- Open Access
Narrowing the field: cancer-specific promoters for mitochondrially-targeted p53-BH3 fusion gene therapy in ovarian cancer
© The Author(s). 2019
- Received: 10 January 2019
- Accepted: 18 April 2019
- Published: 30 April 2019
Despite years of research, the treatment options and mortality rate for ovarian cancer remain relatively stagnant. Resistance to chemotherapy and high heterogeneity in mutations contribute to ovarian cancer’s lethality, including many mutations in tumor suppressor p53. Though wild type p53 gene therapy clinical trials failed in ovarian cancer, mitochondrially-targeted p53 fusion constructs, including a fusion with pro-apoptotic protein Bad, have shown much higher apoptotic potential than wild type p53 in vitro. Due to the inherent toxicities of mitochondrial apoptosis, cancer-specificity for the p53 fusion constructs must be developed. Cancer-specific promoters such as hTERT, hTC, Brms1, and Ran have shown promise in ovarian cancer.
Of five different lengths of hTERT promoter, the − 279/+ 5 length relative to the transcription start site showed the highest activity across a panel of ovarian cancer cells. In addition to − 279/+ 5, promoters hTC (an hTERT/CMV promoter hybrid), Brms1, and Ran were tested as drivers of mitochondrially-targeted p53-Bad and p53-Bad* fusion gene therapy constructs. p53-Bad* displayed cancer-specific killing in all ovarian cancer cell lines when driven by hTC, − 279/+ 5, or Brms1.
Cancer-specific promoters hTC, − 279/+ 5, and Brms1 all display promise in driving p53-Bad* gene therapy for treatment of ovarian cancer and should be moved forward into in vivo studies. -279/+ 5 displays lower expression levels in fewer cells, but greater cancer specificity, rendering it most useful for gene therapeutics with high toxicity to normal cells. hTC and Brms1 show higher transfection and expression levels with some cancer specificity, making them ideal for lowering toxicity in order to increase dose without as much of a reduction in the number of cancer cells expressing the gene construct. Having a variety of promoters available means that patient genetic testing can aid in choosing a promoter, thereby increasing cancer-specificity and giving patients with ovarian cancer a greater chance at survival.
- Ovarian cancer
- Gene therapy
- Cancer-specific promoters
- Mitochondrial targeting
Despite initial response to surgery and chemotherapy, high recurrence rates [1, 2] and difficulty in screening mean that ovarian cancer has a 5-year survival rate of only 47.4% . The most aggressive ovarian cancer subtype, high-grade serous carcinoma (HGSC), is also classified as the most lethal gynecological malignancy [4, 5]. Much of ovarian cancer’s lethality can be attributed to its high level of heterogeneity—over 15 different oncogenes can be mutated [6, 7], complicating targeted therapy—and ability to quickly develop resistance against chemotherapeutic agents. HGSC in particular often possesses resistance mutations such as increased ability to repair DNA damage and upregulated drug efflux pumps, as revealed by whole-genome sequencing of HGSC patients [6–8]. In particular, greater than 96% of HGSCs contain mutated p53—a key tumor suppressor protein—making loss of p53 function a key driver for HGSC development [6–8].
Recently, the Ran (Ras-related nuclear protein) and Brms1 (breast cancer metastasis suppressor 1) promoters were shown to display high activity—comparable to the CMV promoter—as well as cancer specificity in vivo in ovarian cancer (Bg-1 cells), prostate cancer (PC-3 cells) and breast cancer (MCF-7 cells) . Ran, a small GTPase, participates in nuclear transport and is a key player in mitosis that helps to form the mitotic spindle. The cancer-specificity of the Ran promoter is suspected to stem from Ran’s mitotic activity, as rapidly dividing tumor cells require greater amounts of mitotic factors . Brms1 inhibits tumor cell migration by suppressing NF-κB . Though the cancer-specific function of the Brms1 promoter may be somewhat surprising considering Brms1’s anti-tumorigenic function, if a cancer cell has not managed to mutate the Brms1 pathway then high Brms1 activity is logical .
