Between 30% and 70% of patients with breast cancer have pre-existing chronic conditions, and more than half are on long-term non-cancer medication at the time of diagnosis. Preliminary epidemiological evidence suggests that some non-cancer medications may affect breast cancer risk, recurrence, and survival. In this nationwide cohort study, we assessed the association between medication use at breast cancer diagnosis and survival. We included 235,368 French women with newly diagnosed non-metastatic breast cancer. In analyzes of 288 medications, we identified eight medications positively associated with either overall survival or disease-free survival: rabeprazole, alverine, atenolol, simvastatin, rosuvastatin, estriol (vaginal or transmucosal), nomegestrol, and hypromellose; and eight medications negatively associated with overall survival or disease-free survival: ferrous fumarate, prednisolone, carbimazole, pristinamycin, oxazepam, alprazolam, hydroxyzine, and mianserin. Full results are available online from an interactive platform (https://adrenaline.curie.fr). This resource provides hypotheses for drugs that may naturally influence breast cancer evolution.

Background: Breast cancer (BC) is the most frequent cancer and the leading cause of cancer-related death in women. The French National Cancer Institute has created a national cancer cohort to promote cancer research and improve our understanding of cancer using the National Health Data System (SNDS) and amalgamating all cancer sites. So far, no detailed separate data are available for early BC. Objectives: To describe the creation of the French Early Breast Cancer Cohort (FRESH). Methods: All French women aged 18 years or over, with early-stage BC newly diagnosed between 1 January 2011 and 31 December 2017, treated by surgery, and registered in the general health insurance coverage plan were included in the cohort. Patients with suspected locoregional or distant metastases at diagnosis were excluded. BC treatments (surgery, chemotherapy, targeted therapy, radiotherapy, and endocrine therapy), and diagnostic procedures (biopsy, cytology, and imaging) were extracted from hospital discharge reports, outpatient care notes, or pharmacy drug delivery data. The BC subtype was inferred from the treatments received. Results: We included 235,368 patients with early BC in the cohort (median age: 60 years). The BC subtype distribution was as follows: luminal (80.2%), triple-negative (TNBC, 9.5%); HER2⁺ (10.3%), or unidentifiable (n = 44,388, 18.9% of the cohort). Most patients underwent radiotherapy (n = 200,685, 85.3%) and endocrine therapy (n = 165,655, 70.4%), and 38.3% (n = 90,252) received chemotherapy. Treatments and care pathways are described. Conclusions: The FRESH Cohort is an unprecedented population-based resource facilitating future large-scale real-life studies aiming to improve care pathways and quality of care for BC patients.

Hazard ratios are routinely reported as effect measures in clinical trials and observational studies. However, many methodological works have raised concerns about the interpretation of hazard ratios as causal effects. These concerns are often related to three points: (i) depletion of susceptible individuals leads to selection bias and complicates the causal interpretation of the hazard ratio, (ii) the hazard ratio is not collapsible, and (iii) the conventional proportional hazards assumption rarely holds in medical studies. We discuss the relation between these three points. We ground our presentation on an example about effect of endocrine therapy in reducing the risk of recurrence or death in a population of patients with breast cancer. We also describe why survival curves and risk differences do not exhibit any of the undesirable properties of hazard ratios.

The increasing accumulation of medical data brings the hope of data-driven medical decision-making, but data's increasing complexity—as text or images in electronic health records—calls for complex models, such as machine learning. Here, we review how machine learning can be used to inform decisions for individualized interventions, a causal question. Going from prediction to causal effects is challenging, as no individual is seen as both treated and not. We detail how some data can support some causal claims and how to build causal estimators with machine learning. Beyond variable selection to adjust for confounding bias, we cover the broader notions of study design that make or break causal inference. As the problems span across diverse scientific communities, we use didactic yet statistically precise formulations to bridge machine learning to epidemiology.

PURPOSE: Young age is associated with increased risk of recurrence in hormone receptor (HR)–positive early-stage breast cancer (eBC). Lack of adherence to endocrine therapy (ET) is a potential reason for the lower survival proportions observed in younger patients, but the survival benefits of improving adherence to ET in young patients remain unknown. MATERIALS AND METHODS: Using data from the French National Health Data System and target trial emulation methods, we considered three sustained ET persistence strategies (allowing treatment gaps of no more than 30, 90, or 180 continuous days) and estimated the 5-year disease-free survival (DFS) benefit of sustained ET persistence compared with observed ET persistence. RESULTS: A total of 121,601 patients with HR-positive eBC were included in the analyses, of whom 29.8% was younger than 50 years at diagnosis. Younger patients had lower DFS and were more likely to discontinue ET than older patients. In patients 34 years and younger, strict ET persistence (≤30-day gaps) improved 5-year DFS proportions from 74.5% to 78.8% (4.3 percentage points [95% CI, 2.6 to 7.2]) compared with observed persistence. ET persistence strategies allowing for ≤90-day and ≤180-day gaps reduced the 5-year DFS benefit in patients 34 years and younger to 1.3 (95% CI, 0.2 to 3.7) and 1.0 (95% CI, –0.2 to 3.4) percentage points, respectively. By contrast, DFS benefits of improved ET persistence in patients after 50 years old did not exceed 1.9 percentage points, compared with observed persistence, regardless of the persistence definition. CONCLUSION: The survival benefit that could be achieved with strict ET persistence in women 34 years and younger with HR-positive eBC highlights the need for tailored strategies to improve ET persistence in this population.