Cost effectiveness of patient-matched pre- and on-treatment biomarkers in cancer therapy response prediction
Lead Research Organisation:
University of Edinburgh
Department Name: Sch of Molecular. Genetics & Pop Health
Abstract
Summary:
The clinical benefit from specific drug therapies for breast cancer can be predicted by new biomarkers currently under development. When used before radical surgery - the neoadjuvant setting - effective drugs may shrink a cancer, increase the rate of successful surgery and improve the chance of progression-free survival.
The evaluation of the effectiveness and cost-effectiveness of new biomarkers in a routine NHS setting is complex, as it relies on impact through clinical decision making and modifications of the clinical pathway followed by a patient. Being developed for this purpose, new modelling methods rely on the analysis of healthcare datasets and incorporation of real-world clinical data into a mathematical simulation model. This novel approach combines molecular biomarker data with clinical event and clinical outcomes and healthcare costs data within a Bayesian Generalized Evidence Synthesis (BGES) framework as a basis for constructing patient
simulation models. Examples of these methods include semi-Markov state transition models and discrete event simulation.
The project will take as examples a number of biomarkers and genomic signatures arising from a research program in bioinformatics at the Edinburgh Cancer Research Centre. A team at the Institute of Genetics and Molecular Medicine (IGMM) has developed a very promising method for accurately predicting response to neo-adjuvant aromatase inhibitor (AI) therapy in early breast cancer using patient-matched pre- and on-treatment biomarkers. In terms of accuracy, sensitivity and specificity, this method out-performs all established pre-treatment-only assessment methods, but potentially modifies the patient's clinical pathway.
The aim of the project is to develop an analytical and modelling platform for the comparison of the potential clinical effectiveness and cost-effectiveness of in-development biomarkers when used in a routine NHS setting to guide therapy prior to the surgical treatment of early breast cancer. The model will be used to assess the potential value to the NHS, academia and commercial funders of further investment in research and development of specific diagnostic tests.
The project will include a systematic literature review of economic evaluation methods for diagnostic/prognostic tools, and narrative reviews of breast cancer biomarker technology and of the use of real-world NHS data in health economics modelling.
Details of the method developed at the IGMM that will be used in the economic evaluation can be found in:
Turnbull AK, Arthur LM, Renshaw L, Larionov AA, Kay C, Dunbier AK, Thomas JS, Dowsett M, Sims AH, Dixon JM (2015) Accurate prediction and validation of response to endocrine therapy in breast cancer. Journal of Clinical Oncology 33:2270-8
The clinical benefit from specific drug therapies for breast cancer can be predicted by new biomarkers currently under development. When used before radical surgery - the neoadjuvant setting - effective drugs may shrink a cancer, increase the rate of successful surgery and improve the chance of progression-free survival.
The evaluation of the effectiveness and cost-effectiveness of new biomarkers in a routine NHS setting is complex, as it relies on impact through clinical decision making and modifications of the clinical pathway followed by a patient. Being developed for this purpose, new modelling methods rely on the analysis of healthcare datasets and incorporation of real-world clinical data into a mathematical simulation model. This novel approach combines molecular biomarker data with clinical event and clinical outcomes and healthcare costs data within a Bayesian Generalized Evidence Synthesis (BGES) framework as a basis for constructing patient
simulation models. Examples of these methods include semi-Markov state transition models and discrete event simulation.
The project will take as examples a number of biomarkers and genomic signatures arising from a research program in bioinformatics at the Edinburgh Cancer Research Centre. A team at the Institute of Genetics and Molecular Medicine (IGMM) has developed a very promising method for accurately predicting response to neo-adjuvant aromatase inhibitor (AI) therapy in early breast cancer using patient-matched pre- and on-treatment biomarkers. In terms of accuracy, sensitivity and specificity, this method out-performs all established pre-treatment-only assessment methods, but potentially modifies the patient's clinical pathway.
The aim of the project is to develop an analytical and modelling platform for the comparison of the potential clinical effectiveness and cost-effectiveness of in-development biomarkers when used in a routine NHS setting to guide therapy prior to the surgical treatment of early breast cancer. The model will be used to assess the potential value to the NHS, academia and commercial funders of further investment in research and development of specific diagnostic tests.
The project will include a systematic literature review of economic evaluation methods for diagnostic/prognostic tools, and narrative reviews of breast cancer biomarker technology and of the use of real-world NHS data in health economics modelling.
Details of the method developed at the IGMM that will be used in the economic evaluation can be found in:
Turnbull AK, Arthur LM, Renshaw L, Larionov AA, Kay C, Dunbier AK, Thomas JS, Dowsett M, Sims AH, Dixon JM (2015) Accurate prediction and validation of response to endocrine therapy in breast cancer. Journal of Clinical Oncology 33:2270-8
Organisations
People |
ORCID iD |
| Andrew Sims (Primary Supervisor) | |
| Giovanni Tramonti (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| MR/N013166/1 | 01/10/2016 | 30/09/2025 | |||
| 1940692 | Studentship | MR/N013166/1 | 01/09/2017 | 28/02/2021 | Giovanni Tramonti |