Anti-CASP9 (C-terminal) monoclonal antibody, clone 20D22-B23

Purpose This study was conducted to determine the impact of potentially functional polymorphisms in the CASPASE ( CASP) genes on the survival of early-stage non-small-cell lung cancer (NSCLC) patients. Patients and Methods Four hundred eleven consecutive patients with surgically resected NSCLC were enrolled. Nine potentially functional polymorphisms in the CASP3, CASP7, CASP8, CASP9, and CASP10 genes were investigated. The genotype and haplotype associations with overall survival ( OS) and disease-free survival (DFS) were analyzed. Results Patients with the rs2227310 GG genotype had a significantly decreased OS and DFS compared with patients with the CC + CG genotype (adjusted hazard ratio [aHR] for OS, 1.67; 95% CI, 1.19 to 2.35; P = .003; aHR for DFS, 1.62; 95% CI, 1.19 to 2.22; P = .002). The rs4645981C > T genotype also had a significant effect on OS and DFS ( under a recessive model; aHR for OS, 2.00; 95% CI, 1.04 to 3.85; P = .04; aHR for DFS, 2.76; 95% CI, 1.58 to 4.80; P = .0003). When the rs2227310 and rs4645981 genotypes were combined, patients with one or two bad genotypes had worse OS and DFS compared with those who had zero bad genotypes (aHR for OS, 1.75; 95% CI, 1.25 to 2.45; P = .001; aHR for DFS, 1.66; 95% CI, 1.23 to 2.26; P = .001). Conclusion The CASP7 rs2227310 and CASP9 rs4645981 polymorphisms may affect survival in early-stage NSCLC. The analysis of these polymorphisms can help identify patients at high risk for a poor disease outcome.

Motivation: Intrinsically disordered regions are key for the function of numerous proteins, and the scant available experimental annotations suggest the existence of different disorder flavors. While efficient predictions are required to annotate entire genomes, most existing methods require sequence profiles for disorder prediction, making them cumbersome for high- throughput applications. Results: In this work, we present an ensemble of protein disorder predictors called ESpritz. These are based on bidirectional recursive neural networks and trained on three different flavors of disorder, including a novel NMR flexibility predictor. ESpritz can produce fast and accurate sequence-only predictions, annotating entire genomes in the order of hours on a single processor core. Alternatively, a slower but slightly more accurate ESpritz variant using sequence profiles can be used for applications requiring maximum performance. Two levels of prediction confidence allow either to maximize reasonable disorder detection or to limit expected false positives to 5%. ESpritz performs consistently well on the recent CASP9 data, reaching a S-w measure of 54.82 and area under the receiver operator curve of 0.856. The fast predictor is four orders of magnitude faster and remains better than most publicly available CASP9 methods, making it ideal for genomic scale predictions. Conclusions: ESpritz predicts three flavors of disorder at two distinct false positive rates, either with a fast or slower and slightly more accurate approach. Given its state-of-the-art performance, it can be especially useful for high-throughput applications.