Joseph Piekos

PhD thesis:

Statistical Modelling of evolutionary trajectories in breast cancer

From the large pool of genes altered in breast cancer, it is still not well understood which ones are truly drivers, appearing at the origin of the disease and shaping the genomic profile of the tumour, and which ones are passenger and appear as a consequence of genomic instability. My project aims to develop models that characterise evolution patterns in breast cancer tumours, identifying events that increase the risk of relapse and allow patient stratification into different treatments. I will integrate several datasets, including bulk and single cell sequencing data to model tumour evolution using different strategies, such as classical Phylogenetics, clustering of trees and probabilistic modelling.

Who or what inspired you to pursue your research interests?

Some of my close family members have been affected by complex diseases such as Alzheimer's and cancer, driving my ambition to tackle some of the toughest challenges in medical research. During university, I built statistical models for COVID-19 and dementia in my dissertations before spending three years developing predictive models for a sports syndicate. That experience taught me to see the world through a probabilistic lens and confirmed how much I enjoy using data to solve difficult problems. During my PhD and after, I hope to contribute to breakthroughs in cancer research.