Though the vast majority of HGSCs have p53 mutations [6–8], these mutations differ drastically between individual cases. Thus, testing cell lines with a variety of p53 mutation statuses is essential to ensure that p53-Bad/p53-Bad* fusion gene therapy will function regardless of p53 status. Skov3 cells contain a H179R mutation and do not express p53 mRNA, thus they are considered p53 null [22, 23]. Ovcar3 cells contain a R248Q mutation , which is a dominant negative mutation in the DNA binding domain of p53 . Kuramochi cells, considered to have one of the most closely matching genetic profiles to primary patient tumors of all commercially available ovarian cancer cell lines , contain a dominant negative D281Y mutation that likely causes aggregation of p53 [22, 26]. BJ cells, normal human fibroblasts immortalized with telomerase, contain functional wild type p53 .
Other groups have tested a variety of lengths of the hTERT promoter in various types of cancer, and the Ran and Brms1 promoters have been shown to be successful in several different cell lines. This paper aims to test these past successes in a panel of ovarian cancer cell lines with varying p53 statuses. This should result in discovery of the best promoter to impart both cancer specificity as well as potent apoptosis, regardless of p53 status, in ovarian cancer cell lines in preparation for a therapeutic that will be effective in a wide range of patient tumors.
Time point studies
Unlike small molecule therapies, gene therapeutics have a built-in specificity mechanism—promoters—that, if properly harnessed, can lead to a new age of cancer therapies with little to no toxic side effects. The hTERT promoter is one of the most universal cancer-specific promoters and has previously been shown to be ovarian cancer specific , though different studies have reported different lengths of hTERT to be the most successful promoter [28, 29]. To this end, five different lengths of hTERT (− 205/+ 55, − 27/+ 55 , − 279/+ 5 , − 408/+ 5 , and − 408/+ 55) and one hTERT/CMV enhancer fusion promoter (hTC)  were cloned and tested in three ovarian cancer cell lines (Skov3, p53 null; Ovcar3, p53 R248Q DN; Kuramochi, p53 D281Y DN) and one normal cell line (BJ, p53 wt). Promoter − 27/+ 55 was chosen because it was the minimal domain of hTERT reported as effective , promoter − 205/+ 55 because it contains both E boxes and all Sp1 binding sites (Fig. 3), promoters − 279/+ 5 and − 408/+ 5 because they were reported by Horikawa et al. to have the highest expression of all their tested hTERT promoters , and promoter − 408/+ 55 was designed to encompass all the area covered by the other promoters. hTC was chosen because it was reported to achieve higher activity than hTERT-only promoters by combining the − 400/+ 54 length of hTERT with a CMV enhancer (− 1017/− 901) as detailed in the literature . Because the gene therapy construct being developed by our lab is a p53-BH3 fusion construct, ovarian cancer cells were chosen for their varying p53 statuses: p53 null, p53 dominant negative (DN) mutant, and p53 DN/aggregation mutant for Skov3 , Ovcar3 , and Kuramochi , respectively. Additionally, Kuramochi cells are advantageous because they have the highest genetic similarity to primary HGSC tumors out of a large panel of ovarian cancer cell lines . Due to the lack of a commercially available normal human ovary cell line, normal human fibroblasts (BJ cell line, p53 wt27) were used to measure promoter activity in normal cells.
All five hTERT lengths as well as the hTC promoter and CMV promoter (as a positive control) were cloned into plasmids expressing EGFP and transfected into all four cell lines. A separate plasmid containing CMV was used as a control because when a bicistronic plasmid was used, the CMV and cancer-specific promoters interfered with one another. 48 h post-transfection, the CMV promoter (as expected) displayed the highest expression level in all four cell lines, with most cell lines following the general pattern of hTC displaying the next highest expression, then − 279/+ 5 and − 408/+ 5, followed by the other three hTERT promoters (Fig. 4). Skov3 cells veered somewhat from this pattern, displaying much higher transfection levels in general and significantly higher − 279/+ 5 expression compared to that of − 408/+ 5, with − 205/+ 55 and − 408/+ 55 displaying comparable expression levels to − 408/+ 5 (Fig. 4a). Because − 205/+ 55 and − 408/+ 55 both contain two E-boxes (c-myc binding sites) rather than one, it is possible that variations in c-myc expression could be the reason behind this effect; Kuramochi [33, 34] and Ovcar3  have both been reported to have normal c-myc expression, and Skov-3 has fairly normal c-myc expression but slightly higher than Ovcar3 .
