SELDI-TOF MS Profiling of Serum for Detection of the
Progression of Chronic Hepatitis C to Hepatocellular
E. Ellen Schwegler,1,2Lisa Cazares,1,2Laura F. Steel,3Bao-Ling Adam,1David A. Johnson,4O. John Semmes,1,2
Timothy M. Block,3Jorge A. Marrero,5and Richard R. Drake1,2
Proteomic profiling of serum is an emerging technique to identify new biomarkers indicative of
disease severity and progression. The objective of our study was to assess the use of surface-
enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) to
identify multiple serum protein biomarkers for detection of liver disease progression to hepato-
(n ? 38), or HCC (n ? 57) were applied to metal affinity protein chips for protein profiling by
SELDI-TOF MS. Across the four test groups, 38 differentially expressed proteins were used to
had a sensitivity/specificity range of 74% to 95%. For distinguishing chronic HCV from HCV-
HCC, a sensitivity of 61% and a specificity of 76% were obtained. However, when the values of
known serum markers ? fetoprotein, des-gamma carboxyprothrombin, and GP73 were com-
bined with the SELDI peak values, the sensitivity and specifity improved to 75% and 92%,
respectively. In conclusion, SELDI-TOF MS serum profiling is able to distinguish HCC from
liver disease before cirrhosis as well as cirrhosis, especially in patients with HCV infection com-
pared with other etiologies. (HEPATOLOGY 2005;41:634-642.)
marginally improved over the last 20 years. Between 1981
The poor survival rate is in part related to the diagnosis of
HCC at advanced stages, where effective therapies are lack-
ing.3Surveillance of patients at the highest risk for develop-
ing HCC (i.e., patients with cirrhosis) is an important
would benefit from a surveillance program, the poor sensi-
tivity and specificity of currently available tools has pre-
vented widespread implementation of HCC surveillance.
of patients with HCC, particularly during the early stages
(low sensitivity).6Furthermore, elevated AFP levels may be
seen in patients with cirrhosis or exacerbations of chronic
performance characteristics of AFP for HCC surveillance
Conflict of interest: Nothing to report.
he incidence of hepatocellular carcinoma (HCC)
continues to increase in the United States,1while,
unfortunately, patient survival with HCC has only
Abbreviations: SELDI-TOF MS, surface-enhanced laser desorption/ionization
time-of-flight mass spectrometry; HCC, hepatocellular carcinoma; HCV, hepatitis
C virus; AFP, ? fetoprotein; DCP, des-gamma carboxyprothrombin; IMAC-Cu,
copper-coated immobilized metal affinity capture.
From the1Department of Microbiology and Molecular Cell Biology and the
Medical School, Norfolk, VA; the3Drexel Institute for Biotechnology and Virology
Research, Drexel University College of Medicine, Doylestown, PA; and the5Divi-
sion of Gastrotenterology, Department of Internal Medicine, University of Michi-
gan, Ann Arbor, MI.
Received August 12, 2004; accepted December 3, 2004.
This work was supported by National Cancer Institute Early Detection Research
Network grants CA85067 (O.J.S., R.R.D.), CA-86400 (J.A.M.), and CA84951
(T.M.B.); National Institutes of Health grant DK-64909 (J.A.M.); and the Vir-
ginia Prostate Center (R.R.D., O.J.S.).
B.-L.A. is currently affiliated with Center for Biotechnology and Genomic Med-
icine, Medical College of Georgia, Augusta, GA.
Address reprint requests to: Richard R. Drake, Ph.D., Department of Microbi-
ology and Molecular Cell Biology, Eastern Virginia Medical School, 700 W. Olney,
Norfolk, VA 23507. E-mail: email@example.com; fax: 757-624-2255.
Copyright © 2005 by the American Association for the Study of Liver Diseases.
Published online in Wiley InterScience (www.interscience.wiley.com).
91%, and positive predictive values of 9% to 32%.8-10Ab-
dominal ultrasound is the most common imaging modality
survival of patients with cirrhosis who develop HCC by al-
have been reported to be 71% to 78%, 90% to 93%, and
14% to 73%, respectively.6,10,12However, the accuracy of
ultrasound can be limited by the ability of the operator and
the ability to differentiate HCC from nonneoplastic lesions
additional serum markers that will improve the detection
rate of early HCC.
