Patent Application: US-3771305-A

Abstract:
a method for analyzing differential protein expression associated with histopathologic features of breast disease including detecting overexpression or underexpression of a pool of proteins in breast tissues or cells , the pool including at lease one of a protein set including afadin , aurora a , a - catenin , b - catenin , bcl2 , cyclin d1 , cyclin e , cytokeratin 5 / 6 , cytokeratin 8 / 18 , e - cadherin , egfr , erbb2 , erbb3 , erbb4 , estrogen receptor , fgfr1 , fhit , gata3 , ki67 , mucin 1 , p53 , p - cadherin , progesterone receptor , tacc1 , tacc2 , tacc3 , cytokeratin 6 , cytokeratin 18 , ang1 , aurorab , bcrp1 , cathepsind , cd10 , cd44 , ck14 , cox2 , fgf2 , gata4 , hif1 a , mmp9 , mta1 , nm23 , nrg1 a , nrg1beta , p27 , parkin , plau , s100 , scribble , smooth muscle actin , thbs1 and timp1 .

Description:
“ aggressiveness of cancer ” refers to cancer growth rate or potential to metastasize ; a so - called “ aggressive cancer ” will grow or metastasize rapidly or significantly affect overall health status and quality of life . “ adjuvant therapy ” refers to treatment involving radiation , chemotherapy ( drug treatment ), biologic therapy ( vaccines ) or hormone therapy , or any combination given after primary treatment . “ antibody ” is intended to include whole antibodies , e . g ., of any isotype , and includes fragments thereof which are also specifically reactive with a vertebrate , e . g ., mammalian , protein . antibodies can be fragmented using conventional techniques and the fragments screened for utility in the same manner as described above for whole antibodies . thus , the term includes segments generated by proteolytic cleavage or prepared recombinant portions of an antibody molecule capable of selectively reacting with a certain protein . non - limiting examples of such proteolytic and / or recombinant fragments include fab , f ( ab ′) 2 , fab ′, fv , and single chain antibodies ( scfv ) containing a v [ l ] and / or v [ h ] domain joined by a peptide linker . the scfv &# 39 ; s may be covalently or non - covalently linked to form antibodies having two or more binding sites . antibodies may include polyclonal , monoclonal , or other purified preparations of antibodies and recombinant antibodies . “ associated with ” refers to a disease in a subject which is caused by , contributed to by , or causative of an abnormal level of expression of a protein . “ control ” comprises , for example , proteins from a sample of the same patient or from a pool of different patients , or selected among reference proteins which may be already known to be over or under expressed . the expression level of the control can be an average or an absolute value of the expression of reference proteins . these values may be processed to accentuate the difference relative to the expression of the proteins according to the invention . the analysis of the over or under expression of proteins can be carried out on samples such as biological material derived from any mammalian cells , including cell lines , xenografts , human tissues preferably breast tissue and the like . the method according to the invention may be performed on sample from a , e . g ., cell lines , healthy donors , patients or an animal ( for example for veterinary application or preclinical studies ). “ directly or indirectly labeled ” include proteins the sub - constituants of which , i . e ., amino acids or amino acid groups or atoms , are themselves labeled ( directly ), as well as proteins labeled by the intermediate of any element able to recognize and bind to the targeted protein , e . g ., an antibody . “ equivalent ” includes nucleic acids encoding functionally equivalent proteins . equivalent nucleotide sequences include sequences that differ by one or more nucleotide substitutions , additions or deletions , such as allelic variants and , will , therefore , include sequences that differ from the nucleotide sequence of the nucleic acids of the invention because of the degeneracy of the genetic code . “ good - prognosis ” and “ poor - prognosis ,” respectively , refer to favorable ( e . g ., remission ) or unfavorable ( e . g ., metastasis , death ) patient clinical outcome . “ histopathologic features of breast diseases ” includes diseases , disorders or conditions known as , lethally or not , affecting breast cells and / or tissues , including but not limited to breast tumours , for example i ) non cancerous breast diseases , for example , hyperplasias , metaplasias , fibroadenomas , fibrocystic disease , papillomas , sclerosing adenosis or preneoplastic , or ii ) breast cancer . “ breast cancer ” includes but is not limited to : a ) noninvasive breast cancers including i ) ductal carcinoma in situ ( also called “ intraductal carcinoma ” or dcis ), consisting of cancer cells in the lining of the duct , ii ) lobular carcinoma in situ , or lcis ( also known as “ lobular neoplasia ”); b ) invasive cancer occurring when cancer cells spread beyond the basement membrane which covers the underlying connective tissue in the breast , and which include i ) infiltrating ductal carcinoma that penetrates the wall of a duct , and ii ) infiltrating lobular carcinoma which spreads through the wall of a lobule and may sometimes appear in both breasts , sometimes in several separate locations . “ immunohistochemistry ( ihc )” refers to methods using histochemical localization of immunoreactive substances using antibodies as reagents on cells or tissues by technologies such as , but not limited to flow cytometry , elisa , western and southwestern blot analysis , and frozen and paraffin - embedded samples . “ nucleic acids ” refers to polynucleotides , e . g ., isolated , such as deoxyribonucleic acid ( dna ), and , where appropriate , ribonucleic acid ( rna ). the term should also be understood to include , as equivalents , analogs of rna or dna made from nucleotide analogs , and , as applicable to the aspect being described , single ( sense or antisense ) and double - stranded polynucleotides . ests , chromosomes , cdnas , mrnas , and rrnas are representative examples of molecules that may be referred to as nucleic acids . “ over or underexpression ” may comprise the detection of differences in expression of the proteins according to the invention in relation to at least one control . “ predicting clinical outcome ” refers to the ability for one skilled in the art to classify patients into at least two classes “ good prognosis ” and “ bad prognosis ” showing significantly different long - term metastasis free survival ( mfs ). “ protein ” refers to a polypeptide with a primary , secondary , tertiary or quaternary structure , or any portion or modification , e . g ., a mutant , or isoform thereof . a “ portion ” or “ modification ” of a protein retains at least one biological or antigenic characteristic of a native ( wild - type ) protein . “ protein microarray ” refers to a spatially defined and separated collection of individual proteins immobilised on a solid surface . “ treating ” as used herein is intended to encompass treating as well as ameliorating at least one symptom of the condition or disease . we combined ihc and tma to measure the expression levels of selected proteins in a consecutive series of 552 patients with early stage breast cancer . we determined protein combinations to refine tumor classification and improve the prognostic classification of disease . analysis and interpretation of the large amount of data generated ( 552 samples and 26 antibodies , about 14 , 000 data points ) caused us to develop bioinformatic tools . as a first step , we applied pre - existing unsupervised hierarchical clustering algorithms as previously reported . 19 - 24 two recent studies on breast cancer analyzed the expression of 15 proteins in 166 tumors , 22 and 13 proteins on 107 samples , 19 respectively . several of these markers were included in this work ( bcl2 , er , pr , erbb2 , egfr , cyclins , cytokeratins , mib1 , p53 ), allowing for direct comparison of results . in our analysis , clustering allowed the identification of four major coherent protein clusters designated according to the function of most included proteins : “ er - related cluster ”, “ adhesion cluster ”, “ mitosis cluster ” and “ proliferation cluster .” correlated expression of proteins may be due to different mechanisms such as coregulation ( e . g ., er / bcl2 30 ), functional interaction ( e . g ., stk6 / taxins 27 , 28 ), phenotypic association ( e . g ., erbb2 / p53 31 ) or chromosomal location ( e . g ., fgfr1 / tacc1 located on 8p11 ). some co - expressed proteins were previously reported in rna or protein expression profiling studies . for example , er , pr , bcl2 and gata3 clustered together . 8 - 10 , 13 this “ er - related cluster ” was negatively correlated with the “ mitosis ” and “ proliferation ” clusters , in agreement with the higher proliferation index in er - negative tumors 32 and the known proliferation - differentiation balance in carcinomas . the “ er - related cluster ” was close to the “ adhesion cluster ” that included other markers that may correlate positively with er expression such as fhit , 33 ck8 / 18 , 19 , 22 ccnd1 34 and muc1 . 8 our “ proliferation cluster ” had some similarities to that identified by others with the common presence of p53 , ki67 , ccne , erbb2 and ck5 / 6 19 or ccne , erbb2 , egfr and ck5 / 6 . 22 interestingly , this cluster also included cdh3 / p - cadherin , present in a “ basal cluster ” identified in gene expression analyses 9 and previously shown to be overexpressed in a subgroup of breast carcinomas associated with higher proliferation rates and aggressive behavior . 35 hierarchical clustering sorted tumors into three clusters that correlated with relevant histoclinical parameters , including histological type , sbr grade , er status , erbb2 status and the presence or absence of peritumoral vascular emboli . correlations were found between the characteristics of these tumor clusters and their protein expression profiles . for example , the high number of grade iii tumors in cluster b , as well as the high number of erbb2 - positive samples , agreed with the frequent strong expression of the “ proliferation ” cluster — which included erbb2 — and the “ mitosis ” cluster in these tumors . conversely , 99 % of cluster a1 samples were er - positive , and showed a frequent strong expression of the “ er - related ” cluster and low expression of the “ proliferation cluster ”. 32 interestingly , the tumor clusters also correlated with a breast cancer classification recently proposed in two series of analyses that provided a new conceptual framework of mammary oncogenesis . first , phenotypic analyses have established a three - cell phenotypic classification of breast cancer cells . 22 , 36 , 37 these authors suggested that biomarkers such as intermediate filaments cytokeratins ( ck ), encoded by a large number of keratin genes , are able to distinguish between distinct cell subpopulations within the mammary gland epithelial compartment . it has been proposed that “ basal ” cells contain mammary gland progenitor cells able to give raise to both “ luminal ” and “ myoepithelial ” 38 cells . ( 39 for review ) progenitor cells express type ii keratins ck5 and 6 . in contrast , differentiated “ luminal ” cells express type ii keratin ck8 and type i keratin ck18 , which are also observed in normal simple and glandular epithelia . luminal cells also express er . 