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Dear ICBC Team Members:
This issue of the ICBC newsletter has several exciting items. I am pleased to announce that registration for the December ICBC meeting in Singapore is now open and the link to the site is included in this issue. Also, I have started asking expert advisors to the ICBC to provide Technology Updates for the newsletter and Dr. Richard Smith of the Pacific Northwest National Laboratories (PNNL) has generated our first update on a strategy for in-depth characterization of human plasma proteome developed at PNNL. This issue is also highlighting a team led by Dr. Laura Beretta focusing on liver cancer.
I have begun talking with team leaders and setting up videoconferences with each team. It is quite exciting to see the progress being made throughout the consortium.
Best regards,
Lee Hartwell
President and Director
Fred Hutchinson Cancer Research Center
Registration is Open for the December ICBC Meeting in Singapore!
We are looking forward to our next meeting in Singapore on December 1-3, 2006. There will be a welcoming reception held on Friday evening, December 1 at Biopolis that everyone is invited to attend. The scientific meeting will take place on December 2-3.
Each existing ICBC team will be encouraged to bring up to 6 team members. Developing teams will be encouraged to bring up to two representatives. There is no registration fee for this meeting, however, each individual will be responsible for the cost of their airfare, ground transportation and hotel. You may register on line at the following link:
http://www.fhcrc.org/science/international_biomarker/meetings/2006/dec/
We look forward to seeing you there. As the agenda is finalized it will be posted on this site and a notice will be sent to meeting attendees. If you have any questions, please contact me at kkreizen@fhcrc.org or Gail Campagna at gcampagn@fhcrc.org.
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A strategy for in-depth characterization of human plasma proteome developed at PNNL
Dr. Richard Smith, PhD
Methods. While human plasma represents an attractive biofluid for disease biomarker discovery, the extreme complexity and large dynamic range in protein concentrations present significant challenges for characterization, candidate biomarker discovery, and validation. Pacific Northwest National Laboratory (PNNL) has developed a strategy that combines multi-component immunoaffinity subtraction and subsequent solid-phase chemical fractionation based on cysteinyl peptide and N-glycoprotein captures with 2D-LC-MS/MS to increase the dynamic range of analysis for plasma (Figure 1). The advantages afforded by dividing a complex proteome into several subproteomes include 1) reduced complexity of the subproteome samples (particularly, enriched cysteinyl peptide and N-glycopeptide samples), which allows more low-abundance proteins to be identified; 2) the complementary nature of different subproteome fractions, which significantly improves overall proteome coverage (Figure 2); and 3) the relative simplicity, efficiency, and reproducibility afforded by these fractionation methods, which make them amenable to automation.
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Figure 1. Schematic representation of the sample processing and fractionation used to characterize the trauma patient plasma proteome. High-abundance proteins were firstly removed using immunoaffinity subtraction. The resulting less-abundant proteins were split and submitted individually for solid-phase cysteinyl peptide and N-glycoprotein captures. Non-cysteinyl peptides and non-glycopeptides generated at the same time were also collected. All 4 different peptide populations were then fractionated by SCX chromatography and each fraction was analyzed by capillary LC-MS/MS.
Application. Application at PNNL of this "divide-and-conquer" strategy to trauma patient plasma samples significantly improved the overall dynamic range of detection and resulted in confident identification of 22,267 unique peptides from four different peptide populations (cysteinyl peptides, non-cysteinyl peptides, N-glycopeptides, and non-glycopeptides) that covered 3654 non-redundant proteins with 1494 proteins identified by multiple peptides. Numerous low-abundance proteins were identified, exemplified by 78 "classic" cytokines and cytokine receptors and by 136 human cell differentiation molecules. The overlap of peptide and protein identifications of each peptide population is shown in Figure 2.
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Figure 2. Diagram of peptides (A) and proteins (B) identified in multiple peptide populations. All overlaps are shown (2-way, 3-way, and 4-way) for all four peptide populations. The numbers in the small circles indicate the number of proteins that are common only to the two fractions at the opposite positions in the diagram. Cys, cysteinyl peptides; Non-Cys, non-cysteinyl peptides; N-Glyco, N-glycopeptides; Non-Glyco, non-glycopeptides.
