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Dear ICBC Team Members:
Happy New Year to all. We are looking forward to another year of successful collaborations and progress within the Consortium.
It was wonderful to see so many of you in Singapore in early December. I was encouraged by the progress that has been made and the relationships being forged among teams. Presentations from the meeting will be posted on the secure CPAS website soon.
This newsletter includes a technology update from Dr. Daniel Liebler, Vanderbilt University Medical Center, on mapping and kinetic analysis of protein modifications.
CPAS Version 1.7 was released in December and contains several key enhancements. The team highlight for this issue is the Japan pancreas cancer team, led by Dr. Tesshi Yamada.
I would also like to bring to your attention that the next US HUPO meeting will be held in Seattle on March 4-8, 2007 and may provide an opportunity for ICBC members to meet or hold workshops. Please contact gcampagn@fhcrc.org if you would like us to organize a space at the Fred Hutchinson Cancer Research Center for a meeting.
Best regards,
Lee Hartwell
President and Director
Fred Hutchinson Cancer Research Center
Mapping and kinetic analysis of protein modifications. New tools for characterizing protein modifications as potential biomarkers.
Dr. Daniel C. LieblerPosttranslational modifications affect protein functions and also reflect the status of signaling pathways that are critical to the development of cancer and other disease. Protein damage by reactive electrophiles is a hallmark of oxidative stress, inflammation and chemical exposures that contribute to cancer and degenerative diseases (1). These endogenous electrophiles include reactive products of lipid, DNA and carbohydrate oxidation, which may modify protein nucleophiles to produce a variety of adducts, the majority of which have yet to be well characterized.
Analysis of protein modifications is best accomplished by shotgun proteome analyses, which generate MS-MS spectra that encode the sequences of modified peptides and the masses and sequence positions of modifications. In cases where the positional specificity and mass of modifications are known (e.g., phosphorylation, which adds +80 Da to S, T and Y residues) database searches of MS-MS spectra with the specified modifications results in identification of modified sequences. However, for modifications arising from reactive chemical species (e.g., lipid oxidation products and glycation products), the mass and sequence specificity are more difficult to predict and the use of standard database search algorithms produces a high rate of false-positive identifications. The P-Mod algorithm (2) and similar software tools enable identification of MS-MS spectra corresponding to modified peptide sequences, map the modifications to specific sequences and estimate the probability that the matches are nonrandom assignments. A specific advantage of P-Mod is that the masses of sequence specificities of the modifications need not be anticipated or specified in the analysis. Thus, this approach is ideas for discovery of unanticipated, novel modifications.
In studies directed at predicting potential biomarkers of oxidative stress in vivo, we used LC-MS-MS analyses and P-Mod to map 10 sites of covalent modification of human serum albumin (HSA) by the reactive lipid oxidation product 4-hydroxynonenal (HNE) (3). To measure the rates or reaction at six His and Lys residues in the HSA protein, we applied a stable isotope tagging strategy which employs N-terminal tagging of peptides with heavy (13C6-PIC ) and light (12C6-PIC) labeled phenylisocyanate (PIC) (4). Peptides from the last time point in the experiment (which corresponds to the highest adduct levels) are tagged with 13C6-PIC, whereas peptides from the other time points are tagged with the 12C6-PIC (Figure 1). Targeted LC-MS-MS analysis the light and heavy PIC labeled adducts enable selective detection of each. Plots of the ratios of light:heavy PIC-labeled adduct yield measurements of kobs values, which indicate relative reaction rates for all of the detectable sites. This approach allows simultaneous quantitative comparison of all adduction reactions in the same protein.
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Figure 1. Stable isotope tagging and relative quantitation with PIC.
Analysis of the most reactive sites in the context of the HSA protein structure provided interesting insights into factors that dictate reactivity and selectivity in covalent adduction (3). Solvent-exposed Lys and His residues displayed reactivities that varied over about two orders of magnitude (Figure 2).
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Figure 2. A. Kinetic profiles for adduction of HSA at six reaction sites (H242/H247 contains two histidine adducts on the same tryptic peptide). B. Locations of adducted His and Lys residues in the IIA subdomain of HSA. The most reactive target, His242, lies in a hydrophobic pocket and the pK of the imidazole nucleophile is predicted to be reduced by desolvation effects and neighboring basic residues Lys199 and Arg222. (Reproduced with permission from reference 3. Copyright (2006) American Chemical Society.)
