The large amount of data produced by proteomics experiments requires effective bioinformatics tools for the integration of data management and data analysis. Here we introduce a suite of tools developed at Vanderbilt University to support production proteomics. We present the Backup Utility Service tool for automated instrument file backup and the ScanSifter tool for data conversion. We also describe a queuing system to coordinate identification pipelines and the File Collector tool for batch copying analytical results. These tools are individually useful but collectively reinforce each other. They are particularly valuable for proteomics core facilities or research institutions that need to manage multiple mass spectrometers. With minor changes, they could support other types of biomolecular resource facilities.

30 Background: As targeted therapies become widespread in the treatment of cancer, tracking clinical effectiveness as a function of mutation status, outside of the research setting, is needed. We evaluated whether mutation-specific survival statistics could be derived in a completely automated fashion from electronic medical record data sources, for quality assurance and purposes preparatory to research. Methods: Patients with cancer mutation analysis obtained for clinical care at Vanderbilt University up to June 2013 were included. Informatics algorithms were developed to automatically extract tumor mutation status, cancer type, date of diagnosis, and date of death or date of last contact using multiple structured and unstructured clinical data sources including billing codes, narrative pathology reports, and the Social Security Death Index. Survival was modeled using Cox proportional hazards, stratified by mutation type; mutations occurring less than 10 times were aggregated into an “Other” category. Results: 2,636 out of 3,115,904 patients had sufficient data for inclusion. Date of death was recorded for 32% of patients; overall median follow-up was 19 months. The median mutation-specific survival for lung cancer and melanoma patients is shown in the Table. For lung cancer patients, EGFR mutation was associated with superior survival (HR 0.7, 95% CI 0.5-1, p = 0.05). For melanoma patients, GNAQ mutation was associated with inferior survival (HR 2.2, 95% CI 1.3-3.8, p = 0.006) whereas BRAF mutation was not statistically significant (p = 0.32). Conclusions: It is feasible to create survival curves based on cancer mutation status in a fully automated fashion for quality assurance and purposes preparatory to research. Further iterative improvements in the data extraction algorithms are continuing; updates will be presented. Future work will enable stratification by mutation subtype, treatment exposure, staging, and demographics. [Table: see text]