A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
Marc J. van de Vijver, M.D., Ph.D., Yudong D. He, Ph.D., Laura J. van 't Veer, Ph.D., Hongyue Dai, Ph.D., Augustinus A.M. Hart, M.Sc., Dorien W. Voskuil, Ph.D., George J. Schreiber, M.Sc., Johannes L. Peterse, M.D., Chris Roberts, Ph.D., Matthew J. Marton, Ph.D., Mark Parrish, Douwe Atsma, Anke Witteveen, Annuska Glas, Ph.D., Leonie Delahaye, Tony van der Velde, Harry Bartelink, M.D., Ph.D., Sjoerd Rodenhuis, M.D., Ph.D., Emiel T. Rutgers, M.D., Ph.D., Stephen H. Friend, M.D., Ph.D., and René Bernards, Ph.D.
Background A more accurate means of prognostication in breastcancer will improve the selection of patients for adjuvant systemictherapy.
Methods Using microarray analysis to evaluate our previouslyestablished 70-gene prognosis profile, we classified a seriesof 295 consecutive patients with primary breast carcinomas ashaving a gene-expression signature associated with either apoor prognosis or a good prognosis. All patients had stage Ior II breast cancer and were younger than 53 years old; 151had lymph-node–negative disease, and 144 had lymph-node–positivedisease. We evaluated the predictive power of the prognosisprofile using univariable and multivariable statistical analyses.
Conclusions The gene-expression profile we studied is a morepowerful predictor of the outcome of disease in young patientswith breast cancer than standard systems based on clinical andhistologic criteria.
Adjuvant systemic therapy substantially improves disease-freeand overall survival in both premenopausal and postmenopausalwomen up to the age of 70 years with lymph-node–negativeor lymph-node–positive breast cancer.1,2 It is generallyagreed that patients with poor prognostic features benefit themost from adjuvant therapy.3,4 The main prognostic factors inbreast cancer are age, tumor size, status of axillary lymphnodes, histologic type of the tumor, pathological grade, andhormone-receptor status. A large number of other factors havebeen investigated for their potential to predict the outcomeof disease, but in general, they have only limited predictivepower.5
Using complementary DNA (cDNA) microarrays to analyze breast-cancertissue, Perou et al. identified tumors with distinct patternsof gene expression that they termed "basal type" and "luminaltype."6 These subgroups differ with respect to the outcome ofdisease in patients with locally advanced breast cancer.7 Inaddition, microarray analysis has been used to distinguish cancersassociated with BRCA1 or BRCA2 mutations8,9 and to determineestrogen-receptor status6,9,10 and lymph-node status.11,12
Using inkjet-synthesized oligonucleotide microarrays, we recentlyidentified a gene-expression profile that is associated withprognosis in patients with breast cancer.9 We analyzed onlytumors that were less than 5 cm in diameter from lymph-node–negativepatients who were younger than 55 years of age. We found thata classification system based on 70 genes outperformed all clinicalvariables in predicting the likelihood of distant metastaseswithin five years. We estimated that the odds ratio for metastasesamong tumors with a gene signature associated with a poor prognosis,as compared with those having a signature associated with agood prognosis, was approximately 15 using a cross-validationprocedure. Even though these results were encouraging, a limitationof the study was that the results were derived from and evaluatedin two groups of patients selected on the basis of outcome:distant metastases had developed in one group within five years,and the other group remained disease-free for at least fiveyears. Therefore, to provide a more accurate estimate of therisks of metastases associated with the two gene-expressionsignatures and to substantiate that the gene-expression profileof breast cancer is a clinically meaningful tool, we studieda cohort of 295 young patients with breast cancer, some of whomwere lymph-node–negative and some of whom were lymph-node–positive.
Methods
Selection of Patients
Tumors from a series of 295 consecutive women with breast cancerwere selected from the fresh-frozen–tissue bank of theNetherlands Cancer Institute according to the following criteria:the tumor was primary invasive breast carcinoma that was lessthan 5 cm in diameter at pathological examination (pT1 or pT2);the apical axillary lymph nodes were tumor-negative, as determinedby a biopsy of the infraclavicular lymph nodes; the age at diagnosiswas 52 years or younger; the calendar year of diagnosis wasbetween 1984 and 1995; and there was no previous history ofcancer, except nonmelanoma skin cancer. All patients had beentreated by modified radical mastectomy or breast-conservingsurgery, including dissection of the axillary lymph nodes, followedby radiotherapy if indicated. Among the 295 patients, 151 hadlymph-node–negative disease (results on pathological examination,pN0) and 144 had lymph-node–positive disease (pN+). Tenof the 151 patients who had lymph-node–negative diseaseand 120 of the 144 who had lymph-node–positive diseasehad received adjuvant systemic therapy consisting of chemotherapy(90 patients), hormonal therapy (20), or both (20). Sixty-oneof the patients with lymph-node–negative disease werealso part of the previous study used to establish the prognosisprofile.9 All patients were assessed at least annually for aperiod of at least five years. Follow-up information was extractedfrom the medical registry of the Netherlands Cancer Institute.The median duration of follow-up was 7.8 years (range, 0.05to 18.3) for the 207 patients without metastasis as the firstevent and 2.7 years (range, 0.3 to 14.0) for the 88 patientswith metastasis as the first event. The median follow-up amongall 295 patients was 6.7 years (range, 0.05 to 18.3). Therewere no missing data. The study was approved by the medical-ethicscommittee of the Netherlands Cancer Institute.
Clinicopathological variables were determined as described previously.9The level of expression of estrogen receptors was estimatedon the basis of the hybridization results on the microarrayexperiments, which is a reliable assay for estrogen-receptorstatus.9 On the basis of this assay, there were 69 estrogen-receptor–negativetumors (defined by an intensity ratio of less than –0.65U on a logarithmic scale, corresponding to staining of lessthan 10 percent of nuclei on immunohistochemical analysis) and226 estrogen-receptor–positive tumors in the cohort. Thehistologic grade was assessed according to the method describedby Elston and Ellis13; vascular invasion was assessed as absent,minor (one to three vessels), or major (more than three vessels).
