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

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 upgradesto prepare the MODEL platform for commercialization, focusing on improving features related to data integrityand 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 tostandard 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.