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Journal of Clinical Oncology, Vol 26, No 20 (July 10), 2008: pp. 3324-3330 © 2008 American Society of Clinical Oncology. DOI: 10.1200/JCO.2007.14.2471 Young Age at Diagnosis Correlates With Worse Prognosis and Defines a Subset of Breast Cancers With Shared Patterns of Gene Expression
From the Division of Medical Oncology, Department of Medicine, and Institute for Genome Sciences and Policy, Duke University; Cancer Center Biostatistics, Duke University Medical Center, Durham, NC; Veridex Inc, Johnson and Johnson, San Diego, CA; and Erasmus Medical Center, Rotterdam, the Netherlands Corresponding author: Carey K. Anders, MD, Duke University Medical Center, Box 3841, 3829 Duke South, Red Zone, Durham, NC 27710; e-mail: ander118{at}mc.duke.edu
Purpose Breast cancer arising in young women is correlated with inferior survival and higher incidence of negative clinicopathologic features. The biology driving this aggressive disease has yet to be defined.
Patients and Methods Clinically annotated, microarray data from 784 early-stage breast cancers were identified, and prospectively defined, age-specific cohorts (young:
Results Using clinicopathologic variables, young women illustrated lower estrogen receptor (ER) positivity (immunohistochemistry [IHC], P = .027), larger tumors (P = .012), higher human epidermal growth factor receptor 2 (HER-2) overexpression (IHC, P = .075), lymph node positivity (P = .008), higher grade tumors (P < .0001), and trends toward inferior disease-free survival (DFS; hazard ratio = 1.32; P = .094). Using genomic expression analysis, tumors arising in young women had significantly lower ER Conclusion This large-scale genomic analysis illustrates that breast cancer arising in young women is a unique biologic entity driven by unifying oncogenic signaling pathways, is characterized by less hormone sensitivity and higher HER-2/EGFR expression, and warrants further study to offer this poor-prognosis group of women better preventative and therapeutic options.
Breast cancer diagnosed at a young age has been correlated with inferior survival and higher recurrence rates when compared with older counterparts.1-3 Although previous studies have described negative prognostic variables, the underlying biology driving these aggressive features has not been fully elucidated.2,4-8 Over the past decade, gene expression profiling has contributed greatly to our knowledge of the biology of breast cancer, identifying distinct prognostic phenotypes.9-15 Although prognostic gene lists reported in individual studies show little overlap, high concordance rates have been observed when applying models to single data sets, validating their prognostic value.16 These data provide compelling evidence that breast cancer is a complex, heterogeneous disease entity. In this comprehensive evaluation of breast cancer arising in young women, we present an analysis of age-specific differences in prognosis, clinicopathologic variables, gene expression patterns, and oncogenic signaling pathways, thus combining traditional prognostic variables with phenotypes identified via gene expression profiling and providing insight into the unique biology of breast cancer arising in young women.
Patient Selection and Breast Carcinoma Samples The following four publicly available data sets were selected, GSE3143 [NCBI GEO] ,17 GSE2034 [NCBI GEO] ,18 GSE4922 [NCBI GEO] ,19 and the Duke University breast tumor bank (GSE7849 [NCBI GEO] ; Appendix Table A1, online only). Data set selection was based on availability of clinically annotated gene expression probabilities (Affymetrix Human Genome U133A or U95 array; Affymetrix, Santa Clara, CA) from early-stage breast tumors. Chip Comparer was used to map probe sets across various generations of Affymetrix GeneChip arrays (see Appendix, online only).17,20 Overall, 784 clinically annotated breast tumor samples were available for analysis.
