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Molecular Cancer Therapeutics
Molecular Cancer Therapeutics
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Cancer Biology and Translational Studies

Predicting Novel Therapies and Targets: Regulation of Notch3 by the Bromodomain Protein BRD4

Alejandro Villar-Prados, Sherry Y. Wu, Karem A. Court, Shaolin Ma, Christopher LaFargue, Mamur A. Chowdhury, Margaret I. Engelhardt, Cristina Ivan, Prahlad T. Ram, Ying Wang, Keith Baggerly, Cristian Rodriguez-Aguayo, Gabriel Lopez-Berestein, Shyh-Ming Yang, David J. Maloney, Makoto Yoshioka, Jeffrey W. Strovel, Jason Roszik and Anil K. Sood
Alejandro Villar-Prados
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
2School of Medicine, Medical Sciences Campus, University of Puerto Rico, San Juan, Puerto Rico.
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Sherry Y. Wu
3School of Biomedical Sciences, University of Queensland, Queensland, Australia.
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Karem A. Court
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Shaolin Ma
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Christopher LaFargue
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Mamur A. Chowdhury
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Margaret I. Engelhardt
4John P. and Kathrine G. McGovern Medical School, The University of Texas, Houston, Texas.
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Cristina Ivan
5Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
6Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Prahlad T. Ram
6Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas.
7Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Ying Wang
8Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Keith Baggerly
8Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Cristian Rodriguez-Aguayo
5Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
6Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Gabriel Lopez-Berestein
5Department of Experimental Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
6Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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Shyh-Ming Yang
9National Center for Advancing Translational Sciences, NIH, Rockville, Maryland.
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David J. Maloney
9National Center for Advancing Translational Sciences, NIH, Rockville, Maryland.
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Makoto Yoshioka
10ConverGene, Gaithersburg, Maryland.
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Jeffrey W. Strovel
10ConverGene, Gaithersburg, Maryland.
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Jason Roszik
11Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.
12Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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  • For correspondence: asood@mdanderson.org jroszik@mdanderson.org
Anil K. Sood
1Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.
6Center for RNA Interference and Non-Coding RNA, The University of Texas MD Anderson Cancer Center, Houston, Texas.
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  • For correspondence: asood@mdanderson.org jroszik@mdanderson.org
DOI: 10.1158/1535-7163.MCT-18-0365 Published February 2019
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    Figure 1.

    Determining BRD4 as a therapeutic avenue for targeting Notch3 in ovarian cancer with the TPT. A, Screenshot of the TPT demonstrating where the user chooses tumor type, gene of interest (BRD4), and correlate expression to potential downstream target (Notch3) based on RPPA data available for ovarian cancer. TPT can also demonstrate the importance of the downstream target, Notch3, in ovarian cancer by correlating mRNA expression levels with protein levels provided by RPPA as well as correlate protein level (RPPA) with copy number alterations. Blue box pop up summarizes the query results. The scale bar shown in this figure panel only pertains to the highlighted row in the heatmap and not to the entire heatmap displayed in the screen shot. B, GTEx analysis comparing expression of BRD4 in normal ovarian tissue (blue box) with that in ovarian cancer (red box). C, Survival curves for patients with ovarian cancer from The Cancer Genome Atlas datasets comparing low BRD4 expression (green curve) with high BRD4 expression (red curve) in ovarian cancer tumors (P < 0.02). Statistical analysis was done by applying the log-rank test in the “survival” R package. D, RPPA data heatmap expanded from Fig. 1A correlating BRD4 mRNA levels with protein expression levels of indicated proteins.

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    Figure 2.

    BRD4 inhibition using BETis has a beneficial therapeutic effect in ovarian cancer cells. A, Standard curve qRT-PCR analysis of relative BRD4 mRNA relative fold change levels in ovarian cancer cell lines compared with human isolated primary fallopian tube epithelial cells (FTE 4). Experiment done in two independent biological duplicates (****, P < 0.001). One-way ANOVA was used to test for significance. B, BRD4 protein levels in ovarian cancer cells relative to those of FTE 4. Experiment done in two independent biological duplicates. C and D, MTT viability assays of ovarian cancer cells treated for 72 hours with BETi CPI203 or CN210, respectively. Experiment done in three independent biological duplicates. E and F, Colony formation assay of ovarian cancer cells treated for 10 days with BETi CPI203 or CN210, respectively. Experiment done in three independent biological duplicates. G and H, EdU flow cytometry analysis of ovarian cancer cells treated with BETi CPI203 or CN210, respectively, for 72 hours. *, P < 0.05; **, P < 0.01, *** P < 0.001; ns, nonsignificant. Experiment done in three independent biological duplicates. Statistical significance was determined by conducting unpaired Student t test comparing the mean of EdU incorporation of vehicle control versus BETi-treated cells. Experiment done in three independent biological duplicates. I and J, Annexin V flow cytometry analysis of ovarian cancer cells treated with BETi CPI203 or CN210, respectively, for 72 hours. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, nonsignificant. Experiment done in three independent biological duplicates. Statistical significance was determined by conducting unpaired Student t test comparing the mean Annexin V staining of vehicle control versus BETi treatments.

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    Figure 3.