When analyzed for cancer specificity compared with the normal BJ cell line, all of the hTERT promoters and the hTC promoter displayed significant cancer specificity compared to the CMV promoter (Fig. 5). The hTC promoter showed the highest expression levels in each ovarian cancer line but was not as cancer-specific as the hTERT-only promoters—normal BJ cells showed about 75% expression under hTC compared with Kuramochi (Fig. 5), but only about 30% expression under the hTERT-only promoters compared to Kuramochi (the closest cancer cell line to BJ in terms of expression level as well as the cell line most similar to primary patient ovarian tumors ). Thus, hTC shows the highest expression for a cancer-specific promoter, but the hTERT-only promoters show greater cancer-specificity. Based on these results, hTC and − 279/+ 5 (the highest expression amongst the hTERT-only promoter sections) were chosen to move on.
In addition to testing the well-known hTERT promoter, we also wanted to test newly reported ovarian cancer-specific promoters Ran and Brms1 . After cloning Ran and Brms1 into an EGFP plasmid, they were tested compared to CMV, hTC, and − 279/+ 5. EGFP expression under each promoter was measured at 24 h (hence the slightly lower expression numbers compared with the 48-h hTERT data) in anticipation of the 24 h time point used for the TMRE early apoptosis assay. Once again, hTC showed vastly higher expression levels than the other cancer-specific promoters in every cell line (Fig. 7). Interestingly, the relative expression levels of − 279/+ 5, Ran, and Brms1 differed between the three ovarian cancer cell lines, with Brms1 showing clear superiority in Kuramochi cells (Fig. 7c), Ran and Brms1 significantly outstripping − 279/+ 5 in Skov3 cells (Fig. 7a), and a relatively close race between − 279/+ 5 and Brms1 in Ovcar3 cells (Fig. 7b). All four promoters showed cancer specificity compared to the normal BJ cells with the exception of − 279/+ 5 and Ran in Kuramochi cells (Fig. 8). Because − 279/+ 5 previously showed cancer specificity in Kuramochi cells at 48 h (Fig. 5), it was decided to move all four promoters forward to test whether they displayed cancer-specific killing when used to drive expression of the p53-Bad gene constructs.
hTC, − 279/+ 5, Ran, and Brms1 were all cloned into plasmids expressing p53-Bad and p53-Bad*, constructs recently shown by our lab to cause potent apoptosis in ovarian cancer cell lines (data unpublished, submission under review). 24 h post-transfection, cells were assayed for mitochondrial outer membrane permeabilization, an indicator of early mitochondrial apoptosis. Cells were gated for proper morphology and then for EGFP, as p53-Bad and p53-Bad* both have a GFP protein fused to their N-terminus for detection. GFP-positive cells with TMRE are considered to be in the early stages of mitochondrial apoptosis. The − 279/+ 5 promoter displayed significant cancer-specific killing across all cell lines compared to CMV for both p53-Bad and p53-Bad*, though p53-Bad* showed higher killing overall—particularly in Skov3 cells—as well as a greater difference between cancer cells and normal cells (Fig. 10). This shows the potential to use − 279/+ 5 in combination with p53-Bad* for future treatments, as such a high degree of specificity would allow for much higher doses of therapeutic without toxic side effects for patients. The hTC and Brms1 promoters show fairly similar results; both show cancer-specific killing for both p53-Bad and p53-Bad* in all three cancer cell lines compared with normal cells, though the degree of specificity is not as high as in − 279/+ 5 (Figs. 9 and 11). This may be counter-balanced by the higher expression levels seen from Brms1 and hTC driven constructs, however—in practice it may be more practical to use a therapeutic that is slightly less specific but more likely to be expressed in more cancer cells. Additionally, p53-Bad* caused similar or more apoptosis compared with p53-Bad when controlled by cancer-specific promoters, with the exception of hTC in Ovcar3 cells, which showed much higher killing with p53-Bad than p53-Bad* (Fig. 9). Overall, these results indicate that − 279/+ 5, Brms1, and hTC as promoters expressing p53-Bad* should all be moved forward into in vivo studies in order to find the optimum balance of specificity and potency, while Ran—which had too low of expression levels for accurate measurement of MOMP—can be eliminated as a possible promoter. Each of the three promoters may be best suited to different functions, with − 279/+ 5 being ideal for applications where very high specificity is desired, such as a gene therapy with particularly toxic side effects. hTC, which had the highest overall transfection levels, may be ideal for a drug with lower toxicity, and Brms1 may represent a balance between the two.
