BACKGROUND
breast cancer is worldwide the second most common type of cancer after lung cancer.
according to the american cancer society, approximately  <dig>  women in the us will be diagnosed with breast cancer in  <dig>  and about  <dig>  women will die from the disease.
molecular biomarkers have become increasingly important clinical tools for cancer screening, diagnosis, treatment customizations.
there has been an increasing number of research reports on developing breast cancer biomarkers, especially in blood
<cit>
.
many molecular biomarkers with expression level changes have been identified in breast cancer tissue samples or blood, for example, her <dig> <cit>
, pnca <cit>
, lipofilin b <cit>
, cyclin d <dig> <cit>
, ceacam <dig> <cit>
, osteopontin-c <cit>
, rcp <cit>
, and foxa <dig> <cit>
.
most current breast cancer biomarker identification is achieved using functional genomics studies of established breast cancer cell lines
<cit>  <cit>  <cit>  <cit>  <cit>
.
cell lines are widely used in many aspects of laboratory research and particularly as in vitro models in cancer research.
they have a number of advantages, including being easy to access and offering “clean” results with statistically significant signals.
however, human systems are quite complex
<cit>
, and many candidate biomarkers discovered in cell lines do not readily transfer to human tissues or blood, in which clinical testing will be performed.
therefore, profiling human plasma using proteomics techniques offers an appealing alternative to cell lines or tissue biospecimens in developing protein biomarkers
<cit>
, although the debate over this issue is heated
<cit>
.
the question whether protein biomarker identified in blood can be valuable rests primarily on our ability to address the complexity associated with the human plasma proteome.
the inherent presence of measurement noise, inconsistencies due to individual differences, and sample biases of the plasma proteomics approach have been reported
<cit>
.
however, our recent studies also showed, by collecting plasma proteomics into a common proteomics data repository, the hip <dig> database
<cit>
, we could start to reduce the perceived coverage biases for plasma proteomics, and explore a promising goldmine of candidate cancer biomarkers and drug targets
<cit>
.
in addition, bioinformatics and systems biology techniques can help reduce this complexity significantly.
for example, one can use plasma proteomics to derive breast cancer candidate protein markers and then use gene expression mapping to validate candidate protein biomarkers that are known to be secreted.
one can also use advanced visualization or network biology techniques such as
<cit>  <cit>
to model and monitor global patterns of changes in proteomics, instead of candidate biomarkers at the individual protein level
<cit>
.
in this paper, we adopted a systems biology approach to the study of panel protein biomarker discovery in breast cancer using plasma.
for polygenic diseases such as breast cancer and a complex detection platform such as human blood, we recognize that a single protein biomarker approach using “expressions” will not suffice for the high performance requirement of breast cancer screening and diagnosis.
therefore, by enlisting multiple proteins as analytes that are mechanistically linked to breast cancer pathways or functional networks, we believe that the chance of success would be higher than the simpler conventional single-marker approach.
our computational analysis involves several steps.
first, we used a t-statistics and permutation procedure to identify protein biomarker candidates that are significantly differentially detected among different individual plasma samples between the case and the control for breast cancer.
second, we performed an extensive literature curation to determine the constituents of the plasma protein biomarker panel.
third, we performed gene ontology analysis and pathway analysis to validate the list to reveal the intricate breast cancer related molecular mechanism that exists among the protein biomarkers on the panel.
fourth and last, we validated our results using two independent publicly-available breast cancer proteomics datasets derived from other groups.
our results showed that such an integrative systems approach is essential to development and validation of panel protein biomarkers that may withstand rigorous testing for the future steps.