Isolation of RNA and Microarray Expression Profiling
The isolation of RNA, labeling of complementary RNA (cRNA),hybridization of labeled cRNA to 25,000-gene arrays, and assessmentof expression ratios were all performed as previously described.9,14In brief, tumor material was snap-frozen in liquid nitrogenwithin one hour after surgery. Frozen sections were stainedwith hematoxylin and eosin; only samples that had more than50 percent tumor cells were selected. Thirty 30-µm sectionswere used for the isolation of RNA. Total RNA was isolated withRNAzolB and dissolved in RNase-free water. Then 25 µgof total RNA was treated with DNase with use of the Qiagen RNase-freeDNase kit and RNeasy spin columns, the RNA was then dissolvedin RNase-free water to a final concentration of 0.2 µgper microliter, and cRNA was generated by in vitro transcriptionwith the use of T7 RNA polymerase and 5 µg of total RNAand labeled with Cy3 or Cy5 (Cy Dye, Amersham Pharmacia Biotech).Five micrograms of Cy-labeled cRNA from one breast-cancer tumorwas mixed with the same amount of reverse-color Cy-labeled productfrom a pool that consisted of an equal amount of cRNA from eachpatient.
Labeled cRNAs were fragmented to an average size of approximately50 to 100 nucleotides by heating the samples to 60°C inthe presence of 10 mM zinc chloride and adding a hybridizationbuffer containing 1 M sodium chloride, 0.5 percent sodium sarcosine,50 mM morpholino-ethane sulfonic acid (pH 6.5), and formamide(final concentration, 30 percent at 40°C); the final volumewas 3 ml. The microarrays included the 24,479 biologic oligonucleotidesas well as 1281 control probes. After hybridization, the slideswere washed and scanned with a confocal laser scanner (AgilentTechnologies). Fluorescence intensities on scanned images werequantified, and the values were corrected for the backgroundlevel and normalized.
Validation Strategy
We wished to investigate the prognostic value of the gene-expressionprofile in a consecutive series of patients with breast cancer.We included 61 of the 78 patients with lymph-node–negativedisease who were involved in the previous study that determinedthe 70-gene prognosis profile.9 Leaving them out would haveresulted in selection bias, since the previous study includeda disproportionately large number of patients in whom distantmetastases developed within five years. We included these 61patients in the study, but we used the "leave-one-out" cross-validatedclassification established in our previous study to predictthe outcomes among these patients. In this approach, the classificationof the left-out sample was based on its correlation with themean levels of expression of the remaining samples from thepatients with a good-prognosis signature, with the sample inquestion excluded from the gene-selection process.9 This approachminimizes to some extent the possibility of overestimating thevalue of the prognosis profile while it keeps the consecutiveseries complete. We also provide validation results taking onlythe new samples into account.
Correlation of the Microarray Data with the Prognosis Profile
For each of the 234 tumors from patients who were not includedin the previous study, we calculated the correlation coefficientof the level of expression of the 70 genes with the previouslydetermined average profile of these genes in tumors from patientswith a good prognosis (C1).9 A patient with a correlation coefficientof more than 0.4 (the threshold in the previous study of 78tumors that resulted in a 10 percent rate of false negativeresults) was then assigned to the group with a good-prognosissignature, and all other patients were assigned to the groupwith a poor-prognosis signature. For the 61 patients with lymph-node–negativedisease who were included in the previous study, we used a cutoffvalue of 0.55 (corresponding to the threshold that resultedin a 10 percent rate of false negative results in the cross-validatedclassification in our previous study).9
Study Design
Study design, patient selection, RNA isolation from tumor material,histopathological analyses, clinical annotation, and clinicalinterpretation were carried out at the Netherlands Cancer Institute.RNA amplification and microarray hybridization were carriedout at Rosetta Inpharmatics. Bioinformatic and statistical analyseswere performed jointly by authors at both locations. All rawdata were available to all the investigators.
Statistical Analysis
In the analysis of the probability that patients would remainfree of distant metastases, we defined distant metastases asa first event to be a treatment failure; data on all other patientswere censored on the date of the last follow-up visit, deathfrom causes other than breast cancer, the recurrence of localor regional disease, or the development of a second primarycancer, including contralateral breast cancer. Data on patientswere analyzed from the date of surgery to the time of the firstevent or the date on which data were censored, according tothe method of Kaplan and Meier, and the curves were comparedwith use of the log-rank test. Values are expressed as means±SE, calculated according to the method of Tsiatis.15
We used proportional-hazards regression analysis16 to adjustthe association between the correlation coefficient (C1) andmetastases for other variables. All SEs were calculated withuse of the sandwich estimator.17 The histologic grade, extentof vascular invasion, and number of axillary-lymph-node metastases(0 vs. 1 to 3 or 0 vs. 4) were used as variables. The linearityof the relation between the relative hazard ratio and the diameterof the tumor, age, and level of expression of estrogen receptorswas tested with use of the Wald test for nonlinear componentsof restricted cubic splines.18 No evidence of nonlinearity wasfound (P=0.83 for age, P=0.75 for tumor diameter, P=0.65 forthe number of positive nodes, and P=0.27 for the level of expressionof estrogen receptors). We evaluated whether the hazard ratiowas proportional using the method of Grambsch and Therneau.19In addition, we determined the difference between the relativehazard ratio before and after five years of follow-up with respectto the prognosis signature using the Wald test. All calculationswere performed with the S Plus 2000 or S Plus 6 statisticalpackage.
Figure 1. Pattern of Expression of Genes Used to Determine the Prognosis and Clinical Characteristics of 295 Patients with Breast Cancer.
