the International Association for the Study of Lung Cancer106

http://links.lww.com/JTO/A168

http://dtp.nci.nih.gov





used are listed in Supplemental Table 8 (Supplemental DigitalContent 1, http://links.lww.com/JTO/A168).

A secondary step was incubated for 30 minutes withrabbit anti-mouse 1:50 (Z0259 Dako, Glostrup, Denmark)followed by 1:50 diluted alkaline phosphatase anti-alkalinephosphatase method (D0651 Dako). Staining was visualizedusing 20-minute development with New Fuchsine substrate.TMA evaluation and protein staining quantification wereperformed double blinded by a lung pathologist (M.A.d.B.).The intensity of protein staining was classified using a four-grade scale: with 0 indicating fewer than 10% of positive cells,1 for 10 to 25%, 2 for 25 to 50%, and 3 greater than 50%.

RESULTS

Classification of NSCLC in Six SubgroupsUnsupervised clustering of expression profiles revealed

six subclasses within the 90 Erasmus MC NSCLC cases. Theclustering was based on the similarity in global gene expres-sion between the NSCLC samples, and the six distinct sub-groups were recognized using 4791 informative probe sets(Figure 1). All tumor samples were clustered into six groups.Two of these groups correlated well with classical histopa-thology: group3 (G3) displayed a dominant SCC contribu-tion, while the carcinoid (CAR) samples (n � 4) wereexclusively assigned to group5 (G5). By contrast, othergroups showed a weak association with classical histology.They were to varying degrees composed of mixed histopatho-logical NSCLC. The four non-SCCs in G3 were undistin-guishable by expression profiles from the other SCCs in G3,with well-known SCC markers, including TP63, KRTs, andSERPINB, uniformly high expressed. Additional pathologi-cal analysis revealed that two of them presented either posi-tive staining for TP63 or apparent squamous cell elements.

We found that group 4 (G4), G5, and group 6 (G6)comprised neuroendocrine NSCLC, including large-cell neu-roendocrine carcinoma (LCNEC), CAR, and NSCLC with neu-roendocrine features, mainly of the ADC histological subtype.Most LCC and LCNEC cases were mingled with ADC in G4and G6. All CARs were clustered into an independent group(G5). Although the expression profiles of CAR showed to someextent similarity to G4 and G6, the observation that CAR displayeda unique transcriptome profile suggested that CAR is a group ofNSCLC with distinct behavior with respect to tumor cell aggres-siveness, tumor response to therapy, and prognosis.4,21–23

Compared with group 1 (G1) and group 2 (G2), ADC inG4 and G6 displayed gene expression patterns suggestive ofneuroendocrine features. Regardless of histological consis-tency between G1 and G2, the NSCLCs in these two groupswere distinguished by a low degree of cell differentiation inG1 and the expression of a large number of immune-relatedgenes in G2. This suggested that these two groups mightdisplay a different natural course of disease.

Gene signatures that reflect patterns of gene and path-way deregulation, distinguishing these six subclasses, werebioinformatically identified (Supplementary Results, Supple-mental Digital Content 2, http://links.lww.com/JTO/A169)(Supplemental Tables 2 and 3, Supplemental Digital Content1, http://links.lww.com/JTO/A168).

Gene Expression-Based Prediction of Responseto Pemetrexed

The sensitivity of tumors to Pemetrexed treatment isthought to be negatively correlated with the expression levels ofthe enzymes in the nucleotide metabolic pathways.9,24 Expres-sion of the relevant genes was extracted from the microarraydata, and these were subsequently used to develop predictiveschemes for Pemetrexed responsiveness. According to the In-ternal Reference Genes scheme, of 90 NSCLC patients, 35%were predicted as NR and 52% were predicted to be R. Theremaining cases were predicted with intermediate response toPemetrexed. The relative expression of TYMS in predicted NRsis 177.1 (95% confidence interval [CI]: 143.2–210.9), 8.2-foldhigher than that in normal lung tissues; while predicted Rsdisplayed a 2.2-fold increase in relative expression of TYMScompared with normal lungs. When expression of dihydrofolatereductase and phosphoribosylglycinamide formyltransferase wasincluded in the predictive scheme, a similar output was observed.

