Integrated genomic analysis and multi-scale modeling of therapeutic resistance

描述(申请人提供)：治疗抵抗是患者死亡的一个主要原因，在实体肿瘤中几乎是普遍的，包括乳腺癌。例如，曲妥珠单抗(Herceptin)是针对20%的人表皮生长因子受体2(HER2)阳性(HER2+)乳腺癌患者的典型靶向治疗，但治疗只能部分降低早期疾病的复发风险，而在晚期则不能治愈。 布景。虽然肿瘤干细胞(CSCs)和肿瘤内异质性(ITH)都被认为与肿瘤的进展和耐药有关，但耐药机制在人类系统中仍然缺乏特征，只有在识别出耐药亚克隆并成功靶向后才能解决。尽管癌症基因组表面上的混乱令人望而生畏，但肿瘤是由可以测量和利用的进化原则支配的。然而，需要考虑克隆进化、ITH和CSCs的定量方法。为此，我们开发了一个创新的实验和计算框架，该框架利用了躯体获取的关于过去增殖史的报告这一事实 并可用来推断其亚克隆结构和进化轨迹。通过将患者样本的基因组图谱集成到肿瘤生长的多尺度模型和统计推断框架中，该方法能够测量克隆扩张的动力学和患者特定的参数。我们假设，对治疗期间临床样本中肿瘤进化动力学和分子变化的详细描述将使公正地识别新的生物标记物和耐药机制成为可能。鉴于HER2是一个有效的治疗靶点，存在几种有效但不完美的治疗方法，这是一个了解耐药机制的极好模型。我们建议对临床试验中HER2+乳腺癌患者的一系列组织样本进行综合分子分析，以确定耐药的生物标记物(目标1)。基因组数据将在我们的计算框架中进行分析，以量化CSC动力学和在治疗选择压力下克隆进化的时间模式(目标2)。我们将使用患者来源的异种移植模型和短期原发患者培养进一步表征耐药机制、与治疗相关的暂时分子变化和耐药亚群(目标3)。通过询问治疗过程中的克隆进化，我们的创新方法将识别耐药机制和肿瘤动力学，为生物标记物驱动的治疗策略提供信息。这一策略代表了治疗分层的新范例，具有广泛的实用价值。 治疗其他癌症。

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.