In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer

Johnson, Amy; Quintela, Marcos; James, David W.; Garcia, Jetzabel; Edwards, Kadie; Margarit, Lavinia; Das, Nagindra; Lutchman-Singh, Kerryn; Beynon, Amy L.; Rioja, Inmaculada; Prinjha, Rab K.; Harker, Nicola R.; Gonzalez, Deyarina; Conlan, Steve; Francis, Lewis

doi:10.1038/s41416-023-02274-2

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In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer

Amy Johnson, Marcos Quintela

, 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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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...

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Published in:	British Journal of Cancer
ISSN:	0007-0920 1532-1827
Published:	Springer Science and Business Media LLC 2023
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URI:	https://cronfa.swan.ac.uk/Record/cronfa63224
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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. 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spelling	v2 63224 2023-04-21 In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer cd71e22a01d9a5d7e46cd8ef0fc28da1 Amy Johnson Amy Johnson true false 76d053c090d5064ae9558888f7985e92 0000-0002-1359-0359 Kadie Edwards Kadie Edwards true false 0bb6bd247e32fb4249de62c0013b51cb 0000-0002-2562-3461 Steve Conlan Steve Conlan true false 10f61f9c1248951c1a33f6a89498f37d 0000-0002-7803-7714 Lewis Francis Lewis Francis true false 2023-04-21 FGMHL 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. Journal Article British Journal of Cancer 129 1 163 174 Springer Science and Business Media LLC 0007-0920 1532-1827 Ovarian cancer, ovarian tumorigenesis, epigenomic landscapes, SNS-032, EHMT2, inhibitors 27 7 2023 2023-07-27 10.1038/s41416-023-02274-2 http://dx.doi.org/10.1038/s41416-023-02274-2 COLLEGE NANME Medicine, Health and Life Science - Faculty COLLEGE CODE FGMHL Swansea University SU Library paid the OA fee (TA Institutional Deal) Welsh Government and European Development Fund (2017/COL004 and 2017/COL/001) 2023-09-13T15:07:12.2622169 2023-04-21T10:07:27.4623101 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Biomedical Science Amy Johnson 1 Marcos Quintela 0000-0002-9628-695x 2 David W. James 3 Jetzabel Garcia 4 Kadie Edwards 0000-0002-1359-0359 5 Lavinia Margarit 6 Nagindra Das 7 Kerryn Lutchman-Singh 8 Amy L. Beynon 9 Inmaculada Rioja 10 Rab K. Prinjha 11 Nicola R. Harker 12 Deyarina Gonzalez 13 Steve Conlan 0000-0002-2562-3461 14 Lewis Francis 0000-0002-7803-7714 15 63224__27588__1e6a3f250f9243399740edd1ea19351b.pdf 63224.pdf 2023-05-24T11:10:06.2721407 Output 2436888 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng http://creativecommons.org/licenses/by/4.0/ 63224__28213__76faf8a4f6c145e4ba40185a8976eedf.pdf 63224.VOR.pdf 2023-07-31T10:02:00.4445325 Output 2426767 application/pdf Version of Record true © The Author(s) 2023. Distributed under the terms of a Creative Commons Attribution 4.0 License (CC BY 4.0). true eng https://creativecommons.org/licenses/by/4.0/
title	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
spellingShingle	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer Amy Johnson Kadie Edwards Steve Conlan Lewis Francis
title_short	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
title_full	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
title_fullStr	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
title_full_unstemmed	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
title_sort	In silico enhancer mining reveals SNS-032 and EHMT2 inhibitors as therapeutic candidates in high-grade serous ovarian cancer
author_id_str_mv	cd71e22a01d9a5d7e46cd8ef0fc28da1 76d053c090d5064ae9558888f7985e92 0bb6bd247e32fb4249de62c0013b51cb 10f61f9c1248951c1a33f6a89498f37d
author_id_fullname_str_mv	cd71e22a01d9a5d7e46cd8ef0fc28da1_*_Amy Johnson 76d053c090d5064ae9558888f7985e92__Kadie Edwards 0bb6bd247e32fb4249de62c0013b51cb__Steve Conlan 10f61f9c1248951c1a33f6a89498f37d_*_Lewis Francis
author	Amy Johnson Kadie Edwards Steve Conlan Lewis Francis
author2	Amy Johnson Marcos Quintela 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
format	Journal article
container_title	British Journal of Cancer
container_volume	129
container_issue	1
container_start_page	163
publishDate	2023
institution	Swansea University
issn	0007-0920 1532-1827
doi_str_mv	10.1038/s41416-023-02274-2
publisher	Springer Science and Business Media LLC
college_str	Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id	facultyofmedicinehealthandlifesciences
hierarchy_top_title	Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id	facultyofmedicinehealthandlifesciences
hierarchy_parent_title	Faculty of Medicine, Health and Life Sciences
department_str	Swansea University Medical School - Biomedical Science{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Biomedical Science
url	http://dx.doi.org/10.1038/s41416-023-02274-2
document_store_str	1
active_str	0
description	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.
published_date	2023-07-27T15:07:14Z
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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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