10 , 11 use of tissue microarray screening has confirmed this emerging theory . 19 , 22 second , recent gene expression analyses using dna microarrays have led to a similar identification of subclasses of breast tumors that corresponded to the phenotypic classification . 9 - 11 these experiments concurred to establish a distinction between several types of epithelial cells in the mammary gland . the origin of the breast malignant cell remains unknown . two major types of breast cancer may derive from basal / progenitor or luminal cells , respectively . alternatively , most tumors may originate from pluripotent stem cells and reach different stages of differentiation . 40 our results support this new classification model . tumor cluster a1 may be approximated to a cluster of luminal cell - like tumors , with frequent strong expression of er and ck8 / 18 . cluster b may consist of tumors with basal / progenitor , er - negative characteristics , i . e . strong expression of ck5 / 6 and proliferation markers . a2 tumors , with an intermediate profile , may represent a transitory “ baso - luminal ” stage , or consist of tumors that have lost er function . it can be expected that luminal a1 tumors , in which the bulk of cells are more differentiated and express er - related cluster proteins , are of better prognosis , whereas more undifferentiated and proliferative basal b tumors are associated with poor prognosis . the significant differences in clinical outcome observed between the three defined tumor clusters in this study are consistent with this model and recent studies . 9 - 11 , 41 in addition , we discovered that lobular carcinomas are luminal - like tumors , and comprise differentiated luminal cells that express ck8 / 18 . thus , classical unsupervised hierarchical clustering applied to all tested proteins was able to identify biologically and clinically relevant classes of breast cancer . recently , supervised methods have been successfully applied to gene expression data analysis in parallel with unsupervised approaches . in a second step , we thus developed a supervised method to identify the best combination within 26 proteins that would further improve the prognostic classification . to our knowledge , our study is the first application of such supervised methods to large - scale ihc data . we identified a 21 - protein set which optimally classified patients into two classes (“ good - prognosis ” and “ poor - prognosis class ”) with significantly different long - term mfs . initially identified in a random learning set of 368 patients , this prognostic signature was validated in an independent set of 184 patients , showing its robustness . our discriminator set included 10 proteins coded by genes identified across recent gene expression studies , 7 - 15 as well as other proteins with unclear role in disease progression and sensitivity to systemic therapy . the prognostic value of the signature was increasingly accurate with the addition of other proteins as evidenced by univariate and multivariate analyses , further highlighting the strength of large - scale molecular analyses for understanding tumor heterogeneity through the identification of expression signatures . the classification based on the 21 - protein predictor was associated with a highly significant difference in clinical outcome . the 5 - year mfs was 90 % for patients of the “ good - prognosis class ” and only 62 % for patients of the “ poor - prognosis class .” when compared in multivariate analysis with classical prognostic factors and with each tested protein separately , our classification performed significantly better for predicting the occurrence of metastatic relapse . such prognostic association persisted when applied to patients with lymph node - positive and lymph node - negative cancer . interestingly , the mfs of node - negative patients from the “ poor - prognosis class ” was similar to that of node - positive patients from the “ good - prognosis class .” notably , our molecular classification performed better than that defined by st - gallen and nih criteria for node - negative patients . this finding is of particular significance , since about 75 % of node - negative patients candidate for adjuvant chemotherapy based on the st . gallen / nih criteria are currently thought to be over - treated . our 21 - protein predictor assigned fewer node - negative patients to the “ poor - prognosis class ,” and their clinical outcome was more frequently unfavorable than it was for patients assigned to the high - risk class defined by st - gallen or nih criteria . our predictor also performed well in patients irrespective of er status . the 5 - year mfs was 90 % for er - positive patients from the “ good - prognosis class ,” and 58 % for er - positive patients from the “ poor - prognosis class ,” suggesting our 21 - protein set may provide more accurate clinical information than er status alone , possibly reflecting functional differences in the er pathway . additionally , our molecular classification conserved its predictive impact for patients independent of adjuvant systemic therapy . since distant metastasis may be influenced by adjuvant therapy , we separately analyzed the 186 patients who did not receive any chemo - and hormone therapy , as well as the 133 patients who exclusively received adjuvant chemotherapy with anthracyclin - based regimen in most cases . interestingly , we found within the group of 186 untreated patients an odds ratio of 7 . 