Computational models for deciphering the evolution of copy-number alterations in human cancers
Cancer is an evolutionary process in which cells continuously change, compete, and adapt to their environment. Cancer cells acquire genomic alterations that shape their fitness and drive tumour progression. Among these alterations, somatic copy-number alterations (SCNAs)—gains or losses of genomic segments—play a central role in cancer evolution by amplifying favourable regions and deleting unfavourable ones. To understand how tumours form and progress, it is essential to uncover the evolutionary forces shaping cancer genomes, how SCNAs arise, and how they affect cellular fitness and selection. These questions are addressed in this thesis through the development of three complementary computational frameworks. First, I present MEDICC2, an algorithm for reconstructing tumour phylogenies from copy-number data. Using a minimum-evolution principle, MEDICC2 infers evolutionary relationships between multiple tumour samples, providing insight into how copy-number alterations accumulate over time and influence cancer evolution. Second, I introduce SMITH, a stochastic model that simulates tumour growth under fitness and spatial constraints. By extending classical branching-process models with spatial confinement, SMITH captures distinct evolutionary modes arising from competition between clones within limited space and resources. Third, I present SPICE, a model to study selection acting on SCNAs across 5,966 cancers from The Cancer Genome Atlas. It infers individual copy-number events and identifies loci under selection, revealing hundreds of novel oncogenes and tumour suppressors. Together, these projects combine algorithm development, mathematical modelling, machine learning, and large-scale data analysis to provide a unified framework for understanding how somatic copy-number alterations, fitness, and selection jointly shape tumour evolution.
