Landscape of Germline Genetic Variants in AGT, MGMT, and TP53 in Mexican Adult Patients with Astrocytoma.

Astrocytoma is the most common type of primary brain tumor. The risk factors for astrocytoma are poorly understood; however, germline genetic variants account for 25% of the risk of developing gliomas. In this study, we assessed the risk of astrocytoma associated with variants in AGT, known by its role in angiogenesis, TP53, a well-known tumor suppressor and the DNA repair gene MGMT in a Mexican population. A case-control study was performed in 49 adult Mexican patients with grade II-IV astrocytoma. Sequencing of exons and untranslated regions of AGT, MGMT, and TP53 from was carried in an Ion Torrent platform. Individuals with Mexican Ancestry from the 1000 Genomes Project were used as controls. Variants found in our cohort were then assessed in a The Cancer Genome Atlas astrocytoma pan-ethnic validation cohort. Variants rs1926723 located in AGT (OR 2.74, 1.40-5.36 95% CI), rs7896488 in MGMT (OR 3.43, 1.17-10.10 95% CI), and rs4968187 in TP53 (OR 2.48, 1.26-4.88 95% CI) were significantly associated with the risk of astrocytoma after multiple-testing correction. This is the first study where the AGT rs1926723 variant, TP53 rs4968187, and MGMT rs7896488 were found to be associated with the risk of developing an astrocytoma.

Comparing the performance of selected variant callers using synthetic data and genome segmentation.

BACKGROUND: High-throughput sequencing has rapidly become an essential part of precision cancer medicine. But validating results obtained from analyzing and interpreting genomic data remains a rate-limiting factor. The gold standard, of course, remains manual validation by expert panels, which is not without its weaknesses, namely high costs in both funding and time as well as the necessarily selective nature of manual validation. But it may be possible to develop more economical, complementary means of validation. In this study we employed four synthetic data sets (variants with known mutations spiked into specific genomic locations) of increasing complexity to assess the sensitivity, specificity, and balanced accuracy of five open-source variant callers: FreeBayes v1.0, VarDict v11.5.1, MuTect v1.1.7, MuTect2, and MuSE v1.0rc. FreeBayes, VarDict, and MuTect were run in bcbio-next gen, and the results were integrated into a single Ensemble call set. The known mutations provided a level of "ground truth" against which we evaluated variant-caller performance. We further facilitated the comparison and evaluation by segmenting the whole genome into 10,000,000 base-pair fragments which yielded 316 segments. RESULTS: Differences among the numbers of true positives were small among the callers, but the numbers of false positives varied much more when the tools were used to analyze sets one through three. Both FreeBayes and VarDict produced strikingly more false positives than did the others, although VarDict, somewhat paradoxically also produced the highest number of true positives. The Ensemble approach yielded results characterized by higher specificity and balanced accuracy and fewer false positives than did any of the five tools used alone. Sensitivity and specificity, however, declined for all five callers as the complexity of the data sets increased, but we did not uncover anything more than limited, weak correlations between caller performance and certain DNA structural features: gene density and guanine-cytosine content. Altogether, MuTect2 performed the best among the callers tested, followed by MuSE and MuTect. CONCLUSIONS: Spiking data sets with specific mutations -single-nucleotide variations (SNVs), single-nucleotide polymorphisms (SNPs), or structural variations (SVs) in this study-at known locations in the genome provides an effective and economical way to compare data analyzed by variant callers with ground truth. The method constitutes a viable alternative to the prolonged, expensive, and noncomprehensive assessment by expert panels. It should be further developed and refined, as should other comparatively "lightweight" methods of assessing accuracy. Given that the scientific community has not yet established gold standards for validating NGS-related technologies such as variant callers, developing multiple alternative means for verifying variant-caller accuracy will eventually lead to the establishment of higher-quality standards than could be achieved by prematurely limiting the range of innovative methods explored by members of the community.

A rapid procedure for isotopic purification of copper and nickel from seawater using an automated chromatography system.

