Collaborative Research: The Genetic, Epigenetic, and Immunological Foundation of Cancer Evolution

合作研究：癌症进化的遗传、表观遗传和免疫学基础

• 批准号: 1545840
• 负责人: Daniel Fisher
• 金额: $ 34.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-15 至 2020-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1545840&HistoricalAwards=false
• 关键词: Collaborative; Research; Genetic; Epigenetic; Immunological

This award is part of the NSF effort to promote significant advances in the fundamental understanding of cancer biology made possible through multidisciplinary research that involves experts in theoretical physics, applied mathematics, and computer science.The last two decades have seen the development of increasingly effective cancer therapies that target different facets of transformed cells, including aberrant proliferation/survival, immune evasion, hyper-activated signaling pathways and dysregulated transcriptional programs. In a subset of cancers, including acute myeloid leukemia (AML) and non-small cell lung cancer with specific mutations, these therapies lead to dramatic clinical responses in a significant proportion of patients. However, in the majority of AML and subset of lung cancer patients who respond to anti-cancer therapies, therapeutic relapse subsequently ensues, although often after a considerable interval, such that these responses do not lead to long-term cures. In this project the PIs will use theoretical physics and mathematical modeling approaches to investigate the process of response to treatment, the basis for persistence of a subset of cells during clinical response, and the mechanisms driving subsequent therapeutic relapse in these two tumor types. This integrative approach will involve genomic, transcriptional, and phenotypic assays of tumors at the various stages of therapeutic response and resistance. As such, this project is expected to lead to a more fundamental understanding of the evolution of drug resistance and inform the development of novel therapeutic strategies aimed to prevent the emergence of clinical resistance. Although the efforts in this project will focus on lung cancer and AML, the results and approaches described herein will have broader relevance to oncology and are aimed to uncover general principles and models, which are relevant to the spectrum of human cancers. The integrative approach in this project will lead to major advances in the understanding of the genetic and epigenetic evolution of cancer. The studies into the genetic and mechanistic basis for therapeutic relapse, and the detailed genetic, epigenetic, and functional studies of EGFR mutant lung cancer and AML patient samples before therapy, at the time of maximal clinical response, and at disease relapse will allow the PIs to obtain detailed datasets from DNA sequencing and gene expression profiling to probe the dynamics of the genetic and epigenetic diversity at different phases of disease. In turn, this will guide the development of quantitative models of the evolutionary processes that can then be tested in the laboratory. The interplay between the modeling and data will guide strategies for future data collection and in vitro experiments to functionally test hypotheses that emanate from the modeling studies.

该奖项是NSF努力促进对癌症生物学的基本理解的重大进展的一部分程序。在癌症的一部分中，包括急性髓样白血病（AML）和具有特异性突变的非小细胞肺癌，这些疗法在很大一部分患者中导致了巨大的临床反应。但是，在大多数AML和对抗癌疗法反应的肺癌患者的子集中，随后治疗复发随后发生，尽管通常是在相当长的间隔之后，因此这些反应不会导致长期治愈。在该项目中，PI将使用理论物理学和数学建模方法来研究对治疗的反应过程，临床反应过程中细胞子集的持久性的基础以及在这两种肿瘤类型中推动随后的治疗性复发的机制。这种综合方法将涉及在治疗反应和抗性的各个阶段的肿瘤的基因组，转录和表型测定。因此，预计该项目将对耐药性的演变产生更基本的了解，并为旨在防止临床抗性的出现而发展的新型治疗策略发展。尽管该项目的努力将集中在肺癌和AML上，但此处描述的结果和方法将与肿瘤学具有更广泛的相关性，并旨在揭示与人类癌症谱系相关的一般原则和模型。该项目的综合方法将导致理解癌症遗传和表观遗传进化的重大进展。 The studies into the genetic and mechanistic basis for therapeutic relapse, and the detailed genetic, epigenetic, and functional studies of EGFR mutant lung cancer and AML patient samples before therapy, at the time of maximal clinical response, and at disease relapse will allow the PIs to obtain detailed datasets from DNA sequencing and gene expression profiling to probe the dynamics of the genetic and epigenetic diversity at different phases of disease.反过来，这将指导开发进化过程的定量模型，然后在实验室中进行测试。建模和数据之间的相互作用将指导未来数据收集的策略和体外实验，以在功能上测试从建模研究中散发出的假设。