# Advancing Ultra Long-read Sequencing and Chromatin Interaction Analyses for Chromosomal and Extrachromosomal Structural Variation Characterization in Cancer

> **NIH NIH R33** · JACKSON LABORATORY · 2020 · $1,377,810

## Abstract

PROJECT SUMMARY
Structural variants (SVs) such as deletions, insertions, inversions, duplications, and translocations in cancer
genomes can promote tumor progression by perturbing gene structures and expression. Additionally,
extrachromosomal DNA (ecDNA)—an extreme form of SV found in a wide range of cancer types—are a reservoir
of oncogene amplification and contribute to the genetic heterogeneity and evolution of tumors. Thus, a complete
understanding of the structure and distribution of SVs and ecDNAs in tumors would shed light on their roles in
tumor progression. However, the ability to detect and characterize SVs and ecDNAs at the molecular level has
been limited by existing short-read sequencing approaches: large and complex SVs thwart efforts to detect them
and correctly define their structures; and the multi-copy, heterogenous nature of ecDNAs undermines
determination of their primary structures. While ecDNAs can be observed by DAPI-staining of metaphase tumor
cells, determining their sequence content has typically relied on fluorescence in situ hybridization (FISH) to probe
for candidate oncogenes. To support an unbiased and comprehensive molecular approach to the study of SVs,
this project will develop and validate emerging genomic technologies that will enable the detection and
characterization of complex SVs and ecDNAs as standard practices in cancer genomics. In Aim 1, the
read lengths of the nanopore single-molecule sequencing platform will be further extended by improving genomic
DNA quality and optimizing library preparation reactions, with the goal of attaining N50 read lengths of 75-100
Kb. Such long read lengths are expected to span many SVs to more effectively reveal their molecular structures
and phasing information. In parallel, the recent SV-detecting computational pipeline, Picky, will be optimized to
detect molecular signatures of complex SVs and ecDNAs to allow their accurate and sensitive detection in long
read sequencing data to >0.8 precision and recall rates. The active transcription of ecDNAs suggests that they
are associated with RNA polymerase II transcription complexes, making them suitable for unsupervised
detection by the chromatin interaction assay, ChIA-PET. In Aim 2, this method will be employed to map ecDNAs
via their association with RNA polymerase II and reveal transcriptionally relevant interactions between ecDNAs
and the chromosomes. Computational methods will be developed to specifically detect ecDNA-amplified
sequences in ChIA-PET data and their associated oncogenic genes. Additionally, ecDNAs uncovered by ChIA-
PET will be targeted by the CRISPR/dCas9-based targeted capture method to physically isolate ecDNA
molecules for long-read sequencing and structural characterization. Aim 3 will build on the developed methods
to generate a platform for unbiased and unsupervised characterization of SVs and ecDNAs in glioblastoma
neurosphere cultures and in xenograft tumor models of glioblastoma, breast, and lung...

## Key facts

- **NIH application ID:** 9889550
- **Project number:** 1R33CA236681-01A1
- **Recipient organization:** JACKSON LABORATORY
- **Principal Investigator:** Roel GW Verhaak
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,377,810
- **Award type:** 1
- **Project period:** 2020-07-16 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9889550

## Citation

> US National Institutes of Health, RePORTER application 9889550, Advancing Ultra Long-read Sequencing and Chromatin Interaction Analyses for Chromosomal and Extrachromosomal Structural Variation Characterization in Cancer (1R33CA236681-01A1). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/9889550. Licensed CC0.

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