Panel A shows the pattern of expression of the 70 marker genes (also referred to as prognosis-classifier genes9) in a series of 295 consecutive patients with breast carcinomas. Each row represents the prognostic profile of the 70 marker genes for one tumor, and each column represents the relative level of expression of one gene. The tumors are numbered from 1 to 295 on the y axis, and the genes are numbered from 1 to 70 on the x axis. The genes in the horizontal direction are arrayed in the same order as in our previous study.9 Red indicates a high level of expression of messenger RNA (mRNA) in the tumor, as compared with the reference level of mRNA, and green indicates a low level of expression. The dotted line is the previously determined threshold between a good-prognosis signature and a poor-prognosis signature. Tumors are rank-ordered according to their correlation with the previously determined average profile in tumors from patients with a good prognosis. Panel B shows the time in years to distant metastases as a first event for those in whom this occurred, and the total duration of follow-up for all other patients. Panel C shows the lymph-node status (blue marks indicate lymph-node–positive disease, and white lymph-node–negative disease), the number of patients with distant metastases as a first event (blue marks), and the number of patients who died (blue marks).
Table 1. Association between Clinical Characteristics and the Prognosis Signature.
Prognostic Value of Gene-Expression Signature
In our previous study,9 the prognosis profile was determinedin a selected group of patients with lymph-node–negativedisease. In the current study, we evaluated both patients withlymph-node–negative disease and patients with lymph-node–positivedisease. To validate our previous finding, we first calculatedthe estimated odds ratio for the development of metastases withinfive years for the patients with lymph-node–negative diseasein the present series (thus excluding the 61 patients who werealso part of the previous study9) (Table 2). This analysis includedonly patients in whom distant metastases developed within fiveyears and patients who remained disease-free for at least fiveyears. The odds ratio for the development of distant metastaseswithin five years in this group was similar to the ratio inour previous study (15.3 and 15, respectively) (Table 2). Theprognosis signature was also highly predictive of the risk ofdistant metastases among the subgroup of patients with lymph-node–positivedisease and among the subgroup of all new patients (Table 2).These results highlight the value of the prognosis profile andthe robustness of the profiling technique.
Table 2. Odds Ratio for Distant Metastases within Five Years as a First Event, According to the Prognosis Signature.
To obtain a more useful estimate of the clinical outcome, wecalculated the probability of remaining free of distant metastasesand overall survival according to the prognosis profile. Forthis analysis, we first included all 295 patients (Table 3 andFigure 2A and Figure 2B), even the 61 patients with lymph-node–negativedisease who were in the previous study.9 Leaving out these patientswould have resulted in selection bias, since the first seriescontained a disproportionately large number of patients in whomdistant metastases developed within five years. However, a differentclassification strategy was used for these patients, to correctfor overfitting (see the Methods section). The Kaplan–Meiercurves showed a significant difference in the probability thatpatients would remain free of distant metastases and the probabilityof overall survival between the group with a good-prognosissignature and the group with a poor-prognosis signature. Theestimated hazard ratio for distant metastases as a first eventin the group with a poor-prognosis signature as compared withthe group with a good-prognosis signature over the entire follow-upperiod was 5.1 (95 percent confidence interval, 2.9 to 9.0;P<0.001); the prognosis profile was associated with a significantlyhigher hazard ratio during the first five years of follow-up(hazard ratio, 8.8; 95 percent confidence interval, 3.8 to 20;P<0.001) than after five years (hazard ratio, 1.8; 95 percentconfidence interval, 0.69 to 4.5; P=0.24). The hazard ratiofor overall survival was 8.6 (95 percent confidence interval,4 to 19; P<0.001).
Table 3. Rate of Overall Survival and the Probability That Patients Would Remain Free of Distant Metastases at 5 and 10 Years, According to the Prognosis Signature.
Figure 2. Kaplan–Meier Analysis of the Probability That Patients Would Remain Free of Distant Metastases and the Probability of Overall Survival among All Patients (Panels A, and B, Respectively), Patients with Lymph-Node–Negative Disease (Panels C and D, Respectively), and Patients with Lymph-Node–Positive Disease (Panels E and F, Respectively), According to Whether They Had a Good-Prognosis or a Poor-Prognosis Signature.
The P values were calculated with use of the log-rank test.
In the series of 151 patients with lymph-node–negativedisease, the prognosis profile was also extremely useful inpredicting the outcome of disease (Table 3 and Figure 2C andFigure 2D). In this group of patients, the hazard ratio fordistant metastases was 5.5 among those with a poor-prognosissignature as compared with those with a good-prognosis signature(95 percent confidence interval, 2.5 to 12.2; P<0.001). Theprognosis profile was also strongly associated with the outcomein the group of 144 patients with lymph-node–positivedisease (Table 3 and Figure 2E and Figure 2F). In this group,the hazard ratio for distant metastases was 4.5 (95 percentconfidence interval, 2.0 to 10.2; P<0.001).
Multivariable Analysis
Table 4 shows the results of the multivariable analysis of therisk of distant metastases as the first event. The only independentpredictive factors were a poor-prognosis signature, a largerdiameter of the tumor, and the nonuse of adjuvant chemotherapy.During the period in which these patients were treated, mostpremenopausal patients with lymph-node–positive diseasereceived adjuvant chemotherapy, whereas the majority of patientswith lymph-node–negative disease did not receive adjuvanttreatment. Patients who received adjuvant chemotherapy in thisseries had a higher likelihood of remaining free of distantmetastases (hazard ratio for distant metastases, 0.37; 95 percentconfidence interval, 0.20 to 0.66; P<0.001). The poor-prognosissignature was by far the strongest predictor of the likelihoodof distant metastases, with an overall hazard ratio of 4.6 (95percent confidence interval, 2.3 to 9.2; P<0.001).
Table 4. Multivariable Proportional-Hazards Analysis of the Risk of Distant Metastases as a First Event.
