Phenotype Heterogeneity and Dynamics in SCLC
SCLC 的表型异质性和动态
基本信息
- 批准号:10375418
- 负责人:
- 金额:$ 154.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2018
- 资助国家:美国
- 起止时间:2018-04-13 至 2024-06-30
- 项目状态:已结题
- 来源:
- 关键词:AddressAftercareAutomobile DrivingBiological ModelsCD44 geneCancer BiologyCancer cell lineCell CommunicationCell ShapeCell modelCellsCellular MorphologyCellular biologyCollaborationsCommunicationComplexCytometryDNA Sequence AlterationDataData AnalysesEcosystemEducational workshopEpigenetic ProcessExhibitsExperimental ModelsGenesGeneticGenetically Engineered MouseGenotypeGoalsHealthHeterogeneityHumanHuman EngineeringInformation SystemsKnowledgeLeadershipLogicMalignant NeoplasmsMeasurementMeasuresMediatingMethodsMissionModelingMolecularMorphologic artifactsMutationOncologyOutcomePathologicPatientsPharmaceutical PreparationsPharmacotherapyPhenotypePhysiologicalPopulation DynamicsPredispositionProcessProductionPropertyPublicationsRecurrenceRelapseResearch PersonnelResource SharingRoleSideSiteSourceSystemSystems BiologyTP53 geneTechnologyTestingThinkingTissuesTreatment FailureUniversitiesVariantbasecancer cellcancer heterogeneitycancer therapycancer typecell behaviorchemoradiationcomputerized toolsdata exchangedesigndrug sensitivityeffective therapyexosomeexperimental studyhigh dimensionalityimprovedinnovationlung small cell carcinomamathematical modelmeetingsmouse modelmultidimensional dataneoplastic cellnon-geneticnovelonline courseoutreachoutreach programprogramsresistance mutationresponsesingle cell analysissingle cell technologysingle-cell RNA sequencingsuccesssymposiumtherapy resistanttooltranscriptomicstreatment strategytumortumor heterogeneityvirtualweb site
项目摘要
Heterogeneity is a pervasive feature of cancer, central to progression and therapy failure. Both genetic and
nongenetic factors, cell-cell and cell-microenvironment interactions contribute to tumor cell phenotypic variation.
Thus, heterogeneity is a complex multiscale problem difficult to study by reductionist approaches but well
suited to systems thinking. A mechanistic, system-level understanding of heterogeneity would spawn
fundamental advances in cancer treatment strategies. Major challenges include definition of relevant tumor cell
phenotypes, phenotype dynamics emergence from single-cell behavior and interactions, and effective targeting
strategies. In our Center, we will use systems biology approaches to tackle these challenges, focusing on
small cell lung cancer (SCLC), in which the impact of intratumor heterogeneity is particularly compelling. Thus,
SCLC tumors, while histo-pathologically homogeneous with classic “small blue round cell” morphology, are
comprised of phenotypic subpopulations (e.g., tumor-propagating cells; Hes1+ cells; CD44+ cells) that
cooperate to form a tumor ecosystem adaptive to drug treatment. In SCLC genetically engineered mouse
models (GEMMs) and patient tumors, accumulated genetic alterations (e.g., MYC amplification, NOTCH
mutations) may bias phenotypic compositions and consequent drug sensitivity, underscoring the combined role
of genetic and nongenetic sources. The overall goal of our proposed Center is a system-level understanding of
the impact of SCLC phenotype heterogeneity in drug evasion, that will open avenues to novel treatment
strategies. In two highly integrated Projects, we will combine experimentation with mathematical modeling to
generate a comprehensive blueprint of SCLC phenotypic space and identify complex phenotype dynamics
underlying treatment resistance. Project 1 will use high-dimensional cytometry and transcriptomics data to
define human and GEMM core SCLC phenotypes, their susceptibility to genomic alteration bias, and their drug
response plasticity. Project 2 will study SCLC phenotype dynamics initiated by cell-cell and secreted factors or
exosomes in SCLC tumor ecosystems, using models of cell population dynamics driving tumor aggressiveness.