The differential activity of relevant pathways in NRand R determined by a global analysis is shown in Sup-plemental Figure 1 (Supplemental Digital Content 3,http://links.lww.com/JTO/A170). In addition, a set of 426probe sets (346 genes) characterizing putative NRs wasidentified using supervised analysis. Surfactant genes, SOX7,SLC16A4, and SLC46A3, were down-regulated in predictedNRs. The bioinformatics analysis revealed that signaturegenes were functionally enriched in gene sets for cell cycleregulatory functions, such as E2Fs, cyclins, and CDCs; celldivision functions such as GTSE1, KIFs, MCMs, andIGFBPL1; cell growth and invasion including MMP19; andoncogenes and tumor suppressor genes such as MYB, NBL1,and RAS. As expected, other functional pathways includedpyrimidine and purine metabolism and folate biosynthesis(Supplemental Table 4, Supplemental Digital Content 1,http://links.lww.com/JTO/A168). A subset of this signature,represented by 25 probe sets, performed optimally in predict-ing Pemetrexed response (Supplemental Table 5, Supplemen-tal Digital Content 1, http://links.lww.com/JTO/A168).18,19

Correlation of Putative NSCLC Responsivenessto Other Tumor Characteristics

Next, the predicted Pemetrexed resistance profile wasstudied in relation to NSCLC histology. The histologicalsubtype (ADC, SCC, or LCC) was assigned using the histol-ogy signature identified previously.15 Within the three majorsubtypes, LCC contained the highest expression of TYMS(192.0; 95% CI: 125.6–258.4), followed by SCC (86.6; 95%CI: 73.0–100.1) and ADC (76.1; 95% CI: 58.5–93.7; Figure2A). A significant difference in TYMS expression was ob-served between each subtype of NSCLC and noncanceroustissues, with 8.85-, 4-, and 3.5-fold increases in LCC, SCC,and ADC, respectively. The difference in TYMS expressionwas statistically significant between LCC and the other twosubtypes, ADC (p � 0.002) and SCC (p � 0.004), but notbetween ADC and SCC (p � 0.39). Predicted resistance toPemetrexed was found in 27% of ADC, 33% of SCC, and53% of LCC. In contrast, all healthy lung tissue samples (n �65) except for one were stratified as sensitive to Pemetrexed.

Journal of Thoracic Oncology • Volume 7, Number 1, January 2012 Prediction of Pemetrexed Response in NSCLC

Copyright © 2011 by the International Association for the Study of Lung Cancer 107







A Novel NSCLC Group Is Associated withPredicted Pemetrexed Resistance

Because in our cohort predicted Pemetrexed-resistantcases were not significantly overrepresented in any of thehistological subtypes, we correlated predicted Pemetrexed

responsiveness to the six molecularly defined subgroups. Infour of the six groups, fewer than 25% of the cases weredefined as NRs, with percentages of 20%, 0%, 0%, and 25%in G1, G2, G5, and G6, respectively. Approximately 32%cases from G3, which were characterized by SCC, were

FIGURE 1. Identification of six subgroups in the Erasmus MC NSCLC cohort. Six subgroups are indicated by G1 to G6. A,Correlation view of gene expression in the 90 Erasmus MC NSCLC samples. Pairwise correlations between any two samplesare displayed. The colors of the cells represent Pearson’s correlation coefficient values between any two samples, with deeperred indicating higher positive and deeper blue lower negative correlations. The red diagonal line displays the self-to-self com-parison of each sample. B, Relative expression levels of TYMS, cell proliferation genes, and neuroendocrine genes are shownfor each of the six identified NSCLC subgroups. Boxes show the distribution of gene expression in each subgroup, with dotsrepresenting outliers. The dashed line shows the median expression of that gene across all NSCLC samples. NSCLC, non-smallcell lung cancer; G1, group1; G6, group6; TYMS, thymidylate synthase.



predicted as NRs. In contrast, a remarkable proportion (94%,17/18) of NRs predicted by the 25 probe set signature wasobserved in G4 (Figure 3A). We conclude that tumors in G3(32% of SCC) and in particular G4 have the highest proba-bility to be classified as NR.

The expression profile of G4 was distinguishable fromother neuroendocrine tumors. A differential overexpressionof neuroendocrine markers, including ASCL1, DDC, andMAST4, was observed among neuroendocrine groups, with atwo- to fourfold difference between G4 and G6. In contrast,

FIGURE 3. Predicted Pemetrexed sensitivity in NSCLC sub-groups and NSCLC cell lines. A, NSCLC cases predicted tobe resistant (NR) or sensitive (R) to Pemetrexed are corre-lated to expression profiling-based subgroups (G1 to G6). B,Validation of Pemetrexed resistance signature using NSCLCcell lines. Predicted sensitivity to Pemetrexed for NSCLC celllines is compared with experimentally established sensitivity.For each cell line, data from two independent microarrayswere used (number of cases). C, Performance of the Pem-etrexed-resistance prediction signature on the DukeNSCLC Cohort. The 96 primary NSCLC cases were classi-fied into subgroups using the group signature genes (indi-cated by G1 to G6). The predicted response to Pem-etrexed by the 25-probe set resistance signature and itscorrelation to the subgroups are displayed. NSCLC, non-small cell lung cancer; NR, nonresponder; R, responder;G1, group1; G6, group6.

FIGURE 2. Relative expression of TYMS in relation to classi-cal NSCLC histology. Relative expression levels of TYMS inhistology signature-assigned NSCLC groups.15 A, ErasmusMC cohort and (B) Duke Cohort. Boxes show the distribu-tion of TYMS expression in each subgroup with crosses rep-resenting outliers. The band in the box shows the medianexpression of TYMS in that group. TYMS, thymidylate syn-thase; NSCLC, non-small cell lung cancer.



