BACKGROUND
the classification of human tumors using microarray data has been an area of intense research, but it remains a daunting task to validate a new profile and generate a clinically useful test.
many different gene expression-based predictors have been developed for breast cancer  <cit> , and two different gene expression predictors have reached the final step of prospective clinical trial testing  <cit> .
using cdna microarrays, we previously identified five distinct subtypes of breast tumors arising from at least two distinct cell types   <cit> .
this molecular taxonomy was based upon an "intrinsic" gene set, which was identified using a supervised analysis to select genes that showed little variance within repeated samplings of the same tumor, but which showed high variance across tumors  <cit> .
we showed that an intrinsic gene set reflects the stable biological properties of tumors and typically identifies distinct tumor subtypes that have prognostic significance, even though no knowledge of outcome was used to derive this gene set  <cit> .
a major challenge for microarray studies, especially those with clinical implications, is validation  <cit> .
due to the practical barriers of cost and access to large numbers of fresh frozen tumor samples with associated clinical information, very few microarray studies have analyzed enough samples to allow promising initial findings to be sufficiently validated to justify the major investment required for clinical testing.
an efficient approach would be to use public gene expression data repositories as test sets; however, it has been difficult to compare and/or combine data sets from independent laboratories due to differences in sample preparation, experimental design, and microarray platforms.
an accepted method for validation is to derive a prognostic/predictive gene set from a "training set" and then apply it to a completely independent "test set"  <cit> .
the "purest" test sets are comprised of samples not generated by the primary investigators to remove any possibility of bias  <cit> .
in this study, we illustrate the successful application of these principles by  deriving a new breast tumor intrinsic gene list that identifies the "intrinsic" biological features of breast tumors and  validating this predictor using a combined test set of  <dig> breast tumor samples compiled from the public domain.
these analyses show that the breast tumor intrinsic subtypes are significant predictors of outcome when correcting for standard clinical parameters, and that common patterns of expression and outcome predictions can be identified in data sets generated by independent labs.
RESULTS
identification of the intrinsic/unc gene set
our goals were to  create a new breast tumor intrinsic list,  validate this list on an independent dataset to show the clinical significance of the "intrinsic" classifications, and  to derive an objective "intrinsic subtype" classifier that could be used clinically .
an intrinsic analysis is a "within class" versus "across classes" analysis that identifies genes that show low variability within a group , but which show high variation in expression across different tumors; in essence, one is selecting for genes that are consistently expressed when individual tumors are examined, but that vary in expression across different tumors.
to develop a new breast tumor intrinsic gene set , we assayed a training set of  <dig> breast tumor samples and  <dig> normal breast samples, which contained  <dig> sample pairs , using agilent oligo microarrays.
using the intrinsic analysis method as described in sorlie et al.
<dig> <cit> , we identified an intrinsic gene set of  <dig> microarray elements representing  <dig> genes.
we felt it important to create a new intrinsic list because first, we wanted to take advantage of newer microarrays , and second, we wanted to use paired tumor samples that were not before-and-after chemotherapy pairs, but were instead pre-treatment tumor pairs.
the intrinsic/unc gene set showed overlap with a previous breast tumor intrinsic gene set , but also showed a significant increase in gene number likely due to the greater number of genes present on current microarrays.
validation of the intrinsic/unc gene list
to evaluate the intrinsic/unc gene set on an independent test dataset, we applied it to a "combined test set" of  <dig> breast samples  using hierarchical clustering methods as have been done previously  <cit> .
the "combined test set" of  <dig> breast samples was a single data set created by combining together the data from sorlie et al.
<dig> and  <dig>  <cit> , van't veer et al.
<dig>  <cit>  and sotiriou et al.
<dig>  <cit> .
we created a single data table of these three sets by first identifying the common genes present across all three microarray data sets .
next, we used distance weighted discrimination  to combine these three data sets together  <cit> .
dwd is a multivariate analysis tool that is able to identify systematic biases present in separate data sets and then make a global adjustment to compensate for these biases; in essence, each separate data set is a multi-dimensional cloud of data points, and dwd takes two points clouds and shifts one such that it more optimally overlaps the other.
finally, we determined that  <dig> of the  <dig> unique intrinsic/unc genes were present in the combined test set and performed a hierarchical clustering analysis of these  <dig> genes and  <dig> samples .
