1. IOSI Journal Club 2007,05,04 Paolo Kunderfranco PhD Student S-I Hwang, Oncogene 2007 26,65-76

2. What is Proteomics? Proteomics is the systematic identification and characterization of proteins for their structure, function, activity, quantity, and molecular interactions Proteome: A catalog of all proteins Expressed throughout life Expressed under all conditions The goals of proteomics: To catalog all proteins To understand their functions To understand how they interact with each other S-I Hwang, Oncogene 2007 26,65-76

3. The current status of proteomic technologies, the different data typically collected in proteomic research and the available technologies are listed

4. Why might proteomics be more challenging than genomics? Splice variants create an enormous diversity of proteins –~25,000 genes in humans give rise to 200,000 to 2,000,000 different proteins –Splice variants may have very diverse functions Proteins expressed in an organism will vary according to age, health, tissue, and environmental stimuli Post-Translational Modifications –Phosphorylation, Acetylation, Methylation, Acylation, Glycosylation, GPI anchor, Hydroxyproline, Sulfation, Sumoylation, Disulfide-bond formation, Deamidation, Pyroglutamic acid, Ubiquitination and many others…… –Proteolytic cleavege S-I Hwang, Oncogene 2007 26,65-76

5. Why might proteomics be more challenging than genomics? Proteomics requires a broader range of technologies than genomics DIGE Mass spectrometryProtein chips LCMYeast two hybrid Interactome S-I Hwang, Oncogene 2007 26,65-76

6. Practical Applications Comparison of protein expression in diseased and normal tissues –Likely to reveal new targets Today ~500 drug targets Estimates of possible drug targets: 10,000 to 20,000 Help to establish proteomic libraries Protein expression signatures associated with drug toxicity –To make clinical trials more efficient –To make drug treatments more effective Large scale genomics and proteomic characterization of cancer tissues will provide novel insights into the early detection and effective treatment of cancer S-I Hwang, Oncogene 2007 26,65-76 Proteomics is instrumental in discovery of biomarkers

7. What is DTP? Direct tissue proteomics : a shotgun proteomics approach, which provides the chemical identity of proteins in cells, tissues and fluids Direct Tissue Proteomics identifies proteins in formalin-fixed paraffin embedded tissues using shotgun proteomics methods via tandem mass spectrometry (MS/MS) Allows the detection of diagnostic biomarkers and therapeutic targets Uses minute tissue biopsy sections S-I Hwang, Oncogene 2007 26,65-76

8. Goals Determine if proteins can be conclusively identified from small quantities of biopsy tissue samples from clinically relevant prostate cancers. –how many proteins can be conclusively identified in small quantities of prostate cancer biopsy tissues using the shotgun proteomic method? –can a current prostate cancer protein biomarker, such as PSA, be robustly identified using this approach? –can additional proteins involved in prostate tumorigenesis also be detected using this method? S-I Hwang, Oncogene 2007 26,65-76

9. Flow diagram of the DTP procedure (1) Proteomic Analysis H & E staining Gleason Scoring Bioinformatics Validation Immuno- histochemistry AQUA S-I Hwang, Oncogene 2007 26,65-76 A schematic flow diagram depicting the steps for proteomic identification, quantification and validation of prostate tissue arrays

10. Flow diagram of the DTP procedure (2) Commercially available tissue array –5 normal and 25 cancer biopsy sections in duplicates ( 4μm thick x 2mm diameter) Subdivision of all the tissues according to Gleason s. Optimization of protein extraction from PFPE arrays: –Reversing paraformaldehyde crosslinks –Sequencing-grade modified Trypsin digestion of tissues Data dependent mass spectrometry analysis –Separating the tryptic peptides using reverse-phase chromatography –Protein identification using the μ-capillary-LC–MS/MS procedures S-I Hwang, Oncogene 2007 26,65-76 lowmediumhighcontrol 3 610 Gleason s.

