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Currently prostate-specific antigen is used for prostate cancer (PCa) screening, however it lacks the necessary specificity for differentiating PCa from other diseases of the prostate such as benign prostatic hyperplasia (BPH), presenting a clinical need to distinguish these cases at the molecular level. Protein glycosylation plays an important role in a number of cellular processes involved in neoplastic progression and is aberrant in PCa. In this study, we systematically interrogate the alterations in the circulating levels of hundreds of serum proteins and their glycoforms in PCa and BPH samples using multi-lectin affinity chromatography and quantitative mass spectrometry-based proteomics. Specific lectins (AAL, PHA-L and PHA-E) were used to target and chromatographically separate core-fucosylated and highly-branched protein glycoforms for analysis, as differential expression of these glycan types have been previously associated with PCa. Global levels of CD5L, CFP, C8A, BST1, and C7 were significantly increased in the PCa samples. Notable glycoform-specific alterations between BPH and PCa were identified among proteins CD163, C4A, and ATRN in the PHA-L/E fraction and among C4BPB and AZGP1 glycoforms in the AAL fraction. Despite these modest differences, substantial similarities in glycoproteomic profiles were observed between PCa and BPH sera.

Background Quantitative proteomics allows for the discovery and functional investigation of blood-based pre-diagnostic biomarkers for early cancer detection. However, a major limitation of proteomic investigations in biomarker studies remains the biological and technical variability in the analysis of complex clinical samples. Moreover, unlike ‘omics analogues such as genomics and transcriptomics, proteomics has yet to achieve reproducibility and long-term stability on a unified technological platform. Few studies have thoroughly investigated protein variability in pre-diagnostic samples of cancer patients across multiple platforms. Methods We obtained ten blood plasma “case” samples collected up to 2 years prior to breast cancer diagnosis. Each case sample was paired with a matched control plasma from a full biological sister without breast cancer. We measured protein levels using both mass-spectrometry and antibody-based technologies to: (1) assess the technical considerations in different protein assays when analyzing limited clinical samples, and (2) evaluate the statistical power of potential diagnostic analytes. Results Although we found inherent technical variation in the three assays used, we detected protein dependent biological signal from the limited samples. The three assay types yielded 32 proteins with statistically significantly (p

Recent advances in proteome informatics have led to an explosion in tools to analyze mass spectrometry data. These tools operate across the analysis pipeline doing everything from assessing quality control to matching peptides to spectra to quantification. Unfortunately, the vast majority of these tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Consequently, the first step in many protocols is the conversion of data from vendor-specific binary files to open-format files. This protocol details the use of ProteoWizard's msConvert and msConvertGUI software for this conversion, taking format features, coding options, and vendor particularities into account. We specifically describe the various options available when doing conversions and the implications of each option.

Principal Component Analysis. (a) first (x-axis) and second (y-axis) principal components capture more than 90% of variance in both Myriad-RBM and Olink datasets but are unable to separate case and control samples (supported visually by heavily overlapping red and blue density maps). (b) the same PCA plots as (a) with samples colored with anonymized sister pair indices reveal that, when evaluated in covariance, protein levels are not more similar between biological sisters than between unrelated individuals in the antibody-based assays. (DOCX 604Â kb)

Recent advances in proteome informatics have led to an explosion in tools to analyze mass spectrometry data. These tools operate across the analysis pipeline doing everything from assessing quality control to matching peptides to spectra to quantification. Unfortunately, the vast majority of these tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Consequently, the first step in many protocols is the conversion of data from vendor-specific binary files to open-format files. This protocol details the use of ProteoWizard's msConvert and msConvertGUI software for this conversion, taking format features, coding options, and vendor particularities into account. We specifically describe the various options available when doing conversions and the implications of each option.

Ongoing cancer genome characterization studies continue to elucidate the spectrum of genomic abnormalities that drive many cancers, and in the clinical arena assessment of the driver genetic alterations in patients is playing an increasingly important diagnostic and/or prognostic role for many cancer types. However, the landscape of genomic abnormalities is still unknown for less common cancers, and the influence of specific genotypes on clinical behavior is often still unclear. To address some of these deficiencies, we developed Profile, a prospective cohort study to obtain genomic information on all patients at a large tertiary care medical center for cancer-related care. We enrolled patients with any cancer diagnosis, and, for each patient (unselected for cancer site or type) we applied mass spectrometric genotyping (OncoMap) of 471 common recurrent mutations in 41 cancer-related genes. We report the results of the first 5000 patients, of which 26% exhibited potentially actionable somatic mutations. These observations indicate the utility of genotyping in advancing the field of precision oncology.

