| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
Molecular Biology, Pathobiology and Genetics |
1 Institut de Cancérologie de Marseille, Département d'Oncologie Moléculaire, 2 BioPathologie, 3 BioStatistiques, 4 Chirurgie, and 5 Oncologie Médicale et Investigation Clinique, Institut Paoli-Calmettes and UMR599 Institut National de la Santé et de la Recherche Médicale; 6 ERM206 Institut National de la Santé et de la Recherche Médicale; 7 Université de la Méditerranée, UFR de Médecine; and 8 Ipsogen S.A., Marseille, France
Requests for reprints: Daniel Birnbaum, UMR599 Institut National de la Santé et de la Recherche Médicale, 27 Boulevard Leï Roure, 13009 Marseille, France. Phone: 33-4-91-75-84-07; Fax: 33-4-91-26-03-64; E-mail: birnbaum{at}marseille.inserm.fr.
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Key Words: Breast cancer • Expression profiling • Proteomics • Prognosis • Tissue microarray
|
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Large-scale molecular techniques such as DNA microarrays contribute to the understanding of the molecular complexity of breast cancer (5). Several studies have showed the potential clinical utility of gene expression profiles, including the identification of prognostic subclasses (6–15). Their clinical impact must be subsequently evaluated in larger studies, followed by the development of gene expression–based diagnostics adapted to the clinical setting. The cost, complexity, and interpretation of DNA microarrays are currently unsuitable for routine use in standard clinical settings. The sensitivity, specificity, reproducibility, and technical feasibility outside large academic centers have to be addressed, and experimental conditions have to be standardized and data compared in multicenter clinical trials.
Additional opportunities to identify and/or validate molecular signatures are provided by alternative high-through put approaches such as tissue microarrays (TMA; refs. 16–19). The technique can be coupled to immunohistochemistry to study hundreds of specimens simultaneously. Immunohistochemistry is applicable to paraffin-embedded samples, avoiding the requirement for frozen specimens. Immunohistochemistry is relatively inexpensive, straightforward, and well established in standard clinical pathology laboratories. Thus, immunohistochemistry on TMA may be a practical approach both in validation studies and in routine testing. However, analytic methods to efficiently process multiple-target immunohistochemistry data have not been previously developed. Most of the studies have applied unsupervised hierarchical clustering (20–26), and only one has addressed the prognostic issue in breast cancer (27). Supervised analysis, based on Cox regression model, was recently applied to other cancers (28, 29).
Using immunohistochemistry and TMA, we have analyzed the expression of 26 proteins—selected for their relevance in breast cancer and availability of the corresponding antibody—in a retrospective panel of more than 1,600 cancer samples from 552 patients with early breast cancer. Classification of samples based on this multidimensional data set was first done using classic hierarchical clustering. We then developed a supervised method that further improved the prognostic classification.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Tissue Microarrays. TMAs were prepared as previously described (30). For each case, three representative areas from the tumor were carefully selected from a hematoxylin-eosin-safran–stained section of a donor block. Core cylinders (0.6 mm diameter) were punched from each of them and deposited into three separate recipient paraffin blocks using a specific arraying device (Beecher Instruments, Silver Spring, MD). In addition to tumors, the recipient block also received internal controls including 10 normal, breast tissue samples from 10 healthy women that underwent reductive mammary surgery and pellets from cell lines. Five-micrometer sections of the resulting TMA blocks were made and used for immunohistochemistry analysis after transfer onto glass slides. We previously showed the reproducibility of the method notably between multiple interpreters and its reliability by comparison with the standard immunohistochemistry on full sections (
test 
0.95; ref. 30). This high degree of concordance was in the same range as published studies reporting that TMA constructed with three cores per sample are representative of whole specimen (17, 31).
Immunohistochemistry. The selection of the 26 proteins to be tested was based on known or putative importance in breast cancer as prognostic/predictive marker, and availability and suitability of a corresponding antibody for paraffin-embedded tissues (Table 1). They included hormone receptors [estrogen receptor (ER), progesterone receptor (PR)], subclass markers (CK5/6, CK8/18), oncogenes and proliferation proteins (EGFR, ERBB2, ERBB3, ERBB4, BCL2, CCND1, CCNE, Ki-67, FGFR1, Aurora A/STK6, TACC1, TACC2, TACC3), tumor suppressors (P53, FHIT), adhesion molecules (CDH1, CDH3, CTNNA1, CTNNB1, Afadin/AF-6), proteins from amplified genomic regions (ERBB2, CCND1, STK6), and markers identified in previous studies (GATA3, MUC1). Twelve of these proteins were recurrent among the discriminator genes identified in the RNA expression profiling studies that addressed prognosis in breast cancer (5–14).
|
Data Analysis. Expression profiles were analyzed by both unsupervised and supervised methods. First, we applied hierarchical clustering. Data was reformatted as follows: –2 designated negative staining, 2 positive staining, missing data was left blank in the scored table. We used the Cluster program (average linkage, Pearson correlation). Results were displayed with TreeView (34).
