CLINICAL CANCER RESEARCH | PRECISION MEDICINE AND IMAGING

Comprehensive Transcriptomic Profiling IdentifiesBreast Cancer Patients Who May Be Spared AdjuvantSystemic Therapy A C

Martin Sj€ostr€om1,2, S. Laura Chang3, Nick Fishbane4, Elai Davicioni4, Linda Hartman1, Erik Holmberg5,Felix Y. Feng6, Corey W. Speers7, Lori J. Pierce7, Per Malmstr€om1,8, Ma

�rten Fern€o1, and Per Karlsson9,10

ABSTRACT◥

Purpose:There is currently nomolecular signature in clinical usefor adjuvant endocrine therapy omission in breast cancer. Given theunique trial design of SweBCG91-RT, where adjuvant endocrineand chemotherapy were largely unadministered, we sought toevaluate the potential of transcriptomic profiling for identifyingpatients who may be spared adjuvant endocrine therapy.

Experimental Design: We performed a whole-transcriptomeanalysis of SweBCG91-RT, a randomized phase III trial of �radiotherapy after breast-conserving surgery for node-negativestage I–IIA breast cancer. Ninety-two percent of patients wereuntreated by both adjuvant endocrine therapy and chemotherapy.We calculated 15 transcriptomic signatures from the literature andcombined them into an average genomic risk, which was furtherused to derive a novel 141-gene signature (MET141). All signatureswere then independently examined in SweBCG91-RT and in thepublicly available METABRIC cohort.

Results: In SweBCG91-RT, 454 patients were node-negative,postmenopausal, and systemically untreated with ER-positive,HER2-negative cancers, which constitutes a low-risk subgroup andpotential candidates for therapy omission. Most transcriptomicsignatures were highly prognostic for distant metastasis, but con-siderable discordance was observed on the individual patient level.Within the MET141 low-risk subgroup (lowest 25th percentile ofscores), 95% of patients were free of metastasis at 15 years, even inthe absence of adjuvant endocrine therapy. In a clinically low-risksubgroup of the METABRIC cohort not treated with systemictherapy, no breast cancer death occurred among the MET141low-risk patients.

Conclusions: Transcriptomic profiling identifies patients withan excellent outcome without any systemic adjuvant therapy inclinically low-risk patients of the SweBCG91-RT and METABRICcohorts.

IntroductionTreatment of primary breast cancer is becoming more and more

individualized and has entered the era of precision medicine. Due toincreased public awareness and intensified screening programs, theproportion of low-risk tumors has increased with a corresponding riskof overtreatment (1). Thus, in addition to escalating treatment forpatients with high-risk breast cancers, current guidelines focus ondeescalating treatment in low-risk patients (2). Although gene signa-tures assessing recurrence risk have been successful at identifying

patient subgroups in whom adjuvant chemotherapy can be safelyomitted (3–5), there are no tests currently in clinical guidelines toidentify patients who may omit endocrine therapy (2). Adjuvantendocrine therapy reduces the risk of breast cancer death in patientswith estrogen receptor–positive (ERþ) disease by around one-third (6),which can be further reduced by using aromatase inhibitors inpostmenopausal patients (7). However, endocrine therapy may havesubstantial side effects, which is reflected in an adherence rate between50% and 80% (8), and most patients with node-negative disease willnot suffer a recurrence even without adjuvant systemic therapy (6).Thus, developing tools to safely omit endocrine therapy amongpatients with ERþ cancers is highly desirable.

One approach to personalizing therapy is to consider relativetreatment effects constant over subgroups and identify patients atlow risk of recurrences in the absence of the treatment in question (9).The PAM50 risk of recurrence score was shown to identify a subgroupof patients with node-positive hormone-receptor–positive tumorstreated with endocrine therapy but not chemotherapy with a 10-yearmetastasis risk of 6.6%, suggesting that patients in this subgroup maybe spared chemotherapy (3). Amongwomenwith high clinical risk butlow70-gene scores of theMINDACT trial, thefive-yearmetastasis-freesurvival for those that did not receive chemotherapy was similarlyhigh, at 94.7% (5). Furthermore, other studies have focused onidentifying patients at low risk of recurrence despite not receivingany adjuvant systemic therapy. A clinically low-risk subgroup ofpatients with no adjuvant treatment of theOslo1 trial with low PAM50risk of recurrence scores had a 15-year breast cancer–specific survivalof 96.3% (10). Similarly, the 70-gene signature was recently shown toidentify an ultra-low-risk group of patients in the STO-3 trial with abreast cancer–specific survival rate of 94% at 20 years in the absence ofboth endocrine therapy and chemotherapy (11).

1Division of Oncology and Pathology, Department of Clinical Sciences Lund,Faculty of Medicine, Lund University, Lund, Sweden. 2Ska

�ne University Hospital,

Lund, Sweden. 3PFS Genomics, Vancouver, Canada. 4Decipher Biosciences,Vancouver, British Columbia, Canada. 5Regional Cancer Center West, Sahl-grenska University Hospital, Gothenburg, Sweden. 6Department of Urology,Medicine and Radiation Oncology, University of California San Francisco, SanFrancisco, California. 7Department of Radiation Oncology, University of Michi-gan Medical School, Ann Arbor, Michigan. 8Department of Haematology, Oncol-ogy and Radiation Physics, Ska

�ne University Hospital, Lund, Sweden. 9Depart-

ment of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothen-burg University, Gothenburg, Sweden. 10Department of Oncology, SahlgrenskaUniversity Hospital, Gothenburg, Sweden.

