Breast cancer (BC) is the most common cause of female cancer death worldwide. Incidence and mortality rates rise exponentially from age 30-50 (figure; CRUK 2021) and BC is the most common of any cause of death in women in this age group.~The reasons for this dramatic increase in incidence in young women are not well understood and contrast with other endocrine sensitive organs (endometrium and ovary) in which cancer rates increase much later in life. Cervical cancer mortality reduced dramatically, including in young women, following the introduction of widespread screening starting in women in their 20s. This was aided by the identification of key risk factors (sexual activity and HPV infection), direct access to the cervical epithelium and an identifiable premalignant stage. In contrast, in the UK, a strong family history (FH) of BC is the only trigger for BC risk assessment and the opportunity for women aged under 50 to access genetic testing, enhanced screening and primary prevention programmes (NICE 2013). However, in women diagnosed with breast cancer under the age of 40 at least 65% do not have a FH, the proportion increasing further in those diagnosed in older age cohorts (Copson 2018; Eccles 2015).~In addition, BC in young women is more frequently lethal, due to a combination of later stage at presentation, due in part to the lack of screening programmes, and a greater proportion of women developing more aggressive BC subtypes (Colleoni 2002; Paik 2006). The reliance on FH belies the progress over recent decades in the identification of additional BC risk factors including those related to reproductive/lifestyle influences, polygenic risk scores (PRS) and mammographic density (MD) and their incorporation into robust risk prediction algorithms.~The Predicting Risk Of Cancer At Screening (PROCAS NIHR Ref: RP-PG-0707-10031) study recruited over 58,000 women (aged 47-73y) from the Greater Manchester National Health Service Breast Screening Programme (NHSBSP) and showed that it is possible to accurately estimate a woman's individual risk of developing BC through self-report questions and assessment of MD and PRS (van Veen 2018). Using FH alone, only 3.7% of women were identified as being at moderate to high risk of BC according to NICE guidelines (NICE 2017) whereas the comprehensive approach above classified 18% as at least moderate risk and with excellent calibration (observed to expected OR 0.98; 95%CI 0.69-1.28; van Veen 2018). At least 30% of the BC's across the whole population developed in this cohort, a value that exceeded 40% when additional validated single nucleotide polymorphisms (SNPs) were incorporated into the PRS (Brentnall 2020). Women in the moderate/high risk groups can then be offered enhanced screening with predicted improvements in survival (Evans 2014) and preventive medications that result in fewer BC and are cost saving to the NHS (NICE 2017). A follow-on study has been developed to assess the feasibility and acceptability of integrating this approach into the NHSBSP (French 2020).~The current study has been designed to test whether a similar approach is acceptable in women aged 30-39 years and through use of a case control design will assess the impact of mammographic density on BC risk in women of this age. To facilitate the approach, we have developed a web-based application (WBA) based on the Tyrer-Cuzick (T-C) algorithm (see section 1.2 below) and an Automated Low Dose Risk Assessment Mammography (ALDRAM) technique.
The overarching aim of the proposed research is to develop a comprehensive breast cancer risk assessment strategy for women aged 30-39 years.~There are three main objectives to the study.~Objective 1 - Mammographic Density and Risk To define the magnitude of BC risk associated with MD in women aged 30-39 and facilitate its incorporation into risk prediction models.~Objective 2 - Psychological impact~To examine the feasibility of a strategy to offer breast cancer risk-assessment to diverse ethnic and socioeconomic populations of women in their 30s, assessing:~Potential benefits and harms~Impact on health inequalities~Acceptability~Objective 3 - DNA Methylation substudy To explore the potential of DNA Methylation (DNAme) signatures from self-obtained cervical samples to further refine risk prediction algorithms