About the Project
This PhD in the Ovarian Cancer Programme provides a unique opportunity to test molecular hypotheses across sample sizes of more than 2000 patients and to develop high throughput molecular and image analysis methods, including next-generation sequencing and RNA profiling.
Project Description
High grade serous ovarian carcinoma (HGSOC) is the commonest form of ovarian cancer and mortality has not improved over the past 20 years despite large-scale trials of chemotherapy and targeted agents including antiangiogenic and PARP inhibitors. The defining molecular characteristics of HGSOC are mutations in tumour suppressor genes, with frequent involvement of DNA repair pathways, and extreme DNA structural variation. However in contrast to breast and other cancers, robust prognostic molecular classifications have not been identified.
This project will focus on the "systems pathology" of ovarian cancer using very extensive formalin-fixed paraffin-embedded tissue microarray and germline samples held at the Strangeways Research Laboratory from large scale population-based and clinical trial collections. We have pioneered the development of next generation sequencing (NGS) for analysis of FFPE tissues and involved in international studies combining NGS and Nanostring gene expression profiling. The main hypothesis for this work is that combining prevalent molecular and image phenotypes with germline data and outcomes will uncover much more accurate prognostic signatures to help guide patient care.
This project would be ideal for candidates with a strong numerical or computational background. Applications from individuals with a background in mathematics, biostatistics, physics or computer science are particularly encouraged.
Funding Notes
This is one of 20 projects being advertised by the Cambridge Cancer Centre, a partnership between the University of Cambridge, Cancer Research UK and Cambridge University Hospitals NHS Foundation Trust bringing together academic researchers, clinicians, and industry collaborators in the Cambridge area. Up to 10 awards (supporting both clinical and non-clinical students) will be available. Non-clinical studentships fund the University Composition Fee (Home/EU rate), provide a consumables budget, and a stipend, currently £19,000 per annum. Clinical research fellowships cover salary costs for the fellow, a consumables budget, and funding for the University Composition Fee (at staff rate) for three years.
References
1. Köbel M, Piskorz AM, Lee S, Lui S, LePage C, Marass F, et al. Optimized p53 immunohistochemistry is an accurate predictor of TP53 mutation in ovarian carcinoma. J Pathol: Clin Res. 2016.
2. Piskorz AM, Ennis D, Macintyre G, Goranova TE, Eldridge M, Segui-Gracia N, et al. Methanol-based fixation is superior to buffered formalin for next-generation sequencing of DNA from clinical cancer samples. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO. 2016;27(3):532-9.
3. Köbel M, Madore J, Ramus SJ, Clarke BA, Pharoah PD, Deen S, et al. Evidence for a time-dependent association between FOLR1 expression and survival from ovarian carcinoma: implications for clinical testing. An Ovarian Tumour Tissue Analysis consortium study. British journal of cancer. 2014;111(12):2297-307.
4. Sieh W, Köbel M, Longacre TA, Bowtell DD, deFazio A, Goodman MT, et al. Hormone-receptor expression and ovarian cancer survival: an Ovarian Tumor Tissue Analysis consortium study. The Lancet Oncology. 2013;14(9):853-62.
5. Ali HR, Irwin M, Morris L, Dawson SJ, Blows FM, Provenzano E, et al. Astronomical algorithms for automated analysis of tissue protein expression in breast cancer. British journal of cancer. 2013;108(3):602-12.
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