One serum marker being evaluated, des-gamma car-
boxyprothrombin (DCP), or prothrombin induced by
vitamin K absence-II, is an abnormal prothrombin pro-
tein that is increased in the serum of patients with HCC.
In a previous cross-sectional study, DCP was shown to be
better than AFP in differentiating HCC from nonmalig-
among patients with underlying chronic liver disease was
specificity ?90%). Furthermore, high DCP levels in se-
rum or expression in late-stage tumor tissues have been
linked as poor prognostic indicators for patients with
HCC.15,16GP73 is a novel type II Golgi membrane pro-
tein of unknown function that is expressed in the hepato-
cytes of patients with adult giant-cell hepatitis.17,18
a general feature of advanced liver disease.18
identify biomarkers in serum indicative of advanced liver
disease, particularly hepatitis and HCC.19-21One of the
recent technological advances in proteomics is the sur-
face-enhanced laser desorption/ionization time-of-flight
mass spectrometry (SELDI-TOF MS).22-24Applications
of this technology to clinical fluids have suggested great
potential for the early detection of prostate, breast, head-
neck, ovarian, pancreatic, bladder, and liver can-
successfully used at six separate institutions to reproduc-
control sera, and correctly distinguish healthy from pros-
tate cancer subjects based on serum protein profiles.34
Using similar experimental protocols standardized in this
multi-institution study,34the objective of the phase I (ex-
ploratory) cancer biomarker study35reported herein was
to determine if protein profiling using SELDI-TOF MS
could accurately distinguish patients with different stages
virus (HCV) infections ranging from chronic hepatitis to
HCV-associated HCC. An initial comparison of the per-
formance of other serum markers (AFP, DCP, GP73)
singly, grouped, or in combination with the SELDI pro-
tein peaks is also presented.
Patients and Methods
Patient Specimens. All patients were enrolled from
the liver and liver transplantation clinics at the University
of Michigan Medical Center between September 2001
and May 2002 with Institutional Review Board approval.
Written informed consent was obtained from each pa-
tient. Four groups of consecutive subjects were enrolled.
One group included subjects with no history of liver dis-
ease and normal liver biochemistry, no risk factors for
viral hepatitis, and alcohol consumption less than 40
g/wk.14The second group consisted of subjects with his-
tologically confirmed chronic hepatitis. The third group
consisted of patients with histologically proven cirrhosis
and compensated liver disease (Child-Turcotte-Pugh
score ? 7). A fourth group consisted of patients with
histologically proven HCC (Table 1).14A 20-mL blood
sample was drawn from each subject for AFP and DCP
testing more than 2 weeks after liver biopsy was per-
formed. Blood samples were spun and serum was ali-
quoted and stored at ?80°C until testing. Each sample
used for proteomic profiling had not been thawed more
before initiation of treatment.
Serum Liver Biomarker Assays. AFP was tested us-
ing commercially available immunometric assays using
enhanced chemiluminescence at the University of Mich-
igan Hospital Clinical Diagnostic Laboratory. The upper
limit of normal was 8 ng/mL. DCP levels were measured
using an enzyme-linked immunosorbent assay kit (Eitest
PIVKA-II; Eisai Co., Tokyo, Japan) per the manufactur-
er’s instructions and were performed in duplicate.14Lev-
els of GP73 in serum were determined via Western blot
analysis.17,18Equal volumes of patient sera (0.5 ?L/lane)
were separated via SDS-PAGE on 4% to 20% polyacryl-
amide gradient gels. For normalization, each gel also in-
cluded a lane containing 0.5 ?L of serum from a pool of
sources negative for HCV and hepatitis B virus (Sigma,
St. Louis, MO). GP73-specific signals from the 73-kd
notech FluorChem CCD camera with AlphaEase spot
densitometry software (both from Alpha Innotech Corp.,
sity units relative to the GP73 signal detected in Sigma
control serum standard. Values were calculated as the
HEPATOLOGY, Vol. 41, No. 3, 2005SCHWEGLER ET AL.635
mean of duplicate or triplicate determinations for each
SELDI Processing of Serum Samples. Aliquots of
ical Center were mixed in a 2:3 ratio of serum to 8 mol/L
urea, 1% CHAPS, and were frozen at ?80°C before be-
ing shipped to the Center for Biomedical Proteomics at
Eastern Virginia Medical School. Serum samples were
processed robotically on a Biomek 2000 liquid handling
system (Beckman Coulter, Fullerton, CA) in a 96-well
format for SELDI analysis in the following manner: A
urea buffer was made in 1 mol/L urea, 0.125% CHAPS,
and phosphate-buffered saline. Diluted serum was ran-
domly spotted in duplicate onto copper-coated immobi-
lized metal affinity capture (IMAC-Cu) protein chips
(Ciphergen Biosystems, Fremont, CA) for SELDI-TOF
analysis with the aid of a 96-well bioprocessor. The sam-
ple was allowed to bind to the protein chips for 30 min-
utes at room temperature, followed by washes of
arrays were allowed to air dry and a saturated solution of
sinapinic acid in 50% (vol/vol) acetonitrile, 0.5% (vol/
vol) trifluoroacetic acid was added to each spot.
