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REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

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The activity scores of six TF binding profiles for ER alpha in ER+ and ER-. The P-values in the top-right corner are calculated based on Wilcox rank sum test. The six TF binding profiles are from two cell lines (T47d and Ecc1) and under three different conditions (treated with steroid hormone Gen/Estradia for 1h, or with Dmso2 as control).
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Figure 4: The activity scores of six TF binding profiles for ER alpha in ER+ and ER-. The P-values in the top-right corner are calculated based on Wilcox rank sum test. The six TF binding profiles are from two cell lines (T47d and Ecc1) and under three different conditions (treated with steroid hormone Gen/Estradia for 1h, or with Dmso2 as control).

Mentions: First, we examined whether the inferred activity score of ER alpha reflected its actual activity in a sample. We compared activity scores of ER alpha in ER+ versus ER- breast cancer samples. As shown in FigureĀ 4, the activity scores of ER alpha are significantly higher in ER+ than in ER- samples with the exception of Ecc1_ERapha_Dmso2. This suggests that the inferred activity score of ER alpha can correctly reflect the ER status of a breast cancer sample. We compared the activity scores of all TF binding profiles in ER+ versus ER- samples using Wilcox rank sum test (Additional file 4: Table S4). We find that out of the 424 TF binding profiles we collected, 29 have significantly higher activity scores in ER+ samples and 135 have significantly higher activity scores in ER- samples. Consistent with the results of the REACTIN algorithm, the top three most significant TFs with higher activity scores in ER+ than in ER- are ER alpha, FOXA1 and GATA3.


REACTIN: regulatory activity inference of transcription factors underlying human diseases with application to breast cancer.

Zhu M, Liu CC, Cheng C - BMC Genomics (2013)

The activity scores of six TF binding profiles for ER alpha in ER+ and ER-. The P-values in the top-right corner are calculated based on Wilcox rank sum test. The six TF binding profiles are from two cell lines (T47d and Ecc1) and under three different conditions (treated with steroid hormone Gen/Estradia for 1h, or with Dmso2 as control).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750236&req=5

Figure 4: The activity scores of six TF binding profiles for ER alpha in ER+ and ER-. The P-values in the top-right corner are calculated based on Wilcox rank sum test. The six TF binding profiles are from two cell lines (T47d and Ecc1) and under three different conditions (treated with steroid hormone Gen/Estradia for 1h, or with Dmso2 as control).
Mentions: First, we examined whether the inferred activity score of ER alpha reflected its actual activity in a sample. We compared activity scores of ER alpha in ER+ versus ER- breast cancer samples. As shown in FigureĀ 4, the activity scores of ER alpha are significantly higher in ER+ than in ER- samples with the exception of Ecc1_ERapha_Dmso2. This suggests that the inferred activity score of ER alpha can correctly reflect the ER status of a breast cancer sample. We compared the activity scores of all TF binding profiles in ER+ versus ER- samples using Wilcox rank sum test (Additional file 4: Table S4). We find that out of the 424 TF binding profiles we collected, 29 have significantly higher activity scores in ER+ samples and 135 have significantly higher activity scores in ER- samples. Consistent with the results of the REACTIN algorithm, the top three most significant TFs with higher activity scores in ER+ than in ER- are ER alpha, FOXA1 and GATA3.

Bottom Line: REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets.When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer.Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire 03755, USA.

ABSTRACT

Background: Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.

Results: Here we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.

Conclusions: REACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.

Show MeSH
Related in: MedlinePlus