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Title: Proteomic analysis of biomarkers associated with immunotherapy in murine tumour models
Author: Vafadar-Isfahani, B.
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2009
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Emergence of proteomics and high-throughput technologies has allowed the identification of protein expression patterns of disease that potentially hold clinical importance in predictive medicine. The analysis of complex data generated by these technologies incorporates the use of computer algorithms for data mining and identification of important protein biomarkers. Such candidate biomarkers can potentially be used for diagnosis, prognosis and monitoring a variety of diseases as well as the prediction of therapy response. Mass spectrometry has been used widely, for the discovery and quantitation of disease associated biomarkers using a variety of samples such as serum and tissue. In particular, matrix assisted laser desorption/ionisation time of flight mass spectrometry (MALDI-TOF MS) has been used to generate proteomic profiles or “fingerprints” from serum to distinguish patients at different clinical stages of disease. Currently, early stage disease is difficult to diagnose in most cancers as current cancer markers have limited sensitivity and specificity. In advanced stage metastatic disease, treatment options are limited, although it is recognised that some patients may benefit from immunotherapy and in particular vaccine therapy. The use of animal models is critical to evaluate the efficacy of immunotherapies and to investigate tumour immunity in general and the mechanisms involved in tumour progression. These models provide an in vivo environment which cannot be reproduced in vitro, which results in more accurate and reliable information on the host response to immunotherapy and the mechanisms involved. The research presented in this thesis has introduced the use of MALDI-TOF MS proteome profiling and bioinformatic analysis, to detect candidate biomarkers of tumour progression and responce to immunotherapy in a CT26 murine model of colorectal carcinoma. Proteomic profiles from serum and tissue were generated by MALDI-TOF MS followed by artificial neural networks (ANNs) analysis of the complex data. The methods used in this study for sample preparation and analysis demonstrates that good quality proteomic data from serum and tissue can be obtained, and that it is possible to generate discriminatory protein profiles that correlate with clinical outcomes. In the first instance, using the CT26 progression model, serum and tissue samples were collected at four time-points from tumour-bearer and control mice, providing the opportunity to assess the tumour proteome changes with in a time-course from tumour initiation and through different stages of growth. Through the analysis of serum and tissue it is possible to classify samples based on their stage of tumour growth and the discriminatory patterns may reveal novel pathways associated with tumour progression. In addition, this study employed two separate mouse models of colon carcinoma immunotherapy (CT26 tumour model), to investigate biomarkers that are associated with therapy response. Using either disabled infectious single cycle-herpes simplex virus (DISC-HSV) or dendritic cell-based vaccination therapy with CTLA-4 and blockade of VEGFR-2 immunotherapy, up to 70% of the treated tumours tend to regress after receiving the immunotherapy (tumour regressors). Therefore, these models of immunotherapy were used to screen and evaluate serum protein and peptide biomarkers for the detection of progressors from regressors by using MALDI-MS coupled with an ANN algorithm. Comprehensive clean-up methods were conducted on the sera prior to MALDI analysis to reduce the complexity of the specimens. A panel of 4 biomarkers associated with response to DISC-HSV therapy was identified and successfully validated using non-mass spectrometry techniques. Furtheremore, discriminatory patterns corresponding to different stages of tumour progression and immunotherapy were identified in the mouse model with DC-based immunotherapy. Moreover, potential markers associated with response to therapy were proposed using this model. The work presented demonstrates a proof-of-principle that the different types of information that can be obtained from animal models can be expanded and applied to human studies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available