45 for metastatic relapse in the “ poor - prognosis class ” when compared with patients of the “ good - prognosis class .” similar discrimination was observed within the 133 patients treated with chemotherapy alone with a corresponding odds ratio of 3 . thus , the 21 - protein signature may facilitate the selection of appropriate treatment options in early breast cancer patients . it may be an important clinical tool to circumvent unnecessary , toxic and costly treatment of node - negative patients , and it may help for selecting , among patients who need adjuvant chemotherapy , those who might benefit from standard protocol and those who would be candidates to other protocol or other form of systemic therapy . a consecutive series of 552 women with early ( stage i , ii or iii ) breast cancer treated at the institut paoli - calmettes before december 1999 was studied using the tma technology . the stage of disease was defined according to tnm classification ( union internationale contre le cancer , uicc , tnm , 5 th edition ). patients with locally advanced , inflammatory or metastatic disease , or with previous history of cancer were not included . tumors were invasive adenocarinomas including , according to the who histological typing , 388 ductal carcinomas ( 70 %), 72 lobular ( 13 %), 24 mixed ( 4 %), 40 tubular ( 8 %), 8 medullary ( 1 %) and 20 other types ( 4 %). clinical annotation of each sample included patient age , axillary lymph node status , pathological tumor size , scarff - bloom - richardson ( sbr ) grade , peritumoral vascular invasion , estrogen receptor ( er ), progesterone receptor ( pr ) and erbb2 status as evaluated by ihc with positivity cut - off values of 1 % for hormone receptors and with 2 or 3 + score ( herceptest kit scoring guidelines ) for erbb2 . the characteristics of patients are listed in table 2 ( see first column only ). patients were treated according to the following guidelines : all had primary surgery that included complete resection of breast tumor ( modified radical mastectomy in 28 % of cases and lumpectomy in 72 %) and axillary lymph node dissection ; 96 % of patients ( including 100 % of those treated with breast - conservative surgery ) received adjuvant local - regional radiotherapy ; 47 % were given adjuvant chemotherapy ( anthracyclin - based regimen in most cases ), and 42 % received adjuvant hormone treatment ( tamoxifen for most cases ). after completion of local - regional treatment , patients were evaluated at least twice per year for the first 5 years and at least annually thereafter . the median follow - up was 57 months ( range , 2 to 182 ) after diagnosis for the 450 patients who did not experience metastatic relapse as a first event , 37 months ( range , 4 to 151 ) for the 102 patients with metastasis as first event , and 51 months ( range , 2 to 182 ) for all patients . the 5 - year mfs rate was 80 % [ 95 % ci 76 . 2 - 83 . 7 ]. tma &# 39 ; s were prepared as previously described 25 with slight modifications . for each tumor , three representative areas from the primary tumor were carefully selected from a hematoxylin - eosin stained section of a donor block . core cylinders with a diameter of 0 . 6 mm each were punched from each of these areas and deposited into three separate recipient paraffin blocks using a specific arraying device ( beecher instruments , silver spring , md .). the technique of tma allows the analysis of tumors and controls under identical experimental conditions . in addition to tumor tissues , the recipient block also received 10 normal breast tissue samples from 10 healthy women that underwent reductive mammary surgery and pellets from nine mammary cell lines . five - μm sections of the resulting tma block were made and used for ihc analysis after transfer onto glass slides . we previously assessed the reliability of the method by comparison with the standard immunohistochemical method for the usual prognostic parameters ; the value of the kappa test was 0 . 95 . 25 selection of the proteins was performed according to the following criteria : known or potential importance in breast cancer and availability of a corresponding antibody that performed well in ihc on paraffin - embedded tissues . twenty - six proteins were selected including hormone receptors ( er , pr ), subclass markers ( cytokeratins ), oncogenes and proliferation proteins ( erbb family members , bcl2 , cyclins , mib1 , fgfr1 , aurora a , taxins ), tumor suppressors ( p53 , fhit ), adhesion molecules ( cadherins , catenins , afadin ), proteins from oncogenes of amplified genomic regions ( erbb2 , ccnd1 , stk6 ), and other potential prognostic markers identified in specific studies or previous dna microarray experiments ( ccne , gata3 , muc1 ). twelve out of the 26 proteins were mentioned as potential significant genes in rna expression profiling studies in breast cancer . 6 - 15 the characteristics of the antibodies used are listed in table 3 . when available , several antibodies were studied for comparison , and only the reagents that gave the best quality data were kept for the global analysis . ihc was carried out on five - μm sections of tissue fixed in alcohol formalin for 24 h and embedded in paraffin . sections were deparaffinized in histolemon ( carlo erba reagenti , rodano , italy ) and rehydrated in graded alcohol . antigen retrieval was accomplished by incubating the sections in pre - treatment solutions depending on the antibody used . pretreatment conditions are listed in table 3 . the reactions were carried out using an autoimmunostainer ( dako autostainer ). staining was performed at room temperature as follows : rehydrated tissues were washed in phosphate buffer , followed by quenching of endogenous peroxidase activity by treatment with 0 . 