molecular proﬁling of aromatase inhibitor treated ......of differences that were parallel to...

Predictive Biomarkers and Personalized Medicine

Molecular Profiling of Aromatase Inhibitor–TreatedPostmenopausal Breast Tumors Identifies Immune-RelatedCorrelates of Resistance

Anita K. Dunbier1,2,5, Zara Ghazoui1,2, Helen Anderson1,2, Janine Salter1, Ashutosh Nerurkar1, Peter Osin1,Roger A'hern3, William R. Miller4, Ian E. Smith1, and Mitch Dowsett1,2

AbstractPurpose: Estrogen withdrawal by treatment with aromatase inhibitors is the most effective form of

endocrine therapy for postmenopausal estrogen receptor–positive (ERþ) breast cancer. However, response

to therapy varies markedly and understanding of the precise molecular effects of aromatase inhibitors and

causes of resistance is limited. We aimed to identify in clinical breast cancer those genes and pathwaysmost

associated with resistance to aromatase inhibitors by examining the global transcriptional effects of AI

treatment.

Experimental Design: Baseline and 2-week posttreatment biopsies were obtained from 112 postmen-

opausal women with ERþ breast cancer receiving neoadjuvant anastrozole. Gene expression data were

obtained from 81 baseline and 2-week paired samples. Pathway analysis identified (i) the most prevalent

changes in expression and (ii) the pretreatment genes/pathways most related to poor antiproliferative

response.

Results: A total of 1,327 genes were differentially expressed after 2-week treatment (false discovery rate <0.01). Proliferation-associated genes and classical estrogen-dependent genes were strongly downregulated

whereas collagens and chemokineswere upregulated. Pretreatment expressionof an inflammatory signature

correlated with antiproliferative response to anastrozole and this observation was validated in an inde-

pendent study. Higher expression of immune-related genes such as SLAMF8 and TNF aswell as lymphocytic

infiltration were associated with poorer response (P < 0.001) and validated in an independent cohort.

Conclusions: The molecular response to aromatase inhibitor treatment varies greatly between patients

consistent with the variable clinical benefit from aromatase inhibitor treatment. Higher baseline expression

of an inflammatory signature is associated with poor antiproliferative response and should be assessed

further as a novel biomarker and potential target for aromatase inhibitor-treated patients. Clin Cancer Res;

19(10); 1–12. �2013 AACR.

IntroductionApproximately 80% of human breast carcinomas present

as estrogen receptor a–positive (ERþ). In postmenopausalwomen, estrogen withdrawal by treatment with aromatase

inhibitors is the most effective form of endocrine therapy,but response to themvariesmarkedly (1–3).Mechanisms ofresistance appear to be multiple but are poorly character-ized. In vitro studies of acquired resistance to estrogendeprivation have identified several putative mechanismswhich largely involve growth factor–related signal trans-duction pathways (4–7) but there is limited clinical evi-dence to support these. It is also notable that these modelsdo not involve an assessment of the contribution of humanstromal elements.

The presurgical/neoadjuvant setting provides an excep-tionally valuable scenario for linking biology to clinicalresponse and to studymechanisms of resistance; within thissetting, the proliferation marker Ki67 is a validated phar-macodynamic indicator of response to endocrine therapy(8–10). Treatment-dependent changes in sequential mea-surements of Ki67 in neoadjuvant trials of endocrine ther-apy in postmenopausal women (IMPACT, p024, andZ1031; refs. 9, 11, 12) have all revealed differences or lack

Authors' Affiliations: 1Royal Marsden Hospital; 2Breakthrough BreastCancer Research Centre, Institute of Cancer Research; 3Cancer ResearchUK Clinical Trials and Statistics Unit, Section of Clinical Trials, Institute ofCancer Research, London; 4Breast Research Group, University of Edin-burgh, Edinburgh, United Kingdom; and 5Department of Biochemistry,University of Otago, Dunedin, New Zealand

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

A.K. Dunbier, Z.Ghazoui, andH. Anderson contributed equally to thiswork.

Corresponding Author: Anita K. Dunbier, Department of Biochemistry,University of Otago, PO Box 56, Dunedin 9016, New Zealand. Phone:643479-9258; Fax: 643479-7738; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-12-1000

�2013 American Association for Cancer Research.

ClinicalCancer

Research

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of differences that were parallel to recurrence-free survival(RFS) differences in the equivalent adjuvant trials (ATAC,BIG 1-98, and MA27; refs. 1, 13, 14), respectively. Inaddition, Ki67 of patients after 2 weeks’ treatment predictsRFS more closely than pretreatment values (8).

Nearly all patients on an aromatase inhibitor showreduction in Ki67 expression, suggesting that some benefitis derived, although this may be modest (9). This contin-uous marker of response is well-suited to assessment ofmechanisms of resistance that are also likely to have anonbinary effect. This may explain why changes in Ki67have proven to be better predictors of benefit from endo-crine therapy than clinical response during neoadjuvantendocrine therapy (15, 16).

Gene expression profiling of tumor biopsies before andduring treatment has the potential to enable the identifica-tion of the most important genes/pathways involved in theresponse to estrogen deprivation therapy and the pretreat-ment determinants of response and resistance. Availabilityof comprehensive expressiondatasets, as providedhere,willallow the evaluation of candidate genes from experimentalresearch for their clinical relevance.

We present, to our knowledge, the largest study of theglobal transcriptional effects of aromatase inhibitor treat-ment in the neoadjuvant setting with the aims of identify-ing: (i) transcriptional features of response to aromataseinhibitors and (ii) phenotypic and genotypic determinantsof benefit from aromatase inhibitors. This approachrevealed for the first time the importance of immune/inflammatory influences that could not have been detectedby studies of cell lines.

