Overall survival in patients with glioblastoma before and after bevacizumab approval.

OBJECTIVE: Glioblastoma (GBM) is an aggressive disease with limited therapeutic options. While bevacizumab was approved in 2009 for the treatment of patients with progressive GBM, its impact on overall survival (OS) remains unclear. Using US population-based cancer registry data (SEER), this study compared OS of patients diagnosed with GBM before and after bevacizumab approval. METHODS: Adult patients from SEER with a GBM diagnosis were divided into two cohorts: patients diagnosed in 2006-2008 (pre-bevacizumab cohort, n = 6,120) and patients diagnosed in 2010-2012 (post-bevacizumab cohort, n = 6,753). Patients were included irrespective of the treatments received. OS post-diagnosis was compared between the study cohorts utilizing Kaplan-Meier analyses and multivariate Cox proportional hazards regression. RESULTS: Among 12,873 patients with GBM, the median age was 62 years, 41% were women, 31% underwent gross total resection, and 75% received radiation therapy. Survival was stable within the 2006-2008 period (median survival = 9 months for each year), but increased after year 2009 (median survival = 10 and 11 months for years 2010/2011 and 2012, respectively). The adjusted hazard of death was significantly lower in the post-bevacizumab approval cohort (hazard ratio = 0.91, p < .01). CONCLUSIONS: The results of this large population-based study suggested an improvement in OS among patients with a GBM diagnosis in 2010-2012 compared to 2006-2008. While the cause of this improvement cannot be proven in a retrospective analysis, the timing of the survival increase coincides with the approval of bevacizumab for the treatment of patients with progressive GBM, indicating a possible benefit of bevacizumab in this population.

V-Phaser 2: variant inference for viral populations.

BACKGROUND: Massively parallel sequencing offers the possibility of revolutionizing the study of viral populations by providing ultra deep sequencing (tens to hundreds of thousand fold coverage) of complete viral genomes. However, differentiation of true low frequency variants from sequencing errors remains challenging. RESULTS: We developed a software package, V-Phaser 2, for inferring intrahost diversity within viral populations. This program adds three major new methodologies to the state of the art: a technique to efficiently utilize paired end read data for calling phased variants, a new strategy to represent and infer length polymorphisms, and an in line filter for erroneous calls arising from systematic sequencing artifacts. We have also heavily optimized memory and run time performance. This combination of algorithmic and technical advances allows V-Phaser 2 to fully utilize extremely deep paired end sequencing data (such as generated by Illumina sequencers) to accurately infer low frequency intrahost variants in viral populations in reasonable time on a standard desktop computer. V-Phaser 2 was validated and compared to both QuRe and the original V-Phaser on three datasets obtained from two viral populations: a mixture of eight known strains of West Nile Virus (WNV) sequenced on both 454 Titanium and Illumina MiSeq and a mixture of twenty-four known strains of WNV sequenced only on 454 Titanium. V-Phaser 2 outperformed the other two programs in both sensitivity and specificity while using more than five fold less time and memory. CONCLUSIONS: We developed V-Phaser 2, a publicly available software tool (V-Phaser 2 can be accessed via: http://www.broadinstitute.org/scientific-community/science/projects/viral-genomics/v-phaser-2 and is freely available for academic use) that enables the efficient analysis of ultra-deep sequencing data produced by common next generation sequencing platforms for viral populations.