1 % h 2 o 2 , slides , incubated with blocking serum ( dako ) for 30 min ., then with the affinity - purified antibody for one hour . after washing , slides were sequentially incubated with biotinylated antibody against rabbit igg for 20 min . followed by streptadivin - conjugated peroxidase ( dako lsabr2 kit ), then visualized with diaminobenzidine ( 3 - amino - 9 - ethylcarbazole ). slides were counter - stained with hematoxylin , coverslipped using aquatex ( merck , darmstadt , germany ) mounting solution , then evaluated under a light microscope by two pathologists . the results were expressed in terms of percentage ( p ) and intensity ( i ) of positive cells as previously described . 25 for each sample , the mean of the score of a minimum of two core biopsies was calculated . the results were then scored by the quick score ( q ) ( q = p × i ), except for erbb2 status that was evaluated with the dako scale ( herceptest ™ kit scoring guidelines ). quick score allowed separating tumors into two or three classes . homogeneous classes were defined by grouping samples with an equivalent staining level according to the distribution curves as described . 25 two classes ( negative and positive ) were defined for afadin , α and β catenins , bcl2 , cyclins d1 and e , cytokeratins 5 / 6 and 8 / 18 , egfr , erbb3 , erbb4 , fgfr1 , gata3 , mib1 , p53 , p - cadherin , pr and tacc3 , with a positivity cut - off value of q = 1 , except for cyclin d1 and mib1 with a positivity cut - off value of 10 and 20 , respectively . three classes were defined ( negative , moderate and strong staining ) for aurora a , e - cadherin , er , fhit , muc1 , tacc1 , and tacc2 , with negative ( q = 0 ), moderate ( 0 & lt ; q ≦ 100 ) or strong expression ( 100 & lt ; q ≦ 300 ). for erbb2 , three classes ( 0 / 1 +, 2 +, 3 +) were obtained with the dako scale . a combination of exploratory unsupervised and supervised bioinformatic methods was used to analyze these immunohistochemical profiles . first , we applied unsupervised hierarchical clustering similar to that used in gene expression profiling studies . data were reformatted using the following scoring system : − 2 designated negative staining , 1 weakly positive staining , 2 strongly positive staining and missing data were left blank in the scored table . hierarchical clustering investigates relationships between samples and between proteins , based on the similarity of sample immunoreactive scores . we used the cluster program ( average - linkage with pearson correlation as similarity metric ) and results were displayed with the treeview software . 26 we then performed supervised analysis to identify the protein - set that best distinguished between two classes of samples with different clinical outcome . to simplify the analyses , the ihc scores were recorded as negative ( negative staining ) or positive ( weakly and strong positive staining ). the classifier was derived through training on a subset of chosen samples ( ⅔ of population , learning set ) and then validated on the remaining subset ( ⅓ of population , validation set ). the assignment of samples to each set was random , but the ratio between tumors with and without metastatic relapse was preserved . an exhaustive testing comprising all combinations of 1 to 5 proteins , as well as the complementary combinations of 21 to 25 proteins was performed to assess their ability to classify tumors into 2 classes (“ poor - prognosis ” and “ good - prognosis ”) in agreement with their clinical outcome . using the protein expression scores of each combination , we developed a “ metastasis scoring ” system that assigned to each tumor a probability to belong to the “ poor - prognosis class ” or the “ good - prognosis class .” consider a combination of n proteins p 1 , k , p n ( where n ranges from 1 to 5 and 21 to 26 ) and two predefined classes x , y of tumors within the learning set : x ={ x 1 , k , x k } includes samples with metastatic relapse during the follow - up and y ={ y 1 , k , y m } includes samples without any metastatic relapse . for each protein combination tested , one tumor is represented as a ternary vector ( e . g . x 1 ={ x 1 ( p 1 ), k , x 1 ( p n )} where each component is scored 0 for missing data or + 1 /− 1 for positive / negative ihc staining . every tumor z has a score s ( z ) defined as follows . for each protein p i , we compute the frequencies of + 1 /− 1 value in the x class ( adjusted to avoid a 0 probability ): f x i ⁡ ( + 1 ) = card ⁢ { k ⁢ : ⁢ ⁢ x k ⁡ ( p i ) = + 1 } + 1 card ⁢ { k ⁢ : ⁢ ⁢ x k ⁡ ( p i ) ≠ 0 } + 2 ⁢ ⁢ and ⁢ ⁢ f x i ⁡ ( - 1 ) = card ⁢ { k ⁢ : ⁢ ⁢ x k ⁡ ( p i ) = - 1 } + 1 card ⁢ { k ⁢ : ⁢ ⁢ x k ⁡ ( p i ) ≠ 0 } + 2 where , for instance , card { k : x k ( p i )=+ 1 } is the number of x tumors with positive ihc staining for protein p i . similarly we compute the frequencies f y i (+ 1 ) and f y i (− 1 ) in the y class and we define f • i ( 0 )= 1 . the metastasis score of tumor z is the log ratio of the joint probabilities : s ⁡ ( z ) = ∑ i = 1 n ⁢ log ⁡ ( f x i ⁡ ( z ⁡ ( p i ) ) ) - ∑ i = 1 n ⁢ log ⁡ ( f y i ⁡ ( z ⁡ ( p i ) ) ) . samples were then sorted according to their s ( z ) score . the natural threshold that divides the population in 2 classes is s = 0 : if s ( z )& gt ; 0 then z is more similar to the class x and is predicted to belong to the “ poor - prognosis class ” and if s ( z )& lt ; 0 then z is more similar to the class y and is predicted to belong to the “ good - prognosis