We prospectively classified two subsets of breast tumors, those arising in young women age
mRNA Expression Values, Single-Gene and Gene Set Enrichment Analyses
Age-specific differences in single-gene mRNA expression values were detected. P values were based on the pooled t test (SAS Institute, Cary, NC). A two-sided P < .05 was considered statistically significant. Fold change (
Age-specific, single-gene analysis was performed using Gene Pattern using the nearest neighbor method (http://statistics.byu.edu/johnson/ComBat/Abstract.html). Evaluation of age-specific gene sets, which are groups of genes that share common biologic function, chromosomal location, or regulation, was performed using gene set enrichment analysis (GSEA) (http://www.broad.mit.edu/gsea/).22 The ComBat method (http://www.biostat.harvard.edu/
Univariate and Multivariate Analyses
Clinicopathologic variables considered in univariate and multivariate analyses included the following: age at diagnosis (continuous variable with hazard ratios [HRs] denoting increased risk for 10-year decrease in age), clinical ER status (immunohistochemistry [IHC] or enzyme immunoassay; positive v negative), HER-2 by IHC (0, 1+, or 2+ v 3+), tumor grade (1 or 2 v 3), tumor size (
The data set was excellent for imputing missing values as a result of high correlation among variables. For multivariate modeling, SAS Proc MI (SAS Institute, Cary, NC) was used to impute missing data for variables with less than 40% missing values. Therefore, HER-2 by IHC was excluded from all multivariate models. All patients with available data were included in the multivariate model using the backward selection technique with an
Age-Specific Differences in Clinical Outcome Younger women age  45 years illustrated a trend toward inferior DFS when compared with older counterparts age  65 years (HR = 1.32, P = .094; Fig 1). Within the subset of young women, age younger than 40 years conferred an inferior DFS when compared with age of 40 to 45 years at breast cancer diagnosis (HR = 1.69, P = .013; Fig 2A). Further exploration of prognosis among patients age younger than 40 years revealed no significant differences in DFS between age groups younger than 30, 30 to 34, and 35 to 39 years (Fig 2B).
Clinicopathologic Characteristics Unique to Breast Cancer Arising in Young Women Clinicopathologic characteristics and corresponding gene expression data were available for patients across all four data sets (Table 1). PR status and race were available only in the Duke data set, whereas HER-2 status was available only for the GSE3143 [NCBI GEO] data set. Tumor grade was not available for the GSE2034 [NCBI GEO] data set.
When comparing clinicopathologic variables between the age groups ( 45 years, n = 200; and 65 years, n = 211), younger women demonstrated a lower incidence of clinical ER positivity (dichotomous variable, positive v negative; 71% v 80%, respectively; P = .027), higher IHC expression of HER-2 (dichotomous variable, 2+ to 3+ v 0 to 1+; 52% v 24%, respectively; P = .075), higher grade tumors (grade 3 v 1 or 2; 56% v 26%, respectively; P < .0001), larger tumor size (> 2.0 v 2.0 cm; 62% v 47%, respectively; P = .012), and greater incidence of lymph node positivity (38% v 25%, respectively; P = .008; Table 2). Interestingly, further age-specific exploration of clinicopathologic features revealed a greater incidence of lymph node positivity among women age younger than 40 years compared with those age 40 to 45 years (Appendix Table A2, online only).
mRNA Expression Values of ER, PR, HER-2, and EGFR by Age Using gene expression profiling, probes corresponding to the ER  , ERβ, PR, HER-2 (ErbB2), and EGFR genes were identified across all four data sets, and age-specific differences in mRNA expression values were evaluated. Comparing women age 45 years to those age 65 years, mean mRNA ER expression was statistically lower among younger women (7.2 v 9.8, respectively;  = 0.17; P < .0001), as was mRNA expression of ERβ (5.6 v 5.9, respectively; = 0.80; P = .02) and PR (4.1 v 5.0, respectively; = 0.54; P < .0001). Women age 45 years, when compared with older counterparts age 65 years, demonstrated a statistically higher mean mRNA expression of HER-2 (11.1 v 9.4, respectively; = 3.29; P < .0001) and EGFR (7.3 v 6.7, respectively; = 1.54; P < .0001; Table 2, Fig 3).
Age-specific differences in mRNA expression were evaluated solely within the subset of young women's tumors (< 40 v 40 to 45 years). Women age younger than 40 years had lower mean mRNA expression of ER (6.6 v 7.8, respectively; P = .03) and ERβ (5.3 v 5.9, respectively; P = .01). However, mRNA expression of PR, HER-2, and EGFR was similar (Appendix Fig A1, online only).