SELDI Data Analysis. The protein chip arrays were
analyzed using the SELDI ProteinChip System (PBS-II;
Ciphergen Biosystems). The spectra were generated by
the accumulation of 192 shots at laser intensity 220 in a
positive mode. The protein masses were calibrated exter-
nally using purified peptide standards. Spectra were ana-
lyzed with Ciphergen ProteinChip software (version 3.1)
the 1.5- to 20-kd range was performed using Biomarker
Wizard Software (Ciphergen Biosystems) at settings that
provide a 5% minimum peak threshold, 0.2% mass win-
sample pair analyzed and input into BioMarker Patterns
software (Ciphergen Biosystems) for classification tree
analysis as described previously.29,30,36,37Briefly, classifi-
this case were based on the normalized intensity levels of
peaks from the SELDI protein expression profile. Each
peak or cluster identified from the SELDI profile is there-
fore a variable in the classification process. The process of
splitting is continued until terminal nodes are reached
and further splitting has no gain in data classification.
cess, and the best performing tree was chosen for testing.
During the analysis, a pruning step occurs in which
branches are removed and the cost of the removal is de-
Table 1. Demographic Information and Etiology of Liver Disease
(n ? 39)
(n ? 36)
(n ? 38)
(n ? 57)
(? > .05)
Ethnicity (%) NHW/AA/H/Asian
TNM stage % (I/II/III/IV)
51 ? 11
50 ? 6
52 ? 8
54 ? 13
5 ? 0.8
67 ? 41
53 ? 36
0.5 ? 0.4
10.8 ? 23
31 ? 8
6.9 ? 5
4 ? 0.7
28.6 ? 9
22 ? 5
0.4 ? 0.2
2.94 ? 1.6
25 ? 4
5.1 ? 3.4
7.2 ? 1.3
112 ? 124
94 ? 85
0.9 ? 0.6
19.7 ? 38
36 ? 12
10.3 ? 9
8 ? 2.3
81 ? 49
109 ? 59
1.2 ? 0.9
11,788 ? 60,359
1,925 ? 235
16.6 ? 8
NOTE. All data are presented as the mean ? SD.
Abbreviations: NS, nonsignificant; MELD, model for end-stage liver disease; NHW, non-Hispanic white; AA, African American; H, Hispanic; HBV, hepatitis B virus; ALT,
alanine aminotransferase; AST, aspartate aminotransferase; TNM, primary tumor/lymph node/distant metastasis; NA, not applicable.
*Groups 3 and 4 versus groups 1 and 2.
†Group 4 versus groups 1, 2, and 3.
‡Group 4 versus groups 1 and 2.
636SCHWEGLER ET AL.HEPATOLOGY, March 2005
termined to establish a minimal tree size. This is referred
to as a “learning set.” Second, the decision tree was sub-
jected to cross-validation. In this step, the data is parti-
tioned such that randomly selected samples are
categorized with the decision tree being tested to ensure
that the decision tree is valid. Only these cross-validated
values are presented in the data tables herein. The nine
SELDI peaks that formed the main splitters of the tree(s)
with the highest prediction rates in the cross-validation
analysis were selected for further analysis with the differ-
ent serum markers.
