High-performance Proteomics as a Tool in Biomarker Discovery

Education & Training Practical Proteomics 1/2007

© 2007 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim www.practical.proteomics-journal.com

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Introduction

The age of proteomics has been announced in the mid 1990’s and since then a tremendous rush of proteome gold miners started ground-ing the claims. There were people claiming to (i) analyze the whole proteome, (ii) determine the interactome, (iii) determine the global phosphoproteome , (iv) determine the secre-tome, (v) and many more. Such buzzwords give the impression that with some mass spectrometers, computers, and algorithms we

High-performance Proteomics as a Tool in Biomarker DiscoveryHelmut E. Meyer and Kai Stühler

DOI 10.1002/pmic.200700183

Medizinisches Proteom-Center, Ruhr-University Bochum, Bochum,

Germany

Prof. Dr. Helmut E. MeyerMedizinisches Proteom-CenterRuhr-Universität BochumUniversitätsstr. 150D-44801 Bochum, GermanyFax: [email protected]

Abbreviations: IHC, immunohistochemistry PanIN, pancreatic intra-epithelial neoplasias PDAC, pancreatic ductal adenocarcinoma SOP, standard operation procedures

Biomarkers allowing early detec-

tion of disease or therapy control

have a huge infl uence in curing a

disease. A wide variety of methods

were applied to fi nd new biomark-

ers. In contrast to methods focused

on DNA or mRNA techniques, ap-

proaches considering proteins as

potential biomarker candidates

have the advantage that proteins

are more diverse than DNA or

RNA and are more refl ective of a

biological system. Here, we pres-

ent an approach for the identifi -

cation of new biomarkers relying

on our experience from the past

10 years of proteomics, outlining

a concept of “high-performance

proteomics”. This approach is

based on quantitative proteome

analysis using a suffi cient number

of clinical samples and statisti-

cal validation of proteomics data

by independent methods, such as

Western blot analysis or immuno-

histochemistry.

have the Holy Grail in our hand and will soon be able to elucidate the biology.

Now 10 years later, quite a number of studies are published every week dealing with high-throughput proteomics experiments. Very often, in such studies, the technical limi-tations are addressed or long lists of qualita-tively identifi ed proteins are reported, only. However, to fi nd new biomarker protein can-didates that may be useful for further stud-

Education & TrainingPractical Proteomics 1/2007


19

ies or for the development of diagnostic tests or even therapeutic targets, such proteomic studies have to be planned very critically from the beginning to get statistically signifi cant results.

To this end, we have to know what we are doing. What are the limitations of our meth-ods and what can be realistically expected from proteomics studies? That is why we like to give an overview about the experiences and considerations, which may lead to more suc-cessful approaches in proteomics. We call such successful application of proteomics “high-performance proteomics”.

Aims of High-performance Proteomics

High-performance proteomics aims to pro-duce quantitative proteomic data, which are per se statistically signifi cant and can be validated by independent methods with a rea-sonable success rate. To this end, the reality of our technologies applied and their limita-tions have to be taken into account. Moreover, the variability and heterogeneity of biological samples, especially when dealing with human samples (in Joachim Klose’s words: “As hu-mans, we represent a wild-type population”) have to be respected. Even though this later point bears great analytical challenges, we have to fi nd out the right strategies to conquer these problems. At the end we like to present biomarker proteins, which can be validated, found in, e.g. plasma or other body fl uids by Western blotting and, if successful, can be used as specifi c diagnostic marker(s) in an ELISA format for a distinct disease.

Proteomics Tools (Methods Available for Proteomic Studies)

As is well known, many proteomics tools are nowadays available and can help us to reach our goals, when properly applied.

An incomplete list is given below: 2-DE – Ampholine, Immobiline, large

and small gels DIGE-technology (minimal and full) 1-DE → LC-MS/MS Electrospray MS – MALDI-MS Capillary chromatography MS/MS

analysis Multidimensional chromatography Diagonal chromatography Shotgun approach, multidimensional pro-

tein identifi cation technology (MudPIT) Free-fl ow electrophoresis (FFE)

Gradient-centrifugation for prefraction- ation

Isotope Coded Affi nity Tag (ICAT)- technology

Isotope Coded Protein Labeling (ICPL) Isotope Tagging for Relative & Absolute

Quantitation (iTRAQ) Protein biochip-technology (Laser) microdissection Computer-aided image analysis Search engines for PMF Search engines for Peptide Fragment

Fingerprint (PFF) Data analysis – Statistics – Databases Decoy approach with decoy databases etc.

