Deep learning of drug sensitivity and genetic dependency of pediatric cancer cells

儿科癌细胞药物敏感性和遗传依赖性的深度学习

基本信息

批准号：
10620367
负责人：
Yu-Chiao Chiu
金额：
$ 24.9万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-03-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10620367
关键词：
Address Adult Antineoplastic Agents Architecture Area Award Bioinformatics Biological Assay Cancer cell line Cause of Death Cells Characteristics Child Childhood Clustered Regularly Interspaced Short Palindromic Repeats Communities Computer Models Computing Methodologies Data Dependence Development Disease Educational process of instructing Environment Future Genetic Genetic Predisposition to Disease Genetic study Genomics Grant In Vitro Intelligence Investigation Knowledge Learning Light Machine Learning Malignant Childhood Neoplasm Malignant Neoplasms Mentors Methods Modeling Modernization Molecular Profiling Mutation Pattern Pediatric Neoplasm Performance Pharmaceutical Preparations Pharmacogenomics Pharmacotherapy Phase Postdoctoral Fellow Preclinical Testing Publishing Research Research Personnel Research Training Resources Sampling Scheme Screening for cancer Seasons Structure Testing Time Training Training Activity Translations Writing anticancer research base cancer cell cancer genome cancer genomics career chemical genetics clinically relevant cost data resource deep learning deep learning model design drug development drug discovery drug sensitivity drug testing experimental study gene network genetic signature genome-wide genomic profiles genomic signature high dimensionality high throughput analysis high throughput technology in vivo innovation insight knockout gene learning strategy new therapeutic target novel novel therapeutics patient derived xenograft model pre-clinical predicting response predictive modeling programs resistance mechanism response skills small molecule transfer learning tumor tumor heterogeneity

项目摘要

Summary/Abstract The development of novel therapies for pediatric cancers, the second leading cause of death in children, is challenging due to the lack of comprehensive pharmacogenomics resources, unlike the well-established ones in adult cancers. However, breakthroughs in deep learning methods allow learning of intricate pharmacogenomics patterns with unprecedented performance. With a uniquely cross-disciplinary background, the candidate for this proposed K99/R00 has already, as a postdoctoral fellow, (i) developed and published several deep learning models that accurately predicted adult cancer cells’ drug sensitivity and genetic dependency using high- throughput genomics profiles, and (ii) demonstrated the feasibility of transferring the model to predict tumors by a ‘transfer learning’ design. The candidate will extend this research to study pediatric cancers and test the central hypothesis that deep learning extracts genomics signatures to predict the responses of pediatric cancer cells to chemical and genetic perturbations. The proposed study will develop novel deep learning models for predicting drug sensitivity and/or genetic dependency for (Aim 1) currently un-screened pediatric cancer cell lines by learning from screens of adult cells, and (Aim 2) pediatric tumors by learning from adult and/or pediatric cells. Prediction results will be validated by in vitro experiments and data collected from patient-derived xenografts. The proposed study is the first attempt to employ modern computational methods to advance pharmacogenomics studies of pediatric cancer, which would be difficult and costly to pursue via biological assays. Findings will shed light on the optimal drugs and novel therapeutic targets for pediatric malignancies, leading to an optimal and efficient design of preclinical tests. The candidate has a remarkable track record of bioinformatics studies of adult cancer genomics. The focus of this K99 training plan is to develop in-depth understanding of pediatric cancer and preclinical treatment models, and strengthen multifaceted components needed for a successful research career in cancer bioinformatics. The primary mentor, Dr. Peter Houghton, is a renowned leader in pediatric cancer research and preclinical drug testing programs. The candidate also has assembled an outstanding mentor team: Dr. Yidong Chen (co-mentor), a cancer genomics expert and pioneer in bioinformatics analysis of high-throughput technologies; Dr. Jinghui Zhang (collaborator), a computational biologist and leader in integrative genomics studies of major pediatric cancer genome consortiums; Dr. Yufei Huang (collaborator), an expert in state-of-the-art deep learning methods; and two highly knowledgeable consultants with relevant expertise. With this team’s guidance and structured training activities in an ideal training environment, the candidate will strengthen his skills in grant writing and lab management, teaching and mentoring, and broad connections. Overall, the K99/R00 award will be an indispensable support for a timely transition of the candidate to a successful career as a multifaceted, cross-disciplinary investigator in cancer bioinformatics.

摘要/摘要儿科癌症的新疗法的发展是儿童死亡的第二大原因，是由于缺乏全面的药物基因组学资源，具有挑战性成人癌症。但是，深度学习方法的突破允许学习复杂的药物基因组学具有前所未有的性能的模式。具有独特的跨学科背景，是候选人拟议的K99/R00作为博士后研究员已经开发并出版了一些深度学习精确预测成人癌细胞的药物敏感性和遗传依赖性的模型吞吐量基因组学谱，（ii）证明了转移模型以预测肿瘤的可行性 “转移学习”设计。候选人将把这项研究扩展到研究儿科癌症并测试中心假设深度学习提取基因组学特征，以预测小儿癌细胞对化学和遗传扰动。拟议的研究将开发新的深度学习模型来预测药物敏感性和/或遗传依赖性（AIM 1）当前未筛查的小儿癌细胞系，通过向成人和/或儿科细胞学习，从成人细胞的筛查中学习，（AIM 2）小儿肿瘤。预测结果将通过体外实验和从患者衍生的异种移植物收集的数据来验证。拟议的研究是雇用现代计算方法的首次尝试小儿癌的药理学研究，这将很难通过生物测定法进行。调查结果将阐明儿科恶性肿瘤的最佳药物和新的治疗靶标的临床前测试的最佳有效设计。候选人具有生物信息学的杰出记录成人癌症基因组学研究。该K99培训计划的重点是对小儿癌和临床前治疗模型，并加强了所需的多方面组件癌症生物信息学的成功研究职业。主要导师彼得·霍顿（Peter Houghton）博士是著名的小儿癌症研究和临床前药物测试计划的领导者。候选人还组装了杰出的心理团队：癌基因组专家兼生物信息学的先驱伊东·陈（Co-Centor）博士高通量技术的分析；计算生物学家和领导者Jinghui Zhang博士（合作者）在主要儿科癌症基因组联盟的综合基因组学研究中； Yufei Huang博士（合作者），最先进的深度学习方法专家；以及两位知识渊博的顾问专业知识。在这个团队的指导和结构化的培训活动中，在理想的培训环境中候选人将加强他在赠款写作和实验室管理，教学和心理方面的技能以及广泛的技能连接。总体而言，K99/R00奖将是及时过渡的必不可少的支持作为癌症生物信息学的多方面，跨学科研究者的成功职业生涯。