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Title: Quantitative and systems pathology for therapeutic response prediction
Author: Faratian, Dana
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2010
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The measurement of tissue biomarkers for therapeutic response prediction in cancer patients has become standard pathological practice, but only for a very limited number of targets. This is in spite of massive intellectual and financial investment in molecular pathology for translational cancer research. A re-evaluation of current approaches, and the testing of new ones, is required in order to meet the challenges of predicting responses to existing and novel therapeutics, and individualising therapy. Herein I critique the current state of tissue biomarker analysis and quantification in cancer pathology and the reasons why so few novel biomarkers have entered the clinic. In particular, we examine the central role of signalling pathway biology in sensitivity and resistance to targeted therapy. I discuss how accurate quantification, and the ability to simulate biological responses over time and space, may lead to more accurate prediction of therapeutic response. I propose that different mathematical techniques used in the nascent field of systems biology (ordinary differential equation-based, S-systems, and Bayesian approaches) may provide promising new avenues to improve prediction in clinical and pathological practice. I also discuss the challenges and opportunities for quantification in pathological research and practice. I have examined the role of cellular signalling pathways in therapeutic sensitivity and resistance in three different ways. Firstly, I have taken a hypothesis-driven and reductionist approach and shown that decreased Sprouty 2, a feedback inhibitor of MAPK and PI3K signalling, is associated with trastuzumab-resistance in vitro and in a cohort of breast cancer patients treated with trastuzumab. Secondly, I have characterised the activation state of ten growth and survival pathways across different histological subtypes of ovarian cancer using quantitative fluorescence microscopy. I have shown that unsupervised clustering of phosphoprotein expression profiles results in new subgroups with distinct biological properties (in terms of proliferation and apoptosis), and which predict therapeutic response to chemotherapy. Thirdly, I have developed a new mathematical model of PI3K signalling, parameterised using quantitative phosphoprotein expression data from cancer cell lines using reverse-phase protein microarrays, and shown that quantitative PTEN protein expression is the key determinant of resistance to anti-HER2 therapy in silico. Furthermore, the quantitative measurement of PTEN is more predictive of response than other pathway components taken in isolation and when tested by multivariate analysis in a cohort of breast cancers treated with trastuzumab. For the first time, a systems biology approach has successfully been used to stratify patients for personalised therapy in cancer, and is further compelling evidence that PTEN, appropriately measured in the clinical setting, refines clinical decision-making in patients treated with anti-HER2 therapies.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (M.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available