Machine Learning Based Classification of Diffuse Large B-cell Lymphoma Patients by their Protein Expression Profiles

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 (FFPE) tissues of patients with closely related subtypes of diffuse large B-cell lymphoma (DLBCL). 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 yet present in the patient samples. Shotgun proteomic analysis on a quadrupole Orbitrap quantified almost 9000 tumor proteins in 20 patients. The quantitative accuracy of our approach allowed the segregation of DLBCL 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 et al. MCP 2012 PMID 22442255). Expression levels of individual segregation-driving proteins as well as categories such as extracellular matrix proteins behaved consistent with known trends between the subtypes. We employed machine learning (support vector machines) to extract candidate proteins with the highest segregating power. A panel of four proteins (PALD1, MME, TNFAIP8 and TBC1D4) classified the patients with very 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): B Cell, Lymph Node

DISEASE(S): Lymphoma

SUBMITTER: Sally Deeb  

LAB HEAD: Matthias Mann

PROVIDER: PXD002052 | Pride | 2015-09-08

REPOSITORIES: Pride