The purpose of this research was to profile thyroid cancers by their molecular signature (genetic mutation) to determine how this affects each patient outcome. Following this, the goal was to optimise ‘targeted therapy’ for thyroid cancer, where the mutated gene is directly inhibited providing individualised treatment protocols as opposed to more ‘generic’ chemotherapy which is usually ineffective in thyroid cancer.
Our hypothesis was that certain mutations or combinations of mutations can predict the risk of recurrence and poorer outcome. This research focussed on patients who had metastatic or poorly differentiated thyroid cancer (PDTC). PDTC is a distinct subtype of thyroid cancer which confers a poor prognosis. The genetic drivers of PDTC are not well understood. Whole genome sequencing (WGS) can identify structural rearrangements of the genome which contribute to the genesis of this aggressive malignancy.
Genomic DNA for paired-end WGS was extracted from tumour and blood samples from six poorly differentiated thyroid cancer patients. WGS aims to reveal the complete DNA makeup of the tumour. The goal of this was to identify novel structural variants to find new targets for clinical treatments and also to determine if there are predictors from the genome of poor outcome. Significant variant output for all six samples was gathered, including clinically relevant gene fusions. Other insertions were also identified, and novel visualisations were seen that gave insights into rearrangements that had not previously been described. Interpreting these large datasets generated by the WGS bioinformatics is challenging and the next step is to translate this information into actionable results with techniques including RNA-Seq. This can provide insight into the transcriptome (the small percent of the genetic code that is transcribed) of the PDTC.
Ultimately this work will highlight areas in the genome for targeted therapy thus providing our patients options in this challenging malignancy.