Project 3: From Networks and Structures to Hierarchical Whole Cell Models of Cancer

项目 3：从网络和结构到癌症的分层全细胞模型

基本信息

批准号：
10525590
负责人：
Trey Ideker
金额：
$ 53.83万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-14 至 2027-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10525590
关键词：
Affect Affinity Chromatography Architecture Biological Biological Assay Biological Markers Breast Cancer Model Cell model Cells Cellular Structures Clinical Clinical Data Complex Cryoelectron Microscopy DNA Sequence Alteration Data Data Analyses Data Set Development ERBB3 gene Evaluation Expert Systems FRAP1 gene Funding Generations Genes Genetic Head Cancer Head and neck structure Heterogeneity Human Image Immunofluorescence Immunologic Knowledge Learning Lung Lung Neoplasms Malignant Neoplasms Malignant neoplasm of lung Maps Mass Spectrum Analysis Medicine Methodology Methods Modeling Molecular Mutate Mutation Neck Organelles PIK3CA gene Pathway interactions Patients Phenotype Population Proteins Research Personnel Resolution Risk Sampling Somatic Mutation Structural Models Structure System Techniques Training Translations Treatment outcome Work Xenograft procedure base cancer cell cancer genome cancer therapy cancer type clinical translation clinically relevant combinatorial computer framework confocal imaging crosslink data modeling deep learning design drug response prediction machine learning model malignant breast neoplasm multimodality neoplastic cell patient derived xenograft model precision medicine predictive modeling protein complex protein distribution response structural biology three-dimensional modeling transfer learning tumor

项目摘要

CCMI v2.0 Project 3: From Networks and Structures to Hierarchical Whole-Cell Models of Cancer Project Leads: Trey Ideker and Andrej Sali; Co-Investigators: Emma Lundberg, Jennifer Grandis, J. Silvio Gutkind, and Laura van ’t Veer SUMMARY One of the striking discoveries of the cancer genome projects is that each tumor presents a unique set of genetic mutations and molecular alterations. To understand how these alterations give rise to cancer and treatment outcomes, the Cancer Cell Map Initiative (CCMI) has launched systematic efforts to map the physical and functional architecture of tumor cells, capturing the molecular components and pathways on which cancer mutations converge. While parts of this effort are experimental, this Project 3 presents the central computational framework. A first computational theme concerns methods to assemble the structure of the multiscale tumor cell map. Aim 1 focuses on creating 3D models of cancer-associated protein complexes. It will apply established methods of integrative structural biology to data from other projects, including cryo-electron microscopy (cryo-EM), affinity purification mass spectrometry (AP-MS), cross-linking mass spectrometry (XL-MS), and genetic interaction datasets. Initial efforts will focus on PIK3CA-HER3 and mTOR complexes, identified in previous work by the CCMI, then move to new protein complexes identified by our ongoing mapping activities. Aim 2 focuses on mapping tumor cellular components at scales at and above the protein complex, extending to larger cellular components, compartments, and organelles. It will expand on a compelling proof-of-concept for creating an unbiased hierarchical map of human cell components by integration of AP-MS data with protein distribution data from immunofluorescence confocal images. These whole-cell maps will be analyzed to reveal specific cellular components under mutational selection in breast, head-and-neck, and lung cancers. A second computational theme concerns methods to integrate tumor cell maps with functional analysis and predictive medicine. Aim 3 uses the maps to build interpretable deep learning systems for prediction of drug responses. This aim draws from our previous work to establish “visible” learning models (DCell and DrugCell), which are not black boxes but have internal organization determined by prior knowledge of biological structure. We will construct such models from CCMI tumor cell maps, incorporating key improvements over our first- generation pilots. Finally, Aim 4 will use visible deep learning systems alongside other machine learning models to design and evaluate combinatorial biomarkers for breast, head-and-neck, and lung tumors in the patient- derived xenograft (PDX) and clinical settings. Clinical samples and data will be drawn from molecular tumor boards and the I-SPY breast cancer trial. PDX and clinical data will be used for further optimization of our predictive models using nascent techniques from transfer learning. Through these aims, we will advance our basic knowledge of the structure and function of tumors while embedding this knowledge within intelligent systems for precision medicine.

CCMI v2.0 项目 3：从网络和结构到癌症的分层全细胞模型项目负责人：Trey Ideker 和 Andrej Sali；联合研究员：Emma Lundberg、Jennifer Grandis、J. Silvio 古特金德和劳拉·范特·维尔概括癌症基因组计划的一个惊人发现是，每种肿瘤都呈现出一组独特的遗传基因了解这些改变如何导致癌症和治疗。癌症细胞图谱计划 (CCMI) 已启动系统性工作，绘制物理和细胞图谱。肿瘤细胞的功能结构，捕获癌症发生的分子成分和途径虽然这项工作的部分内容是实验性的，但该项目 3 展示了核心计算。框架。第一个计算主题涉及组装多尺度肿瘤细胞图谱结构的方法。 1 专注于创建癌症相关蛋白质复合物的 3D 模型它将应用现有的方法。整合结构生物学与其他项目的数据，包括冷冻电子显微镜 (cryo-EM)、亲和力纯化质谱 (AP-MS)、交联质谱 (XL-MS) 和遗传相互作用最初的工作将集中在 PIK3CA-HER3 和 mTOR 复合物上，这些复合物是在之前的工作中确定的。 CCMI，然后转向我们正在进行的绘图活动确定的新蛋白质复合物，重点是目标 2。在蛋白质复合物及其上方的尺度上绘制肿瘤细胞成分，并扩展到更大的细胞它将扩展一个令人信服的概念验证，以创建一个通过 AP-MS 数据与蛋白质分布数据的整合，绘制人体细胞成分的无偏层次图将分析这些全细胞图谱以揭示特定的细胞。乳腺癌、头颈癌和肺癌中突变选择的成分。第二个计算主题涉及将肿瘤细胞图谱与功能分析相结合的方法 Aim 3 使用这些图来构建可解释的深度学习系统来预测药物。这一目标借鉴了我们之前建立“可见”学习模型（DCell 和 DrugCell）的工作，它们不是黑匣子，而是具有由生物结构的先验知识决定的内部组织。我们将从 CCMI 肿瘤细胞图谱构建此类模型，并结合我们的第一个模型的关键改进最后，Aim 4 将使用可见的深度学习系统以及其他机器学习模型。设计和评估患者乳腺、头颈和肺部肿瘤的组合生物标志物衍生异种移植（PDX）和临床样本和数据将从分子中获取。肿瘤板和 I-SPY 乳腺癌试验将用于进一步优化。我们的预测模型使用来自迁移学习的新兴技术。通过这些目标，我们将增进对肿瘤结构和功能的基础知识，同时将这些知识嵌入到精准医疗的智能系统中。