MCM6, TYMS, and CDCA7 showed a relatively higherexpression in G4 compared with G6 (Figure 1B).

Pemetrexed is transported in and out of cells by mem-brane proteins such as folate receptor 1 (FOLR1), SLC19A1,and ATP-binding cassette family members. Moreover, Pem-etrexed is metabolized by folylpolyglutamate synthetase. Theaberrant expression of such molecules may also contribute toPemetrexed resistance. We observed a lower expression ofFOLR1 and a higher expression of ATP-binding cassette,subfamily C (CFTR/MRP), member 1 in G4 compared witheither other groups or other neuroendocrine tumors (p values�0.015 and � 0.038).

Bioinformatics analysis revealed that pyrimidine me-tabolism and epidermal growth factor signaling pathwayswere more activated in G4 compared with otherNSCLCs (Supplemental Figs. 4 and 5, Supplemental Dig-ital Content 6 and 7, http://links.lww.com/JTO/A174 andhttp://links.lww.com/JTO/A175). Moreover, in comparisonto other neuroendocrine tumors, such as G6, pyrimidinemetabolism was also more up-regulated, while the histidinepathway was more down-regulated in G4 (Figure S6). Weconclude that the newly identified G4 of NSCLC has distinctmolecular characteristics associated with predicted Pem-etrexed resistance.

Validation of the Putative PemetrexedResponsiveness Signature

The expression of resistance-associated genes identi-fied with primary NSCLCs was validated in two independentsample cohorts, in transcriptionally profiled NSCLC cell lines(GSE8332) and primary NSCLCs (GSE3141/Duke Cohort).The performance of our signature was evaluated by compar-ing the predicted Pemetrexed sensitivity of the cell lines tothe measured response in drug sensitivity assays (Figure3B).16 In addition, the sensitivity to Pemetrexed was pre-dicted for primary NSCLCs in the Duke Cohort (Figure 3C).The 25-probe set signature correctly predicted response toPemetrexed in 92% (23/25) of the cell lines. Resistant celllines were all correctly predicted; the sensitivity of predictingresistance was 100% and specificity 93% (Figure 3B).

TYMS Expression as a Predictor of PemetrexedResponse

TYMS expression has been associated with Pemetrexedefficacy previously.6 TYMS is one of the 25 genes in ourPemetrexed response prediction signature. Therefore, wetested if TYMS expression alone could be used as a predictorfor Pemetrexed sensitivity. In the Erasmus MC cohort,TYMS expression correlated significantly with gene signa-ture-predicted Pemetrexed sensitivity (p � 0.0001, �2 test). InNSCLC cell lines, TYMS expression was not related to theobserved sensitivity to Pemetrexed treatment (p � 0.482, �2

test). We conclude that the Pemetrexed responsiveness isdetermined by more genes than TYMS alone, illustrating thevalue of the extended 25-gene Pemetrexed response predic-tion signature that we developed.

Measurement of TYMS Expression in NSCLCby TMAs

TYMS protein staining was performed using TMAs ofNSCLC samples (n � 68) that were used for the microarrays.Staining of the TMAs was graded from scale 0 to 3. ThemRNA expression level for each staining category is shownin Supplemental Figure 2 (Supplemental Digital Content 4,http://links.lww.com/JTO/A171). As only one sample wasgraded 3, it was combined with the grade 2 NSCLCs. ThemRNA expression of TYMS of grade 2 was 3.73-fold and2.52-fold higher than grade 0 and grade 1 (p value � 1.11E-7).The correlation between staining intensity and predicted Pem-etrexed resistance is shown in sSpplemental Figure 2 (Supple-mental Digital Content 4, http://links.lww.com/JTO/A171).Over 87.5% grade 2 NSCLCs were predicted NR to Pem-etrexed. Conversely, 33.5% of grade 1 NSCLC and 19.4%grade 0 were predicted NRs. The expression of TYMS in NRbetween grade 0 and grade 1 was not significantly different (pvalue � 0.993). When the TYMS antibody was more diluted(1:50), over 85% (6/7) grade 2 samples were predicted NR,similar to the results of TMA at titer 1:10. Eight samples hadmoderate staining (grade 1), of which two samples werepredicted NR (25%). The TYMS expression was not detectedwith TMA at titer 1:50 in the rest of the samples (n � 53,78%), illustrating the current limitations of TYMS IHC forassessing Pemetrexed responsiveness, in agreement with pre-vious work.14,25

We conclude that very high protein expression of TYMSdetermined by IHC correlates well with TYMS mRNA expres-sion, while low protein expression of TYMS on the TMAcorrelates poorly with TYMS mRNA expression.

