Micro-Organospheres Drug Screen to Lead Care (MODEL): a Precision Oncology Platform to Guide Breast Cancer Therapy

微有机球药物筛选引导护理 (MODEL)：指导乳腺癌治疗的精准肿瘤学平台

• 批准号: 10383051
• 负责人: Daniel Delubac
• 金额: $ 39.85万
• 依托单位: XILIS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-09 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10383051
• 关键词: Address; Adoption; Automation; Biological Assay; Biology; Biopsy; Breast Cancer Detection; Breast Cancer Patient; Breast Cancer therapy; Cancer Diagnostics; Cancer Patient; Caring; Cells; Clinic; Clinical; Clinical Data; Clinical Laboratory Improvement Amendments; Clinical Protocols; Clinical Research; Complex; Development; Devices; Drug Screening; ERBB2 gene; Emulsions; Enrollment; Equilibrium; Face; Fine needle aspiration biopsy; Funding; Future; Gel; Generations; Genomics; Goals; Institutional Review Boards; Lead; Malignant Neoplasms; Medicine; Methods; Microfluidics; Minority; Modeling; Needle biopsy procedure; Neoadjuvant Therapy; Oncologist; Oncology; Organoids; Outcome; Patient Rights; Patients; Performance; Pharmaceutical Preparations; Phase; Predisposition; Problem Solving; Process; Refractory; Regimen; Risk; Robotics; Running; Sample Size; Sampling; Technology; Temperature; Testing; Therapeutic; Time; Tissues; Validation; Work; arm; base; cancer therapy; cancer type; clinical diagnostics; clinical predictors; commercialization; cost; data integration; data integrity; design; diagnostic assay; drug testing; experience; feasibility testing; flexibility; improved; individual patient; malignant breast neoplasm; microfluidic technology; miniaturize; neoplastic cell; novel; novel strategies; patient response; patient screening; precision oncology; predicting response; predictive modeling; prototype; research clinical testing; response; scale up; screening; standard of care; success; treatment response; tumor; validation studies

PROJECT SUMMARY/ABSTRACT The goal of precision oncology is to match cancer patients with medicines based on the specific biology of their tumor. Crucially, the current precision oncology paradigm – which is largely based on tumor genomic profiling – doesn’t work for the majority of patients. Since every patient’s tumor is uniquely complex, a potential solution to this “precision” problem involves creating a viable functional model of a patient’s individual tumor in order to directly test its susceptibility to different drugs. The broad adoption of such patient-derived functional models into the clinic thus far has been hindered by several limitations centered on scalability, time, and success rate. Specifically, any assay for guiding therapy must be: i) amenable to the amount of material derived from needle biopsies, ii) established with a high success rate, and iii) completed within 10-14 days to minimize unacceptable treatment delays. To address these clinical limitations, we have developed the novel Micro-Organosphere Drug Screen to Lead Care (MODEL) platform. MODEL is based on novel microfluidics technology that generates Patient-Derived Micro-Organospheres (PDMO) from clinical samples (e.g., biopsies) and performs drug screening within 10 days to guide therapy. The objective of our proposal is to further develop and validate our MODEL technology in breast cancer, with a view to advancing it further towards becoming a standard of care diagnostic assay. Phase I of our proposal will focus on preparing our MODEL device for rigorous clinical evaluation. In Aim 1 we will make key upgrades to our device prototype to improve sample efficiency, device performance, and operability. Specifically, the goal of these improvements will be to reduce sample size requirements (extending our capabilities down to fine-needle aspirates), enhance device performance, reinforce consistency of key parameters during and between runs, and increase process automation. In Aim 2, we will rigorously test the ability of our second-generation device to i) successfully generate PDMO from breast cancer biopsies and ii) perform drug screens in less than 10 days total. In Phase II, we will make key device upgrades to prepare the MODEL platform for commercialization, focusing on improving features related to data integrity and ease-of-use (Aim 1). In Aim 2 we will perform the first validation of our MODEL platform in a HER2+ breast cancer clinical protocol consisting of 50 patients, with the goal of testing MODEL’s ability to predict response to standard of care neoadjuvant therapy. If successful, the development of our platform will revolutionize precision oncology by arming oncologists with the information needed to optimally match cancer patients with medicines.

项目总结/摘要 精准肿瘤学的目标是根据癌症患者的特定生物学特征， 肿瘤至关重要的是，目前的精确肿瘤学范式--主要基于肿瘤基因组分析-- 对大多数病人不起作用。由于每个患者的肿瘤都是独特的复杂性， 这个“精确”的问题涉及创建患者个体肿瘤的可行功能模型， 直接测试其对不同药物的敏感性。广泛采用这种患者衍生的功能模型， 到目前为止，诊所受到集中在可扩展性、时间和成功率上的几个限制的阻碍。 具体地，用于指导治疗的任何测定必须： 活组织检查，ii）成功率高，iii）在10-14天内完成，以尽量减少不可接受的 治疗延误。为了解决这些临床局限性，我们开发了新型微有机球药物 筛选到Lead Care（模型）平台。MODEL基于新型微流体技术， 来自临床样品的患者来源的微有机球（PDMO）（例如，活组织检查）和执行药物 在10天内进行筛查以指导治疗。我们提案的目的是进一步发展和验证我们的 MODEL技术在乳腺癌中的应用，以期进一步推动其成为一种标准护理 诊断分析我们提案的第一阶段将专注于为严格的临床试验准备我们的MODEL设备。 评价在目标1中，我们将对我们的设备原型进行关键升级，以提高采样效率， 性能和可操作性。具体而言，这些改进的目标是减少样本量 要求（将我们的能力扩展到细针抽吸），增强设备性能， 在运行期间和运行之间保持关键参数的一致性，并提高工艺自动化程度。在目标2中，我们将 严格测试我们的第二代设备i）成功地从乳腺癌产生PDMO的能力， 活组织检查和ii）在总共少于10天内进行药物筛选。在第二阶段，我们将进行关键设备升级 为商业化做好MODEL平台的准备，重点是改进与数据完整性相关的功能 1.使用方便（Aim 1）。在目标2中，我们将在HER 2+乳腺中对我们的MODEL平台进行首次验证 癌症临床方案由50名患者组成，目的是测试模型预测对 新辅助治疗标准。如果成功，我们平台的发展将彻底改变 精确的肿瘤学，通过武装肿瘤学家所需的信息，以最佳匹配癌症患者与 药