Materials and MethodsPatient samples

Fourteen-gauge core-cut tumor biopsies were obtainedfrom 112 postmenopausal women with stage I to IIIB ERþearly breast cancer before and after 2-week anastrozolemonotherapy in a neoadjuvant trial (17, 18). Patient demo-graphics are shown in Supplementary Table S1. Tissue wasstored in RNAlater at �20�C. Two 4-mm sections from thecore were stained with hematoxylin and eosin (H&E) andexamined by a pathologist (A. Nerurkar and P. Osin) toconfirm the presence of cancerous tissue, assess histopa-thology, and the presence or absence of lymphocytic infil-tration. Tumorswere deemed to be positive for lymphocyticinfiltration if intraepithelial mononuclear cells could beseen within tumor cell nests or in direct contact with tumorcells. Biopsies in which lymphocytic infiltrate could be seenwithout direct contact to tumor cells were not considered tohave lymphocytic infiltration. Samples were assessed blind-ly by both pathologists and the concordance rate was 81%.Discordant samples were then reassessed by A. Nerurkarwho was blinded to the result of the initial assessment andthe more frequent of the 3 assessments was used in theanalysis. Total RNA was extracted using RNeasy (Qiagen).RNA quality was checked using an Agilent Bioanalyser:samples with RNA integrity values of less than 5 wereexcluded from further analysis. ER (H-score) and Ki67(% cells positive) values by centralized immunohistochem-istry were already available (17).

An additional 71 stored sections from paraffin-embed-ded, formalin-fixed core-cut biopsies were obtained from asubset of patients from the IMPACT trial who receivedneoadjuvant anastrozole or tamoxifen (15). Sections wereH&E-stained, and the presence or absence of detectablelymphocytic infiltration was assessed by a pathologist (A.Nerurkar). Ki67 data were obtained from previous analysisof these patients (8, 9).

Gene expression analysis and data preprocessingRNA amplification, labeling, and hybridization on

HumanWG-6 v2 Expression BeadChips (Illumina) wereconducted according to the manufacturer’s instructions ata single Illumina BeadStation facility. Tumor RNA of suf-ficient quality andquantitywas available to generate expres-sion data from 104 pretreatment biopsies and 85 two-weekbiopsies (Supplementary Fig. S1). Datawere extracted usingBeadStudio software and normalized with variance-stabi-lizing transformation (VST) and Robust Spline Normaliza-tion (RSN) method in the Lumi package (19). Probes thatwere not detected in any samples (detection P > 1%) werediscarded from further analysis. Gene expression data fromthis study is deposited (20).

Data analysisGenes differentially expressed between baseline and 2-

week samples were identified using a multivariate permu-tation test (21) implemented in BRB-Array Tools (22).Random variance t-statistics were calculated for each gene

Translational RelevanceMost postmenopausal women with estrogen receptor

(ER)–positive breast cancer receive an aromatase inhib-itor at some stage during their treatment. However,responsiveness to aromatase inhibitors varies greatly.Previous studies of resistance mechanisms have beenconducted in cell lines and have highlighted the impor-tance of signal transduction pathways, but these studiesomit the stromal influences that are increasingly recog-nized to have a major influence on tumor biology. Incontrast, we report genome-wide expression profiling of81 ER-positive breast carcinomas including stromal tis-sues before and during treatment with an aromataseinhibitor in the neoadjuvant setting. We identify aninflammatory signature as the strongest correlate of poorantiproliferative response and confirm this finding in anindependent set of tumors as well as using gross lym-phocytic infiltration as an alternative measure of inflam-matory activity. This work provides a potential newavenue for drug development and for identifyingpatients with a reduced likelihood of response to aro-matase inhibition.

Dunbier et al.

Clin Cancer Res; 19(10) May 15, 2013 Clinical Cancer ResearchOF2




Table 1. Genes differentially expressed between pretreatment and 2 weeks in 81 pairs of patients treatedwith anastrozole

Gene symbol DescriptionFold change(post/pre)

