ovarian cancer has the   highest fatality - to - case ratio of all gynecologic 
 malignancies [ 1 , 2 ] .  
this is attributed to the lack of early 
 warning signs and efficacious early detection techniques 
 [ 1 , 3 ] .
another problem hindering the successful management 
 of the disease is the paucity in prognosticators that could 
 assist the selection of treatment modality .
one of the most 
 promising routes towards improvement in the detection and 
 surveillance of ovarian cancer is the identification of serum 
 markers .
utilization of the ca125 as an ovarian cancer serum 
 marker has improved cancer detection rates during the last few 
 years [ 1 , 2 , 3 ] . nevertheless , ca125 does not diagnose 
 early - stage cancers with high accuracy and is prone to false 
 positives .
therefore , the need to identify additional serum 
 markers for ovarian cancer is paramount to the successful 
 management of this disease . a major obstacle in finding
a diagnostic biomarker is the tremendous molecular 
 heterogeneity that exists for nearly all human cancer , suggesting that simultaneous 
 screening of a patient specimen for multiple biomarkers will be required to improve the 
 early detection / diagnosis of cancer .  
dna chip technologies address this problem at the 
 genomic level , and provide accessibility to gene expression profiles .
however , since 
 proteins are , for the most part ,   the mediators of   a cell 's function , the study of the 
 changes in proteins that result from a pathological lesion , such as cancer , would appear to 
 be a rich source of potential cancer biomarkers .
most of the previous studies in search of diagnostic biomarkers 
 have employed two - dimensional electrophoresis ( 2de ) which can 
 resolve hundreds to thousands of proteins present in complex 
 protein mixtures , such as cell lysates and body fluids .
although 
 some successes have been reported in detecting potential ovarian 
 cancer - associated biomarkers [ 4 , 5 , 6 , 7 ] ,   this classical 
 proteomic technique is very time consuming , not highly 
 reproducible , and   not easily adaptable to a clinical assay 
 format .
a   recently developed mass spectrometry proteomic approach , the 
 seldi ( surface - enhanced laser desorption / ionization ) proteinchip 
 system ( ciphergen biosystems , inc , fremont , 
 calif ) , appears to hold   promise for biomarker discovery and as 
 a potential clinical assay format [ 8 , 9 ] .
( the 
 seldi system and its applications are described in 
 the report by reddy and dalmasso ; and a 
 recent review by wright ) . using this system , 
  distinct protein patterns of normal , 

premalignant , and malignant cells were found   for ovarian , 
 esophageal , prostate , breast , and hepatic cancers 
 [ 12 , 13 , 14 ] .
potential biomarkers for breast and bladder 
 cancers were also detected in nipple aspirate fluid and urine , see respectively [ 15 , 16 ] , by the seldi 
 system .
recent reports also support that analysis of the seldi data by 
  artificial intelligence    algorithms can lead to the 
 identification of protein    fingerprints    specific for 
 prostate , ovarian , and breast cancers , significantly increasing 
 the accuracy in differentiating cancer from the noncancer groups 
 [ 17 , 18 , 19 , 20 ] .   these studies employed different algorithms 
 to analyze the seldi data , including a genetic algorithm 
 , a decision tree [ 17 , 18 ] , and a support vector 
 machine algorithm .
the high dimensionality of the data generated by seldi requires a 
 mathematical algorithm to analyze the data without overfitting . 

since the seldi protein profiling approach is new , it is 
 difficult to determine up - front which algorithm to select for the 
 data analysis and development of a    diagnostic    classifier . 