Discussion
We previously identified a gene-expression profile of 70 genesthat is associated with the risk of early distant metastasesin young patients with lymph-node–negative breast cancer.9In the present study we tested this profile in a series of 295consecutive patients who were treated at the hospital of theNetherlands Cancer Institute. The profile performed best asa predictor of the appearance of distant metastases during thefirst five years after treatment. This finding is not unexpected,since the tumors on which the profile was based had all metastasizedwithin five years. The prognosis profile is also a strong predictorof the development of distant metastases in patients with lymph-node–positivedisease. This finding is important, since the presence of lymph-nodemetastases is by itself a strong predictor of poor survival.Since most patients with lymph-node–positive breast cancerin our study received adjuvant chemotherapy or hormonal therapy(120 of 144 patients), we could not evaluate the prognosticvalue of the profile in patients with untreated lymph-node–positivedisease. There is, however, no indication of an effect of adjuvantchemotherapy on the prognostic value of the profile (data notshown).
Figure 3 shows the Kaplan–Meier estimates of the probabilitythat patients would remain free of distant metastases amongthe 151 patients with lymph-node–negative cancer, accordingto whether the patients were classified with the use of gene-expressionprofiling (Figure 3A), the St. Gallen criteria3 (Figure 3B),or the National Institutes of Health (NIH) consensus criteria4(Figure 3C). The St. Gallen and NIH criteria classify patientsas at low risk or high risk on the basis of various histologicand clinical characteristics. This comparison shows that theprognosis profile assigned many more patients with lymph-node–negativedisease to the low-risk (good-prognosis signature) group thandid the traditional methods (40 percent, as compared with 15percent according to the St. Gallen criteria and 7 percent accordingto the NIH criteria). Moreover, low-risk patients identifiedby gene-expression profiling had a higher likelihood of metastasis-freesurvival than those classified according to the St. Gallen orNIH criteria, and high-risk patients identified by gene-expressionprofiling tended to have a higher rate of distant metastasesthan did the high-risk patients identified by the St. Gallenor NIH criteria. This result indicates that both sets of thecurrently used criteria misclassify a clinically significantnumber of patients. Indeed, the high-risk group defined accordingto the NIH criteria included many patients who had a good-prognosissignature and a good outcome (Figure 3E). Conversely, the low-riskgroup identified by the NIH criteria included patients witha poor-prognosis signature and poor outcome (Figure 3G). Similarsubgroups were identified within the high-risk and low-riskgroups identified according to the St. Gallen criteria (Figure 3Dand Figure 3F, respectively). Since both the St. Gallen andthe NIH subgroups contain misclassified patients (who can bebetter identified through the prognosis signature), these patientswould be either overtreated or undertreated in current clinicalpractice.
Figure 3. Kaplan-Meier Analysis of the Probability That Patients Would Remain Free of Distant Metastases among 151 Patients with Lymph-Node–Negative Breast Cancer with the Use of Gene-Expression Profiling (Good-Prognosis and Poor-Prognosis Signatures) (Panel A), the St. Gallen Criteria for Low-Risk and High-Risk Groups (Panel B), the National Institutes of Health (NIH) Consensus Criteria for Low-Risk and High-Risk Groups (Panel C), the St. Gallen Criteria for a High-Risk Group and Gene-Expression Profiling (Panel D), the NIH Criteria for a High-Risk Group and Gene-Expression Profiling (Panel E), the St. Gallen Criteria for a Low-Risk Group and Gene-Expression Profiling (Panel F), and the NIH Criteria for a Low-Risk Group and Gene-Expression Profiling (Panel G).
For Panels D, E, F, and G, patients were divided into those with a good-prognosis signature and those with a poor-prognosis signature according to gene-expression profiling. The P values were calculated with use of the log-rank test.
Our data indicate that the ability to metastasize to distantsites is an early and inherent genetic property of breast cancer.Our findings argue against the widely accepted idea that metastaticpotential is acquired relatively late during multistep tumorigenesis.20If the metastatic ability of breast cancer is determined earlyin tumorigenesis, early prognostic testing could be undertaken,an approach that would clearly be beneficial. On the other hand,an early onset of metastatic capability theoretically limitsthe benefit of early detection and treatment. Furthermore, ourfindings suggest that the molecular mechanism leading to hematogenous(distant) metastases is distinct from the mechanism of lymphogenic(regional) spread of tumor cells. Our conclusion that the prognosisprofile is independent of lymphogenic metastases is based onits strong predictive power with respect to hematogenous metastases,regardless of the presence or absence of lymph-node involvement.
Supported by grants from the Netherlands Cancer Institute. TheDNA microarray hybridization was carried out by the Kirklandfacility of Rosetta Inpharmatics at the company's cost.
Drs. van de Vijver, He, van 't Veer, Dai, Hart, Roberts, Friend,and Bernards are named inventors on a patent to use microarraytechnology to ascertain breast-cancer prognosis. Drs. He, Dai,Schreiber, Roberts, Bernards, Marton, Parrish, and Friend reporthaving equity in Merck.
We are indebted to Arno Floore, Petra Kristel, and Carla Schippersfor preparing tumor RNA; to Wil van Waardenburg, Kathy van Hees,and Otilia Dalesio for managing the medical-records data; toAaron Benner, David Slade, John McDonald, John Koch, and thestaff of the Gene Expression Laboratory of Rosetta for performingmicroarray experiments; and to Anton Berns, Bas Kreike, MaoMao, Roland Stoughton, and Peter Linsley for helpful suggestions.
Source Information
From the Divisions of Diagnostic Oncology (M.J.V., L.J.V., D.W.V., J.L.P., D.A., A.W., A.G., L.D.), Radiotherapy (A.A.M.H., H.B.), Medical Oncology (S.R.), Biometrics (T.V.), Surgical Oncology (E.T.R.), and Molecular Carcinogenesis (R.B.), Netherlands Cancer Institute, Amsterdam; the Center for Biomedical Genetics, Amsterdam (R.B.); and Rosetta Inpharmatics, Kirkland, Wash. (Y.D.H., H.D., G.J.S., C.R., M.J.M., M.P., S.H.F.). Drs. van de Vijver, He, and van 't Veer contributed equally to this article.
Address reprint requests to Dr. Bernards at the Division of Molecular Carcinogenesis, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, the Netherlands, or at r.bernards{at}nki.nl.
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Gene-Expression Signatures in Breast Cancer
Helmbold P., Haerting J., Kölbl H., Kopans D. B., Kunkler I. H., Ransohoff D. F., van de Vijver M. J., He Y. D., van 't Veer L. J., Bernards R.