RESULTS
normality test
the plasma proteome profiling dataset in study a contains  <dig> peptides, two states  and  <dig> samples each state.
q-q plot and one sample kolmogorov-smirnov test in figure  <dig> showed that the log <dig> transformation intensity values for all  <dig> peptides from  <dig> health women are not normally distributed .
we also found the intensity values from  <dig> women diagnosed with cancer in study a, women diagnosed with cancer from study b and c, and healthy women form study b and c are likewise not normally distributed.
pathway analysis and gene ontology categorization of significant proteins
<dig> peptides in the study a were mapped to  <dig> proteins by searching against the ipi database.
using a t statistics and permutation process described in the methods section and setting a p-value cut-off of  <dig>  after initial anova analysis of mass spectra data, we identified  <dig> statistically significant differentially expressed proteins , among which  <dig> are over-expressed and  <dig> are under-expressed in breast cancer plasma.
compared to the results of traditional statistical testing , our results show that the coupled statistical process outperforms the sensitivity of a parametric traditional statistical test that requires strong and sometimes untenable data assumptions since it is non-parametric and requires no assumption about the distribution under the null hypothesis.
a comparison of the set of  <dig> proteins with published findings from proteomic analysis of human breast cancer cell lines yielded  <dig> differentially expressed proteins in common.
top networks were identified by using ingenuity pathway analysis .
figures 3a and 3b quantified the biological significance of gene ontology biological processes
cross-validation of candidate biomarkers
in order to validate our computational results, the same methods and procedures we used in study a were applied to studies b and c. as shown in the venn diagram ,  <dig> candidate protein biomarkers were identified in study b, of which  <dig> were found in common with study a, and  <dig> candidate protein biomarkers were identified in study c, of which  <dig> were found in common with study a. the similarity measurements with the protein method are 40% for biomarker sets from study a and study b and 35% for biomarker sets from study a and study c.
the pathway-protein association matrix is shown in the additional file  <dig>  the top three pathways: ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’ are ranked top in all three studies .
the similarity measurements with the pathway-protein matrix method are 86% for biomarker sets from study a and study b and 83% for biomarker sets from study a and study c.
using ingenuity pathway analysis and david go analysis, we also found that biomarkers identified from the study b and study c have similar network and function as the  <dig> candidate protein biomarkers identified from study a.
from the cross-validation offered by our ‘systems biology’ approach, we found similar pathway, network and function in those biomarkers identified from the three studies.
the systems biology approach helps to unravel the intricate pathways, networks and functional contexts in which genes or proteins function and is essential to the understanding molecular mechanisms of panel protein biomarkers.
discussion
permutation test
most of protein identification methods were based on fold change.
fold change is easy to use and implement, fast and works with any number of arrays, but it does not take the variability of a protein into account and offers no significance measure of p-value.
in this paper, we used a t-statistics to calculate the initial p-value that can takes the variance into account.
theoretically, the t-test can be used even if the sample sizes are very small , as long as the variables are normally distributed within each group and the variation of scores in the two groups is not reliably different.
if populations from which data to be analyzed by a t-test were sampled violate one or more of the t-test assumptions, the results from the analysis may be incorrect or misleading.
for example, if the assumption of independence is violated, then the two-sample unpaired t-test is simply not appropriate.
if the assumption of normality for the t-test is violated, or outliers are present, then the t-test may not be the most powerful test available.
however, our plasma protein profiling shows no normal distribution .
in order to detect a true difference between health and control samples, a permutation process was used, and should be reliable regardless of whether or not the sample distribution is known.
our results showed that the permutation test was very similar to the t-test in its p-value estimate.
the t-test is a parametric test and the permutation process is non-parametric.
by using the permutation test we made no assumption about the distribution under the null hypothesis.
usually, the assumptions in the null hypothesis are weakened, and it becomes harder to reject.
the permutation process rivals the sensitivity of a parametric t-test assuming equal variances.
to compare the power of the t-test and permutation test, that is, how likely they are to reject the null hypothesis when an alternate hypothesis is true, we assume specific distributions for the alternate hypothesis.
for the t-test, the most natural alternate hypothesis is that the two samples are from different normal distributions.
for large samples, it has been shown that the power of the permutation process using the difference in sample means is equal to the t-test for normally distributed alternates.
we used quantitative method to compare the results of ttperm function in the category package of the r language using the method described in the method section.
all  <dig> biomarkers identified using ttperm function are among the  <dig> panel biomarkers identified by our permutation test method in study a.