Second, we did supervised analysis to identify the protein set that best distinguished between two classes of samples with different survival. The classifier was derived through learning on a subset of samples (two thirds of population, learning set) and then validated on the remaining subset (one third of population, validation set). The assignment of samples to each set was random but preserved the ratio between tumors with and without metastatic relapse. There was no significant difference between the learning and the validation sets for each histoclinical parameter, treatment, and follow-up (data not shown). All combinations of 1 to 5 proteins, as well as the complementary combinations of 21 to 25 proteins, were systematically tested for their ability to classify tumors in two classes ("poor prognosis" and "good prognosis") in agreement with their clinical outcome. An oriented random search through all protein combinations was also done and each combination encountered was tested in the same way (see Supplementary Material for more details). Using the protein expression scores of each combination, we defined a "metastasis score" that assigned to each tumor a probability to belong to the poor-prognosis or the good-prognosis class (see Supplementary Material for details). The best classifier protein set was that with the minimal rate of misclassified tumors. Once identified on the learning set, the prognostic power of the classifier was tested on the validation set by classifying the tumors using the same approach. For each tumor set, the prognostic impact was estimated by univariate analyses that compared the rate of metastatic relapses within the two molecularly defined classes of tumors (Fisher's exact test).
Statistical Methods. Distributions of molecular markers and other categorical variables were compared using either the 
2 or Fisher's exact tests. 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 MFS, calculated from the date of diagnosis, first distant 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. Survival curves were derived from Kaplan-Meier estimates (35) and compared by log-rank test. The influence of molecular grouping, adjusted for other factors, was assessed in multivariate analysis by the Cox proportional hazard models (36). 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.
|
Results |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
|
|
2 test; Fig. 2C, top to bottom). Four major protein clusters were identified (Fig. 2B), including a cluster (designated "ER cluster") of ER-associated proteins (PR, BCL2, GATA3) and a "differentiation cluster" (E-cadherin, 
1-catenin, afadin). We (37) and others (38) have showed that Aurora A (STK6) and Taxins (TACC1-3) are interacting partners and involved in cell division. This translated in the formation of a cluster designated "mitosis cluster." The fourth cluster, designated "proliferation cluster," contained the Ki-67/MIB1 marker and other proteins preferentially overexpressed in highly proliferating tumors (EGFR, ERBB2, P53, CCNE).
The combined protein expression patterns defined two major tumor clusters designated A (n = 471) and B (n = 81) in Fig. 2A. Cluster A was subdivided in two subclusters, A1 (n = 409) and A2 (n = 62). Globally, A1 tumors displayed a strong expression of the ER cluster and the differentiation cluster and a low expression of the proliferation cluster in most of cases, whereas the mitosis cluster was strongly expressed in 50% of samples. B tumors displayed overall a low expression of the ER cluster but a strong expression of the other protein clusters. A2 tumors displayed an intermediate profile characterized overall by a strong expression of the differentiation cluster, a low expression of the proliferation cluster and the mitosis cluster, and a low to strong expression of the ER cluster.
We identified correlations between tumor clusters and biopathologic data. In each cluster, the most frequent histologic type was the ductal type; however, in cluster A1, 18% of samples were of the lobular type compared with 12% in cluster A2 and 7% in cluster B (difference not significant, P = 0.06; 2 test). Figure 2C (middle) shows, in cluster A1, a subcluster of 22 tumors that includes 18 lobular carcinomas with, as expected (39), low expression of E-cadherin. A1 samples were more likely to be ER positive (96% of cases) compared with 39% in cluster A2 and 7% in cluster B (P < 0.0001, 2 test). However, ER-positive and ER-negative cases were scattered across all three clusters, suggesting further heterogeneity among each class. For example, the ER-positive samples from clusters A2 (n = 24) and B (n = 6) were distinguished from ER-positive A1 samples by a low expression of the other proteins included in the ER cluster but a strong expression of some proteins included in the proliferation cluster. This discrimination also existed at the biological level: the ER-positive A2 and B samples were more frequently PR-negative (P = 0.008; Fisher's exact test) and ERBB2-positive (P = 0.001; Fisher's exact test) than ER-positive A1 samples. Similarly, the ER-negative samples from A2 and B clusters differed by a stronger expression of the mitosis and of the proliferation cluster, including CK5/6, in B cases. Correlation also existed with grade; in cluster A1, 40% of cases were grade 1 and 16% were grade 3 compared with 21% and 45% in cluster A2, and 9% and 59% in cluster B (P < 0.0001; 2 test), respectively. Finally, B samples were more likely to be ERBB2-positive (35%) compared with 9% in cluster A1 and 13% in cluster A2 (P < 0.0001, 2 test). No correlation existed with age, pathologic size, axillary lymph node status, and peritumoral vascular invasion.
