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.
+44 114 215 9102
Full contact details
Department of Neuroscience
385a Glossop Road
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
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.
- Whole blood RNA profiles associated with pulmonary arterial hypertension and clinical outcome. American Journal of Respiratory and Critical Care Medicine. View this article in WRRO
- Genomic loci susceptible to systematic sequencing bias in clinical whole genomes. Genome Research, 30(3), 415-426.
- Consensus Genomic Subtypes of Muscle-invasive Bladder Cancer: A Step in the Right Direction but Still a Long Way To Go. European Urology, 77(4), 434-435.
- Epidemiological pathology of Aβ deposition in the ageing brain in CFAS : addition of multiple Aβ-derived measures does not improve dementia assessment using logistic regression and machine learning approaches. Acta Neuropathologica Communications, 7(1), 1-12. View this article in WRRO
- Defining subpopulations of differential drug response to reveal novel target populations. npj Systems Biology and Applications, 5(1). View this article in WRRO
- Looking beyond the hype : applied AI and machine learning in translational medicine. EBioMedicine. View this article in WRRO
- Somatic alteration burden involving non-cancer genes predicts prognosis in early-stage non-small cell lung cancer. Cancers, 11(7). View this article in WRRO
- Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen. Nature Communications, 10. View this article in WRRO
- Characterization of Distinct Populations of Carcinoma-Associated Fibroblasts from Non–Small Cell Lung Carcinoma Reveals a Role for ST8SIA2 in Cancer Cell Invasion. Neoplasia, 21(5), 482-493. View this article in WRRO
- Cell-Specific Computational Modeling of the PIM Pathway in Acute Myeloid Leukemia. Cancer Research, 77(4), 827-838. View this article in WRRO
- Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors. International Journal of Cancer, 140(3), 662-673. View this article in WRRO
- Rational Selection of Syngeneic Preclinical Tumor Models for Immunotherapeutic Drug Discovery. Cancer Immunology Research, 5(1), 29-41. View this article in WRRO
- SOX2 and PI3K Cooperate to Induce and Stabilize a Squamous-Committed Stem Cell Injury State during Lung Squamous Cell Carcinoma Pathogenesis. PLoS Biology, 14(11). View this article in WRRO
- Combenefit: an interactive platform for the analysis and visualization of drug combinations. Bioinformatics, 32(18), 2866-2868. View this article in WRRO
- Cetuximab Inhibits T790M-Mediated Resistance to Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor in a Lung Adenocarcinoma Patient-Derived Xenograft Mouse Model. Clinical Lung Cancer, 17(5), 375-383.e2.
- Identification of Pharmacodynamic Transcript Biomarkers in Response to FGFR Inhibition by AZD4547. Molecular Cancer Therapeutics, 15(11), 2802-2813. View this article in WRRO
- Abstract 4186: Syngenomic fingerprint: the biomic characterization of the mouse syngeneic tumor models. Tumor Biology.
- Integrin α11β1 regulates cancer stromal stiffness and promotes tumorigenicity and metastasis in non-small cell lung cancer. Oncogene, 35(15), 1899-1908. View this article in WRRO
- Clinical Utility of Patient-Derived Xenografts to Determine Biomarkers of Prognosis and Map Resistance Pathways in EGFR-Mutant Lung Adenocarcinoma. Journal of Clinical Oncology, 33(22), 2472-2480.
- Cell surface marker profiling of human tracheal basal cells reveals distinct subpopulations, identifies MST1/MSP as a mitogenic signal, and identifies new biomarkers for lung squamous cell carcinomas. Respiratory Research, 15(1).
- Integrated Omic analysis of lung cancer reveals metabolism proteome signatures with prognostic impact. Nature Communications, 5(1).
- Transcription factor and chromatin features predict genes associated with eQTLs. Nucleic Acids Research, 41(3), 1450-1463.
- Transcription factor co-localization patterns affect human cell type-specific gene expression. BMC Genomics, 13(1), 263-263.
- Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics. BMC Systems Biology, 3(1), 118-118.
- The complete genome of Rhodococcus sp. RHA1 provides insights into a catabolic powerhouse. Proceedings of the National Academy of Sciences, 103(42), 15582-15587.
- Practical Challenges and Recommendations of Filter Methods for Feature Selection. Journal of Information & Knowledge Management, 2040019-2040019.
- Clinical long-read sequencing of the human mitochondrial genome for mitochondrial disease diagnostics. View this article in WRRO
- Loss of Canonical Smad4 Signaling Promotes KRAS Driven Malignant Transformation of Human Pancreatic Duct Epithelial Cells and Metastasis. PLoS ONE, 8(12), e84366-e84366.
- Interoperability with Moby 1.0--It's better than sharing your toothbrush!. Briefings in Bioinformatics, 9(3), 220-231.
Conference proceedings papers
- Neo-antigen Prediction in Bladder Cancer. JOURNAL OF PATHOLOGY, Vol. 245 (pp S12-S12)
- Prognostic significance of soluble and ECM associated VEGFA isoforms in high-grade serous ovarian cancer. International Journal of Experimental Pathology, Vol. 100(4)
- 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
- The Brain Tumour Charity (£119,936) Co-I 2019 – 2022
- 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.