class .” the number of misclassifications ( error rate ) was defined as the number of x tumors classified in the “ good - prognosis class ” plus the number of y tumors classified in the “ poor - prognosis class .” the best classifier protein - set was that with the minimal rate of misclassified tumors . once identified , the prognostic power of the classifier was tested on the validation set by classifying the remaining independent tumors using the same approach . finally , it was assessed on the whole population . for each tumor set , the prognostic impact was further estimated by univariate analyses that compared the rate of metastatic relapses within the two molecularly defined classes of tumors ( fisher exact test ). distributions of molecular markers and other categorical variables were compared using either the standard chi - 2 test or fisher exact test . the follow - up was calculated from the date of diagnosis to the time of metastasis as first event or time of last follow - up for censored patients . the end point was the metastasis - free survival ( mfs ), calculated from the date of diagnosis , first metastasis being scored as an event . all other patients were censored at the time of the last follow - up , death , recurrence of local or regional disease , or development of a second primary cancer , including contralateral breast cancer . survival curves were derived from kaplan - meier estimates and were compared by log - rank test . the influence of molecular grouping , adjusted for other factors including classical prognostic factors and significant ihc measurement , was assessed in multivariate analysis by the cox proportional hazard models . survival rates and odds ratios ( or ) are presented with their 95 % confidence intervals ( 95 % ci ). statistical tests were two - sided at the 5 % level of significance . all statistical tests were done using sas version 8 . 02 . the expression of 26 proteins was studied by ihc on tma containing 552 early stage breast tumor samples and controls ( fig4 a ). as expected , staining for all antibodies was homogeneous among the 10 normal breast samples ( data not shown ), but much more heterogeneous for tumor samples . sixteen proteins were underexpressed in 12 % ( for muc1 ) to 60 % ( for aurora a ) of cases , and overexpressed for 10 proteins in 11 % ( for ki67 / mib1 ) to 66 % ( for erbb4 ) of cases in cancerous tissues compared to normal samples . examples of ihc staining are shown in fig4 ( panels b and c ). results are summarized in table 4 . the overall expression patterns for the 552 samples were first analyzed with hierarchical clustering . results are displayed in a color - coded matrix in fig1 a . the clustering algorithm orders proteins on the horizontal axis and samples on the vertical axis on the basis of similarity of their expression profiles . this similarity is shown as a dendrogram where the length of branch between two elements reflects their degree of relatedness . protein expression scores are represented according to a color scale : red for strong positive staining , brown for weak positive staining and green for negative staining . despite significantly heterogeneous expression , such combinatorial analysis and color display highlighted groups of correlated proteins across correlated samples . fig1 b displays the dendrogram of related proteins . as expected , the three interpretations of er staining made independently by two pathologists were highly correlated ( r 2 between 0 . 87 and 0 . 96 ) ( fig1 c , middle and bottom panels ). furthermore , there was a high degree of concordance for expression of er between ihc on full sections and on tma ( p & lt ; 0 . 0001 , chi - 2 test ). two major protein clusters — designated “ p1 ” and “ p2 ”— were identified ( fig1 b ). these clusters were further divided into smaller sub - groups including a cluster ( thereafter designated “ er - related cluster ”) of er - associated proteins ( pr , bcl2 , gata3 ) and an “ adhesion cluster ” ( e - cadherin , α - catenin , afadin ). we 27 have demonstrated that aurora a ( stk6 ) and taxins ( tacc1 - 3 ) are interacting partners and involved in cell division . this translated in the formation of a third cluster ( thereafter designated “ mitosis cluster ”). the fourth cluster ( thereafter designated “ proliferation cluster ”) defined by the routinely used marker ki67 / mib1 , revealed that proteins such as egfr , erbb2 , p53 and the g1 cyclin ccne are preferentially overexpressed in tumors undergoing rapid growth . the combined protein expression patterns defined two major clusters of tumors designated cluster a ( 462 cases ) and cluster b ( 89 cases ) in fig1 ( 1 case that clustered outside of the 2 clusters was excluded from further analysis ). cluster a could be further subdivided into two subclusters , a1 ( 393 cases ) and a2 ( 89 cases ). globally , cluster a1 tumors displayed a strong expression of the “ er cluster ” and the “ adhesion cluster ” and a low expression of the “ proliferation cluster ” in most of cases , whereas the “ mitosis cluster ” was strongly expressed in about 50 % of samples . in general , cluster b tumors displayed overall a low expression of the “ er cluster ” but a strong expression of the three other protein clusters . cluster a2 included er - positive and er - negative tumors that displayed an intermediate profile characterized overall by strong expression of the “ adhesion cluster ” and a low expression of the “ er cluster ,” the “ proliferation cluster ” and the “ mitosis cluster .” we identified correlations between tumor clusters and relevant biopathological parameters . in each cluster , the most frequent histological type was the ductal type . however , in cluster a1 , 19 % of samples were of the lobular type compared with 12 % in cluster a2 and only 7 % in cluster b ( p = 0 . 