we analyzed the combined test set instead of analyzing each of the  <dig> datasets separately because we believed this would provide more statistical power to perform multivariate analysis, and would yield more meaningful results because any finding would need to be shared/present across all  <dig> datasets.
remarkably, despite the loss of genes in the intrinsic/unc list due to the requirement of having to be present on  <dig> different microarray platforms, the hierarchical clustering analysis in figure  <dig> identified the five main subtypes/groups corresponding to the previously defined her2+/er-, basal-like, luma, lumb and normal breast-like tumor groups  <cit> .
as shown in previous studies, a her2+ expression cluster was observed in the cluster analysis of the "combined test set" and contained multiple genes from the 17q <dig> amplicon including her2/erbb <dig> and grb <dig> .
the her2+ intrinsic subtype  was predominantly er-negative  as previously shown.
a basal-like expression cluster was also present and contained genes  previously identified to be characteristic of basal epithelial cells .
using the program ease <cit> , the gene ontology  categories "extracellular space" and "extracellular region" were over-represented relative to chance in the basal epithelial gene cluster.
as shown in previous studies, a luminal/er+ expression cluster was present and contained er, xbp <dig>  foxa <dig> and gata <dig> .
gata <dig> has recently been shown to be somatically mutated in some er+ breast tumors, and some of the genes in figure 2c are gata3-regulated  <cit> , thus showing the functional clustering of a transcription factor and some of its direct targets.
the gene ontology  categories "transcription regulator activity" and "dna binding" were over-represented relative to chance in the luminal/er+ gene cluster.
the most significant difference between the previous intrinsic/stanford gene lists and the new intrinsic/unc gene list was that the latter contained a large proliferation signature   <cit> .
as expected, ease analysis showed that the go categories "mitotic cell cycle" and "m phase" were over-represented relative to chance in the proliferation signature.
the inclusion of proliferation genes in the intrinsic/unc gene set, but not in the intrinsic/stanford gene set, is likely due to the fact that the intrinsic/stanford lists were based upon before-and-after chemotherapy paired samples of the same tumor, while the intrinsic/unc list was based upon paired samples taken at the same time point with respect to chemotherapy .
this finding suggests that tumor cell proliferation rates do vary before and after chemotherapy, but that proliferation is a reproducible and intrinsic feature of a tumor's expression profile.
a possible new tumor group  characterized by the high expression of interferon -regulated genes was observed in the combined test set analysis .
according to ease, the go categories "immune response" and "defense response" were over-represented relative to chance in the interferon-regulated gene cluster.
this cluster contained stat <dig>  which is thought to be the transcription factor responsible for mediating ifn-regulation of gene expression  <cit> .
genes in the ifn cluster have been linked to lymph node metastasis and poor prognosis  <cit> .
in summary, the intrinsic/unc list contained more genes than previous lists, encompasses most features of the intrinsic/stanford list  and adds the biologically and clinically relevant proliferation signature.
tumor subtypes identified by the intrinsic/unc gene set are predictive of outcome
to determine how many biologically relevant tumor subtypes/groups might be present within the cluster in figure  <dig>  we used  <dig> criteria, which resulted in the identification of  <dig> potential subtypes/groups.
the first criterion was the simple and obvious dendrogram branching pattern  suggesting six groups.
second was the observation that each of the six groups uniquely expressed distinct sets of known biologically relevant genes including the basal, luminal/er+, her2-amplicon, ifn-regulated, and proliferation-associated signatures.
third was our knowledge of the previous classifications made by the sorlie et al.
<dig> intrinsic/stanford list of the stanford/norway samples : there was a high concordance  between the classification of these samples made using either the sorlie et al.
<dig> intrinsic/stanford list or the intrinsic/unc list .
therefore, the  <dig> tumors/patients were stratified into six groups, and we proceeded to look for differences in outcomes and associations with other clinical parameters between these six groups.
the intrinsic/unc gene set identified tumor groups/subtypes that were predictive of relapse-free survival  and overall survival  in kaplan-meier survival analysis on the combined test set.
as previously seen in sorlie et al.
