Breast cancer (BC) is the leading cause of death in UK women aged 35-49. Both the incidence and mortality increase exponentially from the early 30s to the current start of population screening at age 50. Thus, identification of younger women at increased risk of BC will allow enhanced screening approaches to improve early detection and mortality, as has been seen in family history clinics. Young Black women in the UK have a particularly poor BC prognosis. A UK study for women diagnosed with BC under the age of 40, the 5-year distant recurrence free survival (DRFS) in Black women was significantly lower (62.8%) compared to in White women (77.0%), despite comparable treatment  Black women were also more likely to be diagnosed with triple negative BC (TNBC). Furthermore, in those with estrogen receptor (ER)-positive tumours, Black ethnicity was an independent marker of poor DRFS. To address health care disparities between groups from different ethnic backgrounds, it is important to understand these differences in BC outcomes.

These BC differences in the pre-screening age Black population are likely to be multifactorial and will include social, economic and environmental factors. However, recent data show important differences in the mammographic density (MD) and composition of the normal breasts of Black vs. White women. MD refers to the amount of radio-dense tissue on mammography and has a strong positive association with BC risk. Data from 1.28 million women show that MD is strongly associated with ER+ BC in young women. In US studies looking at large populations, Black women were found to be more likely than White to have higher MD, after controlling for BMI and other risk factors. Remarkably, recent studies found that AI deep learning models can predict Black vs White ethnicity from mammogram images alone, suggesting that there are innate differences in tissue structure between Black and White women and that these biological differences may explain different BC incidences. The goal of this project is to measure differences in normal breast tissue and cellular composition between age-matched black and white women and assess whether this can account for differences in risk of breast cancer development. The project will employ state of the art spatial analysis of tissue sections collected from clinical trials, including multiplexed imaging mass cytometry and spatial transcriptomics, coupled with machine learning approaches for data analysis.

Eligibility

Applicants must have obtained or be about to obtain a minimum Upper Second class UK honours degree, or the equivalent qualifications gained outside the UK, in a relevant discipline. Research experience in cancer biology or analysis of ‘omics data sets is desirable.

Before you Apply

Applicants must make direct contact with preferred supervisors before applying. It is your responsibility to make arrangements to meet with potential supervisors, prior to submitting a formal online application.

How to Apply

To be considered for this project you MUST submit a formal online application form – on the application form you must select FBMH Bicentenary PhD Programme - Full-time. If you select the incorrect programme your application cannot be considered. Full details on how to apply can be found on the Bicentenary Website

Your application form must be accompanied by a number of supporting documents by the advertised deadlines. Without all the required documents submitted at the time of application, your application will not be processed and we cannot accept responsibility for late or missed deadlines. Incomplete applications will not be considered.  If you have any queries regarding making an application, please contact our admissions team .

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website.