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

该奖项是国家科学基金会努力促进癌症生物学基础研究取得重大进展的一部分，通过多学科研究，涉及理论物理，应用数学和计算机科学的专家。在过去的二十年里，已经看到了越来越有效的癌症治疗方法的发展，这些治疗方法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失调的转录程序。在包括急性髓性白血病（AML）和具有特定突变的非小细胞肺癌在内的癌症子集中，这些疗法在相当大比例的患者中导致显著的临床反应。然而，在大多数对抗癌疗法有反应的AML和肺癌患者亚组中，治疗复发随后发生，尽管通常在相当长的时间间隔之后，使得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治疗反应的过程，临床反应期间细胞亚群持续存在的基础，以及这两种肿瘤类型中驱动后续治疗复发的机制。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，该项目预计将导致对耐药性演变的更根本的理解，并为旨在预防临床耐药性出现的新型治疗策略的开发提供信息。虽然该项目的工作重点是肺癌和AML，但本文所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相关的一般原理和模型。该项目的综合方法将导致对癌症遗传和表观遗传演变的理解取得重大进展。对治疗复发的遗传和机制基础的研究，以及治疗前EGFR突变型肺癌和AML患者样本在达到最大临床缓解时的详细遗传、表观遗传和功能研究，在疾病复发时，将允许PI从DNA测序和基因表达谱中获得详细的数据集，以探测不同阶段的遗传和表观遗传多样性的动态，疾病反过来，这将指导进化过程的定量模型的开发，然后可以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和体外实验的策略，以功能测试从建模研究中产生的假设。

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.