Preventing Drug Resistance to PARP inhibition in ovarian cancer through novel dosing regimens based on cancer evolutionary dynamics

Primary Supervisor: Michelle Lockley, Queen Mary University London

Secondary Supervisor(s): Rowan Miller, University College Hospital; Trevor Graham, Queen Mary University of London

Project

PARP inhibitors (PARPi) have become standard of care in ovarian cancer, especially in the most common subtype, high grade serous ovarian cancer (HGSOC), but relapse with PARPi-resistant cancer is usual. This Clinical Research Fellowship will 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 drivesadaptionwithinthecancer. OurhypothesisisthattherelativefitnessofthePARPi-sensitiveand-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 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. This concept is known as Adaptive Therapy and the challenge is knowing how to dose a tumour, namely the dosage administered and the 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 to launch the ACTOv clinical trial (Adaptive ChemoTherapy in Ovarian cancer), that will test Adaptive Therapy with carboplatin in HGSOC. ACTOv is the UK?s first clinical trial to test evolution-based therapies for cancer patients and recruitment will commence in 2022. This project will apply the same successful approach to improving the long- term effectiveness of PARPi therapy. All experiments are established in our combined groups.
Specific aims of the CRTF:

1. Use deep targeted sequencing of cell-free DNA to track resistance-associated mutations (typically in-
   frame reversion mutations in BRCA) longitudinally in sequential blood samples collected from PARPi- treated patients. Use these data to measure in-patient fitness values of PARPi-sensitive and resistant populations in the presence and absence of drug.
2. Use mathematical modelling to predict personalised optimal drug dosing schedules derived from the fitness values measured in Aim 1.
3. Test novel adaptive dosing schedules in vivo in murine intraperitoneal co-cultures of paired PARPi- sensitive and resistant HGSOC cells. Explore these models to determine the mechanisms of fitness costs of PARPi resistance

By focusing on evolution in patients, we hope that the data generated here can be readily translated to support the launch of a partner trial to ACTOv within 3-5 years. The Fellow will use model systems in parallel to explore mechanisms of action and run preclinical proof-of-concept experiments.

This PhD will appeal to candidates with an interest in combing ideas from evolution, mathematics and cell biology to tackle fundamental challenges in cancer care. The Fellow will be trained and supported within our combined teams to (a) determine the evolutionary dynamics of resistance in PARPi treated ovarian cancer patients, and (b) establish how these dynamics can be exploited for therapeutic gain by modulation of PARPi dosing schedules. The fellow will leave with skills in genomics, bioinformatics, mathematical modelling and cell biology and will be positioned to develop their original discoveries into a clinical trial at project completion.

References

1. LiquidCNA: tracking subclonal evolution from longitudinal liquid biopsies using somatic copy number alterations. Lakatos E, Hockings H, Mossner M, Huang W, Lockley M, Graham TA; iScience Accepted in principle, July 2021
2. Copy number signatures and mutational processes in ovarian carcinoma. Macintyre G, Goranova TE, De Silva D, Ennis D, Piskorz AM, Eldridge M, Sie D, Lewsley LA, Hanif A, Wilson C, Dowson S, Glasspool RM, Lockley M, Brockbank E, Montes A, Walther A, Sundar S, Edmondson R, Hall GD, Clamp A, Gourley C, Hall M, Fotopoulou C, Gabra H, Paul J, Supernat A, Millan D, Hoyle A, Bryson G, Nourse C, Mincarelli L, Sanchez LN, Ylstra B, Jimenez-Linan M, Moore L, Hofmann O, Markowetz F, McNeish IA, Brenton JD.; Nat Genet. 2018 Sep;50(9):1262-1270. doi: 10.1038/s41588-018-0179-8. Epub 2018 Aug 13.
3. ESMO Recommendations on Homologous Recombination Deficiency Testing to Predict PARP Inhibitor Benefit in Ovarian Cancer. Miller RE, Leary A, Scott C, Serra V, Lord CJ et al.; Ann Oncol. 2020 Dec;31(12):1606-1622. doi: 10.1016/j.annonc.2020.08.2102.
4. Quantification of subclonal selection in cancer from bulk sequencing data. Williams MJ, Werner B, Heide T, Curtis C, Barnes CP*, Sottoriva A*, Graham TA*; Nat Genet 2018 Jun;50(6):895-903. doi: 10.1038/s41588-018-0128-6. Epub 2018 May 28.
5. Subclonal reconstruction of tumors by using machine learning and population genetics. Caravagna G, Heide T, Williams M, Zapata L, Nichol D, Chkhaidze K, Cross W, Cresswell GD, Werner B, Acar A, Barnes CP, Sanguinetti G, Graham TA*, Sottoriva A*.; Nat Genet . 2020 Sep;52(9):898-907. doi: 10.1038/s41588-020-0675-5. Epub 2020 Sep 2.