My research interests lie in the area of translational bioinformatics. Current research projects are focused in high-throughput data analysis, integration with clinical data, databases and software development, particularly for pancreatic cancer and breast cancer.
BCNTB bioinformatics: The next evolutionary step in the bioinformatics of breast cancer tissue banking. Nucleic Acids Res (2018) 46(D1):D1055-D1061. PMID: 29136180
The Pancreatic Expression Database. Nucleic Acids Res (2018) 46(D1):D1107-D1110. PMID: 29059374
SNPnexus: assessing the functional relevance of genetic variation to facilitate the promise of precision medicine. Nucleic Acids Res (2018) 46(W1):W109-W113. PMID: 29757393
A multi-gene signature predicts outcome in patients with pancreatic ductal adenocarcinoma. Genome Medicine (2014) 6(12): 105. PMID: 25587357
Selected relevant ongoing research projects are detailed below:
I lead the IT for the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB) and the bioinformatics for both the PCRFTB and the Breast Cancer Now Tissue Bank (BCNTB). The platforms that we develop bring together the largest collection of multi-dimensional cancer data and allow users to analyse a broad range of specimen/experimental types, including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Ultimately, the aim is to provide the cancer research community with the means to harness the clinical data and molecular findings and create virtual patient models.
My group has a lead in this development by designing SNPnexus to address the data analysis challenge (www.snp-nexus.org). SNPnexus is a powerful platform for understanding a phenotype at a molecular level. SNPnexus has a constantly growing, national and international user community.
We use next-generation sequencing/proteomics analysis to understand the molecular subtypes within matched adjacent normal samples and investigate their distinct prognostic and therapeutic capabilities.
We use next-generation sequencing of matched germline, tumour and serial circulating tumour DNA (ctDNA) samples to explore the clonal evolution of pancreatic cancer, and isolate markers of disease progression, treatment response and acquired resistance.
My team has developed several analytical pipelines, directly applicable to patient data, and the study of the transcriptional/mutational landscapes and evolutionary dynamics of different cancer types to identify therapeutic targets and prognostic biomarkers.
Understanding actinic keratosis and squamous cell carcinoma in the era of whole-exome and -genome sequencing Thomson J, Wang A, Nagano A et al. BRITISH JOURNAL OF DERMATOLOGY (2017) 177(11) 15-16
ALLORESPONSES OF HUMAN T-CELLS FROM ADULT PERIPHERAL BLOOD AND UMBILICAL CORD BLOOD ARE DIFFERENTIALLY IMPACTED BY LENALIDOMIDE - IMPLICATIONS FOR AHSCT Besley C, Kotsiou E, Petty R et al. HAEMATOLOGICA (2017) 102(11) 638-638
The Sharing Experimental Animal Resources, Coordinating Holdings (SEARCH) Framework: Encouraging Reduction, Replacement, and Refinement in Animal Research Morrissey B, Blyth K, Carter P et al. PLOS Biology 15(13) e2000719-e2000719
Phosphoproteomic comparison of Pik3ca and Pten signalling identifies the nucleotidase NT5C as a novel AKT substrate Moniz LS, Surinova S, Ghazaly E et al. Scientific Reports (2017) 7(7)
Alloresponses of Human T-Cells from Adult Peripheral Blood and Umbilical Cord Blood Are Differentially Impacted By Lenalidomide Besley C, Kotsiou E, Petty R et al. Blood (2016) 128(13) 5714-5714
Pancreatic cancer tissue banks: where are we heading? Balarajah V, Ambily A, Dayem Ullah AZ et al. Future Oncol (2016) 12(2) 2661-2663
Exosomes derived from embryonal and alveolar rhabdomyosarcoma carry differential miRNA cargo and promote invasion of recipient fibroblasts. Ghayad SE, Rammal G, Ghamloush F et al. Sci Rep (2016) 6(2) 37088
SEARCHBreast: An online resource designed to increase the efficiency of using materials derived from breast cancer studies in animals. Speirs V, Morrisey B, Holen I et al. The Journal of pathology (2016) 240(18) 120
Introducing SEARCHBreast: a virtual resource to facilitate sharing of surplus animal material developed for breast cancer research. Morrissey B, Holen I, Chelala C et al. NPJ Breast Cancer (2016) 2(1) 16020-16020
SEARCHBreast: A new online resource to make surplus material from in vivo models of breast cancer visible and accessible to researchers Morrissey B, Blyth K, Carter P et al. Breast Cancer Research (2016) 18(7)For additional publications, please click here
In 2002, I was awarded a PhD in Computational Biology/Radiation Biology from Paris-Sud University/Curie Institute and a degree in Structural Bioinformatics from Paris Descartes University. My first post-doctoral experience at the National Centre for Scientific Research (CNRS) involved the development of novel tools to gather information for automated analysis of genome maps and distribution study of the disease-related genes.
In 2004, I joined the Pasteur Institute in Paris to work on large-scale analysis of genetic variation, integration with clinical data and the association with type 1 diabetes. I worked on developing tools to transfer, integrate and analyse the genetic, genomic and proteomic data. My later studies have centred on cancer research.
In 2006, I joined Barts Cancer Institute (BCI) driven by a high motivation to translate my work from a substantial basic/computational research platform into a translational/patient setting. I established an interdisciplinary research team with complementary expertise in translational bioinformatics, clinical informatics, computer science, molecular biology, databases and software engineering. My research is very collaborative with clinical and basic scientists at the Barts & The London School of Medicine (SMD) and Barts Health.
My most challenging, and most rewarding, role is being a mother of two young boys. Not surprisingly, I am very passionate about communicating my love of data science to young children. With the help of my team, we have engaged with primary school children to encourage interest in data science and ultimately inspire the next generation of health data researchers.