Comprehensive transcriptomic profiling identifies breast ... · As systemic adjuvant therapy was...

1

Comprehensive transcriptomic profiling identifies breast cancer

patients who may be spared adjuvant systemic therapy

Martin Sjöström1,2, S. Laura Chang3, Nick Fishbane4, Elai Davicioni4, Linda Hartman1, Erik Holmberg5,

Felix Y. Feng6, Corey W. Speers7, Lori J. Pierce7, Per Malmström1,8, Mårten Fernö1, Per Karlsson9,10.

1Division of Oncology and Pathology, Department of Clinical Sciences Lund, Faculty of Medicine, Lund

University, Lund, Sweden.

2Skåne University Hospital, Lund, Sweden.

3PFS Genomics, Vancouver, Canada.

4Decipher Biosciences, Vancouver, Canada.

5Regional Cancer Center West, Sahlgrenska University Hospital, Gothenburg, Sweden.

6Department of Urology, Medicine and Radiation Oncology, University of California San Francisco, San

Francisco, California, USA.

7Department of Radiation Oncology, University of Michigan Medical School, Ann Arbor, Michigan, USA.

8 Department of Haematology, Oncology and Radiation Physics, Skåne University Hospital, Lund,

Sweden.

9Department of Oncology, Institute of Clinical Sciences, Sahlgrenska Academy, Gothenburg University,

Gothenburg, Sweden

10Department of Oncology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Research. on June 24, 2020. © 2019 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 26, 2019; DOI: 10.1158/1078-0432.CCR-19-1038

http://clincancerres.aacrjournals.org/

2

Corresponding author: Martin Sjöström, email: [email protected],

Address: Department of Oncology and Pathology, Clinical Sciences Lund, Medicon Village By 404:B3,

SE-22381 Lund, Sweden

Phone: +46 733 611 658

Conflict of interest statement

SLC is employed by and reports ownership interest in PFS Genomics, which plans to apply for

patent on presented work. FF, CS, and LP are co-founders of, and report ownership interest in,

PFS Genomics. EH, PM, MF, and PK report patent with PFS Genomics. NF and ED are

employed by and report ownership interest in Decipher Biosciences. MS and LH declare no

competing interests.

Running title: Comprehensive transcriptomic profiling of breast cancer



mailto:[email protected]


3

Statement of Translational Relevance

Some women with primary breast cancer do not require additional endocrine therapy after breast-

conserving surgery, but no tests are in use to find this low-risk group of women. We performed a

transcriptomic analysis of 765 patients of the SweBCG91-RT trial, of whom 454 were node-

negative, post-menopausal and systemically untreated with ER-positive, HER2-negative cancers.

We tested 15 previously-published signatures and showed that most perform well in identifying

women with very low risk of recurrence. However, there was a substantial inter-signature

variation in risk-classification and we therefore combined the signatures into an Average

Genomic Risk and an associated novel signature (MET141). MET141 could identify a low-risk

group of node-negative, post-menopausal, non-systemically treated patients with ER+ and HER2-

negative tumors of which 95% were free of metastasis at 15 years. These results indicate that

transcriptomic profiling may be used to find women who may be spared endocrine treatment.




4

Abstract

Purpose: There is currently no molecular signature in clinical use for adjuvant endocrine therapy

omission in breast cancer. Given the unique trial design of SweBCG91-RT, where adjuvant

endocrine and chemotherapy were largely unadministered, we sought to evaluate the potential of

transcriptomic profiling for identifying patients who may be spared adjuvant endocrine therapy.

Experimental Design: We performed a whole transcriptome analysis of SweBCG91-RT, a

randomized phase III trial of +/- radiotherapy after breast-conserving surgery for node-negative

stage I-IIA breast cancer. 92% of patients were untreated by both adjuvant endocrine therapy and

chemotherapy. We calculated 15 transcriptomic signatures from the literature and combined them

into an Average Genomic Risk, which was further used to derive a novel 141-gene signature

(MET141). All signatures were then independently examined in SweBCG91-RT, and in the

publicly-available METABRIC cohort.

Results: In SweBCG91-RT, 454 patients were node-negative, post-menopausal and systemically

untreated with ER-positive, HER2-negative cancers, which constitutes a low-risk subgroup and

potential candidates for therapy omission. Most transcriptomic signatures were highly prognostic

for distant metastasis, but considerable discordance was observed on the individual patient level.

Within the MET141 low-risk subgroup (lowest 25th

percentile of scores), 95% of patients were

free of metastasis at 15 years e even in the absence of adjuvant endocrine therapy. In a clinically

low-risk subgroup of the METABRIC cohort not treated with systemic therapy, no breast cancer

death occurred among the MET141 low-risk patients.

Conclusion: Transcriptomic profiling identifies patients with an excellent outcome without any

systemic adjuvant therapy in clinically low-risk patients of the SweBCG91-RT and METABRIC

cohorts.




5

Introduction

Treatment of primary breast cancer is becoming more and more individualized and has entered

the era of precision medicine. Due to increased public awareness and intensified screening

programs, the proportion of low-risk tumors has increased with a corresponding risk of over-

treatment.(1) Thus, in addition to escalating treatment for patients with high-risk breast cancers,

current guidelines focus on de-escalating treatment in low-risk patients.(2) While gene signatures

assessing recurrence risk have been successful at identifying patient subgroups in whom adjuvant

chemotherapy can be safely omitted,(3-5) there are no tests currently in clinical guidelines to

identify patients who may omit endocrine therapy.(2) Adjuvant endocrine therapy reduces the

risk of breast cancer death in patients with estrogen receptor-positive (ER+) disease by around

one-third,(6) which can be further reduced by using aromatase inhibitors in post-menopausal

patients.(7) However, endocrine therapy may have substantial side-effects, which is reflected in

an adherence rate between 50-80%, (8) and most patients with node negative disease will not

suffer a recurrence even without adjuvant systemic therapy.(6) Thus, developing tools to safely

omit endocrine therapy among patients with ER+ cancers is highly desirable.

One approach to personalizing therapy is to consider relative treatment effects constant over

subgroups, and identify patients at low risk of recurrences in the absence of the treatment in

question.(9) The PAM50 risk of recurrence score was shown to identify a subgroup of patients

with node-positive hormone-receptor-positive tumors treated with endocrine therapy but not

chemotherapy with a 10-year metastasis risk of 6.6%, suggesting that patients in this subgroup

may be spared chemotherapy.(3) Among women with high clinical risk but low 70-gene scores of

the MINDACT trial, the five-year metastasis-free survival for those that did not receive

chemotherapy was similarly high, at 94.7%.(5) Furthermore, other studies have focused on




6

identifying patients at low risk of recurrence despite not receiving any adjuvant systemic therapy.

