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

by Selleckchem | Jul 11 2023

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.

Comments:

The given background describes a study focused on epigenomic dysregulation in ovarian cancers, specifically high-grade serous carcinoma, which is the most common and aggressive subtype. The researchers aimed to profile re-programmed enhancer locations associated with the disease and utilize this information to improve stratification and therapeutic choices.

The methods involved analyzing publicly available data to examine the enhancer landscapes of normal ovaries and different subtypes of ovarian cancer. The researchers primarily focused on the H3K27ac histone mark, which is associated with active enhancer regions. They developed a computational pipeline to predict drug compound activity based on epigenomic stratification. Furthermore, they validated their predictions in vitro using patient-derived clinical samples and cell lines.

The results of the study demonstrated recurrent and subtype-specific enhancer landscapes in ovarian cancer. The researchers identified differential enrichment of various transcription factors involved in protein complexes across the subtypes, highlighting their potential roles in driving the disease. Based on their computational approach, they predicted the activity of certain drug compounds in high-grade serous carcinoma. Specifically, they identified SNS-032, as well as EHMT2 inhibitors BIX-01294 and UNC0646, as potential therapeutic candidates. The efficacy of these inhibitors was further investigated in vitro using patient-derived samples and cell lines.

In conclusion, this study represents the first attempt to leverage epigenomic landscapes in ovarian cancer for drug discovery. The developed computational pipeline has significant potential for translating epigenomic profiling into leads for therapeutic interventions.