, the luma group had the best outcome while the her2+/er-, basal-like, and lumb groups had significantly worse outcomes.
the new ifn class had a kaplan-meier survival curve similar to that of lumb, and both showed elevated proliferation rates when compared to luma .
in the combined test set, the standard clinical parameters of er status, node status, grade, and tumor size  were significant predictors of rfs using kaplan-meier analysis , thus showing that the act of combining three different patient sets together did not destroy the prognostic abilities of these standard markers.
in a multivariate cox proportional hazards analysis of the combined test set using these standard clinical parameters, size, grade and er status were significant predictors of rfs .
to further evaluate the prognostic/predictive value of the intrinsic subtype classification, we performed multivariate cox proportional hazards analysis of the combined test set using the six intrinsic subtypes/groups defined above and the five standard clinical parameters with rfs, os, or dss as the endpoint .
the intrinsic subtypes, when added to the multivariate model containing the standard clinical variables, resulted in a model significantly more predictive of rfs, os, and dss .
in multivariate analysis for rfs , the basal-like, lumb and her2+/er- subtypes had hazard ratios significantly greater than  <dig> , while the ifn and normal breast-like groups were not significant.
thus, the intrinsic subtypes classifications of luma, lumb, basal-like and her2+/er- add new and important prognostic information beyond what the standard clinical predictors provide.
associations of the intrinsic subtypes with clinical and biological parameters
to further characterize and better understand the intrinsic subtypes, we determined whether an association existed between intrinsic subtype and grade, node status, er status, age, and tumor size in the combined test set.
two-way contingency table analysis showed significant association between grade and subtype, with her2+/er- and basal-like tumors more likely to be grade  <dig> .
the cramer's v statistic <cit> , which measures the strength of association between two variables in a contingency table, indicated a substantial association  between grade and subtype.
two-way contingency table analysis did not show significant association between node status and subtype , but did show significant association between er status and subtype  and between tumor size and subtype .
as would be expected, er+ tumors were more likely to be luma or lumb.
as indicated by the low cramer's v , tumor size and subtype were not strongly correlated.
to determine association between age and subtype, we used an unpaired student's t-test to compare the average ages of diagnosis of each tumor subtype.
interestingly, the average age of diagnosis for her2+/er- tumors was significantly less than that for all other tumor types.
the average age of diagnosis for luma tumors was significantly greater than that for lumb tumors.
derivation and application of a single sample predictor
a caveat to the above analyses is that our classifications were based upon hierarchical clustering, which is a powerful tool for intrinsic class discovery, but which is not suited for individual sample classification because to classify a new sample would require a reanalysis of all samples.
therefore, we wanted to create an unchanging and objective method to classify tumors according to intrinsic subtype that could be clinically applicable.
to this end, we developed a single sample predictor  using the combined test set hierarchically clustered using the  <dig> intrinsic/unc genes .
for the ssp, a mean expression profile  was created for each subtype that was significant in the multivariate analysis  and for the normal breast-like group using the combined test set .
next, any new sample is then compared to each centroid and assigned by the ssp to the nearest subtype/centroid as determined by spearman correlation ; thus, the ssp contains five different idealized profiles, and any new sample is compared to each of the five profiles and assigned a profile label  based upon the single idealized profile it most resembled.
to validate the ssp, we tested it on two additional datasets not used previously.
the first was the 60-patient ma et al.
dataset, which represents a group of early stage er+ tamoxifen-treated patients  <cit> .
the ssp classified these samples as follows:  <dig> basal-like,  <dig> her2+/er-,  <dig> normal breast-like,  <dig> luma, and  <dig> lumb.
the  <dig> basal-like and  <dig> her2+/er- assigned samples were excluded from a survival analysis because they were too few for a meaningful survival analysis and possibly were misclassified er-negative tumors.
among the remaining samples the ssp classification was a significant predictor of rfs , due to the poor outcome of the lumb group.
next, we applied the ssp to a 96-sample test set of local only -treated patients from chang et al.
<cit> .
the tumor groups identified by the ssp showed significant differences in rfs  and os  in kaplan-meier analysis, with the poor outcome groups as expected: lumb, basal-like, and her2+/er-.
thus, the ssp identified tumor groups that are truly prognostic and have significantly different outcomes as was seen before: namely, luma always has the most favorable outcome, while lumb, basal-like and her2+/er- do poorly <cit> .
we also applied the ssp onto the 105-sample dataset used to derive the intrinsic/unc gene list, which is technically not a test set for the ssp because it was used to derive the intrinsic/unc gene set.
the tumor groups identified by the ssp showed significantly different rfs  and os  in kaplan-meier analysis with the poor outcome groups again being lumb, basal-like, and her2+/er-.