<dig> of them are identified in study c. comparing them with the  <dig> candidate protein biomarkers we identified from study b, there are only  <dig> in common.
but the remaining  <dig> proteins not identified by our permutation method are also not chosen as candidate protein biomarkers by previous findings using breast cell lines  <cit>  <cit>  <cit>  <cit> .
gene ontology analysis using the  <dig> biomarkers showed that response to external stimulus was annotated, but cellular metabolic process was not, and acute inflammatory response was annotated but proteolysis not.
the cellular metabolic process  annotated by our permutation method was reported to be related to cancer progression
<cit>
.
all the results show that our permutation test method is highly robust to the equality of the variances, regardless of whether the same sizes are similar and carries more conviction than the other permutation test which doesn’t consider the affect of variance inequality.
candidate protein biomarkers identified
a total of  <dig> peptides were identified from analyzing the plasma protein profiling from  <dig> women diagnosed with breast cancer and from  <dig> healthy women using the permutation test, corresponding to  <dig> unique uniprot protein names.
a comparison of the  <dig> proteins with previously published findings from proteomic analysis of human breast cancer cell lines yielded  <dig> candidate protein biomarkers.
the  <dig> proteins were categorized by their interactive pathway, network and annotated biological process on gene ontology.
an interesting finding from pathway analysis is that those top networks shown in the table  <dig> and figure  <dig>  especially the top  <dig> network  and top  <dig> pathways , are validated by our b and c dataset results, and are similar to previously reported works
<cit>  <cit>  <cit>  <cit>
.
for example, ana-teresa et al.
studied  <dig> candidate genes that are implicated in the etiology of breast cancer and found these genes are functionally involved in complement and coagulation cascades pathway
<cit>
.
carol et al.
reported that the cell migration in breast cancer lines can also be regulated by actin cytoskeleton dynamics
<cit>
.
and michael et al.
reported that increased focal adhesion kinase expression correlates with tgf-β1-mediated activation of p <dig> mapk in metastatic human breast cancer cells and concluded that focal adhesion is essential in mediating oncogenic signaling by transforming growth factor -β
<cit>
.
another interesting finding from our gene ontology work is the role of cellular metabolic process and response to external stimulus  in figure 3a and 3b in breast cancer was also reported by other authors.
for example, cancer, like other diseases, is accompanied by strong metabolic disorders
<cit>
.
and it was also reported that stress and external stimulus such as microbial infections, ultraviolet radiation, and chemical stress from heavy metals and pesticides affect the progression of breast cancer
<cit>
.
CONCLUSIONS
<dig> statistically significantly differentially expressed proteins between  <dig> healthy women and  <dig> women diagnosed with breast cancer were identified from initial lc-ms/ms experiments using a t statistics and permutation process which is useful in independent two-sample hypothesis testing.
top breast cancer activated networks and pathways were identified through systems biology approach.
<dig> candidate protein biomarkers were validated from the pathway/network analysis, literature curation from previous published findings in breast cell lines, and two additional studies.
gene ontology analysis confirmed that cellular metabolic process and response to external stimulus  were enriched in the  <dig> protein biomarker panel.
pathway analysis identified three top enriched pathways: ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’.
our approach integrating computing, basic biomedical research, and clinical applications promises to be able to “translate” between scientific innovations and clinical diagnostic needs for breast cancer.
assay development and clinical trials for panel biomarker from breast cancer patients are needed to assess which of the identified proteins may have diagnostic utility.