11. Flow diagram of the DTP procedure (3) S-I Hwang, Oncogene 2007 26,65-76 Data analysis and interpretation –SEQUEST algorithm ”SEQUEST correlates uninterpreted tandem mass spectra of peptides with amino acid sequences from protein and nucleotide databases” –INTERACT software tool “INTERACT was developed to address the need to curate large datasets from tens to hundreds of LC-MS/MS runs covering multiple tens of thousands of MS/MS spectra” –PROTEOME-3D software “An Interactive Bioinformatics Tool for Large-Scale Data Exploration and Knowledge Discovery” –INTERSECT software tool “Allow the generation of a stage specific prostate expression library

12. N° of identified proteins from each of the 4 Gleason categories Functional characterization of identified proteins into 24 GO categories Results (1) S-I Hwang, Oncogene 2007 26,65-76 Identification of 12631 peptides resulted in a list of 428 unique proteins with high confidence identification Grouping of enzyme implicated in energy metabolism revealed that: 1.Gluconeogenesis enzymes are preferentially expressed in low and medium stage prostate cancer 2.TCA enzymes were detected in multiple stages of prostate cancers 3.Glycolysis enzymes are found in normal tissue as well as in the cancerous prostate tissues

13. Results (2) S-I Hwang, Oncogene 2007 26,65-76 Detection of know markers of prostate cancer Identification of PSA 1.MS/MS spectrum of a PSA peptide Sorting for the KLK3_HUMAN resulted in the identification of 214 tryptic peptides

14. Identification of PSA 2.Peptide sequence coverage of PSA protein Coverage: AA 61,7% (161/261 residues) Mass: 63,0% (18102/28741 Da) 3.Estimation of the relative abundance of PSA peptides in normal and cancerous prostate cancer biopsies Results (2) S-I Hwang, Oncogene 2007 26,65-76 Detection of know markers of prostate cancer

15. How sensitive is the DTP methodology? Scott A. Gerber, PNAS 2003 12,6940 The DTP technology is not quantitative ACQUA: Absolute quantification Direct quantification of differences in protein and post- translationally modified protein expression levels This methodology utilizes a standard peptide with known quantity to compare against a biological sample to establish the absolute quantity of protein in the mixture

16. AQUA Method development A PSA peptide was synthesized with deuterium-labeled valine (8Da heavier than normal valine) S-I Hwang, Oncogene 2007 26,65-76

17. AQUA Quantification experiment (1) 100 femptomole (fmol) of standard peptide were spiked in the tryptic digested tissue sections Two fragment ions from the standard peptide and the endogenous PSA peptide were used for the MRM experiment ABSOLUTE QUANTIFICATION peak area of the endogenous peptide peak area of the AQUA standard peptide amount of standard spiked X S-I Hwang, Oncogene 2007 26,65-76

18. AQUA Quantification experiment (2) Extracted ion chromatograms of standard PSA peptides (black) and endogenous peptides (highlighted) from normal control prostate (blue) and three cancer grades (green, orange and red) Quantification values of PSA from a total of five normal samples and 15 cancerous samples. The values in fmols are converted to pg amounts The range of PSA quantified directly from the tissues was 0.5–140 pg S-I Hwang, Oncogene 2007 26,65-76

19. Search for additional proteins Three broad categories 1.Androgen Rensponsive and Androgen Receptor regulators 2.Known oncoproteins 3.Stromal-associated proteins Results (3) S-I Hwang, Oncogene 2007 26,65-76 Serum PSA levels do not always predict the presence of p.cancer Class Swiss Plot Entry Name Common Name Cancer Grades ControlLowMediumHigh Androgen Regulated Proteins NDR1_HUMANNDRG1 protein (n-myc downstream regulated gene 1 protein)762426 CLUS_HUMANClusterin [Precursor]4103414 BC007997Ras-related estrogen-regulated growth inhibiting protein11135429 JE0350Anterior gradient-2032922 PHB_HUMANProhibitin691421 Androgen Receptor Repressors CRTC_HUMANCalreticulin [Precursor]2121914 FKB5_HUMANfk506-binding protein 510154024 Androgen Receptor Co-regulators CTNB_HUMANBeta-catenin11124314 EZRI_HUMANEzrin6173419 ILK1_HUMANIntegrin-linked protein kinase 1913248 HS9B_HUMANHeat shock protein HSP 90-beta11326566 HS76_HUMANHeat shock 70 kDa protein 6952615 Androgen Receptor Co- activator FLNA_HUMANFilamin A6096111959782 RAN_HUMANGTP-binding nuclear protein Ran22129 IMB1_HUMANImportin beta-1 subunit793020 Oncogene MUC1_HUMANmucin 1 precursor1683 PIM1_HUMANproto-oncogene serine/threonine-protein kinase pim-132146 JC5394DJ-1 protein1012349 PTEN_HUMANphosphatidylinositol-3,4,5-trisphosphate 3-phosphatase pten13118 WNT3_HUMANWnt-3 proto-oncogene protein [Precursor]11153311 WN3A_HUMANWnt-3a protein [Precursor]344714177 ZA2G_HUMANzinc-alpha-2-glycoprotein precursor745311 Angiogenic and cancer stroma associated proteins KIHUGphosphoglycerate kinase2181 LEG1_HUMANgalectin-116133418 LEG3_HUMANgalectin-333112 FBL5_HUMANfibulin-5 precursor102245 MIF_HUMANmacrophage migration inhibitory factor693319 Growth Inhibitors TRFL_HUMANlactotransferrin precursor244011013 THIO_HUMANthioredoxin14146 Cancer associated ENPL_HUMANendoplasmin precursor8247846 SBP1_HUMANselenium-binding protein 1015309

