Birtamimab plus standard of care in light-chain amyloidosis: the phase 3 randomized placebo-controlled VITAL trial.

Amyloid light-chain (AL) amyloidosis is a rare, typically fatal disease characterized by the accumulation of misfolded immunoglobulin light chains (LCs). Birtamimab is an investigational humanized monoclonal antibody designed to neutralize toxic LC aggregates and deplete insoluble organ-deposited amyloid via macrophage-induced phagocytosis. VITAL was a phase 3 randomized, double-blind, placebo-controlled clinical trial assessing the efficacy and safety of birtamimab + standard of care (SOC) in 260 newly diagnosed, treatment-naive patients with AL amyloidosis. Patients received 24 mg/kg IV birtamimab + SOC or placebo + SOC every 28 days. The primary composite end point was the time to all-cause mortality (ACM) or centrally adjudicated cardiac hospitalization ≥91 days after the first study drug infusion. The trial was terminated early after an interim futility analysis; there was no significant difference in the primary composite end point (hazard ratio [HR], 0.826; 95% confidence interval [CI], 0.574-1.189; log-rank P = .303). A post hoc analysis of patients with Mayo stage IV AL amyloidosis, those at the highest risk of early mortality, showed significant improvement in the time to ACM with birtamimab at month 9 (HR, 0.413; 95% CI, 0.191-0.895; log-rank P = .021). At month 9, 74% of patients with Mayo stage IV AL amyloidosis treated with birtamimab and 49% of those given placebo survived. Overall, the rates of treatment-emergent adverse events (TEAEs) and serious TEAEs were generally similar between treatment arms. A confirmatory phase 3 randomized, double-blind, placebo-controlled clinical trial of birtamimab in patients with Mayo stage IV AL amyloidosis (AFFIRM-AL; NCT04973137) is currently enrolling. The VITAL trial was registered at www.clinicaltrials.gov as #NCT02312206.

Long-Term Obeticholic Acid Therapy Improves Histological Endpoints in Patients With Primary Biliary Cholangitis.

BACKGROUND & AIMS: Primary biliary cholangitis (PBC) is an autoimmune disease characterized by bile duct destruction that can progress to cirrhosis. A liver biopsy substudy was conducted in the PBC obeticholic acid (OCA) International Study of Efficacy (POISE) to determine the long-term effects of OCA on liver damage and fibrosis in patients with PBC. POISE is a phase 3, double-blind, placebo-controlled, randomized trial with a 5-year open-label extension that evaluated 5 to 10 mg OCA daily in patients who were intolerant or unresponsive to ursodeoxycholic acid. METHODS: Liver biopsy specimens were collected from 17 patients at time of enrollment in the double-blind phase and after 3 years of OCA treatment. Histologic evaluations were performed by 2 pathologists in a blinded, randomized fashion to determine the effects of OCA on fibrosis and other histologic parameters. Collagen morphometry assessments were performed by automated second harmonic generation and 2-photon excitation microscopy to observe quantitative measures of fibrosis. RESULTS: From the time of enrollment until 3 years of treatment, most patients had improvements or stabilization in fibrosis (71%), bile duct loss (76%), ductopenia (82%), ductular reaction (82%), interface hepatitis (100%), and lobular hepatitis (94%). Over the 3-year period, we found significant reductions in collagen area ratio (median, -2.1; first quartile, -4.6, third quartile, -0.3; P = .013), collagen fiber density (median, -0.8; first quartile, -2.5; third quartile, 0; P = .021), collagen reticulation index (median, -0.1; first quartile, -0.3; third quartile, 0; P = .008), and fibrosis composite score (median, -1.0; first quartile, -2.5; third quartile, -0.5; P = .002). CONCLUSIONS: A subanalysis of data from the POISE study showed that long-term OCA treatment in patients with PBC is associated with improvements or stabilization of disease features, including ductular injury, fibrosis, and collagen morphometry features (ClinicalTrials.gov no: NCT01473524 and EudraCT no: 2011-004728-36).

Genome-Wide Analysis Elucidates the Role of CONSTANS-like Genes in Stress Responses of Cotton.

The CONSTANS (CO)-like gene family has been well studied for its role in the regulation of plant flowering time. However, their role remains poorly understood in cotton. To better understand the possible roles of CO-like in cotton, we performed a comprehensive genome-wide analysis of CO-like genes in cotton. Phylogenetic tree analysis showed that CO-like genes naturally clustered into three groups. Segmental duplication and whole genome duplication (WGD), which occurred before polyploidy, were important contributors to its expansion within the At ("t" indicates tetraploid) and Dt subgenomes, particularly in Group III. Long-terminal repeat retroelements were identified as the main transposable elements accompanying 18 genes. The genotype of GhCOL12_Dt displayed low diversity; it was a candidate involved in domestication. Selection pressure analyses indicated that relaxed purifying selection might have provided the main impetus during the evolution of CO-like genes in upland cotton. In addition, the high expression in the torus and calycle indicated that CO-like genes might affect flowering. The genes from Group II, and those from Group III involved in segmental duplication or WGD, might play important roles in response to drought and salt stress. Overall, this comprehensive genome-wide study of the CO-like gene family would facilitate further detailed studies in cotton.

Evaluating prognostic accuracy of biomarkers under competing risk.

To develop more targeted intervention strategies, an important research goal is to identify markers predictive of clinical events. A crucial step toward this goal is to characterize the clinical performance of a marker for predicting different types of events. In this article, we present statistical methods for evaluating the performance of a prognostic marker in predicting multiple competing events. To capture the potential time-varying predictive performance of the marker and incorporate competing risks, we define time- and cause-specific accuracy summaries by stratifying cases based on causes of failure. Such definition would allow one to evaluate the predictive accuracy of a marker for each type of event and compare its predictiveness across event types. Extending the nonparametric crude cause-specific receiver operating characteristics curve estimators by Saha and Heagerty (2010), we develop inference procedures for a range of cause-specific accuracy summaries. To estimate the accuracy measures and assess how covariates may affect the accuracy of a marker under the competing risk setting, we consider two forms of semiparametric models through the cause-specific hazard framework. These approaches enable a flexible modeling of the relationships between the marker and failure times for each cause, while efficiently accommodating additional covariates. We investigate the asymptotic property of the proposed accuracy estimators and demonstrate the finite sample performance of these estimators through simulation studies. The proposed procedures are illustrated with data from a prostate cancer prognostic study.

Semiparametric estimation of the average causal effect of treatment on an outcome measured after a postrandomization event, with missing outcome data.

In the past decade, several principal stratification-based statistical methods have been developed for testing and estimation of a treatment effect on an outcome measured after a postrandomization event. Two examples are the evaluation of the effect of a cancer treatment on quality of life in subjects who remain alive and the evaluation of the effect of an HIV vaccine on viral load in subjects who acquire HIV infection. However, in general the developed methods have not addressed the issue of missing outcome data, and hence their validity relies on a missing completely at random (MCAR) assumption. Because in many applications the MCAR assumption is untenable, while a missing at random (MAR) assumption is defensible, we extend the semiparametric likelihood sensitivity analysis approach of Gilbert and others (2003) and Jemiai and Rotnitzky (2005) to allow the outcome to be MAR. We combine these methods with the robust likelihood-based method of Little and An (2004) for handling MAR data to provide semiparametric estimation of the average causal effect of treatment on the outcome. The new method, which does not require a monotonicity assumption, is evaluated in a simulation study and is applied to data from the first HIV vaccine efficacy trial.