Proportionof cases

Downregulated genes

1 TFF1 Trefoil factor 1 0.34 84%

2 UBE2C Ubiquitin-conjugating enzyme E2C 0.42 89%

3 TOP2A Topoisomerase (DNA) II alpha 170kDa 0.45 94%

4 IQGAP3 IQ motif containing GTPase-activating protein 3 0.47 88%

5 UHRF1 Ubiquitin-like, containing PHD and RING finger domains, 1 0.50 90%

6 Hs.388347 mRNA; cDNA DKFZp686J0156 0.50 79%

7 SERPINA3 Serpin peptidase inhibitor, clade A, member 3 0.50 92%

8 CDC20 Cell division cycle 20 homolog (S. cerevisiae) 0.51 88%

9 PDZK1 PDZ domain containing 1 0.51 88%

10 FGFR3 Fibroblast growth factor receptor 3 0.51 84%

11 DTL Denticleless homolog (Drosophila) 0.52 85%

12 NUSAP1 Nucleolar and spindle-associated protein 1 0.52 85%

13 PRC1 Protein regulator of cytokinesis 1 0.53 88%

14 Hs.159264 Human clone 23948 mRNA sequence 0.53 83%

15 KIAA0101 KIAA0101 0.53 90%

16 SUSD3 Sushi domain containing 3 0.54 89%

17 AGR2 Anterior gradient homolog 2 (Xenopus laevis) 0.55 84%

18 CCNB2 Cyclin B2 0.56 85%

19 ASPM Asp (abnormal spindle) homolog, microcephaly associated 0.56 89%

20 CDCA5 Cell division cycle associated 5 0.57 92%

22 MYB V-myb myeloblastosis viral oncogene homolog (avian) 0.58 85%

23 STC2 Stanniocalcin 2 0.58 76%

36 STC1 Stanniocalcin 1 0.65 77%

43 MCM4 Minichromosome maintenance complex component 4 0.66 83%

44 GREB1 GREB1 protein 0.66 84%

45 IL17RB Interleukin 17 receptor B 0.66 85%

46 CCNB1 Cyclin B1 0.67 85%

47 PBK PDZ-binding kinase 0.67 88%

50 MYC V-myc myelocytomatosis viral oncogene homolog (avian) 0.67 78%

Upregulated genes

1 LOC651278 Similar to serine hydrolase-like 2 (LOC651278). 1.61 82%

2 THRA Thyroid hormone receptor, alpha (v-erb-a) 1.56 82%

3 PIB5PA Phosphatidylinositol (4,5) bisphosphate 5-phosphatase, A 1.47 80%

4 FBLN1 Fibulin 1 1.45 80%

5 TGFB3 Transforming growth factor, beta 3 1.32 76%

6 PLEKHF1 Pleckstrin homology domain containing, family F member 1 1.30 80%

7 Hs.202577 cDNA FLJ34585 fis, clone KIDNE2008758 1.30 79%

8 DCN Decorin 1.30 78%

9 IQGAP2 IQ motif containing GTPase-activating protein 2 1.28 80%

10 TINF2 TERF1 (TRF1)-interacting nuclear factor 2 1.27 77%

11 CRY2 Cryptochrome 2 (photolyase-like) 1.27 82%

12 DBP D site of albumin promoter (albumin D-box)-binding protein 1.22 80%

13 DHRS10 Hydroxysteroid (17-beta) dehydrogenase 14 1.18 79%

14 SCARB2 Scavenger receptor class B, member 2 1.14 78%

15 CPXM Carboxypeptidase X (M14 family), member 1 1.59 76%

16 HTRA1 HtrA serine peptidase 1 1.39 73%

17 BCAS1 Breast carcinoma–amplified sequence 1 1.33 77%

18 ACACB Acetyl-Coenzyme A carboxylase beta 1.33 76%

19 OLFML1 Olfactomedin-like 1 1.32 79%

20 CDKN1C Cyclin-dependent kinase inhibitor 1C (p57, Kip2) 1.28 78%

NOTE: Genes were selected by class comparison analysis at a FDR of 1%. All genes shown in this table have univariate P� 1� 10�7.The top 20 genes downregulated by anastrozole plus selected additional downregulated genes of interest and the top 20 genesupregulated are shown.

Genomic Profiling Predicts Response to Aromatase Inhibitor Treatment

www.aacrjournals.org Clin Cancer Res; 19(10) May 15, 2013 OF3




(23). Geneset comparison analysis (24) was conductedusing Biocarta pathways (25).

Multiple correlation analysis was conducted in BRB-ArrayTools. A statistical significance level for each gene for testingthehypothesis that theSpearman’s correlationbetweengeneexpression and change in Ki67 was calculated to be 0 and Pvalueswere thenused inamultivariate permutation test (21)from which false discovery rates (FDR) were computed.Proportional 2-week change in Ki67 was defined as (2-weekKi67/baseline Ki67)� 100. Pathway analysis using Ingenu-ityPathwaysAnalysis (Ingenuity Systems)was conductedonthe list of genes correlated with P � 0.005. Other statisticalanalyses were conducted in SPSS for Windows (SPSS Inc.),and Graphpad Prism (Graphpad Software Inc.).

Multivariable analyses were undertaken with log propor-tional change in Ki67 as the dependent variable and theimmune metagene, ESR1, ER H-score, and grade as inde-pendent variables. Backward selection was used. Cases withmissing values for any of the variables in the model wereexcluded from analysis.

Normalized data from the validation set (Edinburgh)were downloaded fromGene Expression Omnibus (ref. 26;Accession number¼GSE20181). Expression of the inflam-matory metagene was derived by extracting data for thecomponent genes from the normalized series matrix file.Ki67 was obtained from a previous study of these samples(27).

Exploratory analysis of the type of immune cell repre-sented by the signature associated with change in Ki67 wascarried out in prediction analysis of microarrays (PAM;ref. 28). Relative expression of the correlated genes wasdetermined by taking the square of correlation coefficient ofthe positively correlated genes and rescaling to the trainingpopulation of all immune cell types from the ReferenceDatabase of Immune Cells (29) from which more than 4examples were available. Leave-one-out cross-validationwas used to determine the accuracy of the classifier and thenormalized expression of the genes correlated with changein Ki67 was entered as an "unknown" to determine thelikely identity of the unknown profile.

ResultsEstrogen deprivation induces profound reductions inproliferation and estrogen-associated genes

Good quality gene expression data were available atbaseline and 2 weeks postanastrozole treatment from 81matched pairs of samples (Supplementary Fig. S1). Usingmultiple testing corrected class comparison analysis, 1,327genes were identified that were significantly differentiallyexpressed at an FDR of 1% (Table 1; Supplementary TableS2A). Of these, 926 were downregulated and 401 upregu-lated. Proliferation-associated genes such as TOP2A,CCNB2 and classical estrogen-dependent genes such asTFF1 and PDZK1were highly represented among the down-regulated genes. Less consistency in function was observedamong upregulated genes, however, collagens and stromalcomponents including immune-related molecules were

prominent. Pathway analysis using Geneset Comparison(24) revealed that 19 of the top 30 statistically significantlyaltered pathways were proliferation-associated (Table 2).Pathways related to estrogen signaling, apoptosis, and thecomplement pathway also changed.

