Dr Dennis Wang PhD, FHEA
Senior Lecturer in Genomic Medicine and Bioinformatics
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
2020 – Present: Senior Lecturer / Group Leader in Genomic Medicine and Bioinformatics
2016 – 2019: Lecturer / Group Leader in Genomic Medicine and Bioinformatics
2018 – Present: Genomics Theme Lead, NIHR Sheffield Biomedical Research Centre
2017 – Present: Scientific Director of the Sheffield Bioinformatics Core
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 statistical genetics 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 to coordinate the genomic sequencing and bioinformatics analysis of patient tumours. 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 teaching and data science as cornerstones of genomics and bioinformatics research.
My group focuses on translating the complex patterns of genomic data generated in the lab to enable the development of personalised medicines that can benefit patients suffering from complex diseases. Of computational interest, this involves integrating genomics information of various data types in order to build machine learning algorithms that predict clinical outcome and identifying biomarkers through feature selection. Of biomedical interest, we survey the genomic landscape of disease subtypes using sequencing techniques to better understand how we can stratify patients for different treatments. Both of these aims enable us to work towards the ultimate goal of developing data driven approaches for personalising 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 (sbc.shef.ac.uk/training).
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.
Genomic characterisation and stratification to enhance patient stratification.
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. We are testing our stratification approaches on the 100,000 Genomes Project cohort and collaborating with industry partners to improve genetic diagnostics (Genomics England, Personalis Inc.).
Probabilistic modelling to identify genetic features associated with clinical outcomes.
Reliable estimation of drug response in patients and their outcomes is an open problem for precision medicine. We collaborate with statisticians at Lancaster University, the AstraZeneca and the Sheffield Institute for Translational Neuroscience to use Gaussian Processes to estimate the uncertainty in treatment response metrics (eg. IC50s) in large screening experiments. Also in collaboration with the Donald Heath Sheffield, Imperial College London and Cambridge University, we are using unsupervised and regression based methods to identify gene signatures that describe different subtypes of pulmonary arterial hypertension, a rare disorder.
Machine learning prediction of drug responses and 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.
Menden, M.P., Wang, D., Guan, Y., Mason, M.J., Szalai, B., Bulusu, K.C., Yu, T., Kang, J., Jeon, M., Wolfinger, R., et al. (2019). Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications. In press. doi: 10.1038/s41467-019-09799-2
Dana Silverbush, Shaun Grosskurth, Dennis Wang, Francoise Powell, Bertie Gottgens, Jonathan Dry, Jasmin Fisher. "Cell-Specific Computational Modeling of the PIM pathway in Acute Myeloid Leukemia". Cancer Research. Dec. 2016.
Mosely, S.I.S., Prime, J.E., Sainson, R.C.A., Koopmann, J.-O., Wang, D.Y.Q., Greenawalt, D.M., Ahdesmaki, M.J., Leyland, R., Mullins, S., Pacelli, L., et al. (2016). Rational selection of syngeneic preclinical tumor models for immunotherapeutic drug discovery. Cancer Immun. Research Dec. 2016
Wang, D., Pham, N.-A., Tong, J., Sakashita, S., Allo, G., Kim, L., Yanagawa, N., Raghavan, V., Wei, Y., To, C., et al. Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors. Int. J. Cancer 140, 662–673. Nov. 2016
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).