Quantitative modeling of extrachromosomal DNA (ecDNA) evolution in tumors

Project Summary. Cancer is governed by evolutionary principles whereby sequential changes at the genetic and epigenetic level enable proliferation, immune evasion, drug resistance, and metastasis. An outstanding goal in cancer biology is to understand the spatiotemporal processes underpinning this evolution. To do so would

greatly improve our ability to create more effective treatment strategies and forecast tumor development far into the future. However, this goal remains elusive due to our incomplete catalog of molecular processes driving evolution and lack of molecular and computational tools for holistically profiling tumors.

One emerging driver is extrachromosomal DNA (ecDNA). Found in approximately half of cancers and strongly associated with poor survival, ecDNAs are a unique form of oncogene amplification: they reside outside of chromosomes and exhibit elevated copy-number, gene expression, and chromatin accessibility as compared

to chromosomal amplifications. New evidence has underscored the importance of ecDNA dynamics and heterogeneity in driving tumor progression: first, ecDNAs asymmetrically segregate during mitosis, leading to accelerated copy-number gains and rapid adaptation to stressful conditions. Second, several varieties of

ecDNAs can exist in single cells where they form cooperative, intermolecular “hubs”. Despite this appreciation, these features of ecDNA have remained elusive to study due to a scarcity of tools for profiling their vast heterogeneity and stochastic evolutionary dynamics. In this project, I will develop the requisite computational

tools for profiling ecDNA variability and evolution in cancer and use these tools to more thoroughly investigate how ecDNA heterogeneity is created, maintained, and leveraged in response to targeted therapies. First, I will build on breakthroughs in long-read sequencing to develop tools that enable unbiased multi-omic profiling of

ecDNA variability across biological conditions (Aim 1). Second, I will leverage new molecular techniques to infer the phylodynamic properties of ecDNA lineages and learn the molecular fitness landscape of ecDNA (Aim 2). Third, I will explore the co-evolutionary principles of ecDNA by combining evolutionary modeling and CRISPR-

based screens (Aim 3). Together, these studies will illuminate properties of ecDNA evolution, nominate new therapeutic strategies, and provide innovative computational tools for the greater scientific community. This work will be performed in the excellent training environment of Stanford University under the

mentorship of Dr. Howard Chang, an expert in epigenomics and ecDNA. An advisory committee of leaders in the fields of ecDNA, cancer biology, bioinformatics, and tumor evolution will provide additional expertise and mentorship. The first half of each aim will be completed predominantly during the K99 phase of the award,

providing a solid foundation for the aims in the R00 phase and eventually an independent R01 application.