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins. Our goal was to quantify these figures of merit for a number of different proteins and peptides, in order to gain more insight in the feasibility of protein biomarker discovery efforts using this technique. Control proteins and peptides were deposited in serial dilutions on thinly sectioned mouse xenograft tissue. Using our experimental setup, coefficients of variation were

Targeted next-generation sequencing (NGS) to capture genes or regions of interest has proven to be a cost-effective alternative to whole genome sequencing (WGS), particularly for cancer research and clinical cancer care. In this context, biologically or clinically relevant selected genes/regions are sequenced to several hundred-fold coverage. However, current algorithms and tools for detecting large structural variants (SVs), such as translocations, fail to achieve either high specificity or sensitivity, due to the fact that most available methods were designed for WGS data, and thus do not take advantage of the reduced size and higher coverage of targeted sequencing to improve SV calling. We developed a novel method, BreaKmer, to detect indels, rearrangements, and translocations from single sample targeted genomic reads. The algorithm extracts mis-mapped single or paired-end NGS reads. From these reads, hypothesized to contain SV breakpoints, contigs are built with a kmer strategy: reads are broken into k-length substrings or kmers, and those occurring in the reference are filtered. The remaining kmers represent sequences containing any sequence variant from the reference, ranging from single nucleotides to larger variants. Contigs are assembled from reads containing sample specific kmers. SVs are called based on alignment of the contigs to the reference sequence. With paired-end (PE) reads, discordantly mapped paired reads are extracted and coupled with SV calls that are made. To demonstrate BreaKmer, we analyzed NGS data from 166 samples enriched using 3 different capture panels (ranging from 305-504 genes). Our dataset contained 25 cancer specimens with known translocation events verified by orthogonal clinical methods and a negative control set of 141 ‘normal’ samples with no known SVs. The samples represented DNA extracted from FFPE, fresh frozen, blood and cell lines. Among 25 samples, 15 had additional replicate samples. Mean target coverage over all the samples was on average 150x. Specimens were barcoded at library preparation and pooled, followed by hybrid-capture targeting cancer genes and sequenced 2x100bp PE. All translocation events from the 25 test samples and their replicates (46) were detected by BreaKmer. An additional 19 translocations were detected among all the samples and their replicates, while no translocation calls were made for the 141 negative control samples. Our novel kmer strategy to detect SVs displayed high sensitivity and specificity. We reliably detected rearrangements of ALK, BCL2-IGH, BCR-ABL1 from lung adenocarcinoma, B-cell lymphoma, and chronic myeloid leukemia samples, respectively; indicating real clinical utility of this algorithm. In addition, our tool effectively detects other SV types - such as indels in FLT3 and KIT, among other genes. Our algorithm thus serves a pressing need for improved SV detection in targeted NGS data, particularly in precision cancer medicine. Citation Format: Ryan P. Abo, Elizabeth P. Garcia, Matthew Ducar, Ravali Adusumilli, Marc Breneiser, Vanessa Rojas-Rudilla, Lynette M. Sholl, Neal I. Lindeman, Matthew L. Meyerson, William C. Hahn, Paul Van Hummelen, Laura E. MacConaill. BreaKmer: Detection of structural rearrangements in targeted next-generation sequencing data using kmers. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5321. doi:10.1158/1538-7445.AM2014-5321

Next-generation sequencing (NGS) is increasingly used in the laboratory and clinic to identify genomic factors contributing to tumorigenesis, such as somatic mutations, DNA insertions and deletions, transcriptome and epigenetic changes, and chromosomal abnormalities. Because tumor specimen tissues are often quite limited or are from rare and precious samples, it is necessary to prepare sequencing libraries from minimal amounts of DNA. Consequently, limited DNA input necessitates amplification during NGS library preparation. Therefore, it is of great importance to amplify the library in an efficient and uniform manner to achieve reproducible amplification of the library fragments and limit GC content bias. Variable GC-bias among libraries is of particular importance for copy number variant (CNV) detection based on sequence coverage. Here, we compare the TruSeq DNA Sample Preparation Kit (Illumina Inc., San Diego, CA) and the KAPA Library Preparation Kit (Kapa Biosystems, Wilmington, MA) to determine which technique achieves optimal library coverage. The major difference between the methods is the DNA polymerase used for library enrichment. The Kapa HiFi polymerase was engineered to have higher fidelity and increased DNA affinity relative to the Herculase II Fusion DNA Polymerase (Agilent Technologies, Inc., Santa Clara, CA) that we used for library enrichment with the TruSeq kit. Thus, the Kapa HiFi polymerase should more efficiently amplify targets across a larger GC content range and improve sequence coverage uniformity. Relative to TruSeq-prepared libraries, the Kapa-prepared libraries had a larger mean insert size, more uniform coverage distribution, and better coverage of GC-rich regions of the genome. This was observed for DNA extracted from blood, cell lines, and formalin-fixed paraffin embedded (FFPE) tumor samples. More experiments are underway to determine the effect of the improved recovery of high-GC content DNA fragments on CNV detection. Citation Format: Aaron R. Thorner, Ashwini Sunkavalli, Ling Lin, Robert T. Jones, Laura Schubert, Matthew D. Ducar, Ravali Adusumilli, Deniz Nesli Dolcen, Liuda Ziaugra, Jack Lepine, Laura E. MacConaill, William C. Hahn, Matthew Meyerson, Paul Van Hummelen. Reducing GC-bias and improving coverage distribution in Illumina sequencing using the Kapa Biosystems library construction method. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3583. doi:10.1158/1538-7445.AM2014-3583

