Characterization of extrachromosomal DNAs in tumors through computational analysis of single-cell and bulk sequencing data

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PROJECT SUMMARY Extrachromosomal DNAs (ecDNAs) are found in 40% of tumors but rarely found in normal cells. Importantly, they contain and express amplified oncogenes derived from chromosomal sequences. In contrast to the chromosomes, ecDNAs segregate unequally to daughter cells during cell division and thus can accumulate at high copy numbers in individual cells within a tumor. This contributes to intratumor heterogeneity (ITH), which can give subsets of tumor cells a selective growth advantage and enable resistance to cancer treatment. While previous studies have focused on how ITH of chromosomal mutations contributes to tumor evolution, little is known about how ecDNAs might impact tumor evolution and patient outcomes. To address how ecDNAs contribute to ITH and tumor evolution, Aim 1 will determine the ITH of ecDNAs for cell lines derived from patient-matched primary and recurrent glioblastoma tumors for which single-cell DNA sequencing (scDNA-seq) and standard bulk short-read whole-genome sequencing (WGS) data have been previously generated. To overcome the technical challenge of detecting individual ecDNAs in scDNA-seq data, we will employ an alternative supervised approach of using `breakpoints' between high-copy number segments in the scDNA-seq data as surrogates for the ecDNA breakpoints and intersect these with the identified ecDNA breakpoint sequences in the reference sets. This approach will enable us to study ecDNA-driven ITH and evolution in single cells between the cell lines derived from the longitudinal glioblastoma tumors. We will also apply this approach to existing scDNA-seq datasets to assess the presence of ecDNAs. Current computational tools used to predict ecDNAs in standard bulk short-read WGS data have limited ability to determine the ecDNA breakpoints in single cells; thus, we anticipate that our proposed approach, while conceptually simple, will have a major impact on improving our understanding of how ecDNAs evolve within the cells of a tumor. In Aim 2, a large cohort of publicly available tumor bulk WGS datasets representing multiple cancer types will be leveraged to more broadly characterize ecDNAs and their effects on tumor evolution. We will perform integrative analysis of ecDNAs and other genomic features using a large number of tumors to characterize ecDNAs and to infer the potential molecular mechanisms underlying their formation. We will build a machine learning classifier that can predict the presence of ecDNAs using non-WGS data (i.e. whole-exome and RNA sequencing) that have been a primary strategy for sequencing patient tumors, and therefore, are more widely available than WGS. We will also systematically analyze a large number of single-time point and longitudinal tumor samples to characterize the effects of ecDNAs on evolutionary selection pressures in tumors. Overall, completion of these Aims will greatly advance our understanding of ecDNAs in tumor evolution, thereby shedding light on how ecDNAs impact patient outcomes and ultimately establishing a basis for novel cancer therapeutics.