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

Corresponding Author: Martin Sj€ostr€om, Lund University, Building 404:B3Medicon Village, Scheelev€agen 2, SE-22381 Lund, Sweden. Phone: 46-733-611-658; E-mail: Martin.Sjostrom@med.lu.se

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When considering the use of baseline risk for gene-expression tests,an emerging problem is the substantial discordance in results for anindividual patient. Indeed, a recent study found the agreement of fivecommon gene-expression tests to be modest, with 39% of patientsclassified uniformly as low risk by all tests, whereas individual testspredicted 61% to 82% to be low risk (12). Other barriers for identifyingpatients for whom adjuvant endocrine therapy can be safely withheldinclude the lack of studies in which patients were not treated withendocrine therapy and lack of studies with long follow-up. As ERþ

breast cancer continues to recur and cause death at a relativelyconsistent rate over 15 years after stopping endocrine therapy, studieswith long follow-up are necessary to identify patients who mayexperience late recurrences (13). Thus, in order to evaluate riskstratification tools for endocrine therapy there is a need for large,well-defined cohorts of patients who were not treated with adjuvantsystemic therapy, have long-term follow-up, and in whom severalgene-expression signatures can be compared.

To that end, we examined the transcriptome of 765 early-stagebreast cancer patients from the SweBCG91-RT trial, a trial random-izing node-negative stage I–IIA breast cancer patients undergoingbreast-conserving surgery to � adjuvant whole breast radiothera-py (14). The vast majority (92%) of patients in the trial were system-ically untreated in the adjuvant setting, and 454 patients were ERþ,HER2-negative (HER2�), postmenopausal, and did not receive adju-vant systemic therapy, making it an ideal data set to study recurrencerisk in the absence of adjuvant systemic therapy. We calculated gene-expression signatures for 15 previously published signatures andaimed to evaluate the potential of transcriptomic profiling in identi-fying patients at such low risk of metastasis that adjuvant endocrinetherapy can be safely omitted.

Materials and MethodsSweBCG91-RT patients

We analyzed gene-expression data of the SweBCG91-RT trial, thedetails of which have been previously described (14–16). Briefly, thetrial randomized 1,178 node-negative, early-stage breast cancerpatients undergoing breast-conserving surgery to adjuvant wholebreast radiotherapy or no radiotherapy. As systemic adjuvant therapywas administered according to regional guidelines at the time, it was

sparsely provided, with only 7% and 2% of patients in the original trialreceiving endocrine therapy and chemotherapy, respectively (15).Subtyping was performed using IHC as detailed previously (14). Theprimary endpoint of this analysis was distant recurrence-free interval(i.e., time tometastasis), defined from the time of surgery until the timeof metastasis, last follow-up or death, with death as a competingevent (17). Patients suffering a contralateral breast cancer or anotherprimary cancer were not censored, as recommended (18). The data forthe metastasis endpoint were collected from patient chart review, andthe median follow-up time was 15.1 years for patients free from event.Additional follow-up was derived from the Swedish cause of deathregistry with a median follow-up time of 20.0 years for patients alive atcensoring, andwe present cumulative incidence of breast cancer death,with death from other causes as competing event, as SupplementaryInformation. The trial and follow-up study were conducted in accor-dance with the declaration of Helsinki and were approved by the LundUniversity Regional Ethical Review Board (approval numbers 2010/127 and 2015/548). Informed oral consent was obtained from allpatients, which was determined appropriate and approved by theEthical Review Board for the original trial and for this gene-expressionstudy.

Gene-expression analysisFormalin-fixed paraffin-embedded tissue was available for 922 of

the original 1,178 patients in the trial (Supplementary Fig. S1). RNAextraction and microarray hybridization were performed in a ClinicalLaboratory Improvement Amendments certified laboratory (DecipherBiosciences). Tumors were profiled with the GeneChip Human Exon1.0 ST microarray (Thermo Fisher) and 765 tumors passed qualitycontrol of RNA, cDNA, and microarray analysis (Gene-ExpressionOmnibus GSE119295). Gene-expression data were normalized usingSingle Channel Array Normalization (19).

Publicly available METABRIC dataWe also examined gene signature scores in the Molecular Tax-

onomy of Breast Cancer International Consortium (METABRIC)cohort. Publicly available clinical and expression data based on theIllumina Human v3 array were downloaded from cBioPortal. Out ofthe 1,904 patients with microarray expression data, 104 patientswere postmenopausal, treated with breast-conserving surgery, withERþ, human epidermal growth factor receptor 2 negative (HER2�)tumors, complete breast cancer–specific death information, andwere not treated with endocrine or chemotherapy. Nearly all werelymph node negative (20). This low-risk systemic treatment na€�vegroup was included for analysis in this study. The median follow-uptime for this low-risk subgroup was 18.1 years for patients alive atcensoring.