Projects will be supported by a Single-Cell Biology and Data Analysis Shared Resource equipped with a vast
palette of state-of-the-art single-cell technologies, including mass cytometry, scRNA-Seq and multidimensional
data analysis. The Center will be located at Vanderbilt University and a satellite site at Stanford contributing
highly-regarded SCLC GEMMs. Experimental systems will encompass human and GEMM SCLC cell lines and
tumors. Our Center investigators have a track record of co-authored publications in SCLC and/or systems
biology. The Administrative Core will provide leadership and communication across the Center while the
Outreach Core will promote collaborations and disseminate knowledge and tools on SCLC and cancer
heterogeneity. We expect that iteration between experiments and modeling will bring about a system-level
understanding of SCLC tumor heterogeneity with lessons generally applicable to any cancer type.
异质性是癌症的普遍特征,是进展和治疗衰竭的核心。遗传和
非固定因子,细胞 - 细胞和细胞 - 微环境相互作用有助于肿瘤细胞表型变异。
这是,异质性是一个复杂的多尺度问题,难以通过还原主义的方法研究,但是很好
适合系统思考。机械级别对异质性的理解将产生
癌症治疗策略的基本进展。主要挑战包括定义相关的肿瘤细胞
表型,单细胞行为和相互作用的表型动力学出现以及有效的靶向
策略。在我们的中心,我们将使用系统生物学方法来应对这些挑战,重点关注
小细胞肺癌(SCLC),其中肿瘤内异质性的影响特别令人信服。那,
SCLC肿瘤虽然与经典的“小蓝色圆形细胞”形态的历史病理学同质性是
积累了表型亚群(例如,肿瘤传播细胞; HES1+细胞; CD44+细胞)
合作形成适应药物治疗的肿瘤生态系统。在SCLC基因设计的鼠标中
模型(GEMM)和患者肿瘤,积累的遗传改变(例如MYC扩增,Notch
突变)可能会偏向表型组成和随之而来的药物敏感性,强调合并作用
遗传和非核源。我们提议的中心的总体目标是系统级的理解
SCLC表型异质性在药物进化中的影响,这将为新治疗开放途径
策略。在两个高度集成的项目中,我们将实验与数学建模相结合
生成SCLC表型空间的全面蓝图并识别复杂的表型动力学
潜在的治疗耐药性。项目1将使用高维细胞仪和转录组学数据
定义人和GEMM核心SCLC表型,它们对基因组改变偏见的敏感性及其药物
响应可塑性。项目2将研究由细胞细胞和分泌因素引发的SCLC表型动力学或
SCLC肿瘤生态系统中的外泌体,使用驱动肿瘤侵袭性的细胞种群动力学模型。
项目将得到单细胞生物学和数据分析共享资源,配备了广泛的资源
最先进的单细胞技术的调色板,包括质量细胞术,SCRNA-SEQ和多维
数据分析。该中心将位于范德比尔特大学和斯坦福大学的卫星站点
备受推崇的SCLC Gemms。实验系统将涵盖人类和GEMM SCLC细胞系以及
肿瘤。我们的中心调查人员在SCLC和/或系统中拥有共同作品的出版物的记录
生物学。行政核心将在中心提供领导和沟通
外展核心将促进合作,并传播有关SCLC和癌症的知识和工具
异质性。我们希望实验和建模之间的迭代将带来系统级
通常适用于任何癌症类型的课程,了解SCLC肿瘤异质性。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Organoids as a Systems Platform for SCLC Brain Metastasis.
- DOI:10.3389/fonc.2022.881989
- 发表时间:2022
- 期刊:
- 影响因子:4.7
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{{ truncateString('Vito Quaranta', 18)}}的其他基金
Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies
定量多尺度成像优化癌症治疗策略
- 批准号:
8703365 - 财政年份:2014
- 资助金额:
$ 154.69万 - 项目类别:
Quantitative Multiscale Imaging to Optimize Cancer Treatment Strategies
定量多尺度成像优化癌症治疗策略
- 批准号:
9131999 - 财政年份:2014
- 资助金额:
$ 154.69万 - 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
- 批准号:
8664820 - 财政年份:2013
- 资助金额:
$ 154.69万 - 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
- 批准号:
8920097 - 财政年份:2013
- 资助金额:
$ 154.69万 - 项目类别:
Image Driven Multi-Scale Modeling to Predict Treatment Response in Breast Cancer
图像驱动的多尺度建模来预测乳腺癌的治疗反应
- 批准号:
8476896 - 财政年份:2013
- 资助金额:
$ 154.69万 - 项目类别:
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