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

该奖项是NSF努力促进癌症生物学基础研究取得重大进展的努力的一部分，该研究涉及理论物理、应用数学和计算机科学方面的专家。过去20年来，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异常转录程序，见证了日益有效的癌症治疗方法的发展。在包括具有特定突变的急性髓系白血病(AML)和非小细胞肺癌在内的一组癌症中，这些疗法在相当一部分患者中产生了戏剧性的临床反应。然而，在大多数对抗癌治疗有反应的AML和肺癌患者亚群中，随后会出现治疗性复发，尽管通常在相当长的间隔之后，因此这些反应不会导致长期治愈。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，临床反应期间细胞子集持续存在的基础，以及推动这两种肿瘤随后的治疗复发的机制。这一综合方法将包括对治疗反应和耐药不同阶段的肿瘤进行基因组、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了解，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。虽然该项目的工作重点将放在肺癌和急性髓细胞白血病上，但这里描述的结果和方法将与肿瘤学具有更广泛的相关性，旨在揭示与人类癌症谱系相关的一般原理和模型。该项目中的综合方法将导致在理解癌症的遗传和表观遗传进化方面取得重大进展。对治疗复发的遗传和机制基础的研究，以及在治疗前、临床反应最大时和疾病复发时对EGFR突变肺癌和AML患者样本的详细遗传、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的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量化模型的开发，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，以从功能上测试源自建模研究的假设。

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.