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An Integrated Systems Approach for Incompletely Penetrant Onco-phenotypes.

针对不完全渗透肿瘤表型的集成系统方法。

基本信息

批准号：
10208800
负责人：
Kevin A Janes
金额：
$ 43.67万
依托单位：
UNIVERSITY OF VIRGINIA
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-15 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10208800
关键词：
3-Dimensional Antineoplastic Agents Appearance Archives Breast Cancer Patient Breast Epithelial Cells Breast biopsy Cancer Patient Carrier Proteins Categories Cell model Cells Characteristics Chemicals Collaborations Combined Modality Therapy Core Biopsy Custom Data Drug Targeting ERBB2 gene Event Exhibits Exportins Frequencies Future Gene Expression Genes Genetic Heterogeneity Genetic Transcription Goals Hand Heritability Heterogeneity Hour Human Hypersensitivity In Situ Intervention Lead Malignant Neoplasms Mammary Neoplasms Mass Spectrum Analysis Measurement Measures Modeling Molecular Mus Mutation Neoplasm Metastasis Nuclear Pore Complex Oncogenes Oncogenic Outcome Pathway interactions Patient-Focused Outcomes Patients Penetrance Pharmaceutical Preparations Phenotype Population Predisposition Receptor Protein-Tyrosine Kinases Research Resistance Rest Sampling Scanning Seeds Signal Transduction Stereotyping Stimulus Structure System Systems Biology Techniques Testing Therapeutic Intervention Transcript Trastuzumab Work base cancer cell cellular engineering data mining experimental study follow-up in vivo knock-down malignant breast neoplasm mammary epithelium meter neoplastic cell network models novel strategies nucleocytoplasmic transport overexpression patient stratification prospective response stem targeted treatment three dimensional cell culture trafficking transcriptome sequencing transcriptomics tumor tumor behavior tumor initiation tumor progression

项目摘要

PROJECT SUMMARY/ABSTRACT Perturbation of cancer cells often leads to heterogeneous outcomes, in that most cells exhibit a dominant phenotype, but the rest appear resistant or hypersensitive to the perturbation. If the penetrance of such a phenotype is heritably incomplete, then it becomes extremely difficult to decipher the upstream molecular events that heterogenize the population and cause response variability. By combining quantitative measurements with dynamical models, systems approaches should be useful if provided with a core network of important biomolecules. The daunting hurdle lies in identifying phenotype-relevant regulatory heterogeneities that define the network for penetrance at the single-cell level. Our proposal seeks to exploit a new approach, called stochastic frequency matching (SFM), for elaborating the molecular networks upstream of incompletely penetrant phenotypes. SFM identifies and parameterizes single-cell heterogeneities—which emerge after a uniform perturbation but before the appearance of a variable phenotype—to hone in on regulatory states corresponding to future penetrance. For an onco-phenotype incompletely triggered by ErbB receptor tyrosine kinase signaling in 3D cultured breast epithelia, we implemented SFM using microarrays to uncover a network of critical nucleocytoplasmic regulators. The goals of this proposal are to apply systems approaches to the ErbB nucleocytoplasmic network and adapt SFM more broadly to RNA sequencing of breast cancer patients with ErbB amplification. Based on our provisional SFM results, we hypothesize that ErbB signaling heterogeneously reconfigures the nucleocytoplasmic shuttling state of cells to determine incomplete penetrance of the onco-phenotype. The aims are to: 1) Identify network-level mechanisms for the incompletely penetrant ErbB1:ErbB2 phenotype. 2) Determine whether drivers of incomplete penetrance in 3D define shuttling states in human cancers and promote ErbB2-driven mammary tumors in mice. 3) Sequence and parameterize regulatory-state heterogeneity in HER2+ breast cancers to assemble patient-specific network models of shuttling variability and sensitivity. Drivers of incomplete penetrance are important for understanding transitions during tumor initiation-progression and for developing therapeutic interventions with more reliable patient outcomes. SFM gives the Cancer Systems Biology Consortium a means to identify driver networks in a comprehensive and hypothesis-driven way.

项目总结/摘要癌细胞的扰动通常导致异质性结果，因为大多数细胞表现出主导的细胞分化。表型，但其余的出现抗性或过敏的扰动。如果这样一个表型遗传上是不完整的，那么就很难破译上游分子使群体异质化并导致反应变异性的事件。通过结合定量如果提供一个核心网络，系统方法应该是有用的重要的生物分子。令人生畏的障碍在于确定表型相关的监管在单细胞水平上定义网络的异质性。我们的建议旨在利用一种新的方法，称为随机频率匹配（SFM），用于阐述上游的分子网络不完全外显的表型SFM识别并参数化单细胞异质性，在一个统一的扰动之后出现，但在一个可变的表型出现之前，对应于未来的过渡状态的调节状态。对于ErbB不完全触发的肿瘤表型受体酪氨酸激酶信号的三维培养的乳腺上皮细胞，我们实现了SFM使用微阵列，揭示了一个关键的核质调节器网络。该提案的目标是应用系统 ErbB核质网络的方法，并使SFM更广泛地适用于乳腺癌的RNA测序 ErbB扩增的癌症患者。基于我们的临时SFM结果，我们假设ErbB 信号传导不均匀地重新配置细胞的核质穿梭状态，以确定不完全的肿瘤表型的突变率。目的是：1）确定网络一级的机制，不完全渗透ErbB 1：ErbB 2表型。2)确定是否在3D中不完全显示驱动程序定义人类癌症中的穿梭状态，并促进小鼠中ErbB 2驱动的乳腺肿瘤。3)序列 HER 2+乳腺癌中的参数调节状态异质性，以组装患者特异性网络穿梭变异性和敏感性的模型。不完全反射的驱动因素对于理解肿瘤发生-进展期间的过渡，并开发更可靠的治疗干预措施，患者结局。SFM为癌症系统生物学联盟提供了一种方法，可以识别综合性和假设驱动的方法。