it is also fair to assume that different bioinformatic tools may 
 be required for different cancer or disease systems .
the 
 objective of this study was to evaluate the commercially 
 available classification algorithm ( biomarker pattern software 
 [ bps ] ) developed by ciphergen biosystems inc for analysis of the 
 seldi serum protein profiling data from patients with ovarian 
 cancer , benign pelvic diseases , and normal women .  
the potential , 
 advantages , and drawbacks of this approach as well as suggestions 
 for improvement are discussed .
serum samples were obtained from patients with epithelial ovarian cancer prior to 
 treatment administration 
 ( n = 44 ) , benign pelvic diseases ( n = 61 ) , and from women with no 
 evidence of pelvic disease ( n = 34 ) enrolled through the division of gynecologic 
 oncology , university of texas , southwestern medical center .
the demographics of the patients and the 
 stage distribution of the ovarian cancers are presented in table 1 .
benign conditions 
 included benign pelvic masses ( endometriosis , cystadenomas , hydrosalpinx , lipoma , 
 brenner tumor , fibroids , endometrial polyp ) . the sera were aliquoted and stored at 
 80c .
serum samples were applied on the strong anion exchange ( sax ) and 
 immobilized - copper ( imac ) chip surfaces . in brief , 21  l 
 of serum   were mixed with 30  l 8 m 
 urea in 1% chaps - pbs ph 7.4 buffer for 30 minutes 
 at 4c , followed by the addition of 
 100  l of 1 m 
 urea in 0.125% chaps - pbs buffer and 
 600  l of binding buffer compatible with the type of 
 surface in use ( pbs for imac and 20  mm hepes 
 containing 0.1% triton for sax ) . 

fifty  l of the diluted 
 samples were then applied onto the chips using a bioprocessor . 
 following a 30-minute incubation ,
nonspecifically bound molecules 
 were removed by 3 brief washes in binding buffer followed by 3 
 washes with hplc - gradient h2o .
acn-0.1%tfa ) was applied to the 
 chip array surface and mass spectrometry was 
 performed using a pbs2 seldi mass spectrometer ( ciphergen 
 biosystems inc ) .
mass calibration was performed using the 
 all - in - one peptide standard ( ciphergen biosystems inc ) which 
 contains vasopressin ( 1084.2 daltons ) , somatostatin ( 1637.9 
 daltons ) , bovine insulin   -chain 
 ( 3495.9 daltons ) , human insulin recombinant ( 5807.6 daltons ) , and 
 hirudin ( 7033.6 daltons ) .
protein spectra of one serum sample processed on the 
 imac metal binding chip array and on the positively charged sax 
 chip array .
note that several different   proteins are captured by 
 the two different chip chemistries .
protein peaks were labeled and their intensities were normalized for total ion current ( mass 
 range 2200  kd ) to account for variation in ionization 
 efficiencies , using the seldi software ( version 3.1 ) .
peak 
 clustering was performed using the biomarker wizard software 
 ( ciphergen biosystems ) and the following specific settings : 
 spectral data from imac surface ; signal / noise ( first pass ) : 4 , 
 minimum peak threshold : 10% , mass error : 0.3% , and signal / noise 
 ( second pass ) : 2 for the 220  kd mass range and signal / noise 
 ( first pass ) : 5 , minimum peak threshold : 10% , mass error : 0.3% , 
 and signal / noise ( second pass ) : 2.5 for the 20100  kd mass 
 range .  
spectral data from the sax surface were analyzed with the 
 same set of settings with the difference that the minimum peak 
 threshold was set to 5% . with these labeling parameters , a total 
 of 122 protein clusters ( 45 from the imac and 77 from the sax 
 surface ) were generated .
peak mass and intensity were exported to 
 an excel file , and the peak intensities from each duplicate 
 spectra were averaged . pattern recognition and sample 
 classification 
 
