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

BI 6727 and gemcitabine combination therapy in pancreatic cancer

Introduction: Though adjuvant chemotherapy improves survival following surgery, pancreatic cancer carries a poor prognosis. Alternatives are required to the current drug regimens consisting of S-phase dependent drugs such as gemcitabine. PLK1 is a passenger protein involved in G2/M phases which presents a novel target to inhibit in combination with current therapies, which may help overcome inate and acquired resistance. Aim: To evaluate the potential role of a novel PLK1 inhibitor, BI 6727 in pancreatic cancer – both as monotherapy and in combination with gemcitabine in vitro. Methods: The IC50 concentrations of both drugs were established in Suit-2, BxPC-3, Panc-1 and MiaPaCa-2 pancreatic cancer cell lines and isobolar analyses undertaken with a variety of combination therapies. Cell cycle analyses were performed with Flow Activated Cell Sorting, with apoptosis and necrosis quantified on the basis of phosphatidylserine cell surface exposure, propidium iodide staining and cleavage of caspase-3. Results: IC50 ranges for BI 6727 and gemcitabine were 53-77nM and 11-34nM respectively across four pancreatic cell lines. Flow cytometry of MiaPaCa-2 cells demonstrated G2 arrest with BI 6727 and S-phase accumulation with gemcitabine monotherapy. Isobolar analyses showed that when added together the combination of drugs was additive, but BI 6727 pretreatment followed by combination was synergistic. Western blotting for cleaved caspase-3 showed evidence of apoptosis with gemcitabine monotherapy but none with BI 6727 treatment. Although membrane inversion was seen with synergistic drug combinations there was no evidence of cleaved-caspase-3, suggesting a modified form of apoptosis. Conclusion: BI 6727 is effective against a variety of pancreatic cancer cells in vitro. Synergy is demonstrated in MiaPaCa-2 cells when BI 6727 is administered prior to combination therapy with gemcitabine, though mode of cell death does not appear to be caspase-dependent. This supports the concept that PLK1 inhibition can overcome gemcitabine resistance in some cells by allowing resistant cells to initiate gemcitabine induced apoptosis, although this is dependent on drug phasing and the full apoptotic pathway may not be achieved