An international phase II trial of single-agent lenalidomide for relapsed or refractory aggressive B-cell non-Hodgkin's lymphoma

Abstract Background Lenalidomide is an immunomodulatory agent with antitumor activity in B-cell malignancies. This phase II trial aimed to demonstrate the safety and efficacy of lenalidomide in patients with relapsed or refractory diffuse large B-cell lymphoma (DLBCL), mantle cell lymphoma (MCL), follicular grade 3 lymphoma (FL-III), or transformed lymphoma (TL). Methods Patients received oral lenalidomide 25 mg on days 1–21 every 28 days as tolerated or until progression. The primary end point was overall response rate (ORR). Results Two hundred and seventeen patients enrolled and received lenalidomide. The ORR was 35% (77/217), with 13% (29/217) complete remission (CR), 22% (48/217) partial remission, and 21% (45/217) with stable disease. The ORR for DLBCL was 28% (30/108), 42% (24/57) for MCL, 42% (8/19) for FL-III, and 45% (15/33) for TL. Median progression-free survival for all 217 patients was 3.7 months [95% confidence interval (CI) 2.7–5.1]. For 77 responders, the median response duration lasted 10.6 months (95% CI 7.0–NR). Median response duration was not reached in 29 patients who achieved a CR and in responding patients with FL-III or MCL. The most common adverse event was myelosuppression with grade 4 neutropenia and thrombocytopenia in 17% and 6%, respectively. Conclusion Lenalidomide is well tolerated and produces durable responses in patients with relapsed or refractory aggressive non-Hodgkin's lymphoma

Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique\u2014Subtype and Stage Inference (SuStaIn)\u2014able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer\u2019s disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 7 10 124 ) or temporal stage (p = 3.96 7 10 125 ). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine

Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise.

To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). V ˙ O 2 peak estimation models were developed in 1891 individuals undergoing CPET at Massachusetts General Hospital (age 45 ± 19 years, 38% female) and validated in a separate test set (MGH Test, n = 448) and external sample (BWH Test, n = 1076). Three penalized linear models were compared: (i) age, sex, and body mass index ('Basic'), (ii) Basic plus standard ECG measurements ('Basic + ECG Parameters'), and (iii) basic plus 320 deep learning-derived ECG variables instead of ECG measurements ('Deep ECG-V˙O2'). Associations between estimated V˙O2peak and incident disease were assessed using proportional hazards models within 84 718 primary care patients without CPET. Inference ECGs preceded CPET by 7 days (median, interquartile range 27-0 days). Among models, Deep ECG-V˙O2 was most accurate in MGH Test [r = 0.845, 95% confidence interval (CI) 0.817-0.870; mean absolute error (MAE) 5.84, 95% CI 5.39-6.29] and BWH Test (r = 0.552, 95% CI 0.509-0.592, MAE 6.49, 95% CI 6.21-6.67). Deep ECG-V˙O2 also outperformed the Wasserman, Jones, and FRIEND reference equations (P < 0.01 for comparisons of correlation). Performance was higher in BWH Test when individuals with heart failure (HF) were excluded (r = 0.628, 95% CI 0.567-0.682; MAE 5.97, 95% CI 5.57-6.37). Deep ECG-V˙O2 estimated V˙O2peak <14 mL/kg/min was associated with increased risks of incident atrial fibrillation [hazard ratio 1.36 (95% CI 1.21-1.54)], myocardial infarction [1.21 (1.02-1.45)], HF [1.67 (1.49-1.88)], and death [1.84 (1.68-2.03)]. Deep learning-enabled analysis of the resting 12-lead ECG can estimate exercise capacity (V˙O2peak) at scale to enable efficient cardiovascular risk stratification