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Wertheim, G. B. W., Yang, T. W., Pan, T.-c., Ramne, A., Liu, Z., Gardner, H. P., Dugan, K. D., Kristel, P., Kreike, B., van de Vijver, M. J., Cardiff, R. D., Reynolds, C., Chodosh, L. A.
(2009). The Snf1-related kinase, Hunk, is essential for mammary tumor metastasis. Proc. Natl. Acad. Sci. USA
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Suva, L. J, Griffin, R. J, Makhoul, I.
(2009). Mechanisms of bone metastases of breast cancer. Endocr Relat Cancer
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Cianfrocca, M., Gradishar, W.
(2009). New Molecular Classifications of Breast Cancer. CA Cancer J Clin
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Coser, K. R., Wittner, B. S., Rosenthal, N. F., Collins, S. C., Melas, A., Smith, S. L., Mahoney, C. J., Shioda, K., Isselbacher, K. J., Ramaswamy, S., Shioda, T.
(2009). Antiestrogen-resistant subclones of MCF-7 human breast cancer cells are derived from a common monoclonal drug-resistant progenitor. Proc. Natl. Acad. Sci. USA
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Mandrekar, S. J., Sargent, D. J.
(2009). Clinical Trial Designs for Predictive Biomarker Validation: Theoretical Considerations and Practical Challenges. JCO
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Min, C., Yu, Z., Kirsch, K. H., Zhao, Y., Vora, S. R., Trackman, P. C., Spicer, D. B., Rosenberg, L., Palmer, J. R., Sonenshein, G. E.
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Burstein, H. J., Souter, I., D'Alessandro, H. A., Sgroi, D. C.
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Grigoriadis, A., Caballero, O. L., Hoek, K. S., da Silva, L., Chen, Y.-T., Shin, S. J., Jungbluth, A. A., Miller, L. D., Clouston, D., Cebon, J., Old, L. J., Lakhani, S. R., Simpson, A. J. G., Neville, A. M.
(2009). CT-X antigen expression in human breast cancer. Proc. Natl. Acad. Sci. USA
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Ponzo, M. G., Lesurf, R., Petkiewicz, S., O'Malley, F. P., Pinnaduwage, D., Andrulis, I. L., Bull, S. B., Chughtai, N., Zuo, D., Souleimanova, M., Germain, D., Omeroglu, A., Cardiff, R. D., Hallett, M., Park, M.
(2009). Met induces mammary tumors with diverse histologies and is associated with poor outcome and human basal breast cancer. Proc. Natl. Acad. Sci. USA
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Correa Geyer, F., Reis-Filho, J. S.
(2009). Microarray-based Gene Expression Profiling as a Clinical Tool for Breast Cancer Management: Are We There Yet?. INT J SURG PATHOL
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Alexe, G., Monaco, J., Doyle, S., Basavanhally, A., Reddy, A., Seiler, M., Ganesan, S., Bhanot, G., Madabhushi, A.
(2009). Towards Improved Cancer Diagnosis and Prognosis Using Analysis of Gene Expression Data and Computer Aided Imaging. Exp. Biol. Med.
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Choi, Y. H., Ahn, J. H., Kim, S.-B., Jung, K.-H., Gong, G.-Y., Kim, M.-J., Son, B.-H., Ahn, S.-H., Kim, W. K.
(2009). Tissue microarray-based study of patients with lymph node-negative breast cancer shows that HER2/neu overexpression is an important predictive marker of poor prognosis. Ann Oncol
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Goldhirsch, A., Ingle, J. N., Gelber, R. D., Coates, A. S., Thurlimann, B., Senn, H.-J., Panel members,
(2009). Thresholds for therapies: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2009. Ann Oncol
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Deblois, G., Hall, J. A., Perry, M.-C., Laganiere, J., Ghahremani, M., Park, M., Hallett, M., Giguere, V.
(2009). Genome-Wide Identification of Direct Target Genes Implicates Estrogen-Related Receptor {alpha} as a Determinant of Breast Cancer Heterogeneity. Cancer Res.
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Matsui, Y., Hadaschik, B. A., Fazli, L., Andersen, R. J., Gleave, M. E., So, A. I.
(2009). Intravesical combination treatment with antisense oligonucleotides targeting heat shock protein-27 and HTI-286 as a novel strategy for high-grade bladder cancer. Molecular Cancer Therapeutics
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Wang, X., Belguise, K., O'Neill, C. F., Sanchez-Morgan, N., Romagnoli, M., Eddy, S. F., Mineva, N. D., Yu, Z., Min, C., Trinkaus-Randall, V., Chalbos, D., Sonenshein, G. E.
(2009). RelB NF-{kappa}B Represses Estrogen Receptor {alpha} Expression via Induction of the Zinc Finger Protein Blimp1. Mol. Cell. Biol.
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Adam, A. P., George, A., Schewe, D., Bragado, P., Iglesias, B. V., Ranganathan, A. C., Kourtidis, A., Conklin, D. S., Aguirre-Ghiso, J. A.
(2009). Computational Identification of a p38SAPK-Regulated Transcription Factor Network Required for Tumor Cell Quiescence. Cancer Res.
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DiMeo, T. A., Anderson, K., Phadke, P., Feng, C., Perou, C. M., Naber, S., Kuperwasser, C.
(2009). A Novel Lung Metastasis Signature Links Wnt Signaling with Cancer Cell Self-Renewal and Epithelial-Mesenchymal Transition in Basal-like Breast Cancer. Cancer Res.
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Rhodes, D. R., Ateeq, B., Cao, Q., Tomlins, S. A., Mehra, R., Laxman, B., Kalyana-Sundaram, S., Lonigro, R. J., Helgeson, B. E., Bhojani, M. S., Rehemtulla, A., Kleer, C. G., Hayes, D. F., Lucas, P. C., Varambally, S., Chinnaiyan, A. M.
