The identification and characterisation of germline genetic variants that affect human cancer

Single nucleotide polymorphisms (SNPs) have great potential to serve as important biomarkers in the clinic to identify those at increased risk for developing cancer, progressing more rapidly, and not responding to therapies. However, the clinical application of cancer-associated SNPs has proven to be more complicated than expected. One of the necessary steps will certainly be the description of the molecular and cellular mechanisms behind the observed associations. The p53 tumour suppressor pathway harbours well-described SNPs that affect p53 signalling and cancer. The aim of the work presented in this thesis was to utilise this knowledge to more efficiently characterise cancer-associated SNPs. Firstly, cancer-associated SNPs in a p53 network gene, CD44, were studied. Specifically, based on CD44’s known roles in both p53-dependent and independent signalling, it was predicted that the cancer-associated SNPs could function as biomarkers for chronic lymphocytic leukaemia progression, and for the response to anti-EGFR therapy for colorectal cancer. Indeed, supportive data is presented. Next, a methodology is presented that aims to identify cancer-associated SNPs in functional p53 binding sites using genome-wide datasets. Interestingly, a SNP is identified that dramatically influences the ability of p53 to regulate transcription of the KITLG oncogene and that associates with one of the largest risks of cancer identified to date. Intriguingly, the SNP is also shown to have undergone positive selection throughout human evolution, signifying a selective advantage, but similar SNPs are demonstrated to be rare in the genome due to negative selection, indicating that polymorphisms in p53 binding sites have been primarily detrimental to humans. Lastly, and in order to begin to explore if other polymorphic transcription factor binding motifs could be found in cancer-associated SNPs, a methodology was designed to identify SNPs in E-box transcription factor binding motifs, as they are sensitive to single base pair changes and affect cancer. Taken together, the work presented in this thesis shows how the study of how SNPs associate with, and impact upon, cancer has great potential to improve both biological knowledge and clinical outcomes.</p

The identification and characterisation of germline genetic variants that affect human cancer

Single nucleotide polymorphisms (SNPs) have great potential to serve as important biomarkers in the clinic to identify those at increased risk for developing cancer, progressing more rapidly, and not responding to therapies. However, the clinical application of cancer-associated SNPs has proven to be more complicated than expected. One of the necessary steps will certainly be the description of the molecular and cellular mechanisms behind the observed associations. The p53 tumour suppressor pathway harbours well-described SNPs that affect p53 signalling and cancer. The aim of the work presented in this thesis was to utilise this knowledge to more efficiently characterise cancer-associated SNPs. Firstly, cancer-associated SNPs in a p53 network gene, CD44, were studied. Specifically, based on CD44’s known roles in both p53-dependent and independent signalling, it was predicted that the cancer-associated SNPs could function as biomarkers for chronic lymphocytic leukaemia progression, and for the response to anti-EGFR therapy for colorectal cancer. Indeed, supportive data is presented. Next, a methodology is presented that aims to identify cancer-associated SNPs in functional p53 binding sites using genome-wide datasets. Interestingly, a SNP is identified that dramatically influences the ability of p53 to regulate transcription of the KITLG oncogene and that associates with one of the largest risks of cancer identified to date. Intriguingly, the SNP is also shown to have undergone positive selection throughout human evolution, signifying a selective advantage, but similar SNPs are demonstrated to be rare in the genome due to negative selection, indicating that polymorphisms in p53 binding sites have been primarily detrimental to humans. Lastly, and in order to begin to explore if other polymorphic transcription factor binding motifs could be found in cancer-associated SNPs, a methodology was designed to identify SNPs in E-box transcription factor binding motifs, as they are sensitive to single base pair changes and affect cancer. Taken together, the work presented in this thesis shows how the study of how SNPs associate with, and impact upon, cancer has great potential to improve both biological knowledge and clinical outcomes.This thesis is not currently available in ORA