The Wolfson Institute for Biomedical Research
at the Cruciform Building
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CANCER RESEARCH U.K.
VIRAL ONCOLOGY GROUP

Bioinformatics

Stephen Henderson PhD
Matthew Trotter PhD

All our research is underpinned by cutting-edge computational and statistical analyses by our bioinformaticians Matthew Trotter and Stephen Henderson. Our Group has been a pioneer in the use of microarrays both for clinical and research studies. Beginning with nylon microarrays and radioactive probes, and now using both standard and custom Affymetrix oligonucleotide arrays.

We have identified molecular signatures for most bone and soft tissue sarcoma, for ovarian cancer, as well as for Kaposi sarcoma (KS). These have provided unique insights into the origins of different mesenchymal tumours, as well as KS. We are currently identifying pathways involved in mesenchymal cell transformation, glycolysis (with Salvador Moncada) and differentiation of embryonic stem cells (ESC, Lucy Smithers). We have explored the expression signature of urine as a predictor of bladder cancer and compared the primary and metastatic signatures of ovarian cancer (Tania Adib). Our interest in KSHV has led to ongoing studies on the profile of putative KSHV targets such as lymphatic endothelial cells (LECs, Hsei-Wei Wang), circulating endothelial progenitors (CEPs, Dimitra Bourmpoulia and Lola Martinez) and dendritic cells (DCs, Dimitrios Lagos).

We are involved in a number of collaborative projects, including studies of ESC differentiation (Roger Pedersen & Enrique Millan, University of Cambridge), several clinical studies within UCH, including diseases such as polycythaemia vera, cancers of unknown primary (CUPs), ovarian cancer and head and neck tumours. We are involved with trials of tyrosine kinase inhibitors in non-small cell lung cancer (TOPICAL trial), and studies of different treatment regimens in biliary tract tumours (BILCAP trial). We collaborate with a number of laboratories to produce a custom Affymetrix chip with wide specificity for a range of oncogenic viral genes (Gary Hayward, Baltimore; Ethel Cesarman, NY; Robert Colgrove, Harvard; John Nicholls, Baltimore). This project presents particular challenges in design of probe sets to detect specific strains, selection of appropriate human housekeeping genes, plus pre-processing and normalisation of data.
Stephen Henderson has a particular interest in the application of machine learning methods to the analyses of microarray data. For example, to automate the detection of signatures and patterns in microarray data. Such approaches are useful for the diagnosis and prognosis of cancers and he has developed software to integrate machine learning methods into a single platform using the R-software language. This work is ongoing and, at present, he aims to integrate survival methods within this framework. We are also interested in the use of statistical methods to identify differential gene expression in low replicate microarray experiments, e.g. stem cell data. Similarly, we have an interest in statistical metrics for the comparison of inter group distances, i.e. to test the relative similarities between groups (or clusters) of arrays rather than single arrays.

We maintain strong links to the Department of Computer Science at UCL and the Bloomsbury Centre for Bioinformatics.

Selected Publications using Computational Analyses:

Henderson-SR, Presneau-N, Guiliano-D, McLean-S, Frow-R, Anderson-J, Whelan-J, Athanasou-N, Flanagan-A, Boshoff-C. A Molecular Map of Mesenchymal Tumours. Genome Biology, 2005 in press

Wang H-W, Trotter_M, Lagos-D, Bourboulia-D, Elliman-S, Mäkinen¬_T, Henderson_S, Flanagan_AM, Alitalo_K, Boshoff-C. KSHV Cellular Reprogramming Contributes to the Lymphatic Endothelial Gene Expression in Kaposi Sarcoma. Nature Genetics 2004, 36:687-693

Adib TR, Henderson S, Perrett C, Hewitt D, Bourmpoulia D, Ledermann J, Boshoff C. Predicting biomarkers for ovarian cancer using gene expression microarrays. Br J Cancer 2004, 90: 686-692

Stebbing J, Bourboulia D, Johnson M, Henderson S, Williams I, Wilder N, Tyrer M, Youle M, Imami N, Kobu T, Kuon W, Sieper J, Gotch F, Boshoff C. Kaposi's sarcoma-associated herpesvirus cytotoxic T lymphocytes recognize and target Darwinian positively selected autologous K1 epitopes. J Virol. 2003 Apr;77(7):4306-14.




Selected publications

Academic Career

Funding

 


Wolfson Institute for Biomedical Research - The Cruciform Building - University College London
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