The Laboratory of Genomic Susceptibility (LGS) in the Division of Cancer Epidemiology and Genetics (DCEG) is committed to understanding the biological basis for genetic susceptibility to cancer, including identification and characterization of cancer susceptibility alleles, characterization of the scope of genetic mosaicism and its contribution to cancer risk, investigation of the genetic architecture of cancer susceptibility, and determination of how germline variation informs our understanding of somatic alterations in cancer. LGS scientists participate in genetic, epidemiologic, clinical and methodological studies of populations, families, and persons at risk for cancer. In addition, they collaborate and consult with experts in statistical genetics, bioinformatics, genetic epidemiology, and molecular epidemiology in interdisciplinary studies. Using large-scale genome-wide association studies, familial linkage studies in high-risk families, and publically available databases, such as TCGA, TARGET, ENCODE, and GTEx, investigators continue to identify loci within the human genome that are associated with cancer risk. The laboratory is at the forefront of applications in bioinformatics and advanced genetic analyses with new platforms designed to evaluate the biological effects of dense sets of single nucleotide polymorphisms (SNPs), which are the most common genetic variants in the human genome. Specifically, the laboratory has integrated approaches to identify and validate common SNPs and ancestral haplotypes, which could be used to dissect the genetic basis of disease susceptibility. Together with NCIs Cancer Genomics Research Laboratory, LGS carries out genome-wide association studies (GWAS). Using data from large-scale GWAS that evaluate millions of SNPs, investigators at the LGS are able to identify in great detail new regions in the human genome that are associated with cancer risk and to estimate the magnitude of effect of these risks. GWAS have been instrumental in the discovery of new regions of the genome, which influence the basic etiological risk factors. In addition, these novel findings may, at a later time, also bear important predictive value for disease, particularly in developing a polygenic model as well as highlight potential molecular pathways related to both disease etiology and perhaps therapeutic intervention. To understand the biology underlying these associations, investigators are following up with focused validation studies, deep-sequencing, and functional analyses, such as analyses of expression levels and methylation patterns. This research relies on multidisciplinary approaches from population genetics, epidemiology and molecular evolution. Investigation of GWAS signals requires extensive bioinformatic follow-up to examine unannotated transcripts, regulatory elements, as well as functional elements for novel transcripts. Regulatory effects are queried with respect to the alteration of gene levels, epigenetics, and long-ranging effects on other genes at a distance using cell lines, normal tissue and tumor tissue, as well as resources from the TCGA and ICGC. The LGS is also investigating several possible biologic mechanisms including whether variations in these identified regions may affect regulatory elements of neighboring genes, the impact of miRNA polymorphisms acting upon fragile chromosomal sites, and epigenetic effects across multi-susceptibility regions. We have developed a series of collaborations with leading epidemiologists and biostatisticians in the Division of Cancer Epidemiology and Genetics (DCEG), the NCI Cohort Consortium, and multiple other international molecular epidemiologic consortia. Data pooling is being used to achieve the statistical power necessary to detect associations between genomic variants and a variety of health outcomes, as well as gene-environment interactions. An interesting outcome from GWAS studies at LGS has been the identification of a sizeablefraction of apparently healthy individuals that harbor large scale (2MB) mosaic events detected as copy number changes or copy-neutral uniparental disomies. Investigating human clonal mosaicism has the potential to offer new insights into genomic maintenance as individuals age as well as explain a portion of the phenotypic heterogeneity of cancer subtypes. With the accumulation of cancer susceptibility regions from GWAS and large new datasets of tumor sequencing data, it is now possible to investigate the interplay between inherited germline genetics and acquired somatic mutations. The expertise of LGS staff and collaborations with DCEG experts enables for novel investigation of how germline genetics combined with somatically acquired mutations may affect susceptibility to cancer.
| Melin, Beatrice S; Barnholtz-Sloan, Jill S; Wrensch, Margaret R et al. (2017) Genome-wide association study of glioma subtypes identifies specific differences in genetic susceptibility to glioblastoma and non-glioblastoma tumors. Nat Genet 49:789-794 |
| Clinical Cancer Genome Task Team of the Global Alliance for Genomics and Health; Lawler, Mark; Haussler, David et al. (2017) Sharing Clinical and Genomic Data on Cancer - The Need for Global Solutions. N Engl J Med 376:2006-2009 |
| Bigot, Pierre; Colli, Leandro M; Machiela, Mitchell J et al. (2016) Functional characterization of the 12p12.1 renal cancer-susceptibility locus implicates BHLHE41. Nat Commun 7:12098 |
| Global Alliance for Genomics and Health (2016) GENOMICS. A federated ecosystem for sharing genomic, clinical data. Science 352:1278-80 |
| Machiela, Mitchell J; Ho, Brian M; Fisher, Victoria A et al. (2015) Limited evidence that cancer susceptibility regions are preferential targets for somatic mutation. Genome Biol 16:193 |
