July 2006 (Vol. 2, No. 4) Scientific Updates Recent Publications Collaborative Software Administrative Updates Biomarker Team Spotlight Key Web Pages at FHCRC

Dear ICBC Team Members:

The summer has been packed with collaborations and we have many things to report. As part of the technology updates for the newsletter, Dr. Sam Hanash of Fred Hutchinson Cancer Research Center has provided us with a strategy for isotopic labeling of intact proteins for quantitative analysis by mass spectrometry. This issue also contains information from Dr. Ruedi Aebersold on the PeptideAtlas project, which contains more than 35,000 peptides from 11,000 human proteins and is nowhere near saturation. As previously promised, we have provided a link to a forthcoming publication from Dr. Leigh Anderson and colleague, of a list of candidate cancer biomarkers for targeted proteomics.

In this issue we are also highlighting a team led by Dr. David Lane and Dr. Khay Guan Yeoh focusing on gastric cancer.

I would also like to remind everyone to register for the upcoming meeting in Singapore Dec. 1-3, 2006. A link to the registration is provided in this issue. There are also some workshops being organized in Korea around liver cancer and glycocapture training — details are listed below.

Best regards,

Lee Hartwell
President and Director
Fred Hutchinson Cancer Research Center

Isotopic labeling of intact proteins for quantitative analysis by mass spectrometry

The quantitative analysis of proteomes has reached a mature stage, with a rich repertoire of technologies available. Mass spectrometry has evolved enough to detect and identify femtomols of peptides, with a dynamic range over 3 to 5 orders of magnitude (Aebersold and Mann, 2003). However, most of cell or biological fluid proteomes are far more complex than what any single separation technique can resolve and have a much greater dynamic range of protein concentration than any mass-spectrometer can handle, thus complicating quantitative protein analysis. One approach to overcome some of these limitations, is to simplify protein complexity prior to analysis. Intact cells can be de-complexed into sub-cellular fractions and lysates and biological fluid proteins can be fractionated using orthogonal separation techniques based on their physical-chemical characteristics (Wang and Hanash, 2005).

Quantitative methods currently available can be grouped into label-free methods, which compare pure peptide ion intensities between MS analysis, and isotope labeling methods, which compare peptide ion intensities in the MS analysis of the mixture of peptides differentially labeled with stable isotopes. Cells grown in culture can be differentially isotope labeled in vivo using medium enriched with ¹⁵N, or stable isotopes of amino acids. The isotope labeling of samples can be performed during tryptic digestion with ¹⁸O, or after enzymatic digestion with a vast variety of tags. Labeled peptides can even be isolated and enriched from peptide mixtures, as with the Isotope Coded Affinity Tag (ICAT) technology (Gygi et al, 1999). However if samples to be compared have to be processed separately until the labeling step, larg quantitation errors may be introduced by artifactual variations. Quantitative proteomics is reviewed in Ong and Mann (2005), Sechi and Oda (2003), and Julka and Regnier (2004).

Isotopic labeling of intact proteins in a mixture has the advantage of reducing artifactual quantitative variation and allowing differential expression of protein isoforms to be uncovered. An approach for intact protein-based analysis consists of cysteine alkylation with acrylamide isotopes (Sechi et al, 2002). Acrylamide is a small reagent (mass = 71) that does not introduce significant mass shift or charge changes in the protein and does not negatively affect protein solubility. The alkylation reaction is performed using standard protein solubilization solutions with a virtually 100% yield. Additionally, the reagents are relatively inexpensive, making it practical to perform experiments starting with large amounts of protein as needed for extensive fractionation and in-depth analysis (Faca et al, 2006). After labeling two different samples with the light (acrylamide) and heavy acrylamide isotopes (D_3-acrylamide or 1,2,3^-13C_3-acrylamide), samples are mixed and submitted to multi-dimensional protein fractionation.

Figure 1
	Human serum, immunodepleted for the top-six most abundant proteins, was labeled with light and heavy acrylamide and mixed at 1:1, in a control experiment. The points are linearly distributed over five orders of magnitude based on ion intensities, indicating the good dynamic range of detection and quantitation. The figure was reproduced from Faca et al, 2006.

We utilized isotope acrylamide labeling of intact proteins to identify changes in human serum associated with cancer. Using a two-dimensional liquid chromatography approach (anion-exchange followed by reverse-phase chromatography) following mixing of differentially labeled samples, intact proteins were fractionated in about 130 fractions that were digested and individually analyzed by liquid chromatography-mass spectrometry, well over a thousand proteins were confidently identified and relative quantitation information was obtained for more than 45%. As a remarkable characteristic of this approach, proteins and protein fragments, as well their posttranslational modifications can be separated and quantified. The type of information about isoforms that may be derived is illustrated in the case of plasmin in the figure below. This approach when applied to a variety of cancer types has yielded a large number of candidate markers.

