Hope of liver cancer blood test

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Hope of liver cancer blood test

Scientists hope new technology will help them develop a blood test to

improve early diagnosis of liver cancer in high risk groups.

A team at the University of Birmingham used sophisticated protein

measurement and computer analysis to detect changes characteristic of

early liver cancer.

The discovery could potentially save lives as liver cancer treatment

is more effective if started early.

Details are published in the British Journal of Cancer.

LIVER CANCER

-About 2,500 people are diagnosed with primary liver cancer in the UK

each year

-The major risk factors are infection with hepatitis B and C and

consumption of foods contaminated with aflatoxin

-Hepatitis B is more common and the distribution of this infection

worldwide largely explains differences in rates of liver cancer

Cancer which first arises in the liver, or hepatocellular carcinoma,

is the sixth most common cancer in the world, being especially

widespread in East Asia.

High-risk groups, such as people with cirrhosis of the liver, are

monitored currently - but tests are not sensitive enough to detect

the disease early.

'First step'

Lead researcher Professor Philip said: " We have shown that

the right combination of technology and computer analysis can 'break

the code' of liver cancer and distinguish people with early liver

cancer from those without the disease.

" Our method was more accurate than the existing liver cancer blood

test.

" However, this is only the first step on a long road towards a test

that can be reliably used for the many people at risk of developing

primary liver cancer.

" We want to improve the technology to make the test even more

accurate. "

Liver diseases, including cirrhosis and hepatitis from the hepatitis

B and C viruses, greatly increase the risk of hepatocellular

carcinoma.

Although vaccinations against the hepatitis B virus are now

administered to children in most countries of the world, there are

millions of people already infected for whom vaccination would be too

late.

And as there is no effective vaccination for hepatitis C, the global

incidence of liver cancer is going to remain high for several decades.

The current methods used to monitor such high-risk groups include

ultrasound scans and a test for the presence of a single protein in

the blood called alpha-fetoprotein.

It is a good indicator of advanced liver cancer, but less able to

detect early disease.

Professor Toy, of Cancer Research UK, said: " More work is needed

to prove that patterns of protein levels associated with liver cancer

can be used as a reliable test for monitoring high-risk groups.â€

Story from BBC NEWS

Changes in the serum proteome associated with the development of

hepatocellular carcinoma in hepatitis C-related cirrhosis

British Journal of Cancer (2006) 94, 287-292.

Published online 10 January 2006

D G Ward1, Y Cheng1, G N'kontchou2, T T Thar2, N Barget2, W Wei1, L J

Billingham1, A 1, M Beaugrand2 and P J 1

1Cancer Research UK Institute for Cancer Studies, School of Medicine,

University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

2Hepto-gastroenterology and Pathology Department, Verdier

Hospital, Assistance Publique-Hospitaux de Paris, UPRES EA 3409, UFR

SMBH, Université Paris 13, Bondy, France

ABSTRACT

Early diagnosis of hepatocellular carcinoma (HCC) is the key to the

delivery of effective therapies. The conventional serological

diagnostic test, estimation of serum alpha-fetoprotein (AFP) lacks

both sensitivity and specificity as a screening tool and improved

tests are needed to complement ultrasound scanning, the major

modality for surveillance of groups at high risk of HCC. We have

analysed the serum proteome of 182 patients with hepatitis C-induced

liver cirrhosis (77 with HCC) by surface-enhanced laser

desorption/ionisation time-of-flight mass spectrometry (SELDI). The

patients were split into a training set (84 non-HCC, 60 HCC) and

a 'blind' test set (21 non-HCC, 17 HCC). Neural networks developed on

the training set were able to classify the blind test set with 94%

sensitivity (95% CI 73-99%) and 86% specificity (95% CI 65-95%). Two

of the SELDI peaks (23/23.5 kDa) were elevated by an average of 50%

in the serum of HCC patients (P<0.001) and were identified as â—Š and

ª immunoglobulin light chains. This approach may permit

identification of several individual proteins, which, in combination,

may offer a novel way to diagnose HCC.

