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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 relationship between survival time of patients with breast cancer and inferred E2F4 activity score. The Vijv dataset is used in the calculation. (a) Two breast cancer samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. (b) Distribution of E2F4 activity scores in the 260 samples. (c) The survival curves of patients with breast cancer. “E2F4>0” shows patients with positive E2F4 activity scores; “E2F4<0” shows patients with negative E2F4 activity scores. (d) The survival curves of four categories patients: ER+ & E2F4>0, ER+ & E2F4<0, ER- & E2F4>0 and ER- & E2F4<0. E2F4 activity score is inferred based on the Sydh_Helas3_E2F4 binding profile.
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Figure 5: The relationship between survival time of patients with breast cancer and inferred E2F4 activity score. The Vijv dataset is used in the calculation. (a) Two breast cancer samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. (b) Distribution of E2F4 activity scores in the 260 samples. (c) The survival curves of patients with breast cancer. “E2F4>0” shows patients with positive E2F4 activity scores; “E2F4<0” shows patients with negative E2F4 activity scores. (d) The survival curves of four categories patients: ER+ & E2F4>0, ER+ & E2F4<0, ER- & E2F4>0 and ER- & E2F4<0. E2F4 activity score is inferred based on the Sydh_Helas3_E2F4 binding profile.

Mentions: Out of the 260 breast cancer samples of the Vijv dataset, 151 have a positive E2F4 activity score and 101 have a negative E2F4 activity score. Figure 5a shows two samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. As shown, in the sample with a negative score genes with higher probability of being E2F4 regulated are more likely to have lower expression levels (i.e. biased to the right side), whereas in the sample with a positive score the opposite is observed. Distribution of E2F4 activity scores in the 260 samples is shown in Figure 5b. Patients with positive E2F4 activity scores (E2F4>0) demonstrate significantly shorter survival time than patients with negative E2F4 activity scores (E2F4<0) with a P-value of 4e-7 (Figure 5c). We note that the ER+ patients show significant longer survival time than ER- patients with P-value of 4e-6. In another words, the inferred activity score of E2F4 achieves better accuracy than ER status when predicting survival time of patients. Combining E2F4 activity score with ER status, we divided samples into four categories as shown in Figure 5d. In ER+ samples, patients with positive E2F4 activity still have significantly shorter survival time than those with negative E2F4 activity (P=1e-5). In ER- samples, the same trend can also be observed, although the difference is not significant due to small sample size.


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 relationship between survival time of patients with breast cancer and inferred E2F4 activity score. The Vijv dataset is used in the calculation. (a) Two breast cancer samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. (b) Distribution of E2F4 activity scores in the 260 samples. (c) The survival curves of patients with breast cancer. “E2F4>0” shows patients with positive E2F4 activity scores; “E2F4<0” shows patients with negative E2F4 activity scores. (d) The survival curves of four categories patients: ER+ & E2F4>0, ER+ & E2F4<0, ER- & E2F4>0 and ER- & E2F4<0. E2F4 activity score is inferred based on the Sydh_Helas3_E2F4 binding profile.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: The relationship between survival time of patients with breast cancer and inferred E2F4 activity score. The Vijv dataset is used in the calculation. (a) Two breast cancer samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. (b) Distribution of E2F4 activity scores in the 260 samples. (c) The survival curves of patients with breast cancer. “E2F4>0” shows patients with positive E2F4 activity scores; “E2F4<0” shows patients with negative E2F4 activity scores. (d) The survival curves of four categories patients: ER+ & E2F4>0, ER+ & E2F4<0, ER- & E2F4>0 and ER- & E2F4<0. E2F4 activity score is inferred based on the Sydh_Helas3_E2F4 binding profile.
Mentions: Out of the 260 breast cancer samples of the Vijv dataset, 151 have a positive E2F4 activity score and 101 have a negative E2F4 activity score. Figure 5a shows two samples with an E2F4 regulatory score of -41.5 and 5.95, respectively. As shown, in the sample with a negative score genes with higher probability of being E2F4 regulated are more likely to have lower expression levels (i.e. biased to the right side), whereas in the sample with a positive score the opposite is observed. Distribution of E2F4 activity scores in the 260 samples is shown in Figure 5b. Patients with positive E2F4 activity scores (E2F4>0) demonstrate significantly shorter survival time than patients with negative E2F4 activity scores (E2F4<0) with a P-value of 4e-7 (Figure 5c). We note that the ER+ patients show significant longer survival time than ER- patients with P-value of 4e-6. In another words, the inferred activity score of E2F4 achieves better accuracy than ER status when predicting survival time of patients. Combining E2F4 activity score with ER status, we divided samples into four categories as shown in Figure 5d. In ER+ samples, patients with positive E2F4 activity still have significantly shorter survival time than those with negative E2F4 activity (P=1e-5). In ER- samples, the same trend can also be observed, although the difference is not significant due to small sample size.

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