Statistical Analysis. Specificity was calculated as the
to the total number of true negative samples. Sensitivity
was calculated as the ratio of the number of correctly
classified diseased samples to the total number of diseased
samples. Comparison of relative peak intensity levels be-
tween groups was calculated using the Student t test.
Sample Processing and SELDI Analysis. The
SELDI-TOF approach was applied to 170 patients as
described in Table 1. A subset with only HCV-related
disease was identified: HCV-HCC (n ? 28), HCV cir-
rhosis (n ? 22), chronic HCV (n ? 27). Each serum
sample was applied to copper-coated immobilized metal
affinity chips (IMAC-Cu) in duplicate. All sample load-
ing, processing and analysis steps were fully automated to
minimize sample processing errors. Following baseline
subtraction and normalization using total ion current,
peaks present in all of the samples were labeled and clus-
tered. The peak intensity values of 39 differentially ex-
mass ranges were used for further analyses.
Comparison of SELDI Spectra. A gel-view represen-
tation of five sample spectra from four groups (healthy,
HCV–no cirrhosis, HCV-cirrhosis, and HCV-HCC) in
the 5,000- to 12,000-Da range is presented in Fig. 1. At
four different distinguishing mass values, a box is drawn
to illustrate the differences in intensities for a given peak
(5,808, 8,939, 9,501, 11,735 m/z). Scatter plots of the
intensities of these same four peaks in all of the HCV-
related samples analyzed are shown in Fig. 2. Using the
mean intensities of each sample as indicated by the bar,
the proteins represented by the 5,808 and 11,735 m/z
peaks increased with disease severity. The levels of the
8,939 m/z protein were also increased following HCV
infection and were actually highest in the serum from
sity of the 9,501 m/z protein decreased in all HCV-asso-
ciated serum samples relative to the healthy subjects.
Table 2 shows the P values of the mean intensities of each
different patient groups. When comparing normal sam-
ples to liver diseases, the P values decreased with disease
severity. The changes were variable when comparing dif-
ferences among chronic hepatitis, cirrhosis, and HCC
samples, although the 5.8 and 11.7 markers were able to
ciated HCC. The cumulative data illustrate how changes
in multiple biomarker proteins can be used as fingerprint
patterns reflective of disease state, even though any single
protein would not be sufficient for classification.
Decision Classification Tree Analysis. For each
to 20-kd range were averaged for duplicate samples and
input into the BioMarker Patterns software (Ciphergen
Biosystems) for classification tree analysis as described in
Patients and Methods. The classification trees split the
data into two nodes, using one rule at a time in the form
of a question. The splitting decisions in this case were
based on the normalized intensity levels of 39 shared
peaks from the SELDI protein expression profile across
each sample, such that each peak was used as a variable in
the classification process. An internal 1/10 sample exclu-
sion, cross-validation process was done automatically for
each decision tree generated, and the most significant and
best performing tree for each condition was chosen. A
separate blinded sample set of sera from healthy individ-
also evaluated for the validation of the classification trees.
samples from healthy, HCV–non-cirrhosis, HCV-cirrhosis, and HCV-HCC
subjects (5 samples per condition) in the 5,000 to 12,000 m/z range.
Each boxed region identifies a differentially expressed peak within one of
the sample groups. The intensity of each band in the gel view reflects the
peak area in the original spectra.
Representative SELDI spectra gel view comparison of serum
HEPATOLOGY, Vol. 41, No. 3, 2005SCHWEGLER ET AL. 637
those described in Table 1 for the learning set sera.
Initially, serum profiles from healthy subjects were
compared individually with serum profiles generated
from either non-cirrhosis, cirrhosis, or HCC samples, in-
cluding the subset of HCV-infected samples (Table 4). A
representative diagram of a decision tree for the compar-
ison of all HCC samples versus healthy patients is shown
each sample pair presented in Tables 4 and 5. Only the
cross-validation results for each decision classification
analysis are presented. Following generation of the
SELDI spectra for the blinded sera on the IMAC-Cu
protein chips, the obtained peak clusters were applied to
the previously optimized decision tree (Fig. 3). A correct
classification of 91% (51 of 56) for HCC and 76% (33 of
42) for healthy patients was obtained.
