Metabolic characterisation of glioblastoma and ovarian tumours and identification of metabolic biomarkers of drug resistance using metabolic imaging

• Applicant: Dr. Islam Alshamleh
• Subject Area: Radiology ; Biochemistry ; Endocrinology, Diabetology, Metabolism ; Medical Physics, Biomedical Technology
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 494873368

A major challenge for cancer therapy is the complex mutational landscape and consequent heterogeneity in treatment response. Genetic classifications have advanced therapy development and choices, nevertheless, prognosis is still challenged by the lack of non-invasive imaging biomarkers for determining prognosis and that can be used subsequently for early detection of treatment response. To address these issues, my proposed host laboratory has performed preliminary studies using magnetic resonance spectroscopic imaging (MRSI) and observed that glioblastoma (GBM) tumours differ in their metabolic signatures, as assessed in patient-derived xenografts (PDXs) as well as in patients. Similar heterogeneity was also observed in ovarian cancer, which translated to heterogenous responses to different therapies. We plan to advance this project by identifying metabolic imaging signatures of GBM and ovarian cancer disease subtypes that will help to predict cancer subtype pre-operatively, and therefore could be used in therapy selection. For the GBM studies, we will profile each of the four different established genetic disease subtypes in PDXs using different non-invasive metabolic imaging techniques (13C MRSI of hyperpolarized [1-13C]pyruvate metabolism, 2H MRSI of [6,6-2H2]glucose metabolism; validated using mass spectrometry imaging (MSI) of tumour sections). The accuracy of these metabolic imaging signatures in predicting disease subtypes will be cross validated in subsequent clinical studies in patients. We will exploit any distinctive metabolic patterns we observe in any of the subtypes to develop new isotope-labelled imaging substrates and we will investigate the potential of targeting these pathways for therapy. Similar to the studies in GBM, we will also characterise the metabolic phenotypes of ovarian cancer disease subtypes (copy number variations) using MRSI and positron emission tomography (PET) in PDXs and in patients. Furthermore, we will assess the potential of metabolic imaging for the early detection of drug resistance by utilizing the novel MRSI approach pioneered by my proposed host laboratory. Using 13C MRSI of hyperpolarized [1-13C]pyruvate metabolism, this approach could detect drug resistance in breast cancer early following treatment initiation (24-48 hours). We will implement this approach to study drug responses in GBM PDXs using standard of care chemo-radiation as well as targeted therapies (Temsirolimus and Ipatasertib). Similarly, we will continue to validate our preliminary observations which demonstrated the superiority of 13C MRSI using hyperpolarized [1-13C]pyruvate over PET scanning in detecting drug resistance in ovarian cancer. In the longer term we will cross validate our findings in GBM and ovarian cancer patients by imaging them pre-treatment and following treatment initiation.

• DFG Programme: WBP Fellowship
• International Connection: United Kingdom
• Host: Professor Dr. Greg Hannon