the 
 decision tree described in the result section was generated using 
 the gini method nonlinear combinations .
a 10-fold cross - validation 
 analysis was performed as an   initial evaluation of the test error 
 of the algorithm .
briefly , this process involves splitting up the 
 dataset into 10 random segments and using 9 of them for training 
 and the 10th as a test set for the algorithm .
multiple trees were 
 initially generated from the 122 classifiers by varying the 
 splitting factor by increments of 0.1 .
the peaks that formed the main 
 splitters of the tree with the highest prediction rates were then 
 selected , the tree was rebuilt based on these peaks alone and 
 evaluated by the test set .
the values of p were calculated 
 based on t - test ( biomarker wizard software ) .
decision tree classification of the ovarian 
 cancer ( c ) and noncancer ( normal and benign or b ) groups .
the 
 blue boxes show the decision nodes with the   peak mass 
 ( m in kd ) , the peak 
 intensity ( i ) cutoff levels , and the number of samples .
the 5.54 , 
 6.65 , and 11.7  kd masses were detected on the imac chip , and 
 the 4.4 and 21.5  kd on the sax chip . these   five masses form 
 the splitting rules .
the red boxes are the 
 terminal   nodes with the classification being either cancer or 
 benign ( normal + benign ) .
serum samples were obtained from patients with epithelial ovarian cancer prior to 
 treatment administration 
 ( n = 44 ) , benign pelvic diseases ( n = 61 ) , and from women with no 
 evidence of pelvic disease ( n = 34 ) enrolled through the division of gynecologic 
 oncology , university of texas , southwestern medical center .
the demographics of the patients and the 
 stage distribution of the ovarian cancers are presented in table 1 .
benign conditions 
 included benign pelvic masses ( endometriosis , cystadenomas , hydrosalpinx , lipoma , 
 brenner tumor , fibroids , endometrial polyp ) . the sera were aliquoted and stored at 
 80c .
serum samples were applied on the strong anion exchange ( sax ) and 
 immobilized - copper ( imac ) chip surfaces . in brief , 21  l 
 of serum   were mixed with 30  l 8 m 
 urea in 1% chaps - pbs ph 7.4 buffer for 30 minutes 
 at 4c , followed by the addition of 
 100  l of 1 m 
 urea in 0.125% chaps - pbs buffer and 
 600  l of binding buffer compatible with the type of 
 surface in use ( pbs for imac and 20  mm hepes 
 containing 0.1% triton for sax ) . 
 fifty  l of the diluted 
 samples were then applied onto the chips using a bioprocessor . 
 following a 30-minute incubation ,
nonspecifically bound molecules 
 were removed by 3 brief washes in binding buffer followed by 3 
 washes with hplc - gradient h2o .
acn-0.1%tfa ) was applied to the 
 chip array surface and mass spectrometry was 
 performed using a pbs2 seldi mass spectrometer ( ciphergen 
 biosystems inc ) .
mass calibration was performed using the 
 all - in - one peptide standard ( ciphergen biosystems inc ) which 
 contains vasopressin ( 1084.2 daltons ) , somatostatin ( 1637.9 
 daltons ) , bovine insulin   -chain 
 ( 3495.9 daltons ) , human insulin recombinant ( 5807.6 daltons ) , and 
 hirudin ( 7033.6 daltons ) .
protein spectra of one serum sample processed on the 
 imac metal binding chip array and on the positively charged sax 
 chip array .
note that several different   proteins are captured by 
 the two different chip chemistries .
protein peaks were labeled and their intensities were normalized for total ion current ( mass 
 range 2200  kd ) to account for variation in ionization 
 efficiencies , using the seldi software ( version 3.1 ) .
peak 
 clustering was performed using the biomarker wizard software 
 ( ciphergen biosystems ) and the following specific settings : 
 spectral data from imac surface ; signal / noise ( first pass ) : 4 , 
 minimum peak threshold : 10% , mass error : 0.3% , and signal / noise 
 ( second pass ) : 2 for the 220  kd mass range and signal / noise 
 ( first pass ) : 5 , minimum peak threshold : 10% , mass error : 0.3% , 
 and signal / noise ( second pass ) : 2.5 for the 20100  kd mass 
 range .  
spectral data from the sax surface were analyzed with the 
 same set of settings with the difference that the minimum peak 
 threshold was set to 5% . with these labeling parameters , a total 
 of 122 protein clusters ( 45 from the imac and 77 from the sax 
 surface ) were generated .
peak mass and intensity were exported to 
 an excel file , and the peak intensities from each duplicate 
 spectra were averaged . pattern recognition and sample 
 classification 
 
the 
 decision tree described in the result section was generated using 
 the gini method nonlinear combinations .
a 10-fold cross - validation 
 analysis was performed as an   initial evaluation of the test error 
 of the algorithm .
briefly , this process involves splitting up the 
 dataset into 10 random segments and using 9 of them for training 
 and the 10th as a test set for the algorithm .
multiple trees were 
 initially generated from the 122 classifiers by varying the 
 splitting factor by increments of 0.1 .
the peaks that formed the main 
 splitters of the tree with the highest prediction rates were then 
 selected , the tree was rebuilt based on these peaks alone and 
 evaluated by the test set . the values of p were calculated 
 based on t - test ( biomarker wizard software ) .
decision tree classification of the ovarian 
 cancer ( c ) and noncancer ( normal and benign or b ) groups .
the 
 blue boxes show the decision nodes with the   peak mass 
 ( m in kd ) , the peak 
 intensity ( i ) cutoff levels , and the number of samples .
the 5.54 , 
 6.65 , and 11.7  kd masses were detected on the imac chip , and 
 the 4.4 and 21.5  kd on the sax chip .
the red boxes are the 
 terminal   nodes with the classification being either cancer or 
 benign ( normal + benign ) .
one hundred 
 thirty - nine serum samples were assayed by seldi mass 
 spectrometry .  
both sax and imac surfaces could effectively 
 resolve low - mass ( < 20  kd ) protein peaks , although the sax 
 surface appeared superior in resolving larger ( > 20  kd ) 
 protein peaks .
figure 1 shows representative protein 
 spectra from one serum sample processed on sax and imac chips . of a total of 139 serum samples , 124 ( 85 controls and 39 cancers ) 

