The TREAT-NMD DMD Global Database: analysis of more than 7,000 Duchenne muscular dystrophy mutations.

Analyzing the type and frequency of patient-specific mutations that give rise to Duchenne muscular dystrophy (DMD) is an invaluable tool for diagnostics, basic scientific research, trial planning, and improved clinical care. Locus-specific databases allow for the collection, organization, storage, and analysis of genetic variants of disease. Here, we describe the development and analysis of the TREAT-NMD DMD Global database (http://umd.be/TREAT_DMD/). We analyzed genetic data for 7,149 DMD mutations held within the database. A total of 5,682 large mutations were observed (80% of total mutations), of which 4,894 (86%) were deletions (1 exon or larger) and 784 (14%) were duplications (1 exon or larger). There were 1,445 small mutations (smaller than 1 exon, 20% of all mutations), of which 358 (25%) were small deletions and 132 (9%) small insertions and 199 (14%) affected the splice sites. Point mutations totalled 756 (52% of small mutations) with 726 (50%) nonsense mutations and 30 (2%) missense mutations. Finally, 22 (0.3%) mid-intronic mutations were observed. In addition, mutations were identified within the database that would potentially benefit from novel genetic therapies for DMD including stop codon read-through therapies (10% of total mutations) and exon skipping therapy (80% of deletions and 55% of total mutations).

Older patients with low Charlson score and high-risk prostate cancer benefit from radical prostatectomy

INTRODUCTION The aim of the study was to identify the appropriate level of Charlson comorbidity index (CCI) in older patients (>70 years) with high-risk prostate cancer (PCa) to achieve survival benefit following radical prostatectomy (RP). METHODS We retrospectively analyzed 1008 older patients (>70 years) who underwent RP with pelvic lymph node dissection for high-risk prostate cancer (preoperative prostate-specific antigen >20 ng/mL or clinical stage ≥T2c or Gleason ≥8) from 14 tertiary institutions between 1988 and 2014. The study population was further grouped into CCI < 2 and ≥2 for analysis. Survival rate for each group was estimated with Kaplan-Meier method and competitive risk Fine-Gray regression to estimate the best explanatory multivariable model. Area under the curve (AUC) and Akaike information criterion were used to identify ideal 'Cut off' for CCI. RESULTS The clinical and cancer characteristics were similar between the two groups. Comparison of the survival analysis using the Kaplan-Meier curve between two groups for non-cancer death and survival estimations for 5 and 10 years shows significant worst outcomes for patients with CCI ≥ 2. In multivariate model to decide the appropriate CCI cut-off point, we found CCI 2 has better AUC and p value in log rank test. CONCLUSION Older patients with fewer comorbidities harboring high-risk PCa appears to benefit from RP. Sicker patients are more likely to die due to non-prostate cancer-related causes and are less likely to benefit from RP.