Dr Lucy Stead
- Position: UKRI Future Leaders Fellow
- Areas of expertise: Neuro-Oncology; Cancer genomics; Cancer transcriptomics; Computational biology; Next generation sequencing; Tumour heterogeneity
- Email: L.F.Stead@leeds.ac.uk
- Phone: +44(0)113 343 8410
- Location: 5.19 Wellcome Trust Brenner Building
- Website: Twitter | LinkedIn | Googlescholar | Researchgate | ORCID
I am a computational cancer biologist interested in the use of high-throughput sequencing to characterise brain tumour genomes and transcriptomes, and the integrated analysis of datasets to further understand the development and progression of brain cancer. My research focuses on inspecting genomic and transcriptomic heterogeneity present within brain tumours and across stages of brain cancer development and progression.
I believe that the most biologically and clinically relevant inferences come from continual iteration of computational and wet-lab approaches, and this is the remit within my group. I am interested in investigating intratumour heterogeneity in glioblastoma (GBM); specifically, I wish to test whether treatment-resistant subclones emerge in recurrent tumours, and characterise them in clinically relevant ways in multiple patients. I am a trained computational biologist with expertise in next-generation sequencing data analysis.
- Head of Glioma Genomics
- Module Leader
Glioblastoma multiforme (GBM) is the most common, most malignant adult brain cancer. Almost 50% of patients die within a year. GBM tumours are surgically removed and patients then receive radio- and chemotherapy but tumours inevitably regrow – on average just 7 months later. We are investigating intra-tumour heterogeneity (ITH) in GBM and its role in tumour recurrence. To do this we have active research projects under three key areas, which are complementary and mutually beneficial to one another: computational genomics, in silico modelling and functional genomics.
We have collected pairs of patient-matched pre-treatment GBM tumour and recurrence following treatment. We plan to apply high-throughput sequencing and array approaches to these paired samples to ascertain which cells survived treatment and why. To do this we are developing integrated approaches at the bulk-tissue and single cell level to yield genotypic and phenotypic information.
In silico Modeling
Many methods exist that attempt to deconvolute the cancer cell populations within a tumour but different methods often give different results and there is no gold standard for determining which is giving the ‘ground truth’ i.e. which of these methods work best. We are developing methods to simulate data from heterogeneous, evolving tumours to enable us to better determine the accuracy of existing methods applied to such data. In developing such methods, we aim to also provide a tool to enable various parameters of tumour evolution to be altered and the results assessed in comparison with real tumour data.
We have developed several models of GBM ITH. Patient-derived models are being used to recreate the patient treatment process in vitro and ascertain whether the cells that survive therapy in this context can further inform us about treatment-resistance mechanisms. However, we are also using the models to control confounding aspects of ITH that commonly co-exist within patient samples so we can focus on, and understand, each in turn.
- PhD Computational biology; University of Leeds 2007-2010
- MSc Bioinformatics and Computational Biology; University of Leeds 2006-2007
- BA Natural Sciences (2.1); University of Cambridge 2000-2003
- Fellow of the Higher Education Academy
- British Neuro-Oncology Research Subcommittee
Module lead for MEDM5101
Research groups and institutes
- Leeds Institute of Medical Research at St James's
- Brain Cancer Research Group
- Genetics and genomics