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FW: NATAP: New Hopeful Liver Cancer Blood Test

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  • alleypat
    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
    Message 1 of 1 , Jan 31, 2006
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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 Johnson 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 John 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.b

      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 Martin1, M Beaugrand2 and P J Johnson1

      1Cancer Research UK Institute for Cancer Studies, School of Medicine,
      University of Birmingham, Edgbaston, Birmingham B15 2TT, UK

      2Hepto-gastroenterology and Pathology Department, Jean Verdier Hospital,
      Assistance Publique-Hospitaux de Paris, UPRES EA 3409, UFR SMBH, UniversitC)
      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 b and B* 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; Johnson, 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 20B18% (meanB1s.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 Wilson, 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 Jean
      Verdier Hospital, Bondy, France. Sample collection was officially registered
      and all patients gave informed consent. Sera were stored at -80B0C. 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 -80B0C 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 -80B0C.

      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 B4 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 B4 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%) (Benjamini 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 60B0C) 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 37B0C 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.

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