An integrated genomic approach for the identification and analysis of single nucleotide polymorphisms that affect cancer in humans - Repapi, E (2013)
The identification of genetic variants such as single nucleotide polymorphisms (SNPs), which affect cancer progression, survival and response to treatments could help in the design of better prevention and treatment strategies. Genome-wide association studies (GWAS) have provided the first step of identifying SNPs associating with cancer risk. However, identifying the causal SNPs responsible for the associations has proven challenging, and GWAS have not been successful for time-to-event phenotypes such as cancer progression, due to the insurmountable obstacle of the large sample size needed. The aim of this thesis is to design and implement strategies that combine the identification of SNPs significantly associated with cancer, focusing on time-to-event phenotypes, with detailed bioinformatics analysis to allow for further experimental validation and modelling, to better understand cancer-associated genomic loci and accelerate their incorporation into the clinic. First, a methodology that utilises the Random Survival Forest is developed and combined with a bioinformatics analysis that ranks SNPs according to their potential to result in differential protein levels or activity, in order to identify SNPs that affect the progression of B-cell chronic lymphocytic leukaemia. Next, an analysis that aims to extend our understanding of the role of SNPs in mediating the cellular responses to chemotherapeutic agents is applied. SNPs that could associate with differential cellular growth responses in cancer cell line panels are identified, and their association with the differential survival of cancer patients is explored. Finally, the potential roles of SNPs in affecting the transcriptional regulation of key cancer genes resulting in differential cancer risk are assessed. First, by focusing on SNPs in an important transcription factor binding motif that has been shown to be extremely sensitive to single base pair changes (the E-box) and next, by exploring the possibility that polymorphic transcription factor binding sites could underlie the significant associations noted in cancer GWAS.