Åke Borg
Principal investigator
Abstract P1-06-01: Putting multigene signatures to the test: Prognostic assessment in population-based contemporary clinical breast cancer
Author
Summary, in English
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.
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.
Department/s
- Breastcancer-genetics
- BioCARE: Biomarkers in Cancer Medicine improving Health Care, Education and Innovation
- Translational Oncogenomics
- Division of Translational Cancer Research
- Faculty office - The medical degree programme board
- Tumor Cell Biology
- Personalized Breast Cancer Treatment
- Surgery (Lund)
- Breast Cancer Surgery
- The Liquid Biopsy and Tumor Progression in Breast Cancer
- Tumor microenvironment
- Clinical Sciences, Helsingborg
- Familial Breast Cancer
Publishing year
2018-02
Language
English
Publication/Series
Cancer research. Supplement
Volume
78
Issue
4
Links
Document type
Conference paper: abstract
Publisher
American Association for Cancer Research Inc.
Topic
- Cancer and Oncology
Conference name
San Antonio Breast Cancer Symposium, 2017
Conference date
2017-12-05 - 2017-12-09
Conference place
San Antonio, United States
Status
Published
Project
- Sweden Cancerome Analysis Network - Breast (SCAN-B): a large-scale multicenter infrastructure towards implementation of breast cancer genomic analyses in the clinical routine
Research group
- Translational Oncogenomics
- Tumor Cell Biology
- Personalized Breast Cancer Treatment
- Breast Cancer Surgery
- The Liquid Biopsy and Tumor Progression in Breast Cancer
- Familial Breast Cancer
ISBN/ISSN/Other
- ISSN: 1538-7445