A clinically low-risk subgroup of patients with no adjuvant treatment of the Oslo1 trial with low

PAM50 risk of recurrence scores had a 15-year breast cancer specific survival of 96.3%.(10)

Similarly, the 70-gene signature was recently shown to identify an ultra-low risk group of

patients in the STO-3 trial with a breast cancer-specific survival rate of 94% at 20 years in the

absence of both endocrine therapy and chemotherapy.(11)

When considering the use of baseline risk for gene expression tests, an emerging problem is the

substantial discordance in results for an individual patient. Indeed, a recent study found the

agreement of five common gene expression tests to be modest, with 39% of patients classified

uniformly as low-risk by all tests, while individual tests predicted 61%-82% to be low-risk.(12)

Other barriers for identifying patients for whom adjuvant endocrine therapy can be safely

withheld include the lack of studies in which patients were not treated with endocrine therapy and

lack of studies with long follow-up. As ER+ breast cancer continues to recur and cause death at a

relatively consistent rate over 15 years after stopping endocrine therapy, studies with long follow-

up are necessary to identify patients who may experience late recurrences.(13) Thus, in order to

evaluate risk stratifications tools for endocrine therapy there is a need for large, well-defined

cohorts of patients who were not treated with adjuvant systemic therapy, have long-term follow-

up, and in whom several gene expression signatures can be compared.

To that end, we examined the transcriptome of 765 early-stage breast cancer patients from the

SweBCG91-RT trial, a trial randomizing node-negative stage I-IIA breast cancer patients

undergoing breast conserving surgery to +/- adjuvant whole breast radiotherapy.(14) The vast

majority (92%) of patients in the trial were systemically untreated in the adjuvant setting, and 454

patients were ER+, HER2-negative (HER2-), postmenopausal and did not receive adjuvant




7

systemic therapy, making it an ideal dataset to study recurrence risk in the absence of adjuvant

systemic therapy. We calculated gene expression signatures for 15 previously published

signatures and aimed to evaluate the potential of transcriptomic profiling in identifying patients at

such low risk of metastasis that adjuvant endocrine therapy can be safely omitted.

Patients and methods

SweBCG91-RT Patients

We analyzed gene expression data of the SweBCG91-RT trial, the details of which have been

previously described.(14-16) Briefly, the trial randomized 1,178 node-negative, early-stage breast

cancer patients undergoing breast-conserving surgery to adjuvant whole breast radiotherapy or no

radiotherapy. As systemic adjuvant therapy was administered according to regional guidelines at

the time, it was sparsely provided, with only 7% and 2% of patients in the original trial receiving

endocrine therapy and chemotherapy, respectively.(15) Subtyping was performed using

immunohistochemistry as detailed previously.(14) The primary endpoint of this analysis was

distant recurrence free interval (i.e. time to metastasis), defined from the time of surgery until the

time of metastasis, last follow-up or death, with death as a competing event.(17) Patients

suffering a contralateral breast cancer or another primary cancer were not censored, as

recommended.(18) The data for the metastasis endpoint was collected from patient chart review

and the median follow-up time was 15.1 years for patients free from event. Additional follow-up

was derived from the Swedish cause of death registry with a median follow-up time of 20.0 years

for patients alive at censoring, and we present cumulative incidence of breast cancer death, with

death from other causes as competing event, as supplemental information. The trial and follow-up

study were conducted in accordance with the declaration of Helsinki and were approved by the




8

Lund University Regional Ethical Review Board (approval numbers 2010/127 and 2015/548).

Informed oral consent was obtained from all patients, which was determined appropriate and

approved by the Ethical Review Board for the original trial and for this gene expression study.

Gene expression analysis

Formalin-fixed paraffin-embedded tissue was available for 922 of the original 1,178 patients in

the trial (eFigure 1). RNA extraction and microarray hybridization were performed in a Clinical

Laboratory Improvement Amendments certified laboratory (DecipherBiosciences). Tumors were

profiled with the GeneChip Human Exon 1.0 ST microarray (ThermoFisher) and 765 tumors

passed quality control of RNA, cDNA and microarray analysis (Gene Expression Omnibus

GSE119295). Gene expression data was normalized using Single Channel Array

Normalization.(19)

Publicly available METABRIC data

We also examined gene signature scores in the Molecular Taxonomy of Breast Cancer

International Consortium (METABRIC) cohort. Publicly available clinical and expression data

based on the Illumina Human v3 array were downloaded from cBioPortal. Out of the 1904

patients with microarray expression data, 104 patients were post-menopausal, treated with breast-

conserving surgery, with ER+, human epidermal growth factor receptor 2 negative (HER2-)

tumors, complete breast cancer specific death information, and were not treated with endocrine or

chemotherapy. Nearly all were lymph node-negative.(20) This low-risk systemic treatment naive

group were included for analysis in this study. The median follow-up time for this low-risk

subgroup was 18.1 years for patients alive at censoring.

Data analysis




9

Statistical 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

published equations or algorithms for calculation.(21-35) Most were created to prognosticate for

the distant recurrence endpoint, although a few (PIK3CAGS, TAMR13) were designed for

tamoxifen sensitivity. The surrogate scores of these previously-published gene expression

signatures were calculated using published algorithms as described below. Cumulative incidences

of metastasis or death from breast cancer were computed with a competing risks approach using

the cmprsk package,(36) and 95% confidence intervals were computed as previously

described.(37) For a direct and unbiased comparison of how the different signatures perform, the

patients were grouped by score quartiles. We further examined rates of metastasis or death from

breast cancer for patients with the lowest quartile of risk scores, hypothesizing that these patients

may be candidates for therapy omission, although aware that this may not directly represent

clinical cut-offs used for the signatures. Cause-specific Cox proportional hazards regression was

used to contrast the differences in hazards between patients with high and low signatures scores,

and p-values were computed with the Wald test. Each continuous risk score was standardized by

dividing the score by its standard deviation in order to create comparable hazard ratios across

signatures, otherwise signatures with smaller ranges of values would have disproportionately

higher hazard ratios and the hazard ratios would not be comparable. Proportional hazards were

checked graphically and by Schoenfeld’s 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 trend was still observed with larger hazard ratios years 0-5 than 5-10, and the

presented HRs should thus be interpreted as the mean over 10 years. To compare how

classification of low-risk patients differs by signature, we identified patients within the lowest

quartile of risk scores of an individual signature and calculated the proportion of those patients




10

also classified in the lowest quartile of each other signature. We then computed the mean

proportion, excluding the signature of interest (in which the proportion is 1). We refer to this as

the “low-risk classification agreement“. In addition, the agreement between signatures split by

quartiles was tested by calculating Cohen’s kappa. We followed REMARK guidelines for

reporting of this study.(39) Adjustment of p-values for multiple testing correction were

performed using the Benjamini-Hochberg false discovery rate (FDR) method, where

applicable.(40)

Estimation of time-dependent area under the curve

Estimation 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 bootstrapped using 1000 bootstrap samples.

