Dartmouth Researchers Receive Substantial NIH Grant as Part of Cooperative Research in Lung Cancer Risk

Significant research funding in the form of a five-year, $12.1 million U19 grant from the National Institutes of Health (NIH) has been awarded to a collaboration of research teams co-led by Geisel School of Medicine professor and Norris Cotton Cancer Center interim director Christopher Amos, PhD, to study lung cancer risk and improve the precision of early screening.

“The goal is to enhance our understanding of gene–environment interactions in lung cancer etiology and to move the observations about risk for lung cancer towards translation,” said Amos.

For more than 30 years lung cancer has remained the most common cancer, and carries with it the highest cancer mortality rate worldwide, largely due to it’s often late-stage diagnosis. With this NIH grant, the team aims to more precisely target lung cancer screening to reduce its burden and improve early detection.

This research funding relates to and greatly extends the team’s recently published Nature Genetics paper, which details the results of a large study that identified several new variants for lung cancer risk that will translate into improved understanding of the mechanisms involved in lung cancer risk. Using the OncoArray genotyping platform developed by multiple cancer consortia, the genome-wide association study identifies new susceptibility loci for lung cancer. Although tobacco smoking is the main risk factor, past studies have also shown heritability of lung cancer as a concern, though much of it remains unexplained.

Dartmouth researchers (L–R) Jiang Gui, Christopher Amos, Ivan Gorlov, and Olga Gorlova were recently awarded a $12 million NIH grant as part of a multi-institutional cooperative project to study lung cancer risk as it pertains to gene — environment interactions, and to improve lung cancer screening methods and efficiency. (Photo: Norris Cotton Cancer Center)

The cooperative grant study will be arranged into three complementary projects working towards a unifying goal. Project 1, Genomic Predictors of Smoking Lung Cancer Risk, studies large samples to identify variants that affect risk through genetic factors and environmental exposures. Project 2, Biomarkers of Lung Cancer Risk evaluates a wide range of risk biomarkers that have been implicated as promising lung cancer risk biomarkers and will identify validated risk biomarkers for use in risk prediction models. Project 3, Translating Molecular and Clinical Data to Population Lung Cancer Risk Assessment establishes an integrated risk prediction model based on lung cancer CT screening populations in the United States, Canada and Europe. It combines personal health and exposure history with targeted molecular and genomic profiles and lung function data, and establishes nodule assessment models for individuals qualified by the probability models.

“We believe that this level of integration will yield novel observations about lung cancer development and provide unique translational opportunities to refine screening eligibility criteria,” said Amos. “Ultimately, it will help improve screening efficiency and further reduce lung cancer mortality.”

In addition to his role as interim director of Norris Cotton Cancer Center, Amos is chair of the Department of Biomedical Data Science, head of the Center for Genomic Medicine, and associate director for Population Sciences at the Geisel School of Medicine. He serves as the communicating principle investigator (PI), the PI of the administrative core and the PI of Project 1. Other Dartmouth investigators include Ivan Gorlov, PhD, Olga Gorlova, PhD, and Jiang Gui, PhD. Paul Brennan, PhD from the International Agency for Research in Cancer, part of the World Health Organization in Lyon, France is the Project 2 leader, focusing on identifying and validating biomarkers of early lung cancer. Rayjean Hung, PhD, at the University of Toronto is the PI of project 3 focusing on applying these biomarkers in all the world’s largest screening cohorts.  Xihong Lin, PhD, at the Chan Harvard School of Public Health is the PI of the biostatistics core.

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