a subset of the 105-sample dataset  had been previously characterized using an immunohistochemical  analysis <cit> , which showed that  all  <dig> basal-like tumors were er-negative and her2-negative ,  all  <dig> luminal subtype tumors were er-positive and her2-negative, and  all  <dig> her2+/er- subtype tumors were er-negative and  <dig> out of these  <dig> showed her2-overexpression .
thus, the ssp correlated with many standard clinical parameters, and was also able to identify clinically relevant groups  not identifiable using the standard clinical assays, thus indicating potential value as an objective classification method that should be developed further as a clinically applicable test.
discussion
the development and validation of gene sets for cancer patients requires significant resources because large training and test sets are required to achieve robust results.
in fact, microarray studies are often criticized for a lack of rigorous validation due to small sample sizes  <cit> .
therefore, we utilized a previously described microarray data set combining method  to create a large validation test set of over  <dig> tumors, and used it to validate a newly derived gene list for breast cancer prognostication and prediction.
this approach allowed us to perform a multivariate analysis in which we show for the first time that the intrinsic subtype classification adds valuable information in the presence of five standard clinical parameters.
we believe this combined test set is a valid test set for use in our analysis because after the multiple data sets were combined, the prognostic abilities of the standard clinical variables such as er and grade remained intact.
the remarkable power of our dwd-based approach is indicated by the fact that although samples came from different platforms, hierarchical clustering analysis of the combined data set managed to group samples and genes based upon biology, and not some artifact caused by combining the data sets together.
evidence that this grouping reflected biology and not some artifact comes from  the finding that various gene ontology terms were significantly over-represented relative to chance in individual gene clusters seen in this analysis and  the groupings of the samples showed inter-dataset mixing and were significant predictors of outcome in univariate kaplan-meier and multivariate cox analysis.
it is also remarkable that this classification was successful in predicting outcome despite the fact that the intrinsic/unc gene set was reduced from  <dig> genes to  <dig> genes in the combined test set; this indicates the robust nature of the intrinsic subtypes as defined by the new intrinsic/unc gene list.
one of the accomplishments of this manuscript was to develop an unchanging and objective intrinsic subtype predictor that could be used routinely in the clinical setting.
this was accomplished by first identifying a new intrinsic gene set and then using this set to develop the single sample predictor  that was shown here to be both prognostic on the local therapy-only patient subset from chang et al.
<cit>  and predictive of outcomes on the er+ tamoxifen-treated data set of ma et al.
<cit> .
many other gene expression based predictors for breast cancer patients have been developed, and in a complementary publication <cit> , we tested the intrinsic subtype ssp developed here, relative to those predictions made by four other previously published breast cancer prognostic/predictive gene sets using a single patient/tumor set of  <dig> cases; the four other expression-based predictors used were  the "70-gene" good vs. poor outcome predictor developed by van't veer and colleagues <cit> ,  the "wound-response" profile developed by chang et al <cit> ,  the "recurrence score " profile developed by paik et al.
<cit> , and  the 2-gene  ratio predictor developed by ma et al.
<cit> .
the results showed that of samples classified as basal-like, her2+/er-, or lumb by the ssp, 93–100% were classified by the 70-gene, rs and wound-response predictors as being in each predictor's bad prognosis group.
these data suggest that a high concordance exists across these multiple predictors, in particular the rs, 70-gene and intrinsic subtypes; thus, the new intrinsic gene list and classification method developed here, when compared to other predictors as accomplished in fan et al.
<cit> , showed that a high concordance across predictors exists, which provides additional validation for each predictor.
CONCLUSIONS
the results of this study advance our current knowledge of the intrinsic breast tumor subtypes and provides an objective method  for prospectively classifying tumors that could be used in the clinical setting.
more broadly speaking, our findings show that while the individual brushstrokes  may sometimes show discordance across data sets, the portraits created by the combined patterns of the individual brushstrokes is conserved and recognizable across datasets because of the similarities to the family portrait  <cit> .
moreover, these data show that the breast tumor intrinsic subtypes identified using the intrinsic/unc gene list can be generalized to many different patient sets, both treated and untreated.

