Case study - Cancer Chronotherapy

Professor Bärbel Finkenstädt Rand

Department of Statistics, University of Warwick

What is the background of your methodology research?

The circadian rhythm displays an endogenous, entrainable oscillation of about 24 hours. Chronotherapy is a form of personalized therapy where treatments are administered to a schedule that corresponds to a person's rhythms to maximize effectiveness and minimize side effects of the therapy. There is now large evidence both in experimental models and in patients that the Circadian Timing System (CTS) of patients is affecting treatment tolerability and efficacy of anticancer drugs while the progression of the disease and treatment with anticancer drugs have disruptive effect on the CTS. Our projects focuses on the development of statistical methods that address the modelling of time varying effects between treatment and circadian rhythm.

Why is your research important?

Our research uses activity data for evaluating and monitoring the endogenous circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. Wearable computing devices allow collection of densely sampled real-time information on movement; enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. To translate the information from such high-volume data, we have developed a modelling approach which solves the problem of thresholding activity into different states in a probabilistic way while respecting time dependence and delivers parameter estimates - for instance probabilities of transitions between rest and activity, which are interpretable and important to circadian research. The model can be used to profile a person’s sleep/wake circadian cycle over many days.

How has your research influenced others?

We have started to use the new analysis methods for sensor data arising in several projects, including cancer patients’ data arising in the French National Project (PiCADo), a planned study of circadian rhythms in 80 cancer patients in the UK, and the French epidemiology study “Circadiem” - coordinated by INSERM, involving 200 nurses. Circadiem will identify the impact of occupational schedule (work at daytime, night or shift work) on health and the circadian timing system. Our research group is engaged with Phillips Respironics USA to discuss actimetry and circadian measures in cancer patients and chronotherapy. The company has now committed to significant further research funding into the development of chronotherapy at Warwick.

How has your research influenced your career?

The research funded has significantly furthered my chances for promotion. However, and most importantly, I am enjoying doing research in an area that has a direct impact on health and quality of life. The project has also transformed the career of the postdoctoral researcher hired on the project. She was originally trained as engineer but she is now developing new skills in statistics and working in a multidisciplinary team at the interface between statistics and medicine.

What is next for your research?

A larger collaborative team is envisaged to develop a patient-centred digital platform to assist patients preserve their health status at their home. The initial clinical context will be the fragile population of cancer patients receiving chemotherapy. Thus, expected end-user benefits will include prolonged hospital-free time for the patients, improved safety of home-based chemotherapy administration, increased systemic symptoms control and reduced health care services costs.

In order to achieve a change in healthcare practice it will be necessary firstly to undertake more development of the quantitative methodologies and secondly to implement these into patient-centred digital platforms.

What has been the role of the MRC-NIHR Methodology Programme?

The programme has enabled the significant start into developing the statistical methodology needed. We now have a pretty good idea of what statistical approaches are useful for modelling telemetric sensor data to monitor a patient’s circadian rhythm.  In fact, as a result of the research funded by the Programme we know approaches that are better suited and turn out to be quite different to what we initially suggested in the grant application.