Uncovering the molecular networks underlying non-genetic heterogeneity in cancer cell populations

项目总结 肿瘤的异质性是癌症患者可变反应和治疗失败的主要原因。 通常，癌症中的异质性被认为是导致耐药的基因突变，这些突变前 在治疗过程中存在或出现。然而，最近的研究，包括我们自己的研究，越来越多地指出 遗传来源的异质性是肿瘤反应早期阶段的关键因素。非遗传性 已知的机制是细胞过程的基础，如干细胞分化和上皮到上皮细胞 间充质转化。在细菌中，同基因的细胞群体已经被证明在 不存在对各种细胞表型的干扰(例如，药物)，每种细胞表型都具有不同的适应性 潜在的压力源。这种“押注对冲”策略增加了一部分人口将 在未来未知的挑战中生存下来。我们和其他人最近假设癌细胞 采用类似的生存策略来抵御抗癌药物的最初冲击。所谓的“毒品” 在获得遗传抵抗力之前，“耐受”细胞可能会在患者体内持续较长时间。 导致肿瘤复发的突变。这项提议的目的是揭示分子因素。 控制癌细胞群体中的非遗传异质性使用组合计算和 实验方法。在目标1中，我建议构建一个详细的生化动力学模型。 控制单个癌细胞分裂和死亡决定的信号网络。它由来已久。 复杂的生化网络可以产生多个稳定的平衡状态，称为 “吸引器。”每个吸引子对应一种细胞表型，可以概念化为一个盆地。 在“表观遗传景观”中。细胞可以在表型之间转换，转换的速度取决于 盆地的深度和分隔盆地的屏障的高度。使用动力系统分析 方法，我将从数学上解决生化分裂/死亡模型的表观遗传图景 并量化所有吸引子的分子签名。在目标2中，使用BRAF突变黑色素瘤和EGFR- 以突变型肺癌为体外模型系统，我将采用克隆和单细胞RNA测序 染色质可及性测序(ATAC-SEQ)以计数细胞的数量和分子特征 非遗传表型状态。我还将利用全外显子组测序来确定非遗传性 表型和免疫功能低下的小鼠模型的性质，以验证模型预测。 实验和电子分子特征之间的差异将导致模型的改进和 进一步的实验。量化癌细胞的表观遗传格局将为 基于合理改变景观以支持药物增加的表型的新疗法 敏感性，一种被称为“有针对性的美化”的方法。这将减少对药物的耐受性 集中和延迟，也许是无限期地，获得遗传耐药突变和肿瘤复发。

PROJECT SUMMARY Tumor heterogeneity is a major contributor to variable response and treatment failure in cancer patients. Usually, heterogeneity in cancer is thought of in terms of resistance-conferring genetic mutations that pre- exist or emerge during treatment. However, recent studies, including our own, increasingly point to non- genetic sources of heterogeneity as critical factors in the early stages of tumor response. Non-genetic mechanisms are known to underlie cellular processes such as stem cell differentiation and epithelial-to- mesenchymal transitions. In bacteria, isogenic cell populations have been shown to diversify in the absence of perturbations (e.g., drugs) into a variety of cellular phenotypes, each with differential fitness to potential stressors. This “bet hedging” strategy increases the odds that a portion of the population will survive a future, unknown challenge. We, and others, have recently hypothesized that cancer cells employ a similar survival strategy to withstand the initial onslaught of anticancer drugs. So-called “drug tolerant” cells may persist within a patient for extended periods of time before acquiring genetic resistance mutations that lead to tumor recurrence. The objective of this proposal is to uncover the molecular factors that control non-genetic heterogeneity in cancer cell populations using a combined computational and experimental approach. In Aim 1, I propose to construct a detailed kinetic model of the biochemical signaling networks that control division and death decisions in individual cancer cells. It is well established that complex biochemical networks can give rise to multiple stable equilibrium states, known as “attractors.” Each attractor corresponds to a cellular phenotype and can be conceptualized as a basin within an “epigenetic landscape.” Cells can transition between phenotypes with rates dependent upon the depths of the basins and the heights of the barriers separating them. Using a dynamical systems analysis approach, I will mathematically solve for the epigenetic landscape of the biochemical division/death model and quantify molecule signatures for all attractors. In Aim 2, using BRAF-mutant melanoma and EGFR- mutant lung cancer as in vitro model systems, I will use clonal and single-cell RNA sequencing and chromatin accessibility sequencing (ATAC-seq) to enumerate the number and molecular signatures of non-genetic phenotypic states. I will also utilize whole-exome sequencing to establish the non-genetic nature of the phenotypes and immunocompromised mouse models to validate model predictions. Differences between the experimental and in silico molecular signatures will lead to model refinement and further experimentation. Quantifying the epigenetic landscapes of cancer cells will lay the groundwork for novel therapies based on rationally modifying the landscape to favor phenotypes with increased drug sensitivity, an approach termed “targeted landscaping.” This would reduce the size of the drug-tolerant pool and delay, perhaps indefinitely, the acquisition of genetic resistance mutations and tumor recurrence.