An inducible molecular memory system to unravel the mechanisms of drug resistance in head and neck cancer

项目总结 HNSCC是癌症相关死亡的第六大原因。大多数死亡是由后转移引起的 治疗失败，但不幸的是，对推动治疗的分子途径有了坚定的理解 抵抗仍然难以捉摸。例如，目前还不清楚为什么绝大多数肿瘤细胞都能成功 通过治疗消除了，但也有一些逃脱了破坏。这些单元是否处于启用以下功能的特权单元状态 逃避这些药物？还是耐药会在治疗后适应性地出现？考虑到发病率和 与HNSCC相关的死亡率，迫切需要回答这些问题，但这一直是 受到两个主要障碍的阻碍。首先，HNSCC肿瘤是高度异质性的，所以大量的基因组方法 不能辨别可能产生抗性克隆的细胞亚群。第二，几乎所有现存的基因组 方法是破坏性的，需要在测量时进行样品裂解。这是一场“毁灭在 观测“使人们不可能把过去发生的分子事件与人类最终的命运联系起来。 发生这些事件的牢房。为了克服这些障碍，我们最近利用了单细胞RNA- SEQ(scRNA-seq)用于表征HNSCC肿瘤之间的异质性，定义了部分上皮到... 预测HNSCC结果的间充质(p-EMT)程序(Puram等人，Cell)。我们还开发了 一种新的单细胞‘呼叫卡’(SCCC)技术，可以记录任何 转录因子(TF)，创建一个永久的分子记忆的所有结合事件在给定的 时刻或纪元(Moudgil等人，Cell)。这使得瞬时的分子相互作用能够被非捕获 破坏性的，并在以后读出(例如，在药物治疗之后)，使我们能够“回到过去”，并确定 细胞状态使细胞能够抵抗治疗。我们通过将任何转铁蛋白融合到iggyBac转座酶上来实现这一点， 这赋予了转座子将转座子插入到其结合的基因组附近的能力。我们会 使用这项技术来确定西妥昔单抗治疗后HNSCC细胞持续存在的机制，然后 进化以产生抗药性克隆，识别可以作为辅助治疗靶点的基因和途径。 具体地说，我们假设存在处于预先存在的p-EMT状态的肿瘤细胞亚群 给予对药物治疗的免疫力。为了验证这一假设，我们将首先使用分子记忆工具 确定为什么有些细胞获得p-EMT，而其他细胞没有(目标1)。这一目标至关重要，因为EMT扮演着 在西妥昔单抗耐药性的发展中起着关键作用，我们在p-EMT方面的丰富经验将使我们能够 使用这个系统来成熟我们的分子存储工具并对其进行基准测试。探索西妥昔单抗的特异性通路 抗性，我们将使用SCCC来评估具有西妥昔单抗抗性和敏感性的HNSCC株系并记录 小区状态中的预先存在和适应性改变。我们将在体内验证这些发现，建立一组 负责治疗耐药的基因和分子通路，从而揭示新的靶点 克服这些机制以及治疗反应的生物标志物预测指标。

PROJECT SUMMARY HNSCC is the sixth leading cause of cancer-related mortality. Most deaths are caused by metastasis after treatment failure, but unfortunately, a firm understanding of the molecular pathways that drive treatment resistance remains elusive. For example, it is unclear why the vast majority of tumor cells are successfully eliminated by treatment, yet a few escape destruction. Are these cells in a privileged cell state that enables evasion of these drugs? Or does resistance emerge adaptively upon treatment? Given the morbidity and mortality associated with HNSCC, there is an urgent need to answer these questions, but this has been prevented by two major obstacles. First, HNSCC tumors are highly heterogeneous, so bulk genomic methods cannot discern subpopulations of cells that might give rise to resistant clones. Second, nearly all existing genomic methods are destructive and require specimen lysis at the time of measurement. This “destruction upon observation” has made it impossible to correlate molecular events that occurred in the past with the final fates of the cells in which these events took place. To overcome these barriers, we have recently utilized single-cell RNA- seq (scRNA-seq) to characterize heterogeneity among HNSCC tumors, defining a partial epithelial-to- mesenchymal (p-EMT) program which predicts HNSCC outcomes (Puram et al., Cell). We have also developed a novel single-cell ‘Calling Card’ (scCC) technology that can record the genome-wide interactions of any transcription factor (TF), creating a permanent molecular memory of all binding events that occur at a given moment or epoch (Moudgil et al., Cell). This allows transient molecular interactions to be captured non- destructively and read out later (e.g. after drug treatment), allowing us to “go back in time” and determine which cell states enabled a cell to resist treatment. We accomplish this by fusing any TF to the piggyBac transposase, which bestows the TF with the ability to direct transposon insertion into the genome near where it binds. We will use this technology to define the mechanisms by which HNSCC cells persist after cetuximab treatment and then evolve to produce resistant clones, identifying genes and pathways that can be targeted by adjuvant therapies. Specifically, we hypothesize there are subpopulations of tumor cells in a pre-existing p-EMT state that confers immunity to drug treatment. To test this hypothesis, we will first use our molecular memory tool to determine why some cells acquire p-EMT but others do not (Aim 1). This Aim is critical because EMT plays a key role in the development of cetuximab resistance, and our extensive experience with p-EMT will allow us to use this system to mature and benchmark our molecular memory tool. To probe pathways specific to cetuximab resistance, we will utilize scCC to evaluate HNSCC lines with cetuximab resistance and sensitivity and record both pre-existing and adaptive changes in cell state. We will validate these findings in vivo, establishing a set of genes and molecular pathways responsible for therapeutic resistance and thereby revealing new targets to overcome these mechanisms as well as biomarker predictors of treatment response.