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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：
10704611
负责人：
Trey Ideker
金额：
$ 47.68万
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/10704611
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 and Neck Cancer Head and Neck Neoplasms 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 Organelles PIK3CA gene Pathway interactions Patients Phenotype Population Protein Dynamics Proteins Research Personnel Resolution Risk Sampling Somatic Mutation Structural Models Structure System Techniques Training Translations Treatment outcome Work Xenograft procedure 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 improved 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