Colorectal cancer remains one of the most common cancers in the US with 146,970 new diagnoses and 49,920 deaths estimated for 2009 (Jemal 2009). Colon cancer is also one of the most familial of cancers. Individuals with a first-degree relative with colon cancer have a 2- to 3-fold increased risk, and those with more than one first-degree relative with colon cancer or a single first-degree relative affected at age d 50 years have a 3- to 6-fold greater risk than those with no family history. The most prominent high-risk colorectal cancer susceptibility genes, APC, MLH1, MSH2, MSH6, PMS2, and PTEN, were all discovered more than a decade ago. Currently, mutation screening of these genes, plus a short list of additional genes that are responsible for a very small fraction of colorectal cancer, plays an important role in the clinical management of individuals with a strong family history of the disease or syndromic evidence for the presence of a gene mutation. At the other end of the risk spectrum, genome-wide association studies have identified a number of common alleles with very modest effects on colorectal cancer risk;their clinical utility has yet to be established. However, taken together, the known spectrum of genetic effects only explain about one quarter of the overall familial excess of colorectal cancer. It should be emphasized that, at present, the vast majority of individuals seen at familial cancer clinics are counseled on the basis of their family history alone because they do not have mutations in the known susceptibility genes. Accordingly, the long-term objective of this project is to identify the majority of genes responsible for the unexplained component of inherited colorectal cancer risk. Over the last few years, new DNA sequencing technologies - referred to as "next generation" or "massively parallel" sequencing - have matured rapidly. They are now ripe for application to research questions in genetic susceptibility for which linkage analysis is confounded by extensive genetic heterogeneity. Taking advantage of unparalleled familial cancer genetics resources available through the Utah Population Database, two massively parallel sequencing strategies will be used to pursue the long term objective of this project: 1) candidate genes will be identified by sequencing all of the gene exons in the human genome from a series of colorectal cancer cases who have a very strong family history of colorectal cancer that is not explained by one of the currently known high-risk susceptibility genes;and 2) colorectal cancer susceptibility genes will be validated by case-control re-sequencing of the candidate genes from step #1 in a much larger series of colorectal cancer cases who have family history of colorectal cancer in comparison with a series of cancer-free controls. The multidisciplinary team assembled for this project has access to an unparalleled resource for studying cancer genetics, has statistical and bioinformatic skills required to analyze massive re-sequencing data, and has the ability to translate findings almost directly to clinical cancer genetics. Thus this team and project are poised to take a huge step towards solving the "problem of missing heritability" in colorectal cancer genetics.
Currently, clinical cancer genetics applied to families with a history of colorectal cancer is only useful to the minority of families in which there is an APC1, mismatch repair gene, BMPR1A, MUTYH, PTEN, SMAD4, or STK11 mutation;unfortunately, mutations in these genes only explain a minority of such families. This project will apply new DNA sequencing technologies to an unparalleled resource of colon cancer cases and families to identify the majority of genes that contribute to familial colon cancer. In the long term, discover of these genes will lead to more effective prevention programs and, potentially, improved treatments.
|Hu, Hao; Roach, Jared C; Coon, Hilary et al. (2014) A unified test of linkage analysis and rare-variant association for analysis of pedigree sequence data. Nat Biotechnol 32:663-9|
|Hu, Hao; Huff, Chad D; Moore, Barry et al. (2013) VAAST 2.0: improved variant classification and disease-gene identification using a conservation-controlled amino acid substitution matrix. Genet Epidemiol 37:622-34|