Most lung cancer deaths result from ineffective treatment of late-stage disease. Currently, there is no satisfactory way to identify patients that will not respond to standard-of-care treatments. Positron emission tomography (PET) imaging offers a potential solution to this clinical problem through the non-invasive assessment of molecular processes that underpin therapy-resistance. The identification of cancer patients that are refractory to treatment will allow the use of innovative new therapies that have the potential to improve patient response and survival.
Here, we will combine expertise in biological, physical, data, and medical sciences to identify and treat therapy-resistant lung cancer. We have developed a PET radiotracer, [18F]FSPG, that can non-invasively detect therapy-resistant tumours in multiple models of lung cancer. Through innovations in artificial intelligence, preclinical [18F]FSPG imaging datasets will be used to extract quantitative parameters from these tumours. Through our collaborations, we will next evaluate molecularly imprinted nanoparticles (nanoMIPs) that deliver a targeted payload of drug to drug-resistant lung cancer. Finally, these innovative technologies will be combined to image, detect, and treat multifocal therapy-resistant disease in a genetically engineered mouse model of lung cancer.
During your PhD you will develop an extensive range of experimental skills spanning in vitro mechanistic evaluation of drug-resistant cancer (flow cytometry, western blotting, biochemical assays, CRISPR/Cas9), to in vivo assessment in advanced animal models of NSCLC. Our objectives over the course of your PhD will be to:
• Yr1: Create an imaging repository for [18F]FSPG across multiple drug-sensitive and drug-resistant lung tumours, required for AI model development; perform initial specificity and selectivity screening of nanoMIPs.
• Yr2: In vitro efficacy studies (dose and time-response) with lead nanoMIPs; use optimised [18F]FSPG-AI protocol for the quantitative assessment of therapy resistance in genetically engineered mouse model of lung cancer.
• Yr3: perform nanoMIP treatment studies in vivo, determine therapeutic index, publish results.