Transfection levels of p53-Bad/p53-Bad* constructs were lower than EGFP, and cancer-specific promoters throughout this study displayed lower numbers of cells expressing the construct of interest compared to the CMV promoter. The lower expression levels from cancer-specific promoters can easily be explained by the relative strengths of the promoters, but the difference in EGFP versus p53-Bad/p53-Bad* expression was explored further through a series of time point studies at 8, 12, and 24 h. CMV-GFP expression stayed steady across all time points for Skov3 (Fig. 12a) and BJ (Fig. 12d), peaked at 12 h for Ovcar3 (Fig. 12b), and peaked at 12 h/stayed steady at 24 h for Kuramochi (Fig. 12c). In distinct contrast, CMV-p53-Bad* expression peaked at 8 h for all 4 cell lines (results were not significant in BJ cells), dropping to much lower levels by 24 h (Fig. 12a-d). This indicates that p53-Bad* inherently either expresses earlier than GFP or simply kills quickly enough for many transfected cells to no longer be detectable at later time points. This makes apoptosis difficult to measure, but shows promise for future in vivo experiments, where overall tumor death can be measured. Interestingly, the overall number of BJ cells expressing p53-Bad* are much lower relative to those expressing GFP than in the other cell lines, indicating that there may be some amount of natural cancer specificity inherent in the p53-Bad* construct as well as in the cancer-specific promoters, making moving this construct forward into in vivo studies even more promising.
Overall, 8 cancer-specific promoters were tested in 4 different cell lines, with − 279/+ 5, hTC, and Brms1 being recommended for further use driving the p53-Bad* construct in in vivo ovarian cancer studies. -279/+ 5 is the most cancer-specific, but may be limited by low expression levels. hTC and Brms1 show slightly less cancer specificity but higher expression levels, with hTC showing the highest expression levels. Future experiments will explore the efficacy of these promoters with p53-Bad* in vivo to determine the best balance between expression levels and cancer specificity for this gene therapy construct, but all three promoters show promise as ovarian cancer-specific gene therapy drivers. -279/+ 5 may be ideal for highly toxic gene therapies, as it has particularly high cancer specificity, while hTC and Brms1 may work best for gene constructs that require less lowering of toxic side effects for normal cells—their higher expression levels would increase the reach of the gene constructs they drive. Additionally, in this new age of personalized medicine, having several promoters available means that genetic testing of a patient’s tumor could lead to choosing the promoter that best targets their particular disease; having several options ready to go means more patients have a greater chance of survival. Eventually, this area of research could lead to drastically lowered toxic side effects for patients, making it possible to increase therapeutic doses to levels that will better destroy cancer cells and thus improve overall survival for countless patients.