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth commonest cancer in the

world today and the overall 5-year survival rate remains less than 5%

(Parkin et al, 2001). Although the incidence rate is likely to fall

with the institution of mass vaccination against the hepatitis B

virus, initiated in the 1980s (Chang et al, 1997), this will not have

a major impact for many years as the age of presentation is over 50

years in most areas of the world. Furthermore, there is no prospect

of a vaccine against the hepatitis C virus, the major aetiological

factor for HCC in the US, Japan and Southern Europe (Di Bisceglie et

al, 1991; El-Sarag and Hashem, 2004). Despite the absence of

randomised clinical trials, there is strong evidence that surgical

resection, liver transplantation or ablative therapies significantly

improve survival (Bruix et al, 2001; Beaugrand et al, 2005). Such

approaches are, however, only applicable to those in whom the tumour

is detected at an early stage, typically less than 3 cm in diameter

without vascular involvement and tumours only rarely present with

symptoms at this stage (Mazzaferro et al, 1996; Bruix et al, 2001;

Beaugrand et al, 2005). Early diagnosis has, therefore, become a

priority. Surveillance of high-risk groups, such as those with

cirrhosis, has been shown to permit detection of small tumours and

there is emerging evidence that this is associated with improved

survival (Sherman, 2005). Currently, surveillance involves both

serological testing with serum alpha-fetoprotein (AFP) estimation and

ultrasound scanning, typically at 6 monthly intervals (Mazzaferro et

al, 1996; Trevisani et al, 2002). However, while AFP may be a useful

diagnostic serum marker in patients with advanced symptomatic

disease, it is much less useful in patients with earlier/small

tumours where its sensitivity is low (Mazzaferro et al, 1996;

, 2001; Trevisani et al, 2002; Sherman, 2005).

Ultrasound is far more sensitive (in the order of 80%) but it is

highly operator dependent (Trevisani et al, 2002; Sherman, 2005).

More specific and sensitive serological tests, to complement

ultrasound scanning, would be of great clinical value.

Surface-enhanced laser desorption/ionisation time-of-flight mass

spectrometry (SELDI) has shown potential for cancer biomarker

discovery (Adam et al, 2002; Li et al, 2002; Chen et al, 2004; Zhang

et al, 2004). A subset of the proteome of a biological sample, such

as serum, which binds to a specific solid-phase chromatographic

surface (known as a 'protein-chip array'), is subsequently ionised

and detected by time-of-flight mass spectrometry. The peak

intensities in the SELDI spectra reflect the abundance of proteins

and peptides in the serum. The technique is relatively high

throughput, allowing samples to be processed in 96-well format at a

rate of up to several hundred serum analyses per day per analyst.

Various computer-based pattern recognition approaches can then be

applied to discriminate between patient groups, for example, those

with and without a particular cancer.

SELDI technology has been applied to identify potential serological

diagnostic markers for several cancers including ovarian cancer

(Zhang et al, 2004), prostate cancer (Adam et al, 2002), breast

cancer (Li et al, 2002) and colon cancer (Chen et al, 2004).

Published studies have indicated that the SELDI approach may also be

used to diagnose HCC (Poon et al, 2003; Paradis et al, 2005;

Schwegler et al, 2005), although only Paradis et al (2005) used an

independent test set and gave details on experimental

reproducibility. All three studies were based on small numbers of

patients with either undefined or late-stage HCC or chronic liver

disease arising from several different aetiologies. In addition,

there has been little consensus on the proteomic features that are

significantly different in the serum of HCC patients.

The present study was confined to patients with cirrhosis and

hepatitis C infection, as hepatitis C infection is the most common

cause of cirrhosis in the western world, carrying a high risk of HCC.

