# Copy Number Variation and Lung Cancer: Disease Risk and Mechanisms

> **NIH NIH R21** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2020 · $186,525

## Abstract

PROJECT SUMMARY
Lung cancer (LC) is the leading cause of cancer related death in the United States. Although genome-wide
association studies have identified many LC susceptibility loci, most of its heritability remains hidden and might
be further explained by copy number variation. To date, studies have provided robust evidence to support the
unique roles of copy number variants (CNVs) in many cancer types, however, their risk effect and molecular
mechanisms contributing to LC is still unclear. The overall objective of this R21 is to conduct a
comprehensive study leveraging datasets from the large-scale Transdisciplinary Research in Cancer of
the Lung (TRICL) consortium, a Lung eQTL dataset and two public data resources to discover high
confidence CNVs predisposing to LC across histological subtypes. The central hypothesis is that CNVs
are associated with LC susceptibility, regulate gene expression and have a potential to serve as novel biomarkers
for prediction of LC. This hypothesis will be tested by pursuing two specific aims: 1) Determine the effect of CNVs
on LC risk; and 2) Characterize the regulatory impact of CNVs on gene expression. In Aim 1, with a large
collection of data from LC patients and controls (n=36,068 total) from the TRICL consortium, we will rigorously
evaluate CNVs as novel biomarkers for lung cancer predisposition. First, CNVs will be generated by a change-
point based method, modSaRa2, and a Hidden Markov Model based approach, PennCNV. Then using a gene-
based collapsing association test, duplications or deletions associated with LC will be determined. These
significant associations will be validated by an independent replication dataset, the Environment and Genetics
in Lung cancer Etiology (EAGLE) dataset (n=4,221). We will identify novel pathways, networks, and interactions
underlying LC, which are significantly enriched by LC-susceptibility CNVs. Gaining insight into the underlying
biological mechanisms of the influence of CNVs on LC risk is critical; therefore, in Aim 2, we will evaluate the
regulatory impact of CNVs on gene expression, which is intermediate to many complex phenotypes. Genomic
measures from the Lung eQTL study (n=1,038) and the public dataset GTEx (n=383) will be used to evaluate the
associations between the identified LC-susceptibility CNVs and expression of their corresponding genes. A functional
study with experimental design will be followed to test the downstream functions of the newly identified CNV
regulated gene expression in growth and progression of cancer cells. This project has the potential to fill a gap in
current knowledge about the utility of CNV as a new type of genetic variation influencing the risk of LC and provide a
better understanding of the underlying molecular mechanisms. Our innovative, integrative genetics, genomics and
bioinformatics approaches will identify novel genetic predictors that predispose to LC. This study has enormous
potential for providing critical new directio...

## Key facts

- **NIH application ID:** 10057045
- **Project number:** 1R21HG010925-01A1
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Feifei Xiao
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $186,525
- **Award type:** 1
- **Project period:** 2020-09-01 → 2022-01-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10057045

## Citation

> US National Institutes of Health, RePORTER application 10057045, Copy Number Variation and Lung Cancer: Disease Risk and Mechanisms (1R21HG010925-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10057045. Licensed CC0.

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