What Are the Key Issues in Pro-teomics or What Makes Proteome Analyses Challenging?

As defi ned, a proteome represents all pro-teins expressed by a genome at a given time point and under certain conditions. Thus, proteome analysis has to address an unprec-edented challenge in respect of a sample’s complexity and dynamic range of protein abundance. Taken for instance a human cell, we estimate that the complexity of a sample is due to post-transcriptional and post-trans-lational modifi cations as well as protein deg-radation ten to hundred times more diverse than the genome, resulting in characteristic under-sampling in protein analytics. Another point, which has to be considered when per-forming proteome analysis, is the dynamic range of protein abundance. As shown for ex-ample for the plasma, the dynamic range of protein abundance comprises up to ten orders of magnitude [1] and cannot be covered by a single analytical techniques without fraction-ation, depletion or concentration.

Furthermore, in cases when proteomics is applied to address quantitative aspects to de-tect proteins differentially expressed between different conditions technical and particularly biological variances have to be considered. From our proteome studies, we estimated er-rors/variances resulting from different sourc-es, when performing a proteome study. Usu-ally, the technical variances can be controlled by standard operation procedures (SOP) very carefully to keep all the steps reproducible. However, the biological variances, which might be small with microorganisms or cell cultures, but which may exhibit great differences from sample to sample from, e.g. human tissues have to be taken as a reality (Table 1).

“To fi nd new biomarker protein

candidates that may be useful

for further studies or for the

development of diagnostic tests

or even therapeutic targets,

proteomic studies have to be

planned very critically from the

beginning to get statistically

signifi cant results.”

“The dynamic range of protein

abundance comprises up to

ten orders of magnitude and

cannot be covered by a single

analytical techniques without

fractionation, depletion or

concentration.”



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How Can We Deal with Technical and Biological Variations?

When starting proteomic studies with sample material from human tissues we have to de-velop and to follow SOP for sample prepara-tion, protein separation, mass spectrometer’s performance and calibration and to evaluate the data in the right way quite strictly. Of course, a close interaction with medical doc-tors and pathologists is mandatory to get the

highest possible quality of sample specimens for the analysis. Indeed, this might be the real bottleneck of such a study. The technical vari-ances can usually be controlled and done so we are able to get reproducible results within an overall 20% SD [2].

However, it is usually a tedious task to deal with the high variances we are confront-ed with using samples from human tissues. As shown in the Table 1, these variances may exceed more than one order of magnitude.

To overcome these problems there are some non-scientifi c solutions available, i.e.:

To perform the experiment only once. To pool the samples to bring the biological

variance to zero. To make technical replicates from the same

sample(s) and name them independent. To use “improved“ statistical methods.

The less common solution is: To take the challenge, work scientifi cally

and repeat the experiments!Of course, this means we have to employ enough independent biological samples and to analyze them individually to get a basis for statistical analysis. Thus, for a proteomic study performed with human tissue samples we have to expect that we need at least be-tween 10 and 20 clinically very well character-ized samples. Since the often employed pool-

Technical variances

sample preparation (SOP) >5–20%

MS-measurement variance <5–10%

errors in data evaluation (FPR) >5%????

Biological variances

cell culture >5–20%

samples from inbreed mice >20–200%

human tissue normal vs. disease >4–5–fold

human cancer tissue >2–20–fold

Table 1. Variances in bioanalytics

“For a proteomic study per-

formed with human tissue

samples we have to expect

that we need at least between

10 and 20 clinically very well

characterized samples.”

Figure 1. The truth about

the tip of the proteomics

iceberg. Besides the limited detection range (left picture), proteome analysis is chal-lenging due to highly abun-dant proteins (right picture).



21

ing of samples destroy any biological variance this is not a proper way to work scientifi cally.

What Are Our Experiences in Pro-teome Analyses?