The results in Table 4 indicate that the SELDI peak
profiles were progressively more effective at distin-
guishing normal samples from non-cirrhosis, cirrhosis,
and HCC conditions as the severity of the disease in-
creased, regardless of etiology. A separate stratification
of these samples was performed based only on an
HCV-associated etiology and analyzed separately. The
sensitivities of HCV-associated diseases versus normal
sera were better compared when all other liver diseases
were combined, as presented in Table 4 (subgroups
I.B., II.B., and III.B.). For example, correct classifica-
tion of HCV-cirrhosis conditions increased to 91%
compared with 72% in the cirrhosis sample set that
included other types of liver disease.
The ability to correctly classify the more clinically rel-
evant scenarios for surveillance of HCV disease progres-
sion from chronic hepatitis to cirrhosis to HCC was
examined using the HCV disease stratified sample set
used in the initial training set. Chronic HCV samples
could be distinguished from HCV-HCC samples with a
both chronic hepatitis and cirrhosis samples were com-
bined and compared with HCV-HCC samples, sensitiv-
ity decreased to 61%, but specificity increased to 76%
(Table 5). To test whether the accuracy of classification
could be increased by including clinical data in the anal-
classification decision tree analysis with all of the SELDI
peaks. These values were considered by the algorithm to
be additional “peaks” to be used with those from the
ble 5, inclusion of these marker protein values increased
the correct classification of disease states to 79%/86%
Fig. 2. Expression level of the (A) 5,808, (B) 8,936, (C) 9,509, and
(D) 11,707 m/z proteins for each sample in all of the indicated HCV
disease-stratified sample sets. Black bars indicate mean normalized
intensity; open circles represent values of individual samples. HCV,
hepatitis C virus infection but no cirrhosis; HCV cirr, HCV-associated
cirrhosis; HCC, HCV-associated hepatocellular carcinoma.
638 SCHWEGLER ET AL. HEPATOLOGY, March 2005
and 75%/92% sensitivity/specificity, respectively, for
both sample sets.
Proteomic analysis of tissue or serum derived from
HCC subjects is an emerging technique for the identifi-
cation of biomarkers indicative of disease severity and
progression.19-21,38To date, new HCC biomarkers have
been sought primarily by using differential two-dimen-
sional gel separations of tumor tissues38or serum19,21to-
gether with mass spectrometry for protein identification.
This is a productive discovery approach for identifying
biomarker proteins of masses greater than 10 kd; how-
ever, the low sample throughput limits the development
of direct diagnostic assays. Use of mass spectrometry pro-
teomic profiling approaches, typified by matrix-assisted
laser desorption/ionization and SELDI instrumentation,
assay effectively with other methods. Using a clinically
defined serum sample set reflective of liver disease pro-
gression to HCC, we have obtained results that support
the potential use of SELDI-TOF profiling as a surveil-
known serum markers or other clinical test data can be
incorporated into the SELDI peak analysis. The SELDI
analysis was comparable to the performance of AFP, the
the SELDI-derived protein peaks with known serum
marker data has also been reported to increase correct
classification of a pancreatic cancer serum cohort.33
Another HCC serum proteomics study using SELDI
and immobilized metal affinity protein chips has been
reported by Poon et al.20The serum samples in this study
were initially prefractionated into six separate compo-
nents before application to the copper affinity chips (or a
ysis algorithm was used for classification following peak
selection. The best sensitivities and specificities of 92%
and 90%, respectively, were obtained for distinguishing
chronic liver diseases (n ? 20) from late-stage HCC (III/
IV, n ? 24) samples. Our study differs significantly in
that more early-stage HCC samples (I/II) were evaluated,
no prefractionation of serum was done, a larger sample
size, and the clinical serum set analyzed was more fully
stratified for the different liver diseases. Use of a decision
tree classification algorithm further enhanced our studies
by allowing the inclusion of other serum marker data.
