AUG 21, 2014 09:45 AM PDT
Pathway based analyses for genetic susceptibility to cancer and autoimmune disease
Presented at the Genetics and Genomics Virtual Event
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  • Associate Director for Population Sciences, Professor of Community and Family Medicine at the Geisel School of Medicine, Norris Cotton Cancer Center, Dartmouth College
      Dr. Christopher Amos received his M.S. and Ph.D. degrees in 1985 and 1988 from the Louisiana State University Medical Center in New Orleans from the Department of Biometry and Genetics. In his graduate studies he developed novel approaches for conducting genetic linkage analysis for multivariate data and in extended families. Subsequently, Dr. Amos completed training in Medical Genetics through the Inter-institute training program in Genetics at the NIH (1992), where he also completed postdoctoral training in the Family Studies Section of the Environmental Epidemiology Branch at the National Cancer Institute. During this period (1988-1992), he studied inheritance patterns of breast and ovarian cancers, publishing seminal papers in the Journal of the American Medical Association and the Journal of the National Cancer Institute. He also developed a novel and widely cited approach for linkage analysis that was published in the American Journal of Human Genetics in 1994(PMID:8116623, 787 citations). He has authored or coauthored 548 peer reviewed papers, 19 reviews or book chapters, 2 books and has an H index of 104 with 43,170 citations. His work is funded by 11 current and 2 approved pending P01 grants from NIH (on one he serves as communicating PI). He has served as PI, multiple PI or core leader on 25 funded grants (including R01, U01, P01, U19, P20, P30 and T32 grants from NIH and ACS grants) and coinvestgator on an additional than 14 grants.

      In 1992 he was recruited to the M.D. Anderson Cancer Center where he joined the Department of Epidemiology and established the Section of Computational and Genetic Epidemiology. There he developed and applied novel bioinformatical approaches for identifying genomic factors influencing lung, breast, head and neck, colon and brain cancer development. In 1994 he received his first NIH R01 to develop model-free linkage analysis methods and develop software to support these methods. He has subsequently received several other NIH grants as PI as well as Co-Investigator that have supported the development of new statistical methods to understand the interplay of genetic and environmental factors that cause lung cancer development. He wrote a book 'Forward-Time Population Genetics Simulations: Methods, Implementation, and Applications') with coauthors Dr. Bo Peng (at M.D. Anderson Cancer Center) and Dr. Marek Kimmel (at Rice) that describes approaches to simulating populations for genetic analyses. He also led studies that recruited and studied individuals with inherited risk for lung, colon and breast cancer. In 2004, in collaboration with Dr. Joan Bailey-Wilson and others, he identified a region of chromosome 6q23-25 that harbors a locus greatly increasing lung cancer risk, and showed in 2010 that this locus identifies individuals exquisitely sensitive to tobacco smoke. This collaboration led to the discovery that mutations in Park2 increase lung cancer risk. In 2008, he led large genetic studies that identified common variants in CHRNA5 (PMID 20554942 cited 1032 times) that have substantial impact on both lung cancer risk and smoking behavior, making it more difficult for smokers to quit. Dr. Amos led an ACS funded grant to study Peutz-Jeghers syndrome a rare condition that causes greatly increased risk for breast, colon and pancreatic cancers with most cases carrying mutations in STK11. He also lead the biostatistics and bioinformatics core for Dr. Louise Strong's P01 'A mutational model for Childhood Cancers' which led to the development of novel approaches for statistical and bioinformatical analysis to identify modifiers of risk for cancer. He also led several other bioinformatical and supported database development for several large projects including the Human Pedigree Analysis Resource core of the M.D. Anderson Cancer Center Support Grant, database development for the Gliogene Consortium, led by Dr. Melissa Bondy to identify genetic factors for familial brain cancers and Bioinformatics leader for the Texas Cancer Genetics Consortium which brought together investigators from UT Southwestern, UT San Antonio, UT M.D. Anderson Cancer Center and the Baylor College of Medicine to identify high risk families with cancer for whom targeted prevention and interventions would be effective.

