Investigating osimertinib resistance in lung cancer using multi-omics and cell biology
Non-small-cell lung cancer (NSCLC) accounts for 80 % of lung cancer cases. Epidermal growth factor receptor mutations (EGFRm) occur in approximately 15 % and 40 % of NSCLC in Western and Asian populations, respectively. Current treatment targets EGFRm with tyrosine kinase inhibitors (TKIs). Resistance to these TKIs however is inevitable. Osimertinib is a 3rd generation EGFR-TKI now used as a first line treatment in the advanced/metastatic setting, exhibiting an average progression free survival of 18.9 months. Dysregulation of metabolism has been suggested to play a role in development of drug resistance. In this thesis, we investigate how aberrant metabolism contributes to the development of osimertinib resistance (OR) using pharmacologically-induced and genetically-modified (GM) resistance models, measured through various -omics analyses; transcriptomics, proteomics, lipidomics and metabolomics. In both pharmacologically-induced and GM OR models, we found alterations in processes relating to metabolism, such as dysregulated sphingolipid metabolism, increased de novo lipogenesis, and links to mitochondrial dysfunction, lipid peroxidation and ferroptosis. In pharmacologically-induced models, we identified that OR lines reduce the free pool of ceramides in favour of complex glycosphingolipids. We went on to show that this avoided ceramide-mediated apoptosis via caspase 3 activation. Importantly, when we combined osimertinib with an inhibitor (D-PDMP) of the key enzyme (GCS) responsible for the conversion of ceramide to glycosphingolipids, the pool of free ceramides was increased and glycosphingolipids decreased. This in turn increased activation of caspase-3 and thus restored apoptosis to OR cells. Here, we identified a novel potential therapeutic target for acquired OR, that when inhibited in combination with osimertinib, sensitivity to osimertinib was restored.
Institutions
- Imperial College London (GB)
Publication Details
- Journal
- Imperial College Research Computing Service Data Repository
- Published
- 2025-02-06
- DOI
- https://doi.org/10.25560/122688
- Primary Topic
- Cell Image Analysis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00