Modeling cancer evolution for prediction with neural networks: methods and applications

利用神经网络对癌症进化进行建模以进行预测：方法和应用

• 批准号: 10675533
• 负责人: Noam Auslander
• 金额: $ 24.9万
• 依托单位: WISTAR INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10675533
• 关键词: Aneuploidy; Award; Biology; Biotechnology; Cancer Model; Characteristics; Chromosome abnormality; Classification; Clinical; Collaborations; Computational Molecular Biology; DNA Damage; Data; Data Reporting; Data Science; Decision Making; Development; Development Plans; Early identification; Event; Evolution; Genetic; Goals; Grant; Human; Investigation; Knowledge; Learning; Machine Learning; Malignant Neoplasms; Medical; Mentors; Methodology; Methods; Mission; Modeling; Molecular Evolution; Mutation; Oncogenic; Outcome; Pathway interactions; Pattern; Performance; Phase; Phenotype; Postdoctoral Fellow; Public Health; Research; Research Personnel; Route; Solid; Techniques; Therapeutic; Time; Training; United States National Institutes of Health; United States National Library of Medicine; anticancer research; cancer type; career; career development; clinical decision-making; clinical predictors; comparative genomics; computer studies; computerized tools; deep learning; driver mutation; genomic data; improved; innovation; insight; learning strategy; mathematical model; member; neural network; novel; novel strategies; predict clinical outcome; predicting response; recurrent neural network; response; treatment response; tumor; tumor progression; tumorigenesis

PROJECT SUMMARY/ABSTRACT The study of tumor evolution can uncover events and interactions that drive tumor development through alternative routes, reveal differences in therapeutic vulnerabilities and improve clinical decision making. Yet, studying tumor evolution is challenging, hindered by the difficulty to interpret noisy genomic data and the lack of temporal ordering of major genetic events. There is therefore a critical need for the development of computational approaches that can facilitate efficient investigation of cancer data under an evolutionary, temporal perspective. The long-term goal of this project is to develop computational tools combining advanced machine learning with molecular evolution techniques and provide novel strategies to investigate tumor evolution. The overall objective is to establish a deep-learning framework to study tumor development that will be used to distinguish early and late genetic events that are associated with tumor characteristics, survival and therapeutic vulnerabilities. The rationale of the proposed research is that the study of tumor evolution through integration of machine learning with molecular evolution techniques could enhance the performance of otherwise difficult clinical classification tasks. The specific aims of this project are to (1) Characterize the interplay between driver mutations and aneuploidy in tumor evolution and identify determinants of clinical outcome and therapeutic vulnerabilities (2) Introduce computational approaches to represent snapshot genomic data through temporal and functional ordering of genetic events (3) Develop a recurrent neural network approach to learn different dynamics in tumor evolution from ordered genomic data, and predict phenotypic features and clinical outcome. The proposed research is innovative because it will combine recent advances in machine learning with evolutionary techniques into a single framework, establishing novel computational tools that will facilitate a comprehensive investigation of cancer development. The proposed framework is significant because it will enable application of temporal modeling with machine learning to cancer data, for prediction of clinical features. To achieve the proposed goals the candidate, Dr. Noam Auslander, requires additional training and mentoring in evolutionary research, comparative genomics and mathematical modeling. During the K99 phase, Dr. Auslander will conduct this research as a postdoctoral fellow at the National Center for Biotechnology Information. She will be mentored by Dr. Eugene Koonin, a recognized expert in the fields of molecular evolution and computational biology, and additional mentoring from senior members from the Koonin lab. Together with her previous training in machine learning and cancer data science, this application for the NIH Pathway to Independence Award (K99/R00) describes a career development plan that will allow Dr. Auslander to achieve her career goals and become an independent investigator and leader in computational research of cancer evolution.

项目摘要/摘要 对肿瘤进化的研究可以揭示推动肿瘤发展的事件和相互作用 通过替代途径，揭示治疗脆弱性的差异并改善临床决策 制作。然而，研究肿瘤进化是具有挑战性的，因为很难解释嘈杂的基因组。 数据和缺乏重大遗传事件的时间顺序。因此，迫切需要 开发计算方法，以促进在以下条件下有效调查癌症数据 一种进化的、时间的视角。这个项目的长期目标是开发计算工具 将高级机器学习与分子进化技术相结合，提供了新的策略 来研究肿瘤的进化。总体目标是建立一个深度学习框架来研究 肿瘤的发展将被用来区分早期和晚期的遗传事件，这些事件与 肿瘤特征、存活率和治疗脆弱性。这项拟议研究的基本原理是 通过机器学习和分子进化相结合来研究肿瘤进化 这些技术可以提高原本困难的临床分类任务的表现。具体的 这个项目的目的是(1)描述肿瘤中驱动突变和非整倍体之间的相互作用 演变和确定临床结果和治疗脆弱性的决定因素(2)介绍 通过时间和函数排序表示快照基因组数据的计算方法 发展一种递归神经网络方法来学习肿瘤中的不同动力学 从有序的基因组数据中进化，并预测表型特征和临床结果。这个 拟议的研究具有创新性，因为它将机器学习的最新进展与 将进化技术整合到单个框架中，建立新的计算工具，以促进 对癌症发展的全面调查。拟议的框架意义重大，因为它 将使具有机器学习的时间建模应用于癌症数据，用于临床预测 功能。为了实现提议的目标，候选人诺姆·奥斯兰德博士需要额外的培训 并在进化研究、比较基因组学和数学建模方面提供指导。在.期间 K99阶段，Auslander博士将作为国家研究中心的博士后研究员进行这项研究 生物技术信息。她将得到该领域公认的专家尤金·库宁博士的指导 分子进化和计算生物学，以及来自 库宁实验室。加上她之前在机器学习和癌症数据科学方面的培训，这一次 NIH独立之路奖(K99/R00)的申请描述了一项职业发展计划 这将使奥斯兰德博士能够实现她的职业目标，成为一名独立的调查员和 癌症进化计算研究领域的领先者。