Even though two distinct sample preparation and algo-
Table 4. Decision Tree Classification Results for Normal
Samples Versus Different Sets of Liver Diseases
Condition Sensitivity Specificity
I.A. All non-cirrhosis liver disease vs. normal
I.B. HCV non-cirrhosis vs. normal
II.A. All cirrhosis vs. normal
II.B. HCV-cirrhosis vs. normal
III.A. All HCC vs. normal
III.B. HCV-HCC vs. normal
III.C. All HCV disease vs. normal
NOTE. All subgroup A samples were the parent cohorts from which the HCV
disease–associated cohorts (subgroup B) were selected. Subgroup C was the
combination of HCV–non-cirrhosis and HCV-cirrhosis samples compared with
normal samples. I indicates non-cirrhosis liver disease; II, cirrhosis; and III, HCC.
Table 2. Comparisons of P Values for the Indicated Markers in Different Sample Pairs
(m/z)Normal vs. HCVNormal vs. HCV-Cirrhosis Normal vs. HCC
HCV vs. HCV-
HCV Cirrhosis vs.
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
?5 ? 10?5
Table 3. Demographic Information of the Validation of the
(n ? 42) HCC (n ? 56)
(? > .05)
TNM stage % (I/II/III/IV)
53 ? 11
55 ? 13
8 ? 2
75 ? 40
111 ? 65
1.5 ? 1
234,871 ? 3,592
5 ? 0.7
21 ? 9
18 ? 5
0.3 ? 0.2
1.8 ? 0.6
NOTE. All data are presented as the mean ? SD.
Abbreviations: NS, nonsignificant; MELD, model for end-stage liver disease;
NHW, non-Hispanic white; AA, African American; H, Hispanic; HBV, hepatitis B
virus; ALT, alanine aminotransferase; AST, aspartate aminotransferase; TNM,
primary tumor/lymph node/distant metastasis; NA, not applicable.
HEPATOLOGY, Vol. 41, No. 3, 2005SCHWEGLER ET AL. 639
study demonstrate that SELDI protein profiling of serum
can provide discriminating protein peaks in classifying
chronic liver diseases and HCC.
profiling of body fluids, the profile itself is diagnostic, and
extending its use does not depend on identification of the
proteins in discriminating peaks. Of course, protein identi-
fication is still possible, and will add biological relevance to
the findings. To move this type of analysis toward a clinical
diagnostic application, it would be optimal to have a classi-
fication algorithm that has the ability to evaluate interspec-
tral relationships between markers, and one with the
capability of distinguishing multiple diagnoses simulta-
neously. The decision classification tree used herein is not
is able to compare only two conditions when four sets re-
quired analysis. However, the methods described in this re-
port illustrate the potential for developing a direct clinical
assay, because the process involves minimal processing of
serum, a fully automated loading and chip-binding proce-
dure, and enough peak features to determine differences in
disease states. This approach emphasizes peak/protein selec-
sion of other clinical data from known tumor or disease
markers (e.g., AFP, DCP, and GP73).
The development and testing of different classification
algorithms for proteomic profiling data is continuing to
evolve,22,39and it remains to be determined if a single
approach will be valid for all applications. Most likely it
will be necessary to empirically evaluate multiple analysis
processing of each spectra (baseline subtraction, instru-
ment noise, and so forth) is necessary to attain maximal
use of the features within the data. Previous applications
data have been questioned as to whether potential non-
biological features present in the spectra (?2,000 m/z)
were useful in the classification process.40,41In contrast, a
separate report has shown that useful biological features
in four separate models to accurately distinguish serum
Fig. 3. Diagram of a decision classification tree for healthy patients and patients with HCC. The squares are the primary nodes and the circles indicate
terminal nodes. The mass value in the root nodes is followed by the intensity value or less. For example, the question forming the first splitting rule is the
following: Are the intensity levels of the peak at 5,808 m/z lower or equal to 1.136? Samples that follow the rule go to the left “yes” terminal node, and
samples that do not follow the rule go to a “no” daughter node to the right. The number of normal or HCC samples in each node are shown.