However, the most reactive target was His242, which resides in a hydrophobic binding pocket known to bind fatty acids and several drugs. Moreover, the predicted pKa (‹2.0) of His242 account for its high reactivity toward HNE. His242 adducts may be preferred products of adduction by lipophilic electrophiles and may comprise a family of biomarkers for oxidative stress.
- Marnett, L. J., Riggins, J. N., and West, J. D. (2003) Endogenous generation of reactive oxidants and electrophiles and their reactions with DNA and protein. J.Clin.Invest 111, 583-593.
- Hansen, B. T., Davey, S. W., Ham, A. J., and Liebler, D. C. (2005) P-Mod: An Algorithm and Software To Map Modifications To Peptide Sequences Using Tandem MS Data. J.Proteome Res. 4, 358-368.
- Szapacs, M. E., Riggins, J. N., Zimmerman, L. J., and Liebler, D. C. (2006) Covalent adduction of human serum albumin by 4-hydroxy-2-nonenal: kinetic analysis of competing alkylation reactions. Biochemistry 45, 10521-8.
- Mason, D. E., and Liebler, D. C. (2003) Quantitative analysis of modified proteins by LC-MS-MS of peptides labeled with phenyl isocyanate. J.Proteome Res. 2, 265-272.
CPAS 1.7 is now available! CPAS Release 1.7 includes many new features. A few key enhancements are listed below.
- MS2 run comparisons using ProteinProphet data
- Mascot support
- Improved sample information handling
- Discussion board enhancements
- Reporting improvements
To learn more about CPAS Release 1.7 or to download a copy, please visit https://cpas.fhcrc.org/Project/home/home.view.
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Baas T, Baskin CR, Diamond DL, Garcia-Sastre A, Bielefeldt-Ohmann H, Tumpey TM, Thomas MJ, Carter VS, Teal TH, Van Hoeven N, Proll S, Jacobs JM, Caldwelll ZR, Gritsenko MA, Hukkanen RR, Camp DG, II, Smith RD, Katze MG. Integrated molecular signature of disease: Analysis of influenza virus-infected macaques through functional genomics and proteomics. J Virology 80(21):10813-10828, 2006.
Callister SJ, Nicora CD, Zeng XH, Roh JH, Dominguez MA, Tavano CL, Monroe ME, Kaplan S, Donohue TJ, Smith RD, Lipton MS. Comparison of aerobic and photosynthetic Rhodobacter sphaeroides 2.4.1 proteomes. J Microbiol Methods 67(3): 424-436, 2006.
Cha MH, Rhim T, Kim KH, Jang AS, Paik YK, Park CS. Proteomic identification of macrophage migration-inhibitory factor upon exposure to TiO2 particles. Mol Cell Proteomics 6(1):56-63, 2007.
Chaurand P, Norris JL, Cornett DS, Mobley JA, Caprioli RM. New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry. J Proteome Res 5(11): 2889-2900, 2006.
Cornett DS, Mobley JA, Dias EC, Andersson M, Arteaga C, Sanders ME, Caprioli RM. A novel histology-directed strategy for MALDI-MS tissue profiling that improves throughput and cellular specificity in human breast cancer. Mol Cell Proteomics 5(10):1975-1983, 2006.
Domon B, Aebersold R. Challenges and opportunities in proteomics data analysis. Mol Cell Proteomics 5(10):1921-1926, 2006.
Flory MR, Lee H, Bonneau R, Mallick P, Serikawa K, Morris DR, Aebersold R. Quantitative proteomic analysis of the budding yeast cell cycle using acid-cleavable isotope-coded affinity tag reagents. Proteomics 6(23): 6146-6157, 2006.
Friedman DB, Wang SZE, Whitwell CW, Caprioli RM, Arteaga CL. Multivariable difference gel electrophoresis and mass spectrometry - A case study on transforming growth factor-beta and ErbB2 signaling. Mol Cell Proteomics 6(1):150-169, 2007.
