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Title: The genomic and metabolomic profiling of pancreas cancer
Author: Sanyal, Sudip
ISNI:       0000 0004 5360 3120
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2015
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Despite the considerable expansion of knowledge in the development of pancreatic cancer, there has been little progress made in facilitating an early diagnosis of this disease and predicting an accurate response to treatment. We aim to translate this knowledge to clinical practice by using a prospective database of precursor cystic lesions in pancreas cancer, assessing the use of over-expressed genes in pancreatic juice as a surrogate marker of these pancreas cancer and finally, downstream of these changes at the genetic level, use metabolomic techniques to look for biomarkers in pancreas cancer in serum. In the first study, we investigate the natural history of pancreatic cystic neoplasms, specifically IPMNs, using a prospectively collected database to examine the profiles and outcomes of main duct IPMN, branch duct IPMN and cystic lesions measuring less than 3 cm in size. A total of 99 patients with suspected pancreatic cystic tumours were enrolled over 3 years. Median follow-up was 24 months (range 0 – 124). Cystic tumours comprised of 13 MD-IPMN, 40 BD-IPMN, 11 MCN and 8 adenocarcinomas among others. The complete cohort showed an overall risk of adenocarcinoma of 8%. Main duct IPMN showed a cumulative risk of 46% with evidence of progression of disease in a further 23%. The associated mortality in MD-IPMN was related to the underlying adenocarcinoma and was 38% in our group. The incidence of adenocarcinoma in branch duct IPMN was 11% with disease progression seen 13.8%. Evidence of extra-pancreatic malignancies was seen in 37.7% of patients with IPMN. In the second study, we explore the feasibility of gene expression profiling from RNA isolated from matched pancreatic juice and tumour tissue in patients with pancreatic cancer and pancreatic cystic tumours. RNA was isolated and Poly(A) PCR was used to globally amplify the RNA. RT-PCR was used to measure expression levels of 18 genes common to both pancreas cancer and pancreatic cystic tumours. Spearman’s rank correlation test was used to examine the relationship of gene expression between pancreatic juice and tissue. One gene out of eighteen, MSLN (p<0.008), showed significant correlation in the expression levels between paired pancreatic juice and tissue samples in pancreas cancer. In the cystic tumour group, only one gene MMP-7 (p<0.01), showed a significant correlation between paired juice and tissue samples. When the whole cohort was analysed for the false discovery rate, these genes did not exhibit statistically significant correlation between the samples. RNA analysis of pancreatic juice is feasible using the Poly(A) cDNA technique and correlation of gene expression is shown to exist, albeit with low sensitivity, indicating its potential use in clinical practice with small tissue and juice samples. In the final study, we performed a literature review on the use of metabolomics in pancreas cancer. We performed metabolic profiling of serum samples from selected cancer patients and noncancerous controls using UPHLC-MS to generate and compare the metabolic profiles in serum samples from a cohort of patients with pancreas cancer, ampullary cancer and endocrine cancer. Thirty nine serum samples (including 19 pancreatic cancers, 9 ampullary cancers and 5 endocrine cancers) and 21 matched HUSERMET controls were analysed using Ultra high performance liquid chromatography mass spectrometry (UHPLC-MS) in both positive and negative ESI modes. The output was generated as a data matrix of mass spectral features with related accurate m/z and retention time pairs. The data was then signal corrected and individual peaks were normalised and the resultant spectra were compared against a metabolite reference library and analysed using univariate and multivariate statistical tests. We found a disparity in the metabolite peaks between the cases and controls on PCA that did not permit the accurate interpretation of the data in the case study set compared to the control set. No obvious reason other than metabolite degradation during storage could account for this difference. PC-DFA analysis of metabolite peaks between pancreas cancer, ampullary cancer and endocrine cancer showed significant difference between endocrine cancers and the other two groups. Significant ESI positive metabolites included those involved in lipid pathways and metabolites involved in glucose metabolism. There is encouraging scope for studies using prospective controls to identify and develop metabolic biomarkers in pancreas cancer.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pancreas cancer ; Genomics ; Poly(A) PCR ; Metabolomics