In addition, we chose to study patients with small HCC (3 cm mean

diameter, range 1-11 cm) in order to detect early changes that could

be usable in a screening situation. Being cognisant of the

controversies that surround the use of SELDI technology to identify

biomarkers (Baggerly et al, 2004; Diamandis, 2004; Ransohoff, 2004),

we were particularly careful to address issues of reproducibility and

validity. We used a 'training' data set to develop artificial neural

networks that permitted classification of patients as HCC or non-HCC

and then applied these networks to a 'blind' test data set. In

addition, we purified and identified two of the most discriminatory

proteomic features with m/z ratios of 23 000 and 23 500.

DISCUSSION

In this study, we have shown that SELDI spectra are reproducible and

capable of detecting differences in the serum proteome associated

with HCC. Artificial neural networks developed using a training set

of 84 non-HCC and 60 HCC patients were able to classify a blind test

set of 21 non-HCC and 17 HCC patients with 94% sensitivity and 86%

specificity. The accuracy of our ANN-based classification is far

higher than the currently accepted biomarker, AFP (Gupta et al, 2003)

(on the patient cohort used in this study, AFP>20 ng ml-1 gave 46%

sensitivity and 89% specificity). However, this is only a phase 1

biomarker discovery study (Pepe et al, 2001). Validation requires

analysis of larger cohorts of patients collected at multiple sites.

Some early SELDI-based biomarker studies have been heavily criticised

for potential bias in their design. This may arise from collection of

the cancer and noncancer sera in a different manner, differing

storage conditions or poor experimental design, for example, temporal

separation between the analysis of the cancer and noncancer samples.

In addition, the small sample numbers and high-dimensionality of the

data require that care is taken not to overfit data. We have

addressed these issues in our experimental design: QC samples were

run on all bioprocessors to exclude trends in the protein-chip array

reader's performance, block randomisation ensured that any variations

in chip quality did not bias the study between HCC and non-HCC or

between training and validation sets and the proteomic team in the UK

that analysed the samples were unaware of the identities of the blind

test set: the 'key' was held in France until the patient

classification had been completed. In addition, the patient groups

were well matched with regard to age (Table 1) and the male/female

ratio was well balanced in the test set. Although the male/female

ratio was not as well balanced in the training set, only two of the

17 peaks used to build ANNs were significantly different between male

and female patients (m/z 4795 and 66 480, P<0.05). Therefore, we can

be confident that the proteomic changes we have identified are

related to HCC. They could be either proteins secreted by the tumour

or arising from secretion from the tumors, induced by inflammatory or

immunological response to the tumour or the hallmark of predisposing

factors to HCC occurrence. The 23/23.5 kDa peak that we have

identified as immunoglobulin light chains may fall into the latter

category: IgG levels are higher in patients with more advanced

chronic liver disease, itself a predictive factor for HCC.

Previous reports have provided evidence that HCC produces changes in

the serum proteome of chronic liver disease patients that can be

detected by SELDI (Poon et al, 2003; Paradis et al, 2005; Schwegler

et al, 2005). Schwegler et al (2005) used SELDI to analyse the serum

of 50 hepatitis C patients (28 with HCC). They achieved 61%

sensitivity and 76% specificity (using decision trees) and found

peaks at 5.8 and 11.7 kDa elevated in HCC. We also see a peak at 5.8

kDa that is upregulated in HCC. Paradis et al (2005) were able to

classify a set of 82 cirrhotic patients (38 with HCC) with 90%

accuracy using logistic regression of data from Zn2+-loaded IMAC

protein-chip arrays and published a list of 30 proteomic features

significantly different between HCC and non-HCC patients. Paradis et

al did not observe our 23/23.5 kDa biomarker, but some discriminatory

features are common to both studies: the intensity of peaks at 33.2

and 66.4 and 102 kDa are decreased (66.4 and 33.2 kDa presumably

representing singly and doubly charged ions of albumin). The use of

Zn2+ rather than Cu2+ as the chromatographic ligand and differences

in sample preparation and Protein-chip reader settings may account

for some differences between the studies in addition to different

underlying causes of chronic liver disease and stage of HCC

progression. The discriminatory peaks in this study also differ from

our earlier work in which serum samples were fractionated prior to

SELDI (Poon et al, 2003) and again this may be due to the greater

mean age and earlier stage of HCC progression in the patients used in

our current work. Additionally, the fractionation may have unmasked

better discriminators than those observed with whole serum.

