The regulation of cancer cell invasive behaviour by Chloride Interacellular Channel 3 (CLIC3) and Transglutaminasae 2

A study into the role of secreted CLIC3 in tumour cell invasion. The initiation and progression of cancers is thought to be linked to their relationship with a population of activated fibroblasts, which are associated with tumours. I have used an organotypic approach, in which plugs of collagen I are preconditioned with fibroblastic cells, to characterise the mechanisms through which carcinoma-associated fibroblasts (CAFs) influence the invasive behaviour of tumour cells. I have found that immortalised cancer-associated fibroblasts (iCAFs) support increased invasiveness of cancer cells, and that this is associated with the ability of CAFs to increase the fibrillar collagen content of the extracellular matrix (ECM). To gain mechanistic insight into this phenomenon, an in-depth SILAC-based mass proteomic analysis was conducted, which allowed quantitative comparison of the proteomes of iCAFs and immortalised normal fibroblast (iNFs) controls. Chloride Intracellular Channel Protein 3 (CLIC3) was one of the most significantly upregulated components of the iCAF proteome. Knockdown of CLIC3 in iCAFs reduced the ability of these cells to remodel the ECM and to support tumour cell invasion through organotypic plugs. A series of experiments, including proteomic analysis of cell culture medium that had been preconditioned by iCAFs, indicated that CLIC3 itself was a component of the iCAF secretome that was responsible for the ability of iCAFs to drive tumour cell invasiveness. Moreover, addition of soluble recombinant CLIC3 (rCLIC3) was sufficient to drive the extension of invasive pseudopods in cancer cell lines, and to promote disruption of the basement membrane in a 3D in vitro model of the ductal carcinoma in situ (DCIS) to invasive ductal carcinoma (IDC) transition. My investigation into the mechanism through which extracellular CLIC3 drives tumour cell invasiveness led me to focus on the relationship between CLIC3 and the ECM modifying enzyme, transglutaminase-2 (TG2). Through this, I have found that TG2 physically associates with CLIC3 and that TG2 is necessary for CLIC3 to drive tumour cell invasiveness. These data identifying CLIC3 as a key pro-invasive factor, which is secreted by CAFs, provides an unprecedented mechanism through which the stroma may drive cancer progression.

Trajectory prediction uncertainty modelling for Air Traffic Management

The anticipated growth of air traffic worldwide requires enhanced Air Traffic Management (ATM) technologies and procedures to increase the system capacity, efficiency, and resilience, while reducing environmental impact and maintaining operational safety. To deal with these challenges, new automation and information exchange capabilities are being developed through different modernisation initiatives toward a new global operational concept called Trajectory Based Operations (TBO), in which aircraft trajectory information becomes the cornerstone of advanced ATM applications. This transformation will lead to higher levels of system complexity requiring enhanced Decision Support Tools (DST) to aid humans in the decision making processes. These will rely on accurate predicted aircraft trajectories, provided by advanced Trajectory Predictors (TP). The trajectory prediction process is subject to stochastic effects that introduce uncertainty into the predictions. Regardless of the assumptions that define the aircraft motion model underpinning the TP, deviations between predicted and actual trajectories are unavoidable. This thesis proposes an innovative method to characterise the uncertainty associated with a trajectory prediction based on the mathematical theory of Polynomial Chaos Expansions (PCE). Assuming univariate PCEs of the trajectory prediction inputs, the method describes how to generate multivariate PCEs of the prediction outputs that quantify their associated uncertainty. Arbitrary PCE (aPCE) was chosen because it allows a higher degree of flexibility to model input uncertainty. The obtained polynomial description can be used in subsequent prediction sensitivity analyses thanks to the relationship between polynomial coefficients and Sobol indices. The Sobol indices enable ranking the input parameters according to their influence on trajectory prediction uncertainty. The applicability of the aPCE-based uncertainty quantification detailed herein is analysed through a study case. This study case represents a typical aircraft trajectory prediction problem in ATM, in which uncertain parameters regarding aircraft performance, aircraft intent description, weather forecast, and initial conditions are considered simultaneously. Numerical results are compared to those obtained from a Monte Carlo simulation, demonstrating the advantages of the proposed method. The thesis includes two examples of DSTs (Demand and Capacity Balancing tool, and Arrival Manager) to illustrate the potential benefits of exploiting the proposed uncertainty quantification method.