Tumors exhibit heterogeneity in transcriptionalresponse to estrogen deprivation

To visualize the degree of variability between the 81tumors in their transcriptional response to estrogen depri-vation, we conducted unsupervised hierarchical clusteringanalysis of the values representing the change (posttreat-ment � baseline) in expression of each of the 1,327 genesupon aromatase inhibitor treatment (Fig. 1). No singlegene was up- or downregulated in all tumors; the mostconsistently altered geneTOP2Awas downregulated in 94%of cases. No gene was upregulated in more than 82% ofcases.

No strong patterns emerged according to this clusteringfor PgR or HER2 status or for DKi67 (Fig. 1B). Most tumorsshowed strong downregulation of classical estrogen-regu-lated genes (ERG; Fig. 1C) and proliferation-associatedgenes (Fig. 1D). However, while a small number of tumorsshowed consistently poor suppression of proliferation-associated genes, variability in the suppression of ERGsdiffered markedly between tumors (Fig. 1C). The majorityof tumors showed consistent upregulation of the clusters ofgenes coding for collagens and chemokines (Fig. 1E and F).Although a small number of tumors with poor Ki67responses also showed lesser increases in the chemokineand collagen clusters, this inverse association was not seenin all tumors. Both supervised segregation of the tumorsbased on DKi67 and HER2 status and unsupervised clus-tering of specific clusters showed an association of littledecrease in proliferation genes for those tumors not show-ing a decrease in Ki67 but only a subtle difference for HER2-positive tumors (Supplementary Fig. S2).

Relationship between pretreatment expression ofESR1 and change in Ki67

This variation in transcriptional response to aromataseinhibitor treatment is consistent with the variationobserved in antiproliferative response as measured byimmunohistochemical assessment of Ki67 (Fig. 2A).Levels of ESR1 could be expected to contribute to theresponsiveness of tumors to estrogen deprivation and thishas previously been shown both by immunohistochem-istry and mRNA analyses in tamoxifen-treated patients(30–32). In this study, ESR1 expression showed a rela-tively weak but statistically significant correlation with theproportional 2-week change in Ki67 (Spearman r ¼�0.29, P ¼ 0.012; Fig. 2B). One patient showing poorchange in Ki67, indicated in Fig. 2A with an asterisk, wasexcluded from further analyses as this patient’s decreasein plasma estradiol was more than 3 SDs less than themean and hence did not meet criteria for estrogendeprivation.

Dunbier et al.





Identification of genes that are correlated withantiproliferative response to aromatase inhibitorsQuantitative trait analysis (QTA) by Spearman correla-

tion was used to identify 471 genes whose expression inbaseline tumor biopsies correlated with proportional 2-week change in Ki67 at P < 0.005 (Table 3; SupplementaryTable S2B). The list of genes associated with poor antipro-liferative response to aromatase inhibitor treatment wasdominated by immune-related genes with SLAMF8, a CD2family member, the most highly correlated gene, and TNF,interleukins and receptors and other cytokines all amongthe top 20 genes (Table 3; Fig. 3A). Less consistency offunction was observed among the genes predicting for agood response to aromatase inhibitors but estrogen signal-ing–associated genes such as GATA3 and STC2 both fea-tured within the top 50 correlated genes (Table 3; Fig. 3B).

Pathway analysis of the list of 471 correlated probesrevealed that inflammatory response–related pathwayswere significantly overrepresented (Table 3; SupplementaryTable S3A). Themost significantly overrepresented networkincluded modules focused upon Inflammatory Response,Inflammatory Disease, and Immunological Disease (P ¼6.83 � 10�23 to 1.83 � 10�2).

Validation of correlation of inflammatory responsesignature with antiproliferative response to aromataseinhibitors

For further analysis, we focused on the 45-gene immuneresponse signature which was most significantly overrepre-sented in the genes correlated with antiproliferativeresponse (Supplementary Table S3). The median baselineexpression of these genes correlated significantly with

Table 2. Pathways affected by aromatase inhibitor treatment

Pathway description Number of genes LS permutation (P)

1 cdc25 and chk1 regulatory pathway in response to DNA damage 9 0.000012 Cyclins and cell-cycle regulation 23 0.000013 Estrogen-responsive protein Efp controls cell-cycle

and breast tumors growth16 0.00001

4 Cell cycle: G1–S checkpoint 27 0.000015 Cell cycle: G2–M checkpoint 24 0.000016 CDK regulation of DNA replication 18 0.000017 How progesterone initiates the oocyte maturation 18 0.000018 Sonic hedgehog (SHH) receptor Ptc1 regulates cell cycle 9 0.000019 Role of Ran in mitotic spindle regulation 9 0.0000110 RB tumor suppressor/checkpoint signaling in response to DNA damage 13 0.0000111 Activation of Src by protein tyrosine phosphatase alpha 10 0.0000112 Regulation of p27 phosphorylation during cell-cycle progression 12 0.0001813 p53 signaling pathway 16 0.0002114 Mechanism of protein import into the nucleus 11 0.000415 Downregulated of MTA-3 in ER-negative breast tumors 15 0.0005416 Role of BRCA1, BRCA2, and ATR in cancer susceptibility 22 0.0008417 Cycling of Ran in nucleocytoplasmic transport 5 0.0008518 Tumor suppressor Arf inhibits ribosomal biogenesis 17 0.0011219 E2F1 destruction pathway 9 0.0012220 BRCA1-dependent Ub ligase activity 8 0.0015221 Cyclin E destruction pathway 8 0.0016622 BTG family proteins and cell-cycle regulation 10 0.001823 Cystic fibrosis transmembrane conductance regulator

(CFTR) and beta 2 adrenergic receptor (b2AR) pathway8 0.00198

24 Classical complement pathway 12 0.0022825 Stathmin and breast cancer resistance to antimicrotubule agents 17 0.0025126 p38 MAPK signaling pathway 34 0.0028427 Regulation of eIF4e and p70 S6 kinase 24 0.0029228 Internal ribosome entry pathway 6 0.0042429 Regulation of cell-cycle progression by Plk3 8 0.0043830 Spliceosomal assembly 14 0.0044731 Apoptotic DNA fragmentation and tissue homeostasis 10 0.00557

NOTE: Pathways identified as differentially expressed between pre- and post aromatase inhibitor treatment by gene set comparisonanalysis are shown. Gray-highlighted rows depict proliferation-associated pathways.






