Engineering and Quantifying Drug Response in Small Cell Lung Cancer

Abstract

Small cell lung cancer (SCLC) is a highly aggressive disease, characterized by a signature “tumor recurrence” during treatment, wherein after initial favorable response, tumors re-establish themselves and no longer respond to therapy, leading to exceedingly poor patient outcomes. Roughly 250,000 people per year succumb to this disease. However, despite its impact, the treatment for SCLC has remained largely unchanged for four decades. My thesis seeks to rectify this, by developing tools to more accurately characterize the genetic plasticity and inherent therapeutic resistance of SCLC, as well as to enable quantification of SCLC drug response using methods that could potentially be extended for use in a translational setting. These tasks can be broadly grouped into the three specific aims of my dissertation project: 1) Engineer a SCLC cell line library to enable experimental testing of SCLC phenotype shift and its relationship to drug response 2) Develop an assay and analysis pipeline capable of continuous quantitation of drug response in SCLC across broad morphologies 3) Computationally model the relationship between real-time luminescence and cell count in order to integrate luminescence derived data into existing direct cell counting analyses. Aim one saw the completion of a highly tunable SCLC cell line library that is fluorescently labelled and contains machinery for the implementation of CRISPRa, CRISPRi, and CRISPRko experimentation. Aim two resulted in a novel experimental method to quantify drug response without cellular labelling, that is also easily scalable to high-throughput workflows as well as translationally relevant to 3-dimensional culture systems. And aim three culminated in the first computational model to link real-time luminescence with cell count, offering insight in to what was found to be a highly non-trivial mathematical relationship.