(2009). AGTR1 overexpression defines a subset of breast cancer and confers sensitivity to losartan, an AGTR1 antagonist. Proc. Natl. Acad. Sci. USA
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Lupien, M., Eeckhoute, J., Meyer, C. A., Krum, S. A., Rhodes, D. R., Liu, X. S., Brown, M.
(2009). Coactivator Function Defines the Active Estrogen Receptor Alpha Cistrome. Mol. Cell. Biol.
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Knijnenburg, T. A., Wessels, L. F. A., Reinders, M. J. T., Shmulevich, I.
(2009). Fewer permutations, more accurate P-values. Bioinformatics
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Kreike, B., Halfwerk, H., Armstrong, N., Bult, P., Foekens, J. A., Veltkamp, S. C., Nuyten, D. S.A., Bartelink, H., van de Vijver, M. J.
(2009). Local Recurrence after Breast-Conserving Therapy in Relation to Gene Expression Patterns in a Large Series of Patients. Clin. Cancer Res.
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Yeatman, T. J.
(2009). Predictive Biomarkers: Identification and Verification. JCO
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Sooriakumaran, P., Henderson, A., Denham, P., Langley, S. EM.
(2009). A Novel Method of Obtaining Prostate Tissue for Gene Expression Profiling. INT J SURG PATHOL
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Onitilo, A. A., Engel, J. M., Greenlee, R. T., Mukesh, B. N.
(2009). Breast Cancer Subtypes Based on ER/PR and Her2 Expression: Comparison of Clinicopathologic Features and Survival. Clin Med Res
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Agarwal, R., Gonzalez-Angulo, A.-M., Myhre, S., Carey, M., Lee, J.-S., Overgaard, J., Alsner, J., Stemke-Hale, K., Lluch, A., Neve, R. M., Kuo, W. L., Sorlie, T., Sahin, A., Valero, V., Keyomarsi, K., Gray, J. W., Borresen-Dale, A.-L., Mills, G. B., Hennessy, B. T.
(2009). Integrative Analysis of Cyclin Protein Levels Identifies Cyclin B1 as a Classifier and Predictor of Outcomes in Breast Cancer. Clin. Cancer Res.
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Liu, K., Bellam, N., Lin, H.-Y., Wang, B., Stockard, C. R., Grizzle, W. E., Lin, W.-C.
(2009). Regulation of p53 by TopBP1: a Potential Mechanism for p53 Inactivation in Cancer. Mol. Cell. Biol.
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Gordon, G. J., Dong, L., Yeap, B. Y., Richards, W. G., Glickman, J. N., Edenfield, H., Mani, M., Colquitt, R., Maulik, G., Van Oss, B., Sugarbaker, D. J., Bueno, R.
(2009). Four-Gene Expression Ratio Test for Survival in Patients Undergoing Surgery for Mesothelioma. JNCI J Natl Cancer Inst
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Slamon, D. J., Press, M. F.
(2009). Alterations in the TOP2A and HER2 Genes: Association With Adjuvant Anthracycline Sensitivity in Human Breast Cancers. JNCI J Natl Cancer Inst
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Scotlandi, K., Remondini, D., Castellani, G., Manara, M. C., Nardi, F., Cantiani, L., Francesconi, M., Mercuri, M., Caccuri, A. M., Serra, M., Knuutila, S., Picci, P.
(2009). Overcoming Resistance to Conventional Drugs in Ewing Sarcoma and Identification of Molecular Predictors of Outcome. JCO
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DE CREMOUX, P., GRANDIN, L., DIERAS, V., SAVIGNONI, A., DEGEORGES, A., SALMON, R., BOLLET, M. A., REYAL, F., SIGAL-ZAFRANI, B., VINCENT-SALOMON, A., SASTRE-GARAU, X., MAGDELENAT, H., MIGNOT, L., FOURQUET, A., ON BEHALF OF THE BREAST CANCER STUDY GROUP OF THE,
(2009). Urokinase-type Plasminogen Activator and Plasminogen-activator-inhibitor Type 1 Predict Metastases in Good Prognosis Breast Cancer Patients. Anticancer Res
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King, B. M., Tidor, B.
(2009). MIST: Maximum Information Spanning Trees for dimension reduction of biological data sets. Bioinformatics
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Cheng, C.-J., Lin, Y.-C., Tsai, M.-T., Chen, C.-S., Hsieh, M.-C., Chen, C.-L., Yang, R.-B.
(2009). SCUBE2 Suppresses Breast Tumor Cell Proliferation and Confers a Favorable Prognosis in Invasive Breast Cancer. Cancer Res.
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Dong, F., Budhu, A. S., Wang, X. W.
(2009). Translating the Metastasis Paradigm from Scientific Theory to Clinical Oncology. Clin. Cancer Res.
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Meyer, R., Fofanov, V., Panigrahi, AnilK., Merchant, F., Zhang, N., Pati, D.
(2009). Overexpression and Mislocalization of the Chromosomal Segregation Protein Separase in Multiple Human Cancers. Clin. Cancer Res.
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Kashani-Sabet, M., Rangel, J., Torabian, S., Nosrati, M., Simko, J., Jablons, D. M., Moore, D. H., Haqq, C., Miller, J. R. III, Sagebiel, R. W.
(2009). A multi-marker assay to distinguish malignant melanomas from benign nevi. Proc. Natl. Acad. Sci. USA
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Hsu, D. S., Acharya, C. R., Balakumaran, B. S., Riedel, R. F., Kim, M. K., Stevenson, M., Tuchman, S., Mukherjee, S., Barry, W., Dressman, H. K., Nevins, J. R., Powers, S., Mu, D., Potti, A.
(2009). Characterizing the developmental pathways TTF-1, NKX2-8, and PAX9 in lung cancer. Proc. Natl. Acad. Sci. USA
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Roukos, D. H.
(2009). Twenty-One-Gene Assay: Challenges and Promises in Translating Personal Genomics and Whole-Genome Scans Into Personalized Treatment of Breast Cancer. JCO
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Parker, J. S., Mullins, M., Cheang, M. C.U., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., Quackenbush, J. F., Stijleman, I. J., Palazzo, J., Marron, J.S., Nobel, A. B., Mardis, E., Nielsen, T. O., Ellis, M. J., Perou, C. M., Bernard, P. S.
