Proteogenomic Analysis of Human Colon Cancer Reveals New Therapeutic Opportunities.

We performed the first proteogenomic study on a prospectively collected colon cancer cohort. Comparative proteomic and phosphoproteomic analysis of paired tumor and normal adjacent tissues produced a catalog of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers, drug targets, and cancer/testis antigens. Proteogenomic integration not only prioritized genomically inferred targets, such as copy-number drivers and mutation-derived neoantigens, but also yielded novel findings. Phosphoproteomics data associated Rb phosphorylation with increased proliferation and decreased apoptosis in colon cancer, which explains why this classical tumor suppressor is amplified in colon tumors and suggests a rationale for targeting Rb phosphorylation in colon cancer. Proteomics identified an association between decreased CD8 T cell infiltration and increased glycolysis in microsatellite instability-high (MSI-H) tumors, suggesting glycolysis as a potential target to overcome the resistance of MSI-H tumors to immune checkpoint blockade. Proteogenomics presents new avenues for biological discoveries and therapeutic development.

Proteogenomic characterization of human colon and rectal cancer.

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.

Detection of Proteome Diversity Resulted from Alternative Splicing is Limited by Trypsin Cleavage Specificity.

Alternative splicing dramatically increases transcriptome complexity but its contribution to proteome diversity remains controversial. Exon-exon junction spanning peptides provide direct evidence for the translation of specific splice isoforms and are critical for delineating protein isoform complexity. Here we found that junction-spanning peptides are underrepresented in publicly available mass spectrometry-based shotgun proteomics data sets. Further analysis showed that evolutionarily conserved preferential nucleotide usage at exon boundaries increases the occurrence of lysine- and arginine-coding triplets at the end of exons. Because both lysine and arginine residues are cleavage sites of trypsin, the nearly exclusive use of trypsin as the protein digestion enzyme in shotgun proteomic analyses hinders the detection of junction-spanning peptides. To study the impact of enzyme selection on splice junction detectability, we performed in-silico digestion of the human proteome using six proteases. The six enzymes created a total of 161,125 detectable junctions, and only 1,029 were common across all enzyme digestions. Chymotrypsin digestion provided the largest number of detectable junctions. Our experimental results further showed that combination of a chymotrypsin-based human proteome analysis with a trypsin-based analysis increased detection of junction-spanning peptides by 37% over the trypsin-only analysis and identified over a thousand junctions that were undetectable in fully tryptic digests. Our study demonstrates that detection of proteome diversity resulted from alternative splicing is limited by trypsin cleavage specificity, and that complementary digestion schemes will be essential to comprehensively analyze the translation of alternative splicing isoforms.

Colorectal Cancer Cell Line Proteomes Are Representative of Primary Tumors and Predict Drug Sensitivity.

BACKGROUND AND AIMS: Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. METHODS: Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. RESULTS: Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. CONCLUSIONS: Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine.

proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data.

To facilitate genome-based representation and analysis of proteomics data, we developed a new bioinformatics framework, proBAMsuite, in which a central component is the protein BAM (proBAM) file format for organizing peptide spectrum matches (PSMs)(1) within the context of the genome. proBAMsuite also includes two R packages, proBAMr and proBAMtools, for generating and analyzing proBAM files, respectively. Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data. First, the interpretation of proteomics data is significantly enhanced with the rich genomic annotation information. Second, PSMs can be easily reannotated using user-specified gene annotation schemes and assembled into both protein and gene identifications. Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration. Finally, proBAM files can be readily visualized in genome browsers and thus bring proteomics data analysis to a general audience beyond the proteomics community. Results from this study establish proBAMsuite as a useful bioinformatics framework for proteomics and proteogenomics research.

The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses.

