Background

Bevacizumab, a humanized antibody to vascular endothelial growth factor (VEGF), shows clinical activity against human cancer, with its addition to standard chemotherapy having been found to improve outcome in patients with advanced nonsquamous non–small cell lung cancer (NSCLC). However, there have been no evidence-based studies to support the continued use of bevacizumab beyond disease progression in such patients treated with the drug in first-line therapy. We have now designed a randomized phase II trial to examine the clinical benefit and safety of continued bevacizumab treatment in patients with advanced nonsquamous NSCLC whose disease has progressed after first-line treatment with bevacizumab plus a platinum-based doublet.

Methods/Design

WJOG 5910L was designed as a multicenter, open-label, randomized, phase II trial by the West Japan Oncology Group of docetaxel (arm A) versus docetaxel plus bevacizumab (arm B) in patients with recurrent or metatstatic nonsquamous NSCLC whose disease has progressed after first-line treatment with bevacizumab plus a platinum-based doublet. Patients in arm A will receive docetaxel at 60 mg/m2 and those in arm B will receive docetaxel at 60 mg/m2 plus bevacizumab at 15 mg/kg, with each drug administered on day 1 every 21 days until progression or unacceptable toxicity. The primary endpoint of the study is progression-free survival, with secondary endpoints including response rate, overall survival, and safety, for patients treated in either arm.

Trial registration

UMIN (University Hospital Medical Information Network in Japan) 000004715

Background

The prevention of serious physical complications in anorexia nervosa (AN) patients is important. The purpose of this study is to clarify which physical and social factors are related to the necessity for urgent hospitalization of anorexia nervosa (AN) patients in a long-term starvation state. We hypothesized that the change of longitudinal BMI, body composition and social background would be useful as an index of the necessity for urgent hospitalization.

Methods

AN patients were classified into; urgent hospitalization, due to disturbance of consciousness or difficulty walking(n = 17); planned admission (n = 96); and outpatient treatment only groups (n = 136). The longitudinal BMI pattern and the clinical features of these groups were examined. In the hospitalization groups, comparison was done of body composition variation and the social background, including the educational level and advice from family members.

Results

After adjusting for age and duration of illness, the BMI of the urgent hospitalization group was lower than that of the other groups at one year before hospitalization (P < 0.01) and decreased more rapidly (P < 0.01). Urgent hospitalization was associated with the fat free mass (FFM) (P < 0.01). Between the groups, no considerable difference in social factors was found.

Conclusions

The longitudinal pattern of BMI and FFM may be useful for understanding the severity in AN from the viewpoint of failure of the homeostasis system.

One major unresolved issue in the analysis of gene expression data is the identification and quantification of gene regulatory networks. Several methods have been proposed for identifying gene regulatory networks, but these methods predominantly focus on the use of multiple pairwise comparisons to identify the network structure. In this article, we describe a method for analyzing gene expression data to determine a regulatory structure consistent with an observed set of expression profiles. Unlike other methods this method goes beyond pairwise evaluations by using likelihood-based statistical methods to obtain the network that is most consistent with the complete data set. The proposed algorithm performs accurately for moderate-sized networks with most errors being minor additions of linkages. However, the analysis also indicates that sample sizes may need to be increased to uniquely identify even moderate-sized networks. The method is used to evaluate interactions between genes in the SOS signaling pathway in Escherichia coli using gene expression data where each gene in the network is over-expressed using plasmids inserts.

Gene expression arrays (gene chips) have enabled researchers to roughly quantify the level of mRNA expression for a large number of genes in a single sample. Several methods have been developed for the analysis of gene array data including clustering, outlier detection, and correlation studies. Most of these analyses are aimed at a qualitative identification of what is different between two samples and/or the relationship between two genes. We propose a quantitative, statistically sound methodology for the analysis of gene regulatory networks using gene expression data sets. The method is based on Bayesian networks for direct quantification of gene expression networks. Using the gene expression changes in HPL1A lung airway epithelial cells after exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin at levels of 0.1, 1.0, and 10.0 nM for 24 hr, a gene expression network was hypothesized and analyzed. The method clearly demonstrates support for the assumed network and the hypothesis linking the usual dioxin expression changes to the retinoic acid receptor system. Simulation studies demonstrated the method works well, even for small samples.