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

摘要/摘要 儿童癌症是儿童死亡的第二大原因，儿童癌症的新疗法的开发是 由于缺乏全面的药物基因组学资源而具有挑战性，不同于 成人癌症。然而，深度学习方法的突破使人们能够学习复杂的药物基因组学。 具有前所未有的性能的模式。拥有独特的跨学科背景，这位候选人 建议的K99/R00已经作为博士后研究员(I)开发并发布了几个深度学习 模型准确地预测了成人癌细胞的药物敏感性和遗传依赖性 以及(Ii)证明了将模型转移到预测肿瘤的可行性 一种“迁移学习”的设计。候选人将把这项研究扩展到研究儿科癌症，并测试中央 深度学习提取基因组学信号以预测儿科癌细胞对 化学和遗传干扰。拟议的研究将开发用于预测的新的深度学习模型 (AIM 1)目前未筛查的儿科癌细胞系的药物敏感性和/或遗传依赖性 从成人细胞的筛选中学习，以及(目标2)通过从成人和/或儿科细胞中学习来学习儿童肿瘤。 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来验证。 这项拟议的研究是首次尝试使用现代计算方法来推动 儿童癌症的药物基因组学研究，这将是通过生物测试进行的困难和昂贵的研究。 研究结果将阐明治疗儿童恶性肿瘤的最佳药物和新的治疗靶点，从而导致 临床前试验的优化和高效设计。这位候选人在生物信息学方面有着出色的记录 成人癌症基因组学研究。本K99培训计划的重点是深入了解 儿科癌症和临床前治疗模式，并加强需要的多方面组件 在癌症生物信息学方面有成功的研究生涯。主要导师彼得·霍顿博士是一位著名的 儿科癌症研究和临床前药物测试项目的领导者。这位候选人还组装了一个 杰出导师团队：癌症基因组学专家、生物信息学先驱陈益东博士(共同导师) 高通量技术分析；计算生物学家、领导者张京辉博士(合作者) 主要儿科癌症基因组联合体的综合基因组学研究；黄宇飞博士(合作者)， 一名最先进的深度学习方法专家；以及两名知识渊博的顾问， 专业知识。在这个团队的指导和有条理的培训活动中，在理想的培训环境下， 应聘者将加强他在补助金撰写和实验室管理、教学和指导方面的技能，以及广泛的 关系。总体而言，K99/R00奖将是候选人及时过渡不可或缺的支持 为在癌症生物信息学领域成为一名多方面、跨学科的调查员而取得成功的职业生涯。

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.