Questions concerning longitudinal data quality and reproducibility of proteomic laboratories spurred the Protein Research Group of the Association of Biomolecular Resource Facilities (ABRF-PRG) to design a study to systematically assess the reproducibility of proteomic laboratories over an extended period of time. Developed as an open study, initially 64 participants were recruited from the broader mass spectrometry community to analyze provided aliquots of a six bovine protein tryptic digest mixture every month for a period of nine months. Data were uploaded to a central repository, and the operators answered an accompanying survey. Ultimately, 45 laboratories submitted a minimum of eight LC-MSMS raw data files collected in data-dependent acquisition (DDA) mode. No standard operating procedures were enforced; rather the participants were encouraged to analyze the samples according to usual practices in the laboratory. Unlike previous studies, this investigation was not designed to compare laboratories or instrument configuration, but rather to assess the temporal intralaboratory reproducibility. The outcome of the study was reassuring with 80% of the participating laboratories performing analyses at a medium to high level of reproducibility and quality over the 9-month period. For the groups that had one or more outlying experiments, the major contributing factor that correlated to the survey data was the performance of preventative maintenance prior to the LC-MSMS analyses. Thus, the Protein Research Group of the Association of Biomolecular Resource Facilities recommends that laboratories closely scrutinize the quality control data following such events. Additionally, improved quality control recording is imperative. This longitudinal study provides evidence that mass spectrometry-based proteomics is reproducible. When quality control measures are strictly adhered to, such reproducibility is comparable among many disparate groups. Data from the study are available via ProteomeXchange under the accession code PXD002114.

Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts.

The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.

Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification.

Lipid identification from data produced with high-throughput technologies is essential to the elucidation of the roles played by lipids in cellular function and disease. Software tools for identifying lipids from tandem mass (MS/MS) spectra have been developed, but they are often costly or lack the sophistication of their proteomics counterparts. We have developed Greazy, an open source tool for the automated identification of phospholipids from MS/MS spectra, that utilizes methods similar to those developed for proteomics. From user-supplied parameters, Greazy builds a phospholipid search space and associated theoretical MS/MS spectra. Experimental spectra are scored against search space lipids with similar precursor masses using a peak score based on the hypergeometric distribution and an intensity score utilizing the percentage of total ion intensity residing in matching peaks. The LipidLama component filters the results via mixture modeling and density estimation. We assess Greazy's performance against the NIST 2014 metabolomics library, observing high accuracy in a search of multiple lipid classes. We compare Greazy/LipidLama against the commercial lipid identification software LipidSearch and show that the two platforms differ considerably in the sets of identified spectra while showing good agreement on those spectra identified by both. Lastly, we demonstrate the utility of Greazy/LipidLama with different instruments. We searched data from replicates of alveolar type 2 epithelial cells obtained with an Orbitrap and from human serum replicates generated on a quadrupole-time-of-flight (Q-TOF). These findings substantiate the application of proteomics derived methods to the identification of lipids. The software is available from the ProteoWizard repository: http://tiny.cc/bumbershoot-vc12-bin64 .

Correcting systematic bias and instrument measurement drift with mzRefinery.

MOTIVATION: Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments. RESULTS: We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0 ppm. Because it is part of the ProteoWizard package, mzRefinery can read and write a wide variety of file formats. AVAILABILITY AND IMPLEMENTATION: The mzRefinery tool is part of msConvert, available with the ProteoWizard open source package at http://proteowizard.sourceforge.net/ CONTACT: samuel.payne@pnnl.gov. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Proteome informatics research group (iPRG)_2012: a study on detecting modified peptides in a complex mixture.

The proteome informatics research group of the Association of Biomolecular Resource Facilities conducted a study to assess the community's ability to detect and characterize peptides bearing a range of biologically occurring post-translational modifications when present in a complex peptide background. A data set derived from a mixture of synthetic peptides with biologically occurring modifications combined with a yeast whole cell lysate as background was distributed to a large group of researchers and their results were collectively analyzed. The results from the twenty-four participants, who represented a broad spectrum of experience levels with this type of data analysis, produced several important observations. First, there is significantly more variability in the ability to assess whether a results is significant than there is to determine the correct answer. Second, labile post-translational modifications, particularly tyrosine sulfation, present a challenge for most researchers. Finally, for modification site localization there are many tools being employed, but researchers are currently unsure of the reliability of the results these programs are producing.

QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics.

