Metabolic imaging comparisons of patient-derived models of renal cell carcinoma

肾细胞癌患者来源模型的代谢成像比较

基本信息

项目摘要

This year, ~62,000 Americans will be diagnosed with kidney cancer and more than 14,000 individuals will die from this disease. Nine of ten kidney cancers are renal cell carcinoma (RCC). To reduce mortality from RCC, improvements are needed at all stages, from diagnosis to prognosis to therapy. In response to the funding opportunity “Biological Comparisons in Patient-derived Models of Cancer (U01)”, we will compare four types of patient-derived models of RCC to investigate the relative authenticity of each as a preclinical model. The first will be patient-derived xenografts (PDXs), widely perceived as the most representative models of human pathophysiology. These have been previously established from a range of pathologic and clinical stages of RCC. The PDXs will serve as the “gold standard” to which to compare three PDX-derived models, including tissue slice cultures (TSCs), primary cell cultures, and xenografts generated from cell cultures. The biological comparison on which we focus is metabolism. Dysregulated metabolism, one of the hallmarks of cancer, is strongly implicated in the development and progression of RCC. Pleiotropic changes include dysregulation of oxygen sensing, energy sensing and nutrient sensing. In particular, high frequency mutations in VHL and FBP1 genes contribute to exhibition of the “Warburg effect” (an elevation of glycolysis in the presence of oxygen) in clear cell RCC, the major subtype of RCC, leading to increased production and excretion of lactate. Comparing metabolism among the four patient-derived models of RCC will capture the functional consequences of genetic, transcriptomic, environmental and other influences to provide a comprehensive picture of the phenotype of each model system. We will use hyperpolarized (HP) 13C magnetic resonance (MR), a remarkably sensitive molecular imaging technique, to surveil dynamic pathway-specific metabolic and physiologic processes in the patient-derived RCC models, yielding biologically and clinically relevant data. Aim 1 will identify the metabolic signature of each of 8 RCC PDXs by HP MR imaging and steady state metabolomic profiling. The metabolic data will be associated with genotypic, transcriptomic and immunotypic features to establish the phenotype of each PDX. In Aim 2, thin precision-cut tissue slices will be prepared from each of the 8 PDXs and placed in a NMR-compatible, 3D tissue culture bioreactor. The metabolic phenotype of the TSCs will be determined by HP MR and steady state studies and compared to that of the original PDXs, along with genetic, transcriptomic and immunohistologic features. Similar studies will be performed in Aim 3 with primary cell cultures derived from PDXs, and in Aim 4 with xenografts generated by the implantation in mice of PDX-derived cell cultures. In Aim 5, the final test of the four types of models will be a comparison of metabolic responses to the clinically relevant glutaminase inhibitor CB-839, which is currently entering clinical trials in RCC.
今年,约62,000名美国人将被诊断患有肾癌,超过14,000人将被诊断患有肾癌。 死于这种疾病。十种肾癌中有九种是肾细胞癌(RCC)。为了降低死亡率, RCC,从诊断到预后再到治疗的各个阶段都需要改进。响应于 资助机会“患者来源的癌症模型的生物学比较(U 01)”,我们将比较四个 类型的RCC患者来源的模型,以调查每个作为临床前模型的相对真实性。 第一种是患者来源的异种移植物(PDX),被广泛认为是最具代表性的异种移植模型。 人类病理生理学这些先前已经建立了一系列的病理和临床 RCC的阶段。PDX将作为比较三种PDX衍生模型的“黄金标准”, 包括组织切片培养物(TSC)、原代细胞培养物和由细胞培养物产生的异种移植物。 我们关注的生物学比较是新陈代谢。代谢失调, 癌症的标志,强烈地牵涉到RCC的发展和进展。多效性变化 包括氧感测、能量感测和营养感测失调。特别是高频 VHL和FBP 1基因的突变有助于表现出“瓦尔堡效应”(糖酵解的增强)。 氧的存在),导致产量增加, 乳酸的排泄。比较四种患者来源的RCC模型之间的代谢将捕获 遗传、转录组、环境和其他影响的功能后果, 每个模型系统的表型的综合图。我们将使用超极化(HP)13 C磁 磁共振(MR),一种非常敏感的分子成像技术,用于监测动态的特定途径 代谢和生理过程中的患者来源的RCC模型,产生生物学和临床 相关数据。 目的1将通过HP MR成像和稳态分析确定8个RCC PDX中每一个的代谢特征。 代谢组学分析代谢数据将与基因型、转录组学和免疫型相关 特征以建立每个PDX的表型。在目标2中,将制备薄的精密切割组织切片 从8个PDX中的每一个中取出,并置于NMR兼容的3D组织培养生物反应器中。代谢 将通过HP MR和稳态研究确定TSC的表型,并与 原始PDX,沿着遗传、转录组学和免疫组织学特征。类似的研究将 在Aim 3中使用源自PDX的原代细胞培养物进行,在Aim 4中使用通过 在小鼠中植入PDX衍生的细胞培养物。在目标5中,四类模型的最终测试将是 比较代谢反应的临床相关的转氨酶抑制剂CB-839,这是目前 进入RCC的临床试验

项目成果

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John Kurhanewicz其他文献

John Kurhanewicz的其他文献

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{{ truncateString('John Kurhanewicz', 18)}}的其他基金

High Field MRI For Optimized Translational 1H Multiparametric and Multinuclear Imaging Research
用于优化平移 1H 多参数和多核成像研究的高场 MRI
  • 批准号:
    10175910
  • 财政年份:
    2021
  • 资助金额:
    $ 63.36万
  • 项目类别:
Preclinical imaging characterization and resource development of PDX SCNC prostate cancer murine models
PDX SCNC 前列腺癌小鼠模型的临床前成像特征和资源开发
  • 批准号:
    10378320
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI
使用超极化 13C MRI 对小细胞神经内分泌前列腺癌进行临床联合定量成像
  • 批准号:
    10669081
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI
使用超极化 13C MRI 对小细胞神经内分泌前列腺癌进行临床联合定量成像
  • 批准号:
    10057724
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI
使用超极化 13C MRI 对小细胞神经内分泌前列腺癌进行临床联合定量成像
  • 批准号:
    10470345
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Characterization of PDX SCNC prostate cancer metastatic murine models and development of associated research resources
PDX SCNC 前列腺癌转移小鼠模型的表征和相关研究资源的开发
  • 批准号:
    10533469
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI
使用超极化 13C MRI 对小细胞神经内分泌前列腺癌进行临床联合定量成像
  • 批准号:
    10256057
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Co-Clinical Quantitative Imaging of Small Cell Neuroendocrine Prostate Cancer Using Hyperpolarized 13C MRI
使用超极化 13C MRI 对小细胞神经内分泌前列腺癌进行临床联合定量成像
  • 批准号:
    10737795
  • 财政年份:
    2020
  • 资助金额:
    $ 63.36万
  • 项目类别:
Metabolic imaging comparisons of patient-derived models of renal cell carcinoma
肾细胞癌患者来源模型的代谢成像比较
  • 批准号:
    9753176
  • 财政年份:
    2017
  • 资助金额:
    $ 63.36万
  • 项目类别:
CLINICAL TRANSLATION OF HYPERPOLARIZED 13C-UREA FOR CANCER MR MOLECULAR IMAGING
超极化 13C-尿素用于癌症 MR 分子成像的临床转化
  • 批准号:
    10116302
  • 财政年份:
    2017
  • 资助金额:
    $ 63.36万
  • 项目类别:

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