Data analysisStatistical analyses were performed using R (3.5.2). We performed a

literature review and identified 15 previously published gene-expression signatures specific to breast cancer risk with publishedequations or algorithms for calculation (21–35). Most were created toprognosticate for the distant recurrence endpoint, although a few(PIK3CAGS and TAMR13) were designed for tamoxifen sensitivity.The surrogate scores of these previously published gene-expressionsignatures were calculated using published algorithms as describedbelow. Cumulative incidences of metastasis or death from breastcancer were computed with a competing risks approach using thecmprsk package (36), and 95% confidence intervals were computed aspreviously described (37). For a direct and unbiased comparison of

Translational Relevance

Some women with primary breast cancer do not require addi-tional endocrine therapy after breast-conserving surgery, but notests are in use to find this low-risk group of women.We performeda transcriptomic analysis of 765 patients of the SweBCG91-RT trial,of whom 454 were node-negative, postmenopausal, and system-ically untreated with ER-positive, HER2-negative cancers. Wetested 15 previously published signatures and showed that mostperformwell in identifyingwomenwith very low risk of recurrence.However, there was a substantial intersignature variation in riskclassification, and we therefore combined the signatures into anaverage genomic risk and an associated novel signature (MET141).MET141 could identify a low-risk group of node-negative, post-menopausal, nonsystemically treated patients with ERþ andHER2-negative tumors, of which 95% were free of metastasis at15 years. These results indicate that transcriptomic profiling maybe used to find women who may be spared endocrine treatment.

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how the different signatures perform, the patients were grouped byscore quartiles. We further examined rates of metastasis or deathfrom breast cancer for patients with the lowest quartile of riskscores, hypothesizing that these patients may be candidates fortherapy omission, although aware that this may not directly rep-resent clinical cutoffs used for the signatures. Cause-specific Coxproportional hazards regression was used to contrast the differencesin hazards between patients with high and low signature scores, andP values were computed with the Wald test. Each continuous riskscore was standardized by dividing the score by its standarddeviation in order to create comparable hazard ratios across sig-natures, otherwise signatures with smaller ranges of values wouldhave disproportionately higher hazard ratios, and the hazard ratioswould not be comparable. Proportional hazards were checkedgraphically and by the Schoenfeld test (38). For most signatures,the hazard ratio (HR) was larger during the first years of follow-up,and we therefore limited this analysis to 10 years. However, a trendwas still observed with larger hazard ratios years 0–5 than 5–10, andthe presented HRs should thus be interpreted as the mean over10 years. To compare how classification of low-risk patients differsby signature, we identified patients within the lowest quartile of riskscores of an individual signature and calculated the proportion ofthose patients also classified in the lowest quartile of each othersignature. We then computed the mean proportion, excluding thesignature of interest (in which the proportion is 1). We refer to thisas the “low-risk classification agreement.” In addition, the agree-ment between signatures split by quartiles was tested by calculatingCohen's kappa. We followed REMARK guidelines for reporting ofthis study (39). Adjustment of P values for multiple testing cor-rection were performed using the Benjamini–Hochberg false dis-covery rate (FDR) method, where applicable (40).

Estimation of time-dependent area under the curveEstimation of time-dependent area under the curve (AUC) was

calculated using the R survivalROC package (version 1.0.3) (41). 95%confidence intervals for time-dependent AUC estimates were boot-strapped using 1,000 bootstrap samples.

Pathway analysisTo assess biological pathways overrepresented in lists of genes, we

used the Panther statistical overrepresentation test (version 13.0,pantherdb.org; ref. 42) using Fisher exact test with Benjamini–Hochberg FDR correction as the test type, and Panther GO-SlimBiological Process gene lists as the annotation data set. As a secondarymethod, we also used Reactome Analysis Tools (reactome.org;refs. 43, 44) with the “project to human” option. The Reactomegenome-wide overview of the pathway analysis visualizes the enrich-ment analysis by organizing Reactome pathways in a hierarchy. Thetop-level pathway is represented as the center of a circular “burst” andeach next level lower on the pathway hierarchy is represented by a stepaway from the center. Pathways overrepresented in the input data setare represented in yellow and pathways not significantly overrepre-sented are represented in gray. For both methods, lists of official genesymbols were entered. Significant enrichment of a pathway wasdefined as FDR < 0.05.

Computation of previously published breast cancer risk scoresPreviously published breast cancer risk scores were developed

on a variety of platforms. We applied gene-expression datafrom microarrays to genomic signature equations to calculatesurrogate continuous risk scores. The following risk scores were

calculated according to their equations as published, usingthe genefu package (version 2.6.0; ref. 45) in R (version 3.3.2):OncotypeDx-like (21), Endopredict-like (22), Genomic Grade

Table 1. SweBCG91-RT patient characteristics.

Allpatients

ERþ, HER2�, postmenopausal,no systemic treatment

Number of patients 765 454Age at surgeryMedian (range) 59 (31–78) 63 (39–78)

�39 19 (3%) 1 (0%)40–49 137 (18%) 16 (4%)50–59 234 (31%) 151 (33%)60–69 284 (37%) 210 (46%)�70 91 (12%) 76 (17%)

Menopausal statusPremenopausal 152 (20%) 0 (0%)Postmenopausal 592 (80%) 454 (100%)Missing 21 0

Histologic grade1 105 (14%) 73 (16%)2 457 (61%) 312 (70%)3 191 (25%) 61 (14%)Missing 12 8

Tumor size (mm)Median (range) 12 (1–40) 11 (1–30)

�10 274 (36%) 198 (43%)11–20 415 (55%) 243 (54%)21–30 70 (9%) 10 (2%)�31 1 (0%) 0 (0%)Missing 5 3

Estrogen receptor status (�1% by IHC)Negative 89 (12%) 0 (0%)Positive 672 (88%) 454 (100%)Missing 4 0

Progesterone receptor status (�20% by IHC)Negative 206 (27%) 90 (20%)Positive 555 (73%) 364 (80%)Missing 4 0

HER2 status by IHC and FISHNegative 702 (93%) 454 (100%)Positive 54 (7%) 0 (0%)Missing 9 0

