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Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer

Staaf, Johan LU ; Vallon-Christersson, Johan LU ; Häkkinen, Jari LU ; Saal, Lao LU ; Hegardt, Cecilia LU ; Larsson, Christer LU ; Ehinger, Anna LU ; Rydén, Lisa LU ; Loman, Niklas LU and Malmberg, Martin LU , et al. (2018) San Antonio Breast Cancer Symposium, 2017 In Cancer research. Supplement 78(4).
Abstract (Swedish)
Abstract

Background

Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.

Patients and methods

Between 2010-09-01 to 2015-03-31, 5101... (More)
Abstract

Background

Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.

Patients and methods

Between 2010-09-01 to 2015-03-31, 5101 (87%) of 5892 patients with invasive primary disease in the healthcare region were included in the SCAN-B study (ClinicalTrials.gov ID: NCT02306096). Inclusion criteria included no generalized/prior contralateral disease and known surgery/treatment status (neo- or adjuvant). 3273 tumors were profiled by RNA sequencing and matched to clinicopathological patient data from the National Breast Cancer Register, with distribution of clinicopathological characteristics reflecting proportions in the catchment region. RNA profiles were classified according to 14 reported gene signatures featuring both GEPs (PAM50, IC10, CIT, TNBCtype) and specific risk predictors (e.g. Oncotype Dx, 70-gene, 76-gene, ROR-variants, genomic grade index). Classifications were investigated for association with patient OS by univariate and multivariate analyses in seven adjuvant clinical treatment groups: TNBC-ACT (adjuvant chemotherapy, n=228), TNBC-untreated (n=83), HER2+/ER- with trastuzumab + ACT treatment (n=101), HER2+/ER+ with trastuzumab + ACT + endocrine treatment (n=210), ER+/HER2- with endocrine treatment (n=1477), ER+/HER2- with endocrine + ACT treatment (n=637), and ER+/HER2- untreated (n=216).

Results

For the majority of signatures, analysis of classification demonstrated prognostic value limited to ER+/HER2- tumors given follow-up time. Several signatures (including Oncotype Dx, 70-gene, ROR-variants) showed strong predictive value in identifying a subset of ER+/HER2- patients receiving a combination of endocrine and ACT therapy with excellent overall survival (>96%), indicating appropriate therapy selection. In addition, for both ER+/HER2- treatment groups signature analysis identified high-risk groups of patients in clear need of additional treatment beyond standard therapeutic regimes, even with less than 5-years of follow-up.

Conclusions

Our results support the prognostic association of gene expression signatures in large unselected population-based primary breast cancer cohorts even with a short follow-up of OS.Importantly, prognostic associations are limited to specific subgroups for different classifiers and in population-based breast cancer some clinically important subgroups constitute a small proportion of cases. In this context, continued population-based inclusion and broad transcriptional profiling of breast cancer patients provides an opportunity for application to broader patient groups (e.g. TNBC and HER2+), and for consensus classification of individual risk assessments that could potentially provide more stable predictions. (Less)
Abstract
Background

Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.

Patients and methods

Between 2010-09-01 to 2015-03-31, 5101 (87%) of 5892... (More)
Background

Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.

Patients and methods

Between 2010-09-01 to 2015-03-31, 5101 (87%) of 5892 patients with invasive primary disease in the healthcare region were included in the SCAN-B study (ClinicalTrials.gov ID: NCT02306096). Inclusion criteria included no generalized/prior contralateral disease and known surgery/treatment status (neo- or adjuvant). 3273 tumors were profiled by RNA sequencing and matched to clinicopathological patient data from the National Breast Cancer Register, with distribution of clinicopathological characteristics reflecting proportions in the catchment region. RNA profiles were classified according to 14 reported gene signatures featuring both GEPs (PAM50, IC10, CIT, TNBCtype) and specific risk predictors (e.g. Oncotype Dx, 70-gene, 76-gene, ROR-variants, genomic grade index). Classifications were investigated for association with patient OS by univariate and multivariate analyses in seven adjuvant clinical treatment groups: TNBC-ACT (adjuvant chemotherapy, n=228), TNBC-untreated (n=83), HER2+/ER- with trastuzumab + ACT treatment (n=101), HER2+/ER+ with trastuzumab + ACT + endocrine treatment (n=210), ER+/HER2- with endocrine treatment (n=1477), ER+/HER2- with endocrine + ACT treatment (n=637), and ER+/HER2- untreated (n=216).

Results

For the majority of signatures, analysis of classification demonstrated prognostic value limited to ER+/HER2- tumors given follow-up time. Several signatures (including Oncotype Dx, 70-gene, ROR-variants) showed strong predictive value in identifying a subset of ER+/HER2- patients receiving a combination of endocrine and ACT therapy with excellent overall survival (>96%), indicating appropriate therapy selection. In addition, for both ER+/HER2- treatment groups signature analysis identified high-risk groups of patients in clear need of additional treatment beyond standard therapeutic regimes, even with less than 5-years of follow-up.