BACKGROUND: Trace metals such as iron, nickel, copper, zinc, and cadmium (Fe, Ni, Cu, Zn, and Cd) are essential micronutrients (and sometimes toxins) for phytoplankton, and the analysis of trace-metal stable isotopes in seawater is a valuable tool for exploring the biogeochemical cycling of these elements in the ocean. However, the complex and often time-consuming chromatography process required to purify these elements from seawater has limited the number of trace-metal isotope samples which can be easily processed in biogeochemical studies. To facilitate the trace-metal stable isotope analysis, here, we describe a new rapid procedure that utilizes automated chromatography for extracting and purifying Ni and Cu from seawater for isotope analysis using a prepFAST-MC™ system (Elemental Scientific Inc.). RESULTS: We have tested the matrix removal effectiveness, recoveries, and procedural blanks of the new purification procedure with satisfactory results. A nearly complete recovery of Ni and a quantitative recovery of Cu are achieved. The total procedural blanks are 0.33 ± 0.24 ng for Ni and 0.42 ± 0.18 ng for Cu, which is negligible for natural seawater samples. The new procedure cleanly separates Ni and Cu from key seawater matrix elements that may cause interferences during mass spectrometry analysis. When the new procedure was used to purify seawater samples for Ni and Cu stable isotope analysis by multi-collector ICP-MS, we achieved an overall uncertainty of 0.07 ‰ for δ60Ni and 0.09 ‰ for δ65Cu (2 SD). The new purification procedure was also tested using natural seawater samples from the South Pacific, for comparison of δ60Ni and δ65Cu achieved in the same samples purified by traditional hand columns. Both methods produced similar results, and the results from both methods are consistent with analyses of δ60Ni and δ65Cu from other ocean locations as reported by other laboratories. SIGNIFICANCE: This study presents a new rapid procedure for seawater stable-metal isotope analysis by automating the chromatography step. We anticipate that the automated chromatography described here will facilitate the rapid and accurate analysis of seawater δ60Ni and δ65Cu in future studies, and may be adapted in the future to automate chromatographic purification of Fe, Zn, and Cd isotopes from seawater.

Proteomics analysis reveals differential acclimation of coastal and oceanic Synechococcus to climate warming and iron limitation.

In many oceanic regions, anthropogenic warming will coincide with iron (Fe) limitation. Interactive effects between warming and Fe limitation on phytoplankton physiology and biochemical function are likely, as temperature and Fe availability affect many of the same essential cellular pathways. However, we lack a clear understanding of how globally significant phytoplankton such as the picocyanobacteria Synechococcus will respond to these co-occurring stressors, and what underlying molecular mechanisms will drive this response. Moreover, ecotype-specific adaptations can lead to nuanced differences in responses between strains. In this study, Synechococcus isolates YX04-1 (oceanic) and XM-24 (coastal) from the South China Sea were acclimated to Fe limitation at two temperatures, and their physiological and proteomic responses were compared. Both strains exhibited reduced growth due to warming and Fe limitation. However, coastal XM-24 maintained relatively higher growth rates in response to warming under replete Fe, while its growth was notably more compromised under Fe limitation at both temperatures compared with YX04-1. In response to concurrent heat and Fe stress, oceanic YX04-1 was better able to adjust its photosynthetic proteins and minimize the generation of reactive oxygen species while reducing proteome Fe demand. Its intricate proteomic response likely enabled oceanic YX04-1 to mitigate some of the negative impact of warming on its growth during Fe limitation. Our study highlights how ecologically-shaped adaptations in Synechococcus strains even from proximate oceanic regions can lead to differing physiological and proteomic responses to these climate stressors.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the United States National Cancer Institute convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: 1) an evolving list of comprehensive quality metrics and 2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

Introduction: In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. Methods: We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. Results: The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Conclusion: Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed upon two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam Principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (1) an evolving list of comprehensive quality metrics and (2) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in the Journal of Proteome Research, Molecular and Cellular Proteomics, Proteomics, and Proteomics Clinical Applications as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

SomaticCombiner: improving the performance of somatic variant calling based on evaluation tests and a consensus approach.

It is challenging to identify somatic variants from high-throughput sequence reads due to tumor heterogeneity, sub-clonality, and sequencing artifacts. In this study, we evaluated the performance of eight primary somatic variant callers and multiple ensemble methods using both real and synthetic whole-genome sequencing, whole-exome sequencing, and deep targeted sequencing datasets with the NA12878 cell line. The test results showed that a simple consensus approach can significantly improve performance even with a limited number of callers and is more robust and stable than machine learning based ensemble approaches. To fully exploit the multi-callers, we also developed a software package, SomaticCombiner, that can combine multiple callers and integrates a new variant allelic frequency (VAF) adaptive majority voting approach, which can maintain sensitive detection for variants with low VAFs.