 the classification of human tumors using microarray data has been an area of intense research, but it remains a daunting task to validate a new profile and generate a clinically useful test. many different gene expression-based predictors have been developed for breast cancer, and two different gene expression predictors have reached the final step of prospective clinical trial testing. using cdna microarrays, we previously identified five distinct subtypes of breast tumors arising from at least two distinct cell types. this molecular taxonomy was based upon an "intrinsic" gene set, which was identified using a supervised analysis to select genes that showed little variance within repeated samplings of the same tumor, but which showed high variance across tumors. we showed that an intrinsic gene set reflects the stable biological properties of tumors and typically identifies distinct tumor subtypes that have prognostic significance, even though no knowledge of outcome was used to derive this gene set. a major challenge for microarray studies, especially those with clinical implications, is validation. due to the practical barriers of cost and access to large numbers of fresh frozen tumor samples with associated clinical information, very few microarray studies have analyzed enough samples to allow promising initial findings to be sufficiently validated to justify the major investment required for clinical testing. an efficient approach would be to use public gene expression data repositories as test sets; however, it has been difficult to compare and/or combine data sets from independent laboratories due to differences in sample preparation, experimental design, and microarray platforms. an accepted method for validation is to derive a prognostic/predictive gene set from a "training set" and then apply it to a completely independent "test set". the "purest" test sets are comprised of samples not generated by the primary investigators to remove any possibility of bias. in this study, we illustrate the successful application of these principles by deriving a new breast tumor intrinsic gene list that identifies the "intrinsic" biological features of breast tumors and validating this predictor using a combined test set of breast tumor samples compiled from the public domain. these analyses show that the breast tumor intrinsic subtypes are significant predictors of outcome when correcting for standard clinical parameters, and that common patterns of expression and outcome predictions can be identified in data sets generated by independent labs.  identification of the intrinsic/unc gene set our goals were to create a new breast tumor intrinsic list, validate this list on an independent dataset to show the clinical significance of the "intrinsic" classifications, and to derive an objective "intrinsic subtype" classifier that could be used clinically. an intrinsic analysis is a "within class" versus "across classes" analysis that identifies genes that show low variability within a group, but which show high variation in expression across different tumors; in essence, one is selecting for genes that are consistently expressed when individual tumors are examined, but that vary in expression across different tumors. to develop a new breast tumor intrinsic gene set, we assayed a training set of breast tumor samples and normal breast samples, which contained sample pairs, using agilent oligo microarrays. using the intrinsic analysis method as described in sorlie et al., we identified an intrinsic gene set of microarray elements representing genes. we felt it important to create a new intrinsic list because first, we wanted to take advantage of newer microarrays, and second, we wanted to use paired tumor samples that were not before-and-after chemotherapy pairs, but were instead pre-treatment tumor pairs. the intrinsic/unc gene set showed overlap with a previous breast tumor intrinsic gene set, but also showed a significant increase in gene number likely due to the greater number of genes present on current microarrays. validation of the intrinsic/unc gene list to evaluate the intrinsic/unc gene set on an independent test dataset, we applied it to a "combined test set" of breast samples using hierarchical clustering methods as have been done previously. the "combined test set" of breast samples was a single data set created by combining together the data from sorlie et al. and, van't veer et al. and sotiriou et al.. we created a single data table of these three sets by first identifying the common genes present across all three microarray data sets. next, we used distance weighted discrimination to combine these three data sets together. dwd is a multivariate analysis tool that is able to identify systematic biases present in separate data sets and then make a global adjustment to compensate for these biases; in essence, each separate data set is a multi-dimensional cloud of data points, and dwd takes two points clouds and shifts one such that it more optimally overlaps the other. finally, we determined that of the unique intrinsic/unc genes were present in the combined test set and performed a hierarchical clustering analysis of these genes and samples. we analyzed the combined test set instead of analyzing each of the datasets separately because we believed this would provide more statistical power to perform multivariate analysis, and would yield more meaningful results because any finding would need to be shared/present across all datasets. remarkably, despite the loss of genes in the intrinsic/unc list due to the requirement of having to be present on different microarray platforms, the hierarchical clustering analysis in figure identified the five main subtypes/groups corresponding to the previously defined her2+/er-, basal-like, luma, lumb and normal breast-like tumor groups. as shown in previous studies, a her2+ expression