Risk prediction of breast cancer

UoM administered thesis: Phd

  • Authors:
  • Elke Van Veen

Abstract

Breast cancer is the most common cancer among women and incidence is still rising. The majority of breast cancer is sporadic (non-familial), and a small proportion is familial. Only ~50% of high risk families have a genetic diagnosis at the present time. Currently, the majority of women are unaware of their personal breast cancer risk. Therefore better risk prediction is warranted as many women without a family history could have a high risk of breast cancer and more breast cancers could be prevented. In this study, two approaches were taken to improve risk prediction in the general population and in high risk families without a genetic diagnosis. The predictive ability of 18 single nucleotide variants (SNPs) associated with breast cancer risk was tested in a prospective cohort of 9363 women. Incorporation of both SNPs and mammographic density into a classical risk factor prediction model was performed to assess the improvement of risk prediction. Promoter methylation analysis and upstream sequencing of BRCA1 was performed in a high-risk cohort of 49 families with breast/ovarian cancer. Functional analysis of upstream variants was carried out using saturation genome editing and luciferase assays. Incorporation of SNPs and mammographic density into a classical risk factor model identified more women at high (>5%) and low (T was identified in 2 of 49 high risk families, explaining their familial disease. The exact functional effect of this variant however, is not yet fully explained. Risk prediction was successfully improved with the addition of SNPs and mammographic density to the currently used risk prediction models. Similarly, identification of a novel pathogenic upstream methylation-associated BRCA1 variant in high risk breast cancer families allows better risk prediction in these families. Both approaches may lead to more personalized risk prediction and preventive management of breast cancer.

Details

Original languageEnglish
Awarding Institution
Supervisors/Advisors
Award date1 Aug 2019