    BRD4 inhibition reduces tumor growth and prolongs survival. A, Schematic for CPI203 treatment in OVCAR5 tumor–bearing mice. B, Tumor nodules (white arrows) in CPI203- or PBS-treated mice. C, Mean tumor weights in PBS (n = 8) and CPI203-treated mice (n = 6); *, P < 0.05. Statistical significance was determined using Student t test for mean difference in tumor weights. D, IHC staining of OVCAR5 tumors for Ki67. Scale bar, 100 μm; N = 4 per group. E, Quantification of Ki67-positive cells from PBS- or CPI203-treated mice, n = 4 per group. Quantification was done using pictures of five random fields from OVCAR5 tumors. Statistical significance was determined using Student t test for mean difference in Ki67-positive cells. F, Timeline for doxycycline-inducible shBRD4 survival experiment. G, IVIS imaging of OVCAR5 tumor–bearing mice 21 days after shRNA induction. H, IHC staining for BRD4 in OVCAR5 tumor–bearing mice to confirm BRD4 knockdown in vivo. Scale bar, 50 μm. I, Quantification of BRD4-positive cells from tumors in H (**, P < 0.01; ***, P < 0.001). Quantification was done using pictures of five random fields from OVCAR5 tumors and statistical significance was determined using Student t test for mean difference in BRD4-positive cells (n = 3 per indicated group). J, Survival curve of each indicated group after shBRD4 induction (***, P < 0.001). Comparison was made to test for any significant difference between curves using log-rank (Mantel–Cox) test.

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    Figure 4.

    BRD4 inhibition decreases Notch3 expression in ovarian cancer. A, C, and E, Protein expression analysis at 72 hours of full-length (FL) and cleaved (C) Notch3 after BRD4 inhibition using a BETi (A and C) or siRNA-mediated knockdown (E). B, D, and F, qRT-PCR analysis of NOTCH3 mRNA levels after BRD4 inhibition using a BETi (B and D) or siRNA-mediated knockdown (F; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001; ns, nonsignificant). Statistical analysis was done by applying unpaired Student t test of mean fold change for vehicle control cells compared with BETi-treated cells. Experiments in (A–F) have been repeated at least in three independent biological replicates. G, Notch3 IHC staining of OVCAR 5 tumor–bearing mice treated with either vehicle control (n = 3) or CN210 (n = 3). H, Notch3 IHC staining of OVCAR 5 tumors after Control (n = 5) or BRD4 (n = 5) siRNA delivery by DOPC nanoliposomes.

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    Figure 5.

    BRD4 is present on the NOTCH3 promoter and impacts Notch3 downstream signaling. A, Schematic of the NOTCH3 promoter and transcription start site. B, Chromatin immunoprecipitation (ChIP) PCR reactions after BRD4 pulldown at the indicated gene promoter regions. Experiments repeated in three independent biological duplicates. C, ChIP qPCR of NOTCH3 transcription start site after BRD4 pulldown (*, P < 0.05). Experiments repeated in three independent biological duplicates. Statistical analysis was done by applying unpaired Student t test for mean fold enrichment of BRD4 over IgG control. D and E, NOTCH3 gene signature generated from RPPA data using NetWalker software. Fold change calculated on the basis of NormLog2 expression difference of DMSO-treated cells versus CPI203-treated cells. Samples were submitted as two independent biological duplicates. F and G, Western blot analysis of cells treated with BETis. Experiments repeated in two independent biological duplicates. H, qRT-PCR analysis of HES1 after BETi treatment. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001. Experiments repeated in three independent biological duplicates. Statistical analysis was done by applying unpaired Student t test of mean fold change for vehicle control cells compared with BETi-treated cells.

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    Figure 6.

    Summary model for use of BETis for targeting NOTCH3 in ovarian cancer. A, BRD4 promoting NOTCH3 transcription and driving ovarian cancer tumor growth. B, BETi inhibiting BRD4-mediated NOTCH3 transcription and inhibiting ovarian cancer tumor growth.

Additional Files

  • Figures
  • Supplementary Data

    • Supplementary Table 5 - Raw RPPA data for OVCAR 5.
    • Supplementary Table 4 - Raw RPPA data for OVCAR 4.
    • Supplementary Materials - File contains synthesis of CN210, sequences for siRNAs, supplementary tables 1 to 3, supplementary figures 1 through 8 and figure legends.
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Molecular Cancer Therapeutics: 18 (2)
February 2019
Volume 18, Issue 2
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Predicting Novel Therapies and Targets: Regulation of Notch3 by the Bromodomain Protein BRD4
Alejandro Villar-Prados, Sherry Y. Wu, Karem A. Court, Shaolin Ma, Christopher LaFargue, Mamur A. Chowdhury, Margaret I. Engelhardt, Cristina Ivan, Prahlad T. Ram, Ying Wang, Keith Baggerly, Cristian Rodriguez-Aguayo, Gabriel Lopez-Berestein, Shyh-Ming Yang, David J. Maloney, Makoto Yoshioka, Jeffrey W. Strovel, Jason Roszik and Anil K. Sood
Mol Cancer Ther February 1 2019 (18) (2) 421-436; DOI: 10.1158/1535-7163.MCT-18-0365

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Predicting Novel Therapies and Targets: Regulation of Notch3 by the Bromodomain Protein BRD4
Alejandro Villar-Prados, Sherry Y. Wu, Karem A. Court, Shaolin Ma, Christopher LaFargue, Mamur A. Chowdhury, Margaret I. Engelhardt, Cristina Ivan, Prahlad T. Ram, Ying Wang, Keith Baggerly, Cristian Rodriguez-Aguayo, Gabriel Lopez-Berestein, Shyh-Ming Yang, David J. Maloney, Makoto Yoshioka, Jeffrey W. Strovel, Jason Roszik and Anil K. Sood
Mol Cancer Ther February 1 2019 (18) (2) 421-436; DOI: 10.1158/1535-7163.MCT-18-0365
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