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Improved identification of subjects genetically at risk for lung cancer

Institution: University of Southern California
Investigator(s): Ite Offringa, Ph.D.
Award Cycle: 2017 (Cycle 26) Grant #: 26IR-0019 Award: $495,000
Subject Area: Early Diagnosis of Tobacco-Related Cancer
Award Type: High Impact Research Project Award

Initial Award Abstract

While smoking is a known strong risk factor for lung cancer, genetics also plays a role: smokers with a close relative who had lung cancer, have increased risk. To date, no gene that strongly predisposes to lung cancer has been identified, and such a gene may not exist. But it is known that there are many small genetic differences between people, called single nucleotide polymorphisms (SNPs or “snips”) that each can increase lung cancer risk, for example by 20%. Knowing a person’s risk for a disease is an important component of screening; the higher a person’s risk, the more important it is to be screened for the disease. Spiral computed tomography can be used to screen long term smokers for lung cancer, but non-smokers or short term smokers may not qualify for this screening. However, if we can show that they are at increased risk because they carry certain lung cancer risk SNPs, we can justify screening them. Screen-detected cancers can be removed by non-invasive robotic surgery before the cancer can spread in the body. The problem with using SNPs to determine lung cancer risk is that the SNPs identified to date (called “tag SNPs”) are more like “labels” or tags for a certain region of the genome. The tag SNP itself, or any of the many other SNPs that lie nearby, could be the “functional” SNP – the SNP that actually CAUSES the increased lung cancer risk. Until we know which SNPs are the functional SNPs, we cannot accurately determine a person’s risk for lung cancer, nor can we understand WHY a SNP causes lung cancer risk to be increased. Another problem is that most of the tag SNPs do not lie in the parts of genes that code for proteins. Some SNPs don’t even lie near any genes! Thus, these SNPs cannot increase lung cancer risk by changing a protein. They must work by changing how genes are regulated. Genes are regulated by the proteins bound to their control regions in the DNA. SNPs can change the DNA sequence in these control regions so that they do not properly bind the proteins that normally sit there. Thus we need to first determine where the regulatory elements are in the cells that could become lung cancers, and then whether a SNP disturbs any of those regulatory elements. A SNP that disturbs a regulatory element in lung alveolar epithelial cells can increase the risk for lung adenocarcinoma, while a SNP that disturbs a regulatory element in bronchial epithelial cells can increase the risk for squamous cell lung cancer. Here we aim to identify the actual SNPs that increase the risk for lung cancer and to determine the genes that are affected by these SNPs. Our project builds on exciting data we obtained from a previous pilot project in which we identified candidate functional risk SNPs for lung adenocarcinoma. We determined that these SNPs disrupt functional elements called enhancers, which act like remote controls to regulate genes that can be located far away. Here we will determine which genes are affected by these enhancers and study how these genes can increase lung cancer risk. We will then apply the same approach to identify squamous cell lung cancer risk SNPs that likewise disrupt enhancers, identify the genes affected by these enhancers, and study their role in bronchial cells, which are progenitors of squamous cell lung cancer. Identifying the actual risk SNPs will increase our ability to predict a subject’s risk for lung cancer, so that subjects at greater risk can be screened and steps can be taken to prevent cancer (e.g. quit smoking). Furthermore, understanding HOW these SNPs increase risk can lead to new strategies to limit lung cancer and possibly prevent it.