Quick Search / Select a Section

Cancer Sciences

The Division of Cancer Sciences encompasses research groups looking at both basic and translational cancer research.

Targeted Cancer Therapy Group

Targeted therapy blocks the growth of cancer cells by interfering with specific targeted molecules needed for carcinogenesis and tumour growth, rather than by simply interfering with all rapidly dividing cells as does current therapies such as chemotherapy. The ultimate aim is to kill the tumour with minimal side effects to the patient. The Targeted Cancer Therapy group, based at The University of Surrey and led by Professor Pandha, are working on several projects:

Viral Therapy:

Investigating so-called oncolytic viruses (viruses that solely replicate in cancer cells causing them to die) including herpes simplex virus (HSV) and reovirus in bladder and prostate models.


Using the tumour associated protein EN2 to target tumour cells for both therapy (vaccines and targeted antibodies) and imaging.


Setting up a bio-repository and accompanying 'database' of 300 patients with all stages of prostate cancer to allow longitudinal blood sampling whilst on different treatment programs, and enable banking and analysis of serum and DNA at 6 monthly intervals.

Novel Cancer Trials:

A clinical team of 12 staff are developing a large programme of clinical research directed at improved treatment for cancer, understanding the causes of cancer and developing new methods of detection.

Find out more at:


Blackbourn Laboratory

Infectious agents, including viruses, are associated with up to 20% of human cancers. The Blackbourn laboratory, led by Professor David Blackbourn and based at the University of Surrey, studies two such viruses that are responsible for causing cancer: Kaposi's sarcoma-associated herpesvirus (KSHV) and Merkel cell polyomavirus (MCV or MCPyV). Their particular interests lie in how such viruses cause disease, evade the immune response and interact with the DNA damage response. Their main research topics are:

  • How KSHV interacts with the DNA damage response.
  • How KSHV modulates the type I interferon (alpha & beta) response.
  • How KSHV deregulates antigen-specific T cell responses.
  • What are the consequences of KSHV infection on endothelial cell biology, including cell-cell interactions and regulating leukocyte recruitment?
  • Understanding the tumour microenvironment and how it contributes to the pathogenesis of Merkel cell carcinoma.
Find out more at:


Cancer Systems Biology

Cancer is a multifactorial disease progressing through an individual trajectory in each patient. Thus, diagnostics, prevention and therapy of the future need to be tailored to the genetic background and lifestyle of the individual. We believe that this requires mechanistic computer simulation of the existing knowledge on human molecular cell biology.

Simulation of the model representing knowledge on molecular interaction network in the cell will allow one to predict trajectories of molecular changes occurring in cells having a particular genetic background and a set of genome damages predisposing to cancer. Comparison of simulation results with experimental data will be used to design experiments filling gaps in fundamental knowledge.

The iteratively refined models will gain sufficient power to predict development of the disease based on patient genetic background and molecular diagnostics. We have developed SurreyFBA (Bioinformatics 2011) software for computer simulation of Genome Scale Metabolic Networks (GSMN). The software has been already applied to modelling of bacterial pathogens (Genome Biology 2007, 2011. PLoS Computational Biology 2011) and is currently being applied to study metabolic reprogramming in cancer.

Recently, we have published Quasi Steady State Petri Net (QSSPN; Bioinformatics 2013), the first method allowing computer simulation of networks describing gene regulation, signalling and whole-cell metabolism. We are currently applying this method to create mechanistic models of genotype-phenotype relationship in cancer.

We have already contributed statistical analysis of a prostate cancer data for Surrey cohort included into recent meta-analysis, which uncovered 23 new susceptibility loci (Nature Genetics 2014). We are now investigating application of molecular network simulations to provide mechanistic interpretation of the observed statistical associations. In all our projects we integrate computer simulations with experimental validation.

The interdisciplinary group is headed by Professor Andrzej Kierzek who supervises method and software development, Dr Nick Plant who leads model development and experimental validation and Dr Mazhar Ajaz who is a clinical oncologist. Members of the group work in Leggett & Dorothy Hodgkin buildings, University of Surrey

Specific Research Projects Include:

  • Analysis of METABRICK transcriptome data in the context of Genome Scale Metabolic Network to study metabolic reprogramming in breast cancer. PhD student Vytautas Leoncikas funded by BBSRC/AstraZeneca CASE studentship. (NJ Plant principal supervisor, AM Kierzek co-supervisor).
  • Mechanistic computer simulation and experimental validation of molecular networks involved in metabolic reprogramming in breast cancer. PhD student Amy Barber funded by Breast Cancer Campaign (NJ Plant principal supervisor, AM Kierzek, M Ajaz co-supervisors).
  • SurreyFBA: Interactive tool for computer simulations of genome scale metabolic networks. BBSRC grant (BB/K015974/1) to AM Kierzek (PI), N Plant, J. McFadden, C Avignone-Rossa
Equipment and Resources

Professor Andrzej Kierzek leads the FHMS Bioinformatics Core Facility equipped with a 100 CPU computational cluster. Drs Carla Moller-Levet and Huihai Wu are Bioinformatics Experimental Officers with expertise in high throughput data analysis and computer simulations. We are happy to collaborate on analysis of Next Generation Sequencing (e.g.: Science 2014), GWAS (e.g.: Nature Genetics 2014), transcriptome (e.g.: PNAS 2014a, 2014b), proteome and metabolome data. We have unique expertise in simulation of genome scale molecular networks with our SurreyFBA (Bioinformatics 2011) and QSSPN (Bioinformatics 2013) tools.