Integrated genomic analysis and multi-scale modeling of therapeutic resistance

描述（申请人提供）：治疗耐药性是患者死亡的主要原因，在实体瘤（包括乳腺癌）中几乎普遍存在。例如，曲妥珠单抗（赫赛汀）是用于20%的人表皮生长因子受体2（HER2）阳性（HER2+）乳腺癌患者的原型靶向疗法，但治疗仅部分降低早期疾病的复发风险，并且在晚期乳腺癌中不能治愈。 设置.虽然癌症干细胞（CSC）和肿瘤内异质性（ITH）都被认为有助于肿瘤进展和耐药性，但耐药机制在人类系统中的特征仍然很差，只有当耐药亚克隆被鉴定并成功靶向时才能得到解决。尽管癌症基因组的明显混乱令人生畏，但肿瘤是由可以测量和利用的进化原则控制的。然而，定量的方法，占克隆进化，ITH和CSC是必要的。为此，我们开发了一个创新的实验和计算框架，利用体细胞获得性报告过去增殖的历史， 并可用于推断其亚克隆结构和进化轨迹。通过将来自患者样本的基因组谱整合到肿瘤生长的多尺度模型和统计推断框架中，这种方法能够测量克隆扩增的动态和患者特异性参数。我们假设，治疗期间临床样本中肿瘤进化动力学和分子变化的详细表征将能够公正地鉴定新的生物标志物和耐药机制。鉴于HER2是一种经过验证的治疗靶点，存在几种有效但不完美的治疗方法，这是了解耐药机制的绝佳模型。我们提出了一个整合的分子分析的系列组织标本从HER2+乳腺癌患者在临床试验中接受新辅助单药和双药HER2靶向治疗，以确定耐药的生物标志物（目标1）。将在我们的计算框架中分析基因组数据，以量化CSC动态和治疗选择压力下克隆进化的时间模式（目的2）。我们将使用患者来源的异种移植模型和短期原代患者培养物进一步表征耐药机制、治疗相关的时间分子变化和耐药亚群（目的3）。通过询问治疗过程中的克隆进化，我们的创新方法将确定耐药机制和肿瘤动力学，为生物标志物驱动的治疗策略提供信息。该策略代表了具有广泛效用的治疗分层的新范例 对于其他癌症。

DESCRIPTION (provided by applicant): Therapeutic resistance is a major cause of patient mortality, and is nearly universal in solid tumors, including breast cancer. For example, trastuzumab (herceptin) is the archetype targeted therapy for the 20% of human epidermal growth factor receptor 2 (HER2)-positive (HER2+) breast cancer patients, but treatment only partially lowers the risk of recurrence in early stage disease, and is not curative in the advanced setting. While both cancer stem cells (CSCs) and intra-tumor heterogeneity (ITH) are thought to contribute to tumor progression and resistance, mechanisms of resistance remain poorly characterized in the human system, and will only be addressed when resistant subclones are identified and successfully targeted. Although the apparent chaos that characterizes cancer genomes is daunting, tumors are governed by evolutionary principles that can be measured and exploited. However, quantitative approaches that account for clonal evolution, ITH, and CSCs are needed. To this end, we have developed an innovative experimental and computational framework that exploits the fact that somatically acquired report on the past proliferative history of cancer cells and can be used to infer their subclonal architecture and evolutionary trajectories. By integrating genomic profiles from patient samples in a multi-scale model of tumor growth and statistical inference framework, this approach enables measurement of the dynamics of clonal expansions and patient-specific parameters. We hypothesize that a detailed characterization of tumor evolutionary dynamics and molecular changes in clinical samples during treatment will enable the unbiased identification of novel biomarkers and mechanisms of resistance. Given that HER2 is a validated therapeutic target for which several effective, but imperfect treatments exist, this is an excellent model in which to understand mechanisms of resistance. We propose an integrated molecular analysis of serial tissue specimens from HER2+ breast cancer patients treated in clinical trials with neoadjuvant single and dual agent HER2-targeted therapies to identify biomarkers of resistance (Aim 1). The genomic data will be analyzed in our computational framework to quantify CSC dynamics and temporal patterns of clonal evolution under treatment selective pressure (Aim 2). We will further characterize mechanisms of resistance, treatment-associated temporal molecular changes, and resistant subpopulations using patient- derived xenograft models and short-term primary patient cultures (Aim 3). By interrogating clonal evolution during therapy, our innovative approach will identify mechanisms of resistance and tumor dynamics that inform biomarker-driven treatment strategies. This strategy represents a new paradigm for treatment stratification with broad utility for other cancers.