In proteomic studies where the analyses are performed with independent biological sam-ples (normally n = 4–10) only a few candidate proteins are found to be reproducibly up- or down-regulated taking a threshold value of twofold up- or down-regulation. Usually, the identifi ed proteins belong to the “class” of the high-abundant proteins. Thus, in a quantita-tive proteomic study it is meaningful to focus on the 100 to 500 most abundant proteins (i.e. in other words the tip of the iceberg), only (Figure 1). Proteins of low abundance can

usually not be measured quantitatively in a given protein solution in a reproducible man-ner by bioanalytical methods like 2-DE or MS-based methods. These low-abundant proteins are quantitatively accessible only when they are analyzed in a targeted approach, e.g. ELI-SA, or when they become “high-abundant” in isolated subfractions of tissues or cells. This may be achieved by, e.g. organelle isolation, membrane isolation, etc., using procedures like gradient/differential centrifugation, mi-crodissection, etc.

Moreover, now in high-throughput pro-teomic studies (shotgun approach) a deluge of MS/MS data are acquired that point to identi-fi ed sequences. However, the success rate is usually very low at the 5–20% level. Addition-ally, the time needed for data validation out-performs the pure analysis time running the LC-MS (MS/MS) experiments four- to tenfold.

Biomarker Discovery by High-performance Proteomics

A wide variety of methods were applied to fi nd new biomarker. In contrast to methods focused on DNA or mRNA techniques, ap-proaches considering proteins as biomarker have the advantage that proteins are more di-verse than DNA or RNA and are responsible for the huge complexity of biological pheno-types. Multiple PTM and alternative splicing result in approximately tens to hundreds of species of proteins derived from a single gene. Furthermore, many physiological changes are mediated by PTM and cannot be predicted from the nucleic acid sequence.

Proteomics have been successfully ap-plied in different fi elds [3–5]. From our point of view considering the trends within pro-teomics in the last few years biomarker dis-covery is still of enormous interest. Therefore, we will explain our concept of high-perfor-mance proteomics describing proteome study performed to detect biomarker of the pancre-atic tumor progression. Before starting a sci-entifi c project including a proteome study a number of questions determining the study design have to be answered (Table 2).

Current Strategies to Discover Biomarkers

As blood is the easiest available source for diagnostics it is not surprising that most of proteomic approaches aim at identifying bio-markers directly in blood specimen (Figure 2). However, as mentioned before due to the

“Before starting a scientifi c

project including a proteome

study a number of questions

determining the study design

have to be answered.”

What is the aim of my proteome study?

Am I interested in the proteome changes after drug treatment or distinct stimuli?

Testing the concentration dependency/time course

Which methods will I use?

How much time I like to invest?

Can I repeat my experiments in this time 5 to 10 times independently?

How much time do I need for the data evaluation?

How many fi nancial resources are available?

Is enough sample material available?

Am I going to pool my samples? Which consequences are expected?

How reproducible is my sample preparation?

Is my sample homogeneous?

Are my results reproducible?

Will I do a qualitative or quantitative proteome analysis?

How can I validate my data by independent methods?

What are valid proteomics data?

Is a functional test available to validate my proteomics data?

Table 2. Pre-considerations before starting a proteomics study



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complexity and enormous dynamic range in protein abundance especially in blood samples such approaches were not very successful in identifying and providing new protein bio-marker for clinical diagnostics so far.

In contrast, different techniques have been developed starting from diseased hu-man tissue samples to obtain biomarker for candidate approaches. Human tissue samples contain candidate biomarkers, if existent, in

higher concentration, which can be analyzed with highly sensitive proteomics techniques (Figure 3). Therefore, we prefer an approach starting with the exhaustive analysis of the dis-eased tissue to identify candidate biomarkers. Since human tissues are quite heterogeneous, subfractions should be prepared by microdis-section. This can be done by manual microdis-section [6, 7] or by laser beam processing [8, 9] in order to select for appropriate cell types.

Figure 2. Serum biomarker

discovery via serum/plasma

profi ling. Despite depletion of high abundant proteins and physico-chemical separa-tion of proteins, identifi ca-tion of candidate serum bio-marker is often impossible. Applying such approaches results in m/z values of un-known identity which allow no further validation.

Figure 3. Biomarker dis-

covery via tissue profi ling.

Tissue samples containing candidate serum biomarker, if existent, in high concentra-tion. Applying highly sensi-tive proteomics techniques identifi cation of proteins is possible allowing indepen-dent verifi cation and candi-date approaches to screen for serum biomarker.