Table 5. Analysis of All 38 SELDI Peaks and Determined
Serum Levels of 3 Marker Proteins (AFP, DCP, GP73)
Chronic HCV vs. HCV-HCC
Chronic HCV vs. HCV-HCC
HCV disease vs. HCV-HCC
HCV disease vs. HCV-HCC
71% (20/28)64% (14/22)
75% (21/28) 92% (45/49)
Abbreviations: Chronic HCV, non-cirrhosis HCV-infected samples; HCV disease,
all non-cirrhosis and cirrhosis HCV-infected samples; HCV-HCC, HCV-infected HCC
640 SCHWEGLER ET AL. HEPATOLOGY, March 2005
samples of ovarian cancer patients from healthy pa-
tients.42,43Whatever methods ultimately evolve, we be-
lieve the algorithm should retain the flexibility to include
the SELDI/matrix-assisted laser desorption/ionization
peak profiles. This multiple marker approach has greater
potential to improve the current single-marker analyses
used in most cancer diagnostic assays.
other serum profiling studies? Are these peaks only repre-
criticism of the proteomic profiling of serum approach,44
or do the peaks reflect immune responses, viral infection
responses, or cancer-specific proteins? Depending on the
clinical characteristics associated with the sample, the
peaks most likely represent components associated with
each possibility. The clinical characteristics of the sample
cohort that was analyzed herein (Table 1) and the differ-
ential protein profiling results that were obtained across
the conditions illustrates this concept. Differential pro-
tein patterns were obtained across the three liver disease
states, indicating the detection of more than just acute
phase reactants. In previous studies, isolation of peptides
bound to circulating serum albumin has indicated multi-
ple fragments of proteins associated with cell growth and
same peptides would be stripped off albumin by the urea/
CHAPS dilutions, which is one explanation for the pres-
the lower-mass polypeptides represent fragments of acute
phase proteins, these could be generated from proteases
specific to the disease state, and thus the pattern of their
appearance—and, ultimately, identification of the pro-
into disease pathogenesis. Additionally, multiple tumor
or disease-specific posttranslational modifications of se-
rum proteins also have to be considered and evaluated as
has been described for apolipoprotien AII.47A mass spec-
trometry platform combined with an immune capture
component could ultimately be the most effective means
to assess these types of modifications of proteins found in
complex clinical fluids.
We chose to use nonfractionation of the serum before
protein chip analysis, because this allows a higher
throughput and minimizes reproducibility problems for
larger sets of serum. As proteomic tools specifically de-
signed for serum fractionation and purifications evolve, a
limited fractionation strategy20,33or removal of major se-
rum proteins such as albumin before analysis remain as
viable options for future studies. Another possibility is to
selectively enrich for major serum carrier proteins, be-
cause these could be the primary carriers of the low-mass
peptides evaluated with our SELDI profiling strategy. A
previous study examining the peptides bound to these
serum carrier proteins indicated that discriminatory pep-
tides previously observed in serum from ovarian cancer
patients were enriched in signal intensity.46Also, in our
study, only one affinity surface, IMAC-Cu, was used. A
combination of protein profiles derived from other affin-
ity surfaces (e.g., hydrophobic, weak cationic, and strong
peaks found with the IMAC-Cu surface. Additionally,
mass spectrometry platforms for evaluating proteins in
clinical fluids will continue to evolve to provide improve-
ments in dynamic mass ranges, increased resolution and
peak detection sensitivities. As these different approaches
are attempted, the results from our study can serve as a
baseline for comparison to any future procedural im-
In conclusion, we present a phase 1 exploratory bi-
omarker study that shows that serum protein profiling
using SELDI-TOF can distinguish patients with HCC
from those with chronic liver disease, particularly those
with HCV infection. This exploratory study provides the
basis for a larger phase 2 biomarker validation study cur-
of the serum from this cohort will allow further verifica-
tion of the methods described herein, as well as provide a
cohort for the evaluation of different mass spectrometry
systems and other classification algorithms. Various puri-
fication and sequencing efforts to identify the low-mass
protein biomarkers identified in this study are also ongo-
ing. We anticipate that data from these studies may help
to improve the outcome for patients with HCC by en-
abling the diagnosis to be made at an earlier stage of the
disease when curative resection and/or transplantation
treatment is possible.
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