Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes

Stefani N. Thomas, Betty Friedrich, Michael Schnaubelt, Daniel W. Chan, Hui Zhang, Ruedi Aebersold

Research output: Other contribution

Abstract

The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) has established a two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) workflow using isobaric tagging to compare protein abundance across samples. The workflow has been used for large-scale clinical proteomic studies with deep proteomic coverage within and outside of CPTAC. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, was recently developed as an alternate proteomic approach. In this study, we analyzed remaining aliquots of peptides using SWATH-MS from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study (Zhang et al., 2016). The SWATH-MS results indicated that both methods confidently identified differentially expressed proteins in enriched pathways associated with the robust Mesenchymal subtype of high-grade serous ovarian cancer (HGSOC) and the homologous recombination deficient tumors also present in the original study. The results demonstrated that SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC harmonized proteomic method, with the advantages of simpler, faster, and cheaper workflows, as well as lower sample consumption. However, the SWATH/DIA-MS workflow resulted in shallower proteome coverage. Overall, we concluded that both analytical methods are suitable to characterize clinical samples such as in the high-grade serous ovarian cancer study, providing proteomic workflow alternatives for cancer researchers depending on the specific goals and context of the studies.
Original languageEnglish (US)
DOIs
StatePublished - Oct 3 2019

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Proteomics
Ovarian Neoplasms
Workflow
Neoplasms
National Cancer Institute (U.S.)
Homologous Recombination
Proteome
Tandem Mass Spectrometry
Liquid Chromatography
Proteins
Research Personnel
Peptides

Cite this

Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes. / Thomas, Stefani N.; Friedrich, Betty; Schnaubelt, Michael; Chan, Daniel W.; Zhang, Hui; Aebersold, Ruedi.

2019.

Research output: Other contribution

Thomas, Stefani N. ; Friedrich, Betty ; Schnaubelt, Michael ; Chan, Daniel W. ; Zhang, Hui ; Aebersold, Ruedi. / Orthogonal proteomic platforms and their implications for the stable classification of high-grade serous ovarian cancer subtypes. 2019.
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abstract = "The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) has established a two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) workflow using isobaric tagging to compare protein abundance across samples. The workflow has been used for large-scale clinical proteomic studies with deep proteomic coverage within and outside of CPTAC. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, was recently developed as an alternate proteomic approach. In this study, we analyzed remaining aliquots of peptides using SWATH-MS from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study (Zhang et al., 2016). The SWATH-MS results indicated that both methods confidently identified differentially expressed proteins in enriched pathways associated with the robust Mesenchymal subtype of high-grade serous ovarian cancer (HGSOC) and the homologous recombination deficient tumors also present in the original study. The results demonstrated that SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC harmonized proteomic method, with the advantages of simpler, faster, and cheaper workflows, as well as lower sample consumption. However, the SWATH/DIA-MS workflow resulted in shallower proteome coverage. Overall, we concluded that both analytical methods are suitable to characterize clinical samples such as in the high-grade serous ovarian cancer study, providing proteomic workflow alternatives for cancer researchers depending on the specific goals and context of the studies.",
author = "Thomas, {Stefani N.} and Betty Friedrich and Michael Schnaubelt and Chan, {Daniel W.} and Hui Zhang and Ruedi Aebersold",
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AU - Thomas, Stefani N.

AU - Friedrich, Betty

AU - Schnaubelt, Michael

AU - Chan, Daniel W.

AU - Zhang, Hui

AU - Aebersold, Ruedi

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AB - The National Cancer Institute (NCI) Clinical Proteomic Tumor Analysis Consortium (CPTAC) has established a two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) workflow using isobaric tagging to compare protein abundance across samples. The workflow has been used for large-scale clinical proteomic studies with deep proteomic coverage within and outside of CPTAC. SWATH-MS, an instance of data-independent acquisition (DIA) proteomic methods, was recently developed as an alternate proteomic approach. In this study, we analyzed remaining aliquots of peptides using SWATH-MS from the original retrospective TCGA samples generated for the CPTAC ovarian cancer proteogenomic study (Zhang et al., 2016). The SWATH-MS results indicated that both methods confidently identified differentially expressed proteins in enriched pathways associated with the robust Mesenchymal subtype of high-grade serous ovarian cancer (HGSOC) and the homologous recombination deficient tumors also present in the original study. The results demonstrated that SWATH/DIA-MS presents a promising complementary or orthogonal alternative to the CPTAC harmonized proteomic method, with the advantages of simpler, faster, and cheaper workflows, as well as lower sample consumption. However, the SWATH/DIA-MS workflow resulted in shallower proteome coverage. Overall, we concluded that both analytical methods are suitable to characterize clinical samples such as in the high-grade serous ovarian cancer study, providing proteomic workflow alternatives for cancer researchers depending on the specific goals and context of the studies.

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