Subtype by IHCLuminal A 421 (56%) 287 (63%)Luminal B (HER2�) 216 (29%) 167 (37%)HER2þ 54 (7%) 0 (0%)Triple-negative 65 (9%) 0 (0%)Missing 9 0

Adjuvant endocrine therapyNo 710 (93%) 454 (100%)Yes 55 (7%) 0 (0%)

Adjuvant chemotherapyNo 755 (99%) 454 (100%)Yes 10 (1%) 0 (0%)

Adjuvant radiotherapyNo 403 (53%) 227 (50%)Yes 362 (47%) 227 (50%)

Distant metastasisNo 658 (86%) 402 (89%)Yes 107 (14%) 52 (12%)

Died from breast cancerNo 628 (82%) 373 (82%)Yes 137 (18%) 81 (18%)

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Index-like (23), PAM50ROR-like (24), Gene70-like (46), Gen-iusM3-like (26), TAMR-like (27), Gene76-like (28), and the PIK3-CAGS-like risk score (29). For signatures that are based on probesfrom specific microarrays, the genefu annotations to Entrez geneidentifiers were used to map probes to the appropriate gene on themicroarray platform. For the few genes not available on micro-array, the term (coefficient and gene-expression value) of that genewas omitted from the signature equation. The genefu functionsused are listed in Supplementary Table S1.

Celera-like risk scoreRisk scores were computed by calculating the sum of the expression

of 14 genes, as previously described (30).

ExagenBC-like ERþ risk scoreRisk scores were computed based on the following equation:

R ¼ 0.128�CYP24 – 0.173�PDCD6IP þ 0.183�BIRC5, as previouslydescribed (31).

Mammostrat-like risk scoreRisk scores were computed based on the following equation: R ¼

1.54�SLC7A5 þ 1.12�TP53 þ 1.06�NDRG1 þ 0.72�HTF9C þ 0.5�

CEACAM5, as previously described (32).

MGI-like risk scoreRisk scores were computed by normalizing the expression levels for

each of the five genes in the score to have a mean of 0 and a standard

Figure 1.Performance of previously published signatures, AGR and a novel signature, MET141, in 454 node-negative, postmenopausal, and systemically untreatedpatients of the SweBCG91-RT trial with ERþ, HER2� cancers. A, Forest plot depicting standardized HRs for each of the 15 previously published gene signatures,the AGR derived as a mean of all signatures, and a novel signature MET141, for the 454 postmenopausal and systemically untreated patients with ERþ, HER2�

cancers, with associated P values from the Cox proportional hazards model. Continuous risk scores were divided by the standard deviation to directly compareHRs between scores with differently distributed values, and the Cox analysis is limited to 10 years. Results are shown for the distant metastasis endpoint.B, Cumulative incidence of distant metastasis in the 454 node-negative postmenopausal patients of the SweBCG91-RT cohort with ERþ, HER2� cancers who didnot receive systemic therapy, for each of the 15 previously published gene signatures, AGR, and MET141.

Figure 2.

Comparison of previously published signatures in 454 low-risk patients of SweBCG91-RT.A,Pearson correlation and hierarchical clustering for the gene signatures. Amoderate to high correlation is seen for most signatures developed in or for breast cancer patients with ERþ cancer. B, Comparison of the previously publishedsignatures on their classification of individual patients. Each row represents an individual patient, and samples are ordered by AGR. Bar plots are colored to indicatewhat quartile the patient was scored per signature, with red indicating that the patient was scored with highest risk (top 25th percentile) and blue indicating that thepatient was scored with lowest risk (bottom 25th percentile). Histologic grade, time to metastasis, and subtype based on IHC scores are also displayed forcomparison.C,Concordance of the signatures in classifyingwhich patients are in the lowest quartile of risk. Bar plots show the proportion of patients classified in thelowest quartile with the title signature, which was also in the lowest quartile of each other signature. This analysis is performed for the 454 postmenopausal andsystemically untreated patients with ERþ, HER2� cancers in the SweBCG91-RT cohort.

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deviation of 1, then combined into a single score as the first principalcomponent, as previously described (33).

Toronto 2017-like risk scoreRisk scores were computed by calculating the linear equation

involving gene expression and coefficients of 95 genes, as previouslydescribed (34).

Two-gene ratio-like risk scoreRisk scores were computed by subtracting the expression of

IL17RB from the expression of HOXB13, as previously described (35).

Average genomic riskTo calculate average genomic risk, each of the 15 signature scores

was scaled from 0 to 1 within the cohort, and then the mean was

Figure 3.

Reactome pathway analysis. Reactome analysis pathway plots that indicate that cell-cycle, DNA replication, and gene transcription pathways are overexpressed inthe gene lists for previously published signatures (A) and for the MET141 signature (B). The analysis shows that MET141 captures largely the same pathways as theprevious signatures.

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computed. The scaling was necessary to prevent signatures with largerranges of values to be overweighted in the calculation of the averagerisk.

