In cancer, oncogenes are frequently amplified on extrachromosomal DNA (ecDNA), circular acentric DNA fragments ranging from hundreds of kilobases to multiple megabases. Oncogene amplification on ecDNA is associated with extremely high copy number and poor prognosis in patients. However, the specific mechanisms through which ecDNA alter cell behavior and tumor evolution are poorly understood. Here, I report that ecDNA are inherited randomly by daughter cells during mitosis. This breakdown of canonical mendelian inheritance patterns results in significant increases in population and tumor heterogeneity. I further demonstrate that cell lines with ecDNA are able to rapidly alter their distribution of ecDNA copy number to adapt and gain resistance to both environmental and therapeutic challenges. Interestingly, investigations into the behavior of multiple species of ecDNA within individual cells demonstrate that while ecDNA is inherited randomly by daughter cells, different ecDNA species are not inherited independently of each other. Further, I show that ecDNA demonstrates an increased frequency of missegregation into micronuclei during mitosis.Finally, I investigate proteins that may be essential for the maintenance and inheritance of ecDNA. Analysis of gene expression data from The Cancer Genome Atlas identifies several proteins involved in chromosomal segregation and DNA repair and metabolism. I specifically analyze the chromokinesins KIF4A and KIF22 and demonstrate that they may play a significant role in ensuring proper segregation of ecDNA into daughter cells. Taken together, the data presented here clearly demonstrate how the random inheritance of ecDNA at each cell division generates a dynamic and heterogeneous distribution of amplified oncogenes. This enables cancer cells to adapt to and resist environmental and therapeutic pressures more readily. I also describe the behavior of ecDNA during mitosis and identify specific proteins that may be able to be targeted to specifically disrupt the proliferation of ecDNA+ cell lines and tumors.

Technological and scientific advances have allowed for a deeper understanding of human cancer and have led to rapid characterization and development of personalized treatments for many patients. Despite these advances, many tumors continue to develop resistance to their prescribed therapies. One mechanism of treatment resistance is through oncogene amplification on extrachromosomal DNA (ecDNA). ecDNA are acentric, circular fragments of genomic DNA, which are prevalent in many of the most aggressive cancers and are associated with poor patient prognosis. Unfettered by the constraints of linear chromosomes, ecDNA leads to novel genomic interactions, highly heterogeneous tumors, and rapid tumor evolution. Here, we report a series of studies investigating the unique cell biology of ecDNA. Due to the lack of centromeres, ecDNA does not follow the laws of Mendelian inheritance and can, therefore, be inherited asymmetrically. Using orthogonal sequencing-based and live-cell imaging technologies, we witness the rapid development of copy number heterogeneity and the dynamic nature of ecDNA copy number. Through live-cell imaging and four-dimensional image analysis, we witness a single cell giving rise to a highly diverse population of cells after only three cell divisions. Additionally, we report that many cancers have amplification of multiple oncogenes on distinct ecDNA species. We show that this co-occurrence is, in part, due to intermolecular interactions that allow for their coordinated inheritance during cell division. Taken together, the series of studies presented herein offer novel insight into ecDNA biology and begin to explain the functional implications of ecDNA copy number heterogeneity and the ability of ecDNA-containing cells to rapidly adapt to their microenvironment. These data indicate that the unique biology of ecDNA allows for the maintenance of important intermolecular interactions while driving rapid development of intratumoral heterogeneity.

Oncogenic mutations in growth factor receptor signaling pathways are common in cancer, including in tumors that arise from or metastasize to the brain. However, most small-molecule inhibitors targeting growth factor receptors have failed to show efficacy for brain cancers, potentially due to inability to achieve sufficient drug levels in the central nervous system (CNS). Targeting tumor co-dependencies provides an alternative approach, particularly if drugs with high brain penetration can be identified. Here we demonstrate that EGFR-mutant cancers, including a highly lethal form of brain cancer glioblastoma (GBM), are remarkably dependent on cholesterol for survival, rendering them sensitive to Liver X receptor (LXR) agonist-dependent cell death. We show that LXR-623, a clinically viable, highly brain-penetrant LXRα-partial/LXRβ-full agonist selectively kills GBM cells in an LXRβ- and cholesterol-dependent fashion, causing significant tumor regression and prolonged survival in mouse models. Thus, a metabolic co-dependency provides a pharmacological means to kill growth factor-activated cancers in the CNS.

Background: Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for yeast survival. Results: Here we investigate the properties of weighted gene co-expression networks formed from multiple microarray datasets. The constructed networks approximate scale-free topology, but this is not universal across all datasets. We show strong positive correlations between gene connectivity within the whole network and gene essentiality as well as gene sequence conservation. We demonstrate the preservation of a modular structure of the networks formed, and demonstrate that, within some of these modules, it is possible to observe a strong correlation between connectivity and essentiality or between connectivity and conservation within the modules particularly within modules containing larger numbers of essential genes. Conclusion: Application of these techniques can allow a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.

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