Optimising outcomes for potentially resectable pancreatic cancer through personalised predictive medicine : the application of complexity theory to probabilistic statistical modeling

Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models.Survival outcomes for pancreatic cancer remain poor. Surgical resection with adjuvant therapy is the only potentially curative treatment, but for many people surgery is of limited benefit. Neoadjuvant therapy has emerged as an alternative treatment pathway however the evidence base surrounding the treatment of potentially resectable pancreatic cancer is highly heterogeneous and fraught with uncertainty and controversy. This research seeks to engage with conjunctive theorising by avoiding simplification and abstraction to draw on different kinds of data from multiple sources to move research towards a theory that can build a rich picture of pancreatic cancer management pathways as a complex system. The overall aim is to move research towards personalised realistic medicine by using personalised predictive modeling to facilitate better decision making to achieve the optimisation of outcomes. This research is theory driven and empirically focused from a complexity perspective. Combining operational and healthcare research methodology, and drawing on influences from complementary paradigms of critical realism and systems theory, then enhancing their impact by using Cilliers’ complexity theory ‘lean ontology’, an open-world ontology is held and both epistemic reality and judgmental relativity are accepted. The use of imperfect data within statistical simulation models is explored to attempt to expand our capabilities for handling the emergent and uncertainty and to find other ways of relating to complexity within the field of pancreatic cancer research. Markov and discrete-event simulation modelling uncovered new insights and added a further dimension to the current debate by demonstrating that superior treatment pathway selection depended on individual patient and tumour factors. A Bayesian Belief Network was developed that modelled the dynamic nature of this complex system to make personalised prognostic predictions across competing treatments pathways throughout the patient journey to facilitate better shared clinical decision making with an accuracy exceeding existing predictive models

Modelling health-related quality of life data for economic evaluation of cancer treatments: Applications in lung cancer

INTRODUCTION: The annual economic burden of treating cancer to the National Health Service (NHS) in the United Kingdom (UK) is over £15 billion; and for non small cell lung cancer (NSCLC), one of the leading causes of cancer deaths in the world, this is £2.4 billion. Economic evaluation plays an essential role in assessing the relative value of lung cancer treatments. Modelling (HRQoL) data is fundamental in determining the cost-effectiveness of cancer treatments. This thesis aims to investigate modelling of HRQoL data collected from lung cancer patients for economic evaluation. In particular, the role of modelling to improve utility prediction is investigated. The sensitivity of disease specific and generic HRQoL measures are also explored. In addition, methods to extrapolate utilities beyond cancer progression and identifying a selection procedure from relevant published algorithms are developed. METHODS: Data from two clinical trials and a prospective observational study in NSCLC patients were designed and executed to develop several mapping models (Linear, Non-Linear, Joint, and Bayesian). The sensitivity of EQ-5D-3L and EQ-5D-5L were compared with a cancer specific measure (QLQ-C30). Simulation methods were used to develop an approach for selecting algorithms. RESULTS: Two and three-part Beta-Binomial models improve predictions. Joint models also contribute to improved prediction of utilities. Bayesian Networks may help reduce the over-prediction in poor health states. The EQ-5D-5L offers better mapping and is more sensitive for detecting treatment benefit compared to EQ-5D-3L. It is also viable to develop decision criteria for selecting between several published algorithms. CONCLUSION: Methodological improvements in modelling HRQoL for the economic evaluation of cancer treatments have been demonstrated. Improvements in model structure, prediction and selection are empirically demonstrated

Vincristine-Induced Peripheral Neuropathy: Assessing Preventable Strategies in Paediatric Acute Lymphoblastic Leukaemia

Background: Acute Lymphoblastic Leukaemia is the most common cancer experienced by children with overall survival rates now exceeding 90%. However, most children will experience vincristine-induced peripheral neuropathy (VIPN) during treatment resulting in sensory-motor abnormalities. To date, there are no approved preventative therapeutics or mitigation strategies for VIPN. This body of work set out to: (1) establish a high-throughput and high-content assay with the capacity to identify neuroprotective compounds, (2) test the feasibility of repurposing olesoxime as a neuroprotectant, and (3) compare traditional statistical methods with machine learning models to identify patients at risk of VIPN. Methods: (1) In vitro neuronal cultures were exposed to vincristine to recapitulate the VIPN phenotype and olesoxime assessed as a positive control. The neurotoxicity assay was miniaturised in 384-well microplates with automation steps to reduce manual handling. (2) Olesoxime and vincristine were applied to proliferating malignant cell lines to ensure the efficacy of vincristine was maintained. (3) Machine learning algorithms were developed using data from a local retrospective cohort to predict VIPN. Results: (1) Neurite length was reduced in a dose-responsive manner with vincristine. Assay miniaturisation and automation steps helped facilitate a high-throughput workflow. An optimised multiplexed dye solution enabled image acquisition and neurite quantification. Further, olesoxime was found to protect neurites and deemed suitable as a positive control (2) Cell viability assays confirmed olesoxime did not interfere with vincristine efficacy in leukemia cells. (3) Machine learning algorithms showed equivalency to traditional univariate analysis. The observation of severe class imbalance meant that patients who were least susceptible to VIPN could be identified. Conclusions: This body of work demonstrates the successful development of a neurotoxicity assay suitable for neuroprotectant drug discovery. Olesoxime was found suitable as a positive control in the assay. Further, viability studies indicated that vincristine retains it efficacy with olesoxime, opening the possibility of its use as an adjunctive therapy. Finally, this work developed machine learning models with the capacity to identify patients with VIPN-free survival. The utility of this model may mean that it can be used to stratify patients prospectively in the clinic based on favourable clinical features