Haab BB, Paulovich AG, Anderson NL, Clark AM, Downing GJ, Hermjakob H, LaBaer J, Uhlen M. A reagent resource to identify proteins and peptides of interest for the cancer community - A workshop report. Mol Cell Proteomics 5(10): 1996-2007, 2006.
Hixson KK, Adkins JN, Baker SE, Moore RJ, Chromy BA, Smith RD, McCutchen-Maloney SL, Lipton MS. Biomarker candidate identification in Yersinia pestis using organism-wide semiquantitative proteomics. J Proteome Res 5(11): 3008-3017, 2006.
Jaffe JD, Mani DR, Leptos KC, Church GM, Gillette MA, Carr SA. PEPPeR, a platform for experimental proteomic pattern recognition. Mol Cell Proteomics 5(10): 1927-1941, 2006.
Jaitly N, Monroe ME, Petyuk VA, Clauss TRW, Adkins JN, Smith RD. Robust algorithm for alignment of liquid chromatography-mass spectrometry analyses in an accurate mass and time tag data analysis pipeline. Anal Chem 78(21):7397-7409, 2006.
Johnson MD, Floyd JL, Caprioli RM. Proteomics in diagnostic neuropathology. J Neuropathology Experimental Neurology 65(9): 837-845, 2006.
Keller A, Eng Ji, Zhang N, Li XJ, Aebersold R. A uniform proteomics MS/MS analysis platform utilizing open XML file formats. Mol Systems Biol doi:10.1038/msb4100024 2005.
Kelly RT, Page JS, Luo QZ, Moore RJ, Orton DJ, Tang KQ, Smith RD. Chemically etched open tubular and monolithic emitters for nanoelectrospray ionization mass spectrometry. Anal Chem 78(22): 7796-7801, 2006.
Kikuchi T, Massion PP, Shyr Y, Altorki NK, Dannenberg AJ, Li M, Gonzalez A, Chaurand P, Caprioli R, Carbone DP. J Clin Onc 24(18): 371S-371S Part 1 Suppl. S, 2006.
Lee NPY, Yeung WSB, Luk JMC. Junction interaction in the seminiferous epithelium: regulatory roles of connexin-based gap junction. Front Biosci 12: 1552-1562, 2007.
Liu T, Qian WJ, Gritsenko MA, Xiao WZ, Moldawer LL, Kaushal A, Monroe ME, Varnum SM, Moore RJ, Purvine SO, Maier RV, Davis RW, Tompkins RG, Camp DG, Smith RD. High dynamic range characterization of the trauma patient plasma proteome. Mol Cell Proteomics 5(10): 1899-1913, 2006.
Luo Q, Page JS, Tang KQ, Smith RD. MicroSPE-nanoLC-ESI-MS/MS using 10-mu m-i.d. Silica-based monolithic columns for proteomics. Anal Chem 79(2):540-545, 2007.
Maclean B, Eng JK, Beavis RC, McIntosh M. General framework for developing and evaluating database scoring algorithms using the TANDEM search engine. Bioinformatics 22(22): 2830-2832, 2006.
Mannova P, Fang RH, Wang H, Deng B, McIntosh MW, Hanash SM, Beretta L. Modification of host lipid raft proteome upon hepatitis C virus replication. Mol Cell Proteomics 5(12): 2319-2325, 2006.
Meistermann H, Norris JL, Aerni HR, Cornett DS, Friedlein A, Erskine AR, Augustin A, Mudry MCD, Ruepp S, Suter L, Langen H, Caprioli RM, Ducret A. Biomarker discovery by imaging mass spectrometry - Transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat. Mol Cell Proteomics 5(10):1876-1886, 2006.
Norbeck AD, Callister SJ, Monroe ME, Jaitly N, Elias DA, Lipton MS, Smith RD. Proteomic approaches to bacterial differentiation. J Microbiol Methods 67(3): 473-486, 2006.
Qian WJ, Jacobs JM, Liu T, Camp DG, Smith RD. Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications. Mol Cell Proteomics 5(10): 1727-1744, 2006.
Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol 24(8): 971-983, 2006.