Various hTERT promoter lengths were cloned into an EGFP plasmid with self-designed primer sets using the transcription initiation site reported in Horikawa et al.29 and the pAdv/TERT vector (AddGene) as a template. Primer sets are as follows. -205/+ 55: TAGTTATTAATCCCAGGACCGCGCTCCCCAC (forward) and TTATATGCTAGCGGATCGCGGGGGTGGCCG (reverse) -27/+ 55: TAGTTATTAATGCCCTCTCCTCGCGGCGCGAG (forward) and GGTAGCGCTAGCGGATCGCGGGGGTGGCCGGGGC -279/+ 5: TAGTTATTAATATACGCGTTGGCCCCTC (forward) and GGTAGCGCTAGCGGATGCTGCCTGAAACTCGCG (reverse) -408/+ 5: TAGTTATTAATGACCCCCGGGTCCG (forward) and GGTAGCGCTAGCGGATGCTGCCTGAAACTCGCG (reverse) -408/+ 55: CTAAGCTATTAATCGATACGCGTTGGCCCCTC (forward) and GTAATGAGCTAGCTAGGCGGGGGTGGCCGGG (reverse). For hTC , a Geneblock (Genewiz) containing hTERT − 400/+ 54 and CMV − 1017/− 901 was ordered with AseI and AgeI cut sites on the ends, then digested and cloned into the EGFP plasmid. -279/+ 5 and hTC were cloned into a plasmid containing p53-Bad* using primer set TAGTTATTAATGACCCCCGG (forward)/ TACATACCGGTGCTGCCTGAAAC (reverse) for − 279/+ 5 and the digested Geneblock from before for hTC.
Brms1 and Ran were cloned using nested PCR . Brms1 PCR was performed using primers CACGACGGAGATTCCCTGAG and CCGCATGCCCATGAACAAAA for the first round, and primers GTGTGTATTAATGCTAGCTCCCTCCCCTAATCTGAGAA and ATATATACCGGTACGGAGATTCCCTGAGA for the second round using the first round product as template. First round Ran PCR used primers ATTTGCGTCACTGGGGTTCC and GAGCGGAGGATGAAACGGGG, while the second round primers were GTGTGTATTAATGCTAGCACGCGTCCAGACTGCAAACA and ATATATACCGGTCGCGATACCTTCCAGAA. Genomic DNA extracted from BJ (normal human fibroblast) cells with the DNeasy Blood and Tissue Kit (Qiagen) was used as a template for both Brms1 and Ran.
Cell maintenance, seeding and transfection
Skov3 human ovarian adenocarcinoma (a kind gift from Dr. Shawn Owen, University of Utah), Kuramochi human ovarian carcinoma (JCRB Cell Bank, Japan), Ovcar3 human ovarian adenocarcinoma (ATCC, Manassas, VA) and BJ normal human fibroblasts (ATCC, Manassas, VA) were all grown in flasks in monolayers and maintained with DMEM (Skov3) or RPMI (Ovcar3, Kuramochi, and BJ) media supplemented with FBS (10% for Skov3/Kuramochi, 20% for Ovcar3/BJ), 1% l-glutamine (Corning), and 1% penicillin-streptomycin (Gibco). Additionally, Ovcar3 cells were supplemented with 0.01 mg/ml bovine insulin (Sigma Aldrich). Cells were seeded in CellBIND 6-well plates (Sigma Aldrich) for assays or 2-well viewing chambers (Thermo Fisher Scientific) for microscopy at varying concentrations in order to account for the difference in growth rates between cell lines. The approximate number of cells seeded per well are as follows: 200,000 cells/well for Skov3, 300,000 cells/well for Ovcar3 and Kuramochi, and 500,000 cells/well for BJ. 24 h after seeding, cells were transfected with 1 pmol of DNA per well using the JetPrime transfection reagent (PolyPlus Transfection) according to manufacturer instructions.
48 h after transfection, cell media was removed and replaced with PBS. Images were taken using NIS software on a Nikon A1R or Olympus FV1000 fluorescence confocal microscope with a 40x objective with a basic GFP filter.
GFP expression assay
24 (Top four promoter experiments) or 48 (hTERT promoter experiments) hours after transfection, cell media was collected and cells were washed with PBS, then harvested with 0.25% Trypsin EDTA (ThermoFisher Scientific). Cells were then pelleted by centrifugation at 1000 RPM for 5 min, after which media was aspirated and cells were re-suspended in 300 μl PBS. The percentage of cells positive for GFP were measured on the FACSCanto-II (BD-Biosciences, University of Utah Flow Cytometry Core), where they were gated for proper morphology as well as GFP (excitation 488 nm, emission filter 530/35) and analyzed with FACSDiva software.
