智信讲坛第（83）期 Toward personalized cancer early detection and prevention: the combination of genome sequencing and electronic medical records

学术报告

同济大学计算机科学与技术系智信讲坛第（83）期

题目：Toward personalized cancer early detection and prevention: the combination of genome sequencing and electronic medical records

报告人：Edwin Wang

时间：     2017年1月7日下午3:00-:5:30pm

地点：     403会议室

组织单位：计算机科学与技术系

邀请人：黄德双

报告人简介：Edwin Wang 博士，加拿大卡尔加利大学讲席教授。具有生物与计算双重教育背景，国际生物信息学知名专家。网络生物学和系统生物学，特别是癌症系统生物学一流学者。开创了microRNA/non-coding RNA基因网络研究领域。有关癌症分子网络模块的研究工作被写进由诺贝尔奖获得者Hartwell博士和系统生物学之父Hood 博士主编的大学《遗传学》教科书（2014年版）。美国癌症研究学会（AACR）癌症系统生物学智囊团（Think Tank）的三十名领域内学术领袖之一，生物信息领域顶级期刊 PLoS Computational Biology 的编委。他带领的研究团队描绘了第一个人类癌症细胞信号通路图谱，开发了领先的鉴定分子标志物的算法。提出癌症特征分子网络计算框架，将20年来传统的癌症特征描述转化为量化网路模型，从而整合癌症组学数据，用于建模和发展假说。主编了癌症系统生物学领域内的第一部专著（2010）。利用计算系统生物学的手段来开发了多项针对肿瘤个体化医疗的分子诊断技术。例如：开发的组合基因标记组算法成功地解决了有关II期结肠癌化疗的争议了20年以上的问题。

内容提要：Cancer is the leading cause of death and the third largest burden in the healthcare system in the world. Each year, more than 15 million new cancer patients are diagnosed and 7-8 million people die from cancer in the world. Current precision oncology is focusing on cancer treatment, however, with some notable exceptions, improvements in overall survival and morbidity over the past few decades have been modest. However, historical data suggest that early detection of cancer is crucial for its ultimate control and prevention. To meet the challenges of the surge in cancer cases in the future, it is envisioned that, besides the promotion of lifestyle changes, improving early diagnosis is the best strategy for reducing the impact of carcinogenesis.

Both genetic and environmental factors (e.g., pollution, lifestyle and so on) interact to induce cancer initiation, progression and metastasis. Therefore, we are aiming to combine the genome sequencing and electronic medical records of individuals to identify high-risk cancer individuals, ‘healthy lifestyle patterns’ for cancer prevention, and monitor high-risk cancer individuals for cancer early detection. To do so, we have complied a cohort which conations 5 million people whose medical records have been collected. Among them, 0.5 million people’ genomic information has been determined. We are developing new algorithms by applying machine learning and deep learning approaches to the cohort to meet the goals mentioned above.