Figure 2

In an experiment comparing a pool of normal human serum and a pool of lung cancer patient serum, after isotopic labeling and intact protein fractionation, the alpha-chain of plasmin could be clearly demonstrated to be up-regulated in cancer while the intact plasminogen precursor was not. The boxes represent peptides identified and the relative cancer:normal acrylamide ratio.

References:
Aebersold, R.; Mann, M. Nature 2003, 422, 198-207.
Faca, V. C., M.; Phanstiel, D.; Glukhova, V.; Zhang, Q.; Fitzgibbon, M.; McIntosh, M.; Hanash, S. J. Proteome Res. 2006, 5, in press.
Gygi, S. P.; Rist, B.; Gerber, S. A.; Turecek, F.; Gelb, M. H.; Aebersold, R. Nat. Biotechnol. 1999, 17, 994-999.
Julka, S.; Regnier, F. J. Proteome Res. 2004, 3, 350-363.
Ong, S. E.; Mann, M. Nat. Chem. Biol. 2005, 1, 252-262.
Schmidt A, Kellermann J, Lottspeich F. Proteomics. 2005, 5, 4-15.
Sechi, S. Rapid Commun. Mass. Spectrom. 2002, 16, 1416-1424.
Sechi, S.; Oda, Y. Curr. Opin. Chem. Biol. 2003, 7, 70-77.
Wang, H.; Hanash, S. Mass Spectrom Rev. 2005, 24, 413-26.

PeptideAtlas Project

The need for public proteomics data repositories is recognized (11) and we intend PeptideAtlas to become a growing database and public resource. The PeptideAtlas currently has more than 35,000 peptides from 11,000 proteins and is no where near saturatjion. We invite researchers to submit their own MS/MS data for incorporation into PeptideAtlas, thus increasing the number of experiments and identified peptides.

Since all MS/MS spectra as well as peptide modifications (e.g. phosphorylation) are stored in the SBEAMS - Proteomics database, we will add functionality for easily accessing this information via the public PeptideAtlas interface.

We are assembling contributed datasets for organisms including mouse, Arabidopsis thaliana and Halobacterium halobium, and subsets such as the human plasma PeptideAtlas (7).

The PeptideAtlas Project by Frank Desiere1, Eric W. Deutsch1, Nichole L. King, Alexey I. Nesvizhskii, Parag Mallick, Jimmy Eng, Sharon Chen, James Eddes, Sandra N. Loevenich, and Ruedi Aebersold
Human Plasma PeptideAtlas, by Eric W. Deutsch, Jimmy K Eng, Hui Zhang,Nichole L. King, Alexey I. Nesvizhskii, Biaoyang Lin, Hookeun Lee, Eugene C. Yi, Reto Ossola and Ruedi Aebersold

In a forthcoming publication in Biomarker Insights, Dr. Leigh Anderson and his colleague Dr. Malu Polanski introduce a list of candidate cancer biomarkers (1261 proteins) from literature and other sources for targeted proteomics.

A List of Candidate Cancer Biomarkers for Targeted Proteomics by Malu Polanski and N. Leigh Anderson

We have received the software for Microsoft LIVE Meeting and will be sending a welcome message to team leaders with the software download in the coming week. This will provide a new option to test for more frequent communication between ICBC sites.

Registration is Open for the December 1-3, 2006 ICBC Meeting in Singapore!

This is a reminder to register for the December ICBC meeting in Singapore. Each existing ICBC team is encouraged to bring up to 6 team members. Developing teams are encouraged to bring up to two representatives. There is no registration fee for this meeting; however, each individual is 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 in Singapore. Once the agenda is finalized, it will be posted on this website and a notice will be sent to meeting attendees. If you have questions, please contact Karma Kreizenbeck at kkreizen@fhcrc.org or Gail Campagna at gcampagn@fhcrc.org.

Liver Cancer Workshop — September 18-19, 2006 in Daejeon, Korea

Dates: September 18-19
Place: Korea Research Institute of Bioscience and Biotechnology at Daejeon, Korea
Lodging: On campus guest house/hotel
Participants: Team leaders and 1-2 researchers from each team
Expenses: KRIBB will cover staying fees and local expenses. Each participant should cover his/her own air fares.

Agenda:

1. Progress in liver cancer biomarker discovery in each team
2. Comparison of different platform technology used in each team to discover biomarkers
3. Possibility of sharing sample information and resources
4. Practical ways to set up a collaboration among the liver cancer biomarker teams.

The main propose of this workshop will be to discuss what we can do to help each other to promote the discovery of biomarkers that can be used for early detection of liver cancers in each ethnic group. For more information, please contact Dr. Hyang-Sook Yoo at yoohyang@kribb.re.kr.