The peptide identification filtering criteria developed based on the reversed database searching1 are much more stringent than the early HUPO criteria2. Its stringency is also supported by the comparable results from the reversed database approach and the Peptide Prophet3 and Protein Prophet4 programs in terms of generating highly overlapped peptide identifications (>95%) and similar numbers of high-confidence protein identifications. A recent study developed a rigorous statistical approach taking into account the length of coding regions in genes, and multiple hypothesis-testing techniques5, resulting a significant reduction in HUPO protein identifications: from the initial 3020 multi-peptide proteins2 to 889 proteins identified with a confidence level of at least 95%. Reanalyzing the data from one of our early plasma profiling study6 using the early HUPO criteria2 yielded 1073 proteins, the length-dependent statistical analysis resulted in approximately a two-fold reduction in the number of protein identifications (433 proteins with confidence >95%)5. Similarly, for the data presented in this study the reversed database analysis also resulted in ~2-fold fewer protein identifications compared to those identifications obtained if the early HUPO criteria was applied (3654 vs. 7928 proteins using previous HUPO criteria), suggesting an approximate comparable level of confidence for protein identifications obtained between the reversed database criteria and the recently published length-dependent statistical analysis5. These comparisons between independent statistical approaches reflect the overall high quality of the currently reported data obtained by the present approach.
To evaluate the overall dynamic range of measurements that resulted from this study, six identified low-abundance proteins (PDGF-B, IL-1RA, VEGF R1, M-CSF, CCL21, and TNF R1) were assayed by ELISA. The results showed that the protein levels from the trauma patient plasma are higher for most of these cytokines or cytokine receptors than those measured from a normal individual except small inducible cytokine A21 (CCL21); however, all six proteins were present in the low ng/mL range for the trauma patient plasma samples, especially VEGF R1 was detected at 556 pg/mL level. An estimated overall dynamic range of detection of >107 was thus verified. In addition, many tissue leakage proteins in the g/mL to ng/mL concentration range were readily detected in multiple peptide populations, which provide a solid basis for candidate disease biomarker (e.g., cancer biomarker) discovery using this strategy.
References:
- Qian, W. J., Liu, T., Monroe, M. E., Strittmatter, E. F., Jacobs, J. M., Kangas, L. J., Petritis, K., Camp, D. G. n., and Smith, R. D. (2005) Probability-based evaluation of peptide and protein identifications from tandem mass spectrometry and SEQUEST analysis: The human proteome. J. Proteome Res. 4, 53-62.
- Omenn, G. S., States, D. J., Adamski, M., Blackwell, T. W., Menon, R., Hermjakob, H., Apweiler, R., Haab, B. B., Simpson, R. J., Eddes, J. S., Kapp, E. A., Moritz, R. L., Chan, D. W., Rai, A. J., Admon, A., Aebersold, R., Eng, J., Hancock, W. S., Hefta, S. A., Meyer, H., Paik, Y. K., Yoo, J. S., Ping, P. P., Pounds, J., Adkins, J., Qian, X. H., Wang, R., Wasinger, V., Wu, C. Y., Zhao, X. H., Zeng, R., Archakov, A., Tsugita, A., Beer, I., Pandey, A., Pisano, M., Andrews, P., Tammen, H., Speicher, D. W., and Hanash, S. M. (2005) Overview of the HUPO Plasma Proteome Project: Results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database. Proteomics 5, 3226-3245.
- Keller, A., Nesvizhskii, A. I., Kolker, E., and Aebersold, R. (2002) Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392.
- Nesvizhskii, A. I., Keller, A., Kolker, E., and Aebersold, R. (2003) A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646-4658.
- States, D. J., Omenn, G. S., Blackwell, T. W., Fermin, D., Eng, J., Speicher, D. W., and Hanash, S. M. (2006) Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat. Biotechnol. 24, 333-338.
- Shen, Y., Jacobs, J. M., Camp, D. G., Fang, R., Moore, R. J., Smith, R. D., Xiao, W., Davis, R. W., and Tompkins, R. G. (2004) Ultra-high-efficiency strong cation exchange LC/RPLC/MS/MS for high dynamic range characterization of the human plasma proteome. Anal. Chem. 76, 1134-1144.
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See above in "Scientific Updates"
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None at this time.
