INSIGHTS INTO THE SELECTION AND FUNCTION OF COPY-NUMBER ALTERATIONS DURING CANCER EVOLUTION
type: CreativeWork
description: Cancer evolution is a complex process mostly driven by genomic instability, including large- and small-scale alterations, such as copy-number alterations (CNAs) or single-nucleotide mutations, respectively. Those alterations play a critical role in tumor development, genetic diversity, and resistance to therapies. This thesis explores the selection and functional role of CNAs, aiming to uncover insights into how these alterations shape cancer evolution. By integrating computational analyses, machine learning approaches, and experimental techniques, we provide insights into the forces that drive CNA patterns, their impact on tumor progression and we examine how CNAs interact with other evolutionary pressures and cellular functions. These approaches reveal how selection and structural constraints influence CNA landscapes, uncovering adaptive mechanisms that enable cancer cells to tolerate genomic instability while maintaining essential functions. The connections between these investigations highlight the interplay of genomic architecture, selective forces, and functional outcomes. By combining data-driven insights with experimental evidence, we identify fundamental principles underlying CNA evolution and its context-dependent dynamics across cancer types. This integrative approach underscores the importance of using diverse methodologies to dissect the complexity of cancer genomes. Ultimately, the findings presented in this thesis advance our understanding of CNA-driven tumor evolution and may help in the identification of new potential vulnerabilities that could inform novel therapeutic strategies.
abstract: Cancer evolution is a complex process mostly driven by genomic instability, including large- and small-scale alterations, such as copy-number alterations (CNAs) or single-nucleotide mutations, respectively. Those alterations play a critical role in tumor development, genetic diversity, and resistance to therapies. This thesis explores the selection and functional role of CNAs, aiming to uncover insights into how these alterations shape cancer evolution. By integrating computational analyses, machine learning approaches, and experimental techniques, we provide insights into the forces that drive CNA patterns, their impact on tumor progression and we examine how CNAs interact with other evolutionary pressures and cellular functions. These approaches reveal how selection and structural constraints influence CNA landscapes, uncovering adaptive mechanisms that enable cancer cells to tolerate genomic instability while maintaining essential functions. The connections between these investigations highlight the interplay of genomic architecture, selective forces, and functional outcomes. By combining data-driven insights with experimental evidence, we identify fundamental principles underlying CNA evolution and its context-dependent dynamics across cancer types. This integrative approach underscores the importance of using diverse methodologies to dissect the complexity of cancer genomes. Ultimately, the findings presented in this thesis advance our understanding of CNA-driven tumor evolution and may help in the identification of new potential vulnerabilities that could inform novel therapeutic strategies.
author: ALFIERI, FABIO
language: eng
keywords: cancer evolution, bioinformatic, genomic, copy-number alteration, aneuploidy, machine learning, Settore MEDS-02/A - Patologia generale
date published: 2025-04-03
date modified: 2026-07-27T20:19:10Z
provider: Università degli Studi di Milano
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Università degli Studi di Milano

PropertyValue
property ID: Handle
value: https://hdl.handle.net/2434/1154659