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.
Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer. BMC Cancer (2022) 22(1):369. PMID: 35392854
COVID-19 in patients with hepatobiliary and pancreatic diseases: a single-centre cross-sectional study in East London. BMJ Open (2021) 11(4):e045077. PMID: 33875444
Characterization of four subtypes in morphologically normal tissue excised proximal and distal to breast cancer. NPJ Breast Cancer (2020). 6:38. PMID: 32885042
SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update). Nucleic Acids Res (2020) 48(W1):W185-W192. PMID: 32496546
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.
Personalized neoantigen viro-immunotherapy platform for triple-negative breast cancer Brito Baleeiro R, Liu P, Chard Dunmall LS et al. Journal for ImmunoTherapy of Cancer (2023) 11(7)
A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma Phillips MM, Pavlyk I, Allen M et al. Pharmacological Reports (2023) 75(7) 570-584
Chairwomen: Diagnostic studies for pancreatic cancer Chelala C, Costello-Goldring E Pancreatology (2023) 23(10) e2
Correction: A role for macrophages under cytokine control in mediating resistance to ADI-PEG20 (pegargiminase) in ASS1-deficient mesothelioma (Pharmacological Reports, (2023), 75, 3, (570-584), 10.1007/s43440-023-00480-6) Phillips MM, Pavlyk I, Allen M et al. Pharmacological Reports (2023) 75(7) 753
541 Gene set enrichment analysis identifies biological networks associated with skin aging in a large Japanese population: Data from the Nagahama cohort Latreille J, Thorn G, Jdid R et al. Journal of Investigative Dermatology (2023) 143(10) s93
902 Development of a web-based platform to facilitate research to uncover the skin ageing mechanism Oscanoa J, Latreille J, Le Clerc S et al. Journal of Investigative Dermatology (2023) 143(10) s154
Dynamic Biobanking for Advancing Breast Cancer Research Abdollahyan M, Gadaleta E, Asif M et al. Journal of Personalized Medicine (2023) 13(7)
Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study Zardab M, Balarajah V, Banerjee A et al. Cancers (2023) 15(7)
A survey on clinical natural language processing in the United Kingdom from 2007 to 2022 Wu H, Wang M, Wu J et al. npj Digital Medicine (2022) 5(7)
Longitudinal profiling of circulating tumour DNA for tracking tumour dynamics in pancreatic cancer Sivapalan L, Thorn GJ, Gadaleta E et al. BMC Cancer (2022) 22(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.