Defining the Role of Metabolic Heterogeneity in Melanoma Dissemination and Therapy Escape

定义代谢异质性在黑色素瘤传播和治疗逃避中的作用

• 批准号: 10264102
• 负责人: Inna Smalley
• 金额: $ 24.9万
• 依托单位: H. LEE MOFFITT CANCER CTR & RES INST
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10264102
• 关键词: Address; Affect; Appearance; Award; BRAF gene; Behavior; Bioinformatics; Biology; Biometry; Blood Circulation; Career Mobility; Cell Culture Techniques; Cell Line; Cells; Chronic; Computational Biology; Dependence; Development; Distant Metastasis; Dose; Drug Exposure; Drug resistance; Educational workshop; Evolution; Failure; Gene Expression; Gene Expression Profile; Genotype; Glycolysis; Goals; Grant; Growth; Heterogeneity; Hypoxia; Immunofluorescence Immunologic; Impairment; In Vitro; Individual; Knowledge; MEKs; Malignant Neoplasms; Mass Spectrum Analysis; Melanoma Cell; Mentors; Metabolic; Metabolic Marker; Metabolism; Modeling; Nature; Neoplasm Metastasis; Oxidative Phosphorylation; Patients; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phase; Phenotype; Population; Research; Research Personnel; Resistance; Role; SLC2A1 gene; Schedule; Scientist; Shapes; Signal Transduction; Site; Specimen; System; Testing; Therapeutic; Time; Treatment Efficacy; Variant; Work; Xenograft procedure; base; drug sensitivity; fitness; glucose uptake; improved; in vivo; in vivo Model; inhibitor/antagonist; large scale data; mRNA Expression; mathematical model; melanoma; metabolic phenotype; metabolomics; mutant; neoplastic cell; patient derived xenograft model; predictive modeling; programs; protein expression; resistance mechanism; response; single-cell RNA sequencing; skills; targeted agent; targeted treatment; treatment response; tumor; tumor heterogeneity

PROJECT SUMMARY/ ABSTRACT Despite promising initial responses, most BRAF-mutant melanoma patients eventually fail on BRAF inhibitor therapy. The task of understanding therapeutic escape and dissemination is complicated further by the countless number of resistance mechanisms identified so far and the realization that multiple cellular phenotypes may exists within a single genotype. The roles of intra-tumoral heterogeneity in progression and drug resistance are poorly understood, and we submit that strategies tackling this diversity are needed for durable therapeutic responses in patients. We hypothesize that: (i) drug-induced tumor cell phenotypic heterogeneity is driven by the development of inter-dependent and cooperative metabolic niches that promote tumor dissemination and affect the response to therapeutics; and (ii) therapeutic approaches that exploit intra- clonal metabolic competition will give more durable responses in melanoma patients. We will define the metabolic landscape of OXPHOS-dependent and glycolysis-dependent subpopulations in cell culture, xenografts, patient-derived xenografts (PDX) and patient specimens using single-cell RNAseq, global metabolomics and targeted expression panels. We will test how OXPHOS-dependent and glycolysis- dependent subpopulations cooperate to drive metastasis and define the drug sensitivities of the individual subpopulations in vitro and in xenografts and PDXs. We will test if disrupting either OXPHOS or glycolysis, pharmacologically or through gene expression targeting, shapes the metastatic potential or the emergence and heterogeneity of drug resistant subpopulations. We will devise a mathematical model to define optimal, evolutionary-informed dosing of BRAF/MEK plus metabolic inhibitor combinations that impair clonal cooperation and maximize duration of the therapeutic response. These models will be validated in vitro and in PDX models. The K99/R00 award would allow the candidate to conduct the proposed studies under the continued guidance of their primary mentor, Dr. John Cleveland, together with a panel of co-mentors who are renowned experts in melanoma biology, PDX modeling, cancer evolution, metabolism, and statistical and computational approaches to systems-wide large-scale data set analysis. During the K99 phase, the candidate will further develop their knowledge in melanoma metabolism and evolutionary biology, as well as their technical abilities in mass-spectrometry-based metabolomics, advanced computational biology, bioinformatics and biostatistics. With the help of their committee and various seminars, courses and workshops, the candidate will become a stronger scientist, manager, grant writer and mentor, all skills necessary to be an independent investigator. Overall, the proposed work will provide the rationale for therapeutic approaches that exploit intra-clonal metabolic competition to give more durable responses in melanoma patients, as well as a timely career transition for a candidate with strong potential to be a successful independent investigator.

项目摘要/摘要 尽管最初的反应很有希望，但大多数BRAF突变黑色素瘤患者最终使用BRAF抑制剂失败 心理治疗。理解治疗逃逸和传播的任务因 到目前为止发现了无数种抗性机制，并意识到多个细胞 表型可能存在于单一的基因中。肿瘤内异质性在肿瘤进展和转移中的作用 人们对耐药性知之甚少，我们认为，需要采取应对这种多样性的策略 患者的持久治疗反应。我们假设：(I)药物诱导的肿瘤细胞表型 异质性是由相互依赖和合作的代谢生态位的发展推动的，这些生态位促进 肿瘤扩散并影响对治疗方法的反应；以及(Ii)利用 克隆代谢竞争将在黑色素瘤患者中产生更持久的反应。我们将定义 氧磷依赖和糖酵解依赖亚群在细胞培养中的代谢格局， 使用单细胞RNAseq的异种移植物、患者来源的异种移植物(PDX)和患者标本，全球 代谢组学和靶向表达小组。我们将测试OXPHOS依赖和糖酵解- 依赖的亚群合作驱动转移并确定个体的药物敏感性 体外、异种移植和PDX中的亚群。我们将测试是否干扰了氧磷酶或糖酵解， 通过药物或通过基因表达靶向，形成转移潜力或出现和 耐药亚群的异质性。我们将设计一个数学模型来定义最优， BRAF/MEK加代谢抑制剂组合的进化知情剂量损害克隆 合作，最大限度地延长治疗反应的持续时间。这些模型将在体外和在 PDX型号。 K99/R00奖将允许候选人在继续指导下进行拟议的研究 他们的主要导师约翰·克利夫兰博士，以及一个共同导师小组，他们是著名的 黑色素瘤生物学、PDX模型、癌症进化、新陈代谢、统计和计算 系统范围内大规模数据集分析的方法。在K99阶段，候选人将进一步 培养他们在黑色素瘤代谢和进化生物学方面的知识，以及他们的技术能力 以质谱学为基础的代谢组学、高级计算生物学、生物信息学和生物统计学。 在他们的委员会以及各种研讨会、课程和工作坊的帮助下，候选人将成为 更强大的科学家、经理、拨款作者和导师，成为一名独立调查员所需的所有技能。 总体而言，拟议的工作将为利用克隆内的治疗方法提供理论基础 代谢竞争，为黑色素瘤患者提供更持久的反应，以及及时的职业生涯 对于一位有很大潜力成为一名成功的独立调查员的候选人来说，这是一个过渡。