# Towards a Simulation of Cancer Genomes for Benchmarking Structural Variant Algorithms

> **NIH NIH F31** · WEILL MEDICAL COLL OF CORNELL UNIV · 2020 · $45,520

## Abstract

Alterations to genomes either drive or mark the processes underlying the pathogenesis of cancer. One such
alteration, rearrangements, heavily impact the structure of cancer genomes. By the drastic nature of these
mutations on cancer genomes, we expect these to be major drivers or signatures of mutational processes.
Thanks to next-generation sequencing and computational/algorithmic advances, the field of cancer research
has made leaps in understanding dynamics of tumor evolution, mutational signatures, and both genetic and
epigenetic drivers of different cancer types. This information has even entered the clinical realm of precision
medicine to effectively treat cancer cases. However, most of this progress has relied on identifying smaller
variants such as single nucleotide variants (SNVs) or small insertions/deletions (indels), largely because,
paradoxically, structural variants remain difficult to detect and characterize using current sequencing
technology. With the availability of widely available sequenced primary tumor tissues from various cancer
consortia, the use of algorithms to reliably identify and characterize structural variants, is not only a preference,
but a necessity. Although many structural variant calling algorithms exist and are in development, most have
not been benchmarked uniformly or even reliably. This poses a major problem in that rearrangements clearly
have major effects and should bear strong mutational signatures, but are not precisely understood because of
the lack of well-developed computational tools to characterize them. No gold standard dataset of structural
variants exist for any cancer type, requiring an accurate simulation to properly benchmark and develop these
algorithms. This proposal will fill this gap by creating an accurate and coherent simulation of structural variants
in addition to small variants to benchmark the most current set of structural variant tools for detection, as well
as characterization of tumor evolution.

## Key facts

- **NIH application ID:** 9937683
- **Project number:** 5F31CA232465-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Kevin Matthew Hadi
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $45,520
- **Award type:** 5
- **Project period:** 2018-07-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9937683

## Citation

> US National Institutes of Health, RePORTER application 9937683, Towards a Simulation of Cancer Genomes for Benchmarking Structural Variant Algorithms (5F31CA232465-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9937683. Licensed CC0.

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