It is possible that the improved accuracy of immunoassays over

SELDI 'quantitation' could improve the performance of biomarkers

originally identified by SELDI. The use of multiple carefully chosen

markers should enhance both the sensitivity and specificity of HCC

screening. Interestingly, unpublished pilot SELDI studies in our

laboratory using Cu2+-loaded IMAC30 protein-chip arrays also indicate

a 23/23.5 kDa peak that is elevated by ~20% in the serum of

colorectal cancer patients (with respect to healthy controls), but

not in the serum of oral, breast or prostate cancer patients.

RESULTS

Surface-enhanced laser desorption/ionisation quality control

Quality control samples were run in triplicate on all six

bioprocessors. ANOVA analysis in Biomarker Wizard software provided

no evidence of significant differences between the QC data from

different bioprocessors. The coefficient of variation (CV) of the 18

intensities measured for each proteomic feature was calculated and

averaged across the 35 most intense peaks. This yielded an average CV

of 20±8% (mean±s.d.) obtained during the course of the survey,

consistent with the manufacturer's specification (15-20%). Visual

inspection of the data revealed no gross differences between

duplicate spectra from each patient's serum samples and no data had

to be discarded on this basis. An example of duplicate 0-20 and 0-200

kDa spectra for one HCC patient is shown in Figure 1.

A number of patient's samples were haemolysed giving rise to atypical

spectra. These were characterised by high haemoglobin peaks (15.1 and

15.9 kDa) and/or a low albumin peak (66.5 kDa). We discarded 34

samples from further data analysis on the basis of a SELDI intensity

>5 at 15.9 kDa or <5 at 66.5 kDa. The haemolysed samples were

distributed evenly among the HCC and non-HCC patients.

Significant differences between the serum of HCC and non-HCC patients

Of the 138 peaks picked and clustered by the Biomarker Wizard

software, 17 were significantly different at P<0.0123 (corresponding

to a false discovery rate of 10%) and these are shown in Table2.

These peaks have areas under the ROC curve ranging from 0.58 to 0.71

indicating possible diagnostic utility, especially if several of

these peaks could be used to build a classifier.

Artificial neural networks

A total of 17 ANN committee models were developed using up to 17 of

the most significant peaks in the training set (P<0.0123). The best

performing committee models were selected by their ability to

correctly assign samples as HCC or non-HCC by 10-fold cross-

validation of the training set. A total of 170 ANNs were trained (10

for each committee model). The committee models using the most

significant 4, 7, 10, 11, 15 and 17 features were selected with

average misclassification rates of 10-15% (cross-validation). Rather

than using individual committee models, the majority vote from these

six committee models was used to classify the blind test set. It

should be emphasised that the blind test set (the key to which was

held exclusively by the Bondy group) was only unblinded when the

classification model had been finalised, hence operator bias can be

excluded from the success of the ANN classification.

The blind test set consisted of 17 HCC and 21 non-HCC patients. The

majority vote of our ANN committee models correctly predicted 16 HCC

patients and 18 non-HCC patients giving 94% sensitivity (95%

confidence interval 73-99%, calculated according to , 1927) and

86% specificity (95% confidence interval 65-95%). Interestingly, all

10 HCC patients in the blind test set that had tumours less than 30

mm were correctly identified, indicating that SELDI can detect liver

tumours at an early stage. One of the non-HCC patients diagnosed as

having HCC by our analytical approach developed a 25 mm diameter

tumour within 6 months of the sample being taken. The area under the

ROC curve for the ANN prediction of the blind test set was 0.92

(Figure 2) comparing favourably with 0.73 for AFP (calculated across

the whole study).