03 ; chi - 2 test ). fig1 c ( top panel ) shows , within cluster a1 , a subcluster of 24 tumors that includes 21 lobular or mixed ( lobular / ductal ) carcinomas with low expression of e - cadherin , consistent with a previous report . 29 correlation also existed with sbr grade ; in cluster a1 , 41 % of cases were grade 1 and 15 % were grade iii compared with 23 % and 35 % in cluster a2 , and 7 % and 63 % in cluster b ( p & lt ; 0 . 0001 ; chi - 2 test ), respectively . in cluster b , samples were more likely to be erbb2 - positive ( 2 + or 3 + in ihc , 36 % of cases ) compared with 8 % in cluster a1 and 12 % in cluster a2 ( p & lt ; 0 . 0001 , chi - 2 test ). conversely , cluster a1 samples were more likely to be er - positive ( 99 % of cases ) compared with 35 % in cluster a2 and 10 % in cluster b ( p & lt ; 0 . 0001 , chi - 2 test ). finally , peritumoral vascular emboli were more frequent in a2 tumors ( 53 % of cases ) than in b ( 37 %) and a1 ( 35 %) tumors ( p = 0 . 02 , chi - 2 test ). interestingly , no correlation was found with age of patients , pathological size of tumors , and axillary lymph node status . importantly , the tumor clusters correlated with clinical outcome . with a median follow - up of 57 months , the 5 - year mfs was significantly different ( p & lt ; 0 . 0001 , log - rank test ) between cluster a1 ( 54 metastases , 86 % mfs [ 95 % ci 82 . 1 - 89 . 9 ]), cluster a2 ( 21 metastases , 68 % mfs [ 95 % ci 79 . 9 - 56 . 5 ]) and cluster b ( 26 metastases , 66 % mfs [ 95 % ci 54 . 3 - 77 . 6 ]) ( data not shown ). we developed a supervised analysis method to search for smaller sets of discriminator proteins that might improve our prognostic classification . analysis was conducted using two equivalent but independent tumor sets ( learning and validation sets ). the learning set of samples ( n = 368 ) allowed the identification of a combination of proteins ( protein expression signature ) that correlated with long - term mfs . the number of proteins in the “ metastatic predictor ” was optimized by iteratively testing all combinations of 1 to 5 proteins and the complementary combinations of 21 to 25 proteins and by assessing their ability for correct classification of samples using a “ metastatic score .” the optimal combination for these tumors contained 21 proteins ( fig2 c ). examples of ihc staining for these 21 proteins are shown in fig4 b . samples from the learning set were ordered using the “ metastatic score .” two classes of samples (“ poor - prognosis class ,” positive scores and “ good - prognosis class ,” negative scores ) were defined using a cut - off value of 0 . as shown in fig2 a , the classifier predicted rather successfully the actual clinical outcome of patients : 47 out of the 128 patients ( 37 %) with positive score displayed metastatic relapse whereas only 21 out of the 240 ( 9 %) with negative score experienced metastasis during follow - up ( odds ratio , or = 6 . 1 [ 95 % ci 3 . 3 - 11 . 3 ], p & lt ; 0 . 0001 , fisher exact test ). we then shown the ability of this multiprotein signature to predict prognosis in an independent set of 184 patients ( validation set ). using the same threshold for the “ metastatic score ” previously described , we identified two classes of patients that strongly correlated with clinical outcome . there were 24 metastatic relapses out of the 63 patients ( 38 %) in the “ poor - prognosis class ” and only 10 out of the 121 ( 8 %) in the “ good - prognosis class ” ( odds ratio , or = 6 . 8 [ 95 % ci 2 . 8 - 17 . 3 ], p & lt ; 0 . 0001 , fisher exact test ) ( fig2 b ). these results confirmed and validated the predictive capacity and robustness of our 21 - protein signature . when all 552 cases ( learning and validation cases ) were analyzed together , the predictor correlated well with long - term mfs . fig2 c shows the expression profiles of the 21 proteins in the 552 tumors in a color - coded matrix . samples are ordered from top to bottom according to their increasing “ metastatic score ” and proteins from left to right according to decreasing δp ( δp is the difference between the probability of positive staining and the probability of negative staining in non - metastatic samples ). the orange dashed line indicates the threshold 0 that separates the two classes , “ good - prognosis ” ( above the line ) and “ poor - prognosis ” ( under the line ). table 2 ( see the three last columns ) shows the characteristics of patients in each class . the histoclinical parameters significantly associated with this classification were sbr grade ( p & lt ; 0 . 0001 , chi - 2 test ), hormone receptor status ( p & lt ; 0 . 0001 , fisher exact test ), erbb2 status ( p & lt ; 0 . 0001 , fisher exact test ), and whether patients received adjuvant chemotherapy ( p = 0 . 001 , fisher exact test ) or hormone therapy ( p & lt ; 0 . 0001 , fisher exact test ). there was no correlation with patient age , tumor size , and number of involved lymph nodes . in contrast , a strong correlation with clinical outcome was observed ( fig2 c ): 65 of 194 patients ( 34 %) assigned to the “ poor - prognosis class ” displayed metastatic relapse whereas only 37 of 358 ( 10 %) assigned to the “ good - prognosis class ” experienced metastasis during follow - up ( odds ratio , or = 4 . 4 [ 95 % ci 2 . 7 - 7 . 0 ], p & lt ; 0 . 0001 , fisher exact test ). the 5 - year mfs was 62 % [ 95 % ci 54 . 7 - 70 . 0 ] in the “ poor - prognosis class ,” and 90 % [ 95 % ci 86 . 0 - 93 . 