Pathway analysis

To assess biological pathways overrepresented in lists of genes, we used the Panther statistical

overrepresentation test (version 13.0, pantherdb.org) (42) using Fisher’s Exact with Benjamini-

Hochberg false discovery rate (FDR) correction as the test type, and Panther GO-Slim Biological

Process gene lists as the annotation data set. As a secondary method, we also used Reactome

Analysis Tools (reactome.org)(43,44) with the “project to human” option. The Reactome

genome-wide overview of the pathway analysis visualizes the enrichment analysis by organizing

Reactome pathways in a hierarchy. The top level pathway is represented as the center of a

circular “burst” and each next level lower on the pathway hierarchy is represented by a step away

from the center. Pathways over-represented in the input dataset are represented in yellow and

pathways not significantly over-represented are represented in grey. For both methods, lists of




11

official gene symbols were entered. Significant enrichment of a pathway was defined as FDR <

0.05.

Computation of previously-published breast cancer risk scores

Previously-published breast cancer risk scores were developed on a variety of platforms. We

applied gene expression data from microarrays to genomic signature equations to calculate

surrogate continuous risk scores. The following risk scores were calculated according to their

equations as published, using the genefu package (version 2.6.0)(45) in R (version 3.3.2):

OncotypeDx-like,(21) Endopredict-like,(22) Genomic Grade Index-like,(23) PAM50ROR-

like,(24) Gene70-like,(46) GeniusM3-like,(26) TAMR-like,(27) Gene76-like,(28) and the

PIK3CAGS-like risk score.(29) For signatures that are based on probes from specific

microarrays, the genefu annotations to Entrez gene identifiers were used to map probes to the

appropriate gene on the microarray platform. For the few genes not available on microarray, the

term (coefficient and gene expression value) of that gene was omitted from the signature

equation. The genefu functions used are listed in eTable 1.

Celera-like risk score

Risk scores were computed by calculating the sum of the expression of 14 genes, as previously

described.(30)

ExagenBC-like ER+ risk score

Risk scores were computed based on the following equation: R = 0.128*CYP24 –

0.173*PDCD6IP + 0.183*BIRC5, as previously described. (31)

Mammostrat-like risk score




12

Risk scores were computed based on the following equation: R = 1.54*SLC7A5 + 1.12*TP53 +

1.06*NDRG1 + 0.72*HTF9C + 0.5* CEACAM5, as previously described.(32)

MGI-like risk score

Risk 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 deviation of 1, then combined into a single score as the

first principal component, as previously described.(33)

Toronto 2017-like risk score

Risk scores were computed by calculating the linear equation involving gene expression and

coefficients of 95 genes, as previously described.(34)

Two gene ratio-like risk score

Risk scores were computed by subtracting the expression of IL17RB from the expression of

HOXB13, as previously described.(35)

Average Genomic Risk

To calculate average genomic risk, each of the fifteen signature scores was scaled from 0 to 1

within the cohort, and then the mean was computed. The scaling was necessary to prevent

signatures with larger ranges of values to be over-weighted in the calculation of the average risk.

MET141

We performed a literature search to identify publicly available gene expression datasets

with metastasis available as an endpoint. These publicly available breast cancer datasets were

Servant (GSE30682), Kao (GSE20685), Wang (GSE2034), Symmans (GSE17705), and van de

Vijver (downloaded from http://microarray-




13

pubs.stanford.edu/wound_NKI/explore.html).(25,28,46-48) We sought a wide range of breast

cancer patients to be able to capture underlying breast cancer risk. All patients in these datasets

were used for analysis and they represent breast cancer patients with a range of clinical risk

factors and treatment. Briefly, the Servant cohort included 343 patients with early-stage breast

cancer all treated with breast conserving surgery and post-operative radiotherapy, with a mix of

adjuvant systemic treatment. The Kao cohort included 327 patients randomly selected from the

institutional tumor bank with a range of low and high risk clinical risk factors. The Wang cohort

included 286 lymph node negative patients who did not receive systemic neoadjuvant or adjuvant

therapy. The Symmans cohort included 508 patients with HER2- tumors, treated with

chemotherapy. The van de Vijver cohort included 295 patients treated with mastectomy or breast

conserving surgery, with a mix of adjuvant treatment.(25,28,47-49) For each dataset, probes were

converted to gene symbols, and the subset of genes in common between the five datasets were

identified (10,990 genes). The fifteen previously-published signatures and average genomic risk

was calculated for each patient in these five cohorts. To assess genes to include in a new

signature, we removed genes in common with genes from the previously published signatures,

and using the remaining 10,315 genes, correlated each gene to the average genomic risk within

each cohort. Genes with a Spearman’s correlation coefficient > 0.4 or <-0.4 to average genomic

risk in all five cohorts were retained, resulting in 141 total genes; 89 positively correlated genes

and 52 negatively correlated genes (eTable 2). The correlation coefficient value was initially

varied from 0.3, 0.4, and 0.5. We found that using cutoffs of 0.3 or 0.5, the signature was not

prognostic in all five training cohorts. The final MET141 score is the average expression of

negatively correlated genes subtracted from the average expression of positively correlated genes.




14

Results

SweBCG91-RT cohort characteristics

The SweBCG91-RT cohort was enriched for ER+ and HER2- tumors. 92% of patients were

systemic treatment naïve and did not receive adjuvant endocrine therapy or chemotherapy (Table

1). We obtained gene expression data from 765 patients (eFigure 1), of which 85% were free of

metastasis event at 15 years. In this gene expression analysis of the SweBCG91-RT cohort, risk

scores from 15 previously-published gene expression signatures were calculated and assessed for

prognostic potential for metastasis and death from breast cancer. Thirteen of the 15 calculated

scores from previously-published signatures were prognostic (p < 0.05) in the full SweBCG91RT

cohort with respect to metastasis (eFigure2), with similar results for death from breast cancer

(eFigure 3).

