A visible machine learning system to discover targeted treatment solutions in cancer

可见的机器学习系统，用于发现癌症的靶向治疗解决方案

• 批准号: 10475249
• 负责人: Yue Qin
• 金额: $ 3.96万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2023-01-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10475249
• 关键词: Address; Affinity Chromatography; Architecture; Award; Awareness; Binding Proteins; Biological; CRISPR screen; Cancer Biology; Cancer Patient; Cancer cell line; Cells; Cellular Structures; Complex; Critical Pathways; Data; Defect; Educational process of instructing; Ensure; Future; Generations; Genes; Genetic; Genetic Engineering; Genetic Predisposition to Disease; Genome engineering; Genomics; Genotype; Image; Immunofluorescence Immunologic; Individual; Interdisciplinary Study; Investigation; Knock-out; Lead; Learning; Machine Learning; Malignant Neoplasms; Maps; Mass Spectrum Analysis; Mentors; Methods; Modality; Mutation; Nature; Oral; Pathway interactions; Phase; Phenotype; Proteins; Proteome; Research; Research Personnel; Research Project Grants; Resolution; Resources; System; Therapeutic; Training; Writing; base; cancer cell; cancer genome; combinatorial; deep neural network; design; empowered; experimental study; fitness; gene interaction; improved; insight; knockout gene; large scale data; loss of function mutation; machine learning framework; metabolome; nanometer; neural network; neural network architecture; preservation; skills; synergism; targeted treatment; therapeutically effective; therapy design; transcriptome; tumor

Project Summary/Abstract Understanding of genetic interactions can lead to therapeutic design for individual cancer patients by targeting the specific genetic vulnerability in the cancer genome. For example, by identifying gene pairs that pose severe fitness defects when knocked out simultaneously (compared to separate knockouts), one can selectively kill cancer cells that harbor loss-of-function mutation in one protein by inhibiting its synthetic-lethal partner. Despite generation of large-scale data delineating the tumor transcriptome, proteome, metabolome, imaging, and so on, little is known regarding how different genes interact with each other and it is unclear how one can design targeted treatments based on the ‘omics data available. To address these challenges, the proposed research will develop a “visible” machine learning framework to systematically understand the higher-order genetic interactions (i.e. di-genic and tri-genic interactions) in cancer and design targeted treatments. The first step for the proposed framework is to gain a holistic view of cancer pathways through combining the ‘omics data available. Multiple approaches have been applied to integrate data of similar forms, but there yet lacks an effective solution for integrating data of vastly different qualities and formats. To address this challenge, Yue Qin has developed a method to infer a hierarchical cancer cell map capturing cancer pathways at multi- scale resolution by fusing immunofluorescence (IF) imaging data and affinity purification-mass spectrometry (AP- MS). During the F99 phase of the proposed research, by tying the architecture of a deep neural network to the hierarchical cancer cell map, Yue will develop a “visible” neural network (VNN) that can predict the cancer cell fitness from genetic perturbation (i.e. knockouts) and genomic backgrounds (i.e. mutations) while providing mechanistic insights in cancer pathways critical for genotype-phenotype prediction. During the K00 phase of the award, Yue will develop genetic engineering approaches to experimentally map higher-order genetic interactions in cancer cells based on the mechanistic insights obtained from VNN during genotype-phenotype prediction. The data generated experimentally can directly inspire targeted treatment designs. In addition, the new data can be integrated into the hierarchical cancer cell map to improve accuracy and resolution of the inferred pathways, thus further improving the “visibility” of VNN in genotype-phenotype prediction. The combination of a computational focused training during F99 phase and experimental focused training during K00 phase will fully prepare Yue leading her own interdisciplinary research in cancer biology. In addition, the personalized training plan covering aspects including mentoring and teaching, scientific writing, and oral presentation will ensure Yue acquiring skills necessary for her future establishment as an independent investigator.

项目概要/摘要 了解遗传相互作用可以通过靶向治疗为个体癌症患者设计治疗方案 例如，通过识别造成严重后果的基因对来识别癌症基因组中的特定遗传脆弱性。 同时击倒时的健康缺陷（与单独击倒相比），可以选择性地杀死 癌细胞通过抑制其合成致死伙伴而在一种蛋白质中产生功能丧失突变。 生成描述肿瘤转录组、蛋白质组、代谢组、成像等的大规模数据， 人们对不同基因如何相互作用知之甚少，也不清楚如何设计 为了应对这些挑战，拟议的研究基于现有的组学数据。 将开发一个“看得见的”机器学习框架来系统地理解高阶遗传 癌症中的相互作用（即双基因和三基因相互作用）并设计靶向治疗。 拟议框架的第一步是通过结合以下方法获得癌症途径的整体视图 已经应用了多种方法来整合类似形式的数据，但目前还没有。 缺乏有效的解决方案来整合质量和格式截然不同的数据来应对这一挑战。 Yueqin 开发了一种方法来推断分层癌细胞图，捕获多个癌症通路。 通过融合免疫荧光 (IF) 成像数据和亲和纯化质谱 (AP- 多发性硬化症）。 在拟议研究的 F99 阶段，通过将深度神经网络的架构与 分层癌细胞图谱，Yue将开发可预测癌细胞的“可见”神经网络（VNN） 遗传扰动（即敲除）和基因组背景（即突变）的适应性，同时提供 对基因型-表型预测至关重要的癌症途径的机制见解。 在该奖项的 K00 阶段，Yue 将开发基因工程方法进行实验 根据从 VNN 获得的机制见解绘制癌细胞中的高阶遗传相互作用 在基因型-表型预测过程中，实验产生的数据可以直接启发靶向治疗。 此外，新数据可以集成到分层癌细胞图谱中以提高准确性。 和推断路径的解析，从而进一步提高 VNN 在基因型-表型中的“可见性” 预言。 F99 阶段以计算为重点的训练与以实验为重点的训练相结合 K00阶段将为Yue领导她自己的癌症生物学跨学科研究做好充分准备。 个性化培训计划，涵盖指导教学、科学写作、口语等方面 演讲将确保岳获得未来作为独立人士建立所需的技能 研究者。