| [세미나] Harnessing genetic interactions to advance whole genome precision cancer treatment(11/20/화, 13:00) | ||||||||||||||||
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| 구분 | 일반 | |||||||||||||||
| 등록일 | 2018/11/19 | |||||||||||||||
| 조회수 | 900 | |||||||||||||||
세미나 안내
ㅁ 일 시 : 2018년 11월 20일 (화) 13:00 ~ 14:00
ㅁ 장 소 : 암병원 B1 대강당 ㅁ 대 상 : Cancer genomics에 관심 있는 모든 분 ㅁ 연 자 : 미국 NCI/NIH 이주상 박사 (National Cancer Institute/ National Institutes of Health) ㅁ 연 제 : Harnessing genetic interactions to advance whole genome precision cancer treatment ㅁ 내 용 : Much of the current focus in cancer research is on studying cancer driver genes. In search for new and effective cancer drugs, this has been translated into searching for ‘actionable’ mutations in these genes, aiming at their therapeutic targeting. However, identifying novel genetic interactions occurring between cancer genes may open new drug treatment opportunities across the whole cancer genome. This talk will focus on studying the utility of two fundamental types of genetic interactions: The first are the well-known synthetic lethal (SL) interactions, describing the relationship between two genes whose combined inactivation is lethal to the cell. The second type are the much less studied synthetic rescues (SR) interactions, where a change in the activity of one gene is lethal to the cell but an alteration of its SR partner gene can rescue cell viability. We shall describe a new approach we have developed for the data-driven identification of these two types of genetic interactions by directly mining patients’ tumor data. Applying it to analyze the Cancer Genome Atlas (TCGA) data, we have identified the first pan-cancer genetic interaction networks shared across many types of cancer, which we then validated via existing and new experimental in vitro and in vivo screens. We find that: (a) SL interactions offer an exciting venue for personalized selective anticancer treatments enabling the prediction of patients’ drug response and providing new selective drug target candidates, and (b) targeting SR genes can mitigate resistance emerging to primary cancer therapy. Importantly, these results are obtained via an unsupervised approach and derived directly from patient data, thus they are more likely to have a significant translational impact.
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