Importantly, the tumor clusters correlated with survival. The 5-year MFS was significantly different (P < 0.0001, log-rank test) between A1 (86%; 95% CI, 82.2-89.7), A2 (62%; 95% CI, 48.7-75.3), and B (64%; 95% CI, 51.2-76.7; data not shown). MFS also significantly differed between the ER-positive samples from A1 cluster and those from merged A2-B clusters (86% versus 52%, P = 0.001, log-rank test). A similar trend was observed between the ER-negative samples from A2 cluster and those from B cluster, but was not significant (64% versus 66%, P = 0.67, log-rank test).
Supervised Analysis
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 (learning and validation) tumor sets.
Identification and Validation of a Prognostic Protein Signature. The learning set (n = 368) allowed the identification of a protein expression signature that correlated with MFS. The number of proteins in the signature was optimized by iteratively testing all combinations of 1 to 5 proteins and the complementary combinations and by assessing their ability for correct classification of samples using a metastatic score. The optimal combination contained 21 proteins (Fig. 3C). Samples were ordered using the metastatic score and sorted in two classes (poor-prognosis class, positive scores; good-prognosis class, negative scores). As shown in Fig. 3A, this classifier predicted rather successfully clinical outcome: 47 (37%) of 128 patients with positive score displayed metastatic relapse, whereas 21 (9%) of 240 patients with negative score experienced metastasis (OR, 6.1; 95% CI, 3.3-11.3; P < 0.0001, Fisher's exact test).
|
Interestingly, the two best combinations identified by alternative algorithms did not improve the discrimination. The signatures identified by bottom-up (13 proteins) or top-down procedure (15 proteins), respectively, included 10 and 14 proteins of the 21-protein signature, but done less correctly in the validation set. Altogether, these results validated the predictive capacity of our 21-protein signature. Examples of staining for these proteins are shown (Fig. 1B).
Classification Based on the 21-Protein Signature. To further define the relationship of our classification with histoclinical data, we combined and analyzed together the learning and validation sets. Figure 3C shows the expression profiles of the 21 proteins in the 552 tumors in a color-coded matrix. The orange dashed line indicates the separation between the good-prognosis class and the poor-prognosis class. Supplementary Table 1 (last three columns) shows the characteristics of patients in each class. The features significantly associated with this classification were pathologic tumor size (P = 0.04, 2 test), grade (P < 0.0001, 2 test), hormone receptor status, ERBB2 status, and whether patients received adjuvant chemotherapy or hormone therapy (P < 0.0001, Fisher's exact test). There was no correlation with patient age, nodal status, and peritumoral vascular invasion. A strong correlation existed with survival (Fig. 3C): 68 (36%) of 189 patients assigned to the poor-prognosis class displayed metastatic relapse, whereas only 34 (9%) of 363 patients assigned to the good-prognosis class experienced metastasis (OR, 5.4; 95% CI, 3.4-8.9; P < 0.0001, Fisher's exact test). The 5-year MFS was 61% (95% CI, 53.2-68.8) in the poor-prognosis class and 90% (95% CI, 86.4-93.5) in the good-prognosis class (P < 0.0001, log-rank test; Fig. 4A). We compared this molecular prognostic classification with those provided by the St-Gallen and NIH criteria (3, 4). These criteria classified the 552 patients in two groups (low versus high risk) on the basis of histoclinical data (high risk if node positive and if node negative with tumor size >2 cm, ER and PR negative, SBR grade 2-3, or age <35 years for St-Gallen; high risk if tumor size >1 cm for NIH). The molecular classification compared favorably in terms of positive (PPV) and negative (NPV) predictive values for metastatic relapse. Respective rates were 36%, 21%, and 20% for PPV and 91%, 96%, and 91% for NPV. Sensitivity was 73% and specificity 67% [receiver operating characteristic (ROC) curve in Fig. 4G].
