Forecasting immune selection and evasion in mismatch repair deficient cancer through evolutionary modelling
Primary Supervisor: Dr Marnix Jansen, UCL Cancer Institute, University College London
Secondary Supervisor: Dr Ben Werner, Barts Cancer Institute, Queen Mary University of London
Tertiary Supervisor: Dr Nischalan Pillay, UCL Cancer Institute, University College London
What is the problem?
Recent years have seen a true revolution in the treatment of cancer. A new class of drugs, called ‘checkpoint inhibitors’, has shown impressive results in the treatment of a range of cancers, including cancers deficient in DNA mismatch repair. These checkpoint inhibitors target the cellular receptors that cancer cells use to masquerade as normal cells; blocking these immune-evasion receptors unleashes the patient’s immune system and kills the tumour cells with great efficacy. However, these new drugs have not been equally effective in every treated patient and in many patients the results have been frankly disappointing. The reasons behind this variable treatment response likely relate to acquired or primary treatment resistance. To harvest the full potential of this powerful new class of drugs, we urgently need resources to study how immune selection drives treatment failure in mismatch repair deficient cancer, and compare responders to non-responders.
We have recently identified a reversible genetic on/off switch that mismatch repair-deficient cancers use to drive immune evasion. This switch operates like a gear box and allows mismatch repair deficient tumour cells to dramatically vary mutation rate within a patient’s tumour across time and space in response to immune selection. Engaging this switch drives rapid clonal diversification of tumour cell populations under immune selection, and identification, through natural selection, of daughter tumour cell lineages that are tolerant to treatment.
We now want to map out in detail the evolutionary trajectories taken by tumour cell lineages as they clonally diversify and adapt to immune selection. To carry out this work we have developed a novel in situ clonal mapping strategy which allows us to visualise the clonal phylogenetic architecture of microsatellite instable cancers in tissue sections of patients’ tumours (Marnix Jansen, clinician scientist and histopathologist UCLH NHS Trust). Armed with this information we can effectively contrast and compare clonal lineages that are successfully adapting to immune selection to those that are not, both within and between patients.
Our aim is to build an evolutionary framework that can be used to derive predictive models and forecast treatment resistance in patients with microsatellite instable cancer undergoing checkpoint inhibitor treatment. We will pair targeted spatial phylogenetic deconvolution to high depth sequencing analyses on tumour tissues derived from patients before and after checkpoint inhibitor treatment. In previous work we have developed a theoretical framework to deconvolute mutation rate and differential lineage survival from multi-region
sampled tumours to quantify tumour evolution (Werner, B. et al., Nature Communications 2020). We will apply and extend this quantitative evolutionary model to sequencing data obtained from patients’ tumours wherein the ground truth clonal architecture is known to analyse the fitness impact of varying mutation rate across time and space.
This multidisciplinary work bridges laboratory science, computational genomics and theoretical modelling and is jointly supported by both research teams. Models can be tested in ongoing clinical trials at UCLH NHS Trust of patients with microsatellite instable cancer undergoing checkpoint inhibitor treatment.
We are looking for a talented PhD candidate with a background in theoretical genetics and statistics or stochastic modelling who shares our enthusiasm for evolutionary cancer genetics and who is keen to develop clinically informed quantitative models for clinical patient benefit. A graduate with a strong interest in evolutionary cancer biology and/or immune-oncology, with or expecting at least an upper second class honours degree in a relevant subject, is required for this project. Previous experience in evolutionary cancer research or stochastic modelling is welcomed, but a strong drive to pursue a scientific career and work at the interdisciplinary frontier between mathematics, evolutionary cancer genetics and clinical medicine is equally important. Evidence of a strong innate mathematical ability will be essential. The studentship will provide advanced training in cutting edge next generation sequencing pipelines, together with the necessary high dimensional data analysis context to analyse this data.
Potential research placements
1. Evolutionary dynamics, Dr Ben Werner, BCI, QMUL
2. Familiarisation in the set-up of clinical trials, sample processing and laboratory techniques in molecular biology. Dr Hamzeh Kayhanian and Dr Kai-Keen Shiu, UCL Clinical Trials Unit & UCL Cancer Institute
3. Training in HTA regulation to provide a solid understanding of patient tissue routing and the wet lab workflow preceding evolutionary modelling. Adriana Resende Alves, and Dominic Patel, UCL Cancer Institute, Histopathology TTP
The funding for this studentship 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 studentships 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. Werner, B. et al., Measuring single cell divisions in human tissues from multi-region sequencing data. Nat Commun. Feb 25;11(1):1035. doi: 10.1038/s41467-020-14844-6. (2020)
2. Cross, W. et al., The Evolutionary Landscape of Colorectal Tumorigenesis. Nature Ecology & Evolution 2 (10). Nature Publishing Group: 1661–72. doi:10.1038/s41559-018-0642-z. (2018)
3. Lavery, D. et al., Evolution of Oesophageal Adenocarcinoma From Metaplastic Columnar Epithelium Without Goblet Cells in Barrett’s Oesophagus. Gut 65 (6): 907–13. doi:10.1136/gutjnl-2015-310748. (2016)
4. Andor, M. et al., Pan-Cancer Analysis of the Extent and Consequences of Intratumor Heterogeneity.” 22 (1): 105–13, doi: 10.1038/nm.3984. (2016)
5. 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)