cluster was observed in the cluster analysis of the "combined test set" and contained multiple genes from the 17q amplicon including her2/erbb and grb. the her2+ intrinsic subtype was predominantly er-negative as previously shown. a basal-like expression cluster was also present and contained genes previously identified to be characteristic of basal epithelial cells. using the program ease, the gene ontology categories "extracellular space" and "extracellular region" were over-represented relative to chance in the basal epithelial gene cluster. as shown in previous studies, a luminal/er+ expression cluster was present and contained er, xbp foxa and gata. gata has recently been shown to be somatically mutated in some er+ breast tumors, and some of the genes in figure 2c are gata3-regulated, thus showing the functional clustering of a transcription factor and some of its direct targets. the gene ontology categories "transcription regulator activity" and "dna binding" were over-represented relative to chance in the luminal/er+ gene cluster. the most significant difference between the previous intrinsic/stanford gene lists and the new intrinsic/unc gene list was that the latter contained a large proliferation signature. as expected, ease analysis showed that the go categories "mitotic cell cycle" and "m phase" were over-represented relative to chance in the proliferation signature. the inclusion of proliferation genes in the intrinsic/unc gene set, but not in the intrinsic/stanford gene set, is likely due to the fact that the intrinsic/stanford lists were based upon before-and-after chemotherapy paired samples of the same tumor, while the intrinsic/unc list was based upon paired samples taken at the same time point with respect to chemotherapy. this finding suggests that tumor cell proliferation rates do vary before and after chemotherapy, but that proliferation is a reproducible and intrinsic feature of a tumor's expression profile. a possible new tumor group characterized by the high expression of interferon -regulated genes was observed in the combined test set analysis. according to ease, the go categories "immune response" and "defense response" were over-represented relative to chance in the interferon-regulated gene cluster. this cluster contained stat which is thought to be the transcription factor responsible for mediating ifn-regulation of gene expression. genes in the ifn cluster have been linked to lymph node metastasis and poor prognosis. in summary, the intrinsic/unc list contained more genes than previous lists, encompasses most features of the intrinsic/stanford list and adds the biologically and clinically relevant proliferation signature. tumor subtypes identified by the intrinsic/unc gene set are predictive of outcome to determine how many biologically relevant tumor subtypes/groups might be present within the cluster in figure we used criteria, which resulted in the identification of potential subtypes/groups. the first criterion was the simple and obvious dendrogram branching pattern suggesting six groups. second was the observation that each of the six groups uniquely expressed distinct sets of known biologically relevant genes including the basal, luminal/er+, her2-amplicon, ifn-regulated, and proliferation-associated signatures. third was our knowledge of the previous classifications made by the sorlie et al. intrinsic/stanford list of the stanford/norway samples: there was a high concordance between the classification of these samples made using either the sorlie et al. intrinsic/stanford list or the intrinsic/unc list. therefore, the tumors/patients were stratified into six groups, and we proceeded to look for differences in outcomes and associations with other clinical parameters between these six groups. the intrinsic/unc gene set identified tumor groups/subtypes that were predictive of relapse-free survival and overall survival in kaplan-meier survival analysis on the combined test set. as previously seen in sorlie et al., the luma group had the best outcome while the her2+/er-, basal-like, and lumb groups had significantly worse outcomes. the new ifn class had a kaplan-meier survival curve similar to that of lumb, and both showed elevated proliferation rates when compared to luma. in the combined test set, the standard clinical parameters of er status, node status, grade, and tumor size were significant predictors of rfs using kaplan-meier analysis, thus showing that the act of combining three different patient sets together did not destroy the prognostic abilities of these standard markers. in a multivariate cox proportional hazards analysis of the combined test set using these standard clinical parameters, size, grade and er status were significant predictors of rfs. to further evaluate the prognostic/predictive value of the intrinsic subtype classification, we performed multivariate cox proportional hazards analysis of the combined test set using the six intrinsic subtypes/groups defined above and the five standard clinical parameters with rfs, os, or dss as the endpoint. the intrinsic subtypes, when added to the multivariate model containing the standard clinical variables, resulted in a model significantly more predictive of rfs, os, and dss. in multivariate analysis for rfs, the basal-like, lumb and her2+/er- subtypes had hazard ratios significantly greater than, while the ifn and normal breast-like groups were not significant. thus, the intrinsic subtypes classifications of luma, lumb, basal-like and her2+/er- add new and important prognostic information beyond what the standard clinical predictors provide. associations of the intrinsic subtypes with clinical and biological parameters to further characterize and better understand the intrinsic subtypes, we determined whether an association existed between intrinsic subtype and grade, node status, er status, age, and tumor size