Shotgun proteomics experiments integrate a complex sequence of processes, any of which can introduce variability. Quality metrics computed from LC-MS/MS data have relied upon identifying MS/MS scans, but a new mode for the QuaMeter software produces metrics that are independent of identifications. Rather than evaluating each metric independently, we have created a robust multivariate statistical toolkit that accommodates the correlation structure of these metrics and allows for hierarchical relationships among data sets. The framework enables visualization and structural assessment of variability. Study 1 for the Clinical Proteomics Technology Assessment for Cancer (CPTAC), which analyzed three replicates of two common samples at each of two time points among 23 mass spectrometers in nine laboratories, provided the data to demonstrate this framework, and CPTAC Study 5 provided data from complex lysates under Standard Operating Procedures (SOPs) to complement these findings. Identification-independent quality metrics enabled the differentiation of sites and run-times through robust principal components analysis and subsequent factor analysis. Dissimilarity metrics revealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or individual metrics were impacted by mass spectrometer and run time. Study 5 data revealed that even when SOPs have been applied, instrument-dependent variability remains prominent, although it may be reduced, while within-site variability is reduced significantly. Finally, identification-independent quality metrics were shown to be predictive of identification sensitivity in these data sets. QuaMeter and the associated multivariate framework are available from http://fenchurch.mc.vanderbilt.edu and http://homepages.uc.edu/~wang2x7/ , respectively.

NCI's Proteomic Data Commons: A Cloud-Based Proteomics Repository Empowering Comprehensive Cancer Analysis Through Cross-Referencing with Genomic and Imaging Data.

Proteomics has emerged as a powerful tool for studying cancer biology, developing diagnostics, and therapies. With the continuous improvement and widespread availability of high-throughput proteomic technologies, the generation of large-scale proteomic data has become more common in cancer research, and there is a growing need for resources that support the sharing and integration of multi-omics datasets. Such datasets require extensive metadata including clinical, biospecimen and experimental and workflow annotations that are crucial for data interpretation and reanalysis. The need to integrate, analyze, and share these data has led to the development of National Cancer Institute's (NCI) Proteomic Data Commons (PDC), accessible at https://pdc.cancer.gov. As a specialized repository within the NCI Cancer Research Data Commons (CRDC), PDC enables researchers to locate and analyze proteomic data from various cancer types and connect with genomic and imaging data available for the same samples in other CRDC nodes. Presently, PDC houses annotated data from nearly 140 datasets across 19 cancer types, generated by several large-scale cancer research programs with cohort sizes exceeding 100 samples (tumor and associated normal when available). In this paper, we review the current state of PDC in cancer research, discuss the opportunities and challenges associated with data sharing in proteomics, and propose future directions for the resource.

IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification.

Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.

Pepitome: evaluating improved spectral library search for identification complementarity and quality assessment.

Spectral libraries have emerged as a viable alternative to protein sequence databases for peptide identification. These libraries contain previously detected peptide sequences and their corresponding tandem mass spectra (MS/MS). Search engines can then identify peptides by comparing experimental MS/MS scans to those in the library. Many of these algorithms employ the dot product score for measuring the quality of a spectrum-spectrum match (SSM). This scoring system does not offer a clear statistical interpretation and ignores fragment ion m/z discrepancies in the scoring. We developed a new spectral library search engine, Pepitome, which employs statistical systems for scoring SSMs. Pepitome outperformed the leading library search tool, SpectraST, when analyzing data sets acquired on three different mass spectrometry platforms. We characterized the reliability of spectral library searches by confirming shotgun proteomics identifications through RNA-Seq data. Applying spectral library and database searches on the same sample revealed their complementary nature. Pepitome identifications enabled the automation of quality analysis and quality control (QA/QC) for shotgun proteomics data acquisition pipelines.

QuaMeter: multivendor performance metrics for LC-MS/MS proteomics instrumentation.

LC-MS/MS-based proteomics studies rely on stable analytical system performance that can be evaluated by objective criteria. The National Institute of Standards and Technology (NIST) introduced the MSQC software to compute diverse metrics from experimental LC-MS/MS data, enabling quality analysis and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufactured by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.

Supporting tool suite for production proteomics.

SUMMARY: 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.

Cullin-independent recognition of HHARI substrates by a dynamic RBR catalytic domain.