Performance of calculated scores from 15 previously-published signatures in potential

candidates for omission of systemic adjuvant treatment

To focus on patients who could be clinical candidates for omission of systemic adjuvant

treatment, we selected patients with ER+, HER2- tumors who were post-menopausal, node-

negative, and did not receive any systemic adjuvant treatment (N=454, 59% of the profiled

cohort). In this low-risk subgroup, 88% of patients were free of metastasis at 15 years.

Twelve of the 15 signatures were significantly associated with metastasis (p<0.05), with scaled

10 year HRs of 1.5 to 2.4 (Figure 1A-B). The same set of signatures were also prognostic for

death from breast cancer (eFigure 4). As risk of late recurrences is a major concern for breast

cancer patients, we analyzed the performance of the different signatures by calculating the AUC




15

at different time points. For most signatures, there was a drop in prognostic ability over time

(eFigure 5), with an average AUC of 0.73, 0.66, and 0.60 at 5, 10 and 15 years, respectively.

Most of the continuous risk scores were highly correlated to each other (Figure 2A). To further

visualize agreement of signatures, we created barplots where each row depicts the calculated

scores and clinical information for an individual patient. All patients are sorted by the average of

the fifteen previously-published signatures. Despite high correlation of signatures, there was

considerable disagreement across signatures for an individual patient (Figure 2B). When

comparing risk scores with subtype, Ki67 and histological grade, grade 3 and the Luminal B

subtype had higher risk compared to grades 1-2 and the Luminal A subtype. In addition, high

Ki67 was strongly correlated with higher risk scores (Figure 2B and eFigure 6A-B). Further,

patients developing early recurrences tended to be classified as higher risk by most continuous

risk scores (Figure 2B), and signatures were better at identifying early recurrences, as shown by a

higher AUC for all prognostic signatures for early recurrences (5-year) than for late recurrences

(15-year) (eFigure 5).

Based on the results analyzing rate of metastasis for patients grouped by score quartiles, we

hypothesized that the lowest score quartile could be candidates for omission of therapy. To

further evaluate the concordance of the 15 signatures for identifying these low-risk patients, we

calculated the low-risk classification agreement, which quantifies the mean proportion of patients

classified in the lowest quartile of risk by one signature also classified in the lowest quartile of

risk by the other signatures. Mean classification agreement ranged from 27% to 51% (Figure 2C).

Similarly, analysis of agreement with Cohen’s kappa showed none to moderate agreement

(eTable 3).

Average Genomic Risk




16

In an effort to increase the stability of the prognostication, we calculated the Average Genomic

Risk (AGR) as the mean of the 15 signatures scores. The prognostic performance of AGR was in

line with the most prognostic individual genomic signatures (HR=2.1 [1.6-2.7], p<0.001 for

metastasis in the low-risk cohort) (Figure 1A-B). Furthermore, the AGR identified a very low-

risk population of patients within the ER+, HER2-, post-menopausal, node-negative, and

systemically untreated subgroup, as patients with the lowest quartile of AGR scores (N=114,

25% of the subgroup) had no distant metastatic event within the first 10 years. Notably, the

proportion of patients free of metastasis at 15 years was 95% (95%CI 88-98%) (Figure 1B).

Signature comparison and related 141-gene signature

Since many signatures were significantly associated with time to metastasis, we performed an

assessment of genes shared between signatures, finding that up to 100% of genes in one signature

(the MGI signature, comprised of five genes) could be found in another (eTable 4). When

removing the Toronto 2017 signature from this analysis, as it had been derived using gene lists

from many 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% of genes in one signature were in common with

others. Enrichment analysis for the published signatures showed that cell cycle and metabolic

pathways were significantly and highly enriched in these signature gene lists (FDR<0.05, eTable

5). We then investigated if a signature that did not heavily share the specific genes found in these

previously-published signatures could still be prognostic in this dataset. To that end, we derived a

signature in five publicly available cohorts by identifying genes highly correlated with AGR but

excluding overlapping genes with previous signatures. This 141-gene signature (MET141,

eTable2) was then independently validated in SweBCG91-RT, with a similar performance as the

AGR: 95% (95%CI 88-98%) free of metastasis at 15 years for the lowest risk quartile in the




17

subgroup (Figure 1B). Gene network analysis of the AGR, comprised of the genes from the

fifteen previously-published signatures, and MET141 gene lists suggested that both were

enriched in similar gene sets with a focus on cell cycle control, DNA replication, transcription

and extracellular matrix organization (Figure 3 A-B, eTable 6).

Performance of calculated scores in METABRIC cohort

We further examined if these gene signatures could identify low-risk patients who may not

require adjuvant system therapy in data from METABRIC, a cohort with breast cancer specific

mortality median follow-up time of 18.1 years in patients alive at censoring. The METABRIC

cohort has 1904 samples linked to microarray gene expression data, 104 of which were from

post-menopausal breast cancer patients with ER+, HER2- cancers, treated with breast conserving

surgery but no adjuvant chemotherapy or endocrine therapy, and nearly all were node-negative

(Table 2). In this low-risk subgroup, 83% of patients were free of breast cancer specific death at

15 years. We calculated the aforementioned 17 signatures. Although these signatures scores were

based on a different microarray platform, the majority of signatures (15/17) were able to identify

a very low risk group of patients in METABRIC with low rates of breast cancer specific death in

patients with lowest 25th

percentile of scores (Figure 4). When classified by MET41, no breast

cancer death occurred among patients with the lowest quartile of risk.




18

Discussion

Herein, we present transcriptomic analyses of the SweBCG91-RT trial, a trial of early-stage

breast cancer with long-term follow-up. As the majority of patients were systemically untreated,

this cohort is uniquely suited to address the question of which patients may be spared endocrine

therapy. We used comprehensive transcriptomic profiling to evaluate the prognostic performance

of 15 previously-described breast cancer signatures and we show that although most signatures

performed well on the group level, there was considerable discordance on the individual patient

level. To overcome this limitation of discordance between individual signatures, we developed

the concept of Average Genomic Risk (AGR) and an associated novel 141-gene signature

(MET141), which were independently validated in SweBCG91-RT and in the METABRIC

dataset. Both AGR and MET141 can identify post-menopausal and systemically untreated

patients with ER+, HER2- cancers with excellent prognosis and who may be candidates for

omission of systemic therapy, including endocrine therapy. Furthermore, unlike AGR, which

requires calculation and summation of risk from 15 different signatures, the MET141 signature

distills similar information into a single signature.

