Clinical genomicist workstation.

The use of NextGen Sequencing clinically necessitates the need for informatics tools that support the complete workflow from sample accessioning to data analysis and reporting. To address this need we have developed Clinical Genomicist Workstation (CGW). CGW is a secure, n-tiered application where web browser submits requests to application servers that persist the data in a relational database. CGW is used by Washington University Genomic and Pathology Services for clinical genomic testing of many cancers. CGW has been used to accession, analyze and sign out over 409 cases since November, 2011. There are 22 ordering oncologists and 7 clinical genomicists that use the CGW. In summary, CGW a 'soup-to-nuts' solution to track, analyze, interpret, and report clinical genomic diagnostic tests.

A method for assessing and maintaining the reproducibility of mass spectrometric analyses of complex samples.

Direct injection mass spectrometric analysis of biological samples is potentially an attractive approach to the discovery of diagnostic patterns for specific pathophysiological conditions because of its speed and simplicity. Despite the possible benefits offered by such a method, its extensive application has been limited so far by several factors, including the inadequate reproducibility of the analytical results. We describe a method for monitoring and optimizing the performance of mass spectrometers used for biomarker discovery studies, based on the analysis of patterns of standardized spectral features. The method was successfully applied to maintaining spectral reproducibility during a multi-day analysis of hundreds of serum samples despite an ion source failure, which necessitated minor maintenance. The monitoring method allowed the early detection of that failure and the restoration of the spectral profiles after the system was restarted.

A method for monitoring and controlling reproducibility of intensity data in complex electrospray mass spectra: a thermometer ion-based strategy.

Reproducibility in mass spectral data is important in both biomarker discovery and spectral database searching. We report a strategy, employing a series of substituted benzylpyridinium thermometer ions that can be used to monitor changes in performance of multiple aspects of an electrospray ionization source that impact the intensity axis of a spectrum. Performance attributes, which could confound even isotope-based quantification strategies, are readily assessed using a mixture of thermometer ions. Based on the observed behavior of the ions, a procedure is proposed for monitoring instrument performance and compensating for factors that affect reproducibility across both time and instruments.

Multianalyte profiling of serum antigens and autoimmune and infectious disease molecules to identify biomarkers dysregulated in epithelial ovarian cancer.

Ovarian cancer is the deadliest gynecologic cancer in the United States. When detected early, the 5-year survival rate is 92%, although most cases remain undetected until the late stages where 5-year survival rates are 30%. Serum biomarkers may hold promise. Although many markers have been proposed and multivariate diagnostic models were built to fit the data on small, disparate sample sets, there has been no systematic evaluation of these markers on a single, large, well-defined sample set. To address this, we evaluated the dysregulation of 204 molecules in a sample set consisting of serum from 294 patients, collected from multiple collection sites, under a well-defined Gynecologic Oncology Group protocol. The population, weighted with early-stage cancers to assess biomarker value for early detection, contained all stages of ovarian cancer and common benign gynecologic conditions. The panel of serum molecules was assayed using rigorously qualified, high-throughput, multiplexed immunoassays and evaluated for their independent ovarian cancer diagnostic potential. Seventy-seven biomarkers were dysregulated in the ovarian cancer samples, although cancer antigen 125, C-reactive protein, epidermal growth factor receptor, interleukin 10, interleukin 8, connective tissue growth factor, haptoglobin, and tissue inhibitor of metalloproteinase 1 stood out as the most informative. When analyzed by cancer subtype and stage, there were differences in the relative value of biomarkers. In this study, using a large sample cohort, we show that some of the reported ovarian cancer biomarkers are more robust than others, and we identify additional informative candidates. These findings may guide the development of multivariate diagnostic models, which should be tested on additional, prospectively collected samples.

Assessment of serum proteomics to detect large colon adenomas.

A noninvasive blood test that could reliably detect early colorectal cancer or large adenomas would provide an important advance in colon cancer screening. The purpose of this study was to determine whether a serum proteomics assay could discriminate between persons with and without a large (> or =1 cm) colon adenoma. To avoid problems of "bias" that have affected many studies about molecular markers for diagnosis, specimens were obtained from a previously conducted study of colorectal cancer etiology in which bloods had been collected before the presence or absence of neoplasm had been determined by colonoscopy, helping to assure that biases related to differences in sample collection and handling would be avoided. Mass spectra of 65 unblinded serum samples were acquired using a nanoelectrospray ionization source on a QSTAR-XL mass spectrometer. Classification patterns were developed using the ProteomeQuest algorithm, performing measurements twice on each specimen, and then applied to a blinded validation set of 70 specimens. After removing 33 specimens that had discordant results, the "test group" comprised 37 specimens that had never been used in training. Although in the primary analysis, no discrimination was found, a single post hoc analysis, done after hemolyzed specimens had been removed, showed a sensitivity of 78%, a specificity of 53%, and an accuracy of 63% (95% confidence interval, 53-72%). The results of this study, although preliminary, suggest that further study of serum proteomics, in a larger number of appropriate specimens, could be useful. They also highlight the importance of understanding sources of "noise" and "bias" in studies of proteomics assays.