|
The same analysis was separately applied to ER-positive and ER-negative tumors. In the ER-positive group (n = 422), 34 of 351 patients from the good-prognosis class displayed metastatic relapse as compared with 30 of 71 patients from the poor-prognosis class (OR, 6.8; 95% CI, 3.6-12.7; P = <0.0001, Fisher's exact test). The corresponding 5-year MFS were 90% (95% CI, 86.3-93.6) and 54% (95% CI, 40.8-67.1), respectively (P < 0.0001, log-rank test). The same was observed for the 129 ER-negative tumors with 5-year MFS of 100% and 65% (95% CI, 55.4-74.5), respectively (P = 0.03, log-rank test).
Finally, because the occurrence of metastasis may be influenced by the delivery of adjuvant systemic therapy, the classification based on the 21-protein signature was applied to 186 women who did not receive adjuvant systemic therapy. The signature successfully predicted prognosis in these patients: 7 metastatic relapses of 124 patients in the good-prognosis class and 18 of 62 in the poor-prognosis class (OR, 6.8; 95% CI, 2.5-20.5; P < 0.0001, Fisher's exact test; Fig. 4E). Similar results were observed for the 133 patients who received adjuvant chemotherapy without hormone therapy. In the good-prognosis class, 11 of 54 patients displayed metastatic relapse, whereas 34 of 79 experienced metastasis in the poor-prognosis class (OR, 3; 95% CI, 1.3-7.2; P = 0.009 Fisher's exact test; Fig. 4F).
Uni- and Multivariate Prognostic Analysis. We compared the prognostic ability of our molecular grouping with classic histoclinical data and individual protein markers. In univariate analysis, the features that correlated with MFS (P < 0.05, log-rank test) were pathologic tumor size (
20, >20 mm), grade (SBR 1, 2, 3), number of positive axillary lymph nodes (0, 1-3, 
4), and peritumoral vascular invasion (negative, positive). Data correlated to longer MFS (P value cutoff, 0.0075 for adjustment on account of multiple comparisons) were positive expression of BCL2 (P < 0.0001), GATA3 (P = 0.0006), ER (P < 0.0001), PR (P = 0.0007) and 1-catenin (P = 0.005) and negative expression of Ki-67 (P < 0.0001) and P53 (P = 0.003; Table 1).
The influence on the risk of metastasis of our multiprotein-based grouping, adjusted for other prognostic factors, was assessed in multivariate analysis. The data entered were dichotomized and included the classification based on the 21-protein combination (good-prognosis class, poor-prognosis class), age (50, >50 years), number of positive axillary lymph nodes (0, 1-3, 4), pathologic tumor size (20 mm, >20), grade (SBR 1, 2, 3), ER status (negative, positive), PR 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 2. Independent prognostic factors included the 21-protein signature, pathologic size of tumors, axillary lymph node status (when dichotomized, 3 versus >3), and Ki-67/MIB1 status. However, the 21-protein signature was the strongest predictor with a hazard ratio of 2.96 for poor-prognosis class compared with good-prognosis class (95% CI, 1.77-4.97; P < 0.0001).
|
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
The clustering sorted tumors in three clusters that correlated with histoclinical data, including grade, ER, and ERBB2 status, in close agreement with their expression profiles. For example, the high number of grade 3 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. Conversely, 99% of cluster A1 samples were ER positive and showed a frequent strong expression of the ER cluster and low expression of the proliferation cluster (40). Although ER expression is a key factor in our classification, ER-positive samples and ER-negative samples displayed heterogeneous expression profiles with the identification of at least two subgroups in each category as recently reported in large-scale expression studies (7, 9, 20, 26). It is probable that the two ER-positive categories represent two distinct groups with different outcome. The same was true for the ER-negative samples. Thus, the grouping of tumors based on the expression of multiple proteins (including ER) was more powerful than ER status alone to tackle the heterogeneity of disease.
The tumor clusters correlated with a phenotypic classification recently proposed (23, 42, 43). "Basal" cells (including progenitors)express keratins CK5/6. In contrast, differentiated "luminal" cells express keratins CK8/18. Gene expression analyses using DNA microarrays have identified subtypes of breast tumors corresponding to this phenotypic classification (8–10). In our study, 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, namely, strong expression of CK5/6, CDH3, and proliferation markers (9, 20). A2 tumors, with an intermediate profile, may represent a transitory "basoluminal" stage, or tumors that have lost ER function. The significant differences in survival observed between these three clusters are consistent with this model (8–10). In addition, we show that lobular carcinomas are luminal-like tumors. Thus, clustering based on expression of multiple proteins identifies relevant subtypes of disease.