Biomarker identification

We selected a broad proteomic feature with peaks at m/z ratios of 22

960 and 23 530 (hereafter referred to as the '23/23.5 kDa peak') that

was significantly elevated in the serum of HCC patients compared to

those with cirrhosis alone as a suitable candidate for purification

and identification. Of the 138 SELDI peaks, these two were the best

discriminators for HCC in this cohort of patients with the exception

of a peak with an m/z ratio of 132 200. Although not formally

identified, this peak copurifies with albumin and most likely

represents a dimer of albumin. The 23/23.5 kDa peak was purified in

parallel from a pool of HCC sera with high intensity at 23/23.5 kDa

and a pool of non-HCC sera with low intensity at 23/23.5 kDa (Figure

3). These sera were denatured with urea at pH 9 and applied to anion

exchange resin. A double peak at 23/23.5 kDa was found in the resin

flow-through ('pH 9 fraction') from the 'high' sample but was less

intense in the pH 9 fraction from the 'low' sample indicating that

our biomarker is a basic protein that does not bind to the resin

under these conditions. The two pH 9 fractions, enriched in the

23/23.5 kDa peak, were applied to an RP-HPLC column and proteins

eluted with an acetonitrile gradient. The 23/23.5 kDa peak was

detected by SELDI in fractions corresponding to 60-70% acetonitrile

and again was more intense in the 'high' sample (Figure 3). The

fractions containing the 23/23.5 kDa peak were concentrated by

centrifugal evaporation and the proteins separated by SDS-PAGE

(Figure 4). A band with ~23 kDa mobility that was more intensely

stained in the 'high' sample (Figure 4) was excised and trypsinised.

LC-MS/MS identified 34 tryptic peptides of immunoglobulin light chain

and eight peptides from immunoglobulin light chain. Several

homologous peptides, each with a unique mass but reflecting the same

part of the sequence of different or light chains were seen. For

example, 12 homologous peptides were found corresponding to the N-

terminal tryptic fragment of light chain. The data are summarised in

Table 3. For each of the seven sets of homologous peptides for the

light chain and five for the light chain, we have provided the

sequence with the highest Xcorr. The peptides listed in Table 3 cover

55 and 34% of the sequence of and light chains, respectively,

assuming a polypeptide length of 215 amino acids. The multiplicity of

peptides demonstrates that the 23/23.5 kDa peak represents a diverse

repertoire of immunoglobulin light chains, consistent with both the

broad elution from the RP-HPLC column and the broad peak(s) in the

SELDI spectra. The identity of the 23/23.5 kDa biomarker was

confirmed using an anti-human IgG polyclonal antibody to probe a blot

of a SDS-PAGE gel of samples with high and low intensities of the

biomarker (Figure 5). The anti-IgG antibody detects more

immunoglobulin protein with an electrophoretic mobility of 20-30 kDa

(light chains) in the serum samples with greater SELDI intensity at

23/23.5 kDa (Figure 4). The anti-IgG antibody also showed increased

binding to proteins of 50 and 100-200 kDa in the samples with greater

SELDI intensity at 23/23.5 kDa (data not shown). This suggests that

there is an overall increase in the level of IgG in the serum of HCC

patients. It is possible that the SELDI peaks at 54 and 149 kDa,

upregulated in HCC patients (Table 2), represent immunoglobulin heavy

chains and intact IgG, respectively.

MATERIALS AND METHODS

Sample collection

Serum samples were collected between May 1994 and January 2005 at

Verdier Hospital, Bondy, France. Sample collection was

officially registered and all patients gave informed consent. Sera

were stored at -80°C. All patients tested positive for hepatitis C

antibodies and hepatitis C RNA on the day of sampling. Hepatocellular

carcinoma was diagnosed histologically or noninvasively, according to

the Barcelona criteria (Bruix et al, 2001). Samples were transported

on dry ice to the University of Birmingham, UK, for analysis in

February 2005, defrosted (on ice) and multiple 20 l aliquots taken

and stored at -80°C pending SELDI analysis. Quality control (QC)

samples were prepared by mixing equal volumes of serum from 27

healthy individuals and stored as multiple aliquots at -80°C.