As done previously [36, 37], cell media was collected 24 h after transfection and cells were washed with PBS then harvested with 0.25% Trypsin EDTA (ThermoFisher Scientific). Cells were then pelleted by centrifugation at 1000 RPM for 5 min, after which media was aspirated and cells were re-suspended in 300 μl of 100 nM tetramethylrhodamine ethyl ester (TMRE, Invitrogen) in 1x Annexin-V buffer (Invitrogen). Cells were incubated at 37 degrees C for 30–45 min, then analyzed on the FACSCanto-II (BD-Biosciences, University of Utah Flow Cytometry Core), where they were gated for proper morphology, GFP (excitation 488 nm, emission filter 530/35), and TMRE (excitation 561, emission filter 585/15) with FACSDiva software.
All statistical analysis was performed using GraphPad Prism version 5.01 for Windows, GraphPad Software, La Jolla California USA, http://www.graphpad.com. For Figs. 4 and 7, one-way ANOVA followed by Tukey’s post-test were performed on the data. For Figs. 5, 8, 9, 10, 11, and 12, two-way ANOVA followed by Bonferroni’s post-test were performed on the data.
We would like to acknowledge funding from The National Institutes of Health Award Number R21-CA187289 as well as The Center for Genomic Innovation and the College of Pharmacy for a Catalyst Grant. The research in this publication was also aided by the University of Utah DNA/Peptide Core, the Flow Cytometry Core (National Center for Research Resources of the National Institutes of Health Award Number 1S10RR026802-01 and National Cancer Institute Award Number 5P30CA042014-24), and the Microscopy Core Facility (National Center for Research Resources Share Equipment Grant #1S10RR024761-01).
This publication was supported by award number NIH CA187289 from the National Institutes of Health.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
KRB cloned the constructs, collected and analyzed the data, and wrote/edited the majority of the manuscript. JHK assisted in collection and analysis of the data. CL helped design experiments, outline the manuscript, and edit the manuscript. All authors read and approved the final manuscript.
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The authors declare that they have no competing interests.
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- Luvero D, Milani A, Ledermann JA. Treatment options in recurrent ovarian cancer: latest evidence and clinical potential. Ther Adv Med Oncol. 2014;6(5):229–39.View ArticleGoogle Scholar
- Wojnarowicz PM, Oros KK, Quinn MC, Arcand SL, Gambaro K, Madore J, et al. The genomic landscape of TP53 and p53 annotated high grade ovarian serous carcinomas from a defined founder population associated with patient outcome. PLoS One. 2012;7(9):e45484.View ArticleGoogle Scholar
- National Cancer Institute. Bethesda M. SEER Cancer Stat Facts: Ovarian Cancer. Available from: https://seer.cancer.gov/statfacts/html/ovary.html. Accessed 15 Dec 2018
- Chen H-J, Huang R-L, Liew P-L, Su P-H, Chen L-Y, Weng Y-C, et al. GATA3 as a master regulator and therapeutic target in ovarian high-grade serous carcinoma stem cells. Int J Cancer. 2018;143(12):3106–19.View ArticleGoogle Scholar
- Dao F, Schlappe BA, Tseng J, Lester J, Nick AM, Lutgendorf SK, et al. Characteristics of 10-year survivors of high-grade serous ovarian carcinoma. Gynecol Oncol. 2016;141(2):260–3.View ArticleGoogle Scholar
