E2F4 regulatory program predicts patient survival prognosis in breast cancer

Khaleel, Sari; Andrews, Erik H.; Ung, Matthew; DiRenzo, James; Cheng, Chao

doi:10.1186/s13058-014-0486-7

Cited by 48 publications

(59 citation statements)

References 57 publications

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“…We have previously developed a method to identify transcriptional regulatory programs that are predictive of cancer prognosis (29, 30). A regulatory program consists of transcription factors and its target genes identified by ChIP-chip or ChIP-seq experiments (31, 32).…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to multigene prognostic signatures identified by supervised models, regulatory programs are defined on the basis of prior knowledge learned from ChIP-seq/chip data. We have previously identified an E2F4 regulatory program that showed robust power in predicting the survival of breast cancer patients (29). E2F4 is a member of the E2F transcription factor family, which plays critical roles in cell-cycle progression and differentiation (34).…”

Section: Introductionmentioning

confidence: 99%

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E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Cheng

Varn

Marsit

2015

Molecular Cancer Research

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Bladder cancer is a common malignant disease, with non–muscle-invasive bladder cancer (NMIBC) representing the majority of tumors. This cancer subtype is typically treated by transurethral resection. In spite of treatment, up to 70% of patients show local recurrences. Intravesical BCG (Bacillus Calmette-Guerin) immunotherapy has been widely used to treat NMIBC, but it fails to suppress recurrence of bladder tumors in up to 40% of patients. Therefore, the development of prognostic markers is needed to predict the progression of bladder cancer and the efficacy of intravesical BCG treatment. This study demonstrates the effectiveness of an E2F4 signature for prognostic prediction of bladder cancer. E2F4 scores for each sample in a bladder cancer expression dataset were calculated by summarizing the relative expression levels of E2F4 target genes identified by ChIP-seq, and then the scores were used to stratify patients into good- and poor-outcome groups. The molecular signature was investigated in a single bladder cancer dataset and then its effectiveness was confirmed in two meta-bladder datasets consisting of specimens from multiple independent studies. These results were consistent in different datasets and demonstrate that the E2F4 score is predictive of clinical outcomes in bladder cancer, with patients whose tumors exhibit an E2F4 score >0 having significantly shorter survival times than those with an E2F4 score <0, in both non–muscle-invasive, and muscle-invasive bladder cancer. Furthermore, although intravesical BCG immunotherapy can significantly improve the clinical outcome of NMIBC patients with positive E2F4 scores (E2F4>0 group), it does not show significant treatment effect for those with negative scores (E2F4<0 group). Implications The E2F4 signature can be applied to predict the progression/recurrence and the responsiveness of patients to intravesical BCG immunotherapy in bladder cancer.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Cheng

Varn

Marsit

2015

Molecular Cancer Research

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“…We and other researchers have shown the prognostic value of E2F4 in breast cancer (21, 39) and bladder cancer (40). Molina-Privado et al have investigated its function in Burkitt lymphoma tumorigenesis, and found reduced protein but not mRNA levels of E2F4 in sporadic Burkitt lymphoma cell lines and tumor samples (41).…”

Section: Discussionmentioning

confidence: 69%

“…We have previously developed computational methods, REACTIN (Regulatory Activity Inference) and BASE (Binding Association with Sorted Expression), to investigate the regulatory programs associated with cancer (19-21). Both of the methods integrate gene expression data with ChIP-seq transcription factor (TF) binding data with the rationale that the regulatory activity of TFs can be reflected by the expression of their target genes.…”

Section: Introductionmentioning

confidence: 99%

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

Cheng

Lou

Andrews

et al. 2016

Molecular Cancer Research

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Liposarcoma is the second most common form of sarcoma, which has been categorized into four molecular subtypes, which are associated with differential prognosis of patients. However, the transcriptional regulatory programs associated with distinct histological and molecular subtypes of liposarcoma have not been investigated. This study uses integrative analyses to systematically define the transcriptional regulatory programs associated with liposarcoma. Likewise, computational methods are used to identify regulatory programs associated with different liposarcoma subtypes as well as programs that are predictive of prognosis. Further analysis of curated gene sets was used to identify prognostic gene signatures. The integration of data from a variety sources including gene expression profiles, transcription factor (TF) binding data from ChIP-seq experiments, curated gene sets, and clinical information of patients indicated discrete regulatory programs (e.g., controlled by E2F1 and E2F4) with significantly different regulatory activity in one or multiple subtypes of liposarcoma with respect to normal adipose tissue. These programs were also shown to be prognostic, wherein liposarcoma patients with higher E2F4 or E2F1 activity associated with unfavorable prognosis. A total of 259 gene sets were significantly associated with patient survival in liposarcoma, among which >50% are involved in cell cycle and proliferation.

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“…REPA's predictions associated 58 TFs with the gene sets identified as differentially expressed in the breast cancer expression profiling study. Out of these 58 TFs, 50 (or 86%) have previously been directly linked to breast cancer; namely, AP2ALPHA [85], AP2GAMMA [85], ATF1 [86], ATF2 [87], ATF3 [88], BAF155 [89], BCL3 [90], BCLAF1 [91], BHLHE40 [92], [93], BRCA1 [94], CBX3 [95], CEBPB [96], CJUN [97], COREST [98], CREB1 [99], E2F4 [100], E2F6 [61], EBF [101], ELF1 [102], ELK1 [103], ETS1 [104], FOXM1 [105], GABP [106], GR [107], HEY1 [108], IKZF1 [109], INI1 [110], KAP1 [111], MAX [112], MBD4 [113], MTA3 [114], MXI1 [115], MYBL2 [116], NFATC1 [117], NFIC [118], NRSF [119], PAX5 [120], PML [121], POL2 [122] RUNX3 [123], SMC3 [124], SP1 [125], SP4 [126], STAT3 [127], STAT5A …”

Section: Differentially Expressed Gene Sets Repa's Predicted Putativementioning

confidence: 99%

REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data

Patra

Izawa

Peña‐Castillo

2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Pathway analysis has been extensively applied to aid in the interpretation of the results of genome-wide transcription profiling studies, and has been shown to successfully find associations between the biological phenomena under study and biological pathways. There are two widely used approaches of pathway analysis: over-representation analysis, and gene set analysis. Recently genome-wide transcription factor binding data has become widely available allowing for the application of pathway analysis to this type of data. In this work, we developed regulatory enrichment pathway analysis (REPA) to apply gene set analysis to genome-wide transcription factor binding data to infer associations between transcription factors and biological pathways. We used the transcription factor binding data generated by the ENCODE project, and gene sets from the Molecular Signatures and KEGG databases. Our results showed that 54 percent of the predictions examined have literature support and that REPA's recall is roughly 54 percent. This level of precision is promising as several of REPA's predictions are expected to be novel and can be used to guide new research avenues. In addition, the results of our case studies showed that REPA enhances the interpretation of genome-wide transcription profiling studies by suggesting putative regulators behind the observed transcriptional responses.

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E2F4 regulatory program predicts patient survival prognosis in breast cancer

Cited by 48 publications

References 57 publications

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

E2F4 Program Is Predictive of Progression and Intravesical Immunotherapy Efficacy in Bladder Cancer

Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value

REPA: Applying Pathway Analysis to Genome-Wide Transcription Factor Binding Data

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