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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要/摘要精确肿瘤学的目的是根据其特定的生物学与癌症患者相匹配瘤。至关重要的是，当前的精度肿瘤学范式主要基于肿瘤基因组分析 - 它不适用于大多数患者。由于每个患者的肿瘤都很复杂，因此这个“精度”问题涉及创建患者个体肿瘤的可行功能模型，以便直接测试其对不同药物的敏感性。这种患者衍生的功能模型广泛采用到目前为止，该诊所受到以可伸缩性，时间和成功率为中心的几个局限性。具体而言，任何指导疗法的测定必须是：i）适合于针线的材料量活检，ii）以高成功率建立，iii）在10-14天内完成，以最大程度地减少无法接受的治疗延迟。为了解决这些临床局限性，我们已经开发了新型的微孔药物屏幕到Lead Care（模型）平台。模型基于生成的新型微流体技术来自临床样本（例如活检）的患者衍生的微孔圈（PDMO）并进行药物在10天内进行筛查以指导治疗。我们建议的目的是进一步发展和验证我们的乳腺癌中的模型技术，以进一步发展成为一种护理标准诊断测定。我们的提案的第一阶段将重点用于准备严格临床的模型设备评估。在AIM 1中，我们将对设备原型进行关键升级，以提高样品效率，设备绩效和操作。具体而言，这些改进的目标是减少样本量要求（将我们的功能降低到罚款质量），增强设备性能，增强运行期间和之间的关键参数的一致性，并增加过程自动化。在AIM 2中，我们将严格测试我们的第二代设备的能力i）成功地从乳腺癌产生PDMO 活检和II）在不到10天的时间内进行药物筛查。在第二阶段，我们将进行关键设备升级为了准备商业化的模型平台，专注于改善与数据完整性相关的功能和易用性（AIM 1）。在AIM 2中，我们将在HER2+乳房中执行模型平台的第一个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反应的能力护理标准新辅助治疗。如果成功，我们平台的开发将彻底改变精确肿瘤学家通过武装肿瘤学家提供最佳匹配癌症患者所需的信息药物。