were randomly selected to form the learning set and 15 ( 10 
 controls and 5 cancers ) to form the blinded test set for the 
 algorithm .  
five peaks were selected by the bps algorithm to 
 discriminate cancer from the noncancer groups . 

figure 2 is the decision tree that was generated from 
  the learning set to 
 classify the two groups .
three peaks ( 5.54 , 6.65 , and 
 11.7  kd ) detected on the imac chip and 2 ( 4.4 and 
 21.5  kd ) detected on the sax surface form the main 
 splitters .  
their mass spectra and gray - scale / gel views are shown 
 in   figures 3 , 4 , 5 , 6 , and 7 .
these peaks have significantly 
 different intensity levels between the cancer and benign or 
 normal controls with the exception of the 6.65 and 21.5  kd 
 peaks , which did not differ significantly between cancers and 
 benigns ( table 2 ) .  
a 10-fold cross - validation 
 analysis was performed as an initial evaluation of the accuracy 
 of the algorithm in predicting ovarian cancer .
a specificity of 
 80% and sensitivity of 84.6% were obtained 
 ( table 3 ) . in the test set , sensitivity and 
 specificity of 80%
the misclassified samples in the test set 
 included one benign ( uterine fibroid ) , one normal , and a stage 
 iii c cancer .
statistical comparison of the intensity levels of the 
 peaks used in the decision tree between the cancer and control 
 groups .
c - n : cancer versus normal ; c - b : cancer versus benign ; 
 and c - b / n : cancer versus normal and benign . performance of the decision tree in predicting ovarian 
 cancer .
numbers in parentheses denote the number of correctly 
 classified sample out of total number of samples in the 
 group .
the high degree of genetic 
 heterogeneity associated with human cancers makes it likely that 
 panels of multiple biomarkers will be needed to improve early 
 detection / diagnosis .  
this entails the development of 
 high - throughput proteomic and genetic approaches as well as of 
 reliable bioinformatic tools for data analysis .
the seldi proteinchip system offers the advantage of rapid and 
 simultaneous detection of multiple proteins from complex biologic 
 mixtures .  
we employed this system in combination with the bps 
 classification algorithm for protein profiling of ovarian cancer 
 in serum .   using this approach , a classifier that was 80% 
 accurate in discriminating patients with ovarian cancer from 
 patients with benign disease and healthy controls from a blinded 
 test set
evaluation of the classifier by 
 cross - validation and the analysis of the independent test set 
 offers statistical confidence of the potential of this approach 
 as an ovarian cancer detection tool .   however , the sample size 
 included in this study decreases the validity of generalized 
 conclusions .  
complete evaluation of this classifier will require 
 testing its prediction rates for larger  blinded  
  and independent serum sets .
however , 
 bps , like other mathematical algorithms , is prone to data 
 overfitting , and also is not reliable when a large number of 
 variables relative to samples sizes are included in the analysis . 