 breast cancer is worldwide the second most common type of cancer after lung cancer. according to the american cancer society, approximately women in the us will be diagnosed with breast cancer in and about women will die from the disease. molecular biomarkers have become increasingly important clinical tools for cancer screening, diagnosis, treatment customizations. there has been an increasing number of research reports on developing breast cancer biomarkers, especially in blood. many molecular biomarkers with expression level changes have been identified in breast cancer tissue samples or blood, for example, her, pnca, lipofilin b, cyclin d, ceacam, osteopontin-c, rcp, and foxa. most current breast cancer biomarker identification is achieved using functional genomics studies of established breast cancer cell lines. cell lines are widely used in many aspects of laboratory research and particularly as in vitro models in cancer research. they have a number of advantages, including being easy to access and offering “clean” results with statistically significant signals. however, human systems are quite complex, and many candidate biomarkers discovered in cell lines do not readily transfer to human tissues or blood, in which clinical testing will be performed. therefore, profiling human plasma using proteomics techniques offers an appealing alternative to cell lines or tissue biospecimens in developing protein biomarkers, although the debate over this issue is heated. the question whether protein biomarker identified in blood can be valuable rests primarily on our ability to address the complexity associated with the human plasma proteome. the inherent presence of measurement noise, inconsistencies due to individual differences, and sample biases of the plasma proteomics approach have been reported. however, our recent studies also showed, by collecting plasma proteomics into a common proteomics data repository, the hip database, we could start to reduce the perceived coverage biases for plasma proteomics, and explore a promising goldmine of candidate cancer biomarkers and drug targets. in addition, bioinformatics and systems biology techniques can help reduce this complexity significantly. for example, one can use plasma proteomics to derive breast cancer candidate protein markers and then use gene expression mapping to validate candidate protein biomarkers that are known to be secreted. one can also use advanced visualization or network biology techniques such as to model and monitor global patterns of changes in proteomics, instead of candidate biomarkers at the individual protein level. in this paper, we adopted a systems biology approach to the study of panel protein biomarker discovery in breast cancer using plasma. for polygenic diseases such as breast cancer and a complex detection platform such as human blood, we recognize that a single protein biomarker approach using “expressions” will not suffice for the high performance requirement of breast cancer screening and diagnosis. therefore, by enlisting multiple proteins as analytes that are mechanistically linked to breast cancer pathways or functional networks, we believe that the chance of success would be higher than the simpler conventional single-marker approach. our computational analysis involves several steps. first, we used a t-statistics and permutation procedure to identify protein biomarker candidates that are significantly differentially detected among different individual plasma samples between the case and the control for breast cancer. second, we performed an extensive literature curation to determine the constituents of the plasma protein biomarker panel. third, we performed gene ontology analysis and pathway analysis to validate the list to reveal the intricate breast cancer related molecular mechanism that exists among the protein biomarkers on the panel. fourth and last, we validated our results using two independent publicly-available breast cancer proteomics datasets derived from other groups. our results showed that such an integrative systems approach is essential to development and validation of panel protein biomarkers that may withstand rigorous testing for the future steps.  normality test the plasma proteome profiling dataset in study a contains peptides, two states and samples each state. q-q plot and one sample kolmogorov-smirnov test in figure showed that the log transformation intensity values for all peptides from health women are not normally distributed. we also found the intensity values from women diagnosed with cancer in study a, women diagnosed with cancer from study b and c, and healthy women form study b and c are likewise not normally distributed. pathway analysis and gene ontology categorization of significant proteins peptides in the study a were mapped to proteins by searching against the ipi database. using a t statistics and permutation process described in the methods section and setting a p-value cut-off of after initial anova analysis of mass spectra data, we identified statistically significant differentially expressed proteins, among which are over-expressed and are under-expressed in breast cancer plasma. compared to the results of traditional statistical testing, our results show that the coupled statistical process outperforms the sensitivity of a parametric traditional statistical test that requires strong and sometimes untenable data assumptions since it is non-parametric and requires no assumption about the distribution under the null hypothesis. a comparison of the set of proteins with