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Pathway analysis reveals functional convergence of gene expression profiles in breast cancer.

ABSTRACT: A recent study has shown high concordance of several breast-cancer gene signatures in predicting disease recurrence despite minimal overlap of the gene lists. It raises the question if there are common themes underlying such prediction concordance that are not apparent on the individual gene-level. We therefore studied the similarity of these gene-signatures on the basis of their functional annotations.We found the signatures did not identify the same set of genes but converged on the activation of a similar set of oncogenic and clinically-relevant pathways. A clear and consistent pattern across the four breast cancer signatures is the activation of the estrogen-signaling pathway. Other common features include BRCA1-regulated pathway, reck pathways, and insulin signaling associated with the ER-positive disease signatures, all providing possible explanations for the prediction concordance.This work explains why independent breast cancer signatures that appear to perform equally well at predicting patient prognosis show minimal overlap in gene membership.

SUBMITTER: Shen R

PROVIDER: S-EPMC2447843 | biostudies-other | 2008 Jun

REPOSITORIES: biostudies-other

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Pathway analysis reveals functional convergence of gene expression profiles in breast cancer.

Shen Ronglai R Chinnaiyan Arul M AM Ghosh Debashis D

BMC medical genomics 20080627

<h4>Background</h4>A recent study has shown high concordance of several breast-cancer gene signatures in predicting disease recurrence despite minimal overlap of the gene lists. It raises the question if there are common themes underlying such prediction concordance that are not apparent on the individual gene-level. We therefore studied the similarity of these gene-signatures on the basis of their functional annotations.<h4>Results</h4>We found the signatures did not identify the same set of ge ...[more]

PMID: 18588682

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Project description:The molecular characteristics of human diseases are often represented by a list of genes termed "signature genes". A significant challenge facing this approach is that of reproducibility: signatures developed on a set of patients may fail to perform well on different sets of patients. As diseases are resulted from perturbed cellular functions, irrespective of the particular genes that contribute to the function, it may be more appropriate to characterize diseases based on these perturbed cellular functions.We proposed a profile-based approach to characterize a disease using a binary vector whose elements indicate whether a given function is perturbed based on the enrichment analysis of expression data between normal and tumor tissues. Using breast cancer and its four primary clinically relevant subtypes as examples, this approach is evaluated based on the reproducibility, accuracy and resolution of the resulting pathway profiles.Pathway profiles for breast cancer and its subtypes are constructed based on data obtained from microarray and RNA-Seq data sets provided by The Cancer Genome Atlas (TCGA), and an additional microarray data set provided by The European Genome-phenome Archive (EGA). An average reproducibility of 68% is achieved between different data sets (TCGA microarray vs. EGA microarray data) and 67% average reproducibility is achieved between different technologies (TCGA microarray vs. TCGA RNA-Seq data). Among the enriched pathways, 74% of them are known to be associated with breast cancer or other cancers. About 40% of the identified pathways are enriched in all four subtypes, with 4, 2, 4, and 7 pathways enriched only in luminal A, luminal B, triple-negative, and HER2+ subtypes, respectively. Comparison of profiles between subtypes, as well as other diseases, shows that luminal A and luminal B subtypes are more similar to the HER2+ subtype than to the triple-negative subtype, and subtypes of breast cancer are more likely to be closer to each other than to other diseases.Our results demonstrate that pathway profiles can successfully characterize both common and distinct functional characteristics of four subtypes of breast cancer and other related diseases, with acceptable reproducibility, high accuracy and reasonable resolution.

Project description:Background: Interval breast cancers can occur through failure to detect an abnormality at the time of screening (missed interval cancer), or as a new event after a negative screen (true interval cancer). The development and progression of true interval tumors (TIBC) is known to be different than screen-detected tumors (SDBC). However, much work still needs to be done to understand the biological characteristics and clinical behaviour of these TIBC. Objectives: To characterize the gene expression profile in TIBC and SDBC aimed to identify biological markers that may be associated with the emergence of symptomatic breast cancer in the screening interval. Material and Methods: An unsupervised exploratory gene expression profile analysis was performed among 10 samples (discovery set, TIBC=5 and SDBC=5) using Affymetrix Human Gene 1.0 ST arrays and interpreted by Ingenuity Pathway Analysis. Differential expression of selected genes was confirmed in validation series of 91 patients (TIBC=12 and SDBC=79) by immunohistochemistry and 24 patients (TIBC=8 and SDBC=16) by RT-qPCR, expanding the analysis to other genes in same pathway (mTOR, 4E-BP1, eIF-4G and S6). Results: Exploratory gene expression analysis identified 1060 transcripts with a p value <0.05 and 132 transcripts with an adjusted p value <0.01 difference between TIBC and SDBC samples. Based on biological implications in breast cancer, four genes were selected for further validation: CP (ceruloplasmin) and RPS6KB2 (ribosomal protein S6 kinase, 70kDa, polypeptide 2) both up-regulated in TIBC vs SDBC and PTEN (phosphatase and tensin homolog) and TGFBR3 (transforming growth factor beta receptor III), down-regulated in TIBC vs SDBC. Their differential expression was confirmed by RT-qPCR and immunohistochemistry, suggesting mTOR pathway overexpression in TIBC at both mRNA and protein level. Further expanded analysis by immunohistochemistry for mTOR pathway activation, including expression of phosphorylated forms of mTOR, 4E-BP1, eIF-4G, RPS6KB2 and S6, confirmed the upregulation of this pathway in TIBC. Conclusions: TIBC and SDBC shows differential expression profile both at the gene and protein levels. The mTOR signaling is significantly upregulated in TIBC compared with SDBC, suggesting an enhanced aggressiveness of TIBC. Besides, CP may also represent novel immunohistochemical marker helpful in distinguishing between TIBC and SDBC. Further studies with larger sets of patients are guaranteed to verify these findings associated with TIBC. 5 TIBC samples where compared with 5 SDBC

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Pathway analysis reveals functional convergence of gene expression profiles in breast cancer.

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Pathway analysis reveals functional convergence of gene expression profiles in breast cancer.

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