Besides mutations, copy number variants (CNVs) impact cancer’s onset, progression, and treatment resistance. Their study is therefore vital for advancing precision oncology. Advancements in cancer genomics, including mutational signatures (patterns of mutations) have enhanced our understanding of cancer development and resistance. However, patterns of CNVs remain understudied. We aim to thoroughly examine CNV signatures in recurring and metastasizing cancers post-treatment. These signatures will be integrated with clinical treatment data, and their prognostic and predictive value will be evaluated. We will utilize extensive clinicogenomic datasets and apply advanced AI and machine learning algorithms, paving the way for the development of genomics-based approaches in precision oncology.