Protein Expression Profiling Predicts Clinical Outcome. We then developed a supervised method to identify the best protein combination that would improve the prognostic classification. We identified a 21-protein signature that optimally classified patients into two classes (good prognosis and poor prognosis) with a highly significant difference in 5-year MFS (90% versus 61%). Initially identified in a set of 368 patients, this signature was validated in an independent set of 184 patients, showing its robustness. It included 10 proteins coded by discriminator genes identified in recent expression studies (6–14) as well as other proteins with possible role in progression. When compared in multivariate analysis with classic prognostic factors and with each protein separately, our classification did significantly better for predicting the occurrence of metastatic relapse. The rate of metastatic relapse was clearly different between our good-prognosis and poor-prognosis classes (9%versus 36%, respectively). Such molecular classification compared favorably with St-Gallen and NIH classification in terms of PPV and NPV for metastatic relapse for all 552 patients as well as node-negative patients. This finding is of particular significance because 75% of node-negative patients candidate for adjuvant chemotherapy based on the St-Gallen/NIH criteria are currently thought to be unnecessarily overtreated. Our predictor assigned fewer 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/NIH criteria. Our predictor also did well in patients irrespective of ER status, suggesting it provides more accurate clinical information than ER status alone, possibly reflecting functional differences in the ER or interacting pathways. Our classification conserved its predictive impact for patients independent of adjuvant therapy. The results obtained in the group not exposed to systemic therapy suggest a true pure prognostic value, whereas those derived from the chemotherapy-treated group might be prognostic and/or reflect response to therapy. Thus, the 21-protein signature might facilitate the selection of appropriate treatment options. 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 therapy. Both hypotheses will require additional retrospective and prospective studies.
|
Acknowledgments |
|---|
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
|
Footnotes |
|---|
Supplementary data for this article are available at Cancer Research Online (http://cancerres.aacrjournals.org/).
Received 8/31/04. Revised 11/ 8/04. Accepted 11/18/04.
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
This article has been cited by other articles:
|
|
 |
|
  S Badve and H Nakshatri Oestrogen-receptor-positive breast cancer: towards bridging histopathological and molecular classifications J. Clin. Pathol., January 1, 2009; 62(1): 6 - 12. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Goncalves, E. Charafe-Jauffret, F. Bertucci, S. Audebert, Y. Toiron, B. Esterni, F. Monville, C. Tarpin, J. Jacquemier, G. Houvenaeghel, et al. Protein Profiling of Human Breast Tumor Cells Identifies Novel Biomarkers Associated with Molecular Subtypes Mol. Cell. Proteomics, August 1, 2008; 7(8): 1420 - 1433. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 G. Sauer, N. Schneiderhan-Marra, C. Kazmaier, K. Hutzel, K. Koretz, R. Muche, R. Kreienberg, T. Joos, and H. Deissler Prediction of Nodal Involvement in Breast Cancer Based on Multiparametric Protein Analyses from Preoperative Core Needle Biopsies of the Primary Lesion Clin. Cancer Res., June 1, 2008; 14(11): 3345 - 3353. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Adelaide, P. Finetti, I. Bekhouche, L. Repellini, J. Geneix, F. Sircoulomb, E. Charafe-Jauffret, N. Cervera, J. Desplans, D. Parzy, et al. Integrated Profiling of Basal and Luminal Breast Cancers Cancer Res., December 15, 2007; 67(24): 11565 - 11575. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 L. Harris, H. Fritsche, R. Mennel, L. Norton, P. Ravdin, S. Taube, M. R. Somerfield, D. F. Hayes, and R. C. Bast Jr American Society of Clinical Oncology 2007 Update of Recommendations for the Use of Tumor Markers in Breast Cancer J. Clin. Oncol., November 20, 2007; 25(33): 5287 - 5312. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
