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

项目摘要 肿瘤异质性是癌症患者中可变应答和治疗失败的主要因素。 通常，癌症中的异质性被认为是预先产生耐药性的基因突变。 在治疗过程中出现或出现。然而，最近的研究，包括我们自己的，越来越多地指出，非- 异质性的遗传来源是肿瘤反应早期阶段的关键因素。非遗 已知的机制是细胞过程的基础，例如干细胞分化和上皮细胞向 间充质转化在细菌中，已显示同基因细胞群体在细胞中多样化。 不存在扰动（例如，药物）转化为各种细胞表型，每种表型对 潜在的压力源这种“赌注对冲”策略增加了一部分人将 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 在获得遗传抗性之前，“耐受”细胞可以在患者体内持续很长一段时间 导致肿瘤复发的突变。这项提案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， 实验方法在目标1中，我建议构建一个详细的生化反应动力学模型， 控制单个癌细胞分裂和死亡决定的信号网络。公认的情况是 复杂的生物化学网络可以产生多种稳定的平衡状态， “吸引子”每个吸引子对应于一个细胞表型，可以被概念化为一个盆地 在“表观遗传景观”中细胞可以在表型之间转换，其速率取决于细胞的生长。 盆地的深度和分隔它们的屏障的高度。使用动力系统分析 方法，我将数学解决生化分裂/死亡模型的表观遗传景观 量化所有吸引子的分子特征在目标2中，使用BRAF突变型黑色素瘤和EGFR- 突变型肺癌作为体外模型系统，我将使用克隆和单细胞RNA测序， 染色质可及性测序（ATAC-seq），以计数 非遗传表型状态。我还将利用全外显子组测序来建立非遗传的 表型和免疫受损小鼠模型的性质，以验证模型预测。 实验和计算机模拟分子特征之间的差异将导致模型改进， 进一步的实验。量化癌细胞的表观遗传景观将为 基于合理修改景观以支持具有增加的药物的表型的新疗法 敏感性，一种被称为“有针对性的美化”的方法。这将减少耐药细胞的大小， 汇集和延迟，也许是无限期地，获得遗传抗性突变和肿瘤复发。

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.