Potential for Practical ApplicationNext, we investigated whether (a combination of) rou-

tine histopathological markers could be used to stratify theputative Pemetrexed Rs/NRs. We used eight well-knownhistopathological markers, including KRT5, EGFR, TTF1,TP53, and neuroendocrine markers. The expression of any ofthese markers failed to stratify patients into the predictedgroups with differential sensitivity. Furthermore, stratifica-tion by any of these routine histopathological markers alsofailed when it was performed with the prior knowledge of theclassical histology or pathological stage (Supplemental Fig. 3,Supplemental Digital Content 5, http://links.lww.com/JTO/A172)(Supplemental Table 6, Supplemental Digital Content 1,http://links.lww.com/JTO/A168). However, when the strati-fication was interpreted with the knowledge of novel group-ing and the expression of routine histopathological markerswas used cooperatively, putative Pemetrexed sensitive andresistant cases could be distinguished, with the best perfor-mance in G3 and still some exceptions in G4 and G6 (Figure4). Fourteen out of 17 predicted Rs in G3 can be identified bya combination of staining for TP53 and EGFR. The expres-sion of neuroendocrine markers was observed in Rs from G1(n � 2) and G5 (n � 4), and both NR (n � 1) and R (n � 1)from G6. There were four cases in G4 for which TMAstaining was inconclusive because it failed for one or allmarkers. We conclude that the cooperative use of routine









histological markers and molecular classification can be usedto predict the sensitivity of a subset of NSCLC to Pem-etrexed.

DISCUSSIONCurrently, the administration of chemotherapy in

NSCLC patients is based on histology, and the response ratesto treatment are approximately 16% for single agents andapproximately 40% for combined regimens.26 The fact thatcancer patients with similar histopathological features re-spond dramatically different to the same therapeutic agentindicates that histology alone is insufficient to predict theresponse to therapeutics. Ideally, therapeutic regimens shouldbe tailored for individual patients to obtain maximal antitu-mor effects. For example, EGFR tyrosine kinase inhibitortreatment in ADC patients improved the response rate toapproximately 68%, but EGFR tyrosine kinase inhibitor innon-ADC patients harboring active EGFR mutations is lesseffective,27,28 illustrating the importance of better defining thetarget group by molecular analysis. Despite this promisingexample, tailored therapy for NSCLC remains largely elu-sive. Most NSCLCs of similar histology and grade receivethe same therapy, and differences in molecular characteristicsare not taken into account routinely. It is therefore importantto develop algorithms for molecular identification of patientswho would be sensitive or resistant to a specific therapy.

The folate antimetabolite Pemetrexed is a promisingagent for the treatment of NSCLC. Currently, the adminis-tration of Pemetrexed is directed by classical histologicalcriteria.10 In this study, we present an approach to implementtailored Pemetrexed therapy in NSCLC. It is based on the useof gene expression profiles to systematically assess the pre-dicted response to Pemetrexed.

Subgrouping of NSCLC Implies CommonMolecular Characteristics of NSCLC Subtypes

Gene expression profiling can be used to reveal tumorfeatures that are relevant to clinical outcome. For example,clustering of ADC or SCC cases based on gene expressionprofiles identified subgroups presenting favorite overall sur-vival.2,3,29–31 Microarray-derived gene signatures have alsodemonstrated the ability to define the risk of NSCLC recur-rence.32 Ultimately, molecular profiling would be expected topredict the response to specific therapies. In breast cancer celllines, gene signatures were identified that reflect the activa-tion status of oncogenic pathways. On the basis of thesesignatures, the coordinated active status of pathways wasobtained that not only defined prognosis in specific patientsubgroups but also predicted the sensitivity to therapeuticagents targeting key components of these pathways.33 In thisstudy, we classified NSCLCs into six groups, independent ofclassical histopathology, using microarray-based molecularprofiles. Tumors clustered in the same subgroups presentsimilar patterns of gene expression and pathway deregulationdespite often variable histopathology (Supplemental Fig. 4, Supple-mental Digital Content 6, http://links.lww.com/JTO/A173)(Supplemental Fig. 5, Supplemental Digital Content 7,http://links.lww.com/JTO/A174). For example, tumors clus-tered in G4 are histologically different—ADC or LCC. Butthey are molecularly similar, with deregulation of the tyrosinemetabolism pathway and expression of the neuroendocrinemarkers.

