MRC Precision Medicine DTP: Molecular pathology data science studies of breast cancer

BACKGROUND: Datasets linked to molecular phenotypes are desperately needed to provide academia and industry the real-world evidence needed for discovery, validation and implementation of new biomarkers for improved cancer outcomes. This DTP project aims to identify and assess how new biomarkers will improve the effectiveness and productivity of National Health Services (NHS) to prevent, detect and treat breast cancers earlier--thereby reducing mortality and morbidity outcomes. Future cancer surveillance, diagnosis and treatment decisions are increasingly emphasising molecular genetics of blood, benign tissues and tumours regardless of anatomical location of where a cancer is diagnosed. This interdisciplinary research project, would capitalise on Scotland’s longitudinal national health record datasets, tissue banks and epidemiology studies, to investigate how molecular genetics, and pathology imaging data might improve earlier detection and precision medicine in a productive and cost-effective manner.

Gene expression data is now publicly available for over 10,000 tumors; however the majority of studies are relatively small, representing 40-300 patients. Little effort has been made to-date to consider how representative these studies are of the different populations and to what extent the incidence of tumours with particular molecular subtypes or prognostic signatures varies and whether this is changing over time. This project will bring together expertise in gene expression analysis (Sims) and breast cancer subtyping and prognostic signatures (Sims and Tobin) with digital pathology and epidemiology (Figueroa), leveraging publicly available data against existing high-quality cohorts from Edinburgh and the Karolinska.

AIMS:
1. Compare how variable and representative the many publicly available primary breast cancer gene expression profiling datasets (microarrays, RNAseq, gene panel arrays) are with cancer registry statistics, relative to local institution specific cohorts from UK and Sweden together representing nearly 15,000 patients. These datasets will be interrogated to assess how the calling of molecular subtypes and prognostic signatures relates to epidemiologic risk factors, treatment response, metastasis and survival, varies across different countries over time. The amount of sample annotation information (such as clinicopathological features, length of follow-up, age, race, BMI) available for each cohort will vary, but we hope to gain an overall picture of general trends and differences between populations over time to inform public health, prevention and treatment.

2. Quantify at the population level how many women are set to benefit from improved stratification of breast cancer using improved molecular subtyping, with the goal of defining the populations that might most benefit from prevention, screening or treatment interventions in the UK and abroad. This will be achieved by analyzing immunohistochemistry and electronic medical records data for over 70,000 breast cancer cases diagnosed since 1997 in Scotland. Collation of this data has been funded with a recent Wellcome Trust Seed award. National dataset has been ethically approved and is already available for analysis. We aim to incorporate mammography imaging data from the Picture archiving system of Scotland to this in 2019/2020.
3. Data from specific aim two will also be used to develop a unique and powerful resource using 1600 samples from two five-year periods (1997-2001 and 2007-2011) which have multi-level ‘-omic, pathology data allowing us to evaluate changes in subtypes and outcomes using current and potential molecular and image-based screening, diagnostic, subtyping and prognostic methods.

TRAINING OUTCOMES:
• Molecular epidemiology and use of bioinformatics in population-based datasets
• Analysis of digital pathology images using automated image analysis algorithms for which Dr. Figueroa has worked on with Breast Cancer Consortium, along with molecular data and medical informatics
• The student will become skilled in a range of computational and statistical analysis techniques. Through joint supervision within the Usher Institute, Edinburgh Cancer Research Centre and the Karolinska Institute (a visiting fellowship would be possible) the student will gain expertise in oncology, digital and molecular pathology and epidemiology.
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This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location:

http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

Please note, you must apply to one of the projects and you should contact the primary supervisor prior to making your application. Additional information on the application process if available from the link above.

For more information about Precision Medicine visit:

http://www.ed.ac.uk/usher/precision-medicine