a preselection process of the most significant variables using 
 statistical analysis ( eg , roc curve , anova ) may help in 
 alleviating this problem .
spectra ( top ) and grey - scale or gel views 
 ( bottom ) of the peaks ( arrows ) forming the splitting rules . the 
 protein peak was detected on imac chip .
the peak appears to be 
 upregulated in the cancer ( c1  c4 ) compared to the benign ( b1-b2 ) 
 and normal ( n1-n2 ) groups .
spectra ( top ) and grey - scale or gel views ( bottom ) of 
 the peaks ( arrows ) forming the splitting rules .
spectra ( top ) and grey - scale or gel views ( bottom ) of 
 the peaks ( arrows ) forming the splitting rules .
the peak appears to be upregulated in 
 cancer ( c1c4 ) compared to the benign ( b1-b2 ) and normal ( n1-n2 ) 
 groups .
spectra ( top ) and grey - scale or gel views ( bottom ) of 
 the peaks ( arrows ) forming the splitting rules .
the peak appears to be 
 upregulated in the cancer ( c1c4 ) compared to the begin ( b1-b2 ) 
 and normal ( n1-n2 ) groups .
spectra ( top ) and grey - scale or gel views ( bottom ) of 
 the peaks ( arrows ) forming the splitting rules .
petricoin et al   recently reported the successful 
 application of a genetic algorithm for the analysis of seldi 
 proteomic data from ovarian cancer patients . in this study ,
five 
 discriminatory peptides were detected , moleculalr mass range 
 5002500 daltons , and the accuracy in predicting ovarian cancer 
 in a blinded set of samples was 97.4% .  
we focused on the 
 analysis of potential biomarkers in higher mass ranges ( > 2000 
 daltons ) .
furthermore , in contrast to the case where bps algorithm 
 is processed , that is ,   labeled peak information is analyzed , 
 the   genetic algorithm employed by petricoin et 
 al analyzes time - of - flight  raw    seldi data .  
in this case , 
 prerequisite for the further identification of the potential 
 discriminatory markers is the coupling of the genetic algorithm 
 with a peak identification system where the raw data are 
 translated into protein peak information .  
bps employs the peak 
 identification system of the seldi software facilitating 
 biomarker detection .
it should be noted , however , that careful 
 and precise selection of the peak labeling settings and 
 normalization of peak intensities are considered critical for 
 biomarker identification and for the   efficient and 
 reliable performance of any learning algorithm used in 
 conjunction with the seldi system .
besides providing a preliminary evaluation of the suitability of 
 bps for the comparison of seldi data , our study also demonstrates 
 the potential of combining spectral data from different types of 
 surfaces as a means to increase protein resolution .
although , 
 compared to seldi , the resolving power of 2d gel electrophoresis 
 remains unchallenged , we have found that this combinatorial 
 approach can significantly enhance biomarker discovery and 
 increase test accuracy for ovarian and breast cancers from 
 7075% up to 90%   . in conclusion , the bps software appears to be potentially 
 suitable for analysis of the high - dimensional seldi spectral data . 

avenues for improvement of the algorithm performance include 
 optimization of the peak labeling process as well as preselection 
 of the most significant peaks by statistical approaches .  
more 
 extended studies will be required to validate the potential and 
 reliability of bps as a bioinformatic tool for proteomic studies . 

it should also be emphasized that comparative analysis of 
 different types of algorithms will be of paramount importance for 
 the better evaluation of their performance and the selection of 
 the bioinformatic features needed for effective biomarker 
 discovery and discrimination of cancer .
this study was supported by grants from the gustavus and louise 
 pfeiffer research foundation ,   the early detection research 
 network ,   nci ( ca85067 ) , and the virginia prostate center .
 recent reports from our laboratory and others support the seldi 
 proteinchip technology as a potential clinical diagnostic tool 
 when combined with n - dimensional analyses algorithms . 
 the 
 objective of this study was to determine if   the commercially 
 available classification algorithm biomarker patterns software 
 ( bps ) , which is based on a classification and regression tree 
 ( cart ) , would be effective in discriminating ovarian cancer from 
 benign diseases and healthy controls .   
 serum protein mass 
 spectrum profiles from 139 patients with either ovarian cancer , 
 benign pelvic diseases , or    healthy women were analyzed using 
 the bps software . 
 a decision tree , using five protein peaks , 
 resulted in   an accuracy of 81.5% in the cross - validation 
 analysis and 80% in a blinded set of samples in 
 differentiating the ovarian cancer from the control groups . 
 the 
 potential , advantages , and drawbacks of the bps system as a 
 bioinformatic tool for the analysis of the seldi high - dimensional 
 proteomic data are discussed . 
INTRODUCTION
METHODS
Serum samples
SELDI processing of serum samples
Processing of SELDI data
RESULTS
DISCUSSION
ACKNOWLEDGMENT