- Bast RC Jr, Hennessy B, Mills GB. The biology of ovarian cancer: new opportunities for translation. Nat Rev Cancer. 2009;9(6):415–28.View ArticleGoogle Scholar
- Cancer Genome Atlas Research N. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15.View ArticleGoogle Scholar
- Vang R, Shih Ie M, Kurman RJ. Ovarian low-grade and high-grade serous carcinoma: pathogenesis, clinicopathologic and molecular biologic features, and diagnostic problems. Adv Anat Pathol. 2009;16(5):267–82.View ArticleGoogle Scholar
- Zeimet AG, Marth C. Why did p53 gene therapy fail in ovarian cancer? Lancet Oncol. 2003;4(7):415–22.View ArticleGoogle Scholar
- Erster S, Mihara M, Kim RH, Petrenko O, Moll UM. In vivo mitochondrial p53 translocation triggers a rapid first wave of cell death in response to DNA damage that can precede p53 target gene activation. Mol Cell Biol. 2004;24(15):6728–41.View ArticleGoogle Scholar
- Vaseva AV, Moll UM. The mitochondrial p53 pathway. Biochim Biophys Acta. 2009;1787(5):414–20.View ArticleGoogle Scholar
- Matissek KJ, Okal A, Mossalam M, Lim CS. Delivery of a monomeric p53 subdomain with mitochondrial targeting signals from pro-apoptotic Bak or Bax. Pharm Res. 2014;31(9):2503–15.View ArticleGoogle Scholar
- Delbridge AR, Strasser A. The BCL-2 protein family, BH3-mimetics and cancer therapy. Cell Death Differ. 2015;22(7):1071–80.View ArticleGoogle Scholar
- Shamas-Din A, Brahmbhatt H, Leber B, Andrews DW. BH3-only proteins: orchestrators of apoptosis. Biochim Biophys Acta. 2011;1813(4):508–20.View ArticleGoogle Scholar
- Datta SR, Katsov A, Hu L, Petros A, Fesik SW, Yaffe MB, et al. 14-3-3 proteins and survival kinases cooperate to inactivate BAD by BH3 domain phosphorylation. Mol Cell. 2000;6(1):41–51.View ArticleGoogle Scholar
- Kyo S, Takakura M, Fujiwara T, Inoue M. Understanding and exploiting hTERT promoter regulation for diagnosis and treatment of human cancers. Cancer Sci. 2008;99(8):1528–38.View ArticleGoogle Scholar
- Maraei AAM, Hatta AZ, Shiran MS, Tan GC. Human telomerase reverse transcriptase expression in ovarian tumors. Indian J. Pathol. Microbiol. 2012;55(2):187.View ArticleGoogle Scholar
- Ramlee MK, Wang J, Toh WX, Li S. Transcription regulation of the human telomerase reverse transcriptase (hTERT) gene. Genes. 2016;7(8):50.View ArticleGoogle Scholar
- Reyes-González JM, Armaiz-Peña GN, Mangala LS, Valiyeva F, Ivan C, Pradeep S, et al. Targeting c-MYC in platinum-resistant ovarian Cancer. Mol Cancer Ther. 2015;14(10):2260–9.View ArticleGoogle Scholar
- Chen Y-J, Campbell HG, Wiles AK, Eccles MR, Reddel RR, Braithwaite AW, et al. PAX8 regulates telomerase reverse transcriptase and telomerase RNA component in Glioma. Cancer Res. 2008;68(14):5724.View ArticleGoogle Scholar
- Chen X, Scapa JE, Liu DX, Godbey WT. Cancer-specific promoters for expression-targeted gene therapy: ran, brms1 and mcm5. J Gene Med. 2016;18(7):89–101.View ArticleGoogle Scholar
- Yaginuma Y, Westphal H. Abnormal Structure And Expression Of The P53 Gene In Human Ovarian-Carcinoma Cell-Lines. Cancer Res. 1992;52(15):4196–9.PubMedGoogle Scholar
- Mullany LK, Wong K-K, Marciano DC, Katsonis P, King-Crane ER, Ren YA, et al. Specific TP53 mutants overrepresented in ovarian Cancer impact CNV, TP53 activity, responses to Nutlin-3a, and cell survival. Neoplasia (New York, NY). 2015;17(10):789–803.View ArticleGoogle Scholar