published findings from proteomic analysis of human breast cancer cell lines yielded differentially expressed proteins in common. top networks were identified by using ingenuity pathway analysis. figures 3a and 3b quantified the biological significance of gene ontology biological processes cross-validation of candidate biomarkers in order to validate our computational results, the same methods and procedures we used in study a were applied to studies b and c. as shown in the venn diagram, candidate protein biomarkers were identified in study b, of which were found in common with study a, and candidate protein biomarkers were identified in study c, of which were found in common with study a. the similarity measurements with the protein method are 40% for biomarker sets from study a and study b and 35% for biomarker sets from study a and study c. the pathway-protein association matrix is shown in the additional file the top three pathways: ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’ are ranked top in all three studies. the similarity measurements with the pathway-protein matrix method are 86% for biomarker sets from study a and study b and 83% for biomarker sets from study a and study c. using ingenuity pathway analysis and david go analysis, we also found that biomarkers identified from the study b and study c have similar network and function as the candidate protein biomarkers identified from study a. from the cross-validation offered by our ‘systems biology’ approach, we found similar pathway, network and function in those biomarkers identified from the three studies. the systems biology approach helps to unravel the intricate pathways, networks and functional contexts in which genes or proteins function and is essential to the understanding molecular mechanisms of panel protein biomarkers. discussion permutation test most of protein identification methods were based on fold change. fold change is easy to use and implement, fast and works with any number of arrays, but it does not take the variability of a protein into account and offers no significance measure of p-value. in this paper, we used a t-statistics to calculate the initial p-value that can takes the variance into account. theoretically, the t-test can be used even if the sample sizes are very small, as long as the variables are normally distributed within each group and the variation of scores in the two groups is not reliably different. if populations from which data to be analyzed by a t-test were sampled violate one or more of the t-test assumptions, the results from the analysis may be incorrect or misleading. for example, if the assumption of independence is violated, then the two-sample unpaired t-test is simply not appropriate. if the assumption of normality for the t-test is violated, or outliers are present, then the t-test may not be the most powerful test available. however, our plasma protein profiling shows no normal distribution. in order to detect a true difference between health and control samples, a permutation process was used, and should be reliable regardless of whether or not the sample distribution is known. our results showed that the permutation test was very similar to the t-test in its p-value estimate. the t-test is a parametric test and the permutation process is non-parametric. by using the permutation test we made no assumption about the distribution under the null hypothesis. usually, the assumptions in the null hypothesis are weakened, and it becomes harder to reject. the permutation process rivals the sensitivity of a parametric t-test assuming equal variances. to compare the power of the t-test and permutation test, that is, how likely they are to reject the null hypothesis when an alternate hypothesis is true, we assume specific distributions for the alternate hypothesis. for the t-test, the most natural alternate hypothesis is that the two samples are from different normal distributions. for large samples, it has been shown that the power of the permutation process using the difference in sample means is equal to the t-test for normally distributed alternates. we used quantitative method to compare the results of ttperm function in the category package of the r language using the method described in the method section. all biomarkers identified using ttperm function are among the panel biomarkers identified by our permutation test method in study a. of them are identified in study c. comparing them with the candidate protein biomarkers we identified from study b, there are only in common. but the remaining proteins not identified by our permutation method are also not chosen as candidate protein biomarkers by previous findings using breast cell lines. gene ontology analysis using the biomarkers showed that response to external stimulus was annotated, but cellular metabolic process was not, and acute inflammatory response was annotated but proteolysis not. the cellular metabolic process annotated by our permutation method was reported to be related to cancer progression. all the results show that our permutation test method is highly robust to the equality of the variances, regardless of whether the same sizes are similar and carries more conviction than the other permutation test which doesn’t consider the affect of variance inequality. candidate protein biomarkers identified a total of peptides were identified from analyzing the plasma protein profiling from women diagnosed with breast cancer and from healthy women using the permutation test, corresponding to unique uniprot protein names. a