(2009). Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. JCO
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Zhu, Z.-H., Sun, B.-Y., Ma, Y., Shao, J.-Y., Long, H., Zhang, X., Fu, J.-H., Zhang, L.-J., Su, X.-D., Wu, Q.-L., Ling, P., Chen, M., Xie, Z.-M., Hu, Y., Rong, T.-H.
(2009). Three Immunomarker Support Vector Machines-Based Prognostic Classifiers for Stage IB Non-Small-Cell Lung Cancer. JCO
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Klatte, T., Seligson, D. B., LaRochelle, J., Shuch, B., Said, J. W., Riggs, S. B., Zomorodian, N., Kabbinavar, F. F., Pantuck, A. J., Belldegrun, A. S.
(2009). Molecular Signatures of Localized Clear Cell Renal Cell Carcinoma to Predict Disease-Free Survival after Nephrectomy. Cancer Epidemiol. Biomarkers Prev.
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Eeckhoute, J., Lupien, M., Meyer, C. A., Verzi, M. P., Shivdasani, R. A., Liu, X. S., Brown, M.
(2009). Cell-type selective chromatin remodeling defines the active subset of FOXA1-bound enhancers. Genome Res
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Ojalvo, L. S., King, W., Cox, D., Pollard, J. W.
(2009). High-Density Gene Expression Analysis of Tumor-Associated Macrophages from Mouse Mammary Tumors. Am. J. Pathol.
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Boutros, P. C., Lau, S. K., Pintilie, M., Liu, N., Shepherd, F. A., Der, S. D., Tsao, M.-S., Penn, L. Z., Jurisica, I.
(2009). Prognostic gene signatures for non-small-cell lung cancer. Proc. Natl. Acad. Sci. USA
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Holli, K., Hietanen, P., Saaristo, R., Huhtala, H., Hakama, M., Joensuu, H.
(2009). Radiotherapy After Segmental Resection of Breast Cancer With Favorable Prognostic Features: 12-Year Follow-Up Results of a Randomized Trial. JCO
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(2009). Gene-Expression Signatures in Breast Cancer. NEJM
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Ghayad, S. E, Vendrell, J. A, Bieche, I., Spyratos, F., Dumontet, C., Treilleux, I., Lidereau, R., Cohen, P. A
(2009). Identification of TACC1, NOV, and PTTG1 as new candidate genes associated with endocrine therapy resistance in breast cancer. J Mol Endocrinol
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Gao, D., Du, J., Cong, L., Liu, Q.
(2009). Risk Factors for Initial Lung Metastasis from Breast Invasive Ductal Carcinoma in Stages I-III of Operable Patients. Jpn J Clin Oncol
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van Agthoven, T., Sieuwerts, A. M., Meijer-van Gelder, M. E., Look, M. P., Smid, M., Veldscholte, J., Sleijfer, S., Foekens, J. A., Dorssers, L. C.J.
(2009). Relevance of Breast Cancer Antiestrogen Resistance Genes in Human Breast Cancer Progression and Tamoxifen Resistance. JCO
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Schmidt, M., Victor, A., Bratzel, D., Boehm, D., Cotarelo, C., Lebrecht, A., Siggelkow, W., Hengstler, J. G., Elsasser, A., Gehrmann, M., Lehr, H. -A., Koelbl, H., von Minckwitz, G., Harbeck, N., Thomssen, C.
(2009). Long-term outcome prediction by clinicopathological risk classification algorithms in node-negative breast cancer--comparison between Adjuvant!, St Gallen, and a novel risk algorithm used in the prospective randomized Node-Negative-Breast Cancer-3 (NNBC-3) trial. Ann Oncol
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Beck, A. H., Espinosa, I., Edris, B., Li, R., Montgomery, K., Zhu, S., Varma, S., Marinelli, R. J., van de Rijn, M., West, R. B.
(2009). The Macrophage Colony-Stimulating Factor 1 Response Signature in Breast Carcinoma. Clin. Cancer Res.
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Martinez-Iglesias, O., Garcia-Silva, S., Tenbaum, S. P., Regadera, J., Larcher, F., Paramio, J. M., Vennstrom, B., Aranda, A.
(2009). Thyroid Hormone Receptor {beta}1 Acts as a Potent Suppressor of Tumor Invasiveness and Metastasis. Cancer Res.
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Wouters, B. J., Lowenberg, B., Delwel, R.
(2009). A decade of genome-wide gene expression profiling in acute myeloid leukemia: flashback and prospects. Blood
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Lukes, L., Crawford, N. P.S., Walker, R., Hunter, K. W.
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Morris, P. G., McArthur, H. L., Dang, C. T., Hudis, C. A.
(2009). Selection of Appropriate Patients for Anthracycline Therapy. Am Soc Clin Oncol Ed Book
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Chiang, A. C., Massague, J.
(2008). Molecular Basis of Metastasis. NEJM
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Dowsett, M., Dunbier, A. K.
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Balleine, R. L., Webster, L. R., Davis, S., Salisbury, E. L., Palazzo, J. P., Schwartz, G. F., Cornfield, D. B., Walker, R. L., Byth, K., Clarke, C. L., Meltzer, P. S.
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Saxena, N. K., Taliaferro-Smith, L., Knight, B. B., Merlin, D., Anania, F. A., O'Regan, R. M., Sharma, D.
(2008). Bidirectional Crosstalk between Leptin and Insulin-like Growth Factor-I Signaling Promotes Invasion and Migration of Breast Cancer Cells via Transactivation of Epidermal Growth Factor Receptor. Cancer Res.
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Lee, S.
