Prediction of breast cancer risk based on profiling with common genetic variants

BACKGROUND: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. METHODS: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived from relative risk estimates and UK incidence and mortality rates. RESULTS: There was no strong evidence for departure from a multiplicative model for any SNP pair. Women in the highest 1% of the PRS had a three-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio [OR] = 3.36, 95% confidence interval [CI] = 2.95 to 3.83). The ORs for ER-positive and ER-negative disease were 3.73 (95% CI = 3.24 to 4.30) and 2.80 (95% CI = 2.26 to 3.46), respectively. Lifetime risk of breast cancer for women in the lowest and highest quintiles of the PRS were 5.2% and 16.6% for a woman without family history, and 8.6% and 24.4% for a woman with a first-degree family history of breast cancer. CONCLUSIONS: The PRS stratifies breast cancer risk in women both with and without a family history of breast cancer. The observed level of risk discrimination could inform targeted screening and prevention strategies. Further discrimination may be achievable through combining the PRS with lifestyle/environmental factors, although these were not considered in this report.

A strategy-based framework for assessing the flood resilience of cities – the Hamburg case study

Climate change and continuous urbanization contribute to an increased urban vulnerability towards flooding. Only relying on traditional flood control measures is recognized as inadequate, since the damage can be catastrophic if flood controls fail. The idea of a flood-resilient city – one which can withstand or adapt to a flood event without being harmed in its functionality – seems promising. But what does resilience actually mean when it is applied to urban environments exposed to flood risk, and how can resilience be achieved? This paper presents a heuristic framework for assessing the flood resilience of cities, for scientists and policy-makers alike. It enriches the current literature on flood resilience by clarifying the meaning of its three key characteristics – robustness, adaptability and transformability – and identifying important components to implement resilience strategies. The resilience discussion moves a step forward, from predominantly defining resilience to generating insight into “doing” resilience in practice. The framework is illustrated with two case studies from Hamburg, showing that resilience, and particularly the underlying notions of adaptability and transformability, first and foremost require further capacity-building among public as well as private stakeholders. The case studies suggest that flood resilience is currently not enough motivation to move from traditional to more resilient flood protection schemes in practice; rather, it needs to be integrated into a bigger urban agenda.