RING-between-RING (RBR) E3 ligases mediate ubiquitin transfer through an obligate E3-ubiquitin thioester intermediate prior to substrate ubiquitination. Although RBRs share a conserved catalytic module, substrate recruitment mechanisms remain enigmatic, and the relevant domains have yet to be identified for any member of the class. Here we characterize the interaction between the auto-inhibited RBR, HHARI (AriH1), and its target protein, 4EHP, using a combination of XL-MS, HDX-MS, NMR, and biochemical studies. The results show that (1) a di-aromatic surface on the catalytic HHARI Rcat domain forms a binding platform for substrates and (2) a phosphomimetic mutation on the auto-inhibitory Ariadne domain of HHARI promotes release and reorientation of Rcat for transthiolation and substrate modification. The findings identify a direct binding interaction between a RING-between-RING ligase and its substrate and suggest a general model for RBR substrate recognition.

ScanRanker: Quality assessment of tandem mass spectra via sequence tagging.

In shotgun proteomics, protein identification by tandem mass spectrometry relies on bioinformatics tools. Despite recent improvements in identification algorithms, a significant number of high quality spectra remain unidentified for various reasons. Here we present ScanRanker, an open-source tool that evaluates the quality of tandem mass spectra via sequence tagging with reliable performance in data from different instruments. The superior performance of ScanRanker enables it not only to find unassigned high quality spectra that evade identification through database search but also to select spectra for de novo sequencing and cross-linking analysis. In addition, we demonstrate that the distribution of ScanRanker scores predicts the richness of identifiable spectra among multiple LC-MS/MS runs in an experiment, and ScanRanker scores assist the process of peptide assignment validation to increase confident spectrum identifications. The source code and executable versions of ScanRanker are available from http://fenchurch.mc.vanderbilt.edu.

Sequence tagging reveals unexpected modifications in toxicoproteomics.

Toxicoproteomic samples are rich in posttranslational modifications (PTMs) of proteins. Identifying these modifications via standard database searching can incur significant performance penalties. Here, we describe the latest developments in TagRecon, an algorithm that leverages inferred sequence tags to identify modified peptides in toxicoproteomic data sets. TagRecon identifies known modifications more effectively than the MyriMatch database search engine. TagRecon outperformed state of the art software in recognizing unanticipated modifications from LTQ, Orbitrap, and QTOF data sets. We developed user-friendly software for detecting persistent mass shifts from samples. We follow a three-step strategy for detecting unanticipated PTMs in samples. First, we identify the proteins present in the sample with a standard database search. Next, identified proteins are interrogated for unexpected PTMs with a sequence tag-based search. Finally, additional evidence is gathered for the detected mass shifts with a refinement search. Application of this technology on toxicoproteomic data sets revealed unintended cross-reactions between proteins and sample processing reagents. Twenty-five proteins in rat liver showed signs of oxidative stress when exposed to potentially toxic drugs. These results demonstrate the value of mining toxicoproteomic data sets for modifications.

TagRecon: high-throughput mutation identification through sequence tagging.

Shotgun proteomics produces collections of tandem mass spectra that contain all the data needed to identify mutated peptides from clinical samples. Identifying these sequence variations, however, has not been feasible with conventional database search strategies, which require exact matches between observed and expected sequences. Searching for mutations as mass shifts on specified residues through database search can incur significant performance penalties and generate substantial false positive rates. Here we describe TagRecon, an algorithm that leverages inferred sequence tags to identify unanticipated mutations in clinical proteomic data sets. TagRecon identifies unmodified peptides as sensitively as the related MyriMatch database search engine. In both LTQ and Orbitrap data sets, TagRecon outperformed state of the art software in recognizing sequence mismatches from data sets with known variants. We developed guidelines for filtering putative mutations from clinical samples, and we applied them in an analysis of cancer cell lines and an examination of colon tissue. Mutations were found in up to 6% of identified peptides, and only a small fraction corresponded to dbSNP entries. The RKO cell line, which is DNA mismatch repair deficient, yielded more mutant peptides than the mismatch repair proficient SW480 line. Analysis of colon cancer tumor and adjacent tissue revealed hydroxyproline modifications associated with extracellular matrix degradation. These results demonstrate the value of using sequence tagging algorithms to fully interrogate clinical proteomic data sets.