A Translational Model of Evolution of Myeloma Adhesion-Mediated Drug Resistance

骨髓瘤粘附介导的耐药性进化的转化模型

基本信息

批准号：
8230163
负责人：
Ariosto S Silva
金额：
$ 23.54万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-24 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8230163
关键词：
Adhesions Affect Aspirate substance Biological Markers Biopsy Blood flow Bone Marrow Bone Marrow Cells Bone marrow biopsy Cell Adhesion Cell Adhesion Molecules Cell Line Cells Cessation of life Clinical Trials Coculture Techniques Complex Computational Technique Computer Simulation Data Development Devices Disease Dose Doxorubicin Drug Combinations Drug Delivery Systems Drug resistance Elements Environment Evolution Extracellular Matrix Fibronectins Fluorescence Future Grant Growth Heterogeneity Hypoxia Immune response Immunohistochemistry In Vitro Individual Integrins Label Life Location Malignant - descriptor Malignant Neoplasms Marrow Mediating Melphalan Mesenchymal Microfluidics Modeling Multiple Myeloma Newly Diagnosed Oxygen Patients Pharmaceutical Preparations Phenotype Plasma Cells Proliferating Proteins Protocols documentation Refractory Relapse Resistance Sampling Serum Simulate Staging Stromal Cells Structure System Techniques Time Tissue Sample Treatment Failure Tumor Burden Validation Variant Vascular Cell Adhesion Molecule-1 chemotherapy cytokine density drug efficacy drug mechanism host neoplasm interaction in vivo indexing novel pre-clinical research clinical testing research study response simulation standard of care therapy outcome

项目摘要

DESCRIPTION (provided by applicant): Multiple Myeloma (MM) is an incurable disease in which malignant plasma cells proliferate excessively in the bone marrow (BM). Most patients initially respond well to therapy, which can extend survival for several years. However, these patients inevitably become refractory to therapy, resulting in relapse and death. Thus, persistence of MM cells following initial therapy leads to evolution of resistance and is the major cause of treatment failure. Development of MM is the result of complex tumor-host interactions in the bone marrow. These interactions also protect the MM cells from immune response and chemotherapy?termed cell-adhesion mediated drug resistance (CAM-DR). Our group has recently shown that relapsed patients (n=7) show significantly higher levels of CAM-DR markers (levels of a4 integrin in CD138+ MM cells from bone marrow aspirates) than observed in newly diagnosed patients (n=7). This strongly suggests that MM cells adapt to therapy at least in part by upregulating adhesion-mediated interactions. MM response to therapy can be evaluated using in vitro co-culture models. However, these data are limited to fixed time-points and drug concentrations, so that temporal and spatial heterogeneity that typically occur in the phenotype of MM cells and the BM environment are not examined. Thus, while traditional in vitro techniques are necessary for demonstrating drug efficacy and mechanisms, they are insufficient to fully characterize the range of cell response in vivo due to variation in CAM-DR, disordered vascularity and blood flow. Thus, factors likely to affect the outcome of therapy in individual patients (such as gradients of drugs and oxygen, or variability in regional density of bone marrow mesenchymal or adhesion molecules) have not, until now, been included in pre-clinical evaluation of MM therapies. In this study we propose an integrated computational/experimental approach that better recreates the temporal and spatial BM heterogeneity, utilizing a novel micro-fluidic device that can generate gradients of chemotherapeutic drugs and CAM-DR dynamics. These data will be used to parameterize computational models of the 3D BM structure, capable of reproducing the evolution of CAM-DR and therapy response. Preliminary validation of this proof of concept will consist in treatment simulations of personalized patient BM computational models, and comparing the model predictions to obtained BM biopsies. The end point of this exploratory grant is the proof-of-concept of this approach, namely a set of preliminary results from a small group of patients (n=9, split in three different stages of the disease), a customizable computational model of patient myelomatous bone marrow, and an optimized protocol for use of patient samples, immunohistochemistry and microfluidics for generating these models, which we plan to use in the future to support a clinical trial as an ancillary biomarker. PUBLIC HEALTH RELEVANCE: Multiple Myeloma (MM) is an incurable malignant cancer of the bone marrow (BM), and although therapy can extend survival of most patients for several years, the cancer inevitably becomes resistant, resulting in relapse and death. There are many different drugs available for MM treatment, but due to the complexity of the disease, it is difficult for a clinician to choose the best treatment for a given patient. We propose a novel combination of experimental and computational techniques to use information extracted from live cells and tissue samples from MM patients, to build personalized models of estimation of patient response to therapy.

描述（由申请人提供）：多发性骨髓瘤（MM）是一种无法治愈的疾病，其中恶性血浆细胞在骨髓（BM）中过度增殖。大多数患者最初对治疗的反应良好，可以将生存延长几年。但是，这些患者不可避免地会成为治疗的难治性，从而导致复发和死亡。因此，初始治疗后MM细胞的持久性会导致抗药性的演变，并且是治疗失败的主要原因。 MM的发展是骨髓中复杂的肿瘤宿主相互作用的结果。这些相互作用还可以保护MM细胞免受免疫反应和化学疗法的影响，称为细胞粘附介导的耐药性（CAM-DR）。我们的小组最近表明，复发患者（n = 7）显示出比新诊断的患者观察到的CAM-DR标记水平（CD138+ MM细胞中的A4整合素水平）（n = 7）。这强烈表明，MM细胞至少部分通过上调粘附介导的相互作用而适应治疗。可以使用体外共培养模型评估MM对治疗的反应。但是，这些数据仅限于固定时间点和药物浓度，因此未检查通常发生在MM细胞表型中的时间和空间异质性和BM环境。因此，尽管传统的体外技术对于证明药物疗效和机制是必需的，但由于CAM-DR，血管无序和血流的变化，它们不足以完全表征体内细胞反应的范围。因此，直到现在，直到现在，可能会影响个别患者（例如药物和氧梯度）的因素（例如药物和氧梯度，或骨髓间充质或粘附分子的区域密度的变异性），直到现在，尚未包括在MM治疗的临时评估中。在这项研究中，我们提出了一种综合计算/实验方法，可以利用一种可以生成化学治疗药物和CAM-DR动力学梯度的新型微富集设备来更好地重现时间和空间BM异质性。这些数据将用于参数化3D BM结构的计算模型，能够重现CAM-DR和治疗反应的演变。此概念证明的初步验证将包括对个性化患者BM计算模型的治疗模拟，并将模型预测与获得的BM活检进行比较。这种探索性赠款的终点是这种方法的概念证明，即一组少数患者（n = 9，在疾病的三个不同阶段分裂），患者骨髓骨髓的可定制计算模型以及对患者样本的使用优化的协议，免疫组织和微生物的使用，该模型是在这些模型中使用这些模型，以供这些模型，我们的生成模型，以供这些模型，我们的初步疾病的初步阶段（n = 9，分为n = 9，分为n = 9，分裂）。辅助生物标志物。公共卫生相关性：多发性骨髓瘤（MM）是骨髓（BM）无法治愈的恶性癌，尽管治疗可以扩大大多数患者的生存，但癌症不可避免地会抗性，导致复发和死亡。有许多不同的药物可用于MM治疗，但是由于疾病的复杂性，临床医生很难为给定患者选择最佳治疗方法。我们提出了一种新型的实验和计算技术组合，以利用从MM患者的活细胞和组织样本中提取的信息，以建立对患者对治疗反应的估计的个性化模型。