Conclusions

Our results support the prognostic association of gene expression signatures in large unselected population-based primary breast cancer cohorts even with a short follow-up of OS.Importantly, prognostic associations are limited to specific subgroups for different classifiers and in population-based breast cancer some clinically important subgroups constitute a small proportion of cases. In this context, continued population-based inclusion and broad transcriptional profiling of breast cancer patients provides an opportunity for application to broader patient groups (e.g. TNBC and HER2+), and for consensus classification of individual risk assessments that could potentially provide more stable predictions. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Cancer research. Supplement
volume
78
issue
4
publisher
American Association for Cancer Research Inc.
conference name
San Antonio Breast Cancer Symposium, 2017
ISSN
1538-7445
DOI
10.1158/1538-7445.SABCS17-P1-06-01
language
English
LU publication?
yes
id
df2af0f7-4bd2-490c-b8f2-9456284e1ec7
date added to LUP
2018-03-05 15:49:50
date last changed
2018-05-29 12:18:55
@misc{df2af0f7-4bd2-490c-b8f2-9456284e1ec7,
  abstract     = {Background<br>
<br>
Gene expression signatures hold promise for a molecularly driven division of primary breast cancer with clinical implications. A gap still remains in the application/validation of such signatures in actual clinical treatment groups from unselected, population-based, primary breast cancer receiving current standard of care therapy. We analyzed classification proportions and overall survival (OS) of 14 reported gene expression phenotypes (GEPs) and risk predictors (RPs) in seven clinical treatments groups from an 3273-sample breast cancer cohort representative of population-based disease in the South Swedish healthcare region.<br>
<br>
Patients and methods<br>
<br>
Between 2010-09-01 to 2015-03-31, 5101 (87%) of 5892 patients with invasive primary disease in the healthcare region were included in the SCAN-B study (ClinicalTrials.gov ID: NCT02306096). Inclusion criteria included no generalized/prior contralateral disease and known surgery/treatment status (neo- or adjuvant). 3273 tumors were profiled by RNA sequencing and matched to clinicopathological patient data from the National Breast Cancer Register, with distribution of clinicopathological characteristics reflecting proportions in the catchment region. RNA profiles were classified according to 14 reported gene signatures featuring both GEPs (PAM50, IC10, CIT, TNBCtype) and specific risk predictors (e.g. Oncotype Dx, 70-gene, 76-gene, ROR-variants, genomic grade index). Classifications were investigated for association with patient OS by univariate and multivariate analyses in seven adjuvant clinical treatment groups: TNBC-ACT (adjuvant chemotherapy, n=228), TNBC-untreated (n=83), HER2+/ER- with trastuzumab + ACT treatment (n=101), HER2+/ER+ with trastuzumab + ACT + endocrine treatment (n=210), ER+/HER2- with endocrine treatment (n=1477), ER+/HER2- with endocrine + ACT treatment (n=637), and ER+/HER2- untreated (n=216).<br>
<br>
Results<br>
<br>
For the majority of signatures, analysis of classification demonstrated prognostic value limited to ER+/HER2- tumors given follow-up time. Several signatures (including Oncotype Dx, 70-gene, ROR-variants) showed strong predictive value in identifying a subset of ER+/HER2- patients receiving a combination of endocrine and ACT therapy with excellent overall survival (&gt;96%), indicating appropriate therapy selection. In addition, for both ER+/HER2- treatment groups signature analysis identified high-risk groups of patients in clear need of additional treatment beyond standard therapeutic regimes, even with less than 5-years of follow-up.<br>
<br>
Conclusions<br>
<br>
Our results support the prognostic association of gene expression signatures in large unselected population-based primary breast cancer cohorts even with a short follow-up of OS.Importantly, prognostic associations are limited to specific subgroups for different classifiers and in population-based breast cancer some clinically important subgroups constitute a small proportion of cases. In this context, continued population-based inclusion and broad transcriptional profiling of breast cancer patients provides an opportunity for application to broader patient groups (e.g. TNBC and HER2+), and for consensus classification of individual risk assessments that could potentially provide more stable predictions.},
  author       = {Staaf, Johan and Vallon-Christersson, Johan and Häkkinen, Jari and Saal, Lao and Hegardt, Cecilia and Larsson, Christer and Ehinger, Anna and Rydén, Lisa and Loman, Niklas and Malmberg, Martin and Borg, Åke},
  issn         = {1538-7445},
  language     = {eng},
  note         = {Conference Abstract},
  number       = {4},
  publisher    = {American Association for Cancer Research Inc.},
  series       = {Cancer research. Supplement},
  title        = {Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer},
  url          = {http://dx.doi.org/10.1158/1538-7445.SABCS17-P1-06-01},
  volume       = {78},
  year         = {2018},
}