Can genetic risk profiling in colorectal cancer guide prevention and screening?


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Malcolm Dunlop1
1University of Edinburgh, Edinburgh, UK

Abstract

Common cancer has a heritable component. Common genetic variation has been shown to contribute to cancer risk, including colorectal cancer (CRC). Population screening for CRC has been introduced using faecal occult blood testing (FOBT), whilst there are promising results for flexible sigmoidoscopy applied to age-defined risk categories. Colonoscopic surveillance is already offered to people with a modestly elevated risk due to a personal or family history. More intensive surveillance is offered to high risk individuals from Lynch Syndrome families. Similarly, multi-locus genotype data for common variants offers the possibility of partitioning risk within the average risk population. Thus, stratifying the general population into risk categories offers the potential of tailoring the intensity of surveillance or preventative approach to the predicted level of risk. The heritable component of CRC variance is ~35%, but only ~5% of cases are attributable to highly penetrant mutations. Risk associated with the genetic loci identified by recent genome-wide association studies is individually modest. However, the impact on overall CRC incidence in the population as a whole is substantial, because of the high allele frequencies. High absolute risks, exceeding thresholds triggering clinical intervention, could be apparent in population subgroups with multiple risk alleles. We developed and validated CRC risk prediction models incorporating age, gender, family history and genotype data from common genetic risk variants. Model performance in profiling individual genetic risk of CRC will be explored and the utility of categorising risk subgroups within the population will be assessed by applying the risk models to available population data. Identifying additional genetic risk factors is likely to further improve predictive performance, but relatively small numbers of people fulfilling age/gender risk categories could already be offered genotyping. Applying risk prediction models could identify high risk groups for intensive surveillance as part of public health measures to control colorectal cancer.

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