Study design

The study consisted of 84 non-HCC patients and 60 HCC patients (the

training set) and 38 samples where the classification of HCC/non-HCC

identities was not revealed to the UK-based proteomics team (the

blind test set) (Table 1). Independent duplicate SELDI spectra were

collected for all serum samples using Cu2+-loaded IMAC30 protein-chip

arrays. Samples were processed using six 96-well bioprocessors over a

2-week period. Three spots per bioprocessor were devoted to identical

QC samples, one spot to a 0-20 kDa calibration mix and one spot to a

20-200 kDa calibration mix. Block randomisation was utilised: to the

eight spots on each protein-chip array, we applied in random sequence

three serum samples from patients without HCC, three serum samples

from patients with HCC and two serum samples from the blind test set

or one serum sample from the blind test set and either one QC sample

or calibration mix. All samples were analysed once on bioprocessors 1-

3 and then a separate aliquot of each patient's serum was analysed a

second time on bioprocessors 4-6, ensuring that the measurement

duplicates were not processed on the same day.

Surface-enhanced laser desorption/ionisation procedure

An initial experiment using pooled sera from HCC and non-HCC patients

was conducted to decide whether H50, CM10, Q10 or Cu2+-loaded IMAC30

protein-chip arrays were best able to detect changes in the serum

proteome characteristic of HCC. The Cu2+-loaded IMAC30 performed best

both in terms of the total number of peaks detected and the number of

peak intensities that were significantly different between the HCC

and non-HCC pooled samples. We proceeded to analyse all of the

patient's samples in duplicate using Cu2+-loaded IMAC30 protein-chip

arrays. The protein-chip arrays were placed in a 96-well bioprocessor

and prepared by a 5 min incubation with 50 l of 100 mM CuSO4 followed

by a water rinse and 3 ´ 10 min equilibrations with 200 l of binding

buffer (100 mM NaCl, 500 mM NaH2PO4/NaOH (pH 7.0)). Serum samples

were defrosted on ice and diluted five-fold with 9 M urea, 2% CHAPS,

50 mM Tris/HCl (pH 9.0). Following a brief vortex, the samples were

left on ice for 30 min prior to a 10-fold dilution in binding buffer.

These 50-fold final dilution samples were loaded on the bioprocessor

(100 l per spot) and incubated at room temperature for 1 h with

shaking at 900 r.p.m. After this period, the nonbound material was

discarded and the protein-chip arrays were washed (4 ´ 10 min

incubations with 200 l of binding buffer followed by a water rinse).

The protein-chip arrays were allowed to dry for 30 min prior to

addition of 1 l of 50% saturated sinapinic acid in 50% acetonitrile,

0.5% trifluoroacetic acid. The spots were then allowed to dry for

another 30 min prior to a second 1 l addition of sinapinic acid. The

protein-chip arrays were analysed in a PBS IIc protein chip reader

equipped with an autoloader (Ciphergen, UK). Spectra were collected

over 0-20 and 0-200 kDa ranges (488 laser shots) using laser

intensities of 165 and 210, respectively. Spectra were externally

calibrated in the 0-20 kDa range using all-in-one peptide standard

(Ciphergen) with added cytochrome c and myoglobin (Sigma). The 0-200

kDa range was calibrated using chymotrypsinogen, bovine serum albumin

and phosphorylase b (Sigma). Spectra were normalised using the total

ion current from 2 to 20 and 20 to 200 kDa. Peaks were selected and

clustered using Biomarker Wizard software (Ciphergen) with the signal

to noise ratio >5 for the first pass and >2 for the second, a cluster

mass window of 0.2%, and a requirement for peaks to be present in

>20% of the spectra. The peak intensities from the duplicate spectra

from each patient were averaged and the resulting peak intensities of

the 60 HCC patients and 84 non-HCC patients in the training set were

compared by two-sample t-test and the area under the receiver

operator characteristic (ROC) curve used to assess the discriminatory

power of each peak.

