Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2008 Feb;172(2):495-509.
doi: 10.2353/ajpath.2008.061079. Epub 2008 Jan 10.

Transcriptional networks inferred from molecular signatures of breast cancer

Ron Tongbai  1 , Gila Idelman, Silje H Nordgard, Wenwu Cui, Jonathan L Jacobs, Cynthia M Haggerty, Stephen J Chanock, Anne-Lise Børresen-Dale, Gary Livingston, Patrick Shaunessy, Chih-Hung Chiang, Vessela N Kristensen, Sven Bilke, Kevin Gardner
Affiliations

Transcriptional networks inferred from molecular signatures of breast cancer

Ron Tongbai et al. Am J Pathol. 2008 Feb.

Abstract

Global genomic approaches in cancer research have provided new and innovative strategies for the identification of signatures that differentiate various types of human cancers. Computational analysis of the promoter composition of the genes within these signatures may provide a powerful method for deducing the regulatory transcriptional networks that mediate their collective function. In this study we have systematically analyzed the promoter composition of gene classes derived from previously established genetic signatures that recently have been shown to reliably and reproducibly distinguish five molecular subtypes of breast cancer associated with distinct clinical outcomes. Inferences made from the trends of transcription factor binding site enrichment in the promoters of these gene groups led to the identification of regulatory pathways that implicate discrete transcriptional networks associated with specific molecular subtypes of breast cancer. One of these inferred pathways predicted a role for nuclear factor-kappaB in a novel feed-forward, self-amplifying, autoregulatory module regulated by the ERBB family of growth factor receptors. The existence of this pathway was verified in vivo by chromatin immunoprecipitation and shown to be deregulated in breast cancer cells overexpressing ERBB2. This analysis indicates that approaches of this type can provide unique insights into the differential regulatory molecular programs associated with breast cancer and will aid in identifying specific transcriptional networks and pathways as potential targets for tumor subtype-specific therapeutic intervention.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Inference of tumor subtype-associated genetic signatures from meta-analysis of gene expression profiles that reproducibly distinguish breast cancer subtypes. A: Gene lists for promoter analysis were derived from the data of Sorlie and colleagues, in which hierarchical clustering of 534 genes and 115 breast tumors showed five distinct gene expression signatures (boxed) subdividing tumors into five clinically relevant subgroups (Modified from Sorlie et al with permission, Copyright 2003, National Academy of Sciences, USA). Boxed regions overlap the starting list of genes used to construct tumor subtype-specific gene lists for proximal promoter analysis after significance ranking of subtype discriminators by analysis of variance (see Supplemental Table S1 at http://ajp.amjpathol.org). B: Median expression profiles of subtype-specific gene groups based on dendrogram boundaries. The genes are ordered from left to right according to the order generated by hierarchical clustering (top to bottom) in A. C: Rederivation of hierarchical clustering of tumor subtypes based on gene lists derived from significance ranking of subtype discriminators by analysis of variance. Subtype discriminators were then used as subtype-specific genetic signatures.
Figure 2
Figure 2
Tumor subtypes show nonrandom enrichment for specific TFBS. A: Shown is reduced representation a two-way hierarchical clustering of 409 TFBS and the 5 tumor subtypes based on overrepresentation of TFBS matrices scored from P values derived using a background model of 15,318 RefSeq genes (see Materials and Methods). B–F: Magnifications of matrix cluster overrepresented in: ERBB2+ tumor subtype (B); luminal subtype A (C); normal-like breast tumor subtype (D); matrix clusters enriched in basal-like tumor subtype and luminal B subtype (E); and matrix clusters enriched preferentially in luminal B subtype (F). The scale bar represents the log2 transformation of the inverse of the P value (P value <0.05 corresponds to >4.3 on the scale). G: Dendrogram showing classification of tumor subtypes based on TFBS composition in the proximal promoter.
Figure 3
Figure 3
Principal components analysis (PCA) of TFBS enrichment reduces redundancy and highlights the most effective tumor subtype discriminators. A: PCA profile of the promoter regions for the gene set in each of the tumor subtypes based on TFBS’s enrichment scores (P values). Each sphere represents the comparative statistical averaging of the TFBS enrichment profile for each tumor subtype based on the enrichment score of 409 matrices (ie, 409 vectors). The data are color-coded according to respective tumor subtype: red, basal-like tumor subtype; purple, luminal B subtype; green, luminal A subtype; blue, ERBB2+-overexpressed tumor subtype; orange, normal-like breast tumor subtype. B: Plot of the contribution of each principal component (blue) to the cumulative variance (red). C: Two-dimensional plotting of PCA principal components (PC): PC1 versus PC2, PC 1 versus PC3, and PC1 versus PC4. D: Forty-four matrices with PC loading correlations greater than 0.75 in at least one subtype and with a P value less than 0.05 in at least one subtype were chosen for PCA biplot analysis. E: Hierarchical clustering of the 44 matrices shown in D according to enrichment P values.
Figure 4
Figure 4
Pathway analysis of composite list of expressed genes and enriched transcription factors implicates specific regulatory networks. The number of original genes is the number of genes in the network (highlighted in green) that were in the original gene list (excluding the genes encoding the transcription factors). The number of transcription factor genes is the number of genes in the network that encode significant transcription factors. The functions shown are the functions that are most significant for that network. A: Network derived from expressed genes and significant transcription factors in basal-like tumor subtype. B: Network derived from expressed genes and significant transcription factors in luminal subtype A. C: Network derived from expressed genes and significant transcription factors in normal-like breast tumor subtype. D: Network derived from expressed genes and significant transcription factors in ERBB2+ tumor subtype. Highlighted regions indicate feed forward autoregulatory loop inferred from the presence of NF-κB TFBS in the ERRB2 and GRB7 promoters (see Supplementary Figure S4 at http://ajp.amjpathol.org). E: Network derived from expressed genes and significant transcription factors in luminal subtype B.
Figure 5
Figure 5
NF-κB complexes associate with the ERBB2 promoter in vivo. A: Analysis of EGF-1-dependent association of NF-κB complexes with the ERBB2 promoter by chromatin immunoprecipitation in MCF-7 and the ERBB2 overexpressing cell line, MDA-MB-231, in the presence or absence of 12 ng/ml of EGF-1. Relative enrichment was determined by quantitative PCR and normalized to nonspecific IgG. Data represent the average of triplicate determinations and error bars show ±SEM. B: Schematic of NF-κB/ERBB feed-forward autoregulatory loop. ERBB1 = EGFR; ERBB2 = Neu/Her2; ERBB3 = Her3; RTK = receptor tyrosine kinases other than ERBB family. Dashed lines indicate interactions that have positive feedback or self-amplifying influence. Solid lines indicate regulatory influences that enhance expression or function.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537. - PubMed
    1. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R, Geisler S, Demeter J, Perou CM, Lonning PE, Brown PO, Borresen-Dale AL, Botstein D. Repeated observation of breast tumor subtypes in independent gene expression data sets. Proc Natl Acad Sci USA. 2003;100:8418–8423. - PMC - PubMed
    1. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de RM, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein LP, Borresen-Dale AL. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA. 2001;98:10869–10874. - PMC - PubMed
    1. Perou CM, Sorlie T, Eisen MB, van de RM, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. - PubMed
    1. Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A, Chinnaiyan AM. Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Proc Natl Acad Sci USA. 2004;101:9309–9314. - PMC - PubMed

Publication types

Substances