MEDI1814 selectively reduces free Aß42 in cerebrospinal fluid of non-clinical species and Alzheimer's disease patients.

INTRODUCTION: Small molecules and antibodies are being developed to lower amyloid beta (Aß) peptides. METHODS: We describe MEDI1814, a fully human high-affinity monoclonal antibody selective for Aß42, the pathogenic self-aggregating species of Aß. RESULTS: MEDI1814 reduces free Aß42 without impacting Aß40 in the cerebrospinal fluid of rats and cynomolgus monkeys after systemic administration. MEDI1814 administration to patients with Alzheimer's disease (AD; n = 57) in single or repeat doses up to 1800 mg intravenously or 200 mg subcutaneously was associated with a favorable safety and tolerability profile. No cases of amyloid-related imaging abnormalities were observed. Predictable dose-proportional changes in serum exposures for MEDI1814 were observed across cohorts. Cerebrospinal fluid (CSF) analysis demonstrated central nervous system penetration of MEDI1814. Pharmacodynamic data showed dose-dependent suppression of free Aß42, increases in total (bound and free) Aß42, but no change in total Aß40 in CSF across doses. DISCUSSION: MEDI1814 offers a differentiated approach to impacting Aß in AD via selective reduction of free Aß42.

Flexible diagnostic measures and new cut-point selection methods under multiple ordered classes.

Medical diagnosis is essentially a classification problem and usually it is done with multiple ordered classes. For example, cancer diagnosis might be "non-malignant," "early stage," or "late stage." Therefore, appropriate measures are needed to assess the accuracy of diagnostic markers under multiple ordered classes. However, all existing measures fail to differentiate among some distinctly different biomarkers. This paper presents a multi-step procedure for evaluating biomarker accuracy under multiple ordered classes. This procedure leads to two new flexible overall measures as well as three new cut-point selection methods with great computational ease. The performance of proposed measures and cut-point selection methods are numerically explored via a simulation study. In the end, an ovarian cancer dataset from the Prostate, Lung, Colorectal, and Ovarian cancer study is analyzed. The proposed accuracy measures were estimated for markers CA125 and HE4, and cut-points were estimated for the risk of ovarian malignancy algorithm score.

Issues and solutions in biomarker evaluation when subclasses are involved under binary classification.

In practice, it is common to evaluate biomarkers in binary classification settings (e.g. non-cancer vs. cancer) where one or both main classes involve multiple subclasses. For example, non-cancer class might consist of healthy subjects and benign cases, while cancer class might consist of subjects at early and late stages. The standard practice is pooling within each main class, i.e. all non-cancer subclasses are pooled together to create a control group, and all cancer subclasses are pooled together to create a case group. Based on the pooled data, the area under ROC curve (AUC) and other characteristics are estimated under binary classification for the purpose of biomarker evaluation. Despite the popularity of this pooling strategy in practice, its validity and implication in biomarker evaluation have never been carefully inspected. This paper aims to demonstrate that pooling strategy can be seriously misleading in biomarker evaluation. Furthermore, we present a new diagnostic framework as well as new accuracy measures appropriate for biomaker evaluation under such settings. In the end, an ovarian cancer data set is analyzed.

Daily Time of Radiation Treatment Is Associated with Subsequent Oral Mucositis Severity during Radiotherapy in Head and Neck Cancer Patients.

BACKGROUND: Limited treatment options are available for oral mucositis, a common, debilitating complication of cancer therapy. We examined the association between daily delivery time of radiotherapy and the severity of oral mucositis in patients with head and neck cancer. METHODS: We used electronic medical records of 190 patients with head and neck squamous cell carcinoma who completed radiotherapy, with or without concurrent chemotherapy, at Roswell Park Comprehensive Cancer Center (Buffalo, NY) between 2015 and 2017. Throughout a 7-week treatment course, patient mouth and throat soreness (MTS) was self-reported weekly using a validated oral mucositis questionnaire, with responses 0 (no) to 4 (extreme). Average treatment times from day 1 until the day before each mucositis survey were categorized into seven groups. Multivariable-adjusted marginal average scores (LSmeans) were estimated for the repeated- and maximum-MTS, using a linear-mixed model and generalized-linear model, respectively. RESULTS: Radiation treatment time was significantly associated with oral mucositis severity using both repeated-MTS (n = 1,156; P = 0.02) and maximum-MTS (n = 190; P = 0.04), with consistent patterns. The severity was lowest for patients treated during 8:30 to <9:30 am (LSmeans for maximum-MTS = 2.24; SE = 0.15), increased at later treatment times and peaked at early afternoon (11:30 am to <3:00 pm, LSmeans = 2.66-2.71; SEs = 0.16/0.17), and then decreased substantially after 3 pm. CONCLUSIONS: We report a significant association between radiation treatment time and oral mucositis severity in patients with head and neck cancer. IMPACT: Although additional studies are needed, these data suggest a potential simple treatment time solution to limit severity of oral mucositis during radiotherapy without increasing cost.

Optimal threshold selection methods under tree or umbrella ordering.

Receiver operating characteristic (ROC) curve is a popular tool for evaluating diagnostic accuracy of biomarkers. In ROC framework, there exist several optimal threshold selection methods for binary classification. For diseases with multi-classes, an important category of scenarios is tree or umbrella ordering in which the marker measurement for one particular class is lower or higher than those for the rest classes. Tree or umbrella ordering has important clinical applications, especially in the molecular diagnostics of cancer subtypes. The ROC curve has been extended to a typical ROC framework for tree or umbrella ordering (denoted as TROC). In this paper, we investigate several methods for optimal threshold selection under tree or umbrella ordering. Simulation studies are carried out to explore the performance of these threshold selection methods. A real microarray data set on lung cancer is analyzed using the proposed methods.

Measuring diagnostic accuracy for biomarkers under tree-ordering.

In the field of diagnostic studies for tree or umbrella ordering, under which the marker measurement for one class is lower or higher than those for the rest unordered classes, there exist a few diagnostic measures such as the naive AUC ( NAUC), the umbrella volume ( UV), and the recently proposed TAUC, i.e. area under a ROC curve for tree or umbrella ordering (TROC). However, an important characteristic about tree or umbrella ordering has been neglected. This paper mainly focuses on promoting the use of the integrated false negative rate under tree ordering ( ITFNR) as an additional diagnostic measure besides TAUC, and proposing the idea of using ( TAUC, ITFNR) instead of TAUC to evaluate the diagnostic accuracy of a biomarker under tree or umbrella ordering. Parametric and non-parametric approaches for constructing joint confidence region of ( TAUC, ITFNR) are proposed. Simulation studies under a variety of settings are carried out to assess and compare the performance of these methods. In the end, a published microarray data set is analyzed.