3 ] in the “ good - prognosis class ” ( p & lt ; 0 . 0001 , log - rank test ) ( fig3 a ). our protein expression signature also classified the 255 patients with node - positive disease into two classes that correlated with clinical outcome . in the “ good - prognosis class ,” 28 out of 158 patients experienced metastatic relapse during follow - up as compared with 43 out of 97 in the “ poor - prognosis class ” ( odds ratio , or = 3 . 7 [ 95 % ci 2 . 0 - 6 . 8 ], p & lt ; 0 . 0001 , fisher exact test ) ( fig3 b ). the same was true for the 292 patients with node - negative breast cancer . in this group , the odds ratio for metastasis was 6 . 5 ([ 95 % ci 2 . 7 - 16 . 8 ], p & lt ; 0 . 0001 , fisher exact test ) among the 93 women from the “ poor - prognosis class ,” as compared with the 199 women from the “ good - prognosis class ” ( fig3 b ). as shown , there was no significant difference for mfs between the 158 node - positive patients from the “ good - prognosis class ” and the 93 node - negative patients from the “ poor - prognosis class ” ( p = 0 . 142 , log - rank test ). we compared our prognostic classification of node - negative patients with those provided by the consensus criteria established during the st - gallen and nih conferences . 3 , 4 these criteria classified all 292 patients into two groups ( low risk versus high risk ) ( fig3 c and 3d ). our multiprotein signature classified many more patients into the “ good - prognosis class ” ( 199 vs 80 vs 43 , respectively ) and less patients in the “ poor - prognosis class ” ( 93 vs 209 vs 245 ) as compared with st - gallen and nih classifications , and interestingly , with a percentage of metastatic relapse similar in the classes with low risk ( 4 . 5 % vs 5 % vs 7 %, respectively ), but greater in the classes with high risk ( 24 % vs 13 % vs 11 %, respectively ). in fact , the low - risk group and the high - risk group defined according to consensual criteria could further be subdivided in prognostic subgroups when the 21 - protein signature was applied ( data not shown ). the same analysis was separately applied to er - positive and er - negative tumors . in the er - positive group ( n = 422 ), 35 of 345 patients from the “ good - prognosis class ” displayed metastatic relapse as compared with 29 of 77 from the “ poor - prognosis class ” ( odds ratio , or = 5 . 4 [ 95 % ci 2 . 8 - 9 . 9 ], p =& lt ; 0 . 0001 , fisher exact test ). the corresponding 5 - year mfs were 90 % [ 95 % ci 85 . 9 - 93 . 3 ] and 58 % [ 95 % ci 45 . 4 - 70 . 6 ], respectively ( p & lt ; 0 . 0001 , log - rank test ) ( data not shown ). the same trend was observed , although not significant ( p = 0 . 21 , log - rank test ), for the 129 er - negative tumors with 5 - year mfs of 91 % [ 95 % ci 76 . 0 - 100 . 0 ] and 66 % [ 95 % ci 56 . 0 - 75 . 1 ], respectively . since the occurrence of metastatic relapse may be influenced by the delivery of adjuvant systemic therapy , the classification based on our 21 - protein signature was applied to 186 women who received neither chemotherapy nor hormone therapy after local - regional treatment . importantly , the 21 - protein signature successfully predicted prognosis in these patients : 6 metastatic relapses of 119 patients in the “ good - prognosis class ” and 19 of 67 in the “ poor - prognosis class ” ( odds ratio , or = 7 . 4 [ 95 % ci 2 . 6 - 23 . 9 ], p & lt ; 0 . 0001 , fisher exact test ) ( fig3 e ). similar results were observed when we focused on the 133 patients who received adjuvant chemotherapy without hormone therapy . in the “ good - prognosis class ,” 12 of the 58 patients displayed metastatic relapse whereas 33 of 75 experienced metastasis in the “ poor - prognosis class ” ( odds ratio , or = 3 [ 95 % ci 1 . 3 - 7 . 2 ], p = 0 . 006 fisher exact test ) ( fig3 f ). we finally compared the prognostic ability of our molecular grouping of tumors with classical histoclinical factors and individual protein markers . in univariate analysis , the histoclinical factors that correlated with mfs ( p & lt ; 0 . 05 , log - lank test ) were pathological tumor size (≦ 20 mm , & gt ; 20 ), tumor grade ( sbr i , ii , iii ), number of positive axillary lymph nodes ( 0 , 1 - 3 , ≧ 4 ), and peritumoral vascular invasion ( negative , positive ). proteins significantly correlated to mfs were bcl2 ( p & lt ; 0 . 0001 ), gata3 ( p = 0 . 0006 ), mib1 ( p & lt ; 0 . 0001 ), er ( p & lt ; 0 . 0001 ), pr ( p = 0 . 0007 ), p53 ( p = 0 . 003 ) and α - catenin ( p = 0 . 005 ) ( table 5 ). the influence on the risk of distant metastasis of our multiprotein - based grouping , adjusted for other prognostic factors , was assessed in multivariate analysis by the cox proportional hazards model . the parameters entered in the model were dichotomised and included the classification based on the discriminator 21 - protein set (“ good - prognosis class ” and “ poor - prognosis class ”), age of patients (≦ 50 years , & gt ; 50 years ), number of positive axillary lymph nodes ( 0 , 1 - 3 , ≧ 4 ), pathological tumor size (≦ 20 mm , & gt ; 20 ), tumor grade ( sbr i , ii , iii ), estrogen receptor status ( negative , positive ), progesterone receptor status ( negative , positive ), peritumoral vascular invasion ( negative , positive ), chemotherapy ( delivery or not ), hormone therapy ( delivery or not ) and each of the proteins ( negative , positive ) significantly associated with survival in univariate analyses . results are shown in table 5 . several independent factors predictive of distant metastasis as first event were evidenced including the prognosis signature based on the 21 - 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