An evaluation of treatment response and remission definitions in adult obsessive-compulsive disorder: A systematic review and individual-patient data meta-analysis.

INTRODUCTION: Expert consensus operationalized treatment response and remission in obsessive-compulsive disorder (OCD) as a Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) reduction ≥35% and score ≤12 with ≤2 on Clinical Global Impressions Improvement (CGI-I) and Severity (CGI-S) scales, respectively. However, there has been scant empirical evidence supporting these definitions. METHODS: We conducted a systematic review and an individual participant data meta-analysis of randomized-controlled trials (RCTs) in adults with OCD to determine optimal Y-BOCS thresholds for response and remission. We estimated pooled sensitivity/specificity for each percent reduction threshold (response) or posttreatment score (remission) to determine response and remission defined by a CGI-I and CGI-S ≤ 2, respectively. RESULTS: Individual participant data from 25 of 94 eligible RCTs (1235 participants) were included. The optimal threshold for response was ≥30% Y-BOCS reduction and for remission was ≤15 posttreatment Y-BOCS. However, differences in sensitivity and specificity between the optimal and nearby thresholds for response and remission were small with some uncertainty demonstrated by the confidence ellipses. CONCLUSION: While the empirically derived Y-BOCS thresholds in our meta-analysis differ from expert consensus, given the predominance of data from more recent trials of OCD, which involved more refractory participants and novel treatment modalities as opposed to first-line therapies, we recommend the continued use of the consensus definitions.

Searching for molecular markers in head and neck squamous cell carcinomas (HNSCC) by statistical and bioinformatic analysis of larynx-derived SAGE libraries.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans. The average 5-year survival rate is one of the lowest among aggressive cancers, showing no significant improvement in recent years. When detected early, HNSCC has a good prognosis, but most patients present metastatic disease at the time of diagnosis, which significantly reduces survival rate. Despite extensive research, no molecular markers are currently available for diagnostic or prognostic purposes. METHODS: Aiming to identify differentially-expressed genes involved in laryngeal squamous cell carcinoma (LSCC) development and progression, we generated individual Serial Analysis of Gene Expression (SAGE) libraries from a metastatic and non-metastatic larynx carcinoma, as well as from a normal larynx mucosa sample. Approximately 54,000 unique tags were sequenced in three libraries. RESULTS: Statistical data analysis identified a subset of 1,216 differentially expressed tags between tumor and normal libraries, and 894 differentially expressed tags between metastatic and non-metastatic carcinomas. Three genes displaying differential regulation, one down-regulated (KRT31) and two up-regulated (BST2, MFAP2), as well as one with a non-significant differential expression pattern (GNA15) in our SAGE data were selected for real-time polymerase chain reaction (PCR) in a set of HNSCC samples. Consistent with our statistical analysis, quantitative PCR confirmed the upregulation of BST2 and MFAP2 and the downregulation of KRT31 when samples of HNSCC were compared to tumor-free surgical margins. As expected, GNA15 presented a non-significant differential expression pattern when tumor samples were compared to normal tissues. CONCLUSION: To the best of our knowledge, this is the first study reporting SAGE data in head and neck squamous cell tumors. Statistical analysis was effective in identifying differentially expressed genes reportedly involved in cancer development. The differential expression of a subset of genes was confirmed in additional larynx carcinoma samples and in carcinomas from a distinct head and neck subsite. This result suggests the existence of potential common biomarkers for prognosis and targeted-therapy development in this heterogeneous type of tumor.

Simcluster: clustering enumeration gene expression data on the simplex space.

BACKGROUND: Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. RESULTS: Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. CONCLUSION: Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.