A

C Sushi domain containing 3 N-acetyl transferase 1 Leucine-a-2-glycoprotein DA375949 Phorbol-12-myristate induced 1 FLJ14346 Serum/glucocorticoid regulated kinase 3 Cadherin EGF receptor 2 Pl-P3-dependent RAC exchanger 1 Sema doma (Ig) short chain 7-dehydrocholesterol reductase PDZ domain containing 1 PDZ domain containing 1 PDZ domain containing 1 RNA binding motif protein 24 Reticulon 1 Stanniocalcin 1 Retinoic acid induced 2 Acetyl-Coenzyme A oxidase 2 SEC14-like 2 Fructose-2,6-biphosphatase 3 RAB30 member RAS oncogene family RAB30 member RAS oncogene family Interleukin 17 receptor B Transmembrane protease, serine 3 Trefoil factor 1 Trefoil factor 3 Protein kinase Ib Aminocarboxymuconate DC Solute carrier family 27 member 2 CYTP4502B7 pseudogene 1 Testis expressed 14 Heat shock protein 22 protein 8 Tubulin, alpha 3e Tubulin, alpha 2, transcript var 2 Serine peptidase inhibitor, 4 Prostaglandin E synthase

PR Her2 ΔKi67

B PgR positive HER2 positive Ki67 increase on treatment Ki67 decrease <50% Ki67 decrease 50–75% Ki67 decrease>75%

IHC Data color key:

IQ containing GTPase activating 3Centromere protein F, 350/400kaCell division cycle 20 homologCell division cycle associated 5Asp (abnormal spindle) homologNon-SMC condesin complex GKinesin family member 20AProtein regulator of cytokinesis 1Topoisomerase II alpha 170 kDaUbiquitin-conjugaing enzyme E2CCyclin B2Nucleoloar and spindle associated 1Centrosomal protein 55kDaAurora kinase ACell division assoicated 3TPX2, microtubule-associated, homologCyclin B1Stathmin 1/oncoprotein 18E2F transcription factor 2Chromosome 18 ORF 56Ubiquitin-conjugaing enzyme E2TExonuclease 1Fanconi anaemia, group ITTK protein kinaseRAD51 associated protein 1Ubiquitin-like 1KIAA0101Kinesin family member 11Thymidylate synthetasePituitary tumour-transforming 1Maternal embryonic leucine zipper kinaseCell division cycle 2, G1 to S and G2 to MCyclin A2BUB1 homolog betaHyaluronan-mediated mobility receptorDiscs, large homolog 7PDZ binding kinaseCentromere protein ACell division cycle 25 homolog CThyroid hormone receptor interactor 13Centromere protein E, 313kDa

D

Fibronectin leucine rich protein 2Runt-related transcription factor 1cDNA clone IMAGE:5922621Basic helix-loop-helix domain, BSimilar to hypothetical proteinTransforming growth factor beta 3Sortillin related receptor 2Leucine rich repeat containing 17Nectin homologZinc finger protein 521Coiled-coil domaing containing 80Chromosome 6 open reading frame 65Protein associated with Tlr4Ring finger protein 144AMatrix metallopeptidase 2Carboxypeptidase ZProcollagen C-endopeptidase enhanceCathepsin KDecorinSecreted frizzled-related protein 2cDNA clone IMAGE:5261213CKLF-like 3Predicted: KIAA1644 proteinAnthrax toxin receptorTransforming, coiled-coil containing 1V-maf musculoaponeurotic fibrosarcoma BAngiopoietin-like 2Collagen, type II, alpha 1Collagen, type I, apha 1Collagen, type VI, alpha 3

E

Complement factor HRa1 GND stimulator-like 1Transient receptor cation channel, C1Faftlin family member 2Chromosome 21 open reading frame 34Sine oculis binding protein homologPhosphodiesterase 1ALa ribonucleoprotein, member 6Heat shock 27 kDa protein 2Sema domain proteinChromosome 6 open reading frame 189High density lipoprotein-binding proteinClaudin 5LIM domain binding 2Heat shock 70kD protein 12BMHC, class II, DP beta 1Phospholipase D family member 3MHC, class II, DM alphaComplement component 3GTPase, IMAP family member 4CXADR adhesion molecule, 1Chromosome 6 open reading frame 192Pecanex homolog (Drosophila)Ubiquitin-activating enzyme E1-likeProtein phosphatase 1MWAS/WASL interacting protein, 1Apolipoprotein C-IMHC, class II, DM beta

F

Dunbier et al.





2-week proportional change in Ki67 (r ¼ 0.44, P � 0.0001;Fig. 3C). Multivariable analysis including grade, ERH-score,and baseline Ki67 found that the inflammatory responsesignature was an independent predictor of change in Ki67(Supplementary Table S4). In an independent set of 58tumors treated with the aromatase inhibitor letrozole (33)baseline expression of the immune response metagene wassignificantly correlated with change in Ki67 (r ¼ 0.41, P ¼0.0045; Fig. 3D) as in our own data.

