Preventing Drug Resistance to PARP inhibition in ovarian cancer through novel dosing regimens based on cancer evolutionary dynamics
Primary Supervisor: Prof Michelle Lockley, Barts Cancer Institute, Queen Mary University London
Secondary Supervisors : Dr Rowan Miller University College Hospital and Prof Trevor Graham, Barts Cancer Institute, Queen Mary University of London
PARP inhibition therapy (PARPi) has become standard of care in ovarian cancer. Despite promising initial responses to PARPi, relapse of drug-resistant cancer is common. We are interested in how novel, potentially patient-bespoke, drug dosing regimens can improve the efficacy of treatment. In this Clinical Research Training Fellowship we propose to study the evolutionary dynamics of drug resistance in PARPi treated ovarian cancer, and determine how to exploit these dynamics through variable drug dosing to improve patient outcomes.
Evolutionary theory states that all adaptions will come at some cost. In other words, if a cancer clone evolves to become optimal at some trait, such as rapid growth, it will inevitably come at the price of being worse at another trait, such as accurate DNA replication. Treatments such as PARPi represent a new selective pressure that drives adaption within the cancer. Our hypothesis is that the relative fitness of the PARPi-sensitive and -resistant cells is flipped by PARPi therapy: in the presence of drug PARPi-resistant cells are fitter, whereas in the absence of drug PARPi-sensitive cells have higher fitness. If this hypothesis is correct (and we have already shown that it is the case for platinum sensitive/resistant populations in high grade serious ovarian cancer – HGSOC), then switching on-off therapy at the correct intervals would prevent the emergence of resistant cells and enable continued sensitivity to PARPi treatment over the long-term. The challenge is knowing how to dose a tumour, namely the dosage and duration of intervals on/off therapy. These parameters are determined by the underlying evolutionary dynamics of the drug resistant and sensitive clones.
Our current CRUK-funded clinical fellow has progressed this concept in platinum-resistant cancer and is already developing the first clinical trial of this approach in platinum-resistant cancer. This project will apply the same successful approach to PARPi therapy. All experiments are established in our combined groups.
Specific aims of the project:
1. Evolve resistance to PARPi in a human HGSOC cell line and mouse model of HGSOC, and perform in vitro/vivo experiments to measure the fitness cost of resistance in the absence of drug.
2. Use mathematical modelling to predict optimal drug dosing scheduled to the fitness values measured in Aim 1, and then test novel schedules in vitro and in vivo.
3. Use deep circulating free DNA targeted sequencing to track resistance-associated mutations longitudinally in blood samples from PARPi treated patients. Use these data to measure in-patient fitness values of PARPi sensitive and resistant populations.
This project will enable the clinical fellow to (a) determine the evolutionary dynamics of resistance in PARPi treated ovarian cancer, and (b) establish how these dynamics can be exploited for therapeutic gain by modulation of PARPi dosing schedules.
Potential placement opportunities
1. Dr Miller, UCL/UCLH. Good Clinical Practice accreditation plus training in trial conduct, tissue collection and processing. The fellow will also gain experience of the clinical management of ovarian cancer.
2. Prof Graham, BCI, QMUL. Generation of simple differential equation models of resistant/sensitive sub-clone evolution, and fitting these models to clinical data. This will provide the mathematical foundation for the work proposed in the PhD.
3. Prof Sandy Anderson, Integrated Mathematical Oncology (IMO) Centre at Moffitt Cancer Centre, Florida. Mathematical modelling of treatment response.
We are looking for a committed candidate with an enthusiasm for improving the systemic treatment of malignant diseases to join our diverse team. An interest in using convergence science to tackle the pervasive clinical problem of cancer treatment resistance is essential as is a focus on clinical translation of research outcomes for patient benefit. This fellowship is most likely to suit a more mathematically-minded clinician and they will be trained and supported within our combined teams. Some prior research experience, either lab-based or clinical, is desirable. A demonstrated ability to communicate well, work effectively within a team and maintain accurate records are all essential criteria.
The funding for this fellowship covers students with home tuition fee status only. For more information on home tuition fee status please visit the UKCISA website. Please note that we will only be able to offer fellowships to candidates that have home tuition fee status or provide evidence that they can fund the international portion of the tuition fee from external sources (i.e. not self-funded).
1. Hoare, J.I. et al., Platinum resistance induces diverse evolutionary trajectories in high grade serous ovarian cancer. Preprint: https://www.biorxiv.org/content/10.1101/2020.07.23.200378v1
2. Macintyre, G. et al., Copy number signatures and mutational processes in ovarian carcinoma. Nat Genet. Sep;50(9):1262-1270. doi: 10.1038/s41588-018-0179-8 (2018)
3. Miller, R.E. et al., ESMO Recommendations on Homologous Recombination Deficiency Testing to Predict PARP Inhibitor Benefit in Ovarian Cancer. Annals of Oncology, available online Sept 28, 2020. DOI: https://doi.org/10.1016/j.annonc.2020.08.2102
4. Williams, M.J. et al., Quantification of subclonal selection in cancer from bulk sequencing data. Nat Genet, Jun;50(6):895-903. doi: 10.1038/s41588-018-0128-6. (2018)
5. Caravagna, G. et al., Model-based tumor subclonal reconstruction. Nat Genet. Sep;52(9):898-907. doi: 10.1038/s41588-020-0675-5 (2020)