Impact of prior treatment on selinexor, bortezomib, dexamethasone outcomes in patients with relapsed/refractory multiple myeloma: Extended follow-up subgroup analysis of the BOSTON trial.

OBJECTIVES: To analyze the impact of prior therapies on outcomes with selinexor, bortezomib, and dexamethasone (SVd) versus bortezomib and dexamethasone (Vd) in 402 patients with relapsed/refractory multiple myeloma (RRMM) in the phase 3 BOSTON trial. METHODS: Post hoc analysis of progression-free survival (PFS), overall survival (OS), and safety for lenalidomide-refractory, proteasome inhibitor (PI)-naïve, bortezomib-naïve, and one prior line of therapy (1LOT) patient subgroups. RESULTS: At a median follow-up of over 28 months, clinically meaningful improvements in PFS were noted across all groups with SVd. The median SVd PFS was longer in all subgroups (lenalidomide-refractory: 10.2 vs. 7.1 months, PI-naïve: 29.5 vs. 9.7; bortezomib-naïve: 29.5 vs. 9.7; 1LOT: 21.0 vs. 10.7; p < .05). The lenalidomide-refractory subgroup had longer OS with SVd (26.7 vs. 18.6 months; HR 0.53; p = .015). In all subgroups, overall response and ≥very good partial response rates were higher with SVd. The manageable safety profile of SVd was similar to the overall patient population. CONCLUSIONS: With over 2 years of follow-up, these clinically meaningful outcomes further support the use of SVd in patients who are lenalidomide-refractory, PI-naïve, bortezomib-naïve, or who received 1LOT (including a monoclonal antibody) and underscore the observed synergy between selinexor and bortezomib.

Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes.

Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.