Project ID BE-MI2024_22


Co Supervisor 1A Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Imaging Chemistry & BiologyWebsite

Co Supervisor 1B Faculty of Life Sciences & Medicine, School of Biomedical Engineering & Imaging Sciences, Department of Imaging Chemistry & BiologyWebsite

AIRIaL: Artificial Intelligence and Resistance Imaging in Lung Cancer

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.

Representative Publications

1. ‘H.E. Greenwood*, R. Edwards*, N. Koglin, M. Berndt, F. Baark, J. Kim, G. Firth, E. Khalil, A. Mueller & T.H. Witney (2022). Radiotracer stereochemistry affects substrate affinity and kinetics for improved imaging of system xC- in tumors. Theranostics 12, pp.1921-1936.

2. R. Pereira*, R.L. Flaherty*, R. Edwards*, H.E. Greenwood, A.J. Shuhendler & T.H. Witney (2022). A Prodrug Strategy for the In Vivo Imaging of Aldehyde Dehydrogenase Activity. RSC Chem Bio 3, pp.561-570.

3. P.N. McCormick, H.E. Greenwood, M. Glaser, O.D.K. Maddocks, T. Gendron, K. Sander, G. Gowrishankar, A. Hoehne, T. Zhang, A.J. Shuhendler, D.Y. Lewis, M. Berndt, N. Koglin, M.F. Lythgoe, S.S. Gambhir, E. Årstad and T.H. Witney (2019). Assessment of tumor redox status through (S)-4-(3-[18F]fluoropropyl)-L-glutamic acid positron emission tomography imaging of system xc- activity. Cancer Res 79, pp.853-63

1. Baghdadi NE, Burke BP, Alresheedi T, et al: Multivalency in CXCR4 chemokine receptor targeted iron oxide nanoparticles. Dalton Transactions 50:1599-1603, 2021

2. Burke BP, Miranda CS, Lee RE, et al: Cu-64 PET Imaging of the CXCR4 Chemokine Receptor Using a Cross-Bridged Cyclam Bis-Tetraazamacrocyclic Antagonist. Journal of Nuclear Medicine 61:123-128, 2020

3. Burke BP, Grantham W, Burke MJ, et al: Visualizing Kinetically Robust (Co4L6)-L-III Assemblies in Vivo: SPECT Imaging of the Encapsulated Tc-99m TcO4- Anion. Journal of the American Chemical Society 140:16877-16881, 2018