Moreover, G6 tumors are also histologically ADC orLCC but molecularly characterized by altered histidine me-tabolism (Supplemental Fig. 6, Supplemental Digital Content8, http://links.lww.com/JTO/A176). Importantly, the group

FIGURE 4. Utility of routine IHCmarkers to identify putative NR andR to Pemetrexed therapy. The ex-pression of eight IHC markers in 68of 90 NSCLC and two normal lungtissues (G0) was detected by TMAand used for cluster analysis. Highstaining is shown in red and lowstaining is shown in green. FailedIHC staining is shown in purple.NSCLC cases are annotated withpredominantly present expressionprofile-assigned subgroup(s) (G1 toG6) on the right and predictedPemetrexed responsiveness (resis-tant case [NR]: gray; sensitive case[R]: orange) on the left. IHC, im-munohistochemistry; NR, nonre-sponder; R, responder; NSCLC,non-small cell lung cancer.






signature defined five subgroups of similar size and compo-sition in the independent Duke NSCLC Cohort (Figure 3C)(Supplemental Table 7, Supplemental Digital Content 1,http://links.lww.com/JTO/A168). G5/CAR was absent be-cause this tumor type was not represented in the DukeCohort. This strongly supports the notion that at least fivecommon subtypes of NSCLC exist and that these can beclassified by oncogenomics. This molecular classificationmight be used to further refine conventional histopathology indepicting tumor biology and revealing the underlying onco-genic phenotypes. The identification of these distinct NSCLCgroups provides an opportunity to explore molecular classi-fication as a tool to tailor the therapeutic regimens forindividual NSCLC patients. We have applied this to predictPemetrexed response in NSCLC cases.

Molecular Characteristics of NSCLC Associatedwith Putative Sensitivity to PemetrexedTreatment

We did not observe a significant difference in patientcharacteristics, such as gender, age, or smoking habit, norpostoperation survival among the six subgroups. This issupported by the fact that the group signature does notshow overlap with our previously determined survivalsignature.15 However, distinct patterns of oncogenic path-way deregulation in each group suggest that the responseto a particular pharmacological treatment might differbetween the NSCLC subgroups (Supplemental Table 2,Figs. 4 and 5, Supplemental Digital Content 1, 6, and 7,http://links.lww.com/JTO/A168, http://links.lww.com/JTO/A173,and http://links.lww.com/JTO/A174).We found that theTYMS expression-based predictions of Pemetrexed responsedid not correlate well with classical histology, challenging thecurrent guideline to limit its use to ADC and LCC cases. Weidentified a group of NSCLC (G4), composed mainly of ADCand LCC cases, in which a large proportion of tumors arepredicted to be resistant to Pemetrexed. In contrast, ADC andLCC cases classified in G1 and G6 were identified as candi-dates for Pemetrexed therapy as they were predicted torespond favorably (Figure 3).

According to previous clinical trials, LCC patientsshowed the best response to Pemetrexed.11 This is contradic-tory to the predicted response in the Erasmus MC cohort.This paradox may result from the difficulty in distinguishingLCC from other types of NSCLC. The classification of LCCby routine pathology is prone to considerable interobservervariation, for instance 62.5% of LCC cases in our previousstudy were differentially classified between two pathologists.15

A refined classification of LCC is possible with the use ofadditional IHC markers which are not routinely used, as exem-plified by our TMA results (Supplemental Table 6, Supplemen-tal Digital Content 1, http://links.lww.com/JTO/A168).

In this study, NSCLCs were histologically classifiedwith the aid of a 75-gene histology signature identified in ourprevious study, resulting in molecularly defined NSCLCsubtypes sharing a distinct gene expression profile.15 Addi-tional IHC showed that at least one of three LCC markers(NCAM1, CHGA, or synaptophysin) was expressed in 59% ofthe gene signature-assigned LCC samples, compared with only

7.5% in the remaining samples (Supplemental Table 6, Supple-mental Digital Content 1, http://links.lww.com/JTO/A168).

Remarkably, approximately 68% of SCC in our cohortand 47% of SCC in the Duke Cohort were predicted to besensitive to Pemetrexed therapy. The molecular differencesbetween SCC NR and R were similar to those betweennon-SCC NR and R. The expression of FOLR1 is lower inboth SCC NR and non-SCC NR, while ATP-binding cassette,subfamily C (CFTR/MRP), member 1 and TP53 are overex-pressed in the same patients (Supplemental Table 6, Supple-mental Digital Content 1, http://links.lww.com/JTO/A168)(Supplemental Fig. 7, Supplemental Digital Content 9,http://links.lww.com/JTO/A177). Pyrimidine and purine me-tabolism were activated at a higher level in NRs comparedwith R. This indicates that Pemetrexed may be an effectivetherapeutic agent for a subset of SCC patients identified bythis approach. This observation is supported by a phase IIIstudy in which no significant association between advantageof Pemetrexed treatment and histological subtype was ob-served.34 In a recently published meta-study by Scagliotti etal., differential efficacy of Pemetrexed by NSCLC histologywas evaluated in three large Phase III trials. Although re-sponse rate to Pemetrexed in SCC patients was inferior to thatin non-SCC patients, a subset of SCC patients (23.4%, com-pared with 28.6% in non-SCC) sensitive to Pemetrexedtherapy was observed.35

Chemotherapy is not a standard of care for patients withpulmonary CAR tumors. Currently, the options for chemo-therapy of CAR patients are quite limited and CAR patientsusually do not respond well to such treatments.36 To ourknowledge, the efficacy of Pemetrexed has not been tested inCAR patients. Our observation that the CAR cases (G5) werepredicted Pemetrexed Rs suggests a possible therapeuticbenefit of Pemetrexed therapy in such cases.