The recent EBCTCG meta-analysis showed that late recurrences are a significant clinical

problem, and that efforts to avoid endocrine therapy must rely on long-term follow-up data.(13)

In this study, we show that the performance of calculated scores from previously-published

signatures deteriorates with longer follow-up. Despite this, many of the signatures can identify a

large proportion of patients where over 90% are free of metastasis at 15 years, and rates of death

from breast cancer less than 5% at 15 years, even without any systemic therapy. Signatures for

treatment prediction are often validated by performing analysis of treatment effect in subgroups.

However, for treatment omission, it has been argued that it may be more appropriate to consider




19

the relative treatment effect constant over subgroups and to assess baseline risk.(9) This should

apply for adjuvant endocrine therapy for breast cancer, where few studies find subgroups within

ER+ tumors without any treatment effect, and where a long-term excellent prognosis means

modest absolute effect of therapy. Therefore, we here present the long-term results for a low-risk

patient subgroup that were not given any systemic adjuvant treatment, and we stratify the results

for score quartiles for each signature to allow an unbiased evaluation of what can potentially be

achieved by transcriptomic profiling. We deliberately do not select a specific cut-off, as there is

no consensus for which rate of metastasis is acceptable, but highly individual and dependent on

patient preferences, co-morbidities and side-effects experienced. However, we chose to highlight

results for the lowest risk quartile, where several signatures can identify a group of patients

without any metastasis during the first 10 years, and deaths from breast cancer below 5% at 15

years. We believe that these predicted rates may be low enough to discuss omission of endocrine

therapy in select patients, but the decision will ultimately be up to the patient and treating

physician following a balanced discussion of risks and benefits. Ideally, de-escalation of

endocrine therapy should be investigated in prospective trials.

An emerging dilemma is the considerable discordance between results of multiple gene

expression tests currently in clinical use and risk prediction for individual patients. Indeed, we

have largely confirmed the results by Bartlett et al., in a different cohort, which showed only 39%

of patients classified uniformly by five tests as low-risk, while individual tests predicted a much

larger proportion as low-risk. The same authors showed that three different subtyping tests

disagreed for 41% of tumors.(12) In our current work, we present a strategy of overcoming this

by using a whole transcriptome platform and the average of all the signatures. This approach

produced results consistent with the best individual signatures and could potentially improve




20

inter-signature variability since it relies on more data points. However, we have included all the

signatures in the calculation of AGR and there are likely additional methods or modifications that

could further improve risk stratification, such as removal of the signatures with the lowest

individual performance or reweighting the signatures. These approaches will be tested in future

studies.

Although these data suggest it may be valuable to profile tumors with all available signatures,

this is not feasible for numerous practical reasons including cost and availability of enough

sample material from the tumor. To that end, we developed a novel 141-gene signature in

publicly available cohorts that is based on genes correlated with AGR. We show that MET141

captures the same biology as the AGR and has a similar performance but would be considerably

more feasible in the clinical setting. Although promising in this validation study, it remains to be

tested in further patient cohorts if the performance and robustness is superior to currently

available signatures.

There are several strengths of this study. First, we utilize a CLIA-certified comprehensive whole

transcriptome approach that produces quality results for FFPE tissue and allows us to assess

multiple previously-described signatures simultaneously. Further, this study examined a large

patient cohort from a well-defined randomized trial. In addition to the benefits of using sample

material from an unconfounded randomized phase III trial, the fact that so many of these patients

were systemically untreated and followed for such a long time is 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 previously-published signatures. This may produce slightly different

scores than using the approved and commercially available diagnostic tests. However, the




21

surrogate scores show the expected high correlation with Ki67 and histological grade, and we

demonstrate that these surrogate scores are able to prognosticate for recurrence risk in two

separate datasets, which supports that the calculated scores are incorporating similar information

to the clinically used scores. Further, we are not using thresholds originally specified for the

individual signatures and the exact definition of low-risk or high-risk tumor may be slightly

different in this study. Instead, we group scores by quartiles and when presenting hazard ratios,

normalize the scores to the standard deviation of each score. This is done deliberately to directly

compare between signatures. If transferring these results to a clinical setting, further stratification

by cut-point determination may be desirable to select those patients at lowest risk for systemic

recurrence. Another limitation, inherent in all trials with such long follow-up, is use of outdated

or less relevant treatments as compared to contemporary practice. In this study however, since we

are specifically investigating systemically untreated patients in the adjuvant setting, this is not a

major concern. The length of follow-up should not influence the time to metastasis or breast

cancer death, except for possible current therapies for treatment of relapses, which could slightly

improve the outcome. With regards to radiotherapy, the patients in the trial were randomized to

receive either whole breast radiotherapy or no radiotherapy. We chose to combine the RT+ and

RT- patients in this study to increase power, since the original study did not find difference

between +/- RT with respect to distant metastasis or death from breast cancer. Besides treatment,

baseline risk may change over time due to systematic changes in detection or staging. In the

original study, 65% of patients had screen detected tumors and lymph node status was defined

based on axillary lymph node dissection, which is likely less sensitive to small-volume lymph

node metastases compared to sentinel lymph node biopsies, which is performed today. Thus, if

any change in baseline risk, we would anticipate the baseline risk to be even lower.




22

In conclusion, calculated scores from previously-developed breast cancer signatures are largely

prognostic in a breast cancer cohort who are node-negative, post-menopausal and systemically

untreated with ER+, HER2- tumors. However, the signatures are discordant on an individual

patient level, and we therefore propose that an average of the signatures can result in more robust

patient-level results. Using this average, or an associated 141-gene signature, patients can be

identified with an excellent long-term freedom from metastasis even in the absence of endocrine

treatment.




23

Acknowledgments

We thank Kristina Lövgren for expert technical assistance, Sara Baker for database management

and administrative support, and Fredrika Killander for updating the SweBCG91-RT clinical

information.

This work was supported by PFS Genomics, 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, Swedish Cancer and

Allergy Foundation, Skåne County Research Foundation (FOU and PhD studies grant), Mrs.

Berta Kamprad Research Foundation, King Gustav V Jubilee Clinic Cancer Foundation in

Gothenburg and the LUA/ALF-agreement in West and South Sweden.




24

References

1. Hosseini A, Khoury AL, Esserman LJ. Precision surgery and avoiding over-treatment. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2017;43(5):938-43 doi 10.1016/j.ejso.2017.02.003.

