Supervisor: Dr. Maxime Tarabichi
Thyroid cancers are amongst the most diagnosed cancers worldwide in women. Though the survival rate of thyroid cancer is overall one of the highest, anaplastic thyroid carcinomas, a rare form of aggressive undifferentiated cancer, are responsible for half of thyroid-cancer deaths, often within the first months after diagnosis.
It is believed that they evolve from their more differentiated and less aggressive counterparts. Also, thyroid cancers become more genomically unstable as they progress and accumulate more mutations, including copy number events and whole-genome doublings, important prognostic factors across cancer types.
There have been very few studies looking at intra-tumour genetic heterogeneity and the underlying evolution of anaplastic thyroid cancer.
In this thesis, the candidate will analyse spatially-resolved multi-region whole-genome sequencing (WGS) data of a growing series of anaplastic thyroid cancers collected across different hospital sites; we will characterise their genomic landscape and their intra-tumour genetic heterogeneity to shed light on their evolution; and will quantify the amount of genomic instability at the single-cell level through single-nuclei whole-genome sequencing. These data will then integrate with other omics layers being derived for the same samples, including transcriptomic data.
Altogether, this thesis in computational biology will elucidate some of the main questions pertaining to anaplastic thyroid cancer evolution, and hopefully ultimately provide the necessary knowledge to help improve the poor clinical outcome of these patients.