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For the proteome analysis of such a small sample amount, different approaches have been established. One approach applied for the analysis of microdissected cells without direct protein quantifi cation is the so-called “shotgun sequencing” where the proteins are treated with an appropriate protease. The derived peptides are subsequently separated and detected by MS (LC-MS/MS). Similar to this approach, an LC-MS/MS-based strategy for protein identifi cation and quantifi cation has been established with 16O/18O post-diges-tion labeling [10]. Altogether, the analysis of 10 000 cells (1–4 µg protein) from breast duc-tal carcinoma resulted in the identifi cation of 76 proteins, whereof 7– 8 proteins showed a signifi cant regulation. Another strategy allow-ing protein profi ling of microdissected cells is based on SELDI-MS. SELDI-MS exhibits a sensitivity down to the femtomole/attomole level but is mostly applicable for low molecu-lar weight proteins (<20 kDa) and does not allow the identifi cation of selected marker proteins [11].

An alternative approach for protein quan-tifi cation and identifi cation with much higher protein resolution is 2-DE. We have shown that using the recently introduced 2D-DIGE saturation labeling technique the analysis of 1000 microdissected cells is feasible [12].

High-performance Proteomics: Studying Clinical Sam-ples to Discover New Biomarker

In terms of high-performance proteomics, we apply microdissection to prepare the appro-priate sample material from clinical samples for quantitative proteome analysis. Microdis-section offers the advantage to analyze the biologically relevant cell type that is often in the minority when analyzing precursor cells of a disease like, e.g. pancreatic ductal adeno-carcinoma (PDAC).

In 2005, we published an article applying our ideas of high-performance proteomics [12]. In this article, we described a method which allows performing quantitative pro-teomics studies on the level of 1000 microdis-sected cells from thin sections of microscopic specimens using the 2-D-DIGE saturation labeling to identify new biomarker of the pancreatic tumor progression. Patients with PDAC would benefi t from early detection and targeted treatment. CA-19-9, the only widely used serum marker of PDAC, is not applica-ble for screening purposes. The specifi city of CA-19-9 is only 60 –90% because its level can

be elevated in various disorders. Therefore, according to the recent ASCO guidelines, CA-19-9 is not recommended as a screening marker for PDAC [13]. No other reliable se-rum biomarker of PDAC has been defi ned to date.

Meanwhile, this strategy has been used for a number of different tissue types, including kidney, liver, lung and pancreas [12, 14–16]. In the later case, we validated the proteomics results by immunohistochemistry (IHC), us-ing tissue arrays coming from 130 individual patients [5].

In the case of liver cirrhosis, only sequen-tial liver biopsies currently allow for a precise evaluation of fi brosis progression. Neverthe-less, liver biopsies are accompanied by seri-ous side effect like, e.g. post-punctional bleed-ing, biliom, pneumothorax and renal trauma. The application of non-invasive biomarkers of hepatic fi brosis has been suboptimal [17–19] and underscored the need for more feasible markers of progression risk in liver fi brosis. Applying our approach of high-performance proteomics, biomarker candidates could be detected by Western blotting in the serum of affected patients and are now under further investigation [15].

High-performance Proteomics: Studying PDAC

To demonstrate the strategy of high-perfor-mance proteomics the workfl ow for the pan-creatic cancer study is given in the following (Figure 4). Due to the high technical demands and the huge amount of data, quantitative proteome studies are not suitable for analyz-ing large patient cohorts routinely to identify valid marker proteins in a “one-step” analysis. Therefore, a meaningful reduction of the ana-lyzed samples is necessary. We analyzed 37 single lesions of nine patients using 2D-DIGE to select the candidate proteins.

All samples have been analyzed individu-ally by 2-DE using the fl uorescence dye satu-ration labeling. In our hands, 2-DE in combi-nation with fl uorescence dye protein labeling is the method of choice for the analysis of complex protein mixtures. After determina-tion of the sensitivity of the labeling and spot detection, we performed the study using 1000 cells (~2 µg total protein) to avoid pooling of samples derived from several patients. Ap-plying fl uorescence dye protein labeling we were successful in revealing 2500 proteins for quantitative proteome analysis considering ~ 2 µg protein from 1000 microdissected cells



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(Figure 5). After differential analysis using image analysis software, we determined 86 proteins differentially regulated within pan-creatic tumor progression. Altogether, we identified 39 non-redundant proteins by MS, using a reference proteome extracted from PDAC tissue providing sufficient amount of protein for standardization and identifi-cation by MS. These differentially regulated

proteins will help to elucidate biological processes involved in pancreatic tumor pro-gression and furthermore, are good candi-dates to be used as biomarker or as serum biomarker, if released into blood.