Exploration of association between lymphocyticinfiltration and antiproliferative response toaromatase inhibitorsOne form of inflammatory activity that can be readily

assessed in histologic specimens is lymphocytic infiltration.Two pathologists (A. Nerurkar and P. Osin) identified thepresence of visible lymphocytic infiltration in 15 andabsence in 64 tumors. Consistent with the gene expressionobservations, tumors with lymphocytic infiltration showedsignificantly poorer antiproliferative response to aromataseinhibitor treatment (Mann–Whitney,P¼0.011,n¼79; Fig.3E). The presence of lymphocytic infiltration correlatedwith expression of the immune response metagene (Sup-plementary Fig. S3A). In addition, the proportion of sam-ples containing a lymphocytic infiltrate in this cohortincreased over the duration of treatment (SupplementaryFig. S3B). We also examined lymphocytic infiltration in asubset ofmaterial from the tamoxifen and anastrozole armsof the IMPACT trial (15). Although the relationship was notsignificant, possibly due to the smaller number assessed, asimilar effect size was observed (Mann—Whitney, P¼ 0.15,

n ¼ 52; Fig. 3F). No significant association was observedusing the anastrozole- or tamoxifen-only subgroups of thiscohort (n ¼ 29 and 32 patients, respectively).

Genes associated with response are correlated with thetranscriptional profile of dendritic cells

The association between lymphocytic infiltration andchange in Ki67 suggests that the gene expression signaturepredicting response to aromatase inhibitors may be derivedfrom infiltrating immune cells, rather the tumor cells them-selves. To investigate the likely source of the inflammatorysignature predictive of change in Ki67, we defined PAMcentroids typifying the main immune cell types using pro-files from publicly available gene expression data (29).Leave-one-out cross-validation of the centroids identifiedhematopoietic stem cells, B cells, T cells, natural killer (NK)cells, and dendritic cells with 100% accuracy. Predictionanalysis using the normalized relative expression of thegenes in our 471-gene response predictor identified theprofile as being most closely aligned to that of dendriticcells (Supplementary Table S5).

DiscussionIn this study, the largest reported of the global transcrip-

tional consequences of aromatase inhibitor treatment inbreast tumors, we have identified an inflammatory geneexpression signature in baseline samples that is indepen-dently associated with poor antiproliferative response toneoadjuvant aromatase inhibitor treatment (8, 15). Thesignature we identified appears to contain the transcrip-tional fingerprint of infiltrating immune cells, a possibility

Figure 1. Heatmap of changes of expression of genes which alter upon aromatase inhibitor treatment. A, heatmap of changes in 1327 genes differentiallyexpressed at FDR<1%.B, dendrogramof 81pairs of tumors color codedwith immunohistochemical information corresponding to eachsample.Heatmapsofgenes comprising the core of the (C) estrogen-associated gene cluster; (D) proliferation-associated gene cluster; (E) ECMcluster; and (F) immune cluster. Reddenotes upregulation, green denotes downregulation.

Figure 2. Ki67 changes in responseto aromatase inhibitor treatment. A,profiles of%Ki67 staining before and2 weeks after aromatase inhibitortreatment as measured byimmunohistochemistry. B,relationship between pretreatmentexpression of ESR1 and changein Ki67.






Table 3. Genes and pathways associated with antiproliferative response to anastrozole treatment

Rank Gene symbol DescriptionCorrelationcoefficient

Parametricp-value or score

Genes associated with poor response1 SLAMF8 Signaling lymphocytic activation molecule 8 0.520 3.0E-062 P2RY6 Pyrimidinergic receptor P2Y, G-protein coupled, 6 0.507 5.6E-063 Hs.370503 mRNA; cDNA DKFZp313O229 0.505 5.9E-064 ZBED2 Zinc finger, BED-type containing 2 0.492 1.11E-055 PITPNM1 Phosphatidylinositol transfer protein, 1 0.487 1.40E-056 IL21R Interleukin 21 receptor 0.481 1.81E-057 LAIR2 Leukocyte-associated immunoglobulin-like R2 0.476 2.29E-058 RGS19 Regulator of G-protein signaling 19 0.474 2.54E-059 HAVCR2 Hepatitis A virus cellular receptor 2 0.465 3.75E-0510 IL32 Interleukin 32 0.464 3.92E-0511 ADAM8 ADAM metallopeptidase domain 8 0.464 3.84E-0512 PLCL3 Phospholipase C, eta 1 0.464 3.77E-0513 FPRL2 Formyl peptide receptor-like 2 0.462 4.25E-0514 LAG3 Lymphocyte activation gene 3 0.461 4.31E-0515 SGK Serum/glucocorticoid regulated kinase 0.460 4.66E-0516 TNF Tumor necrosis factor 0.458 4.93E-0517 Hs.560728 cDNA clone IMAGE:38786 3 0.457 5.12E-0518 CARD9 Caspase recruitment domain family, member 9 0.457 5.24E-0519 TRAF3 TNF receptor–associated factor 3 0.452 6.47E-0520 AKR1B1 Aldo-keto reductase family 1, member B1 0.452 6.38E-0524 TNFAIP3 Tumor necrosis factor, alpha-induced protein 3 0.438 0.00011325 CD53 CD53 molecule 0.438 0.00011233 IRF8 Interferon regulatory factor 8 0.427 0.00017335 CD86 CD86 molecule 0.426 0.00017736 IL10RA Interleukin 10 receptor, alpha 0.426 0.00017737 CD84 CD84 molecule 0.425 0.00018440 ITGAL Integrin, alpha L 0.423 0.00019943 IGSF6 Immunoglobulin superfamily, member 6 0.420 0.00022646 ITGB2 Integrin, beta 2 0.419 0.00022848 LPXN Leupaxin 0.418 0.00024249 SLAMF1 Signaling lymphocytic activation molecule 1 0.417 0.00025350 TNFSF7 CD70 molecule 0.417 0.000251

