Dr Jun Wang

PhD
Senior Lecturer
Group Leader
Twitter
Research Focus

I have broad research interests and experience in bioinformatics, cancer genomics and data analytics. These research areas mainly involve developing and applying bioinformatics and computational approaches to analyse large-scale cancer datasets to uncover novel diagnostic and prognostic biomarkers. I also lead the Cancer Research UK Barts Centre Bioinformatics Core Facility.

Key Publications

ACSNI: An unsupervised machine-learning tool for prediction of tissue-specific pathway components using gene expression profiles. Patterns (2021) 2(6), 100270. PMID: 34179848

The genomic landscape of actinic keratoses. J Invest Dermatol (2021) 141(7):1664-1674.e7. PMID: 33482222

Applications and analysis of targeted genomic sequencing in cancer studies. Computational and Structural Biotechnology Journal (2019) 17, 1348-1359. PMID: 31762958

The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature. Nature Commun (2018) 9(2) 3667. PMID: 30202019

Major Funding
  • 2021-2024- Barts Charity Strategic Award, Barts Centre for Squamous Cancer, Co-applicant, £2.64M in total, £160,000 to JW
  • 2019-2023- Barts Charity Large Project Award, Investigation of the immune environment of keratinocyte skin cancer progression with implications for risk prediction and targeted prevention, Co-Investigator, £498,534.52 in total, £166,000 awarded to JW
  • 2018-2023- Cancer Research UK Accelerator Award, Early detection and intervention: Understanding the mechanisms of transformation and hidden resistance of incurable haematological malignancies, Co-investigator, £1.6M in total, £149,000 to JW
Other Activities
  • Turing Fellow, The Alan Turing Institute (2021-)
  • Member, The Genetics Society (2021-)
  • External examiner, MSc Bioinformatics (Teesside University) (2021-)
  • Collaborator, The UK Keratinocyte Cancer Collaborative (UKKCC) (2021-)
  • Member, Barts Cancer Centre Patient and Public Involvement Advisory Group (2019-)
Research

My main research interests lie in developing and applying bioinformatics and computational approaches to analyse large-scale cancer datasets to uncover novel diagnostic and prognostic features. In particular, I am interested in applying machine learning / AI algorithms to integrate multi-omics and clinicopathological data to derive diagnostic and prognostic tools for patient stratification.

I also lead the CRUK Barts Centre Bioinformatics Core Service.

Biomedical science, especially cancer research, is increasingly data driven, as new bioanalytical techniques deliver ever more data about DNA, RNA, proteins, metabolites and the interactions between them, in the whole tissue and single-cell levels. Given the increasing amount of omics datasets (big-data), the challenges are in how to analyse large-scale datasets and interpret the results accurately and thoroughly, and to identify “driver” events and predictive biomarkers in tumour development and progression.

Our research interests include the following:

Cancer genomics and evolution

Focusing on large-scale multi-omics datasets, we develop analytic pipelines and identify novel driver events, molecular subtypes, and diagnostic / prognostic signatures in cancer development and progression based on machine learning and data integration techniques. Using bulk tissue RNA-seq data, we are also interested in investigating immune and stromal landscape and signatures for patient subgrouping and stratification. Currently we are working on multi-omics datasets of cutaneous and oesophageal squamous cell carcinoma. We also investigate the clonal evolutionary patterns of these tumours and further understand how clonal / subclonal architecture affects clinical features of patients.

Noncoding sequence variants and RNA genes in cancer

Using publicly available whole-genome, ChIP-seq and RNA-seq data, we investigate functionally important noncoding mutations and dysregulated long noncoding RNAs in pancreatic and ovarian cancer. Using big-data and bioinformatic approaches, we first identify top novel candidates that are then taken to the lab for further in vitro validation using high-through screening (e.g., STARR-seq) and CRISPR/Cas9.

Single cell analytics

We have constructed a cross-package toolkit, named IBRAP (https://github.com/connorhknight/IBRAP), that provides the most comprehensive workflow from data pre-processing to automatic annotation of cell types, and enables users to interchange analytical components and individual methods. Benchmarking metrices are provided that distinguishes pipeline performance(s), thus providing dataset-specific pipeline production for single-cell studies. Currently, we are implementing IBRAP to construct normal reference maps using publicly available single cell data.

Computational histopathology and imaging analysis using AI

Despite recent advances in understanding the molecular pathogenesis of many cancers, disease assessment is still based on clinical and histopathological staging, with few objective prognostic biomarkers. A rapid, simple and cost-effective tool that augments clinicopathologic staging and allows clinicians to stratify patients according to their risk of progression is a priority for translational research.

Currently we are developing deep learning-based resources to automatically extract core histological features from digitised whole slide images and map these to molecular and clinical features in cutaneous and oesophageal squamous cell carcinoma. We aim to create a risk stratification tool which can be incorporated into routine pathology workflow, significantly improving patient outcomes.

