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Title:
The molecular portraits of breast tumors are conserved across microarray platforms | BMC Genomics
Description:
Background Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. Results A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. Conclusion This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.
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Keywords {🔍}
gene, set, intrinsic, breast, pubmed, data, tumor, sample, analysis, article, test, expression, genes, google, scholar, samples, cancer, combined, tumors, microarray, clinical, subtype, figure, cas, intrinsicunc, ssp, cluster, sets, list, subtypes, central, survival, basallike, significant, microarrays, lumb, herer, predictors, normal, datasets, showed, standard, outcome, perou, size, predictor, classification, university, single, groups,
Topics {✒️}
cox-mantel log-rank test open access article unc-ch microarray database[37] article download pdf intrinsic/unc gene set intrinsic/unc gene list cancer patient risk-stratification kaplan-meier survival analysis intrinsic/stanford gene set intrinsic/unc list contained her2-amplicon gene clusters pre-treatment tumor pairs prognostic/predictive gene set node-negative breast cancer gene-expression profiling intrinsic/unc list due single patient/tumor set interferon-regulated gene cluster gene expression-based predictors luminal/er+ gene cluster cy5-labeled experimental sample genome-wide expression patterns related subjects intrinsic subtypes/groups defined false-positive intrinsic genes final pre-processing step identify prognostic subclasses multivariate cox analysis luminal/er+ expression cluster custom designed 1av1-based misclassified er-negative tumors kaplan-meier analysis privacy choices/manage cookies univariate kaplan-meier agilent human oligonucleotide breast cancer patients breast cancer subtypes siteman cancer center intrinsic gene set natl cancer inst intrinsic/unc list breast tumor intrinsic gata3-regulated genes highlighted inflammatory breast cancer breast cancer prognostication intrinsic gene list authors’ original file article hu full size image poor outcome groups
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- Ioannidis JP: Microarrays and molecular research: noise discovery?
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headline:The molecular portraits of breast tumors are conserved across microarray platforms
description:Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.
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Recurrence Score
Intrinsic Subtype
Intrinsic Gene
Poor Outcome Group
Normal Breast Sample
Life Sciences
general
Microarrays
Proteomics
Animal Genetics and Genomics
Microbial Genetics and Genomics
Plant Genetics and Genomics
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headline:The molecular portraits of breast tumors are conserved across microarray platforms
description:Validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. However, validation is often unconvincing because the size of the test set is typically small. To overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. A 105-tumor training set containing 26 sample pairs was used to derive a new breast tumor intrinsic gene list. This intrinsic list contained 1300 genes and a proliferation signature that was not present in previous breast intrinsic gene sets. We tested this list as a survival predictor on a data set of 311 tumors compiled from three independent microarray studies that were fused into a single data set using Distance Weighted Discrimination. When the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into LumA, LumB, Basal-like, HER2+/ER-, and Normal Breast-like tumor subtypes that we demonstrated in previous datasets. These subtypes were associated with significant differences in Relapse-Free and Overall Survival. Multivariate Cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. From the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles (i.e. centroids), which is designed for single sample predictions (SSP). The SSP approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups. This study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. In addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile.
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Recurrence Score
Intrinsic Subtype
Intrinsic Gene
Poor Outcome Group
Normal Breast Sample
Life Sciences
general
Microarrays
Proteomics
Animal Genetics and Genomics
Microbial Genetics and Genomics
Plant Genetics and Genomics
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