A phase I safety and pharmacokinetic study of the death receptor 5 agonistic antibody PRO95780 in patients with advanced malignancies.

PURPOSE: PRO95780 is a fully human IgG1 monoclonal antibody that triggers the extrinsic apoptosis pathway through death receptor 5. This first-in-human study assessed the safety, tolerability, pharmacokinetics, and any early evidence of efficacy of PRO95780 in patients with advanced malignancies. EXPERIMENTAL DESIGN: Target concentrations were predicted to occur at 10 mg/kg. Patients received up to eight cycles of PRO95780 i.v. using a 3+3 dose escalation design at 1 to 20 mg/kg every 14 days (every 28 days in cycle 1; stage 1), with cohort expansion at either the maximum tolerated dose or 10 mg/kg, whichever was lower (stage 2). Patients were evaluated for response every other cycle. RESULTS: The maximum tolerated dose was not reached within this study. Four (8%) of 50 patients reported adverse events of greater than grade 2 at least possibly related to PRO95780, including 2 patients with reversible grade 3 transaminase elevation. The mean terminal half-life was 8.8 to 19.3 days, with dose-dependent increases in exposure (peak plasma concentration and area under the concentration) across 1 to 15 mg/kg. Most patients treated with 10 mg/kg or above achieved trough concentration above the target efficacious concentration at day 15 with moderate accumulation after multiple doses. No objective responses occurred, although three minor responses were observed in patients with colorectal and granulosa cell ovarian cancers (each treated with 4 mg/kg) and chondrosarcoma (10 mg/kg). CONCLUSIONS: PRO95780 is safe and well tolerated at doses up to 20 mg/kg. Evidence of activity was noted in several different tumor types at 4 and 10 mg/kg. Pharmacokinetic analysis supports a dosing regimen of 10 to 15 mg/kg every 2 to 3 weeks.

Variance-stabilizing transformations for two-color microarrays.

MOTIVATION: Authors of several recent papers have independently introduced a family of transformations (the generalized-log family), which stabilizes the variance of microarray data up to the first order. However, for data from two-color arrays, tests for differential expression may require that the variance of the difference of transformed observations be constant, rather than that of the transformed observations themselves. RESULTS: We introduce a transformation within the generalized-log family which stabilizes, to the first order, the variance of the difference of transformed observations. We also introduce transformations from the 'started-log' and log-linear-hybrid families which provide good approximate variance stabilization of differences. Examples using control-control data show that any of these transformations may provide sufficient variance stabilization for practical applications, and all perform well compared to log ratios.

Estimation of transformation parameters for microarray data.

MOTIVATION AND RESULTS: Durbin et al. (2002), Huber et al. (2002) and Munson (2001) independently introduced a family of transformations (the generalized-log family) which stabilizes the variance of microarray data up to the first order. We introduce a method for estimating the transformation parameter in tandem with a linear model based on the procedure outlined in Box and Cox (1964). We also discuss means of finding transformations within the generalized-log family which are optimal under other criteria, such as minimum residual skewness and minimum mean-variance dependency. AVAILABILITY: R and Matlab code and test data are available from the authors on request.

Approximate variance-stabilizing transformations for gene-expression microarray data.

MOTIVATION: A variance stabilizing transformation for microarray data was recently introduced independently by several research groups. This transformation has sometimes been called the generalized logarithm or glog transformation. In this paper, we derive several alternative approximate variance stabilizing transformations that may be easier to use in some applications. RESULTS: We demonstrate that the started-log and the log-linear-hybrid transformation families can produce approximate variance stabilizing transformations for microarray data that are nearly as good as the generalized logarithm (glog) transformation. These transformations may be more convenient in some applications.

Modeling uncertainty in the measurement of low-level analytes in environmental analysis.

The use of analytical chemistry measurements in environmental monitoring is dependent on an assessment of measurement error. Models for variation in measurements are needed to quantify uncertainty in measurements, set limits of detection, and preprocess data for more sophisticated analysis in prediction, classification, and clustering. This article explains how a two-component error model can be used to accomplish all of these objectives. In addition, we present applications to quantitating biomarkers of exposure to toxic substances using gene expression microarrays.