Personalized Medicine and HTA
In the last decade, progress has been made in diagnostics and treatment for several diseases. However, for some of those diseases it is not yet known how these develop or which patients respond to treatment. Targeting treatment based on individual patient characteristics may therefore improve clinical outcomes while avoiding toxicity and overtreatment. Oncology is one of the areas in which personalized medicine is frequently applied, with an increasing number of biomarkers to target treatment.
In our research program, we develop new modeling approach to simulate patient-level outcomes of biomarker based targeted treatment. Such models do allow individual clinical pathway modelling as well as the inclusion of treatment decisions based on individual risk-predictions. As part of our program, several online tools are available that either predict risk of recurrences, forecast survival as well as use expert beliefs to fill evidence gaps that may be present in modeling patient-level outcomes. These online tools also allow for re-evaluation of outcome measures according to the user’s own beliefs or if new evidence is being generated.
On this site we will share three prediction models that were developed as part of our work on the health economic evaluation of personalized medicine. The examples are in breast and prostate cancer respectively.
Health Economic Modeling of Liquid Biopsies in Breast Cancer
Estimating the cost-effectiveness of using circulating tumour cell detection to guide systemic therapy in stage IA primary breast cancer: an online tool.
Berghuis AM, Koffijberg H, Terstappen LWMM, Rack B, Neubauer H, Fehm T, IJzerman MJ (submitted and presented at SMDM 2016, Vancouver)
Breast cancer is one of the most frequently diagnosed form of cancer among women, with a relatively high survival of 89%. However, metastatic disease still largely is an incurable condition, with survival rates below 25%. To reduce the development of metastases it is important to start treatment and search for metastases as early as possible. However, detection and staging of distant metastases is usually done by using several imaging techniques. Unfortunately, these imaging techniques seem to be not sensitive enough. None of the currently used techniques is able to detect micro metastases at time of diagnosis due to their low resolution.
A possible solution for enabling earlier detection of these micro metastases is the use of liquid biopsies. Which can provide important information on prognosis and, hence, could eventually guide treatment options for cancer patients. Circulating tumour cells (CTCs) in blood can provide important prognostic information for cancer patients. However, existing CTC detection tests use small blood samples and therefore have low sensitivity. Currently the EU CTC Therapeutic Apheresis consortium is developing a new approach to separate CTCs in whole blood, aiming for increased sensitivity. This study evaluates the potential health economic impact of using CTC enumeration in whole blood to guide systemic therapy in stage IA breast cancer. However, as there still is a lot of uncertainty around several parameters used to calculate the cost-effectiveness, we converted this model into an online tool.
The online tool was based on the previous model based cost-effectiveness analysis and was built in R using the Shiny package. The mean estimated value and a range of uncertainty of several input parameters that have substantial influence on the outcome can be adjusted in the tool. Parameters that can be reset in the tool include for example sensitivity, specificity and costs of the CTC test. When all parameter values are reevaluated the tool calculates a base-case ICER and plots the results of a probabilistic sensitivity analysis. This tool allows for easy and rapid reevaluation of the cost-effectiveness of CTCs compared to usual care under the user’s own beliefs. We found that different beliefs used as input for the analysis had substantial effect on the expected impact of CTC detection in terms of health benefits, cost savings and cost-effectiveness. However, as common in model based cost-effectiveness analysis, not all input parameters had substantial influence on the outcomes.
Optimisation of Breast Cancer Follow-up Based on Individual Risk Prediction
Personalisation of breast cancer follow-up: a time-dependent prognostic nomogram for the estimation of annual risk of locoregional recurrence in early breast cancer patients
Witteveen A, Vliegen IMH, Sonke GS, Klaase JM, IJzerman MJ, Siesling S. Published in Breast Cancer Res Treat. 2015 Jul 11;152(3):627–36.
A locoregional recurrence (LRR) has a high risk of distant metastasis, and thus confers a poor prognosis. LRRs are defined as the reappearance of breast cancer on the same site as the primary tumour, in the chest wall or ipsilateral, infraclavicular, supraclavicular or parasternal lymph nodes after curative treatment. Factors that influence the risk of recurrence include tumour size, age, vascular invasion, multifocality, histological grade, hormone receptor status and treatment of the primary tumour. Regular follow-up is aimed at detecting LRRs in an early stage to improve survival. In the Netherlands, patients are followed clinically for at least 5 years after their treatment. Still, most of the recurrences are detected by the women themselves in between follow-up visits and some are detected after the 5 years of clinical follow-up. In a Dutch multicentre study, Geurts et al. found that only 34 % of the LRRs were detected asymptomatically during routine visits. Due to the increase in survival, the burden of follow-up on health care is rising. Even though the risk factors are known, follow-up is the same for all patients and not dependent on the personal risk of the individual breast cancer patient. Since 2012, the national guideline of the Netherlands recommends an individualised follow-up by shared decision making, but does not provide recommendations on how to effectuate it. To achieve this, good insight into time-dependent individual LRR risk is necessary.
