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/摘要小儿癌症是儿童死亡的第二大原因，其新疗法的开发是由于缺乏全面的药物基因组学资源，与现有的成熟资源不同，这具有挑战性然而，深度学习方法的突破使得复杂的药物基因组学的学习成为可能。具有前所未有的表现的模式，具有独特的跨学科背景，这个候选人。作为博士后研究员，提出的 K99/R00 已经 (i) 开发并发表了多项深度学习使用高准确度预测成体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概况，以及（ii）证明了通过转移模型来预测肿瘤的可行性候选人将把这项研究扩展到儿科癌症并测试中心。假设深度学习提取基因组学特征来预测儿童癌细胞的反应拟议的研究将开发新的深度学习模型来预测。 (目标 1) 目前未筛选的儿科癌细胞系的药物敏感性和/或遗传依赖性从成人细胞筛选中学习，以及（目标 2）通过从成人和/或儿科细胞学习来学习儿科肿瘤。预测结果将通过体外实验和从患者来源的异种移植物收集的数据进行验证。这项研究是首次尝试采用现代计算方法来推进小儿癌症的药物基因组学研究，通过生物检测进行起来既困难又昂贵。研究结果将揭示儿科恶性肿瘤的最佳药物和新的治疗靶点，从而促进最佳且高效的临床前测试设计该候选人在生物信息学方面拥有出色的记录。 K99 培训计划的重点是深入了解成人癌症基因组学。儿科癌症和临床前治疗模型，并加强治疗所需的多方面组成部分其主要导师 Peter Houghton 博士是一位在癌症生物信息学方面取得成功的研究生涯。该候选人还组建了一个儿科癌症研究和临床前药物测试项目的领导者。优秀导师团队：癌症基因组学专家、生物信息学先驱陈一东博士（联合导师）高通量技术分析；张景辉博士（合作者），计算生物学家和领导者主要儿童癌症基因组联盟的综合基因组学研究；Yufei Huang 博士（合作者），一位最先进的深度学习方法专家；以及两位知识渊博的相关顾问；凭借该团队的指导和在理想的培训环境中进行的结构化培训活动，候选人将加强他在资助写作和实验室管理、教学和指导以及广泛的方面的技能总体而言，K99/R00奖项将是候选人及时过渡不可或缺的支持。作为癌症生物信息学领域的多方面、跨学科研究者，取得成功的职业生涯。