However, before considering proteins as serum biomarker it is meaningful to validate the data of the proteome study by a larger number of patients. Therefore, we favor IHC using tissue microarrays (Figure 6). Tissue microarrays allow high parallel analysis of multiple tissue specimens for statistical validation by limited methodical variances. One relevant criterion for select-ing candidate proteins for further valida-tion by IHC using tissue microarrays has been the abundance of the distinct proteins during pancreatic tumor progression. Be-sides this stage-dependent abundance, we also considered tumor-relevant function as a criterion for validation by IHC. Seven out of the 39 non-redundant regulated pro-teins could be taken for further validation by IHC. The rationale for this low number is very simple: So far, we could not obtain antibodies for the other protein candidates, which are suited for IHC. This is another bottleneck nowadays!

The stained tissue microarrays reflecting 290 tissue slices from 130 different patients were statistically evaluated. After classifi-cation of the lesion, a pathologist does the scoring for each tissue slice (Figure 6).

To demonstrate the power of this strat-egy one example is given in Figures 5 and 6. The candidate protein 1 exhibits a very high variability that exceeds a 20-fold difference in abundance in pancreatic intra-epithelial neoplasias (PanIN) 1A, for example (Figure 5). Analyzing the immunohistochemical data for this candidate marker protein, we could show (Figure 6) that these data are validating our proteomics data perfectly well with a very good statistical significance.

Outlook

High-performance proteomics describes our understanding of quantitative proteome analysis and is not restricted to 2-DE ap-proaches. However, other non-gel-based technologies are still in their infancy and currently very time consuming if they are applied scientifically for biomarker iden-tification. However, there is a lot of hope that future developments in bioanalytics will overcome this bottleneck by using bet-ter strategies allowing analysis of 50 to 100

Figure 4. Workfl ow for the

identifi cation of biomarker

using microdissection, pro-tein fl uorescence dye label-ing (DIGE saturation dyes), differential 2-DE, protein identifi cation by MALDI-MS (MS/MS) and validation of the candidates (IHC).

“Before considering proteins as

serum biomarker it is meaning-

ful to validate the data of the

proteome study by a larger

number of patients.”



25

human samples individually within 1 to 2 weeks. Further, we hope that in the near fu-ture we will have better antibody resources which will allow us to take the next steps in validating our high-performance proteomics data.

Acknowledgements

The authors would like to thank all partners successfully collaborating within the last 4 years helping to realize our concept of High-Perfor-

Figure 5. Result of the 2-D-

DIGE analysis for a candidate

biomarker protein. Applying fl uorescence dye protein la-beling 2500 proteins have been revealed for quantita-tive proteome analysis con-sidering ~ 2 µg protein from 1000 microdissected cells from normal and PanIN 1b precursor lesion.

mance Proteomics, especially, Barbara Sitek, Kathy Pfeiffer, Eva Hawranke from the Med-izinischen Proteom-Center, Bochum; Bence Sipos, Günter Klöppel, Jutta Lüttges, Sabine Burkert and Birgit Streletzki from the Depart-ment of Pathology; Kiel, and Stephan Hahn, Wolff Schmiegel from the Bergmannsheil Hos-pital, Bochum. This work was supported by grants from the Deutsche Krebshilfe (B.S., J.L., S.A.H and K.S., 70-2988-Schm3), Bundesmin-isterium für Bildung und Forschung (NGFN, FZ 031U119) and the Nordrhein Westfalen Ministerium für Wissenschaft und Forschung.

Figure 6. Validation of

candidate biomarker. Tissue microarrays refl ecting 290 tissue slices from 130 differ-ent patients with different PanIN lesions. Statistical evaluation after classifi ca-tion of the lesions by a pa-thologist verifi ed the results from the proteome study (see Figure 5) and validated the candidate biomarker as signifi cantly regulated be-tween PanIN 1A and normal ductal epithelium.



