Evolution of overall survival and receipt of new therapies by subtype among 20 446 metastatic breast cancer patients in the 2008-2017 ESME cohort

BACKGROUND: Treatment strategies for metastatic breast cancer (MBC) have made great strides over the past 10 years. Real-world data allow us to evaluate the actual benefit of new treatments. ESME (Epidemio-Strategy-Medico-Economical)-MBC, a nationwide observational cohort (NCT03275311), gathers data of all consecutive MBC patients who initiated their treatment in 18 French Cancer Centres since 2008. PATIENTS AND METHODS: We evaluated overall survival (OS) in the whole cohort (N = 20 446) and among subtypes: hormone receptor positive, human epidermal growth factor 2 negative (HR+/HER2-; N = 13 590), HER2+ (N = 3919), and triple-negative breast cancer (TNBC; N = 2937). We performed multivariable analyses including year of MBC diagnosis as one of the covariates, to assess the potential OS improvement over time, and we described exposure to newly released drugs at any time during MBC history by year of diagnosis (YOD). RESULTS: The median follow-up of the whole cohort was 65.5 months (95% CI 64.6-66.7). Year of metastatic diagnosis appears as a strong independent prognostic factor for OS [Year 2016 HR 0.89 (95% CI 0.82-0.97); P = 0.009, using 2008 as reference]. This effect is driven by the HER2+ subcohort, where it is dramatic [Year 2016 HR 0.52 (95% CI 0.42-0.66); P 70% of patients received pertuzumab from 2016 onwards), use of everolimus or eribulin was recorded in less than one-third of HR+/HER2- and TNBC cohorts, respectively, whatever YOD. CONCLUSION: OS has dramatically improved among HER2+ MBC patients, probably in association with the release of several major HER2-directed therapies, whose penetrance was high. This trend was not observed in the other subtypes, but the impact of CDK4/6 inhibitors cannot yet be assessed

ERP Source Analysis Guided by fMRI During Familiar Face Processing

Event related potentials (ERPs) provide precise temporal information about cognitive processing, but with poor spatial resolution, while functional magnetic resonance imaging (fMRI) reliably identifies brain areas involved, but with poor temporal resolution. Here we use fMRI to guide source localization of the ERPs at different times for studying the temporal dynamics of the neural system for recognizing familiar faces. fMRI activation areas were defined in a previous experiment applying the same paradigm used for ERPs. The Bayesian model averaging (BMA) method was used to estimate the generators of the ERPs to unfamiliar, visually familiar, and personally-familiar faces constraining the model by fMRI activation results. For this, higher prior probabilities in the solution space were assigned to the fMRI-defined regions, which included face-selective areas and other areas related to “person knowledge” retrieval. Source analysis was carried out in three-time windows: early (150–210 ms), middle (300–380 ms) and late (460–580 ms). The early and middle responses were generated in fMRI-defined areas for all face categories, while these areas do not contribute to the late response. Different areas contributed to the generation of the early and middle ERPs elicited by unfamiliar faces: fusiform (Fus), inferior occipital, superior temporal sulcus and the posterior cingulate (PC) cortices. For familiar faces, the contributing areas were Fus, PC and anterior temporal areas for visually familiar faces, with the addition of the medial orbitofrontal areas and other frontal structures for personally-significant faces. For both unfamiliar and familiar faces, more extended and reliable involvement of contributing areas were obtained for the middle compare with early time window. Our fMRI guide ERP source analysis suggested the recruitment of person-knowledge processing areas as early as 150–210 ms after stimulus onset during recognition of personally-familiar faces. We concluded that fMRI-constrained BMA source analysis provide information regarding the temporal-dynamics in the neural system for cognitive processsing