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  Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

Deeb, S. J., Tyanova, S., Hummel, M., Schmidt-Supprian, M., Cox, J., & Mann, M. (2015). Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles. MOLECULAR & CELLULAR PROTEOMICS, 14(11), 2947-2960. doi:10.1074/mcp.M115.050245.

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 Creators:
Deeb, Sally J.1, Author              
Tyanova, Stefka1, 2, Author              
Hummel, Michael3, Author
Schmidt-Supprian, Marc3, Author
Cox, Juergen2, Author              
Mann, Matthias1, Author              
Affiliations:
1Mann, Matthias / Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Max Planck Society, ou_1565159              
2Cox, Jürgen / Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Max Planck Society, ou_2063284              
3external, ou_persistent22              

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Free keywords: CHRONIC LYMPHOCYTIC-LEUKEMIA; PARAFFIN-EMBEDDED TISSUE; GENE-EXPRESSION; QUANTITATIVE PROTEOMICS; MASS-SPECTROMETRY; TYROSINE KINASE; NEXT-GENERATION; CANCER; IDENTIFICATION; SURVIVAL
 Abstract: Characterization of tumors at the molecular level has improved our knowledge of cancer causation and progression. Proteomic analysis of their signaling pathways promises to enhance our understanding of cancer aberrations at the functional level, but this requires accurate and robust tools. Here, we develop a state of the art quantitative mass spectrometric pipeline to characterize formalin-fixed paraffin-embedded tissues of patients with closely related subtypes of diffuse large B-cell lymphoma. We combined a super-SILAC approach with label-free quantification (hybrid LFQ) to address situations where the protein is absent in the super-SILAC standard but present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9,000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of diffuse large B-cell lymphoma patients according to their cell of origin using both their global protein expression patterns and the 55-protein signature obtained previously from patient-derived cell lines (Deeb, S. J., D'Souza, R. C., Cox, J., Schmidt-Supprian, M., and Mann, M. (2012) Mol. Cell. Proteomics 11, 77-89). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistently with known trends between the subtypes. We used machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8, and TBC1D4) is predicted to classify patients with low error rates. Highly ranked proteins from the support vector analysis revealed differential expression of core signaling molecules between the subtypes, elucidating aspects of their pathobiology.

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Language(s): eng - English
 Dates: 2015
 Publication Status: Published in print
 Pages: 14
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000365636800009
DOI: 10.1074/mcp.M115.050245
 Degree: -

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Title: MOLECULAR & CELLULAR PROTEOMICS
Source Genre: Journal
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Publ. Info: 9650 ROCKVILLE PIKE, BETHESDA, MD 20814-3996 USA : AMER SOC BIOCHEMISTRY MOLECULAR BIOLOGY INC
Pages: - Volume / Issue: 14 (11) Sequence Number: - Start / End Page: 2947 - 2960 Identifier: ISSN: 1535-9476