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，它们 不要工作。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、多西紫杉醇、长春瑞滨、灯盏花素和依沙比平。多条线路 来自我们实验室和其他机构的大量证据支持染色体不稳定(CIN)是 控制紫杉醇和其他抗微管药物反应的癌症特征。中心假设是 先前存在的CIN水平最高的乳腺肿瘤对增强的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的原因是多极 患者肿瘤中的纺锤波，类似浓度的其他抗微管药物会导致多极纺锤波 间期FISH检测CIN与转移瘤紫杉烷反应相关 乳腺癌。目标1将确定临床上有用的微管毒素是否普遍导致多极 纺锤。紫杉醇、多西紫杉醇、灯盏花素、长春瑞滨和伊克沙比隆对有丝分裂纺锤体的影响将被测试。 在细胞模型、小鼠模型和从人类乳腺癌中获得的样本中的形态和功能 作为标准护理的一部分，患者将这些治疗作为单一药物接受治疗。因此，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效应。目标 2将确定哪些类型和程度的CIN对不同的微管靶向药物具有敏感性。四 CIN模型将被用来产生特定的有丝分裂缺陷，包括多极分裂、极分裂、 染色体、滞后染色体和染色体桥以确定的速率，这些将被测试 在多种模型中对微管靶向药物的敏感性。患者起源的原发器质性乳腺癌 具有明确的CIN机制的培养将进行平行测试。目标3将建立一个标准化的方法 目的：量化CIN作为乳腺癌的生物标志物。将使用四个CIN模型进行比较 已提出的CIN定量方法包括间期FISH、批量DNA和RNA测序以及数字测序 来自低通单细胞DNA测序的核型。我们预计，这将为准确地 从数以千计的已测序肿瘤中推断CIN，这些肿瘤的数据是公开的。这些措施包括 CIN还将被评估其预测转移性乳腺癌患者紫杉烷反应的能力， 使用存档的肿瘤样本，以验证预测紫杉醇疗效的能力。这项工作意义重大 因为它将增进我们对广泛使用的抗癌药物的机制的了解，以及CIN如何 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 在每个目标中都包含了模型和人体样本。最终，所获得的知识将允许 准确预测哪些患者将从广泛使用的治疗中受益，哪些患者不会受益，从而使 有可能解决目前癌症过度治疗和治疗无效的问题。

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.