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| Project Title: | Hepatitis C Virus-associated Hepatocellular Carcinoma | |
| Cancer Site(s): | Liver | |
| Principal Investigator(s): |
Laura BERETTA, Ph.D. | |
| Participating Institutions: |
Fred Hutchinson Cancer Research Center (FHCRC - Coordinating Center) | |
| Mouse Model(s): | Xenograft models Transgenic mouse models under development | |
| Clinical Sample(s): | Liver tissue and plasma/sera from National Taiwan University Hospital Liver tissue and plasma/sera from the French National Hepatocellular Carcinoma Collection (INSERM) Liver tissue and plasma/sera from University of Michigan School of Medicine | |
| Technical Approaches: | High performance liquid chromatography and LTQ-FT MS (FHCRC) Two-dimensional gel electrophoresis (NTU) MALDI-TOF MS + SELDI-TOF MS (NTU) Glycopeptide enrichment (FHCRC + NTU) |
Brief Description of Project:
"Hepatitis C Virus-associated Hepatocellular Carcinoma" is an international, multi-institutional research project to identify biomarkers that reveal the progression of hepatocellular carcinoma (HCC) in patients with hepatitis C virus (HCV) infection. We will determine which of these biomarkers may be effective markers for early diagnosis, which may be effective markers for disease recurrence after ablation or resection, and which may predict risk of progression. Samples from several tissue banks (including matching tissue and serum samples) will be available and the samples will be processed in the proteomics laboratories at FHCRC and NTU.
The World Health Organization (WHO) estimates that about 170 million people are chronically infected with HCV worldwide. These patients are at high risk of developing liver cirrhosis and HCC. The pathway that leads from HCV to HCC is well defined, beginning with chronic inflammation of the liver and proceeding through fibrosis and cirrhosis to HCC. In chronically infected HCV patients, the progression from chronic inflammation to the onset of cirrhosis often takes 20 to 40 years. Once cirrhosis has developed, between 3% and 8% of HCV patients will progress to HCC annually.
Currently, the diagnostic marker for HCC is alpha-fetoprotein (AFP). However, the sensitivity of AFP runs as low .65 and the specificity of AFP runs as low as .69. We hope to identify some candidate markers with a significantly higher sensitivity and a significantly higher specificity.
The research project will have two specific goals:
- identification of serum markers for early detection of HCC in cirrhotic patients, and
- identification of serum markers associated with the states of fibrosis progression
Three basic strategies will be used in this project: tissue-based, serum-based, and animal model-based. In the tissue-based strategy, we will compare the proteomic profiles of neoplastic liver tissue with adjacent tissue. In the serum-based strategy, we will compare samples from healthy individuals, samples from patients with fibrosis at different stages, and samples from patients with HCC. In the animal model-based strategy, xenografts will be developed by injecting HCC cell lines into nude mice. Animal models of HCV-related HCC are currently in development.
Team Members and Expertise:
Laura BERETTA, Ph.D. (Fred Hutchinson Cancer Research Center - Biomarker Team PI) lberetta@fhcrc.org
Christian BRÉCHOT, Ph.D. (Institut National de la Santé et de la Recherche Médicale) brechot@tolbiac.inserm.fr
Chi-Ling CHEN, Ph.D. (National Taiwan University) chlnchen@ntumc.org
Chien-Hung CHEN, M.D., Ph.D. (National Taiwan University) chenhcc@ntumc.org
Ding-Shinn CHEN, M.D. (National Taiwan University) dschen@ntumc.org
Pei-Jer CHEN, M.D., Ph.D. (National Taiwan University)
Jia-Horng KAO, M.D., Ph.D. (National Taiwan University) kjh@ntumc.org
Ming-Yang LAI, M.D., Ph.D. (National Taiwan University) mylai@ntumc.org
Shwu-Bin LIN, Ph.D. (National Taiwan University) sblin@ntumc.org
Chun-Jen LIU, M.D., Ph.D. (National Taiwan University) cjliu@ntumc.org
Martin MCINTOSH, Ph.D. (Fred Hutchinson Cancer Research Center) mmintos@fhcrc.org
Ya-Chien YANG, Ph.D. (National Taiwan University) ycyang@ntumc.org
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