Other Activities
  • Turing Fellow, The Alan Turing Institute (2021-)
  • Member, The Genetics Society (2021-)
  • External examiner, MSc Bioinformatics (Teesside University) (2021-)
  • Collaborator, The UK Keratinocyte Cancer Collaborative (UKKCC) (2021-)
  • Member, Barts Cancer Centre Patient and Public Involvement Advisory Group (2019-)
Major Funding
  • 2022-2025- Barts Centre for Squamous Cancer Alexandra Carrell PhD Studentship, £80,000
  • 2021-2024- Barts Charity Strategic Award, Barts Centre for Squamous Cancer, Co-applicant, £2.64M in total, £160,000 to JW
  • 2019-2022- Sanofi and Regeneron Investigator Sponsored Studies (ISS) Program, Personalised medicine for immunotherapy of cutaneous squamous cell carcinoma (PERMEDID), Co-Investigator £639,037.55, £70,000 to JW
  • 2019-2023- Barts Charity Large Project Award, Investigation of the immune environment of keratinocyte skin cancer progression with implications for risk prediction and targeted prevention, Co-Investigator, £498,534.52 in total, £166,000 awarded to JW
  • 2018-2023- Cancer Research UK Accelerator Award, Early detection and intervention: Understanding the mechanisms of transformation and hidden resistance of incurable haematological malignancies, Co-investigator, £1.6M in total, £149,000 to JW
  • 2018-2022- MRC Doctoral Training Partnership (DTP) Studentship, £83,280
  • 2018-2021- Academy of Medical Sciences Springboard Award, Characterisation of functional non-coding mutations in follicular lymphoma, £100,000
  • 2017-2022- Cancer Research UK, Programme Award, Improving outcome for patients with Poor Risk Acute Myeloid Leukaemia, £1.9M in total, Co-investigator
  • 2017-2021- MRC Doctoral Training Partnership (DTP) Studentship, £83,280
Recent Publications

Longitudinal expression profiling identifies a poor risk subset of patients with ABC-type Diffuse Large B Cell Lymphoma. Bewicke-Copley F, Korfi K, Araf S et al. Blood Adv (2022) (2)
https://www.ncbi.nlm.nih.gov/pubmed/35947123

ERG activity is regulated by endothelial FAK coupling with TRIM25/USP9x in vascular patterning D'Amico G, Fernandez I, Gómez-Escudero J et al. Development (Cambridge, England) (2022) 149(7)

Characterization of the Intra-tumoral B Cell Immunoglobulin Repertoire Is of Prognostic Value for Esophageal Squamous Cell Carcinoma Wang Z, Cheng Z, Lu S et al. Frontiers in Immunology (2022) 13(7)

Decosus: An R Framework for Universal Integration of Cell Proportion Estimation Methods Anene CA, Taggart E, Harwood CA et al. Frontiers in Genetics (2022) 13(7)

Disruption of pancreatic stellate cell myofibroblast phenotype promotes pancreatic tumor invasion Murray ER, Menezes S, Henry JC et al. Cell Reports (2022) 38(7)

Genome-wide whole-blood transcriptome profiling across inherited bone marrow failure subtypes Walne AJ, Vulliamy T, Bewicke-Copley F et al. Blood Advances (2021) 5(7) 5360-5371

Vitamin B5 and Succinyl-CoA Improve Ineffective Erythropoiesis in SF3B1 Mutated Myelodysplasia Mian S, Philippe C, Maniati E et al. Blood (2021) 138(10) 324-324

Erratum: mTOR-dependent translation amplifies microglia priming in aging mice (Journal of Clinical Investigation (2021) 131:1 (1–16) DOI: 10.1172/JCI132727) Keane L, Antignano I, Riechers SP et al. Journal of Clinical Investigation (2021) 131(7)

Optogenetic modeling of human neuromuscular circuits in Duchenne muscular dystrophy with CRISPR and pharmacological corrections Paredes-Redondo A, Harley P, Maniati E et al. Science Advances (2021) 7(1)

KDM5 inhibition offers a novel therapeutic strategy for the treatment of KMT2D mutant lymphomas Heward J, Konali L, D'Avola A et al. Blood (2021) 138(7) 370-381

For additional publications, please click here
Team

Postdoctoral Bioinformaticians

PhD Students

  • Sheila Barasa
  • Connor Knight
  • Yue Chen

Academic Clinical Fellow

  • Dr Emilia Peleva

Former lab members

  • Dr Chinedu A. Anene (2020-2021), Lecturer at London South Bank University, honorary lecturer at BCI, Queen Mary University of London
  • Dr Faraz Khan (2019-2022), Computational Immunologist at Quell Therapeutics
  • Mr Firat Uyulur (2019-2021), Bioinformatician at Achilles Therapeutics
  • Dr Minal Patel (2017-2021), post-doc at National Cancer Institute, NIH, USA
Biography

I received my first degree in biological engineering at Shanghai Jiao Tong University. This was followed by an MSc degree of quantitative genetics and genome analysis, and a PhD in evolutionary genetics studying comparative genomics and evolution of noncoding sequences in Drosophila, both at the University of Edinburgh. I then joined Rothamsted Research as a postdoc working on plant genomics and genetic linkage mapping as part of the international Brassica rapa genome project. I moved to Barts Cancer Institute, Queen Mary University of London, as a bioinformaticist in 2010 to work on cancer genomics and biomarker discovery as part of the bioinformatics core. I became a Lecturer in Bioinformatics and group leader in 2016, and have also been leading the CRUK Barts Centre Bioinformatics Core Facility since 2018. I was promoted to Senior Lecturer in 2019.

I am Programme Director for the Cancer Genomics & Data Sciences MSc Programme at BCI, Queen Mary University of London.
Find out more about the programme

Address

Barts Cancer Institute,
Queen Mary University of London,
Charterhouse Square,
London EC1M 6BQ

Email address: bci-admin@qmul.ac.uk
Telephone: +44 20 7882 5555

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