20. Results (3) S-I Hwang, Oncogene 2007 26,65-76 Wnt-3, wingless-type MMTV integration site family, member 3 Identified with multiple peptides controllowmediumhigh WNT3_HUMAN Wnt-3 proto-oncogene protein [Precursor]11153311 WNT3A_HUMANWnt-3a protein [Precursor]344714177

21. Results (3) S-I Hwang, Oncogene 2007 26,65-76 Wnt proteins form a family of highly conserved secreted signaling molecules that regulate cell-to-cell interactions during embryogenesis Wnt proteins are secreted protein ligands for cell surface receptors of the frizzled and lipoprotein receptor- related protein family The Wnt family of proteins is known to cause oncogenic transformation in a number of cell systems including the prostate cells Wnt-3a protein was shown to support the androgen-independent growth of LNCaP prostate cancer cells Wnt-3, wingless-type MMTV integration site family, member 3

22. Results (3) S-I Hwang, Oncogene 2007 26,65-76 Wnt-3, wingless-type MMTV integration site family, member 3 Wnt-3 protein immunohistochemistry on the prostate cancer tissue arrays a) Basal epithelial cells from normal prostate glands Immuno-reactivity restricted to a few clusters of basal epithelial cells of the prostate glands b) Luminal epithelial cells of the prostatic intraepithelial neoplasia Significant upregulation of Wnt-3 protein was detectable

23. Results (3) S-I Hwang, Oncogene 2007 26,65-76 Wnt-3, wingless-type MMTV integration site family, member 3 Wnt-3 protein immunohistochemistry on the prostate cancer tissue arrays c) Advanced prostate cancer Strong immunoreactivity was seen in the neoplastic and invasive cells d) Invasive prostate cancer

24. Results (3) S-I Hwang, Oncogene 2007 26,65-76 Wnt-3, wingless-type MMTV integration site family, member 3 Two fold increase in staining intensities was seen in the cancerous glands of the prostate Indeed DTP provides additional protein targets tha may participate in prostate carcinogenesis

25. Remarks S-I Hwang, Oncogene 2007 26,65-76 Primary focus of this study is not about the discovery of novel biomarkers, but to 1. Prove that protein identification from archival FFPE is feasible 2.To demonstrate that PSA can be detected and quantified by this method 3.To demonstrate that this method allows identification of new biologically interesting proteins But... Discrepancy between published microarray cDNA data sets and identified proteins

26. Remarks & Comments S-I Hwang, Oncogene 2007 26,65-76 Cancer tissues are heterogeneous in nature Most cancer cells are mixed with normal cells Cellular to stroma ratios can be very different in distinct areas How to normalize the cellular and matrix components??? Proteomic shotgun approaches suffer from undersampling the expressed proteins The complexity is huge................. Duty cicle of the latest MS are relatively slow Compatibility between MS analysis and extraction buffer How to increase the yield for the extracted proteins???

27. Remarks & Comments S-I Hwang, Oncogene 2007 26,65-76 These results sugget that More comprehensive characterization of proteomes and mRNAs from normal and cancerous cells Ability to analize proteins from pure cell populations, LCM, robots??? Develop methodology to identify also low abundant and membrane bounds proteins, key enzymes and regulators??? Are there only some limited genetic mechanisms for each tissue or cell specific cancer???