2. Curigliano G, Burstein HJ, E PW, Gnant M, Dubsky P, Loibl S, et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2017;28(8):1700-12 doi 10.1093/annonc/mdx308.

3. Gnant M, Sestak I, Filipits M, Dowsett M, Balic M, Lopez-Knowles E, et al. Identifying clinically relevant prognostic subgroups of postmenopausal women with node-positive hormone receptor-positive early-stage breast cancer treated with endocrine therapy: a combined analysis of ABCSG-8 and ATAC using the PAM50 risk of recurrence score and intrinsic subtype. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2015;26(8):1685-91 doi 10.1093/annonc/mdv215.

4. Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer. The New England journal of medicine 2018;379(2):111-21 doi 10.1056/NEJMoa1804710.

5. Cardoso F, van't Veer LJ, Bogaerts J, Slaets L, Viale G, Delaloge S, et al. 70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer. The New England journal of medicine 2016;375(8):717-29 doi 10.1056/NEJMoa1602253.

6. Davies C, Godwin J, Gray R, Clarke M, Cutter D, Darby S, et al. Relevance of breast cancer hormone receptors and other factors to the efficacy of adjuvant tamoxifen: patient-level meta-analysis of randomised trials. Lancet 2011;378(9793):771-84 doi 10.1016/s0140-6736(11)60993-8.

7. Aromatase inhibitors versus tamoxifen in early breast cancer: patient-level meta-analysis of the randomised trials. Lancet 2015;386(10001):1341-52 doi 10.1016/s0140-6736(15)61074-1.

8. Chlebowski RT, Geller ML. Adherence to endocrine therapy for breast cancer. Oncology 2006;71(1-2):1-9 doi 10.1159/000100444.

9. Peto R. Current misconception 3: that subgroup-specific trial mortality results often provide a good basis for individualising patient care. British journal of cancer 2011;104(7):1057-8 doi 10.1038/bjc.2011.79.

10. Ohnstad HO, Borgen E, Falk RS, Lien TG, Aaserud M, Sveli MAT, et al. Prognostic value of PAM50 and risk of recurrence score in patients with early-stage breast cancer with long-term follow-up. Breast cancer research : BCR 2017;19(1):120 doi 10.1186/s13058-017-0911-9.

11. Esserman LJ, Yau C, Thompson CK, van 't Veer LJ, Borowsky AD, Hoadley KA, et al. Use of Molecular Tools to Identify Patients With Indolent Breast Cancers With Ultralow Risk Over 2 Decades. JAMA oncology 2017;3(11):1503-10 doi 10.1001/jamaoncol.2017.1261.

12. Bartlett JM, Bayani J, Marshall A, Dunn JA, Campbell A, Cunningham C, et al. Comparing Breast Cancer Multiparameter Tests in the OPTIMA Prelim Trial: No Test Is More Equal Than the Others. Journal of the National Cancer Institute 2016;108(9) doi 10.1093/jnci/djw050.

13. Pan H, Gray R, Braybrooke J, Davies C, Taylor C, McGale P, et al. 20-Year Risks of Breast-Cancer Recurrence after Stopping Endocrine Therapy at 5 Years. The New England journal of medicine 2017;377(19):1836-46 doi 10.1056/NEJMoa1701830.

14. Sjostrom M, Lundstedt D, Hartman L, Holmberg E, Killander F, Kovacs A, et al. Response to Radiotherapy After Breast-Conserving Surgery in Different Breast Cancer Subtypes in the




25

Swedish Breast Cancer Group 91 Radiotherapy Randomized Clinical Trial. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2017;35(28):3222-9 doi 10.1200/jco.2017.72.7263.

15. Killander F, Karlsson P, Anderson H, Mattsson J, Holmberg E, Lundstedt D, et al. No breast cancer subgroup can be spared postoperative radiotherapy after breast-conserving surgery. Fifteen-year results from the Swedish Breast Cancer Group randomised trial, SweBCG 91 RT. European journal of cancer (Oxford, England : 1990) 2016;67:57-65 doi 10.1016/j.ejca.2016.08.001.

16. Malmström P, Holmberg L, Anderson H, Mattsson J, Jonsson PE, Tennvall-Nittby L, et al. Breast conservation surgery, with and without radiotherapy, in women with lymph node-negative breast cancer: a randomised clinical trial in a population with access to public mammography screening. European journal of cancer (Oxford, England : 1990) 2003;39(12):1690-7.

17. Gourgou-Bourgade S, Cameron D, Poortmans P, Asselain B, Azria D, Cardoso F, et al. Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials)dagger. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO 2015;26(5):873-9 doi 10.1093/annonc/mdv106.

18. Hudis CA, Barlow WE, Costantino JP, Gray RJ, Pritchard KI, Chapman JA, et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2007;25(15):2127-32 doi 10.1200/jco.2006.10.3523.

19. Piccolo SR, Sun Y, Campbell JD, Lenburg ME, Bild AH, Johnson WE. A single-sample microarray normalization method to facilitate personalized-medicine workflows. Genomics 2012;100(6):337-44 doi 10.1016/j.ygeno.2012.08.003.

20. Curtis C, Shah SP, Chin SF, Turashvili G, Rueda OM, Dunning MJ, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012;486(7403):346-52 doi 10.1038/nature10983.

21. Paik S, Shak S, Tang G, Kim C, Baker J, Cronin M, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. The New England journal of medicine 2004;351(27):2817-26 doi 10.1056/NEJMoa041588.

22. Filipits M, Rudas M, Jakesz R, Dubsky P, Fitzal F, Singer CF, et al. A new molecular predictor of distant recurrence in ER-positive, HER2-negative breast cancer adds independent information to conventional clinical risk factors. Clinical cancer research : an official journal of the American Association for Cancer Research 2011;17(18):6012-20 doi 10.1158/1078-0432.ccr-11-0926.

23. Sotiriou C, Wirapati P, Loi S, Harris A, Fox S, Smeds J, et al. Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. Journal of the National Cancer Institute 2006;98(4):262-72 doi 10.1093/jnci/djj052.

24. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2009;27(8):1160-7 doi 10.1200/jco.2008.18.1370.

25. van de Vijver MJ, He YD, van't Veer LJ, Dai H, Hart AA, Voskuil DW, et al. A gene-expression signature as a predictor of survival in breast cancer. The New England journal of medicine 2002;347(25):1999-2009 doi 10.1056/NEJMoa021967.