Glycocapture Workshop — September 18-22, 2006 in Seoul, Korea

Dr. Myeong-Hee Yu's lab spent some resources adopting the glycocapture technology from Dr. Ruedi Aebersold's lab, where it was originally developed, and would like to host a workshop to share the technology with other members of the ICBC on September 18-22, 2006 at KIST in Seoul, Korea. Two members of Dr. Aebersold's lab will be assisting with the training. Registration is open to post-docs, technicians, students, and junior investigators in the ICBC. The workshop is limited to a total of 10 participants. The workshop is free and all materials will be provided. In addition the following will also be provided:

1. Lodging and meals for the attendees at the KIST guest house
2. Attendees may get the results of MS analysis for their own samples (e. g. frozen serum) if they would like to bring samples to the workshop

For more information, please e-mail Dr. Yu at mhyu@kistmail.kist.re.kr.

Brief Description of Project:

Gastric adenocarcinoma is the second leading cause of cancer death worldwide. It is particularly common in Asia and especially in China and Japan. In Singapore it is the fourth most common cancer in males who have a 1:50 lifetime risk of developing gastric cancer. Gastric cancer traditionally carries a poor prognosis with 79% of tumors diagnosed at stage IV and five year survival less than 5%. Advanced gastric cancer is generally refractory to chemotherapy, which leads to poor prognosis. It has been shown that if it is diagnosed at an early stage, it is a curable disease. Therefore it is most important to be able to identify clinically useful early markers that can detect gastric cancer at an early stage.

The Gastric Cancer Epidemiology and Genetics Programme (GCEP), a research program which aims to identify clinical and genetic biomarkers for gastric cancer, initiated the GCEP Cohort Study for subjects at high risk of gastric cancer in January 2004. People aged >50 years who are at high risk for gastric cancer will be offered screening by endoscopy with careful and systematic follow-up over a minimum of 5 years. An enrollment of 4000 subjects over 5 years is planned with 202 subjects already prospectively recruited since 2004. Specimens collected for this Cohort Study to be used for biomarker discovery will include gastric mucosal biopsies, sera, gastric juice, leucocyte DNA and RNA, and a culture of Helicobacter pylori, a gastric pathogen bacterium strongly associated with gastric cancer.

The proteomics platform for biomarker discovery will include the following strategies:

Shotgun proteomics
Quantitative proteomics
Protein identification by MALDI-TOF/TOF mass spectrometry
Bioinformatics and data mining

The cancer genomics platform for biomarker discovery will employ two main technologies to identify genetic changes associated with gastric cancer. Gene Identification Signature (GIS) analysis will be used to annotate all key translocations and abnormal transcripts in primary gastric cancers. In addition, a novel and powerful technology called chromatin immunoprecipitation pair-end tag sequencing (ChIP-PET) will be used to identify all binding sites of the RUNX3 nuclear oncogene which has been found to be directly involved in gastric cancer development, and of p53 which has been implicated in the progression of gastric cancers.

Team Members and Expertise:

Max CHUNG, Ph.D. (NUS) [bchcm@nus.edu.sg] (bio)
Yoshiaki ITO, M.D., Ph.D. (NUS; IMCB) [itoy@imcb.a-star.edu.sg] (bio)
David LANE, Ph.D. (IMCB) (Biomarker Team PI) [d.p.lane@imcb.a-star.edu.sg] (bio)
Hew Choy LEONG, Ph.D. (NUS) [dbshead@nus.edu.sg] (bio)
Qinsong LIN, Ph.D. (NUS) [dbslinqs@nus.edu.sg] (bio)
Edison LIU, M.D. (GIS; NUS) [liue@gis.a-star.edu.sg] (bio)
JJ LIU, Ph.D. (GIS; NUS) [liuj3@gis.a-star.edu.sg] (bio)
Lance MILLER, Ph.D. (GIS) [millerl@gis.a-star.edu.sg] (bio)
Nallasivam PALANISAMY, Ph.D. (GIS) [palanisamyn@gis.a-star.edu.sg] (bio)
Gunaretnam RAJAGOPAL, Ph.D. (Bioinformatics Institute, Singapore) [guna@bii.a-star.edu.sg] (bio)
Yijun RUAN, Ph.D. (GIS) [ruanyj@gis.a-star.edu.sg] (bio)
Mark SEIELSTAD, Ph.D. (GIS; NUS) [seielstadm@gis.a-star.edu.sg] (bio)
Chia Lin WEI, Ph.D. (GIS) [weicl@gis.a-star.edu.sg] (bio)
K.G. YEOH, MBBS, M.Med. (NUS) (Biomarker Team Co-PI) [mdcykg@nus.edu.sg] (bio)

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