Tracking the Mechanisms of Adaptation to Autophagy Inhibition

摘要 对抗癌药物的获得性耐药已被证明是 癌细胞生物学，因为癌细胞具有非凡的适应能力 条件。例如，当必要的代谢过程受阻时，一些癌细胞就会死亡， 但细胞亚群可以存活并获得抵抗力。细胞器回收过程， 自噬为研究癌症中的代谢适应提供了一个极好的范例。许多 癌细胞沉迷于自噬，以维持体内平衡和再生营养，但 以前的工作强调了稀有细胞快速适应和获得新依赖关系的能力 在另一条代谢途径上。对压力的快速和短暂的适应，表现在 代谢组、表观基因组和转录组的研究还不够深入。 这一提议表明，抵抗机制比预先存在的机制更为复杂 异质肿瘤细胞之间的遗传差异，但相反包括快速信号 事件，广泛的应激和代谢反应，表观遗传变化，以及新的 基因突变。这些因素中的每一个是如何以及何时导致耐药性的仍然存在。 未知。许多研究分析了适应的种群经过选择后的情况。这个 这里采取的方法是不同的：这些研究的目的是观察选择和 行动中的适应。拟议的项目将开发一套新的工具和模型系统 为了跟踪快速信号、应激和代谢反应的动态相互作用，以及 转录变化、表观遗传变化和基因变化--所有这些都具有时间精确度。 尽管对治疗耐药性进行了数十年的研究，但根本问题仍然存在。为 例如，关键是要确定：a)癌细胞是否经历状态变化和适应 响应于处理，或者B)处理简单地选择预先存在的状态，即 异源的并且已经具有抗性。区分这两者之间的动态是至关重要的 确定给定的抗性机制是否应该作为组合的目标的模型 治疗(模式A的结果)，或作为患者选择的生物标记物(a 模式B的后果)。一些患者对自噬抑制的反应非常好， 这一领域迫切需要与这些患者相关的两种生物标志物来改善患者 选择，并寻找防止治疗抵抗的方法。为此，这些研究将有助于 更好的自噬靶向癌症疗法的发展。此外，理解 不同类型的适应在时间上的动态贡献将产生新的 癌细胞的耐药性，超越了那些模拟自噬调节的基因。

SUMMARY Acquired resistance to anti-cancer therapeutics has proven to be one of the largest hurdles in cancer cell biology because cancer cells have the remarkable ability to adapt to diverse conditions. For example, when essential metabolic processes are blocked, some cancer cells die, but subsets of cells can survive and acquire resistance. The organelle recycling process, autophagy, provides an excellent paradigm to study metabolic adaptations in cancer. Many cancer cells are addicted to autophagy to maintain homeostasis and regenerate nutrients, but previous work highlighted the ability of rare cells to rapidly adapt and acquire new dependencies on alternate metabolic pathways. Rapid and transient adaptations to stress that manifest in the metabolome, epigenome and transcriptome have been understudied. This proposal suggests that resistance mechanisms are more complex than just pre-existing genetic differences between heterogeneous tumor cells, but instead include rapid signaling events, broad stress and metabolic responses, epigenetic changes, and the acquisition of new genetic mutations. How and when each of these factors contribute to resistance remains unknown. Many studies analyze adapted populations after they have undergone selection. The approach taken here is different: these studies aim to observe the process of selection and adaptation in action. The proposed projects will develop a set of novel tools and model systems to track the dynamic interactions of rapid signaling, stress and metabolic responses, along with transcriptional changes, epigenetic changes, and genetic alterations – all with temporal precision. Despite decades of studies on therapeutic resistance, fundamental questions remain. For example, it is critical to determine whether: A) cancer cells undergo a change in state and adapt in response to a treatment, or B) a treatment simply selects for a pre-existing state that is heterogenous and already resistant. It is critical to differentiate the dynamics between these two models to determine whether a given resistance mechanism should be targeted as a combination therapy (a consequence of Model A), or instead used as a biomarker for patient selection (a consequence of Model B). Some patients respond remarkably well to autophagy inhibition and the field is desperate for both biomarkers associated with these patients to improve patient selection, and for ways to prevent therapy resistance. To this end, these studies will facilitate the development of better autophagy-targeting cancer therapeutics. Moreover, understanding the temporally dynamic contributions of different kinds of adaptations will generate new models of cancer cell drug resistance, beyond those that model autophagy modulation.