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
- 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) - 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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/df2af0f7-4bd2-490c-b8f2-9456284e1ec7
- author
- 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. (More)
- 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
; Malmberg, Martin
LU
and Borg, Åke
LU
(Less)
- organization
-
- Breastcancer-genetics
- BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
- Translational Oncogenomics (research group)
- Division of Translational Cancer Research
- Faculty office - The medical degree programme board
- Tumor Cell Biology (research group)
- Personalized Breast Cancer Treatment (research group)
- Surgery (Lund)
- Breast Cancer Surgery (research group)
- The Liquid Biopsy and Tumor Progression in Breast Cancer (research group)
- Tumor microenvironment
- Clinical Sciences, Helsingborg
- Familial Breast Cancer (research group)
- publishing date
- 2018-02
- 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
- conference location
- San Antonio, United States
- conference dates
- 2017-12-05 - 2017-12-09
- ISSN
- 1538-7445
- DOI
- 10.1158/1538-7445.SABCS17-P1-06-01
- project
- Sweden Cancerome Analysis Network - Breast (SCAN-B): a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine
- language
- English
- LU publication?
- yes
- id
- df2af0f7-4bd2-490c-b8f2-9456284e1ec7
- date added to LUP
- 2018-03-05 15:49:50
- date last changed
- 2022-10-21 02:39:53
@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 (>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}},
doi = {{10.1158/1538-7445.SABCS17-P1-06-01}},
volume = {{78}},
year = {{2018}},
}