CAREER: Reconfigurable Electro-Fluidic Prescriptions (REFRx): Data-Driven Biosensors for Detection and Treatment of Multidrug-Resistant Cancers

Rutgers University New Brunswick, New Brunswick NJ

Investigators

Abstract

This proposal is to develop an instrument that can rapidly identify drug resistant cancer cells in tumors and prescribe a course of treatment for the patient that minimizes chance of cancer recurrence. Drug resistance is one of the greatest impediments to treating both cancer and infectious disease and has been identified as one of the greatest public health threats of the next several decades. The proposed miniaturized instrument can be utilized for rapidly screening cancer patients for drug resistance and identifying the key molecular players involved and selecting optimal cancer treatment drugs. In this work, a microfluidics/electronic/data-driven crosscut approach is proposed to enable a rapid technology that can identify drug resistant cells using machine learning and examine the key protein pathways resulting in resistance using a label-free sensing array. The proposed platform is adaptive and reconfigures itself to assay the relevant proteins on-demand and avoids a resource-hungry brute force approach. This interdisciplinary project will engage and train both graduate and undergraduate students in various areas. The PI will also engage K-12 students through outreach workshops, local industry through educational lectures, and the general public through development of an online course, resulting in broad dissemination of knowledge. A new class of data-driven biosensors will be developed that can adapt themselves on-demand to detect and treat multidrug resistant cancers. Treatment of multi-drug resistance in cancer is difficult using static analysis platforms because of the rapid ability of tumor sub-clones to mutate and become insusceptible to a chemotherapeutic drug, thus an adaptive approach can be more efficient. An all-electronic platform will be developed for rapidly sorting drug resistant cells and adaptively analyzing the molecular pathways involved in resistance using a reconfigurable array of sensors. The analyzer will iteratively detect drug resistant cells using reconfigurable impedance cytometry in conjunction with machine learning, then sort them using dielectrophoresis (DEP), analyze them using a reconfigurable array of label-free protein sensors, and then use a machine learning classifier to further sub-type the drug resistant cells to select a drug combination to test in the subsequent iteration. An integrated closed-loop feedback system will be fabricated and characterized for sorting drug-sensitive and drug-resistant cells, and analyze differential expression of potential drug-resistance pathways iteratively against key drug candidates. The result of this research will be a new class of data-driven biosensors that can detect and sub-type drug resistant cancer cells from a breast tumor tissue that can be broadly applied to a multitude of biomedical applications including anti-microbial resistance. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.