Dr Dennis Wang PhD
Lecturer in Bioinformatics and Genomic Medicine
Department of Neuroscience
Sheffield Institute for Translational Neuroscience
University of Sheffield
385a Glossop Road
Telephone: +44 (0)114 215 9102
Fax: +44 (0)114 22 22290
2016 – Present: Lecturer in Bioinformatics and Genomic Medicine
2014 – 2016: Senior Scientist, AstraZeneca
2012 – 2014: Scientific Associate, Princess Margaret Cancer Centre, Toronto, Canada
I graduated from the University of British Columbia (Vancouver, Canada) with a BSc in Computer Science, Microbiology and Immunology. I then moved to the University of Cambridge for an MPhil in Computational Biology working on signalling pathways with Prof Jasmin Fisher (Microsoft Research), and a PhD in Biostatistics working on epigenetics with Prof Lorenz Wernisch (MRC Biostatistics Unit).
Following the completion of my PhD in 2012, I undertook postdoctoral training to investigate genetic prognostic markers at the Princess Margaret Cancer Centre in Toronto with Prof Ming-Sound Tsao and Prof Frances Shepherd. I was promoted to a staff scientist in 2013 tocoordinate the genomic sequencing and bioinformatics analysis of patient tumors. With a greater interest in drug development, I went back to Cambridge in 2014 to join the early drug discovery division of AstraZeneca where I developed computational methods to identify pharmacological biomarkers that predict drug response. I joined the University of Sheffield in 2016 as lecturer to further establish genomics and bioinformatics as cornerstones within the education and translational research programmes here.
Translational bioinformatics is an emerging field that is transforming healthcare by bringing genomics and informatics closer to patients. Our research focuses on translating the complex patterns of genomic data generated in the lab to enable the development of personalized medicines that can benefit patients suffering from complex diseases. On the computational side, this involves integrating genomics information of various data types in order to build algorithms that predict clinical outcome and identifying genetic biomarkers through feature selection. On the biological side, it is important to survey the genomic landscape of disease subtypes, as well preclinical models, to better understand how we can stratify patients for different treatments. Both of these aims enables us to work towards the ultimate goal of developing data driven approaches for personalized medicine.
I am a co-lead for the MSc in Genomic Medicine.
I am also developing online training courses in bioinformatics for learning across universities.
Peer reviewer for computational and biological journals and grant awarding bodies
Member of the International Society of Computational Biology, International Biometric Society, American Society of Human Genetics, and American Association for Cancer Research.
Preclinical model characterization and stratification to improve patient selection.
In order to translate evidence from preclinical studies to the clinic, we must use multi-omic profiling to characterize in vitro, in vivo and patient samples used in drug development. In collaboration with
Microsoft Research, we combined proteomics data to predict cell fate when perturbed by a combination of inhibitors. We are also characterizing the genomes of syngeneic mouse models (in collaboration with MedImmune) and patient-derived xenografts (in collaboration with Univ. of Toronto) to identify genetic markers of response to immunotherapies and targeted therapeutics. The next step is to validate these biomarkers in the clinic.
Statistical and machine learning models to identify genetic features associated with drug response (pharmacogenomics).
Reliable estimation of drug response in patients and preclinical models is an open problem for the drug development community. We collaborate with statisticians at Lancaster University and the Sanger Institute to use Gaussian Processes to estimate the uncertainty in drug response metrics (eg. IC50s) in large screening experiments. Also in collaboration with AstraZeneca, we are using matrix decomposition to stratify cell lines with different levels of drug response and to improve biomarker detection.
In silico testing of drug combinations.
Large-scale in vitro screening of drug combinations are extremely costly and can only test a small proportion of all possible combinations. To help alleviate this problem, we have developed software tools to quantify drug synergy and helped launch the AstraZeneca-Sanger DREAM challenge for predicting drug combinations. By stimulating computational scientists from around the world with a large experimental data set of drug combinations, we have sourced new machine learning methods that are highly predictive of synergistic drug combinations in different cancer types. Currently, we are assessing the translatability of these methods for predicting synergy in completely new drugs and combinations.
Dennis Wang, Nhu-An Pham, Shingo Sakashita, Ghassan Allo, Lucia Kim, Naoki Yanagawa, Chang-Qi Zhu, Ming Li, Ethan Kaufman, Nadeem Moghal, Geoffrey Liu, Melania Pintilie, Igor Jurisica, John D. McPherson, Frances Shepherd, Ming S. Tsao. "Integrative and comparative genomic analysis of patient-derived xenografts in non-small cell lung cancer". Journal of Pathology. In press.
Suzanne I. S. Mosely, John Prime, Jens-Oliver Koopman, Dennis Wang, Danielle Greenawalt, Miika J. Ahdesmaki, Rebecca Leyland, Stefanie Mullins, Luciano Pacelli, Danielle Marcus, Judith Anderton, Amanda Watkins, Jane Coates Ulrichsen, Zach Brohawn, Brandon Higgs, Athula Herath, Robert Kozarski, James A. Harper, Viia Valge-Archer, Ross Stewart, Simon Dovedi, Michelle Morrow, Richard C. A. Sainson, Robert Wilkinson "Characterisation of murine syngeneic tumour models enables rational preclinical model selection for immuno-oncology drug discovery". Cancer Immunology Research. In press.
Di Veroli, G.Y., Fornari, C., Wang, D., Richards, F.M., Jodrell, D.I. "Combenefit: An interactive platform for the analysis and visualisation of drug combinations". Bioinformatics. April 2016. (2016). doi:10.1093/bioinformatics/btw230
Stewart, E.L., Mascaux, C., Pham, N.-A., Sakashita, S., Sykes, J., Kim, L., Yanagawa, N., Allo, G., Ishizawa, K., Wang, D., Zhu, C.-Q., Li, M., Ng, C., Liu, N., Pintilie, M., Martin, P., John, T., Jurisica, I., Leighl, N.B., Neel, B.G., Waddell, T.K., Shepherd, F.A., Liu, G., Tsao, M.-S., 2015. Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma. Journal of Clinical Oncology. 2014.60.1492. doi:10.1200/JCO.2014.60.1492
Li, L., Wei, Y., To, C., Zhu, C.-Q., Tong, J., Pham, N.-A., Taylor, P., Ignatchenko, V., Ignatchenko, A., Zhang, W., Wang, D., Yanagawa, N., Li, M., Pintilie, M., Liu, G., Muthuswamy, L., Shepherd, F.A., Tsao, M.S., Kislinger, T., Moran, M.F., 2014. Integrated omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. Nature Communications 5, 5469. doi:10.1038/ncomms6469
Leung, L., Radulovich, N., Zhu, C.-Q., Wang, D., To, C., Ibrahimov, E., Tsao, M.-S., 2013. Loss of canonical Smad4 signaling promotes KRAS driven malignant transformation of human pancreatic duct epithelial cells and metastasis. PLoS ONE 8, e84366. doi:10.1371/journal.pone.0084366
Wang, D. , Rendon, A., Ouwehand, W., Wernisch, L. “Transcription factor and chromatin features predict genes associated with eQTLs.” Nucleic Acids Research. 41(3):1450-63 (Feb. 2013).
Wang, D. , Rendon, A., Ouwehand, W., Wernisch, L. “Transcription factor co-localization patterns affect human cell type-specific gene expression.” BMC Genomics 13:263 (2012).
Wang, D., Cardelli, L., Phillips, A., Piterman, N., Fisher, J. “Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics.” BMC Systems Biology 3, 118 (2009).