서울대학교 생물정보연구소와 생물정보학 협동과정 공동 주최로 특별 세미나를 아래와 같이 열고자 하오니, 많은 참여 바랍니다.

일시: 2017년 4월 24일 월요일 오후 4시

연사: 황태현 교수 (Cleveland Clinic)

장소: 서울대학교 220동 625호

Title : Delineating complex genetic variants of cancer genomes from mutation to structural variation for precision oncology

Abstract : Tens of thousands of molecular profiles of cancer patients and preclinical models have been generated and analyzed for cancer research. These “Big Data” analyses have provided the unprecedented opportunity to translate knowledge into the discovery of novel therapeutic targets and treatment strategies for cancer patients. Although significant efforts have focused on genetic variations that disrupt the coding sequence of important cancer-related genes, the role of complex genomic variation and rearrangements that present in either coding or non-coding is still largely unknown for therapy response in cancer. In this talk, I will present our ongoing efforts to develop novel computational tool and machine learning algorithms to (1) detect and estimate heterogeneity percentages for structural variants in subclones that are present even at minor frequencies but that nevertheless play critical roles in tumor progression and therapy resistance; (2) predict individual tumor sensitivity to drug(s) and chemical compound(s) based on information on genetic variants; and (3) stratify patients into clinically distinct subgroups and predict risk of survival based on an integrative large scale somatic mutation analysis. Our results show that incorporating information from complex genetic variants can identify a predictive biomarker-based on genetic variations that can guide therapy for patients with cancer in the clinical setting. Moreover, our results demonstrated that the predictive model that integrates genetic variations of clinically relevant cancer-related genes and multiple drug sensitivity profiles could improve prediction of sensitivity of a panel of FDA-approved anticancer drugs and novel synthetic compounds. Finally, our results also showed that a large-scale integrative somatic mutation analysis can improve to better stratify patients into clinically distinct phenotypes and accurately predict risk of survival after surgery. Our computational algorithms and findings could improve our understanding of how complex genetic variations affect clinical decision-making and treatment stratification, and thus improve therapeutic efficacy in cancer.

At the end of my talk, I will introduce our current efforts to lead "Data Driven Precision Medicine" at Cleveland Clinic.

Bio

Tae Hyun Hwang is an assistant professor and serves as a director of Bioinformatics Research to lead and oversee bioinformatics, computational biology, data-driven personalized medicine research at Cleveland Clinic. His primary expertise is in developing and applying novel computational approaches to gain new insights about the genetic variants of cancers through the integrative analysis of various genome-scale data, including next-generation sequencing data. He is particularly interested in developing new computational approaches and tools to integrate large-scale genomic, genetics, phenotypic, interactome (e.g. protein-protein interaction networks) networks, image and clinical data to discover the genetic variants that might be used clinically to guide therapy decisions for cancer patients. He has developed various bioinformatics approaches based on microarray gene expression, SNP, and copy number alteration for the discovery of biomarkers, and for building predictive models for better cancer treatment, diagnosis and prevention (Hwang et al, 2008, Tian et al, 2009, Hwang et al, 2011, Hwang et al, 2013). Recently he also developed new computational approaches and pipelines to identify genomic alterations (e.g., mutation, SNPs, structural variation, etc.) associated with therapy resistance, cancer progression and development using next-generation sequencing data from patients, cell lines, and patient-derived xenografts (PDXs) (Li et al, 2012, Li et al, 2013, and Nyquista et al, 2013). Most recently, he developed the first computational tool that detects heterogeneous subclonal populations within a cancer patient by estimating their representative percentages of genomic variants (e.g., mutation, structural variation, etc.) and allowing for selective personal genome assembly of sample subpopulations using next-generation sequencing data (Landman et al, 2014, Park et. al, 2016, Chen et. al, 2016). He and his research group are currently working on developing and applying novel computational tools to analyze high-throughput chemical compound screening and drug screening profiles with genomic data (e.g., whole exome, genome, and RNA-seq data) for the development of predictive biomarker, drug sensitivity prediction and novel therapeutic agents. He serves as a bioinformatics core director for NASA Specialized Center of Research (NSCOR) grant and bioinformatics core co-director for Kidney Cancer Specialized Programs of Excellence (SPORE) grant and co-Investigator on projects for the NIH and other grantors for developing bioinformatics and statistical methodology. He is also a recipient for Lung Cancer SPORE Career Development Award and American Cancer Society Young Investigator Award.

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