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Supervised Risk Predictor Of Breast Cancer Based On Intrinsic Subtypes.
J. Parker, M. Mullins, M. Cheang, S. Leung, D. Voduc, T. Vickery, S. Davies, C. Fauron, X. He, Z. Hu, John F. Quackenbush, I. Stijleman, J. Palazzo, J. S. Marron, A. Nobel, E. Mardis, T. Nielsen, M. Ellis, C. M. Perou, P. Bernard
Published 2009 · Medicine
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UNLABELLED PURPOSE To improve on current standards for breast cancer prognosis and prediction of chemotherapy benefit by developing a risk model that incorporates the gene expression-based "intrinsic" subtypes luminal A, luminal B, HER2-enriched, and basal-like. METHODS A 50-gene subtype predictor was developed using microarray and quantitative reverse transcriptase polymerase chain reaction data from 189 prototype samples. Test sets from 761 patients (no systemic therapy) were evaluated for prognosis, and 133 patients were evaluated for prediction of pathologic complete response (pCR) to a taxane and anthracycline regimen. RESULTS The intrinsic subtypes as discrete entities showed prognostic significance (P = 2.26E-12) and remained significant in multivariable analyses that incorporated standard parameters (estrogen receptor status, histologic grade, tumor size, and node status). A prognostic model for node-negative breast cancer was built using intrinsic subtype and clinical information. The C-index estimate for the combined model (subtype and tumor size) was a significant improvement on either the clinicopathologic model or subtype model alone. The intrinsic subtype model predicted neoadjuvant chemotherapy efficacy with a negative predictive value for pCR of 97%. CONCLUSION Diagnosis by intrinsic subtype adds significant prognostic and predictive information to standard parameters for patients with breast cancer. The prognostic properties of the continuous risk score will be of value for the management of node-negative breast cancers. The subtypes and risk score can also be used to assess the likelihood of efficacy from neoadjuvant chemotherapy.
This paper references
Diagnosis of multiple cancer types by shrunken centroids of gene expression
R. Tibshirani (2002)
Molecular heterogeneity of breast carcinomas and the cancer stem cell hypothesis
J. Stingl (2007)
SiZer for Exploration of Structures in Curves
P. Chaudhuri (1999)
Molecular Breast Cancer Subtypes in Premenopausal African-American Women, Tumor Biologic Factors and Clinical Outcome
C. Ihemelandu (2007)
Distinct patterns of DNA copy number alteration are associated with different clinicopathological features and gene‐expression subtypes of breast cancer
A. Bergamaschi (2006)
Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer.
S. Paik (2006)
Statistical methods for identifying differentially expressed genes in DNA microarrays.
John M. D. Storey (2003)
Cluster analysis and display of genome-wide expression patterns
C. Ferris (1999)
A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes.
R. Neve (2006)
Gene expression predictors in breast cancer: current status, limitations and perspectives.
C. Desmedt (2008)
Genes harbouring susceptibility SNPs are differentially expressed in the breast cancer subtypes
Silje H. Nordgard (2007)
Stromal gene expression predicts clinical outcome in breast cancer
Greg Finak (2008)
Predictors of Resistance to Preoperative Trastuzumab and Vinorelbine for HER2-Positive Early Breast Cancer
L. Harris (2007)
A prediction-based resampling method for estimating the number of clusters in a dataset
S. Dudoit (2002)
Supervised risk predictor of breast cancer based on intrinsic subtypes
JS Parker (2008)
Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
Yufeng Liu (2008)
A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer.
S. Paik (2004)
Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer.
R. Rouzier (2005)
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
Concordance among gene-expression-based predictors for breast cancer.
C. Fan (2006)
Stem/Progenitor Cells in Mouse Mammary Gland Development and Breast Cancer
Y. Li (2005)
Origins of breast cancer subtypes and therapeutic implications
A. Sims (2007)
Presence of bone marrow micrometastasis is associated with different recurrence risk within molecular subtypes of breast cancer
B. Naume (2007)
Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.
S. Loi (2007)
Tumor stroma and regulation of cancer development.
T. Tlsty (2006)
Breast cancer classification and prognosis based on gene expression profiles from a population-based study
C. Sotiriou (2003)
Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival.
H. Chang (2005)
Intrinsic molecular signature of breast cancer in a population-based cohort of 412 patients
S. Calza (2006)
Molecular portraits of human breast tumours
C. Perou (2000)
Agreement in breast cancer classification between microarray and quantitative reverse transcription PCR from fresh-frozen and formalin-fixed, paraffin-embedded tissues.
M. Mullins (2007)
Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis.
C. Sotiriou (2006)
The prognostic role of a gene signature from tumorigenic breast-cancer cells.
R. Liu (2007)
Supervised risk predictor of breast cancer based on intrinsic subtypes
JS Parker (2008)
Expression genomics in breast cancer research: microarrays at the crossroads of biology and medicine
L. Miller (2007)
Extracellular matrix signature identifies breast cancer subgroups with different clinical outcome
A. Bergamaschi (2008)
The molecular portraits of breast tumors are conserved across microarray platforms
Z. Hu (2006)
Repeated observation of breast tumor subtypes in independent gene expression data sets
T. Sørlie (2003)
Classification of microarrays to nearest centroids
A. Dabney (2005)
Reasons for breast cancer heterogeneity
F. Bertucci (2008)
Molecular classification of breast tumors: toward improved diagnostics and treatments.