- Soragni A, Janzen DM, Johnson LM, Lindgren AG, Nguyen AT-Q, Tiourin E, et al. A designed inhibitor of p53 aggregation rescues p53 tumor suppression in ovarian carcinomas. Cancer Cell. 2016;29(1):90–103.View ArticleGoogle Scholar
- Domcke S, Sinha R, Levine DA, Sander C, Schultz N. Evaluating cell lines as tumour models by comparison of genomic profiles. Nat Commun. 2013;4:2126.View ArticleGoogle Scholar
- Lu P, Vander Mause ER, Bowman KER, Brown SM, Lim CS. Mitochondrially targeted p53 is superior to wild type p53 in ovarian Cancer cells even with strong dominant negative mutant p53. Journal of Ovarian Research. Submitted December 2018.Google Scholar
- Morales CP, Ouellette M, Kaur KJ, Yan Y, White MA, Wright WE, et al. Absence of cancer-associated changes in human fibroblasts immortalized with telomerase. Nat Genet. 1999;21(1):115–8.View ArticleGoogle Scholar
- Takakura M, Kyo S, Kanaya T, Hirano H, Takeda J, Yutsudo M, et al. Cloning of human telomerase catalytic subunit (hTERT) gene promoter and identification of proximal core promoter sequences essential for transcriptional activation in immortalized and cancer cells. Cancer Res. 1999;59(3):551–7.PubMedGoogle Scholar
- Horikawa I, Cable PL, Afshari C, Barrett JC. Cloning and characterization of the promoter region of human telomerase reverse transcriptase gene. Cancer Res. 1999;59(4):826–30.PubMedGoogle Scholar
- Davis JJ, Wang L, Dong F, Zhang L, Guo W, Teraishi F, et al. Oncolysis and suppression of tumor growth by a GFP-expressing Oncolytic adenovirus controlled by an hTERT and CMV hybrid promoter. Cancer Gene Ther. 2006;13(7):720–3.View ArticleGoogle Scholar
- Braunstein I, Cohen-Barak O, Shachaf C, Ravel Y, Yalon-Hacohen M, Mills GB, Tzukerman M, Skorecki KL. Human telomerase reverse transcriptase promoter regulation in Normal and malignant human ovarian epithelial cells. Cancer Res 2001;61:5529–5536.Google Scholar
- Elias KM, Emori MM, Papp E, MacDuffie E, Konecny GE, Velculescu VE, et al. Beyond genomics: critical evaluation of cell line utility for ovarian cancer research. Gynecol Oncol. 2015;139(1):97–103.View ArticleGoogle Scholar
- COSMIC [Available from: cancer.sanger.ac.uk. Accessed 15 Dec 2018
- Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, et al. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2017;45(D1):D777–D83.View ArticleGoogle Scholar
- Russo P, Arzani D, Trombino S, Falugi C. C-myc down-regulation induces apoptosis in human cancer cell lines exposed to RPR-115135 (C31H29NO4), a non-peptidomimetic farnesyltransferase inhibitor. J Pharmacol Exp Ther. 2003;304(1):37.View ArticleGoogle Scholar
- Okal A, Matissek KJ, Matissek SJ, Price R, Salama ME, Janát-Amsbury MM, et al. Re-engineered p53 activates apoptosis in vivo and causes primary tumor regression in a dominant negative breast Cancer Xenograft model. Gene Ther. 2014;21(10):903–12.View ArticleGoogle Scholar
- Okal A, Mossalam M, Matissek KJ, Dixon AS, Moos PJ, Lim CS. A chimeric p53 evades mutant p53 Transdominant inhibition in Cancer cells. Mol Pharm. 2013;10(10):3922–33.View ArticleGoogle Scholar
- Vaseva AV, Moll UM. The mitochondrial p53 pathway. BBA -Bioenergetics. 2009;1787(5):414–20.View ArticleGoogle Scholar
- Yang E, Zha J, Jockel J, Boise LH, Thompson CB, Korsmeyer SJ. Bad, a heterodimeric partner for Bcl-XL and Bcl-2, displaces Bax and promotes cell death. Cell. 1995;80(2):285–91.View ArticleGoogle Scholar