Personalized early detection and prevention of breast cancer: ENVISION consensus statement

Abstract

The European Collaborative on Personalized Early Detection and Prevention of Breast Cancer (ENVISION) brings together several international research consortia working on different aspects of the personalized early detection and prevention of breast cancer. In a consensus conference held in 2019, the members of this network identified research areas requiring development to enable evidence-based personalized interventions that might improve the benefits and reduce the harms of existing breast cancer screening and prevention programmes. The priority areas identified were: 1) breast cancer subtype-specific risk assessment tools applicable to women of all ancestries; 2) intermediate surrogate markers of response to preventive measures; 3) novel non-surgical preventive measures to reduce the incidence of breast cancer of poor prognosis; and 4) hybrid effectiveness-implementation research combined with modelling studies to evaluate the long-term population outcomes of risk-based early detection strategies. The implementation of such programmes would require health-care systems to be open to learning and adapting, the engagement of a diverse range of stakeholders and tailoring to societal norms and values, while also addressing the ethical and legal issues. In this Consensus Statement, we discuss the current state of breast cancer risk prediction, risk-stratified prevention and early detection strategies, and their implementation. Throughout, we highlight priorities for advancing each of these areas.

Fig. 1. A schematic outlining a personalized approach to early detection and prevention of breast cancer.

Women entering a personalized early detection programme would initially be assessed using a validated tool to determine their estimated risk of breast cancer. Subsequently, the women would be stratified into appropriate risk groups such that they can receive tailored interventions. This approach might mean that some women start mammographic screening at a younger age, have different screening intervals or have supplemental screening with another imaging modality, such as MRI. Women deemed to be at higher risk of breast cancer could, in addition, be offered prophylactic treatment. A healthy lifestyle would be recommended to all women, independent of risk level.

Fig. 2. Risk-stratified early detection and prevention programmes as complex adaptive systems.

Various questions will define the risk-stratified programme, including which risk factors to include in risk assessments, what risk threshold to use for risk stratification, how many risk groups to have, when to do risk assessments, how often to screen and to whom screening should be offered, as well as which interventions should be used in individuals deemed to be at high risk. Decision-making regarding these questions will be influenced by the research evidence, the available resources, the health-care setting and societal values, preferences and social norms. The choices made in addressing each of these questions will determine whether the programme will be effective in reducing cancer-specific death and improving the benefit–harm balance of screening and be cost-effective, acceptable, accessible and feasible to implement. Dynamic interactions exist between each of these factors, and thus a change in one factor affects all others. Hence, the importance of a holistic, ‘systems thinking’ approach.

Fig. 3. Overview of personalized risk reduction and breast cancer prevention paradigms.

Various risk factors contribute to field defects in breast tissues that favour the development of breast cancer. The presence of such field defects can be assessed using biomarkers and/or imaging to guide personalized prevention strategies, the success of which can be monitored on an ongoing basis through intermediate surrogates (for example, reduction or resolution of the field defect) that reflect the ultimate goal of a decreased incidence of breast cancers with features indicative of a poor prognosis.

Fig. 4. Implementation of risk-stratified early detection and prevention programmes in a learning health-care system.

The schematic illustrates the various multilevel interactions between the different components needed for the implementation of risk-stratified programmes for the early detection and prevention of cancer. The ultimate goal is an improvement in population health outcomes. To achieve this goal, the process has to be iterative within a learning health-care system.