in the combined test set. two-way contingency table analysis showed significant association between grade and subtype, with her2+/er- and basal-like tumors more likely to be grade. the cramer's v statistic, which measures the strength of association between two variables in a contingency table, indicated a substantial association between grade and subtype. two-way contingency table analysis did not show significant association between node status and subtype, but did show significant association between er status and subtype and between tumor size and subtype. as would be expected, er+ tumors were more likely to be luma or lumb. as indicated by the low cramer's v, tumor size and subtype were not strongly correlated. to determine association between age and subtype, we used an unpaired student's t-test to compare the average ages of diagnosis of each tumor subtype. interestingly, the average age of diagnosis for her2+/er- tumors was significantly less than that for all other tumor types. the average age of diagnosis for luma tumors was significantly greater than that for lumb tumors. derivation and application of a single sample predictor a caveat to the above analyses is that our classifications were based upon hierarchical clustering, which is a powerful tool for intrinsic class discovery, but which is not suited for individual sample classification because to classify a new sample would require a reanalysis of all samples. therefore, we wanted to create an unchanging and objective method to classify tumors according to intrinsic subtype that could be clinically applicable. to this end, we developed a single sample predictor using the combined test set hierarchically clustered using the intrinsic/unc genes. for the ssp, a mean expression profile was created for each subtype that was significant in the multivariate analysis and for the normal breast-like group using the combined test set. next, any new sample is then compared to each centroid and assigned by the ssp to the nearest subtype/centroid as determined by spearman correlation; thus, the ssp contains five different idealized profiles, and any new sample is compared to each of the five profiles and assigned a profile label based upon the single idealized profile it most resembled. to validate the ssp, we tested it on two additional datasets not used previously. the first was the 60-patient ma et al. dataset, which represents a group of early stage er+ tamoxifen-treated patients. the ssp classified these samples as follows: basal-like, her2+/er-, normal breast-like, luma, and lumb. the basal-like and her2+/er- assigned samples were excluded from a survival analysis because they were too few for a meaningful survival analysis and possibly were misclassified er-negative tumors. among the remaining samples the ssp classification was a significant predictor of rfs, due to the poor outcome of the lumb group. next, we applied the ssp to a 96-sample test set of local only -treated patients from chang et al.. the tumor groups identified by the ssp showed significant differences in rfs and os in kaplan-meier analysis, with the poor outcome groups as expected: lumb, basal-like, and her2+/er-. thus, the ssp identified tumor groups that are truly prognostic and have significantly different outcomes as was seen before: namely, luma always has the most favorable outcome, while lumb, basal-like and her2+/er- do poorly. we also applied the ssp onto the 105-sample dataset used to derive the intrinsic/unc gene list, which is technically not a test set for the ssp because it was used to derive the intrinsic/unc gene set. the tumor groups identified by the ssp showed significantly different rfs and os in kaplan-meier analysis with the poor outcome groups again being lumb, basal-like, and her2+/er-. a subset of the 105-sample dataset had been previously characterized using an immunohistochemical analysis, which showed that all basal-like tumors were er-negative and her2-negative, all luminal subtype tumors were er-positive and her2-negative, and all her2+/er- subtype tumors were er-negative and out of these showed her2-overexpression. thus, the ssp correlated with many standard clinical parameters, and was also able to identify clinically relevant groups not identifiable using the standard clinical assays, thus indicating potential value as an objective classification method that should be developed further as a clinically applicable test. discussion the development and validation of gene sets for cancer patients requires significant resources because large training and test sets are required to achieve robust results. in fact, microarray studies are often criticized for a lack of rigorous validation due to small sample sizes. therefore, we utilized a previously described microarray data set combining method to create a large validation test set of over tumors, and used it to validate a newly derived gene list for breast cancer prognostication and prediction. this approach allowed us to perform a multivariate analysis in which we show for the first time that the intrinsic subtype classification adds valuable information in the presence of five standard clinical parameters. we believe this combined test set is a valid test set for use in our analysis because after the multiple data sets were combined, the prognostic abilities of the standard clinical variables such as er and grade remained intact. the remarkable power of our dwd-based approach is indicated by the fact that although samples came from different platforms, hierarchical clustering analysis of the combined data set managed to group samples and genes based upon biology, and not some artifact caused by combining the data sets together. evidence that this grouping reflected biology and not some artifact comes from the finding that various gene ontology terms were significantly over-represented relative to chance in individual