title: Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.
abstract: 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 prOTOF 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.
authors.affiliation: Clinical Proteomics Reference Laboratory, Gaithersburg, MD, USA.
authors.forename: Chenwei
authors.initials: C
authors.lastname: Liu
authors.forename: Nancy
authors.initials: N
authors.lastname: Shea
authors.forename: Sally
authors.initials: S
authors.lastname: Rucker
authors.forename: Linda
authors.initials: L
authors.lastname: Harvey
authors.forename: Paul
authors.initials: P
authors.lastname: Russo
authors.forename: Richard
authors.initials: R
authors.lastname: Saul
authors.forename: Mary F
authors.initials: MF
authors.lastname: Lopez
authors.forename: Alvydas
authors.initials: A
authors.lastname: Mikulskis
authors.forename: Scott
authors.initials: S
authors.lastname: Kuzdzal
authors.forename: Eva
authors.initials: E
authors.lastname: Golenko
authors.forename: David
authors.initials: D
authors.lastname: Fishman
authors.forename: Eric
authors.initials: E
authors.lastname: Vonderheid
authors.forename: Susan
authors.initials: S
authors.lastname: Booher
authors.forename: Edward W
authors.initials: EW
authors.lastname: Cowen
authors.forename: Sam T
authors.initials: ST
authors.lastname: Hwang
authors.forename: Gordon R
authors.initials: GR
authors.lastname: Whiteley
mesh_terms: D001798:Blood Proteins / Q000032:analysis; D030562:Databases, Protein; D005260:Female; D006801:Humans; D016410:Lymphoma, T-Cell, Cutaneous / Q000097:blood / Q000145:classification* / Q000175:diagnosis*; D009363:Neoplasm Proteins / Q000097:blood*; D010051:Ovarian Neoplasms / Q000097:blood / Q000145:classification* / Q000175:diagnosis*; D011499:Protein Processing, Post-Translational; D040901:Proteomics / Q000379:methods*; D012680:Sensitivity and Specificity; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods
chemical_list: D001798:Blood Proteins; D009363:Neoplasm Proteins