Matrix effects of calcium on high-precision sulfur isotope measurement by multiple-collector inductively coupled plasma mass spectrometry.

Multiple-collector inductively coupled plasma mass spectrometry (MC-ICP-MS) has been successfully applied in the rapid and high-precision measurement for sulfur isotope ratios in recent years. During the measurement, the presence of matrix elements would affect the instrumental mass bias for sulfur and these matrix-induced effects have aroused a lot of researchers' interest. However, these studies have placed more weight on highlighting the necessity for their proposed correction protocols (e.g., chemical purification and matrix-matching) while less attention on the key property of the matrix element gives rise to the matrix effects. In this study, four groups of sulfate solutions, which have different concentrations of sulfur (0.05-0.60mM) but a constant sequence of atomic calcium/sulfur ratios (0.1-50), are investigated under wet (solution) and dry (desolvation) plasma conditions to make a detailed evaluation on the matrix effects from calcium on sulfur isotope measurement. Based on a series of comparative analyses, we indicated that, the matrix effects of calcium on both measured sulfur isotope ratios and detected (32)S signal intensities are dependent mainly on the absolute calcium concentration rather than its relative concentration ratio to sulfur (i.e., atomic calcium/sulfur ratio). Also, for the same group of samples, the matrix effects of calcium under dry plasma condition are much more significant than that of wet plasma. This research affords the opportunity to realize direct and relatively precise sulfur isotope measurement for evaporite gypsum, and further provides some suggestions with regard to sulfur isotope analytical protocols for sedimentary pore water.

Dissection of the sets of genes that control the behavior of biglycan-deficient pre-osteoblasts using oligonucleotide microarrays.

Biglycan (bgn) is a small leucine-rich proteoglycan (SLPR) that is enriched in the extracellular matrix of skeletal tissues. Bgn-deficient mice develop age-related osteopenia with a phenotype that resembles osteoporosis. In order to identify sets of genes that play a key role in the skeletal abnormality, we determined the global gene expression patterns in bgn-deficient (bgn-KO) pre-osteoblasts using oligonucleotide microarray technology. Calvarial cells were harvested from newborn mice and cultured in the presence or absence of BMP-4 for 7 days. The total RNA was purified, labeled and hybridized to Affymetrix chips (U74A), and analyzed with a software program called GeneSpring. Our data suggested that biglycan regulates the activity of osteoblastic progenitors through sets of genes associated with cell cycle, cell growth, and differentiation. The biological outcome from the altered expression of these genes could cause a defect in the quantity and quality of osteoblastic progenitors, which could contribute to the development of age-related osteopenia in bgn-KO mice. Moreover, the data from this approach also revealed that biglycan deficiency affected the genes that control inflammation, immune response, and growth of tumor cells. These are new and unexpected findings that lead to the formation of new paradigms for biglycan function. Based on these findings, we propose that the reduction of this small proteoglycan with aging may increase the risk of infection and autoimmune diseases, impair wound healing, and cause higher incidences of malignancy. This study provides a broad and deep foundation for understanding SLRP function at a more complex level.

Rapid and high-precision measurement of sulfur isotope and sulfur concentration in sediment pore water by multi-collector inductively coupled plasma mass spectrometry.

We have developed a technique for the rapid, precise and accurate determination of sulfur isotopes (δ(34)S) by MC-ICP-MS applicable to a range of sulfur-bearing solutions of different sulfur content. The 10 ppm Alfa-S solution (ammonium sulfate solution, working standard of the lab of the authors) was used to bracket other Alfa-S solutions of different concentrations and the measured δ(34)SV-CDT values of Alfa-S solutions deviate from the reference value to varying degrees (concentration effect). The stability of concentration effect has been verified and a correction curve has been constructed based on Alfa-S solutions to correct measured δ(34)SV-CDT values. The curve has been applied to AS solutions (dissolved ammonium sulfate from the lab of the authors) and pore water samples successfully, validating the reliability of our analytical method. This method also enables us to measure the sulfur concentration simultaneously when analyzing the sulfur isotope composition. There is a strong linear correlation (R(2)>0.999) between the sulfur concentrations and the intensity ratios of samples and the standard. We have constructed a regression curve based on Alfa-S solutions and this curve has been successfully used to determine sulfur concentrations of AS solutions and pore water samples. The analytical technique presented here enable rapid, precise and accurate S isotope measurement for a wide range of sulfur-bearing solutions - in particular for pore water samples with complex matrix and varying sulfur concentrations. Also, simultaneous measurement of sulfur concentrations is available.

