New strategies for exploiting the fitness costs of drug resistance in cancer
Application closing date: 16/11/25
Project background
Drug resistance is a major clinical challenge in cancer treatment, underlying both tumour recurrence and patient mortality (Vasan et al, 2019). Yet when cancer cells develop resistance to a drug, there can be an evolutionary “trade-off” that occurs as cells allocate resources to resistance rather than to other cellular functions such as proliferation (Aktipis et al, 2013). This means that resistant cells may have reduced fitness in a drug-free environment. The idea of a “fitness cost to resistance” underpins a series of new strategies for the “adaptive treatment” of tumours, in which a population of drug-sensitive cells are maintained, and when the drug is removed these can then out-compete the drug-resistant population (Gatenby et al, 2009). Re-introducing the drug can then be effective at reducing tumour burden as there is a significant population of sensitive cells that will die. Such adaptive interventions could provide a route to circumvent drug resistance, by controlling a tumour rather than attempting to eradicate it.
For adaptive therapies to reach their potential in the clinic, we need a deeper understanding of the factors that influence the cost of resistance; tumour (epi)genetics, therapy type, microenvironment, and mechanism of resistance are all likely to be important. This project proposes to systematically characterise the mechanisms and fitness costs of resistance by performing a large-scale experimental screen across diverse cancer cell lines exposed to a range of standard-of-care drugs.
We previously showed that resistance to platinum chemotherapy has a fitness cost for ovarian cancer cells (Hockings et al, 2025), and subsequently opened a clinical trial testing adaptive dosing of chemotherapy in patients with this disease (Mukherjee et al, 2024). We also developed a mathematical modelling framework that infers the temporal dynamics of cancer cell drug resistance phenotypes using only genetic lineage tracing and population size data (Whiting et al, 2025). This project will build upon this methodology, with the aim of identifying novel therapeutic strategies where adaptive dosing could be used to outcompete resistant clones.This project is part of the EU-funded Evolutionary Medical Genomics Doctoral Network (EvoMG-DN), which is currently recruiting a cohort of 15 PhD students based at leading universities, research institutes, and industry partners across 7 European countries. The programme offers the students multiple travel opportunities, summer schools, and placements in other participating labs across Europe.
The candidate will receive a monthly salary of Euros 4,917 (subject to tax and National Insurance deductions), which includes both a living allowance and a mobility allowance, for a duration of 36 months. Additionally, candidates with a family may be eligible for a monthly family allowance of Euros 380 (also subject to tax and National Insurance deductions), provided they meet the eligibility criteria for the MSCA family allowance. In the fourth year of the PhD, the candidate will receive the standard ICR stipend rate.
Project aims
- Barcode a panel of cancer cell lines to enable genetic lineage tracing
- Design a large-scale experimental screen in which clinically relevant drugs are applied to barcoded cancer cell lines, generating resistant clones in vitro
- Use single cell DNA sequencing to track genetic lineages through treatment and resistance, and single cell RNA sequencing to characterise the resistant phenotype
- Apply mathematical frameworks to learn the dynamics of resistance evolution
- Combine experimental results with computational modelling of evolutionary dynamics to propose new therapeutic strategies for adaptive intervention
Further details & requirements
Hypothesis: Drug resistance carries a fitness cost which can be exploited in new therapeutic strategies to direct cancer evolution
The student will generate data in the wet-lab to determine the cost of resistance in a large panel of cancer cell lines and patient derived organoids, treated with a broad range of standard-of-care chemotherapies.
We then intend to use our established methodology to barcode a panel of cancer cell lines in vitro, before treating them long-term with a range of clinically relevant chemotherapy drugs. At various intervals, cells will be removed for single cell DNA sequencing (to trace genetic lineages) and single cell RNA sequencing (to characterise the resistant phenotype). A suite of in vitro assays will be used to determine if resistant clones grow slower than their drug-sensitive counterparts, and map environmental dependence and potential mechanisms.
In the dry-lab, the student will lead mathematical and bioinformatic analysis on the sequencing data they have generated, refining and applying our existing mathematical framework (Whiting et al, 2025) to understand the evolutionary dynamics of resistance. By combining wet-lab and computational approaches, the student will determine the relationship between fitness costs and resistance mechanisms. Experiments will be replicated to determine under what conditions the resistant phenotype is reproducible, and therefore predictable. Finally the student will perform integrative analysis of all datasets to propose new therapeutic concepts for adaptive treatment.
The studentship will be based in the Genomics and Evolutionary Dynamics group, within the Centre for Evolution and Cancer. We are a highly diverse and interdisciplinary team of about 15 people, consisting of clinicians, biologists, mathematicians and computational scientists. Our lab has around 12 years of experience in both experimental and bioinformatic analyses of cancer evolution, and full training in both wet and dry lab techniques will be provided to the successful candidate.
To complement the core research, the candidate will benefit from targeted international secondments. At the University of Cambridge (with Prof Jamie Blundell) during months 19–20, the student will receive training in modelling clonal evolution in cancer. At Chalmers University (Gothenburg, with Dr Eszter Lakatos) in month 23, a two-week stay will provide complementary training in mathematical modelling. Finally, at the Centre for Genomic Regulation (Barcelona, with Prof Manuel Irimia) in month 24, a short secondment will focus on transcriptomic analyses in the context of protective cancer signatures and their relationship with resistance. These placements will ensure a strong interdisciplinary foundation, linking experimental, computational, and theoretical approaches.
BSc (First or 2:1) or Master’s degree, ideally in a quantitative subject (for example physics, mathematics, or computer science) but experience of cell or molecular biology is highly valued.
A strong interest experimental cancer biology is essential, but prior wet-lab training is not essential. Experience with computational or mathematical modelling would be valuable. We are looking for a highly motivated candidate with the willingness to learn and an enthusiasm for tackling complex questions in evolutionary dynamics. A keen interest in how resistance evolves and how it can be managed is essential.
- “A view on drug resistance in cancer” Vasan et al, Nature. 2019 Nov;575(7782):299-309. doi: 10.1038/s41586-019-1730-1.
- “Life history trade-offs in cancer evolution” Aktipis et al, Nat Rev Cancer. 2013 Dec;13(12):883-92. doi: 10.1038/nrc3606.
- “Adaptive therapy” Gatenby et al, Cancer Res. 2009 Jun 1;69(11):4894-903. doi: 10.1158/0008-5472.CAN-08-3658.
- “Adaptive Therapy Exploits Fitness Deficits in Chemotherapy-Resistant Ovarian Cancer to Achieve Long-Term Tumor Control” Hockings et al. Cancer Res. 2025 Sep 15;85(18):3503-3517. doi: 10.1158/0008-5472.CAN-25-0351.
- “Study protocol for Adaptive ChemoTherapy for Ovarian cancer (ACTOv): a multicentre phase II randomised controlled trial to evaluate the efficacy of adaptive therapy (AT) with carboplatin, based on changes in CA125, in patients with relapsed platinum-sensitive high-grade serous or high-grade endometrioid ovarian cancer” Mukerjee et al. BMJ Open. 2024 Dec 22;14(12):e091262. doi: 10.1136/bmjopen-2024-091262.
- “Quantitative measurement of phenotype dynamics during cancer drug resistance evolution using genetic barcoding” Whiting et al, Nat Commun. 2025 Jun 20;16(1):5282. doi: 10.1038/s41467-025-59479-7.