MET141We performed a literature search to identify publicly

available gene-expression data sets with metastasis available asan endpoint. These publicly available breast cancer data sets wereServant (GSE30682), Kao (GSE20685), Wang (GSE2034), Sym-mans (GSE17705), and van de Vijver (downloaded from http://smd.princeton.edu/; refs. 25, 28, 46–48). We sought a wide rangeof breast cancer patients to be able to capture underlying breastcancer risk. All patients in these data sets were used for analysisand they represent breast cancer patients with a range of clinicalrisk factors and treatment. Briefly, the Servant cohort included343 patients with early-stage breast cancer all treated with breast-conserving surgery and postoperative radiotherapy, with a mix ofadjuvant systemic treatment. The Kao cohort included 327patients randomly selected from the institutional tumor bankwith a range of low-risk and high-risk clinical risk factors. TheWang cohort included 286 lymph node–negative patients whodid not receive systemic neoadjuvant or adjuvant therapy. TheSymmans cohort included 508 patients with HER2� tumors,treated with chemotherapy. The van de Vijver cohort included295 patients treated with mastectomy or breast-conserving sur-gery, with a mix of adjuvant treatment (25, 28, 47–49). For eachdata set, probes were converted to gene symbols, and the subset ofgenes in common between the five data sets was identified(10,990 genes). The 15 previously published signatures and averagegenomic risk were calculated for each patient in these five cohorts. Toassess genes to include in a new signature, we removed genes incommon with genes from the previously published signatures, andusing the remaining 10,315 genes, correlated each gene to the averagegenomic risk within each cohort. Genes with a Spearman correlationcoefficient > 0.4 or <�0.4 to average genomic risk in all five cohortswere retained, resulting in 141 total genes: 89 positively correlatedgenes and 52 negatively correlated genes (Supplementary Table S2).The correlation coefficient value was initially varied from 0.3, 0.4, and0.5. We found that using cutoffs of 0.3 or 0.5, the signature was notprognostic in all five training cohorts. The final MET141 score is theaverage expression of negatively correlated genes subtracted from theaverage expression of positively correlated genes.

ResultsSweBCG91-RT cohort characteristics

The SweBCG91-RT cohort was enriched for ERþ and HER2�

tumors. Ninety-two percent of patients were systemic treatment na€�veand did not receive adjuvant endocrine therapy or chemotherapy(Table 1). We obtained gene-expression data from 765 patients(Supplementary Fig. S1), of which 85% were free of metastasis eventat 15 years. In this gene-expression analysis of the SweBCG91-RTcohort, risk scores from 15 previously published gene-expressionsignatures were calculated and assessed for prognostic potential formetastasis and death from breast cancer. Thirteen of the 15 calculatedscores frompreviously published signatures were prognostic (P < 0.05)in the full SweBCG91RT cohort with respect to metastasis (Supple-mentary Fig. S2), with similar results for death from breast cancer(Supplementary Fig. S3).

Table 2. METABRIC patient characteristics.

All patients

ERþ, HER2�,postmenopausal,treated with BCSbut no systemictreatment

Number of patients 1,904 104Age at surgery

Median (range) 61.8 (21.9–96.3) 63.2 (50–87.3)�39 116 (6%) 0 (0%)40–49 295 (15%) 0 (0%)50–59 431 (23%) 38 (37%)60–69 552 (29%) 38 (37%)�70 510 (27%) 28 (27%)

Menopause statusPremenopausal 411 (22%) 104 (100%)Postmenopausal 1,493 (78%) 0 (0%)

Histologic grade1 165 (9%) 19 (18%)2 740 (39%) 60 (58%)3 927 (49%) 19 (18%)NA 72 (4%) 6 (6%)

Tumor size (mm)Median (range) 23 (0–182) 17 (10–43)�10 80 (4%) 4 (4%)11–20 752 (39%) 76 (73%)21–30 650 (34%) 22 (21%)�31 404 (21%) 2 (2%)Missing 18 (1%) 0 (0%)

Estrogen receptor statusNegative 445 (23%) 0 (0%)Positive 1,459 (77%) 104 (100%)

Progesterone receptor statusNegative 895 (47%) 24 (23%)Positive 1,009 (53%) 80 (77%)

HER2 statusNegative 1,668 (88%) 104 (100%)Positive 236 (12%) 0 (0%)

SubtypeER�/HER2� 290 (15%) 3 (3%)ERþ/HER2� high proliferation 603 (32%) 35 (34%)ERþ/HER2� low proliferation 619 (33%) 55 (53%)HER2þ 188 (10%) 2 (2%)Missing 204 (11%) 9 (9%)

Surgery typeBreast-conserving surgery 755 (40%) 104 (100%)Mastectomy 1,127 (59%) 0 (0%)Missing 22 (1%) 0 (0%)

Adjuvant endocrine therapyNo 730 (38%) 104 (100%)Yes 1,174 (62%) 0 (0%)

Adjuvant chemotherapyNo 1,508 (79%) 104 (100%)Yes 396 (21%) 0 (0%)

Adjuvant radiotherapyNo 767 (40%) 15 (14%)Yes 1,137 (60%) 89 (86%)

Died from breast cancerNo 1,281 (67%) 82 (79%)Yes 622 (33%) 22 (21%)Missing 1 (0%) 0 (0%)

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Performance of calculated scores from 15 previously publishedsignatures in potential candidates for omission of systemicadjuvant treatment

To focus on patients who could be clinical candidates for omissionof systemic adjuvant treatment, we selected patients with ERþ, HER2�

tumors who were postmenopausal, node-negative, and did not receiveany systemic adjuvant treatment (N¼ 454, 59% of the profiled cohort).In this low-risk subgroup, 88% of patients were free of metastasis at15 years.

Twelve of the 15 signatures were significantly associated withmetastasis (P < 0.05), with scaled 10 year HRs of 1.5 to 2.4(Fig. 1A and B). The same set of signatures were also prognostic fordeath from breast cancer (Supplementary Fig. S4). As risk of laterecurrences is a major concern for breast cancer patients, we analyzedthe performance of the different signatures by calculating the AUC atdifferent time points. For most signatures, there was a drop inprognostic ability over time (Supplementary Fig. S5), with an averageAUC of 0.73, 0.66, and 0.60 at 5, 10, and 15 years, respectively.

