||1R21CA256575-01A1 Interpret this number
||Characterization of Extrachromosomal Dnas in Tumors Through Computational Analysis of Single-Cell and Bulk Sequencing Data
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
Extrachromosomal DNA amplifications in cancer.
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