V. Kulasingam and E. P. Diamandis Proteomics Analysis of Conditioned Media from Three Breast Cancer Cell Lines: A Mine for Biomarkers and Therapeutic Targets Mol. Cell. Proteomics, November 1, 2007; 6(11): 1997 - 2011. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. S. Lee, S.-B. Cho, H. E. Lee, M. A Kim, J. H. Kim, D. J. Park, J. H. Kim, H.-K. Yang, B. L. Lee, and W. H. Kim Protein Expression Profiling and Molecular Classification of Gastric Cancer by the Tissue Array Method Clin. Cancer Res., July 15, 2007; 13(14): 4154 - 4163. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. Diallo-Danebrock, E. Ting, O. Gluz, A. Herr, S. Mohrmann, H. Geddert, A. Rody, K.-L. Schaefer, S. E. Baldus, A. Hartmann, et al. Protein Expression Profiling in High-Risk Breast Cancer Patients Treated with High-Dose or Conventional Dose-Dense Chemotherapy Clin. Cancer Res., January 15, 2007; 13(2): 488 - 497. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. G. Haffty, Q. Yang, M. Reiss, T. Kearney, S. A. Higgins, J. Weidhaas, L. Harris, W. Hait, and D. Toppmeyer Locoregional Relapse and Distant Metastasis in Conservatively Managed Triple Negative Early-Stage Breast Cancer J. Clin. Oncol., December 20, 2006; 24(36): 5652 - 5657. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. Dolled-Filhart, L. Ryden, M. Cregger, K. Jirstrom, M. Harigopal, R. L. Camp, and D. L. Rimm Classification of breast cancer using genetic algorithms and tissue microarrays. Clin. Cancer Res., November 1, 2006; 12(21): 6459 - 6468. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. Bertucci, D. Birnbaum, and A. Goncalves Proteomics of Breast Cancer: Principles and Potential Clinical Applications Mol. Cell. Proteomics, October 1, 2006; 5(10): 1772 - 1786. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
C. Ginestier, N. Cervera, P. Finetti, S. Esteyries, B. Esterni, J. Adelaide, L. Xerri, P. Viens, J. Jacquemier, E. Charafe-Jauffret, et al. Prognosis and Gene Expression Profiling of 20q13-Amplified Breast Cancers Clin. Cancer Res., August 1, 2006; 12(15): 4533 - 4544. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
F. Bertucci, P. Finetti, N. Cervera, E. Charafe-Jauffret, E. Mamessier, J. Adelaide, S. Debono, G. Houvenaeghel, D. Maraninchi, P. Viens, et al. Gene Expression Profiling Shows Medullary Breast Cancer Is a Subgroup of Basal Breast Cancers. Cancer Res., May 1, 2006; 66(9): 4636 - 4644. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G. M. Callagy, P. D. Pharoah, S. E. Pinder, F. D. Hsu, T. O. Nielsen, J. Ragaz, I. O. Ellis, D. Huntsman, and C. Caldas Bcl-2 Is a Prognostic Marker in Breast Cancer Independently of the Nottingham Prognostic Index Clin. Cancer Res., April 15, 2006; 12(8): 2468 - 2475. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 V. Gelsi-Boyer, B. Orsetti, N. Cervera, P. Finetti, F. Sircoulomb, C. Rouge, L. Lasorsa, A. Letessier, C. Ginestier, F. Monville, et al. Comprehensive Profiling of 8p11-12 Amplification in Breast Cancer Mol. Cancer Res., December 1, 2005; 3(12): 655 - 667. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Isidoro, E. Casado, A. Redondo, P. Acebo, E. Espinosa, A. M. Alonso, P. Cejas, D. Hardisson, J. A. Fresno Vara, C. Belda-Iniesta, et al. Breast carcinomas fulfill the Warburg hypothesis and provide metabolic markers of cancer prognosis Carcinogenesis, December 1, 2005; 26(12): 2095 - 2104. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
J. D. Brenton, L. A. Carey, A. A. Ahmed, and C. Caldas Molecular Classification and Molecular Forecasting of Breast Cancer: Ready for Clinical Application? J. Clin. Oncol., October 10, 2005; 23(29): 7350 - 7360. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Singhal, A. Vachani, D. Antin-Ozerkis, L. R. Kaiser, and S. M. Albelda Prognostic Implications of Cell Cycle, Apoptosis, and Angiogenesis Biomarkers in Non-Small Cell Lung Cancer: A Review Clin. Cancer Res., June 1, 2005; 11(11): 3974 - 3986. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Cancer Research | Clinical Cancer Research |
| Cancer Epidemiology Biomarkers & Prevention | Molecular Cancer Therapeutics |
| Molecular Cancer Research | Cancer Prevention Research |
| Cancer Prevention Journals Portal | Cancer Reviews Online |
| Annual Meeting Education Book | Meeting Abstracts Online |
P. ER*, means of two independent ER analyses. 
) or not (
) of metastatic relapse for each patient.