      In 2012, Dr. Amos moved to the Geisel School of Medicine at Dartmouth College where he became the inaugural Chair for the Department of Biomedical Data Science, which includes Divisions of Biostatistics, Biomedical Informatics and Behavioral Studies. The Department grew under his leadership to 24 faculty members and about 80 staff. He also heads the Center of Genomic Medicine and became the Norris Cotton Cancer Center's Associate Director for Population Studies. Dr. Amos continued the work to identify and characterize genetic factors influencing lung cancer risk. He led an international team that studied over 50,000 individuals and identified uncommon mutations in BRCA2 that confer a 250% higher risk for developing squamous lung cancer. Dr. Amos is leading an international collaboration entitled 'Transdisciplinary Research in Cancer of the Lung' that had been supported by a U19 award from the NCI, which integrates genetic studies from international sites for discovery and epidemiological analysis of lung cancer risk. Work being performed by this consortium has been organized into a P01 submission that recently was approved for funding at the NCI with an expected start date of June 1, 2017. Dr. Amos moved to the Baylor College of Medicine on November 1, 2017 where he is the Associate Director for Quantitative Research and the Director of the Institute for Clinical and Translational Research.

      In addition to lung cancer, Dr. Amos has been involved in cutting edge research in other malignancies.
      He led the OncoArray Consortium that recently completed genotyping of about 500,000 individuals using a customized array developed with Illumina that comprises 500,000 markers. It was developed to decipher the genetic architecture of common cancers including prostate, colon, breast and ovarian cancers. Managing population-based analysis of this very large study required the development of new analytical approaches for characterizing the ethnic origin of participants to avoid confounding and the approach (published in BMC Bioinformatics). Results of studies using this array have been published in Nature Genetics for Head and Neck, Glioma (led by Dr. Melissa Bondy at the Baylor College of Medicine), and Ovarian Cancer, and there are additional accepted papers forthcoming in Nature and Nature Genetics. These papers have revealed the genetic architectures and more novel variants influencing Lung cancer, and ER+ and ER- breast cancer.

      Since 2014, Dr. Amos has been collaborating with Drs. Hashem El-Serag at Baylor College of Medicine and Manal Hassan at the M.D. Anderson Cancer Center in the Hepatobiliary Cancer Consortium which brings together North American Investigators to understand its environmental and genetic etiologies. This research has established a national consortium to identify genetic and environmental factors contributing to the epidemic of hepatobiliary cancer. Dr. Amos leads the design and analytical team responsibilities for the consortium.

      At Dartmouth, Dr. Amos co-leads the New Hampshire Colonoscopy Registry. Currently, he is serving at the interim Director of the Norris Cotton Cancer Center (NCCC), an NCI-designated cancer center. He leads the Center for Genomic Medicine, which was developed in collaboration with leaders in Pathology to develop approaches for routine sequencing for cancer NCCC patients. These data have been integrated with the electronic medical record, and are being used to identify clinical characteristics of patients associated with carriage of actionable mutations.

      Dr. Amos is also leader in cancer research mentoring. He is director of a Center of Biomedical Research Excellence grant (P20) 'Quantitative Biomedical Research Institute' that mentors junior faculty toward their initial award of an R01, and supports bioinformatics and personalized medicine at Dartmouth College and surrounding institutions in New Hampshire, Maine and Vermont. Dr. Amos serves as leader of the Biostatistics Epidemiology and Research Design component of the Clinical and Translational Institute at Dartmouth College, and has been directing the biomedical informatics team. Finally, Dr. Amos is leading a T32 training grant funded by the Knowledge to Big Data initiative for training graduate students in biomedical data sciences.

      Dr. Amos's expertise is recognized nationally and internationally. Dr. Amos became the President of the International Genetic Epidemiology Society in 2002, and served as its Secretary/Treasurer from 2007 to 2012. He served on the board of external scientific advisors for the Centre Etude Polymorphism Humain from 2007-2014. He was elected as a fellow of the American Association for the Advancement of Science in 2012. He currently serves on the Board of Scientific Counselors for the National Institute of Environmental Health Sciences. He completed a term as reviewer for the Access Committee for the Center for Inherited Disease Research from 2007-2011. He has also served as a reviewer for NIH, Cancer Research UK, and Canadian Institute for Health Research and numerous other panels. He has served or is serving as an associate editor for the American Journal of Human Genetics, Genes and Immunity, Genetic Epidemiology and Human and Molecular Genetics, and as a statistical editor for the Journal of the National Cancer Institute.

    In this presentation I describe pathway based analyses of genotyping data to identify pathways related to the development of complex diseases, with a focus on lung cancer and selected autoimmune diseases. The goal of this research has been to identify sets of genes that influence disease risk using extensive data that have been developed by collaborative studies. These studies involve research groups from multiple locations across the world, which raises issues about joint analysis of the data. Rather, we adopt an approach in which analyses are performed by center and then merged, to perform first pass meta-analyses. Additionally, we apply novel approaches to organizing the data into pathways while allowing for correlations among markers to reduce discovery of false positive findings. Results from applications to lung cancer and selected autoimmune diseases will be described.

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