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: 1546101
• 负责人: Chang Chan
• 金额: $ 34.46万
• 依托单位: Rutgers, The State University of New Jersey-RBHS-CINJ
• 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=1546101&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.

该奖项是美国国家科学基金会（NSF）努力的一部分，旨在通过涉及理论物理学、应用数学和计算机科学专家的多学科研究，促进对癌症生物学基础理解的重大进展。在过去的二十年里，针对转化细胞的不同方面，包括异常增殖/存活、免疫逃避、过度激活的信号通路和转录程序失调，癌症治疗方法的发展越来越有效。在一些癌症中，包括急性髓性白血病（AML）和具有特定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显著的临床反应。然而，在大多数对抗癌治疗有反应的AML和部分肺癌患者中，治疗性复发随后发生，尽管通常要经过相当长的一段时间，因此这些反应不能导致长期治愈。在这个项目中，pi将使用理论物理和数学建模方法来研究对治疗的反应过程，临床反应期间细胞亚群持续存在的基础，以及驱动这两种肿瘤类型随后治疗复发的机制。这种综合方法将涉及肿瘤在治疗反应和耐药的各个阶段的基因组、转录和表型分析。因此，该项目有望导致对耐药性演变的更基本的理解，并为旨在防止临床耐药性出现的新治疗策略的发展提供信息。虽然这个项目的工作将集中在肺癌和AML上，但这里描述的结果和方法将与肿瘤学有更广泛的相关性，旨在揭示与人类癌症谱系相关的一般原理和模型。该项目的综合方法将导致对癌症的遗传和表观遗传进化的理解取得重大进展。研究治疗性复发的遗传和机制基础，以及在治疗前、最大临床反应时和疾病复发时对EGFR突变肺癌和AML患者样本进行详细的遗传学、表观遗传学和功能研究，将使pi能够从DNA测序和基因表达谱中获得详细的数据集，以探索疾病不同阶段遗传和表观遗传多样性的动态。反过来，这将指导进化过程的定量模型的发展，然后可以在实验室进行测试。建模和数据之间的相互作用将指导未来数据收集和体外实验的策略，以功能测试从建模研究中产生的假设。