Sato Ah, Anderson GL, Urban N, McIntosh MW. Comparing adaptive and non-adaptive algorithms for cancer early detection with novel biomarkers. Cancer Biomark 2(3-4): 151-162, 2006.
Scholler N, Garvik B, Quarles T, Jiang S, Urban N. Method for generation of in vivo biotinylated recombinant antibodies by yeast mating. J Immunol Methods 317(1-2):132-43, 2006.
Seliger B, Fedorushchenko A, Brenner W, Ackermann A, Atkins D, Hanash S, Lichtenfels R. Ubiquitin COOH-terminal hydrolase 1: A biomarker of renal cell carcinoma associated with enhanced tumor cell proliferation and migration. Clin Cancer Res 13(1):27-37, 2007.
Shvartsburg AA, Bryskiewicz T, Purves RW, Tang K, Guevremont R, Smith RD. Field asymmetric waveform ion mobility spectrometry studies of proteins: Dipole alignment in ion mobility spectrometry? J Phys Chem B 110(43):21966-21980, 2006.
Song K, Hanash S. Unraveling the complex proteome for biomarker discovery in gastrointestinal and liver diseases. Gastroenterology 131(5):1375-1378, 2006.
Tolmachev AV, Monroe ME, Jaitly N, Petyuk VA, Adkins JN, Smith RD. Mass measurement accuracy in analyses of highly complex mixtures based upon multidimensional recalibration. Anal Chem 78(24): 8374-8385, 2006.
Valentine SJ, Plasencia MD, Liu XY, Krishnan M, Naylor S, Udseth HR, Smith RD, Clemmer DE. Toward plasma proteome profiling with ion mobility-mass spectrometry. J Proteome Res 5(11): 2977-2984, 2006.
Wang P, Tang H, Zhang H, Whiteaker J, Paulovich A, McIntosh M. Normalization regarding non-random missing values in high-throughput mass spectrometry data. Pac Symp Biocomput: 315-326, 2006.
Xue XF, Wu SF, Wang ZS, Zhu YP, He FC. Protein probabilities in shotgun proteomics: Evaluating different estimation methods using a semi-random sampling model. Proteomics 6(23): 6134-6145, 2006.
Zhang H, Liu AY, Loriaux P, Wollscheid B, Zhou Y, Watts JD, Aebersold R. Mass spectrometric detection of tissue proteins in plasma. Mol Cell Proteomics 6(1):64-71, 2007.
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None at this time.
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Minutes from the December ICBC meeting are nearly finalized and will be distributed when complete.
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Pancreas — National Cancer Center Research Institute (Coordinating Center)
| Project Title: | Improving Early Detection of Pancreatic Cancer with a Blood Test | |
| Cancer Site(s): | Pancreas | |
| Principal Investigator(s): |
Tesshi YAMADA, M.D., Ph.D. |
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| Participating Institutions: |
National Cancer Center Research Institute (Coordinating Center) |
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| Mouse Model(s): | Under consideration | |
| Clinical Samples: | Plasma/sera from Jichi Medical School Hospital Tokyo Medical University Hospital National Cancer Center Research Hospital Osaka Medical Center for Cancer and Cardiovascular Diseases Fukuoka University Hospital |
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| Technical Approaches: | Glycopeptide enrichment Surface Enhanced Laser Desorption/Ionization hybrid Quadrupole Time-of-Flight Mass Spectrometry (SELDI-QqTOF-MS) Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-MS) Two Dimensional Difference Gel Electrophoresis (2D-DIGE) Protein/peptide quantitation using isotope-coded affinity tags (ICAT) Two Dimensional Image Converted Analysis of nano-flow Liquid chromatography and Mass Spectrometry (2D-ICAL-MS) Two-dimensional microflow liquid chromatography/tandem mass spectrometry (2-D microLC-MS/MS) |
Brief Description of Project:
Pancreatic cancer is the fifth leading cause of cancer-related mortality in Japan and was responsible for 22260 deaths in Japan in 2004. The number has increased about 2.5 fold over the last 20 years, and will continue to increase in the future. Since the clinical manifestations of pancreatic cancer, except obstructive jaundice, are often not apparent until the advanced stages of the disease, and the anatomical location of the pancreas deep in the abdomen makes physical and ultrasonic detection of pancreatic cancer difficult, about 95% of all cases are diagnosed in stage III or IV, and the 5-year survival rate of pancreatic cancer patients is the lowest among patients with common solid tumors. Early detection by mass screening seems to be one of the most feasible strategies for improving the outcome of pancreatic cancer patients. Since the 5-year survival rate of pancreatic cancer patients with stage I, II, III, IVa, and IVb disease has been reported to be 59%, 51%, 26%, 12%, and 3%, respectively, detection of stage I or II disease would significantly improve overall patient survival.