gene clusters seen in this analysis and the groupings of the samples showed inter-dataset mixing and were significant predictors of outcome in univariate kaplan-meier and multivariate cox analysis. it is also remarkable that this classification was successful in predicting outcome despite the fact that the intrinsic/unc gene set was reduced from genes to genes in the combined test set; this indicates the robust nature of the intrinsic subtypes as defined by the new intrinsic/unc gene list. one of the accomplishments of this manuscript was to develop an unchanging and objective intrinsic subtype predictor that could be used routinely in the clinical setting. this was accomplished by first identifying a new intrinsic gene set and then using this set to develop the single sample predictor that was shown here to be both prognostic on the local therapy-only patient subset from chang et al. and predictive of outcomes on the er+ tamoxifen-treated data set of ma et al.. many other gene expression based predictors for breast cancer patients have been developed, and in a complementary publication, we tested the intrinsic subtype ssp developed here, relative to those predictions made by four other previously published breast cancer prognostic/predictive gene sets using a single patient/tumor set of cases; the four other expression-based predictors used were the "70-gene" good vs. poor outcome predictor developed by van't veer and colleagues, the "wound-response" profile developed by chang et al, the "recurrence score " profile developed by paik et al., and the 2-gene ratio predictor developed by ma et al.. the results showed that of samples classified as basal-like, her2+/er-, or lumb by the ssp, 93–100% were classified by the 70-gene, rs and wound-response predictors as being in each predictor's bad prognosis group. these data suggest that a high concordance exists across these multiple predictors, in particular the rs, 70-gene and intrinsic subtypes; thus, the new intrinsic gene list and classification method developed here, when compared to other predictors as accomplished in fan et al., showed that a high concordance across predictors exists, which provides additional validation for each predictor.  the results of this study advance our current knowledge of the intrinsic breast tumor subtypes and provides an objective method for prospectively classifying tumors that could be used in the clinical setting. more broadly speaking, our findings show that while the individual brushstrokes may sometimes show discordance across data sets, the portraits created by the combined patterns of the individual brushstrokes is conserved and recognizable across datasets because of the similarities to the family portrait. moreover, these data show that the breast tumor intrinsic subtypes identified using the intrinsic/unc gene list can be generalized to many different patient sets, both treated and untreated. 
these subtypes were associated with significant differences in relapse-free and overall survival. multivariate cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. when the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into luma, lumb, basal-like, her2+/er-, and normal breast-like tumor subtypes that we demonstrated in previous datasets. a 105-tumor training set containing sample pairs was used to derive a new breast tumor intrinsic gene list. from the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles, which is designed for single sample predictions. to overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list. this intrinsic list contained genes and a proliferation signature that was not present in previous breast intrinsic gene sets. validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification.
 validation of a novel gene expression signature in independent data sets is a critical step in the development of a clinically useful test for cancer patient risk-stratification. however, validation is often unconvincing because the size of the test set is typically small. to overcome this problem we used publicly available breast cancer gene expression data sets and a novel approach to data fusion, in order to validate a new breast tumor intrinsic list.  a 105-tumor training set containing sample pairs was used to derive a new breast tumor intrinsic gene list. this intrinsic list contained genes and a proliferation signature that was not present in previous breast intrinsic gene sets. we tested this list as a survival predictor on a data set of tumors compiled from three independent microarray studies that were fused into a single data set using distance weighted discrimination. when the new intrinsic gene set was used to hierarchically cluster this combined test set, tumors were grouped into luma, lumb, basal-like, her2+/er-, and normal breast-like tumor subtypes that we demonstrated in previous datasets. these subtypes were associated with significant differences in relapse-free and overall survival. multivariate cox analysis of the combined test set showed that the intrinsic subtype classifications added significant prognostic information that was independent of standard clinical predictors. from the combined test set, we developed an objective and unchanging classifier based upon five intrinsic subtype mean expression profiles, which is designed for single sample predictions. the ssp approach was applied to two additional independent data sets and consistently predicted survival in both systemically treated and untreated patient groups.  this study validates the "breast tumor intrinsic" subtype classification as an objective means of tumor classification that should be translated into a clinical assay for further retrospective and prospective validation. in addition, our method of combining existing data sets can be used to robustly validate the potential clinical value of any new gene expression profile. 