Abstract We propose to continue our established Genomics Data Analysis Center for the analysis of structural variants in cancer, including somatic copy-number alterations (SCNAs) and rearrangements, addressing the Copy Number competency required by GDAN. We also add capabilities to analyze long- and linked-read data, addressing a second competency. We led SCNA analyses throughout The Cancer Genome Atlas (TCGA) and the first iteration of the Genomics Data Analysis Network (GDAN). We also co-led the Structural Variations Working Group of the International Cancer Genome Consortium Pan-Cancer Analysis of Whole Genomes (PCAWG). For these efforts, we have developed a large suite of tools and deep expertise covering all aspects of analysis of SVs in cancer. Specifically, we will accomplish five Aims: In Aims 1 and 2, we will determine SCNA and rearrangement profiles from either short-read (whole exome or whole genome) or long-/linked-read sequencing data, and determine germline genotypes, parental haplotypes, and ancestry groups. The haplotype information will be used to improve our copy-number resolution. In Aim 3, we reconstruct the tumor genome and its evolutionary history. We evaluate sample heterogeneity including tumor purity, ploidy, and subclonal alterations, and phase rearrangements to homologous chromosomes—determining somatic distances between all pairs of loci. Using these data, we determine mechanisms of DNA damage and repair and infer the events that occurred over tumor evolution. In Aim 4, we integrate data across samples to identify SVs and genomic loci that impact tumor evolution, detect associations with other molecular and clinical features, and evaluate potential SCNA-determined subclasses. Moreover, we perform association and admixture analyses with the germline genotypes detected in Aim 1. In Aim 5, we indicate ways in which we will immerse ourselves within the GDAN and disseminate our analysis results both within the GDAN and to the wider community. Within this, we offer secondary competencies in single-cell and circulating DNA analysis. Our GDAC will provide a comprehensive analysis of the roles of structural variations in cancer development and progression through treatment among GDAN samples. We will also optimize interactions with the wider GDAN and scientific community to make maximal use of these data.

Project narrative Somatic copy-number alterations (SCNAs) and the rearrangements that generate them are, together with mutations, the major somatic genome alterations in human cancer, and understanding them yields insights into how to diagnose and treat cancers. We have extensive experience in developing methods to detect and interpret SCNAs and rearrangements and have applied these methods across tens of thousands of tumors, discovering new molecular subtypes of cancer that may benefit from new therapeutic strategies. We propose to renew our Genomics Data Analysis Center that will specialize in conducting state-of-the-art analyses of SCNAs and rearrangements from both short read and long read sequencing data to answer clinically and biologically relevant questions that are tailored to the needs of the wider Genomics Data Analysis Network.