T. Sørlie (2007)
Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling
S. V. Laere (2005)
Identification of conserved gene expression features between murine mammary carcinoma models and human breast tumors
J. Herschkowitz (2006)
A reliable data-based bandwidth selection method for kernel density estimation
S. Sheather (1991)
Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications
T. Sørlie (2001)
Classification and risk stratification of invasive breast carcinomas using a real-time quantitative RT-PCR assay
L. Perreard (2005)
Gene expression profiling predicts clinical outcome of breast cancer
L. J. Veer (2002)
Conservation of Breast Cancer Molecular Subtypes and Transcriptional Patterns of Tumor Progression Across Distinct Ethnic Populations
Kun Yu (2004)
Gene expression and benefit of chemotherapy in women with nodenegative, estrogen receptor-positive breast cancer
S Paik (2006)
Microarray Database: GEO Data Sets for Breast Cancer Research Published Papers (Clinical Data updated on 11-062007 for Data I, 4-7-2008 for Data II). https:// genome.unc.edu/pubsup/breastGEO
North Carolina (2008)
Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.
A. V. Ivshina (2006)
Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
Y. Wang (2005)
Gene expression profiling of breast cancer: a new tumor marker.
L. V. Van’t Veer (2005)
Gene Expression Programs in Response to Hypoxia: Cell Type Specificity and Prognostic Significance in Human Cancers
Jen-Tsan Chi (2006)
An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival.
L. Miller (2005)
Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer.
K. Hess (2006)
Geneexpression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
Y Wang (2005)
This paper is referenced by
Clinical significance and immunogenomic landscape analyses of the immune cell signature based prognostic model for patients with breast cancer.
Shiyuan Wang (2020)
The Role of Proliferation in Determining Response to Neoadjuvant Chemotherapy in Breast Cancer: A Gene Expression–Based Meta-Analysis
D. Stover (2016)
Prognosis and predictive factors in human breast cancer during tumor progression
E. Karlsson (2013)
Beyond synthetic lethality: multiple gene interaction types play a key functional role in cancer
Assaf Magen (2018)
Matrix Metalloproteinases: A challenging paradigm of cancer management.
Ali Alaseem (2019)
Down regulation of ADAM33 as a Predictive Biomarker of Aggressive Breast Cancer
G. C. Mânica (2017)
Intrinsic Subtypes and Gene Expression Profiles in Primary and Metastatic Breast Cancer.
J. M. Cejalvo (2017)
Reparameterization of PAM50 Expression Identifies Novel Breast Tumor Dimensions and Leads to Discovery of a Genome-Wide Significant Breast Cancer Locus at 12q15
M. J. Madsen (2018)
Molecular classification of breast cancer: what the pathologist needs to know.
E. Rakha (2017)
CDK4/6 Inhibition in Breast Cancer: Mechanisms of Response and Treatment Failure
A. Garrido-Castro (2017)
Comprehensive characterization of claudin-low breast tumors reflects the impact of the cell-of-origin on cancer evolution
Roxane M. Pommier (2020)
The impact of distinct triple-negative breast cancer subtypes on misdiagnosis and diagnostic delay
C. Elfgen (2019)
Distinct Receptor Tyrosine Kinase Subsets Mediate Anti-HER2 Drug Resistance in Breast Cancer*
Peter B. Alexander (2016)
Reconstruction of nuclear receptor network reveals that NR2E3 is a novel upstream regulator of ESR1 in breast cancer
Y. Park (2012)
Induction of Wnt-Inducible Signaling Protein-1 Correlates with Invasive Breast Cancer Oncogenesis and Reduced Type 1 Cell-Mediated Cytotoxic Immunity: A Retrospective Study
D. Klinke (2014)
The role of the AR/ER ratio in ER-positive breast cancer patients.
Nelson Rangel (2018)
Targeting androgen receptor in estrogen receptor-negative breast cancer.
M. Ni (2011)
Molecular subtyping of early-stage breast cancer identifies a group of patients who do not benefit from neoadjuvant chemotherapy
S. Glueck (2013)
Intrinsic Subtypes from the PAM50 Gene Expression Assay in a Population-Based Breast Cancer Survivor Cohort: Prognostication of Short- and Long-term Outcomes
B. Caan (2014)
Modeling complex patterns of differential DNA methylation that associate with gene expression changes
Christopher E Schlosberg (2017)
Whole transcriptome profiling of patient-derived xenograft models as a tool to identify both tumor and stromal specific biomarkers
J. Bradford (2016)
Pathologic response and long-term follow-up in breast cancer patients treated with neoadjuvant chemotherapy: a comparison between classifications and their practical application.
A. Corben (2013)
Reply to Y.Yamamoto et al.
A. Prat (2013)
RNA Helicase DDX5 Regulates MicroRNA Expression and Contributes to Cytoskeletal Reorganization in Basal Breast Cancer Cells*
D. Wang (2011)
Use of a Novel Embryonic Mammary Stem Cell Gene Signature to Improve Human Breast Cancer Diagnostics and Therapeutic Decision Making
G. Wahl (2013)
Molecular Testing in Breast Cancer
K. Allison (2017)
"Triple negative breast cancer": Translational research and the (re)assembling of diseases in post-genomic medicine.
P. Keating (2016)
Novel RNA-Affinity Proteogenomics Dissects Tumor Heterogeneity for Revealing Personalized Markers in Precision Prognosis of Cancer.
L. Wang (2018)
The value of genomics in dissecting the RAS-network and in guiding therapeutics for RAS-driven cancers.
Gajendra Shrestha (2016)
A Similarity Regression Fusion Model for Integrating Multi-Omics Data to Identify Cancer Subtypes
Yang Guo (2018)
A Preclinical Model for ERα-Positive Breast Cancer Points to the Epithelial Microenvironment as Determinant of Luminal Phenotype and Hormone Response.
G. Sflomos (2016)
Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma
S. Kas (2017)See more