Alview: Portable Software for Viewing Sequence Reads in BAM Formatted Files.

The name Alview is a contraction of the term Alignment Viewer. Alview is a compiled to native architecture software tool for visualizing the alignment of sequencing data. Inputs are files of short-read sequences aligned to a reference genome in the SAM/BAM format and files containing reference genome data. Outputs are visualizations of these aligned short reads. Alview is written in portable C with optional graphical user interface (GUI) code written in C, C++, and Objective-C. The application can run in three different ways: as a web server, as a command line tool, or as a native, GUI program. Alview is compatible with Microsoft Windows, Linux, and Apple OS X. It is available as a web demo at https://cgwb.nci.nih.gov/cgi-bin/alview. The source code and Windows/Mac/Linux executables are available via https://github.com/NCIP/alview.

Recommendations for mass spectrometry data quality metrics for open access data (corollary to the Amsterdam principles).

Policies supporting the rapid and open sharing of proteomic data are being implemented by the leading journals in the field. The proteomics community is taking steps to ensure that data are made publicly accessible and are of high quality, a challenging task that requires the development and deployment of methods for measuring and documenting data quality metrics. On September 18, 2010, the U.S. National Cancer Institute (NCI) convened the "International Workshop on Proteomic Data Quality Metrics" in Sydney, Australia, to identify and address issues facing the development and use of such methods for open access proteomics data. The stakeholders at the workshop enumerated the key principles underlying a framework for data quality assessment in mass spectrometry data that will meet the needs of the research community, journals, funding agencies, and data repositories. Attendees discussed and agreed up on two primary needs for the wide use of quality metrics: (i) an evolving list of comprehensive quality metrics and (ii) standards accompanied by software analytics. Attendees stressed the importance of increased education and training programs to promote reliable protocols in proteomics. This workshop report explores the historic precedents, key discussions, and necessary next steps to enhance the quality of open access data. By agreement, this article is published simultaneously in Proteomics, Proteomics Clinical Applications, Journal of Proteome Research, and Molecular and Cellular Proteomics, as a public service to the research community. The peer review process was a coordinated effort conducted by a panel of referees selected by the journals.

Expression signature developed from a complex series of mouse models accurately predicts human breast cancer survival.

PURPOSE: The capability of microarray platform to interrogate thousands of genes has led to the development of molecular diagnostic tools for cancer patients. Although large-scale comparative studies on clinical samples are often limited by the access of human tissues, expression profiling databases of various human cancer types are publicly available for researchers. Given that mouse models have been instrumental to our current understanding of cancer progression, we aimed to test the hypothesis that novel gene signatures possessing predictability in clinical outcome can be derived by coupling genomic analyses in mouse models of cancer with publicly available human cancer data sets. EXPERIMENTAL DESIGN: We established a complex series of syngeneic metastatic animal models using a murine breast cancer cell line. Tumor RNA was hybridized on Affymetrix MouseGenome-430A2.0 GeneChips. With the use of Venn logic, gene signatures that represent metastatic competency were derived and tested against publicly available human breast and lung cancer data sets. RESULTS: Survival analyses showed that the spontaneous metastasis gene signature was significantly associated with metastasis-free and overall survival (P < 0.0005). Consequently, the six-gene model was determined and showed statistical predictability in predicting survival in breast cancer patients. In addition, the model was able to stratify poor from good prognosis for lung cancer patients in most data sets analyzed. CONCLUSIONS: Together, our data support that novel gene signature derived from mouse models of cancer can be used for predicting human cancer outcome. Our approaches set precedence that similar strategies may be used to decipher novel gene signatures for clinical utility.