Genes associated with good response1 LOC441425 LOC441425 �0.522 1.9E-062 SLC3A1 Solute carrier family 3, member 1 �0.495 7.3E-063 LOC645636 LOC645636 (similar to AIP1) �0.470 2.29E-054 TMC4 Transmembrane channel-like 4 �0.443 7.18E-055 Hs.99785 cDNA: FLJ21245 fis, clone COL01184 �0.438 8.92E-056 EML2 Echinoderm microtubule–associated protein like 2 �0.437 9.31E-057 C1orf182 Chromosome 1 open reading frame 182 �0.433 0.0001098 GATA3 GATA-binding protein 3 �0.431 0.0001189 LOC388564 Hypothetical gene supported by BC052596 �0.428 0.00013310 C17orf79 Chromosome 17 open reading frame 79 �0.425 0.00014811 GSTO2 Glutathione S-transferase omega 2 �0.422 0.00016512 PEX19 Peroxisomal biogenesis factor 19 �0.421 0.00017813 PAK6 P21(CDKN1A)-activated kinase 6 �0.417 0.00020614 Hs.570838 FLJ36037 �0.408 0.00028415 Hs.58384 BX090362 �0.407 0.00029216 NME5 Non-metastatic cells protein 5 �0.406 0.00030217 HIST1H2AC Histone cluster 1, H2ac �0.406 0.000306

(Continued on the following page)

Dunbier et al.





that is supported by our observation that tumors withdetectable lymphocytic infiltration appear to obtain lesserbenefit from aromatase inhibitors. Importantly, the rela-tionship of the signature with change in Ki67 was validatedin an independent set of tumors.The immune system has conflicting potential roles in

both suppressing tumor growth by destroying cancer cellsand promoting tumors through the production of cytokinesand growth factors (34). Investigation of the associationbetween tumor-associated lymphocytes and response toneoadjuvant chemotherapy has revealed that the presenceof lymphocytes is an independent predictor of goodresponse to cytotoxic chemotherapy (35) in patients withbreast cancer. Similarly, Mahmoud and colleagues recentlyshowed that tumor-infiltratingCD8þ lymphocytes are indi-cators of good clinical outcome in patients with ER� breastcancer (36). Our observation that infiltrating immune cellsare associated with poor response to endocrine therapysuggests that their role may be significantly different intumors treated with endocrine therapy as opposed to che-motherapy and between the ERþ and ER� subgroups. Thedifferential cytotoxicities of chemotherapy and endocrinetherapy on lymphocytes and the effect of estrogen on theimmune systemmay partially explain these differences withchemotherapeutic agents potentially killing infiltratingimmune cells, hence negating their proproliferative effect.Exploratory analysis of the inflammatory signature sug-

gested that dendritic cells could be involved in the poorresponse of tumors with high expression of the signature toestrogen deprivation. Dendritic cells have been implicatedin promoting breast tumorigenesis by polarizing CD4þ Tcells (37) and hence could aid resistance through thismanner. In addition, a dendritic cell metagene (38) hasbeen shown to be associated with endocrine resistance inhigh proliferation, high estrogen-related score tumors (39).

However, further work is needed to define the contributionof these cells to aromatase inhibitor response.

The prominence of the immune system as a determinantof resistance to endocrine therapy contrasts with evidencefrom cell lines of growth factor pathways as the majordeterminants (4–7). This is readily explained by the cellline work, even if conducted in rodent models, excludinghuman stromal components. It is also clear, however, thatimmune influences as characterized by the current work canexplain resistance in only a proportion of patients and lessstatistically prominent pathways may be important deter-minants of resistance. Larger patient numbers and a greaterfocus on the pathways activated in individual patients arerequired to define the proportional influence of the variousputative mechanisms. The public availability of our com-prehensive molecular characterization of this set of tissuesfrom a carefullymonitored clinical trial provides a referenceservice for other experimentalists to assess the clinicalrelevance for their findings in relation to estrogen depriva-tion therapy.

The changes in gene expression were very substantial butvariable between tumors as previously reported (40, 41).Some clusters of proliferation, estrogen-regulated, extracel-lular matrix (ECM), and immune genes were obvious, butwithin these clusters, major variability between the changesin gene expression existed.While proliferation gene changescorrelated with immunohistochemical Ki67 changes,changes in estrogen-regulated genes showed little obviouspattern but this may be due to almost all patients showingsuppression of nearly all of these genes and thus creatinglimited opportunity for segregation of patient groups onthis basis. The most consistently downregulated gene wasTOP2A, a key target for anthracycline-based therapies.This observation supports the current practice of sequenc-ing aromatase inhibitor treatment after chemotherapy,

Table 3.Genes and pathways associatedwith antiproliferative response to anastrozole treatment (Cont'd )

Rank Gene symbol DescriptionCorrelationcoefficient

Parametricp-value or score

18 CYB5D2 Cytochrome b5 domain containing 2 �0.403 0.00033919 CELSR2 Cadherin, EGF LAG seven-pass G-type receptor 2 �0.403 0.0003420 POLR2L Polymerase (RNA) II polypeptide L, 7.6kDa �0.400 0.00038150 STC2 Stanniocalcin 2 �0.361 0.001688

Top networks associated with change in Ki671 Inflammatory response, inflammatory disease, immunological disease 362 Hematologic system development and function, tissue morphology,

cell-to-cell signaling and interaction32

3 Inflammatory response, cell-to-cell signaling and interaction,hematologic system development and function

21

4 Gene expression, cellular growth and proliferation, cell morphology 215 Cell-to-cell signaling and interaction, hematologic system development

and function, immune cell trafficking19

NOTE: The top 20 genes associatedwith poor response, selected immune-related genes from the top 50, the top 20 genes associatedwith good response and the top gene networks as defined by Ingenuity Pathway Analysis of the complete list of 471 genes are shown.






particularly anthracyclins which directly target Topo-isomerase-2. It should be noted, however, that proapop-totic genes are also downregulated, hence the balance ofmolecular effects in relation to their benefit or detrimentfor combination with specific chemotherapy is difficult topredict.