For the prediction of breast cancer recurrence, the first model was developed by Gail et al. This model, as well as other well-known models (e.g. BRCAPRO, BOADICE) is aimed at predicting the general risk of primary breast cancer. To get towards personalised follow-up, models predicting LRRs are required. In this paper, logistic regression is used to calculate the risks. Not only the single risk estimated for the overall follow-up period of 5 years, but also the annual time-dependent risk. To facilitate uptake in clinical practice, ease of use and accessibility are crucial. This can be achieved by using a nomogram: a graphical representation of the underlying model. Our aim is to develop and validate a time-dependent logistic regression model and nomogram suitable for the annual risk prediction of LRRs in individual breast cancer patients. Knowing this individual risk could facilitate the decision on a personalised follow-up plan.
Patient Level Modeling of Biomarked Based Treatment Switches in mCRPC
Timed automata modeling of the personalized treatment decisions in metastatic castration resistant prostate cancer
K. Degeling, S. Schivo, N. Mehra, H. Koffijberg, R. Langerak, J. S. de Bono, M.J. IJzerman
(Submitted and presented ISPOR Milan, 2015)
The Timed Automata modeling paradigm has emerged from Computer Science as a mature tool for the functional analysis and performance evaluation of timed distributed systems. This study is a first exploration of the suitability of Timed Automata for health economic modeling, using a case study on personalized treatment for metastatic Castration Resistant Prostate Cancer (mCRPC).
The treatment process has been modeled by creating several independent timed automata, where an automaton represents a patient, a physician, a test, or a treatment/testing guideline schedule. These automata interact via message passing and are fully parameterized with quantitative information. Messages can be passed, asynchronously, from one automaton to one or more other automata, at any point in time, thereby triggering events and decisions in the treatment process. In the automata time is continuous, and both QALYs and costs can be incorporated using (assignable) local clocks. Uncertainty can be modeled using probabilities and timing intervals that can be uniformly or exponentially distributed. Software for building timed automata is freely available for academic use and includes procedures for statistical model checking (SMC) to validate the (internal) behavior and results of the model.
In several days a Timed Automata model has been produced that is compositional, easy to understand and easy to update. The behavior and results of the model have been assessed using the SMC tool. Actual results for the mCRPC case study obtained from the Timed Automata model are compared with results of a Discrete Event Simulation model in a separate study. The Timed Automata paradigm can be successfully applied to evaluate the potential benefits of a personalized treatment process of mCRPC. The compositional nature of the resulting model provides a good separation of all relevant components. This leads to models that are easy to formulate, validate, understand, maintain and update.
Lorente D, Ravi P, Mehra N, Pezaro CJ, Omlin AG, Miranda M, et al. Interrogating metastatic prostate cancer treatment switch decisions. ASCO Meeting Abstracts. 2016 Jan 10;34(2_suppl):296.
Malone DC, Berg NS, Claxton K, Garrison LP, IJzerman MJ, Marsh K, et al. International Society for Pharmacoeconomics and Outcomes Research Comments on the American Society of Clinical Oncology Value Framework. Journal of Clinical Oncology. 2016 Jun 13.
IJzerman MJ, Manca A, Keizer J, Ramsey SD. Implementing Comparative Effectiveness Research in Personalized Medicine Applications in Oncology: Current and Future Perspectives. Comparative Effectiveness Research. 2015 Nov 26;5:65–72.
Retèl VP, Linn SC, van Harten WH. Molecular profiling is rather likely to be cost effective. Journal of Clinical Oncology. 2015 May 10;33(14):1626–7.
Retèl VP, Joore MA, Drukker CA, Bueno-de-Mesquita JM, Knauer M, van Tinteren H, et al. Prospective cost-effectiveness analysis of genomic profiling in breast cancer. Eur J Cancer. 2013 Dec;49(18):3773–9.
University of Twente – Personalized Medicine and HTA Group (HTSR)
Please feel free to contact us for further information:
Prof. dr. Maarten J. IJzerman, professor (email@example.com)
Prof. dr. Sabine Siesling, professor (firstname.lastname@example.org)
Prof. dr. Wim van Harten, professor (email@example.com)
Dr. ir. Erik Koffijberg, associate professor (firstname.lastname@example.org)
Dr. Valesca Retel, post-doctoral fellow (email@example.com)
Sofie Berghuis, PhD student (firstname.lastname@example.org)
Koen Degeling, PhD student (email@example.com)
Annemieke Witteveen, PhD student (firstname.lastname@example.org)
University of Twente
Drienerlolaan 5, 7522 NB Enschede
Building Ravelijn (on the campus, building number 10 on the map)