Medullary thyroid cancer is a rare, aggressive neuroendocrine tumour. Treatment of MTC poses a major challenge due to its propensity of early metastasis and no available adjuvant systemic treatment options. The human REarranged during Transfection (RET) proto-oncogene is the key driver of MTC and the major focus of multiple intracellular pathways that determine the morphologic outcome of malignancy. However the therapeutic efficacy of tyrosine kinase inhibitors (TKIs) targeting RET appears to be quite modest in clinics, due to the significant adverse effects and the clinical resistance to TKIs, including primary resistance and the onsets of acquired resistance to TKIs.
Recent developments in the understanding of the biology of miRNAs have highlighted their therapeutic potential, in particular their functional roles in modulating molecularly targeted therapies. Our in vitro data has found that specific RET-associated miRNAs inhibited cell growth and modulated TKI responses (Thyroid, in press, 2019). Further, preliminary in vivo studies indicated that TKI cabozantinib treatment may have triggered acquired resistance in mice over time while miRNA could potentially enhance the tumour suppression effect of cabozantinib.
This proposal seeks to rationally develop innovative miRNA plus TKI treatments in MTC pre-clinical models. We propose to investigate how combined treatment of miRNAs plus TKIs would reverse the TKI resistance and improve therapeutic efficacy.
However, using miRNAs as a novel therapeutics, the most critical challenge is the lack of efficient tumour-targeted delivery vehicles, leading to potential collateral, off target effects. We have formed the collaboration with the EnGeneIC Biotechnology Company (Australia) and have granted access to their patented nanoparticle EnGeneIC Delivery Vehicles (EDVs).
The use of the effective EDV nanoparticle delivery system will remain the key to translating such therapeutics into the clinic.
The ideal candidate has expertise in cell culture, RNA biology and bioinformatics of high throughput sequencing data"
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