Dr Dennis Wang

BSc, MPhil, PhD, FHEA

Department of Neuroscience

Senior Lecturer in Genomic Medicine and Bioinformatics

Also attached to Department of Computer Science, Faculty of Engineering.

Member of the Machine Learning and Natural Language Processing research groups.

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+44 114 215 9102

Full contact details

Dr Dennis Wang
Department of Neuroscience
Room B44
385a Glossop Road
S10 2HQ

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 Sheffield Institute for Translational Neuroscience, University of Sheffield in 2016 as a Lecturer (now Senior Lecturer) of Genomic Medicine and was jointly appointed to the Dept. of Computer Science in 2018.

I am also the Scientific Director of the Sheffield Bioinformatics Core and Genomics Theme Lead within the NIHR Sheffield Biomedical Research Centre.

  • 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
Research interests

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

Current Projects

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 characterise 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 characterising 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.


Journal articles

Conference proceedings papers

  • Otero-Núñez P, Rhodes C, Wharton J, Swietlik E, Kariotis S, Harbaum L, Dunning M, Elinoff J, Errington N, Thomson R , Iremonger J et al (2020) Multi-omic profiling in pulmonary arterial hypertension. Pulmonary hypertension RIS download Bibtex download
  • Aguero BL, Valluru M, Bradford J, Wang D, Tozer GM & English WR (2019) Prognostic significance of soluble and ECM associated VEGFA isoforms in high-grade serous ovarian cancer. INTERNATIONAL JOURNAL OF EXPERIMENTAL PATHOLOGY, Vol. 100(4) (pp A8-A9) RIS download Bibtex download
  • Griffin JL, Freeman TM & Wang D (2018) Neo-antigen Prediction in Bladder Cancer. JOURNAL OF PATHOLOGY, Vol. 245 (pp S12-S12) RIS download Bibtex download
  • Sakashita S, Pham N-A, Alberts P, Ludkovski O, Li M, Ng C, Wang D, Allo G, Kim L, Yanagawa N , Zhu C-Q et al (2015) Patient-Derived Xenograft Studies Suggest FGFR1 Amplification Is Insufficient to Predict Response to FGFR Inhibitors in Lung SqCC. JOURNAL OF THORACIC ONCOLOGY, Vol. 10(9) (pp S595-S595) RIS download Bibtex download
  • Navab R, Pasko E, Kim KS, Walker GC, Gullberg D & Tsao MS (2015) Integrin a11b1 Regulates Cancer Stromal Stiffness and Promotes Tumorigenicity and Metastasis in Non-Small Cell Lung Cancer. JOURNAL OF THORACIC ONCOLOGY, Vol. 10(9) (pp S480-S480) RIS download Bibtex download
  • Pham N-A, Wang D, Sakashita S, Allo G, Kim L, Yanagawa N, Zhu C-Q, Li M, Kaufman E, Moghal N , Liu G et al (2015) Genomic Profiling of Patient-Derived Xenografts Identify Passenger Aberrations Associated with Better Prognosis in Non-Small Cell Lung Cancer. JOURNAL OF THORACIC ONCOLOGY, Vol. 10(9) (pp S313-S314) RIS download Bibtex download
  • Navab R, Hao J, Sakashita S, Wang D, Pintilier M, Wang Y, Zhu C-Q, Venkat K, Jurisica I & Tsao M-S (2014) Abstract 4800: Heterogeneity of cancer associated fibroblasts in non-small cell lung cancer is defined by the level of collagen gel contraction activity (pp 4800-4800) RIS download Bibtex download


Research group
  • Tim Freeman
  • Tzen Szen Toh
  • Brenda Lourdes Aguero Burgos
  • Dr Matthew Parker
  • Dr Mark Dunning
  • Dr Emily Chambers
  • Dr Emmanuel Jammeh
  • Dr. Nat Ilenkovan
  • Dr Tzen S Toh
  • Tim Freeman
  • Niamh Errington
  • Mohammed Rajab
  • Sokratis Kariotis
  • Katjusa Koler
  • The Brain Tumour Charity (£119,936) Co-I 2019 – 2022
    Ex-vivo 3D models of post-surgical residual disease in glioblastoma to improve biological understanding and treatment.
  • NIHR Efficacy of Mechanism Evaluation (£815,275) Co-I 2019 – 2024
    Genotype of Ureothelial cancer: Stratified Treatment and Oncological outcomes (GUSTO).
  • AMS Springboard Award (£99,411) PI 2019 – 2021
    Evidencing subtypes of disorders through consensus of clinical and multi-omic traits.
  • Rosetrees Seedcorn Award (£30,428) PI 2019 – 2020
    Machine learning prediction of uncertainty in cancer therapy response using genomic biomarkers
  • EPSRC Centre for Doctoral Training Studentship (£65,585) PI 2018 – 2022
    Tensor-based machine learning for personalized medicine
  • Donald Heath PhD Studentship (£87,973) PI 2018 – 2022
    Epigenetic data integration for subtyping pulmonary hypertension
  • BBSRC – Bioinformatics Resource Fund (£428,223) Co-I 2018 – 2020
    Developing an in vivo CRISPR-interference Screening Resource
  • Weston Park Cancer Charity – Large Grant (£47,840) Co-I 2018 – 2019
    Liquid biopsy-based screening biomarkers
  • NHS Health Education England (£49,660) PI 2018 – 2019
    Genomic Medicine Framework - CPPD modules along with short courses
Teaching activities

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).

Professional activities

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.

Member of the Machine Learning and Natural Language Processing research groups