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References

[1] Anderson, N.L., Anderson, N.G., The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteomics 2002, 1, 845–867.

[2] Jung, K., Gannoun, A., Sitek, B., Apostolov, O. et al., Statistical evaluation of methods for the analysis of dynamic protein expression. Data from a tumor study. Rev. Stat. 2006, 4, 67–80.

[3] Souchelnytskyi, S., Lomnytska, M., Dubrovska, A., Hellman, U., Volodko, N., Towards early detection of breast and ovarian cancer: plasma proteomics as a tool to fi nd novel. Proteomics 2006, 6, 69–71.

[4] O’Connor, M., Novel biomarkers for DNA damage response pathways: Insights and applications for cancer therapy. Proteomics 2006, 6, 65–68.

[5] Sitek, B., Sipos, B.I., Alkatout, I.G., Poschmann G. et al., Identifi cation and validation of molecular markers of pancreatic tumour progression by proteomics and immunohistochemistry, manuscript to be submitted.

[6] Whetsell, L., Maw, G., Nadon, N., Ringer, D.P. et al., Polymerase chain reaction microanalysis of tumors from stained histological slide. Oncogene 1992, 7, 2355–2361.

[7] Zhuang, Z., Bertheau, P., Emmert-Buck, M.R., Liotta, L.A. et al., A microdissection technique for archival DNA analysis of specifi c cell populations in lesions <1 mm in size. Am. J. Pathol. 1995, 146, 620–625.

[8] Emmert-Buck, M.R., Bonner, R.F., Smith, P.D., Chuaqui, R.F. et al., Laser capture microdissection. Science 1996, 274, 998 –1001.

[9] Schütze, K., Lahr, G., Identifi cation of expressed genes by laser-mediated manipulation of single cells. Nat. Biotechnol. 1998, 16, 737–742.

[10] Zang, L., Toy, D.P., Hancock, W.S., Sgroi, D.C. et al., Proteomic analysis of ductal carcinoma of the breast using laser capture microdissection, LC-MS, and 16O/18O isotopic labeling. J. Proteome Res. 2004, 3, 604 – 612.

[11] Craven, R.A., Banks, R.E., Laser capture microdissection and proteomics: possibilities and limitation. Proteomics 2001, 1, 1200 –1204.

[12] Sitek, B., Lüttges, J., Marcus, K., Klöppel, G. et al., Application of DIGE saturation labeling for the analysis of microdissected precursor lesions of pancreatic adenocarcinoma. Proteomics 2005, 5, 2665–2679.

[13] Locker, G.Y., Hamilton, S., Harris, J., Jessup, J.M. et al., ASCO 2006 update of recommendations for the use of tumor markers in gastrointestinal cancer. J. Clin. Oncol. 2006, 24, 5313–5327.

[14] Sitek, B., Potthoff, S., Schulenborg, T., Stegbauer, J. et al., Novel approaches to analyze glomerular proteins from smallest scale murine and human samples using DIGE saturation labeling. Proteomics 2006, 6, 4337– 4345.

[15] Mölleken, C., Sitek, B., Sipos, B., Henkel, C. et al., Detection of novel biomarkers of hepatitis C-associated fi brosis by proteomic analysis, manuscript in preparation.

[16] Poschmann, G., Sipos, B., Sitek, B., Ulrich, A. et al., Identifi cation and validation of proteins highly expressed in lung squamous carcinoma using a proteomic approach, manuscript in preparation.

[17] Olaso, E., Friedman, S.L., Molecular regulation of hepatic fi brogenesis. J. Hepatol. 1998, 29, 836 – 847.

[18] Poynard, T., Imbert-Bismut, F., Munteanu, M., Ratziu, V., FibroTest-FibroSURE: towards a universal biomarker of liver fi brosis? Expert Rev. Mol. Diagn. 2005, 5, 15–21.

[19] Leroy, V., De Traversay, C., Barnoud, R., Hartmann, J.D. et al., Changes in histological lesions and serum fi brogenesis markers in chronic hepatitis C patients non-responders to interferon alfa. J. Hep. 2001, 35, 26.

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High-performance Proteomics as a Tool in Biomarker Discovery