Comprehensive transcriptomic profiling identifies breast ... · As systemic adjuvant therapy was...
Transcript of Comprehensive transcriptomic profiling identifies breast ... · As systemic adjuvant therapy was...
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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.
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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
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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.
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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.
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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
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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
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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
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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
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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
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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
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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
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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-
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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.
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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
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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
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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
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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.
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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
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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
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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
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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.
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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.
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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.
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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
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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)
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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.
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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%)
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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%)
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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
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