Cancer proteomics : Method development for mass spectrometry based analysis of clinical materials

To improve cancer treatment, biomarkers for diagnostics and therapeutic guidance are desperately needed. Mass spectrometry (MS) based proteomics is one of the most promising methods for biomarker discovery. Clinical materials such as blood and tumor tissue provide an excellent starting material for biomarker discovery studies. However, at present, there are several analytical challenges related to biomarker discovery from clinical materials using mass spectrometry. In this thesis several methodological aspects in mass spectrometry based biomarker discovery workflows are optimized, including sample preparation, sample prefractionation and data management. In paper I an analytical workflow for SELDI-TOF MS of acute myeloid leukemia (AML) cells is presented including sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. The study illustrates the benefit of combining the information from several data analysis methods when dealing with complex data from global proteomics analysis. Papers II, III and IV, deals with analytical challenges when performing biomarker discovery studies using plasma as a starting material. The studies highlight the benefit of prefractionation on the analytical depth and in addition show the importance of identifying a large number of proteins to reach low abundant tissue leakage proteins. Paper IV shows the added value of combining high abundant protein depletion and narrow range peptide isoelectric focusing for plasma biomarker discovery studies. In paper IV, pleural effusion, a proximal fluid in lung cancer, is collected and prepared according to the same protocol as plasma; an approach that previously has not been described. The potential of using pleural effusion as discovery material is also shown. Paper V describes a protocol for removal of blood contamination and enrichment of tumor cells from lung cancer tumor tissue. By removal of blood and stromal contaminants, twice as many proteins could be identified from lung cancer tissue, as compared with direct lysis of fresh frozen tissue. In general this thesis highlights the importance of experimental design and optimization prior to performing biomarker discovery experiments from clinical materials, especially as clinical materials usually are limited both in amounts and numbers and the sample sets contains a high inherent variability. LIST OF PUBLICATIONS I. Forshed, J.; Pernemalm, M. ; Tan, C. S. ; Lindberg, M. ; Kanter, L. ; Pawitan, Y. ; Lewensohn, R. ; Stenke, L. ; Lehtio, J., Proteomic data analysis workflow for discovery of candidate biomarker peaks predictive of clinical outcome for patients with acute myeloid leukemia. J Proteome Res 2008, 7, (6), 2332-41. II. Pernemalm, M. ; Orre, L. M. ; Lengqvist, J.; Wikstrom, P.; Lewensohn, R. ; Lehtio, J., Evaluation of three principally different intact protein prefractionation methods for plasma biomarker discovery. J Proteome Res 2008, 7, (7), 2712-22 III. Pernemalm, M. ; Lewensohn, R. ; Lehtio, J., Affinity prefractionation for MSbased plasma proteomics. Proteomics 2009 Mar;9(6):1420-7. IV. Pernemalm M. ; De Petris L. ; Eriksson H. ; Branden E. ; Koyi H. ; Kanter L. ; Lewensohn R. ; Lehtio J., Use of narrow-range peptide IEF to improve detection of lung adenocarcinoma markers in plasma and pleural effusion. Proteomics. 2009 Jul;9(13):3414-24 V. De Petris, L. ; Pernemalm, M. ; Elmberger, G. ; Bergman, P.; Orre, L. ; Lewensohn, R. ; Lehtio, J., A novel method for sample preparation of fresh lung cancer tissue preparation for high resolution mass spectrometry-based proteomics. Manuscript, submitted