This study investigates the patterns of somatic copy number alterations (SCNAs) across 4,934 cancers from The Cancer Genome Atlas (TCGA) Pan-Cancer data set. SCNAs are critical in cancer development, influencing oncogene activation and tumor suppressor inactivation. The study identifies common SCNA patterns, including the association of whole-genome doubling (WGD) with increased rates of various SCNAs, TP53 mutations, and CCNE1 amplifications. SCNAs internal to chromosomes are shorter than those bounded by telomeres, suggesting different mechanisms of generation. Recurrent focal SCNAs were found in 140 regions, with 102 without known oncogene or tumor suppressor targets and 50 with significantly mutated genes. Amplified regions without known oncogenes were enriched for genes involved in epigenetic regulation. When genomic disruption levels were accounted for, 7% of region pairs were anticorrelated, suggesting related functions. SCNAs affect a larger fraction of the genome than any other somatic genetic alteration. Understanding SCNAs is crucial for cancer diagnostics and therapeutics. The study addresses challenges in distinguishing driver events from passenger SCNAs and identifying oncogene and tumor suppressor targets. It uses integrated statistical approaches and computational tools to analyze SCNA events and their temporal ordering. The results provide insights into the mechanisms and functional consequences of cancer-related SCNAs. The study highlights the importance of genomic disruption in analyzing SCNA correlations and the role of epigenetic regulators in cancer. It also identifies significant anticorrelations between SCNAs, suggesting functional redundancies. The findings contribute to understanding the role of SCNAs in cancer progression and the development of targeted therapies.This study investigates the patterns of somatic copy number alterations (SCNAs) across 4,934 cancers from The Cancer Genome Atlas (TCGA) Pan-Cancer data set. SCNAs are critical in cancer development, influencing oncogene activation and tumor suppressor inactivation. The study identifies common SCNA patterns, including the association of whole-genome doubling (WGD) with increased rates of various SCNAs, TP53 mutations, and CCNE1 amplifications. SCNAs internal to chromosomes are shorter than those bounded by telomeres, suggesting different mechanisms of generation. Recurrent focal SCNAs were found in 140 regions, with 102 without known oncogene or tumor suppressor targets and 50 with significantly mutated genes. Amplified regions without known oncogenes were enriched for genes involved in epigenetic regulation. When genomic disruption levels were accounted for, 7% of region pairs were anticorrelated, suggesting related functions. SCNAs affect a larger fraction of the genome than any other somatic genetic alteration. Understanding SCNAs is crucial for cancer diagnostics and therapeutics. The study addresses challenges in distinguishing driver events from passenger SCNAs and identifying oncogene and tumor suppressor targets. It uses integrated statistical approaches and computational tools to analyze SCNA events and their temporal ordering. The results provide insights into the mechanisms and functional consequences of cancer-related SCNAs. The study highlights the importance of genomic disruption in analyzing SCNA correlations and the role of epigenetic regulators in cancer. It also identifies significant anticorrelations between SCNAs, suggesting functional redundancies. The findings contribute to understanding the role of SCNAs in cancer progression and the development of targeted therapies.