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In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
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David W. James,
Jetzabel Garcia,
Kadie Edwards ,
Lavinia Margarit,
Nagindra Das,
Kerryn Lutchman-Singh,
Amy L. Beynon,
Inmaculada Rioja,
Rab K. Prinjha,
Nicola R. Harker,
Deyarina Gonzalez,
Steve Conlan ,
Lewis Francis
British Journal of Cancer, Volume: 129, Issue: 1, Pages: 163 - 174
Swansea University Authors:
Amy Johnson, Kadie Edwards , Steve Conlan , Lewis Francis
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© The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0).
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DOI (Published version): 10.1038/s41416-023-02274-2
Abstract
Background: Epigenomic dysregulation has been linked to solid tumour malignancies, including ovarian cancers. Profiling of re-programmed enhancer locations associated with disease has the potential to improve stratification and thus therapeutic choices. Ovarian cancers are subdivided into histologic...
| Published in: | British Journal of Cancer |
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| ISSN: | 0007-0920 1532-1827 |
| Published: |
Springer Science and Business Media LLC
2023
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63224 |
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| Abstract: |
Background: Epigenomic dysregulation has been linked to solid tumour malignancies, including ovarian cancers. Profiling of re-programmed enhancer locations associated with disease has the potential to improve stratification and thus therapeutic choices. Ovarian cancers are subdivided into histological subtypes that have significant molecular and clinical differences, with high-grade serous carcinoma representing the most common and aggressive subtype. Methods: We interrogated the enhancer landscape(s) of normal ovary and subtype-specific ovarian cancer states using publicly available data. With an initial focus on H3K27ac histone mark, we developed a computational pipeline to predict drug compound activity based on epigenomic stratification. Lastly, we substantiated our predictions in vitro using patient-derived clinical samples and cell lines. Results: Using our in silico approach, we highlighted recurrent and privative enhancer landscapes and identified the differential enrichment of a total of 164 transcription factors involved in 201 protein complexes across the subtypes. We pinpointed SNS-032 and EHMT2 inhibitors BIX-01294 and UNC0646 as therapeutic candidates in high-grade serous carcinoma, as well as probed the efficacy of specific inhibitors in vitro. Conclusion: Here, we report the first attempt to exploit ovarian cancer epigenomic landscapes for drug discovery. This computational pipeline holds enormous potential for translating epigenomic profiling into therapeutic leads. |
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| Keywords: |
Ovarian cancer, ovarian tumorigenesis, epigenomic landscapes, SNS-032, EHMT2, inhibitors |
| College: |
Faculty of Medicine, Health and Life Sciences |
| Funders: |
Welsh Government and European Development Fund (2017/COL004 and 2017/COL/001) |
| Issue: |
1 |
| Start Page: |
163 |
| End Page: |
174 |