Validation of the Predicted PemetrexedResponsiveness Signature

The role of TYMS expression in the efficacy of Pem-etrexed therapy has been established by several stud-ies.6,9,16,24 However, TYMS expression alone was not able tostratify NSCLC cell lines with respect to Pemetrexed sensi-tivity, and approximately 57% of Pemetrexed-sensitive can-cer cell lines displayed high TYMS expression (�60th per-centile, n � 184, GSE8332). Furthermore, a large variation inTYMS expression was observed among LCNEC and CARprimary tumors, which are sensitive to Pemetrexed.37 Westratified NSCLC patients into different groups by a multi-gene signature, and this gene signature accurately predictedPemetrexed response of the NSCLC cell lines. Because ofseveral factors, including the lack of availability of suitabletumor material from Pemetrexed-treated patients to performmicroarray analysis, especially from patients treated withPemetrexed as a single agent, our study does not includeretrospective or prospective validation in NSCLC patients.Our cohort recruited only operated patients who were nottreated with Pemetrexed. Furthermore, Pemetrexed is ap-proved as second-line treatment of NSCLC or first-line treat-ment in combination with platinum agents. This precludesassessment of the impact of Pemetrexed per se. Clinical trials




http://links.lww.com/JTO/A168,

http://links.lww.com/JTO/A173,






where Pemetrexed is administrated as a first-line single agentfor therapy for NSCLC are ongoing and tumor samples fromthese patients may become available for future gene expres-sion profiling experiments.

Gene Expression Profiles May Guide theChoice of Chemotherapy Regimens

In this study, we show that gene expression profileshold promise for classification of patients with respect toPemetrexed sensitivity. The identification of the novel G4classification, and the 25-probe set resistance signature, indi-cates that rather than by a single gene, Pemetrexed efficacy isdetermined by a gene interaction network (SupplementalTable 5, Supplemental Digital Content 1,http://links.lww.com/JTO/A168) (Supplemental Fig. 4, Sup-plemental Digital Content 6, http://links.lww.com/JTO/A174). This is confirmed by the analysis of resistant andsensitive NSCLC cell lines.16 A retrospective study tested thepredictive role of TYMS expression and gene polymorphismfor clinical response of colorectal cancer to 5-fluorouracil, aTYMS inhibitor widely used as anticancer drug. The authorsconcluded that TYMS expression is one of multiple determi-nants of the response to 5-fluorouracil.38 These observationsalso support the notion that the use of gene expressionprofiles may improve the specificity and sensitivity of pre-dicting NSCLC NRs to Pemetrexed.

Presently, gene expression profiling of cancer predic-tive markers in the clinic has not been commonly applied dueto the practical limitations of microarray techniques andsubsequent analytic expertise.39 In contrast, IHC is a standardassay that is routinely used in clinical pathology. Thus,immunophenotyping holds promise to classify cancer-intrin-sic subtypes defined by gene expression profiles while by-passing the difficulties in implementing microarray techniquein clinical practice. For example, the expression status estrogenreceptor and human epidermal growth factor receptor 2 can beused to classify breast cancer into four subtypes, which wereoriginally established by a 50-gene signature.40–43

Potential to Use Surrogate MarkersThe assessment of TYMS expression by IHC lacks

sensitivity and is therefore of limited value for the predictionof Pemetrexed efficacy.14,37 We tested if (a combination of)routine histological markers (Supplemental Tables 6 and 8,Supplemental Digital Content 1, http://links.lww.com/JTO/A168) could be used as an alternative approach to identifyNSCLCs that were predicted to be resistant to Pemetrexed.Negative TP53 staining assigned the NSCLC in G3 as po-tentially sensitive to Pemetrexed therapy. The likelihood ofresistance is predicted by high expression of TP53 andreinforced by concurrent high EGFR expression. Similarly,tumors in G4 with strong staining for TP53 might be resistantto Pemetrexed. In contrast, high expression of TP53, EGFR,or both does not predict resistance for the NSCLCs in G1 orG6. Positive staining of TTF1 predicts a good response toPemetrexed in tumors from G1 or G6, which is in agreementwith a recently published study by Sun et al.44 Furthermore,the expression of any of three neuroendocrine markers, syn-

aptophysin, NCAM1, and CHGA, in G5 may be predictive ofPemetrexed sensitivity.

The relationship between TP53 and EGFR expressionwith Pemetrexed sensitivity in G3 potentially provides aninstant and practical manner to stratify SCC patients forPemetrexed treatment (Supplemental Fig. 8A, SupplementalDigital Content 10, http://links.lww.com/JTO/A178). Highexpression of either TP53 or neuroendocrine markers predictsPemetrexed resistance in G4 NSCLCs (Supplemental Fig.8B, Supplemental Digital Content 10,http://links.lww.com/JTO/A178), although a few exceptionswere observed in this group. For the other NSCLC subgroups,a more specific and sensitive marker other than the cooper-ative use of currently available routine markers is needed.

