Professor Jun Wang
Group Leader
Overview
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.
Team
Kevin Augustsson
PhD Student
Dr Katie Baird
Bioinformatician
Bernhard Finke
PhD Student
Dr Eleni Maniati
Lead Bioinformatician
Dr Ankit Patel
Teaching Fellow - Bioinformatics
Dr Emilia Peleva
Clinical Research Fellow
Benjamin Roche
PhD Student
Wendy Tran
Bioinformatician
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 Barts Cancer Institute 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.
Key Publications
The integrated molecular and histological analysis defines subtypes of esophageal squamous cell carcinoma. Nat Commun (2024) 15(1):8988. PMID: 39419971
Transcriptomic analysis of cutaneous squamous cell carcinoma reveals a multi-gene prognostic signature associated with metastasis. J Am Acad Dermatol (2023) 89(6):1159-1166. PMID: 37586461
IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline. Brief Bioinform (2023) bbad061. PMID: 36847692
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
The genomic landscape of cutaneous SCC reveals drivers and a novel azathioprine associated mutational signature. Nat Commun (2018) 9(2):3667. PMID: 30202019
See recent publicationsMajor Funding
- 2025-2028 – Cancer Research UK Biomarker Project Award. Evaluation and refinement of a multigene prognostic signature and digital histology AI features for the prediction of metastasis in cutaneous squamous cell carcinoma. PI. £290,000
- 2023-2027 – Wellcome HARP Clinical PhD Fellowship. Improving outcomes for cutaneous squamous cell carcinoma in organ transplant recipients. PI. £316,000
- 2023-2026 – British Association of Dermatologists and British Skin Foundation. UK Keratinocyte Cancer Collaborative – Generating an Atlas for Skin Cancer. co-PI / Bioinformatics Lead. £300,000 in total. £123,000 to JW
- 2023-2027 – BBSRC/UKRI AI for Drug Discovery PhD Studentship. Predicting metastasis and drug response in skin cancer using multimodal deep learning. PI. £120,000
- 2022-2026 – Barts Centre for Squamous Cancer Alexandra Carrell PhD Studentship. PI, £105,000
- 2021-2028- Barts Charity Strategic Award, Barts Centre for Squamous Cancer, Co-applicant, £2.64M in total, £160,000 to JW
- 2019-2025 – Sanofi and Regeneron Investigator Sponsored Studies (ISS) Program. Personalised medicine for immunotherapy of cutaneous squamous cell carcinoma (PERMEDID). Co-investigator. £681k in total, £108,000 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-)
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 and Professor in 2025.
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.
Related News
New AI model sheds light on high-risk skin cancer: Q&A with the authors
Researchers have developed an artificial intelligence (AI) tool that could help doctors identify which skin cancers are most likely to spread. We spoke to Professor Jun (Alex) Wang, a group leader at BCI, and Dr Emilia Peleva, a Clinical Research Fellow and dermatologist in his team, about their new study, published in npj Precision Oncology.
General News 30 September 2025
Shining a Spotlight on AI Research at the Barts Cancer Institute
We explore Nobel-Prize-winning research and the theory behind AI, and speak with Dr Vivek Singh, Dr Oscar Maiques and Professor Claude Chelala, three researchers using cutting-edge AI in their work at the BCI.
General News 16 December 2024
International study reveals four distinct types of oesophageal cancer
New research reveals that the most common type of oesophageal cancer, oesophageal squamous cell carcinoma (ESCC), is composed of four distinct subtypes—each of which may benefit from different treatment approaches.
General News 23 October 2024
New tool predicts risk of skin cancer spread more accurately than human inspection
These results could aid the treatment of individuals most at risk of aggressive skin cancer.
General News 12 October 2023
Dissecting complex biological pathways with machine learning
We spoke with Group Leader Dr Jun Wang and Postdoctoral Researcher Dr Anthony Anene from Barts Cancer Institute’s Centre for Cancer Genomics & Computational Biology about their most recent publication. Published in Patterns, the paper describes the development of a machine-learning tool called ACSNI that can be used to predict tissue-specific pathway components from large biological datasets.
General News 19 July 2021
Study links widely-used drug azathioprine to skin cancers
A drug used to treat inflammatory bowel disease and arthritis, and prevent organ rejection in transplant patients, has been identified as an important contributor to skin cancer development in a study by researchers from Queen Mary University of London, including our Barts Cancer Research UK Centre (BCC) Bioinformatics team, the University of Dundee and the Wellcome Sanger Institute.
General News 14 September 2018