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 了解遗传相互作用可以通过靶向单个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，通过识别构成严重的基因对 当被淘汰时，健身缺陷（与单独的淘汰赛相比）时，可以有选择地杀死 通过抑制其合成致命伴侣，癌细胞在一种蛋白质中丧失功能突变。尽管 生成大规模数据描绘肿瘤转录组，蛋白质组，代谢组，成像等 关于不同基因如何相互作用的知之甚少，目前尚不清楚如何设计 基于“ OMICS可用数据”的有针对性处理。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，以系统地了解高阶通用 癌症中的相互作用（即双基因和三基因相互作用），并设计了有针对性的治疗方法。 提议的框架的第一步是通过合并来获得癌症途径的整体视野 可用的“ OMICS数据”。多种方法已应用于相似形式的集成数据，但是 缺乏整合质量和格式截然不同的数据的有效解决方案。为了应对这个挑战， Yue Qin开发了一种推断分层癌细胞图的方法，该细胞图捕获了多数 通过融合免疫荧光（IF）成像数据和亲和力纯化质量光谱法（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结构与 分层癌细胞图，YUE将开发一个可以预测癌细胞的“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 癌症途径的机械见解对于基因型 - 表型预测至关重要。 在该奖项的K00阶段，YUE将开发基因工程方法 基于从VNN获得的机械见解，癌细胞中的高阶遗传相互作用 在基因型 - 表型预测期间。实验生成的数据可以直接激发目标治疗 设计。此外，可以将新数据集成到分层癌细胞图中以提高准确性 和分辨率的途径，从而进一步提高了基因型 - 表型中VNN的“可见性” 预言。 在F99阶段和实验性培训期间以计算为中心的培训的组合 在K00阶段，将完全准备自己领导自己在癌症生物学领域的跨学科研究。此外， 个性化培训计划涵盖包括心理和教学，科学写作和口头的方面 演讲将确保Yue获得其未来建立所必需的技能，作为独立 研究者。