comparison of the proteins with previously published findings from proteomic analysis of human breast cancer cell lines yielded candidate protein biomarkers. the proteins were categorized by their interactive pathway, network and annotated biological process on gene ontology. an interesting finding from pathway analysis is that those top networks shown in the table and figure especially the top network and top pathways, are validated by our b and c dataset results, and are similar to previously reported works. for example, ana-teresa et al. studied candidate genes that are implicated in the etiology of breast cancer and found these genes are functionally involved in complement and coagulation cascades pathway. carol et al. reported that the cell migration in breast cancer lines can also be regulated by actin cytoskeleton dynamics. and michael et al. reported that increased focal adhesion kinase expression correlates with tgf-β1-mediated activation of p mapk in metastatic human breast cancer cells and concluded that focal adhesion is essential in mediating oncogenic signaling by transforming growth factor -β. another interesting finding from our gene ontology work is the role of cellular metabolic process and response to external stimulus in figure 3a and 3b in breast cancer was also reported by other authors. for example, cancer, like other diseases, is accompanied by strong metabolic disorders. and it was also reported that stress and external stimulus such as microbial infections, ultraviolet radiation, and chemical stress from heavy metals and pesticides affect the progression of breast cancer.  statistically significantly differentially expressed proteins between healthy women and women diagnosed with breast cancer were identified from initial lc-ms/ms experiments using a t statistics and permutation process which is useful in independent two-sample hypothesis testing. top breast cancer activated networks and pathways were identified through systems biology approach. candidate protein biomarkers were validated from the pathway/network analysis, literature curation from previous published findings in breast cell lines, and two additional studies. gene ontology analysis confirmed that cellular metabolic process and response to external stimulus were enriched in the protein biomarker panel. pathway analysis identified three top enriched pathways: ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’. our approach integrating computing, basic biomedical research, and clinical applications promises to be able to “translate” between scientific innovations and clinical diagnostic needs for breast cancer. assay development and clinical trials for panel biomarker from breast cancer patients are needed to assess which of the identified proteins may have diagnostic utility. 
a comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker. our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers. we validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive candidate protein biomarkers in a panel. using a two-sample t-statistics and permutation procedure, we identified statistically significant, differentially expressed proteins, among which are over-expressed and are under-expressed in breast cancer plasma. plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. additional gene ontology analysis of the proteins also showed that cellular metabolic process and response to external stimulus were enriched functional annotations of the proteins identified in the breast cancer plasma samples. using the pathway analysis, we observed that the “activated” plasma proteins were present in several cancer pathways, including ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’, and match well with previously reported studies. breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. breast cancer is worldwide the second most common type of cancer after lung cancer. in this study, we analyzed a liquid chromatography coupled tandem mass spectrometry proteomics dataset from plasma samples of healthy women and women diagnosed with breast cancer. we presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies.
 breast cancer is worldwide the second most common type of cancer after lung cancer. plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. a comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.  in this study, we analyzed a liquid chromatography coupled tandem mass spectrometry proteomics dataset from plasma samples of healthy women and women diagnosed with breast cancer. using a two-sample t-statistics and permutation procedure, we identified statistically significant, differentially expressed proteins, among which are over-expressed and are under-expressed in breast cancer plasma. we validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive candidate protein biomarkers in a panel. using the pathway analysis, we observed that the “activated” plasma proteins were present in several cancer pathways, including ‘complement and coagulation cascades’, ‘regulation of actin cytoskeleton’, and ‘focal adhesion’, and match well with previously reported studies. additional gene ontology analysis of the proteins also showed that cellular metabolic process and response to external stimulus were enriched functional annotations of the proteins identified in the breast cancer plasma samples. by cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.  we presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers. 13– july the international conference on bioinformatics & computational biology las vegas, nv, usa 