5/3 (목) 최정현 박사 강연 안내
- 성균융합원
- 2018-04-17
지능정보융합원에서는 최정현 박사님과 함께 '암 후성 유전학을 위한 생물 정보학의 응용'이라는 주제로 강연을 진행하고자 합니다.
주제 : "암 후성 유전학을 위한 생물 정보학의 응용"
강연자 : 최정현 박사 (국립해양생물자원관 국가해양생명자원센터 센터장)
일 시 : 2018년 5월 3일 (목) 오후 4:00 ~ 6:00
장 소 : 성균관대학교 자연과학캠퍼스 산학협력센터 85712 강의실
요 약 :
- Human cancers exist as a highly complex system consisting of heterogeneous cell populations that exhibit diverse molecular and phenotypic features. Cancer cells often evolve through a reiterative process of clonal expansion, genetic diversification, clonal selection, and adaptation within tumor microenvironments. Clinically, the presence of a small percentage of drug-resistant or tumor-initiating cells may ultimately determine the patient’s outcome and survival. The significant intratumoral heterogeneity represents a formidable challenge to the discovery of effective cancer treatments, and therefore dissection of the tumor heterogeneity holds the key to the development of more effective drugs to control cancer growth and metastasis. The overarching goal of this project is to develop bioinformatics tools for dissecting the epigenetic heterogeneity of human cancer. Cancer genome sequencing has opened a new window to investigate the evolution and genetic tumor heterogeneity in many malignancies. However, the number of mutated genes identified in cancer is still limited, and many cancer driver genes are mutated at relatively low frequency in a number of cancers. On the other hand, epigenetic differences are vast between tumor and normal tissues, as well as between patients, typically involving thou-sands of loci in a particular genome. Several recent studies have revealed the coevolution of genetic and epige-netic aberrations and highlighted the influential role of epigenetic hierarchy in tumor cell evolution. One of the innovative tools that can examine intratumoral epigenetic heterogeneity is NOMe-seq (Nucleosome Occupancy and Methylome Sequencing) or MAPit-BGS (Methyltransferase Accessibility Protocol for individual templates-Bisulfite Genome Sequencing), which allow simultaneously profiling chromatin accessibility and DNA methylation on single molecules. NOMe-seq uses a GpC methyltransferase (M.CviPI) to methylate GpCs in nucleosome-depleted regions followed by bisulfite sequencing that measures the de novo methylation of cytosines by M.CviPI. Since the methylation of GpCs and CpGs represent chromatin accessibility and DNA methylation, respectively, NOMe-seq can footprint active (unmethylated and nucleosome-depleted), repressed (unmethylated and nucleo-some-occupied), and silent (methylated and nucleosome-occupied) promoters. Using deep sequencing and long paired-end sequencing, it is possible to detect the minority subpopulations of tumor cells that display different chromatin and DNA methylation profiles from the bulk tumor population using NOMe-seq. In our preliminary study, we have successfully sequenced and analyzed one wild type and 3 DNMT knockout HCT116 cell lines using NOMe-seq. Since normal tissues surrounding the tumors complicate the analysis of the somatic tumor specific epigenetic alteration, we will use cancer-specific and normal cell-specific signatures. For instance, tumor-infiltrating immune cells will have very different chromatin and DNA methylation patterns in the promoters of immune responsive genes when compared to tumor cells and other normal epithelial or stromal cells. Therefore, these immune cell-specific epigenetic signatures can be used to quantify the fraction of tumor infiltrating immune cells. Because NOMe-seq is DNA-based analysis and normal cells are generally copy number neutral, we in fact can quantify the fraction of normal cell types based on epigenetic signatures that represent the specific cell types. In addition, hypermethylation in certain tumor suppressor genes such as p16 (CDKN2A) is highly specific to tumor cells; this is supported by a vast amount of published literatures. Using these cancer-specific and normal cell-specific epigenetic signatures, we will to develop a similar “Cancer Cell Fraction” (CCF) concept used in clonality analysis using exome sequencing for analyzing NOMe-seq; clonal lineages will be identified by cluster-ing mutations exhibiting shared CCF. Despite highly innovative technology of NOMe-seq, there are currently no appropriate bioinformatics programs and statistical approaches that are suitable for this study.
강연자 최정현 박사님 소개
- 부산대학교 물리학과를 졸업, 동 대학 전자계산학과 박사 취득. 2005년부터 2010년까지 인디애나 대학교 유전체 및 생물정보학 센터 연구원으로 근무했고 2011년부터 2016년까지는 조지아 의과대학 생물통계학과 조교수를 역임. 2016년부터 국립해양생물자원관 국가해양생명자원센터 센터장을 맡아 국내 해양생물자원 활용에 큰 이바지를 하고 있음