Sample classification

Artificial neural networks (ANNs) were used to build committee models

to classify serum samples into HCC and non-HCC groups using different

numbers of significant peaks. The feed-forward neural networks

consisted of three layers: an input layer, a hidden layer and an

output layer. The number of input nodes was determined by the number

of significant peaks from which the models were trained. The hidden

layer connected the input and output layers, and the number of nodes

in this layer controlled the complexity and performance of the neural

networks. The output layer consisted of a single node whose output

was used to classify sample status, representing HCC or non-HCC. The

ANN had full connection from the input nodes to the hidden nodes and

from the hidden nodes to the output node. All of the connection

weights were randomly initialised in the range (-1, +1). The ANNs

were trained using the back propagation algorithm. In the procedure

of training a committee model, a 10-fold cross-validation approach

was used to reduce the risk of 'over fit' (Khan et al, 2001). The

training data set was randomly partitioned into 10 subvalidation sets

(10%) and 10 subtraining sets (90%). Each sample was contained only

once in the subvalidation sets. Thus, 10 different ANNs were combined

to create a committee model. A stepwise approach was used in which

many committee models were built using various numbers of the most

significant peaks. Significant peaks were identified by two-sample t-

test if P is less than 0.0123 (determined by a false discovery rate

of 10%) (i and Hochberg, 1995). The classification of the

blind test set was made according to the majority decision of the six

best committee models.

Biomarker purification and identification

Two pooled samples were prepared, one containing serum from five HCC

patients with high SELDI intensity at 23/23.5 kDa and one containing

serum from five non-HCC patients with low SELDI intensity at 23/23.5

kDa. These two samples were diluted four-fold with 9 M urea, 2%

CHAPS, 50 mM Tris/HCl (pH 9.0) and applied to Q Ceramic HyperD F

anion exchange resin. Proteins were eluted stepwise from the resin

using buffers at pH 9, 7, 5, 4, 3 and an organic wash. The proteins

in these fractions were monitored by SELDI using Cu2+-loaded IMAC30

protein-chip arrays and the fractions containing the 23/23.5 kDa

biomarkers were applied to a monolithic C18 RP-HPLC column

(BeckmanCoulter PF-2D system) and eluted with an acetonitrile

gradient in 0.1% trifluoroacetic acid at a flow rate of 0.75 ml min-

1. Fractions were collected (0.6 min) and analysed by SELDI on Cu2+-

loaded IMAC30 protein-chip arrays. Fractions containing the 23/23.5

kDa peak were concentrated and further purified by non-reducing 12%

SDS-PAGE using MES running buffer (Invitrogen). The bands

corresponding to the 23/23.5 kDa biomarkers were excised and washed

in 40 mM ammonium bicarbonate/50% acetonitrile. The gel slices were

then treated with 50 mM DTT in 40 mM ammonium bicarbonate/10%

acetonitrile (1 h at 60°C) followed by 100 mM iodoacetamide (30 min

at room temperature in the dark). After several washes with 40 mM

ammonium bicarbonate/10% acetonitrile, 20 l of 12.5 g ml-1 sequencing

grade trypsin (Promega) was added to the dried gel bands and

digestion allowed to proceed at 37°C overnight. Peptides were

extracted with 100 l of 3% formic acid and analysed by LC-MS/MS using

a ThermoFinnigan LCQ Deca XP Plus Ion-Trap linked directly to an LC

Packings/Dionex Ultimate nanobore HPLC system. MS/MS data were

searched against a database of nonredundant human protein sequences

extracted from NCBI using SEQUEST. Data were filtered using Xcorr

values of 1.5, 2 and 2.5 for singly, doubly and triply charged parent

ions, respectively, and only first hits were considered.