The ability to harness the power of next generation sequencing for characterizing the cancer genome would be extremely valuable to the cancer research community and ultimately improve diagnosis and individualized therapy. We present a targeted approach using massively parallel sequencing to simultaneously detect mutations, translocations and copy-number variations in archived clinical tumor specimen. Targeted sequencing was achieved by designing RNA baits to capture the exons of 504 genes with relevance to cancer. The bait set was augmented with specific intronic sequences to detect translocations often involved in cancer. The overall performance of the hybrid capture was improved by optimizing the tiling strategy of the baits. These improvements significantly shifted fragments with low or no-coverage closer to the overall mean, resulting in a more uniform coverage distribution. To validate the targeted gene panel we sequenced tumor samples harboring mutations, translocations and copy-number alterations that were previously identified by clinically approved assays. All the known alterations were confirmed with additional potentially actionable mutations. The ultimate goal of the targeted panel is to screen cancer patients quickly and economically allowing simultaneous detection of all common genomic alterations in the cancer genome. Citation Format: Paul Van Hummelen, Matthew Ducar, Robert T. Jones, Alina Raza, Ashwini Sunkavalli, Megan Hanna, Adri Mills, Ravali Adusumilli, Prateek Kumar, Laura Schubert, Marc Breneiser, Anna C. Cooley, Elizabeth Garcia, Lynette M. Scholl, Neal I. Lindeman, Nikhil Wagle, Levi Garraway, Kristian Cibulskis, Scott L. Carter, Michael Lawrence, Gad Getz, Matthew L. Meyerson, William C. Hahn, Laura E. MacConaill. Targeted sequencing to detect somatic mutations, translocations and copy-number variation in human tumors simultaneously. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 819. doi:10.1158/1538-7445.AM2013-819

Subject characteristics of blood plasma samples from the Northern California site of the Breast Cancer Family Registry. Table S2 Targeted proteins in the antibody-based proteomics platforms. (DOCX 25Â kb)

Subject characteristics of blood plasma samples from the Northern California site of the Breast Cancer Family Registry. Table S2 Targeted proteins in the antibody-based proteomics platforms. (DOCX 25Â kb)

Benchmark challenges, such as the Critical Assessment of Structure Prediction (CASP) and Dialogue for Reverse Engineering Assessments and Methods (DREAM) have been instrumental in driving the development of bioinformatics methods. Typically, challenges are posted, and then competitors perform a prediction based upon blinded test data. Challengers then submit their answers to a central server where they are scored. Recent efforts to automate these challenges have been enabled by systems in which challengers submit Docker containers, a unit of software that packages up code and all of its dependencies, to be run on the cloud. Despite their incredible value for providing an unbiased test-bed for the bioinformatics community, there remain opportunities to further enhance the potential impact of benchmark challenges. Specifically, current approaches only evaluate end-to-end performance; it is nearly impossible to directly compare methodologies or parameters. Furthermore, the scientific community cannot easily reuse challengers' approaches, due to lack of specifics, ambiguity in tools and parameters as well as problems in sharing and maintenance. Lastly, the intuition behind why particular steps are used is not captured, as the proposed workflows are not explicitly defined, making it cumbersome to understand the flow and utilization of data. Here we introduce an approach to overcome these limitations based upon the WINGS semantic workflow system. Specifically, WINGS enables researchers to submit complete semantic workflows as challenge submissions. By submitting entries as workflows, it then becomes possible to compare not just the results and performance of a challenger, but also the methodology employed. This is particularly important when dozens of challenge entries may use nearly identical tools, but with only subtle changes in parameters (and radical differences in results). WINGS uses a component driven workflow design and offers intelligent parameter and data selection by reasoning about data characteristics. This proves to be especially critical in bioinformatics workflows where using default or incorrect parameter values is prone to drastically altering results. Different challenge entries may be readily compared through the use of abstract workflows, which also facilitate reuse. WINGS is housed on a cloud based setup, which stores data, dependencies and workflows for easy sharing and utility. It also has the ability to scale workflow executions using distributed computing through the Pegasus workflow execution system. We demonstrate the application of this architecture to the DREAM proteogenomic challenge.

Formalin fixation followed by paraffin embedment (FFPE) is the most common method of preserving resected tissues. FFPEs can be easily stored, retrieved and processed for further analysis when compared to the logistical complications of processing fresh frozen (FF) material. Conversely, they may not be ideal for sequencing because of the nucleic acid fragmentation and artifacts introduced by fixation. Here we report our efforts in performing next-generation sequencing on more than 70 specimens comprising >40 FFPE samples, from low to high quality, and compared the performance of FFPE, FF and commercially available cell lines.