Development and Validation of a Predictive Model of Chemotherapy in Breast Cancer

乳腺癌化疗预测模型的开发和验证

• 批准号: 9050374
• 负责人: Matthew T. McKenna
• 金额: $ 2.82万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2019-11-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9050374
• 关键词: Accounting; Address; Affect; Algorithms; Behavior; Bioreactors; Breast Cancer Patient; Breast Cancer cell line; Breast Cancer therapy; Cell Count; Cell Death; Cell Density; Cell Line; Cell Proliferation; Cell division; Cell model; Cells; Cessation of life; Cisplatin; Clinic; Clinical Trials; Combined Modality Therapy; Computer Simulation; Computing Methodologies; Consensus; Cyclophosphamide; Cytotoxic Chemotherapy; Cytotoxic agent; Data; Death Rate; Development; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Distant; Dose; Doxorubicin; Goals; Growth; Healthcare; Image; Imaging Techniques; In Vitro; Link; Literature; MDA MB 231; MDA-MB-468; Magnetic Resonance Imaging; Measurement; Measures; Methods; Microscopy; Modeling; Molecular Genetics; Molecular Target; Neoadjuvant Therapy; Operative Surgical Procedures; Paclitaxel; Patients; Pharmaceutical Preparations; Physicians; Population; Prediction of Response to Therapy; Prevalence; Recurrence; Regimen; Research; Residual Tumors; Resolution; Risk; Sampling; Schedule; Scientist; Selection for Treatments; Subgroup; System; Testing; Therapeutic; Time; Translating; Treatment Protocols; Validation; Work; cancer subtypes; chemotherapy; clinically relevant; cytotoxic; design; imaging modality; improved; in vitro Assay; in vitro Model; in vivo; in vivo Model; inhibitor/antagonist; insight; malignant breast neoplasm; mathematical model; mouse model; neoplastic cell; next generation; personalized medicine; precision medicine; predicting response; predictive marker; predictive modeling; prospective; public health relevance; research clinical testing; research study; response; standard of care; therapy design; training opportunity; treatment response; triple-negative invasive breast carcinoma; tumor; tumor growth

 DESCRIPTION (provided by applicant) Triple negative breast cancer (TNBC) accounts for 15% of all breast cancers, and patients with this disease have an increased likelihood of distant recurrence and shorter overall survival compared to non-TNBC patients. The standard of care for TNBC is neoadjuvant therapy (NAT), consisting of a panel of cytotoxic therapies, followed by surgery. However, the field currently lacks a consensus on the appropriate combination of therapies and an ability to predict how any patient will response to a given therapeutic regimen. This proposal addresses these issues through construction of a mathematical model utilizing tumor-specific imaging data. Several attempts have been made to capture tumor growth and treatment response within a mathematical framework, but many of those attempts have relied on parameters and data that are difficult or impossible to measure with the requisite temporal and spatial resolution. This effort is distinguished by proposing a model parameterized exclusively with experimentally available data. Specifically, the proposal builds on recent advances in time-resolved automated fluorescent microscopy and diffusion-weighted magnetic resonance imaging (DW-MRI) to populate the proposed model. Fluorescent microscopy can track cell populations in two dimensions over time, and DW-MRI can provide quantitative information on cell density in three dimensions. We have developed in vitro assays to leverage each of these imaging modalities to track tumor status noninvasively throughout the course of therapy. We hypothesize that this data can be used to initialize a computational model to predict: 1) the temporospatial response of TNBC to therapy and 2) optimal NAT regimens for a given tumor. To test this hypothesis, we propose three specific aims: 1) to measure and model TNBC cell line response to NAT in 2D using fluorescent microscopy, 2) to measure and model TNBC cell line response to NAT in 3D using MRI, and 3) to evaluate model predictions in an in vivo model of TNBC. The proposal will subject a representative sample of TNBC cell lines to a panel of clinically relevant therapies evaluating tumor response in both 2D and 3D. Preliminary data indicates that data collected via fluorescent microscopy can describe tumor-scale data collected via MRI with appropriate temporospatial scaling factors. The last decade has produced significant advances in the genetic and molecular characterization of tumors and their response to therapy. Our proposal will provide insight into how these cell-scale observations translate to clinically relevant measures of tumor status. Further, this proposal will quantitatively characterize the response of TNBC to NAT. The field requires this quantitative understanding to properly evaluate next- generation therapies. Finally, this proposal will demonstrate the utility of a computational approach to therapy design through in vivo experiments. Ultimately, these Aims will move the field towards the goal of precision medicine: delivering the optimal drug in its optimal dose on an optimal schedule to each patient.

 描述（由申请人提供） 三阴性乳腺癌（TNBC）占所有乳腺癌的15%，与非TNBC患者相比，患有这种疾病的患者远端复发的可能性增加，总生存期缩短。TNBC的标准治疗是新辅助治疗（NAT），由一组细胞毒性治疗组成，然后进行手术。然而，该领域目前缺乏对适当的治疗组合的共识，以及预测任何患者将如何响应给定治疗方案的能力。该提案通过利用肿瘤特异性成像数据构建数学模型来解决这些问题。已经进行了几次尝试以在数学框架内捕获肿瘤生长和治疗反应，但是这些尝试中的许多都依赖于难以或不可能以必要的时间和空间分辨率测量的参数和数据。这一努力的特点是提出了一个模型参数化专门与实验可用的数据。具体来说，该提案建立在时间分辨自动荧光显微镜和扩散加权磁共振成像（DW-MRI）的最新进展，以填充所提出的模型。荧光显微镜可以随时间的推移在两个维度上跟踪细胞群，DW-MRI可以提供三维细胞密度的定量信息。我们已经开发了体外测定，以利用这些成像方式中的每一种在整个治疗过程中无创地跟踪肿瘤状态。我们假设该数据可用于初始化计算模型以预测：1）TNBC对治疗的时空响应和2）给定肿瘤的最佳NAT方案。为了检验这一假设，我们提出了三个具体目标：1）使用荧光显微镜在2D中测量和建模TNBC细胞系对NAT的响应，2）使用MRI在3D中测量和建模TNBC细胞系对NAT的响应，以及3）在TNBC的体内模型中评估模型预测。该提案将使TNBC细胞系的代表性样本接受一组临床相关治疗，以评估2D和3D的肿瘤反应。初步数据表明，通过荧光显微镜收集的数据可以描述通过MRI收集的肿瘤规模的数据与适当的时空比例因子。 过去十年在肿瘤的遗传和分子特征及其对治疗的反应方面取得了重大进展。我们的建议将提供深入了解这些细胞规模的观察如何转化为肿瘤状态的临床相关措施。此外，该提案将定量表征TNBC对NAT的反应。该领域需要这种定量理解来正确评价下一代疗法。最后，该提案将通过体内实验证明计算方法在治疗设计中的实用性。最终，这些目标将使该领域朝着精确医学的目标发展：在一个特定的时间内以最佳剂量提供最佳药物。 为每位患者提供最佳治疗方案。