In conclusion, we suggest a refined classification ofNSCLC subtypes based on gene expression profiles. Thisnew molecular classification may aid tailored Pemetrexedtherapy for individual NSCLC patients. Although this hy-pothesis needs to be further validated in clinical settings, ourobservations indicate that NSCLC resistant to Pemetrexedcan be identified by molecular means. Furthermore, the ap-proach we have followed in this study could be generallyapplicable to other therapeutic agents and other types ofcancer.

ACKNOWLEDGMENTSSupported by the Netherlands Genomics Initiative

(NGI) and a nonrestrictive grant from Eli Lilly and Companyfor the tissue microarrays.

REFERENCES1. Travis WD, Gal AA, Colby TV, et al. Reproducibility of neuroendocrine

lung tumor classification. Hum Pathol 1998;29:272–279.2. Takeuchi T, Tomida S, Yatabe Y, et al. Expression profile-defined

classification of lung adenocarcinoma shows close relationship withunderlying major genetic changes and clinicopathologic behaviors.J Clin Oncol 2006;24:1679–1688.

3. Bhattacharjee A, Richards WG, Staunton J, et al. Classification of humanlung carcinomas by mRNA expression profiling reveals distinct adenocar-cinoma subclasses. Proc Natl Acad Sci U S A 2001;98:13790–13795.

4. Anbazhagan R, Tihan T, Bornman DM, et al. Classification of small celllung cancer and pulmonary carcinoid by gene expression profiles.Cancer Res 1999;59:5119–5122.

5. Hanauske AR, Chen V, Paoletti P, et al. Pemetrexed disodium: a novelantifolate clinically active against multiple solid tumors. Oncologist2001;6:363–373.

6. Hanauske AR, Eismann U, Oberschmidt O, et al. In vitro chemosensi-tivity of freshly explanted tumor cells to pemetrexed is correlated withtarget gene expression. Invest New Drugs 2007;25:417–423.

7. Esteban E, Casillas M, Cassinello A. Pemetrexed in first-line treatmentof non-small cell lung cancer. Cancer Treat Rev 2009;35:364–373.

8. Longo-Sorbello GS, Chen B, Budak-Alpdogan T, et al. Role of pem-etrexed in non-small cell lung cancer. Cancer Invest 2007;25:59–66.

9. Bepler G, Sommers KE, Cantor A, et al. Clinical efficacy and predictivemolecular markers of neoadjuvant gemcitabine and pemetrexed in re-sectable non-small cell lung cancer. J Thorac Oncol 2008;3:1112–1118.

10. Ciuleanu T, Brodowicz T, Zielinski C, et al. Maintenance pemetrexedplus best supportive care versus placebo plus best supportive care fornon-small-cell lung cancer: a randomised, double-blind, phase 3 study.Lancet 2009;374:1432–1440.

11. Scagliotti G, Hanna N, Fossella F, et al. The differential efficacy ofpemetrexed according to NSCLC histology: a review of two Phase IIIstudies. Oncologist 2009;14:253–263.

12. Scagliotti GV, Parikh P, von Pawel J, et al. Phase III study comparingcisplatin plus gemcitabine with cisplatin plus pemetrexed in chemother-










apy-naive patients with advanced-stage non-small-cell lung cancer.J Clin Oncol 2008;26:3543–3551.

13. Ceppi P, Volante M, Saviozzi S, et al. Squamous cell carcinoma of thelung compared with other histotypes shows higher messenger RNA andprotein levels for thymidylate synthase. Cancer 2006;107:1589–1596.

14. Monica V, Scagliotti GV, Ceppi P, et al. Differential ThymidylateSynthase Expression in Different Variants of Large-Cell Carcinoma ofthe Lung. Clin Cancer Res 2009;15:7547–7552.

15. Hou J, Aerts J, den Hamer B, et al. Gene expression-based classificationof non-small cell lung carcinomas and survival prediction. PLoS One;5:e10312.

16. Li T, Ling YH, Goldman ID, et al. Schedule-dependent cytotoxicsynergism of pemetrexed and erlotinib in human non-small cell lungcancer cells. Clin Cancer Res 2007;13:3413–3422.

17. Tusher VG, Tibshirani R, Chu G. Significance analysis of microarraysapplied to the ionizing radiation response. Proc Natl Acad Sci U S A2001;98:5116–5121.

18. Tibshirani R, Hastie T, Narasimhan B, et al. Diagnosis of multiplecancer types by shrunken centroids of gene expression. Proc Natl AcadSci U S A 2002;99:6567–6572.

19. Golub T, Slonim D, Tamayo P, et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring.Science 1999;286:531–536.

20. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichmentanalysis: a knowledge-based approach for interpreting genome-wideexpression profiles. Proc Natl Acad Sci U S A 2005;102:15545–15550.

