Impact of chromosomal instability on sensitivity to microtubule-targeting drugs in breast cancer

项目摘要 几种微管靶向药物通常用于治疗乳腺癌，但对许多患者来说， 不工作。这项研究的长期目标是准确预测哪些患者将受益于 微管靶向药物包括紫杉醇、多西他赛、长春瑞滨、艾日布林和伊沙匹隆。多行 来自我们实验室和其他实验室的大量证据支持这样一种观点，即染色体不稳定性（CIN）是 这是癌症对紫杉醇和其他抗微管药物反应的特点。核心假设是 具有最高水平的预先存在的CIN的乳腺肿瘤对引起的增强的CIN最敏感， 通过微管靶向治疗。我们的初步数据显示，紫杉醇导致CIN由于多极 类似浓度的其他抗微管药物会导致多极纺锤体 在培养的细胞中，通过间期FISH测量的CIN与转移性肿瘤中的紫杉烷反应相关， 乳腺癌目的1将确定临床上有用的微管毒物是否普遍诱导多极 纺锤。将检测紫杉醇、多西他赛、艾日布林、长春瑞滨和伊沙匹隆对有丝分裂纺锤体的影响 在细胞模型、小鼠模型和从人乳腺癌中获得的样品中， 作为标准治疗的一部分接受这些治疗的患者。这一目标将 确定这些微管靶向药物对癌症是否具有相似或不同的生物学作用。目的 2将确定哪些类型和程度的CIN赋予不同的微管靶向药物的敏感性。四 CIN的模型将用于产生特定的有丝分裂缺陷，包括多极分裂、极分裂和极分裂。 染色体、落后染色体和染色体桥，这些将被测试， 在多种模型中对微管靶向药物的敏感性。患者源性原发性类器官乳腺癌 将平行检测具有明确CIN机制的培养物。目标3将建立标准化方法 来量化CIN作为人类乳腺癌的生物标志物。四个CIN模型将用于比较 提出的定量CIN的方法，包括间期FISH，批量DNA和RNA测序，以及数字 低通单细胞DNA测序的核型。我们预计，这将提供一个基础， 从数以千计的测序肿瘤中推断CIN，这些肿瘤的数据在医学上可用。的这些举措 还将评估CIN预测转移性乳腺癌患者中紫杉烷反应的能力， 使用存档的肿瘤样品，以验证预测对紫杉醇的反应的能力。这项工作意义重大 因为它将推进我们对广泛使用的癌症药物的机制以及CIN（一种 肿瘤生物学的共同特征，影响对这些药物的反应。它通过以下方式确保临床相关性 在每个目标中结合模型和人体样本。最终获得的知识将允许 准确预测将从广泛使用的治疗中受益和不会受益的患者，从而使 这可能解决目前存在的过度治疗和无效治疗癌症的问题。

Project Summary Several microtubule-targeted drugs are commonly used to treat breast cancer, but for many patients they do not work. The long-term goal of this research is to accurately predict which patients will benefit from microtubule-targeted drugs including paclitaxel, docetaxel, vinorelbine, eribulin, and ixabepilone. Multiple lines of evidence from our laboratories and others support the idea that chromosomal instability (CIN) is the key feature of cancer governing response to paclitaxel and other anti-microtubule drugs. The central hypothesis is that breast tumors with the highest levels of pre-existing CIN are most sensitive to the enhanced CIN caused by microtubule-targeted therapies. Our preliminary data show that paclitaxel causes CIN due to multipolar spindles in patient tumors, that similar concentrations of other anti-microtubule drugs cause multipolar spindles in cultured cells, and that CIN measured by interphase FISH correlates with taxane response in metastatic breast cancer. Aim 1 will determine whether clinically useful microtubule poisons universally induce multipolar spindles. Paclitaxel, docetaxel, eribulin, vinorelbine, and ixabepilone will be tested for effects on mitotic spindle morphology and function in cell models, mouse models, and in samples obtained from human breast cancer in patients receiving these treatments as single agents as part of the standard of care. This aim will thereby determine whether these microtubule-targeted drugs have similar or disparate biologic effects on cancer. Aim 2 will determine which types and degrees of CIN confer sensitivity to diverse microtubule targeted agents. Four models of CIN will be used to generate specific mitotic defects including multipolar divisions, polar chromosomes, lagging chromosomes, and chromosome bridges at defined rates, and these will be tested for sensitivity to microtubule-targeted drugs in multiple models. Patient-derived primary organoid breast cancer cultures with defined mechanisms of CIN will be tested in parallel. Aim 3 will establish a standardized method to quantify CIN to use as a biomarker in human breast cancer. The four CIN models will be used to compare proposed methods to quantify CIN including interphase FISH, bulk DNA and RNA sequencing, and digital karyotypes from low-pass single-cell DNA sequencing. We anticipate that this will provide a basis to accurately infer CIN from the thousands of sequenced tumors for which data is publically available. These measures of CIN will also be evaluated for their ability to predict taxane response in metastatic breast cancer patients, employing archived tumor samples, to verify ability to predict response to paclitaxel. The work is significant because it will advance our knowledge of the mechanism of widely used cancer drugs as well as how CIN, a common feature of tumor biology, affects response to these agents. It ensures clinical relevance by incorporating both models and human samples in each aim. Ultimately the knowledge gained will allow for accurate prediction of patients who will and will not benefit from widely used treatments, and thereby has the potential to address the ongoing problem of overtreatment and ineffective treatment of cancer.