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Sturgeon, C. M., Duffy, M. J., Stenman, U.-H., Lilja, H., Brunner, N., Chan, D. W., Babaian, R., Bast, R. C. Jr., Dowell, B., Esteva, F. J., Haglund, C., Harbeck, N., Hayes, D. F., Holten-Andersen, M., Klee, G. G., Lamerz, R., Looijenga, L. H., Molina, R., Nielsen, H. J., Rittenhouse, H., Semjonow, A., Shih, I.-M., Sibley, P., Soletormos, G., Stephan, C., Sokoll, L., Hoffman, B. R., Diamandis, E. P.
(2008). National Academy of Clinical Biochemistry Laboratory Medicine Practice Guidelines for Use of Tumor Markers in Testicular, Prostate, Colorectal, Breast, and Ovarian Cancers. Clin. Chem.
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Khalid, O., Baniwal, S. K., Purcell, D. J., Leclerc, N., Gabet, Y., Stallcup, M. R., Coetzee, G. A., Frenkel, B.
(2008). Modulation of Runx2 Activity by Estrogen Receptor-{alpha}: Implications for Osteoporosis and Breast Cancer. Endocrinology
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Weichselbaum, R. R., Ishwaran, H., Yoon, T., Nuyten, D. S. A., Baker, S. W., Khodarev, N., Su, A. W., Shaikh, A. Y., Roach, P., Kreike, B., Roizman, B., Bergh, J., Pawitan, Y., van de Vijver, M. J., Minn, A. J.
(2008). An interferon-related gene signature for DNA damage resistance is a predictive marker for chemotherapy and radiation for breast cancer. Proc. Natl. Acad. Sci. USA
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Somlo, G., Chu, P., Frankel, P., Ye, W., Groshen, S., Doroshow, J. H., Danenberg, K., Danenberg, P.
(2008). Molecular profiling including epidermal growth factor receptor and p21 expression in high-risk breast cancer patients as indicators of outcome. Ann Oncol
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Provenzano, P. P., Inman, D. R., Eliceiri, K. W., Beggs, H. E., Keely, P. J.
(2008). Mammary Epithelial-Specific Disruption of Focal Adhesion Kinase Retards Tumor Formation and Metastasis in a Transgenic Mouse Model of Human Breast Cancer. Am. J. Pathol.
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Nolan, M. E., Aranda, V., Lee, S., Lakshmi, B., Basu, S., Allred, D. C., Muthuswamy, S. K.
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Allen, W. L., Coyle, V. M., Jithesh, P. V., Proutski, I., Stevenson, L., Fenning, C., Longley, D. B., Wilson, R. H., Gordon, M., Lenz, H.-J., Johnston, P. G.
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Moon, A., Yong, H.-Y., Song, J.-I., Cukovic, D., Salagrama, S., Kaplan, D., Putt, D., Kim, H., Dombkowski, A., Kim, H.-R. C.
(2008). Global Gene Expression Profiling Unveils S100A8/A9 as Candidate Markers in H-Ras-Mediated Human Breast Epithelial Cell Invasion. Mol Cancer Res
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Menard, S., Balsari, A., Tagliabue, E., Camerini, T., Casalini, P., Bufalino, R., Castiglioni, F., Carcangiu, M. L., Gloghini, A., Scalone, S., Querzoli, P., Lunardi, M., Molino, A., Mandara, M., Mottolese, M., Marandino, F., Venturini, M., Bighin, C., Cancello, G., Montagna, E., Perrone, F., De Matteis, A., Sapino, A., Donadio, M., Battelli, N., Santinelli, A., Pavesi, L., Lanza, A., Zito, F. A., Labriola, A., Aiello, R. A., Caruso, M., Zanconati, F., Mustacchi, G., Barbareschi, M., Frisinghelli, M., Russo, R., Carrillo, G., On the behalf of the OMERO group,
(2008). Biology, prognosis and response to therapy of breast carcinomas according to HER2 score. Ann Oncol
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Haibe-Kains, B., Desmedt, C., Sotiriou, C., Bontempi, G.
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Wang, S. E., Xiang, B., Guix, M., Olivares, M. G., Parker, J., Chung, C. H., Pandiella, A., Arteaga, C. L.
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Goetz, M. P., Suman, V. J., Couch, F. J., Ames, M. M., Rae, J. M., Erlander, M. G., Ma, X.-J., Sgroi, D. C., Reynolds, C. A., Lingle, W. L., Weinshilboum, R. M., Flockhart, D. A., Desta, Z., Perez, E. A., Ingle, J. N.
(2008). Cytochrome P450 2D6 and Homeobox 13/Interleukin-17B Receptor: Combining Inherited and Tumor Gene Markers for Prediction of Tamoxifen Resistance. Clin. Cancer Res.
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Hwang, T., Sicotte, H., Tian, Z., Wu, B., Kocher, J.-P., Wigle, D. A., Kumar, V., Kuang, R.
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Goldstein, L. J., Gray, R., Badve, S., Childs, B. H., Yoshizawa, C., Rowley, S., Shak, S., Baehner, F. L., Ravdin, P. M., Davidson, N. E., Sledge, G. W. Jr, Perez, E. A., Shulman, L. N., Martino, S., Sparano, J. A.
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Hanczar, B., Dougherty, E. R.
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Cheville, J. C., Karnes, R. J., Therneau, T. M., Kosari, F., Munz, J.-M., Tillmans, L., Basal, E., Rangel, L. J., Bergstralh, E., Kovtun, I. V., Savci-Heijink, C.D., Klee, E. W., Vasmatzis, G.
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[Abstract][Full Text]
4) were used as variables. The linearity of the relation between the relative hazard ratio and the diameter of the tumor, age, and level of expression of estrogen receptors was tested with use of the Wald test for nonlinear components of restricted cubic splines.18 No evidence of nonlinearity was found (P=0.83 for age, P=0.75 for tumor diameter, P=0.65 for the number of positive nodes, and P=0.27 for the level of expression of estrogen receptors). We evaluated whether the hazard ratio was proportional using the method of Grambsch and Therneau.19 In addition, we determined the difference between the relative hazard ratio before and after five years of follow-up with respect to the prognosis signature using the Wald test. All calculations were performed with the S Plus 2000 or S Plus 6 statistical package. 
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