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.

项目摘要/摘要 对基因相互作用的了解可以通过靶向引导针对个别癌症患者的治疗设计 癌症基因组中的特定遗传脆弱性。例如，通过识别构成严重威胁的基因对 体能缺陷当同时被击倒时(与单独的击倒相比)，一个人可以选择性地杀死 癌细胞通过抑制其合成致死伙伴而在一种蛋白质中存在功能丧失突变。尽管 生成描绘肿瘤转录组、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，也不清楚一个人是如何设计的 根据现有的组学数据进行靶向治疗。为了应对这些挑战，拟议的研究 将开发一个“可见的”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)并设计有针对性的治疗方法。 提出的框架的第一步是通过结合 可获得的组学数据。已经应用了多种方法来集成相似表单的数据，但还没有 缺乏有效的解决方案来集成质量和格式大相径庭的数据。为了应对这一挑战， 秦悦开发了一种方法来推断癌症细胞的分层图谱，该图谱捕捉了多个 融合免疫荧光(IF)成像数据和亲和纯化-质谱仪(AP-MS)的尺度分辨率 Ms)。 在拟议研究的F99阶段，通过将深度神经网络的体系结构与 癌细胞分层图，悦将开发一种可预测癌细胞的可视神经网络(VNN) 适应于遗传扰动(即基因敲除)和基因组背景(即突变)，同时提供 癌症途径的机械性洞察对基因-表型预测至关重要。 在K00奖项的阶段，岳将开发基因工程方法来进行实验 基于VNN获得的机制洞察力绘制癌细胞中的高阶遗传相互作用图 在基因-表型预测过程中。实验产生的数据可以直接激发靶向治疗 设计。此外，新数据可以集成到分级癌细胞图中，以提高准确性 以及对所推断的途径的分辨，从而进一步提高了VNN在基因型-表型中的可见性 预测。 F99阶段计算性集中训练与实验性集中训练的结合 在K00阶段，岳阳将为领导自己在癌症生物学领域的跨学科研究做好充分准备。此外, 个性化的培训计划，包括指导和教学、科学写作和口语 演讲将确保岳获得必要的技能，为她未来成为一名独立的 调查员。