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.
Highlights
From the ‡Proteomics and Signal Transduction Group and §Computational Systems Biochemistry, Max Planck Institute of Biochemistry, D-82152 Martinsried, Germany, ¶Institute of Pathology, Campus Benjamin Franklin, Molecular Diagnostics, Charite Universitatsmedizin Berlin, 12200 Berlin, Germany, and Institute of Oncology and Hematology, III
Workflow for Quantitative Proteome Measurements of diffuse large B-cell lymphoma (DLBCL) formalin-fixed paraffin-embedded (FFPE) Patient Samples—One of the most commonly used methods for tissue preservation involves fixing the sample in neutral buffered formalin followed by embedding it in paraffin, termed FFPE tissues
Despite attempts to improve the quality of extracted RNA samples from FFPE tissues and to provide standardized protocols, currently snap frozen tissues are greatly preferred in that workflow [10, 29]
Summary
Generation of the Lymphoma super-SILAC Mix—The super-SILAC mix was generated by combining equal amounts of heavy lysates from six lymphoma cell lines (Ramos, Mutu, BL-41, U2932, L428, and DB) as described [18]. Protein Extraction from FFPE DLBCL Tissues—For each patient sample, two FFPE slices of macrodissected tissue were collected (10-m thickness) They were processed for mass spectrometrybased proteome analysis by extraction and digestion according to the filter-aided sample preparation (FASP) protocol (FFPE-FASP) [17, 21]. For MS/MS scans, the target ion value was set to 1,000,000 with a maximum injection time of 60 ms, a resolution of 17,500 at m/z 400, and dynamic exclusion of 25 s. To ensure the widest applicability of the results, both the predictor training and the feature selection are done in a cross-validation procedure This means that the data set is split into training and test subsets multiple times with feature selection and predictor training performed only on the training set. The cross-validation was performed using random sampling with 90% of the data for training and 1,000 repetitions
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