21. Borczuk AC, Gorenstein L, Walter KL, et al. Non-small-cell lung cancermolecular signatures recapitulate lung developmental pathways. Am JPathol 2003;163:1949–1960.

22. Righi L, Volante M, Rapa I, et al. Neuro-endocrine tumours of the lung.A review of relevant pathological and molecular data. Virchows Arch2007;51(Suppl 1):S51–S59.

23. Gustafsson BI, Kidd M, Chan A, et al. Bronchopulmonary neuroendo-crine tumors. Cancer 2008;113:5–21.

24. Giovannetti E, Mey V, Nannizzi S, et al. Cellular and pharmacogeneticsfoundation of synergistic interaction of pemetrexed and gemcitabine inhuman non-small-cell lung cancer cells. Mol Pharmacol 2005;68:110–118.

25. Tubbs RR, Pettay JD, Roche PC, et al. Discrepancies in clinical labo-ratory testing of eligibility for trastuzumab therapy: apparent immuno-histochemical false-positives do not get the message. J Clin Oncol2001;19:2714–2721.

26. Rosell R, Felip E, Garcia-Campelo R, et al. The biology of non-small-cell lung cancer: identifying new targets for rational therapy. LungCancer 2004;46:135–148.

27. Jackman DM, Miller VA, Cioffredi LA, et al. Impact of epidermalgrowth factor receptor and KRAS mutations on clinical outcomes inpreviously untreated non-small cell lung cancer patients: results of anonline tumor registry of clinical trials. Clin Cancer Res 2009;15:5267–5273.

28. Shukuya T, Takahashi T, Kaira R, et al. Efficacy of gefitinib for

non-adenocarcinoma non-small-cell lung cancer patients harboring epi-dermal growth factor receptor mutations: a pooled analysis of publishedreports. Cancer Sci 2011;102:1032–1037.

29. Raponi M, Zhang Y, Yu J, et al. Gene expression signatures forpredicting prognosis of squamous cell and adenocarcinomas of the lung.Cancer Res 2006;66:7466–7472.

30. Inamura K, Fujiwara T, Hoshida Y, et al. Two subclasses of lungsquamous cell carcinoma with different gene expression profiles andprognosis identified by hierarchical clustering and non-negative matrixfactorization. Oncogene 2005;24:7105–7113.

31. Garber ME, Troyanskaya OG, Schluens K, et al. Diversity of geneexpression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A2001;98:13784–13789.

32. Wigle DA, Jurisica I, Radulovich N, et al. Molecular profiling ofnon-small cell lung cancer and correlation with disease-free survival.Cancer Res 2002;62:3005–3008.

33. Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures inhuman cancers as a guide to targeted therapies. Nature 2006;439:353–357.

34. Gronberg BH, Bremnes RM, Flotten O, et al. Phase III study by theNorwegian lung cancer study group: pemetrexed plus carboplatin com-pared with gemcitabine plus carboplatin as first-line chemotherapy inadvanced non-small-cell lung cancer. J Clin Oncol 2009;27:3217–3224.

35. Scagliotti G, Brodowicz T, Shepherd FA, et al. Treatment-by-histologyinteraction analyses in three phase III trials show superiority of pem-etrexed in nonsquamous non-small cell lung cancer. J Thorac Oncol2011;6:64–70.

36. Bertino EM, Confer PD, Colonna JE, et al. Pulmonary neuroendocrine/carcinoid tumors: a review article. Cancer 2009;115:4434–4441.

37. Ceppi P, Volante M, Ferrero A, et al. Thymidylate synthase expressionin gastroenteropancreatic and pulmonary neuroendocrine tumors. ClinCancer Res 2008;14:1059–1064.

38. Moreira LR, Schenka AA, Latuff Filho P, et al. Correlation betweenthymidylate synthase protein expression and gene polymorphism withclinicopathological parameters in colorectal carcinoma. Int J SurgPathol 2009;17:181–186.

39. Koscielny S. Why most gene expression signatures of tumors have notbeen useful in the clinic. Sci Transl Med 2010;2:14ps12.

40. Perou CM, Sorlie T, Eisen MB, et al. Molecular portraits of humanbreast tumours. Nature 2000;406:747–752.

41. Parker JS, Mullins M, Cheang MC, et al. Supervised risk predictor ofbreast cancer based on intrinsic subtypes. J Clin Oncol 2009;27:1160–1167.

42. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 2006;355:560–569.

43. Finnegan TJ, Carey LA. Gene-expression analysis and the basal-likebreast cancer subtype. Future Oncol 2007;3:55–63.

44. Sun JM, Han J, Ahn JS, et al. Significance of thymidylate synthase andthyroid transcription factor 1 expression in patients with nonsquamousnon-small cell lung cancer treated with pemetrexed-based chemother-apy. J Thorac Oncol 2011;6:1392–1399.



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