Characterising the evolution of anaplastic thyroid carcinomas

Supervisor: Dr. Maxime Tarabichi

Thyroid cancers (TC), represented by the most common papillary thyroid carcinomas (PTC), are amongst the most diagnosed cancers worldwide. Though the survival rate of TC is overall one of the highest, anaplastic thyroid carcinomas (ATC), a rare form of aggressive undifferentiated TC, kill the majority of patients within the first months after diagnosis. It is believed that they evolve from their more differentiated and less aggressive counterparts, as in 40-60% of the cases these two forms of the disease coexist within the same patient while sharing the underlying driver mutations. Also, similarly to other cancer types, as TC progress they become more genomically instable and accumulate mutations, including copy number events, an important prognostic factor across other cancer types.

Because of their rarity, ATC are substantially understudied relative to their impact on patient lives, and the paucity of genomic studies limits our understanding of the evolution and treatment options for those cancers. Especially, although a main therapeutic challenge in cancer treatment, there have been very few studies looking at intra-tumour genetic heterogeneity and the underlying evolution of ATC.

In this thesis, the candidate will analyse multi-region whole-genome sequencing (WGS) data of a growing series of both PTC and ATC collected across different hospital sites; characterise their genomic landscape and their intra-tumour genetic heterogeneity; this will add to the scarce and limited public DNA sequencing data on PTC and ATC that we will collect and homogenise into a meta-dataset; furthermore, to finally shed light on the evolution of ATC from differentiated TC, we will reconstruct the phylogenetic relationships between ATC and their differentiated counterparts within the same patients; finally, we 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 ATC origins, differences with and evolution from differentiated TC and the associated treatment challenges, and hopefully ultimately provide the necessary knowledge to help improve the poor clinical outcome of patients presenting with the more advanced stages of TC.

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