| dc.description.abstract | Cancer is one of the most common causes of human mortality worldwide and can be divided into three main classes; sporadic, familial and hereditary. Familial and hereditary cancers are believed to be initiated by germline mutations and are often clustering within families. Regular surveillance of these cancer-predisposed individuals is one of the most effective strategies to reduce cancer risk and mortality. However, traditional diagnostic methods are not able to efficiently deal with the enormous amount of genes associated with hereditary cancers and are further impaired due to phenotypic overlap between syndromes. Next generation sequencing can be used to overcome these difficulties due to the enormous analysing power achievable with this new technology. The cost- and time-efficient next generation sequencing technology has resulted in an increased interest in the diagnostic community. In this thesis, an Illumina MiSeq platform (next generation sequencing platform) was established at the Medical Genetics Laboratory, St. Olavs Hospital to enable comprehensive genetic screening of cancer-predisposed individuals. The main goal was to optimize a targeted next generation sequencing method using a gene-panel of 124 genes associated with cancer risk. The method included both wet- and dry-laboratory stages and involved use of a regional-specific (Sør-Trøndelag, Norway) variant database constructed during this study. In total, 162 blood samples from healthy and cancer-affected individuals were used to evaluate and optimize the method. The results of this study showed that the sensitivity of the method was equivalent to the Sanger-sequencing method. The method also showed satisfactory result with respect to specificity when Sanger-sequencing was used as a verification tool. These abilities demonstrate that the next generation sequencing method is appropriate for implementation into diagnostic facilities without compromising results. Another benefit of this method is the enhanced cost- and time-efficiency compared to the methods used today at St. Olavs Hospital. The implementation of this method at St. Olavs Hospital will be a step towards a more personalized medical practice, which will improve patient care. | nb_NO |
