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

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;共同研究者：Emma Lundberg、Jennifer Grandis、J. Silvio 古特金德和劳拉货车没有转向 总结 癌症基因组计划的一个惊人发现是，每个肿瘤都呈现出一组独特的基因表达。 突变和分子改变。为了了解这些改变如何引起癌症和治疗， 结果，癌症细胞图谱倡议（CCMI）已经启动了系统的努力，以绘制物理和 肿瘤细胞的功能结构，捕获癌症发生的分子成分和途径， 突变会趋同虽然这项工作的一部分是实验性的，这个项目3提出了中央计算 框架. 第一个计算主题涉及组装多尺度肿瘤细胞图的结构的方法。目的 1专注于创建癌症相关蛋白质复合物的3D模型。它将采用既定的方法， 将结构生物学与其他项目的数据相结合，包括冷冻电子显微镜（cryo-EM），亲和性 纯化质谱（AP-MS）、交联质谱（XL-MS）和遗传相互作用 数据集。最初的努力将集中在PIK 3CA-HER 3和mTOR复合物上，这些复合物是在以前的工作中由 CCMI，然后转移到我们正在进行的绘图活动确定的新蛋白质复合物。目标2侧重于 在蛋白质复合物处和蛋白质复合物上方的尺度上映射肿瘤细胞组分，延伸到更大的细胞， 组件、隔室和细胞器。它将扩展一个令人信服的概念验证， 通过整合AP-MS数据和蛋白质分布数据的人类细胞组分的无偏分层图 免疫荧光共聚焦图像。这些全细胞图谱将被分析，以揭示特定的细胞 在乳腺癌、头颈癌和肺癌的突变选择下， 第二个计算主题涉及将肿瘤细胞图谱与功能分析整合的方法， 预测医学Aim 3使用这些地图构建可解释的深度学习系统，用于预测药物 应答这一目标借鉴了我们以前建立“可见”学习模型（DCell和DrugCell）的工作， 它们不是黑箱，而是由生物结构的先验知识决定的内部组织。 我们将从CCMI肿瘤细胞图谱构建这样的模型，结合我们第一个模型的关键改进- 一代飞行员。最后，Aim 4将与其他机器学习模型一起使用可视深度学习系统 设计和评估患者乳腺、头颈和肺肿瘤的组合生物标志物- 衍生的异种移植物（PDX）和临床环境。临床样本和数据将从分子生物学 肿瘤委员会和I-SPY乳腺癌试验。PDX和临床数据将用于进一步优化 我们的预测模型使用了迁移学习的新生技术。 通过这些目标，我们将推进我们对肿瘤结构和功能的基础知识， 将这些知识嵌入到精准医疗的智能系统中。

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.