Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Research Abstract Details 

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Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Abstract Text:

Chenwei Liu,Nancy Shea,Sally Rucker,Linda Harvey,Paul Russo,Richard Saul,Mary F Lopez,Alvydas Mikulskis,Scott Kuzdzal,Eva Golenko,David Fishman,Eric Vonderheid,Susan Booher,Edward W Cowen,Sam T Hwang,Gordon R Whiteley,Chenwei Liu,Nancy Shea,Sally Rucker,Linda Harvey,Paul Russo,Richard Saul,Mary F Lopez,Alvydas Mikulskis,Scott Kuzdzal,Eva Golenko,David Fishman,Eric Vonderheid,Susan Booher,Edward W Cowen,Sam T Hwang,Gordon R Whiteley,Chenwei Liu,Nancy Shea,Sally Rucker,Linda Harvey,Paul Russo,Richard Saul,Mary F Lopez,Alvydas Mikulskis,Scott Kuzdzal,Eva Golenko,David Fishman,Eric Vonderheid,Susan Booher,Edward W Cowen,Sam T Hwang,Gordon R Whiteley,

Proteomic patterns as a potential diagnostic technology has been well established for several cancer conditions and other diseases. The use of machine learning techniques such as decision trees, neural networks, genetic algorithms, and other methods has been the basis for pattern determination. Cancer is known to involve signaling pathways that are regulated through PTM of proteins. These modifications are also detectable with high confidence using high-resolution MS. We generated data using a prOTOFtrade mark mass spectrometer on two sets of patient samples: ovarian cancer and cutaneous t-cell lymphoma (CTCL) with matched normal samples for each disease. Using the knowledge of mass shifts caused by common modifications, we built models using peak pairs and compared this to a conventional technique using individual peaks. The results for each disease showed that a small number of peak pairs gave classification equal to or better than the conventional technique that used multiple individual peaks. This simple peak picking technique could be used to guide identification of important peak pairs involved in the disease process.

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Publishing Authors By Initials

C Liu,N Shea,S Rucker,L Harvey,P Russo,R Saul,MF Lopez,A Mikulskis,S Kuzdzal,E Golenko,D Fishman,E Vonderheid,S Booher,EW Cowen,ST Hwang,GR Whiteley,C Liu,N Shea,S Rucker,L Harvey,P Russo,R Saul,MF Lopez,A Mikulskis,S Kuzdzal,E Golenko,D Fishman,E Vonderheid,S Booher,EW Cowen,ST Hwang,GR Whiteley,C Liu,N Shea,S Rucker,L Harvey,P Russo,R Saul,MF Lopez,A Mikulskis,S Kuzdzal,E Golenko,D Fishman,E Vonderheid,S Booher,EW Cowen,ST Hwang,GR Whiteley,

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Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Proteomics

VOLUME: 7

Page Numbers: 4045-52

Journal Abbreviation: Proteomics

ISSN: 1615-9853

DAY: 20

MONTH: Nov

YEAR: 2007

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications. Information

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LANGUAGE: eng

NlmUniqueID: 101092707

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Grant and Affiliation Information for Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.

AFFILIATION: Clinical Proteomics Reference Laboratory, Gaithersburg, MD, USA.

Country: Germany

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MEDLINETA: Proteomics

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