A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline

Rudnick, Paul; Markey, Sanford P.; Roth, Julius A.; Mirokhin, Yuri A.; Yan, Xiaojun; Tchekhovskoi, Dmitrii V.; Edwards, Nathan; Thangudu, Ratna R.; Ketchum, Karen A.; Kinsinger, Christopher R.; Mesri, Mehdi; Rodriguez, Henry; Stein, Stephen E.

doi:10.1021/acs.jproteome.5b01091

Cited by 103 publications

(88 citation statements)

References 18 publications

(36 reference statements)

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“…The downloaded data had been processed using the Common Data Analysis Pipeline (CDAP) (14), which standardizes the treatment of data across the consortium. CDAP software is flexible to accommodate different types of mass spectrometry data.…”

Section: Experimental Procedures and Resultsmentioning

confidence: 99%

Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal

Heins

Muller

et al. 2018

Preprint

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SummaryThe Clinical Proteomic Tumor Analysis Consortium (CPTAC) has produced extensive mass spectrometry based proteomics data for selected breast, colon and ovarian tumors from The Cancer Genome Atlas (TCGA). We have incorporated the CPTAC proteomics data into the cBioPotal to support easy exploration and integrative analysis of these proteomic datasets in the context of the clinical and genomics data from the same tumors. cBioPortal is an open source platform for exploring, visualizing, and analyzing multi-dimensional cancer genomics and clinical data. The public instance of the cBioPortal (http://cbioportal.org/) hosts more than 100 cancer genomics studies including all of the data from TCGA. Its biologist-friendly interface provides many rich analysis features, including a graphical summary of gene-level data across multiple platforms, correlation analysis between genes or other data types, survival analysis, and network visualization. Here, we present the integration of the CPTAC mass spectrometry based proteomics data into the cBioPortal, consisting of 77 breast, 95 colorectal, and 174 ovarian tumors that already have been profiled by TCGA for mutations, copy number alterations, gene expression, and DNA methylation. As a result, the CPTAC data can now be easily explored and analyzed in the cBioPortal in the context of clinical and genomics data. By integrating CPTAC data into cBioPortal, limitations of TCGA proteomics array data can be overcome while also providing a user-friendly web interface, a web API and an R client to query the mass spectrometry data together with genomic, epigenomic, and clinical data.

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Section: Experimental Procedures and Resultsmentioning

confidence: 99%

Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal

Heins

Muller

et al. 2018

Preprint

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“…While some large-scale efforts, such as the Clinical Proteomic Tumor Analysis Consortium (CPTAC), are addressing this shortcoming, proteomics and metabolomics is still catching up to the massively parallel nucleic acid profiling approaches in terms of ease of data generation, standardization, and accessibility. [104][105][106] Further improvements in multiplexing, more rapid and standardized separations, and improved mass-spectrometry scan speed and automation will be important criteria for continuing to advance the field. Proteomics and metabolomics raw data processing and feature identification…”

Section: Mass Spectrometry Data Acquisitionmentioning

confidence: 99%

Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

2018

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The objective of omics studies is to globally measure the different classes of cellular biomolecules present in a biological specimen (e.g. proteins, metabolites) as accurately as possible in order to investigate the corresponding 'states' of biological systems. High throughput omics technologies are emerging as an increasingly powerful toolkit in the rapidly advancing field of systems biology, enabling the systematic study of dynamic molecular processes that drive core cell functions like growth, sensing, and environmental adaptation. Advances in high resolution mass spectrometry, in particular, now allow for the near comprehensive study of cellular proteins and metabolites that underlie physiological homeostasis and disease pathogenesis. Yet while the expression levels, modification states, and functional associations of diverse molecular species are now measurable, existing proteomic and metabolomic data generation and analysis workflows are often specialized and incompatible. Hence, while there are now many reports of ad hoc combinations of unimolecular proteomic and metabolomic workflows, only a limited number of multi-omic profiling approaches have been reported for obtaining different molecular measurements (proteins, metabolites, nucleic acids) in parallel from a single biological sample. Moreover, elucidating how the myriad of measured cellular components are linked together functionally within the metabolic processes, signal transduction pathways, and macromolecular interaction networks central to living systems remains a massive, complicated, and uncertain endeavor. Presented here is a review of convergent mass spectrometry-based multi-omic methodologies, with a focus on notable recent advances and remaining challenges in terms of efficient sample preparation, biochemical separations, data acquisition, and integrative computational strategies. We outline a unifying network-based integrative framework to better derive biological knowledge from integrated profiling studies with the goal of realizing the full potential of multi-omic data sets.

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“…They evaluated the performance of the proposed method on synthetic data and on real DNA methylation, gene expression, and miRNA expression data from ovarian cancer samples extracted from TCGA. The second axis illustrated by four of the 15 pre-selected articles [13][14][15][16] is the rapid growth of proteomics data resource analysis and integration with genomics data enabling the emerging of proteogenomic approaches. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) [3] has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by TCGA program.…”

Section: Description Of Candidate Best Papers and Selected Best Papersmentioning

confidence: 99%

“…The Clinical Proteomic Tumor Analysis Consortium (CPTAC) [3] has produced large proteomics data sets from the mass spectrometric interrogation of tumor samples previously analyzed by TCGA program. In [13], the authors describe a tool named Common Data Analysis Platform (CDAP) produced by the CPTAC. Thanks to a dedicated pipeline, the goal of the CDAP is to provide standard and uniform reports for all CPTAC data (peptide-spectrum-match reports and gene-level reports), hence reducing the variability generated by disparate data analysis platforms and enabling comparisons between different samples and cancer types as well as across the major omics fields.…”

Section: Description Of Candidate Best Papers and Selected Best Papersmentioning

confidence: 99%

Best Paper Selection

2017

Yearb Med Inform

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SummaryObjectives: To summarize excellent current research and propose a selection of best papers published in 2016 in the field of Bioinformatics and Translational Informatics with applications in the health domain and clinical care. Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2017, from which we attempt to derive a synthetic overview of current and future activities in the field. As in 2016, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section coverage. Each section editor evaluated separately the set of 951 articles returned and evaluation results were merged for retaining 15 candidate best papers for peer-review. Results: The selection and evaluation process of papers published in the Bioinformatics and Translational Informatics field yielded four excellent articles focusing this year on the secondary use and massive integration of multi-omics data for cancer genomics and non-cancer complex diseases. Papers present methods to study the functional impact of genetic variations, either at the level of the transcription or at the levels of pathway and network. Conclusions: Current research activities in Bioinformatics and Translational Informatics with applications in the health domain continue to explore new algorithms and statistical models to manage, integrate, and interpret large-scale genomic datasets. As addressed by some of the selected papers, future trends would include the question of the international collaborative sharing of clinical and omics data, and the implementation of intelligent systems to enhance routine medical genomics. KeywordsTranslational medical research; computational biology; gene genome expression; genome; medical informatics Yearb Med Inform 2017:188-92 http://dx

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A Description of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Common Data Analysis Pipeline

Cited by 103 publications

References 18 publications

Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal

Integration and analysis of CPTAC proteomics data in the context of cancer genomics in the cBioPortal

Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

Best Paper Selection

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