Stem cell ecological dynamics, leukaemia prognosis, and the robustness of the niche
Stem cells interact in fundamental ways with the cancer environment. In the haematopoietic system, the stem cell niche is believed to maintain ‘‘stemness’‘ through communication with and interactions between stem cells and other cell-types that combine to constitute the niche conditions. Disrupted regulation of this system can lead to cancer, highlighting the systemic nature of this disease. We demonstrate the importance of niche dynamics in chronic myeloid leukaemia by integrating mathematical models and data in an ecological framework. Bayesian parameter inference leads to insight into the transient blood cell dynamics of at-risk patients: we are able to predict changes in progenitor cell dynamics indicative of relapse. We show how the ecological framework our analysis is based upon appears to be intricately linked to disease outcome in leukaemia. We go on to apply this approach to stem cell-niche interactions in order to test their robustness in response to general perturbations. Although the number of feasible states depends on the model, the system stability in general does not. Thus niche-mediated interactions lend (surprisingly) high levels of robustness to the stem and progenitor cell dynamics. Further, complex niche interaction networks are not necessary for this robustness; simple networks suffice. This suggests that repairing the niche dynamics that have been disrupted by cancer might be a viable therapeutic strategy.
Date:
21 April 2016, 10:30
Venue:
WTCHG, Seminar room A
Speaker:
Adam Maclean (Imperial College London)
Organising department:
Department of Oncology
Organisers:
Anastasia Samsonova (University of Oxford),
Christopher Yau (University of Oxford)
Organiser contact email address:
cyau@well.ox.ac.uk
Host:
Christopher Yau (University of Oxford)
Part of:
Cancer Bioinformatics Seminar Series
Topics:
Booking required?:
Not required
Audience:
Members of the University only
Editors:
Christopher Yau,
Anastasia Samsonova