26. Haibe-Kains B, Desmedt C, Rothe F, Piccart M, Sotiriou C, Bontempi G. A fuzzy gene expression-based computational approach improves breast cancer prognostication. Genome biology 2010;11(2):R18 doi 10.1186/gb-2010-11-2-r18.

27. Loi S, Haibe-Kains B, Desmedt C, Wirapati P, Lallemand F, Tutt AM, et al. Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen. BMC genomics 2008;9:239 doi 10.1186/1471-2164-9-239.




26

28. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, Yang F, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet 2005;365(9460):671-9 doi 10.1016/s0140-6736(05)17947-1.

29. Loi S, Haibe-Kains B, Majjaj S, Lallemand F, Durbecq V, Larsimont D, et al. PIK3CA mutations associated with gene signature of low mTORC1 signaling and better outcomes in estrogen receptor-positive breast cancer. Proceedings of the National Academy of Sciences of the United States of America 2010;107(22):10208-13 doi 10.1073/pnas.0907011107.

30. Tutt A, Wang A, Rowland C, Gillett C, Lau K, Chew K, et al. Risk estimation of distant metastasis in node-negative, estrogen receptor-positive breast cancer patients using an RT-PCR based prognostic expression signature. BMC cancer 2008;8:339 doi 10.1186/1471-2407-8-339.

31. Davis LM, Harris C, Tang L, Doherty P, Hraber P, Sakai Y, et al. Amplification patterns of three genomic regions predict distant recurrence in breast carcinoma. The Journal of molecular diagnostics : JMD 2007;9(3):327-36 doi 10.2353/jmoldx.2007.060079.

32. Ring BZ, Seitz RS, Beck R, Shasteen WJ, Tarr SM, Cheang MC, et al. Novel prognostic immunohistochemical biomarker panel for estrogen receptor-positive breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2006;24(19):3039-47 doi 10.1200/jco.2006.05.6564.

33. Ma XJ, Salunga R, Dahiya S, Wang W, Carney E, Durbecq V, et al. A five-gene molecular grade index and HOXB13:IL17BR are complementary prognostic factors in early stage breast cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2008;14(9):2601-8 doi 10.1158/1078-0432.ccr-07-5026.

34. Bayani J, Yao CQ, Quintayo MA, Yan F, Haider S, D'Costa A, et al. Molecular stratification of early breast cancer identifies drug targets to drive stratified medicine. NPJ Breast Cancer 2017;3:3 doi 10.1038/s41523-016-0003-5.

35. Ma XJ, Wang Z, Ryan PD, Isakoff SJ, Barmettler A, Fuller A, et al. A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen. Cancer cell 2004;5(6):607-16 doi 10.1016/j.ccr.2004.05.015.

36. Gray B. cmprsk: Subdistribution analysis of competing risks. R package version 2.2-7. Available at: http://CRAN.R-project.org/package=cmprsk. Accessed May 17, 2019.

37. Scrucca L, Santucci A, Aversa F. Competing risk analysis using R: an easy guide for clinicians. Bone marrow transplantation 2007;40(4):381-7 doi 10.1038/sj.bmt.1705727.

38. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika 1982;69(1):239-41.

39. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. REporting recommendations for tumour MARKer prognostic studies (REMARK). European journal of cancer (Oxford, England : 1990) 2005;41(12):1690-6 doi 10.1016/j.ejca.2005.03.032.

40. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B (Methodological) 1995;57(1):289-300.

41. Heagerty PJ, Lumley T, Pepe MS. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 2000;56(2):337-44.

42. Mi H, Huang X, Muruganujan A, Tang H, Mills C, Kang D, et al. PANTHER version 11: expanded annotation data from Gene Ontology and Reactome pathways, and data analysis tool enhancements. Nucleic acids research 2017;45(D1):D183-d9 doi 10.1093/nar/gkw1138.

43. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic acids research 2018;46(D1):D649-d55 doi 10.1093/nar/gkx1132.



http://cran.r-project.org/package=cmprsk


27

44. Milacic M, Haw R, Rothfels K, Wu G, Croft D, Hermjakob H, et al. Annotating cancer variants and anti-cancer therapeutics in reactome. Cancers 2012;4(4):1180-211 doi 10.3390/cancers4041180.

45. Gendoo DM, Ratanasirigulchai N, Schroder MS, Pare L, Parker JS, Prat A, et al. Genefu: an R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics (Oxford, England) 2016;32(7):1097-9 doi 10.1093/bioinformatics/btv693.

46. van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415(6871):530-6 doi 10.1038/415530a.

47. Servant N, Bollet MA, Halfwerk H, Bleakley K, Kreike B, Jacob L, et al. Search for a gene expression signature of breast cancer local recurrence in young women. Clinical cancer research : an official journal of the American Association for Cancer Research 2012;18(6):1704-15 doi 10.1158/1078-0432.ccr-11-1954.

48. Kao KJ, Chang KM, Hsu HC, Huang AT. Correlation of microarray-based breast cancer molecular subtypes and clinical outcomes: implications for treatment optimization. BMC cancer 2011;11:143 doi 10.1186/1471-2407-11-143.

49. Symmans WF, Hatzis C, Sotiriou C, Andre F, Peintinger F, Regitnig P, et al. Genomic index of sensitivity to endocrine therapy for breast cancer. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2010;28(27):4111-9 doi 10.1200/jco.2010.28.4273.




28

Figure legends

Figure 1. Performance of previously-published signatures, Average Genomic Risk and a

novel signature, MET141, in 454 node-negative, post-menopausal and systemically

untreated patients with ER+, HER2- cancers of SweBCG91-RT.

(A) Forest plot depicting standardized hazard ratios (HRs) for each of the 15 previously-

published gene signatures, the Average Genomic Risk derived as a mean of all signatures, and a

novel signature MET141, for the 454 post-menopausal and systemically untreated patients with

ER+, HER2- cancers, with associated p-values from the Cox proportional hazards model.

Continuous risk scores were divided by the standard deviation to directly compare of hazard

ratios 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 post-menopausal patients that did not receive systemic

therapy with ER+, HER2- cancers in the SweBCG91-RT cohort, for each of the 15 previously-

published gene signatures, Average Genomic Risk, and MET141.

Figure 2. Comparison of previously-published signatures in 454 low-risk patient of

SweBCG91-RT.

(A) Pearson correlation and hierarchical clustering for the gene signatures. A moderate to high

correlation is seen for most signatures developed in or for breast cancer patients with ER+ cancer.

