Predicting and understanding ovarian cancer risk

Background

Ovarian cancer has a poor prognosis which is partly attributed to its usually late presentation. Some risk factors have been identified, including genetic variants and reproductive-related factors, and risk prediction models have been developed incorporating such factors to predict risk of a future diagnosis. The aim of this project will be to explore aspects of explaining and predicting risk in different populations. Specific objectives may include:

1. To evaluate models for predicting risk of ovarian cancer in the Million Women Study and other large cohort studies.
2. To assess the contribution of different types of data to risk, e.g. how much rare and common genetic variation, family history, reproductive, lifestyle and other factors contribute to risk.
3. To explore differences by histological subtypes.
4. To identify features from digital pathology studies and explore associations of risk factors with such features.

Methodological aspects relating to any of these may be part of the project if of interest.

research experience, research methods and skills training

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

There are various opportunities for training within the department and externally. There will be opportunity to work with diverse teams in the department with a range of backgrounds and skills, as well as regular research activities such as seminars. 

PROSPECTIVE STUDENT

The ideal candidate will have a Bachelor’s/Master’s degree in a relevant area (e.g. statistics/epidemiology/public health/medicine).