Most of the continuous risk scores were highly correlated toeach other (Fig. 2A). To further visualize agreement of signa-tures, we created barplots where each row depicts the calculatedscores and clinical information for an individual patient. Allpatients are sorted by the average of the 15 previously publishedsignatures. Despite high correlation of signatures, there wasconsiderable disagreement across signatures for an individualpatient (Fig. 2B). When comparing risk scores with subtype,Ki67 and histologic grade, grade 3 and the Luminal B subtype hadhigher risk compared with grades 1 to 2 and the Luminal Asubtype. In addition, high Ki67 was strongly correlated withhigher risk scores (Fig. 2B; Supplementary Fig. S6A and S6B).Further, patients developing early recurrences tended to beclassified as higher risk by most continuous risk scores(Fig. 2B), and signatures were better at identifying early recur-rences, as shown by a higher AUC for all prognostic signaturesfor early recurrences (5 years) than for late recurrences (15 years;Supplementary Fig. S5).

Based on the results analyzing rate of metastasis for patientsgrouped by score quartiles, we hypothesized that the lowest scorequartile could be candidates for omission of therapy. To furtherevaluate the concordance of the 15 signatures for identifying theselow-risk patients, we calculated the low-risk classification agreement,which quantifies the mean proportion of patients classified in thelowest quartile of risk by one signature also classified in the lowestquartile of risk by the other signatures. Mean classification agreementranged from 27% to 51% (Fig. 2C). Similarly, analysis of agreementwith Cohen's kappa showed none to moderate agreement (Supple-mentary Table S3).

Average genomic riskIn an effort to increase the stability of the prognostication, we

calculated the average genomic risk (AGR) as the mean of the 15signatures scores. The prognostic performance of AGRwas in linewith

the most prognostic individual genomic signatures [HR ¼ 2.1 (1.6–2.7), P < 0.001 for metastasis in the low-risk cohort; Fig. 1A and B].Furthermore, theAGR identified a very low-risk population of patientswithin the ERþ, HER2�, postmenopausal, node-negative, and sys-temically untreated subgroup, as patients with the lowest quartile ofAGR scores (N¼ 114, 25% of the subgroup) had no distant metastaticevent within the first 10 years. Notably, the proportion of patients freeof metastasis at 15 years was 95% (95% CI, 88%–98%; Fig. 1B).

Signature comparison and related 141-gene signatureBecause many signatures were significantly associated with time to

metastasis, we performed an assessment of genes shared betweensignatures, finding that up to 100% of genes in one signature (theMGI signature, comprised of five genes) could be found in another(Supplementary Table S4). When removing the Toronto 2017 signa-ture from this analysis, as it had been derived using gene lists frommany of the signatures included in this work, and the MGI signature,which has a small total number of genes, we found that at most 69% ofgenes in one signature were in common with others. Enrichmentanalysis for the published signatures showed that cell-cycle andmetabolic pathways were significantly and highly enriched in thesesignature gene lists (FDR < 0.05; Supplementary Table S5). We theninvestigated if a signature that did not heavily share the specific genesfound in these previously published signatures could still be prognosticin this data set. To that end, we derived a signature in five publiclyavailable cohorts by identifying genes highly correlated with AGR butexcluding overlapping genes with previous signatures. This 141-genesignature (MET141; Supplementary Table S2) was then independentlyvalidated in SweBCG91-RT, with a similar performance as the AGR:95% (95% CI, 88%–98%) free of metastasis at 15 years for the lowestrisk quartile in the subgroup (Fig. 1B). Gene network analysis of theAGR, comprised of the genes from the 15 previously publishedsignatures, and MET141 gene lists suggested that both were enrichedin similar gene sets with a focus on cell-cycle control, DNA replication,transcription, and extracellular matrix organization (Fig. 3A and B;Supplementary Table S6).

Performance of calculated scores in the METABRIC cohortWe further examined if these gene signatures could identify low-risk

patients who may not require adjuvant system therapy in data fromMETABRIC, a cohort with breast cancer–specific mortality medianfollow-up time of 18.1 years in patients alive at censoring. TheMETABRIC cohort has 1904 samples linked to microarray gene-expression data, 104 ofwhichwere frompostmenopausal breast cancerpatients with ERþ, HER2� cancers, treated with breast-conservingsurgery but no adjuvant chemotherapy or endocrine therapy, andnearly all were node negative (Table 2). In this low-risk subgroup, 83%of patients were free of breast cancer–specific death at 15 years. Wecalculated the aforementioned 17 signatures. Although these signa-tures scores were based on a different microarray platform, themajority of signatures (15/17) were able to identify a very low riskgroup of patients in METABRIC with low rates of breast cancer–

Figure 4.Performance of previously published signatures, AGR, and the novel signature MET141, in systemically untreated and clinically low-risk patients in the METABRICcohort. A, Forest plot depicting standardized HRs for each of the 15 previously published gene signatures, the AGR, and the novel signature MET141, in thepostmenopausal and systemically untreated patients with ERþ, HER2� cancers of the METABRIC cohort, where nearly all were node negative. P values are fromthe Cox proportional hazards model. Continuous risk scores were divided by the standard deviation to directly compare HRs between scores with differentlydistributed values, and the Cox analysis is limited to 10 years. Results are shown for endpoint breast cancer death. B, Cumulative incidence of breast cancer deathin the postmenopausal patients of theMETABRIC cohort with ERþ, HER2� cancerswho did not receive systemic therapy, for each of the 15 previously published genesignatures, AGR, and MET141.