Mass screening by computerized tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) may not be cost-effective because of the relatively low incidence of pancreatic cancer, and the long-term safety of these modalities has not been established. Thus, the development of a new diagnostic modality that enables the early detection of pancreatic cancer in a safe/non-invasive and cost-effective way is needed. Human blood serum and plasma contain a large variety of proteins, and their relative abundance and modification may precisely reflect the disease status of organs and tissues. Recent advances in MS-based proteomic technologies coupled with bioinformatics may revolutionize medical diagnosis and cancer screening. The purpose of the team is to identify new diagnostic biomarkers of pancreatic cancer by comprehensive serum/plasma protein profiling. This team is composed of clinicians and scientists specialized in proteomics and bioinformatics. A large number of serum/plasma samples will be prospectively collected from participating institutions under the same protocol. Protein profiling will be performed by using various cutting-edge technologies, such as SELDI-QqTOF-MS, MALDI-MS, 2D-DIGE, ICAT, 2D-ICAL-MS, and 2-D microLC-MS/MS.
This team is supported by the grant "Third Term Comprehensive Control Research for Cancer" from the Ministry of Health, Labor and Welfare of Japan.
Team Members and Expertise:
Kazufumi HONDA, D.D.s., Ph.D. (National Cancer Center Research Institute)
Section Head of National Cancer Center Research Institute Cancer Proteomics Project
Bioinformatics and Proteomics (SELDI-QqTOF-MS and MALDI-MS)
Tatsuya IOKA, M.D. (Osaka Medical Center for Cancer and Cardiovascular Diseases)
Chief Physician of Osaka Medical Center for Cancer and Cardiovascular Diseases
Chemotherapy and Mass Survey
Provision of Clinical Samples
Tadashi KONDO, M.D., Ph.D. (National Cancer Center Research Institute)
Section Head and Project Leader of National Cancer Center Research Institute Proteome Bioinformatics Project
Bioinformatics and Proteomics (2D DIGE)
Hideo NAGAI, M.D., Ph.D. (Jichi Medical School)
Professor of Jichi Medical School
General and Pancreatic Surgery
Provision of Clinical Samples
Toshihide NISHIMURA, Ph.D. (Tokyo Medical University)
Professor of Tokyo Medical University
Proteomics (2-D microLC-MS/MS)
Takuji OKUSAKA, M.D., Ph.D. (National Cancer Center Hospital)
Chief Physician of National Cancer Center Hospital
Hepatobiliary and Pancreatic Oncology
Provision of Clinical Samples
Masaya ONO, M.D., Ph.D. (National Cancer Center Research Institute)
Section Head of National Cancer Center Research Institute Cancer Proteomics Project
Proteomics (2D-ICAL-MS)
Miki SHITASHIGE, Ph.D. (National Cancer Center Research Institute)
Staff Scientist of National Cancer Center Research Institute Cancer Proteomics Project
Proteomics (ICAT)
Akihiko TSUCHIDA, M.D., Ph.D. (Tokyo Medical University)
Associate Professor of Tokyo Medical University
Hepatobiliary and Pancreatic Surgery
Provision of Clinical Samples
Tesshi YAMADA, M.D., Ph.D. (National Cancer Center Research Institute-Biomarker Team PI) [tyamada@ncc.go.jp]
Chief of Chemotherapy Division and Project Leader of Cancer Proteomics Project
Bioinformatics
Yohichi YASUNAMI, M.D., Ph.D. (Fukuoka University)
Associate Professor of Fukuoka University
Gastrointestinal Surgery
Provision of Clinical Samples
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