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Quantitative modeling of extrachromosomal DNA (ecDNA) evolution in tumors

Award Number: R00CA286968
ORGANIZATION: NATIONAL CANCER INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 01/01/2024
PERIOD OF PERFORMANCE END DATE: 01/31/2029

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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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $248,618 )
2026	2026	MASSACHUSETTS INSTITUTE OF TECHNOLOGY	77 MASSACHUSETTS AVE	CAMBRIDGE	MA	02139	MIDDLESEX	USA	Cancer Biology Research	000	3	3/5/2026	EXTENSION WITH OR WITHOUT FUNDS	$248,618
														Subtotal = $248,618

Grand Total All Awards = $248,618

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Quantitative modeling of extrachromosomal DNA (ecDNA) evolution in tumors

Award Number: R00CA286968

ORGANIZATION: NATIONAL CANCER INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 01/01/2024

PERIOD OF PERFORMANCE END DATE: 01/31/2029

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