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specific death in patients with lowest 25th percentile of scores (Fig. 4).When classified by MET41, no breast cancer death occurred amongpatients with the lowest quartile of risk.

DiscussionHerein, we present transcriptomic analyses of the SweBCG91-RT

trial, a trial of early-stage breast cancer with long-term follow-up. Asthe majority of patients were systemically untreated, this cohort isuniquely suited to address the question of which patients may bespared endocrine therapy. We used comprehensive transcriptomicprofiling to evaluate the prognostic performance of 15 previouslydescribed breast cancer signatures and we show that although mostsignatures performed well on the group level, there was considerablediscordance on the individual patient level. To overcome this limita-tion of discordance between individual signatures, we developed theconcept of AGR and an associated novel 141-gene signature(MET141), which were independently validated in SweBCG91-RTand in the METABRIC data set. Both AGR and MET141 can identifypostmenopausal and systemically untreated patients with ERþ,HER2� cancers with excellent prognosis and who may be candidatesfor omission of systemic therapy, including endocrine therapy. Fur-thermore, unlike AGR, which requires calculation and summation ofrisk from 15 different signatures, theMET141 signature distills similarinformation into a single signature.

The recent EBCTCGmeta-analysis showed that late recurrences area significant clinical problem, and that efforts to avoid endocrinetherapy must rely on long-term follow-up data (13). In this study, weshow that the performance of calculated scores from previouslypublished signatures deteriorates with longer follow-up. Despite this,many of the signatures can identify a large proportion of patientswhere over 90% are free of metastasis at 15 years, and rates of deathfrom breast cancer less than 5% at 15 years, even without any systemictherapy. Signatures for treatment prediction are often validated byperforming analysis of treatment effect in subgroups. However, fortreatment omission, it has been argued that itmay bemore appropriateto consider the relative treatment effect constant over subgroups and toassess baseline risk (9). This should apply for adjuvant endocrinetherapy for breast cancer, where few studies find subgroupswithin ERþ

tumors without any treatment effect, and where a long-term excellentprognosis means modest absolute effect of therapy. Therefore, we herepresent the long-term results for a low-risk patient subgroup that werenot given any systemic adjuvant treatment, and we stratify the resultsfor score quartiles for each signature to allow an unbiased evaluation ofwhat can potentially be achieved by transcriptomic profiling. Wedeliberately do not select a specific cutoff, as there is no consensusfor which rate of metastasis is acceptable, but highly individual anddependent on patient preferences, comorbidities and side effectsexperienced. However, we chose to highlight results for the lowestrisk quartile, where several signatures can identify a group of patientswithout anymetastasis during the first 10 years, and deaths frombreastcancer below 5% at 15 years. We believe that these predicted rates maybe low enough to discuss omission of endocrine therapy in selectpatients, but the decision will ultimately be up to the patient andtreating physician following a balanced discussion of risks and benefits.Ideally, deescalation of endocrine therapy should be investigated inprospective trials.

An emerging dilemma is the considerable discordance betweenresults of multiple gene-expression tests currently in clinical use andrisk prediction for individual patients. Indeed, we have largelyconfirmed the results by Bartlett and colleagues, in a different

cohort, which showed only 39% of patients classified uniformly byfive tests as low-risk, whereas individual tests predicted a muchlarger proportion as low risk. The same authors showed that threedifferent subtyping tests disagreed for 41% of tumors (12). In ourcurrent work, we present a strategy of overcoming this by using awhole-transcriptome platform and the average of all the signatures.This approach produced results consistent with the best individualsignatures and could potentially improve intersignature variabilitybecause it relies on more data points. However, we have included allthe signatures in the calculation of AGR and there are likelyadditional methods or modifications that could further improverisk stratification, such as removal of the signatures with the lowestindividual performance or reweighting the signatures. Theseapproaches will be tested in future studies.

Although these data suggest it may be valuable to profile tumorswith all available signatures, this is not feasible for numerous practicalreasons, including cost and availability of enough sample materialfrom the tumor. To that end, we developed a novel 141-gene signaturein publicly available cohorts that is based on genes correlated withAGR. We show that MET141 captures the same biology as the AGRand has a similar performance but would be considerablymore feasiblein the clinical setting. Although promising in this validation study, itremains to be tested in further patient cohorts if the performance androbustness is superior to currently available signatures.

There are several strengths of this study. First, we utilize a CLIA-certified comprehensive whole-transcriptome approach that producesquality results for FFPE tissue and allows us to assess multiplepreviously described signatures simultaneously. Further, this studyexamined a large patient cohort from a well-defined randomized trial.In addition to the benefits of using sample material from an uncon-founded randomized phase III trial, the fact that so many of thesepatients were systemically untreated and followed for such a long timeis unique and allows for the novel findings reported herein.

