Grant: Collaborative Health Research Projects (NSERC partnered)

The goal of genome-based precision medicine is to selectively target drugs to responsive patients. This is particularly important in cystic fibrosis (CF), where ~75% of eligible patients, carrying the F508del-CFTR mutation, do not show demonstrable improvement in lung function with the clinically-available drug Orkambi. Orkambi treatment costs >$250,000/yr and is not formally covered by publically funded health-care in Canada at this time. New drugs in phase 2 clinical trials may be more effective than others for individual patients. To identify which patients are most likely to benefit from CF modulator drugs, it is important to study CFTR at the protein level as patient-patient variation is not only dependent on genomic differences (e.g. specific CFTR mutations). We will apply machine learning based network analysis of single molecule localization super-resolution microscopy (SMLM) to develop an imaging-based assay to identify patients likely to benefit from a particular therapeutic. Optimization of therapies for individual patients will increase drug efficacy and substantially reduce cost of treatment for Canadian CF patients. SMLM generates resolution ten-fold beyond the diffraction limit. However, quantitative approaches to analyze the large data sets of 3D point localizations generated are limited. We developed a novel and powerful approach to model SMLM particle localization data sets, transforming 3D localizations into a network by virtual connections between the points. This approach allows us to determine molecular architecture, i.e. how proteins within a protein complex interact. We now propose to refine and apply the technology to CFTR and thereby validate our analysis pipeline for personalized medicine applications in CF and other diseases with a known drug target. We will partner with Quorum Technologies, a Canadian microscopy company, to commercialize the software for use by super-resolution microscopy users and pharmaceutical companies worldwide.