(B) Comparison of the previously-published signatures on their classification of individual

patients. Each row represents an individual patient and samples are ordered by Average Genomic

Risk. Bar plots are colored to indicate what quartile the patient was scored per signature, with red

indicating that the patient was scored with highest risk (top 25th percentile) and blue indicating




29

that the patients was scored with lowest risk (bottom 25th percentile). Histological grade, time to

metastasis, and subtype based on immunohistochemistry scores are also displayed for

comparison. (C) Concordance of the signatures in classifying which patients are in the lowest

quartile of risk. Bar plots show the proportion of patients classified in the lowest quartile with the

title signature, that was also in the lowest quartile of each other signature. This analysis is

performed for the 454 post-menopausal and systemically untreated patients with ER+, HER2-

cancers in the SweBCG91-RT cohort.

Figure 3. Reactome pathway analysis

Reactome analysis pathway plots that indicate that cell cycle, DNA replication, and gene

transcription pathways are overexpressed in the gene lists for previously-published signatures

(A), and for the MET141 signature (B). The analysis shows that MET141 captures largely the

same pathways as the previous signatures.

Figure 4. Performance of previously published signatures, Average Genomic Risk, and the

novel signature MET141, in systemically untreated and clinically low-risk patients in the

METABRIC cohort.

(A) Forest plot depicting standardized hazard ratios (HRs) for each of the 15 previously-

published gene signatures, the Average Genomic Risk, and the novel signature MET141, in the

post-menopausal and systemically untreated patients with ER+, HER2- cancers of the

METABRIC cohort, where nearly all were node-negative. P-values are from the Cox

proportional hazards model. Continuous risk scores were divided by the standard deviation to

directly compare of hazard ratios between scores with differently distributed values and the Cox

analysis is limited to 10 years. Results are shown for endpoint breast cancer death. (B)




30

Cumulative incidence of breast cancer death in the post-menopausal patients that did not receive

systemic therapy with ER+, HER2- cancers of the METABRIC cohort, for each of the 15

previously-published gene signatures, Average Genomic Risk, and MET141.




31

Table 1. SweBCG91-RT Patient characteristics

All patients ER+, HER2-, post-menopausal, no systemic treatment

Number of patients 765 454

Age at surgery

Median (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 status

Pre-

menopausal 152 (20%) 0 (0%)

Post-menopausal 592 (80%) 454 (100%)

Missing 21 0

Histological grade

1 105 (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 FISH

Negative 702 (93%) 454 (100%)

Positive 54 (7%) 0 (0%)

Missing 9 0

Subtype by IHC

Luminal A 421 (56%) 287 (63%)

Luminal B (HER2-) 216 (29%) 167 (37%)

HER2+ 54 (7%) 0 (0%)

Triple-Negative 65 (9%) 0 (0%)

Missing 9 0

Adjuvant endocrine therapy

No 710 (93%) 454 (100%)

Yes 55 (7%) 0 (0%)

Adjuvant chemotherapy

No 755 (99%) 454 (100%)

Yes 10 (1%) 0 (0%)

Adjuvant radiotherapy

No 403 (53%) 227 (50%)

Yes 362 (47%) 227 (50%)

Distant metastasis

No 658 (86%) 402 (89%)

Yes 107 (14%) 52 (12%)

Died from breast cancer

No 628 (82%) 373 (82%)

Yes 137 (18%) 81 (18%)




32

Table 2. METABRIC Patient characteristics

All patients ER+,HER2-,post-menopausal, treated with BCS but

no systemic treatment

Number of patients 1904 104

Age 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 Status

Pre-menopausal 411 (22%) 104 (100%)

Post-menopausal 1493 (78%) 0 (0%)

Histological grade

1 165 (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 status

Negative 445 (23%) 0 (0%)

Positive 1459 (77%) 104 (100%)

Progesterone receptor status

Negative 895 (47%) 24 (23%)

Positive 1009 (53%) 80 (77%)

HER2 Status

Negative 1668 (88%) 104 (100%)

Positive 236 (12%) 0 (0%)

Subtype

ER-/HER2- 290 (15%) 3 (3%)

ER+/HER2- High Proliferation 603 (32%) 35 (34%)

ER+/HER2- Low Proliferation 619 (33%) 55 (53%)

HER2+ 188 (10%) 2 (2%)

Missing 204 (11%) 9 (9%)

Surgery Type

Breast-conserving surgery 755 (40%) 104 (100%)

Mastectomy 1127 (59%) 0 (0%)

Missing 22 (1%) 0 (0%)

Adjuvant endocrine therapy

No 730 (38%) 104 (100%)

Yes 1174 (62%) 0 (0%)

Adjuvant chemotherapy

No 1508 (79%) 104 (100%)

Yes 396 (21%) 0 (0%)

Adjuvant radiotherapy

No 767 (40%) 15 (14%)

Yes 1137 (60%) 89 (86%)

Died from breast cancer

No 1281 (67%) 82 (79%)

Yes 622 (33%) 22 (21%)

Missing 1 (0%) 0 (0%)
















Published OnlineFirst September 26, 2019.Clin Cancer Res Martin Sjöström, S. Laura Chang, Nick Fishbane, et al. cancer patients who may be spared adjuvant systemic therapyComprehensive transcriptomic profiling identifies breast

Updated version

10.1158/1078-0432.CCR-19-1038doi:

Access the most recent version of this article at:

Material

Supplementary

http://clincancerres.aacrjournals.org/content/suppl/2019/09/26/1078-0432.CCR-19-1038.DC1

Access the most recent supplemental material at:

Manuscript

Authoredited. Author manuscripts have been peer reviewed and accepted for publication but have not yet been

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

Subscriptions

Reprints and

[email protected] at

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

Permissions

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

.http://clincancerres.aacrjournals.org/content/early/2019/09/26/1078-0432.CCR-19-1038To request permission to re-use all or part of this article, use this link



http://clincancerres.aacrjournals.org/lookup/doi/10.1158/1078-0432.CCR-19-1038

http://clincancerres.aacrjournals.org/content/suppl/2019/09/26/1078-0432.CCR-19-1038.DC1

http://clincancerres.aacrjournals.org/cgi/alerts

mailto:[email protected]

http://clincancerres.aacrjournals.org/content/early/2019/09/26/1078-0432.CCR-19-1038


Date post:	17-Jun-2020
Category:	Documents
Upload:	others
View:	3 times
Download:	0 times

Comprehensive transcriptomic profiling identifies breast ... · As systemic adjuvant therapy was...

Documents