Despite these strengths, there remain some limitations to this study.One limitation is the use of surrogate scores for the previouslypublished signatures. This may produce slightly different scores thanusing the approved and commercially available diagnostic tests.However, the surrogate scores show the expected high correlationwith Ki67 and histologic grade, and we demonstrate that thesesurrogate scores are able to prognosticate for recurrence risk in twoseparate data sets, which supports that the calculated scores areincorporating similar information to the clinically used scores. Fur-ther, we are not using thresholds originally specified for the individualsignatures and the exact definition of low-risk or high-risk tumor maybe slightly different in this study. Instead, we group scores by quartilesand when presenting HRs, normalize the scores to the standarddeviation of each score. This is done deliberately to directly comparebetween signatures. If transferring these results to a clinical setting,further stratification by cutoff point determinationmay be desirable toselect those patients at lowest risk for systemic recurrence. Anotherlimitation, inherent in all trials with such long follow-up, is the use ofoutdated or less relevant treatments as compared with contemporarypractice. In this study, however, because we are specifically investi-gating systemically untreated patients in the adjuvant setting, this isnot a major concern. The length of follow-up should not influence thetime to metastasis or breast cancer death, except for possible currenttherapies for treatment of relapses, which could slightly improve theoutcome. With regard to radiotherapy, the patients in the trial wererandomized to receive either whole breast radiotherapy or no radio-therapy. We chose to combine the RTþ and RT� patients in this studyto increase power, because the original study did not find difference

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between � RT with respect to distant metastasis or death from breastcancer. Besides treatment, baseline risk may change over time due tosystematic changes in detection or staging. In the original study, 65%ofpatients had screen detected tumors and lymph node status wasdefined based on axillary lymph node dissection, which is likely lesssensitive to small-volume lymph node metastases compared withsentinel lymph node biopsies, which is performed today. Thus, if anychange in baseline risk, we would anticipate the baseline risk to be evenlower.

In conclusion, calculated scores from previously developed breastcancer signatures are largely prognostic in a breast cancer cohort whoare node-negative, postmenopausal and systemically untreated withERþ, HER2� tumors. However, the signatures are discordant on anindividual patient level, andwe therefore propose that an average of thesignatures can result in more robust patient-level results. Using thisaverage, or an associated 141-gene signature, patients can be identifiedwith an excellent long-term freedom from metastasis even in theabsence of endocrine treatment.

Disclosure of Potential Conflicts of InterestS.L. Chang is an employee/paid consultant for and reports receiving commercial

research grants fromPFSGenomics, and holds ownership interest (including patents)in PFS Genomics and Decipher Biosciences. N. Fishbane is an employee/paidconsultant for and holds ownership interest (including patents) in Decipher Bios-ciences. E. Davicioni is an employee/paid consultant for Decipher Biosciences.E. Holmberg reports receiving other commercial research support from PFSGenomics research contract. F.Y. Feng holds ownership interest (includingpatents) in and is an advisory board member/unpaid consultant for PFS Genomics.C.W. Speers holds ownership interest (including patents) in and is an advisory boardmember/unpaid consultant for PFS Genomics. L.J. Pierce holds ownership interest(including patents) in and is an advisory board member/unpaid consultant forPFS Genomics. P. Malmstr€om reports receiving other remuneration from PFSGenomics (royalty agreement). P. Karlsson reports receiving other remunerationfrom PFS Genomics (research contract). No potential conflicts of interest weredisclosed by the other authors.

Authors’ ContributionsConception and design:M. Sj€ostr€om, S.L. Chang, F.Y. Feng, C.W. Speers, L.J. Pierce,P. Malmstr€om, M. Fern€o, P. KarlssonDevelopment of methodology: S.L. Chang, N. Fishbane, E. Davicioni, F.Y. Feng,C.W. SpeersAcquisition of data (provided animals, acquired and managed patients, providedfacilities, etc.): E. Davicioni, P. Malmstr€om, M. Fern€o, P. KarlssonAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): M. Sj€ostr€om, S.L. Chang, N. Fishbane, E. Davicioni,L. Hartman, F.Y. Feng, C.W. Speers, L.J. Pierce, P. Malmstr€om, P. KarlssonWriting, review, and/or revision of the manuscript: M. Sj€ostr€om, S.L. Chang,N. Fishbane, E. Davicioni, L. Hartman, E. Holmberg, F.Y. Feng, C.W. Speers,L.J. Pierce, P. Malmstr€om, M. Fern€o, P. KarlssonAdministrative, technical, or material support (i.e., reporting or organizing data,constructing databases): S.L. Chang, E. Davicioni, L. Hartman, E. Holmberg,F.Y. Feng, M. Fern€o, P. KarlssonStudy supervision: F.Y. Feng, P. Malmstr€om, M. Fern€o, P. Karlsson

AcknowledgmentsThe authors thank Kristina L€ovgren for expert technical assistance, Sara Baker for

database management and administrative support, and Fredrika Killander forupdating the SweBCG91-RT clinical information. This work was supported by PFSGenomics, Swedish Breast Cancer Association (BRO), Swedish Cancer Society,Faculty of Medicine at Lund University, Lund University Research Foundation,Gunnar Nilsson Cancer Foundation, Anna and Edwin Berger Foundation, SwedishCancer and Allergy Foundation, Ska

�ne County Research Foundation (FOU and PhD

studies grant), Mrs. Berta Kamprad Research Foundation, King Gustav V JubileeClinic Cancer Foundation in Gothenburg, and the LUA/ALF-agreement inWest andSouth Sweden.

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 29, 2019; revised June 3, 2019; accepted September 17, 2019;published first September 26, 2019.

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2020;26:171-182. Published OnlineFirst September 26, 2019.Clin Cancer Res   Martin Sjöström, S. Laura Chang, Nick Fishbane, et al.   Patients Who May Be Spared Adjuvant Systemic TherapyComprehensive Transcriptomic Profiling Identifies Breast Cancer

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