A small number of other groups have explored genomicprofiles in relation to clinical response to aromatase inhi-bitors. Consistent with the current report, Miller and col-leagues (40) found that themost prominent downregulatedgenes by aromatase inhibitors, in their case, letrozole, to bethose related to proliferation and those that are increased toincludemany stroma-related genes. However, in that study,the genes most associated with resistance to the aromataseinhibitors were related to cellular biosynthetic processes, inparticular those coding for ribosomal proteins (33). Thisdifference from the current set of findings may be due toclinical response rather than Ki67 change being the end-point for response in the Miller study: the need to getshrinkage of tumor for assignment of response may placean increased emphasis on metabolic processes. A recentstudy of 377patients randomized to neoadjuvant treatment

with 1 of the 3 third-generation aromatase inhibitors foundthat both luminal A and B subgroups responded, wellalthough the luminal B group remained at poorer prognosisposttreatment (12). This study did not report on the explo-ration of other signatures to segregate responsive and non-responsive subgroups.

The use of Ki67 change as an intermediate endpoint fortreatment benefit may be considered a limitation of thisstudy. However, this approach allows the identification ofgenes/signatures that are specific to response. In contrast,clinical response requires objective regression of tumorsand this has dependence on initial growth rate as well theanti-growth effects of therapy. In addition, the continuousnature of Ki67 expression provides greater statistical powerthan the categorical nature of clinical response analyses.Finally, as pointed out above, Ki67 on treatment is a betterpredictor for long-term outcome than clinical response toendocrine treatment (8, 15).

In conclusion, as well as immune-related functions hav-ing recently described prognostic value and predictive valuefor chemotherapy in early breast cancer, they are related toreduced antiproliferative response to aromatase inhibitor

Figure 3. Immune involvement andantiproliferative response to Ki67.A, scatter plot of relationshipbetween pretreatment expressionof SLAMF8 and proportionalchange in Ki67. B, relationshipbetween pretreatment expressionofGATA3 and proportional changein Ki67. C, relationship betweenpretreatment expression of theinflammatory response metagenesand proportional change in Ki67 inour discovery cohort. D,relationship between pretreatmentexpression of the inflammatoryresponse metagenes andproportional change in Ki67 in theEdinburgh patient cohort. E, plot ofproportional change in Ki67 intumors with detectablelymphocytic infiltration versusthose without in our discoverycohort. F, plot of proportionalchange in Ki67 in tumors withlymphocytic infiltration versusthose with no detectablelymphocytic infiltration in theIMPACT series. Dotted lines areshown at 100% (no change in Ki67after treatment) and 50% (datapoints above this have a less than50% decrease in Ki67 and as suchare defined as nonresponders;ref. 10).

Dunbier et al.





therapy. Further study is needed to determinewhether theserelationships are causative and therefore potentially subjectto intervention.

Disclosure of Potential Conflicts of InterestH. Anderson has ownership interest (including patents) in shares in

AstraZeneca. M. Dowsett has a commercial research grant, honoraria fromspeakers’ bureau, and is a consultant/advisory board member of AstraZe-neca. No potential conflicts of interest were disclosed by the other authors.

Authors' ContributionsConception and design: A.K. Dunbier, Z. Ghazoui, P. Osin, M. DowsettDevelopment of methodology: A.K. Dunbier, Z. Ghazoui, H. AndersonAcquisitionofdata (provided animals, acquired andmanagedpatients,provided facilities, etc.): A.K. Dunbier, Z. Ghazoui, H. Anderson, A.Nerurkar, W.R. Miller, I.E. SmithAnalysis and interpretation of data (e.g., statistical analysis, biosta-tistics, computational analysis): A.K. Dunbier, Z. Ghazoui, H. Anderson,R. A’hern, W.R. Miller, M. Dowsett

Writing, review, and/or revision of the manuscript: A.K. Dunbier, Z.Ghazoui, H. Anderson, A. Nerurkar, R. A’hern, W.R. Miller, I.E. Smith, M.DowsettAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): Z. Ghazoui, H. Anderson, J. SalterStudy supervision: A.K. Dunbier, M. Dowsett

Grant SupportA.K. Dunbier, H. Anderson, and Z. Ghazoui were supported by the Mary-

Jean Mitchell Green Foundation. This work was also supported by a Break-through Breast Cancer Research Grant (to M. Dowsett), a Health ResearchCouncil of New Zealand Sir Charles Hercus Fellowship (to A.K. Dunbier),and National Health Service funding to the NIHR Biomedical ResearchCentre.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received March 26, 2012; revised February 11, 2013; accepted March 1,2013; published OnlineFirst March 14, 2013.

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Published OnlineFirst March 14, 2013.Clin Cancer Res Anita K. Dunbier, Zara Ghazoui, Helen Anderson, et al. Correlates of ResistancePostmenopausal Breast Tumors Identifies Immune-Related

Treated−Molecular Profiling of Aromatase Inhibitor

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