Additionally, gene expression profiling was used to evaluate age-specific differences in concurrent mRNA expression of ER, PR, and HER-2. On the basis of available literature, triple-negative breast tumors were defined by ER
Univariate and Multivariate Analysis: Combining Clinicopathologic Variables and Gene Expression Profiles
In univariate analysis among women age
In a multivariate analysis among women age 45 years, younger age at breast cancer diagnosis (HR = 1.96, P = .004), lower mRNA expression of ERβ (HR = 1.41, P = .012), and higher mRNA expression of EGFR (HR = 1.24, P = .026) were predictive of inferior DFS (Table 3). Among women age 65 years, positive lymph node status (HR = 1.88, P = .04) and lower mRNA expression of ERβ (HR = 1.40, P = .034) were predictive of inferior outcome (Table 3). Similar to results obtained in univariate analysis, age was not a significant predictor of DFS among women age 65 years in multivariate modeling.
Age-Specific Analysis of Single Genes and Gene Sets
We applied GSEA and found 367 significant gene sets among young women's tumors that specifically distinguished them from tumors arising in older women, using a false discovery rate
This comprehensive study confirms our belief that breast cancer arising in young women is a unique disease entity driven by complex biologic processes extending beyond hormone receptors and hereditary cancer syndromes. Our large data set of clinically annotated breast tumors combines clinicopathologic and gene expression variables confirming age at breast cancer diagnosis to be the most important variable in determining outcome among young women. Most importantly and consistent with our working hypothesis, 367 gene sets were identified as differentially expressed in young women's tumors, whereas tumors arising in older patients did not share any common gene sets. The identification of genomic pathways specific to breast cancer arising in a younger host provides both a clearer understanding of the interplay of age and contributing biologic processes and a unique opportunity to explore therapeutic targets with the goal of improving outcomes for young women diagnosed with this aggressive disease. Population-based studies have identified young age as an independent predictor of adverse breast cancer–specific outcome.1,3,5-7 Our results strengthen this historical observation. Our analysis is unique because the study population spans many countries, in contrast to previous work conducted within single institutions or geographic regions. Moreover, previous studies were conducted before the advent of modern day therapeutics. Our analysis, incorporating patients treated with a variety of cytotoxic/hormonal agents, adds to the existing body of literature reflecting outcomes expected in contemporary clinical practice. Historically, studies report an inverse correlation between ER status and age, and our results support this observation.3,4,6 Our analysis illustrates, in addition to IHC, that quantitative ER mRNA expression was lower among breast tumors arising in younger women. Recently, a strong positive correlation between quantitative measurements of ER expression, above that of IHC or ligand binding methodologies, and distant recurrence–free interval among tamoxifen-treated patients has been illustrated.25 Studies have reported inferior prognosis among young women with ER-positive breast tumors compared with negative hormonal profiles.2,8,26 In contrast, our results indicate that clinical ER status in univariate and multivariate analyses was not predictive of inferior DFS among young women. A higher incidence of HER-2 overexpression conferring a more aggressive phenotype has been reported among breast tumors arising at a younger age.27-29 We report a higher incidence of clinical HER-2 overexpression (2 to 3+ via IHC) among breast tumors arising in younger women; however, current controversy surrounding optimal HER-2 testing via IHC remains a possible limitation to this conclusion. A recent study reports that quantitative ErbB2 mRNA (Affymetrix GeneChip) correlates with HER-2 status determined by IHC or fluorescent in situ hybridization.24 Our analysis illustrates higher HER-2 mRNA expression among tumors arising in younger women; however, HER-2 status via mRNA expression did not prove to be predictive of DFS among this cohort of young patients. Higher mRNA expression of EGFR, a transmembrane receptor sharing partial homology and intrinsic tyrosine kinase activity with HER-2, was predictive of inferior outcome among younger women.30 EGFR lost predictive clinical significance among older women. A high proportion of BRCA1/2-associated breast carcinomas and those characterized by the basal phenotype overexpress EGFR.31,32 Because BRCA1/2 mutations and the basal phenotype are unique among breast cancer arising in young women, this may provide an explanation for age-specific differences observed between EGFR mRNA expression and prognosis.33 Triple-negative breast cancer is correlated with shorter survival times and an overall incidence of 11.2% across all ages.13,33-35 A higher prevalence of triple-negative breast cancer has been reported in premenopausal women of African American descent compared with postmenopausal women regardless of ancestry (39% v 14% to 16%, respectively; P < .001).33 Our results illustrate lower quantitative mRNA expression of ER, PR, and HER-2 among tumors arising in younger versus older women (7.0% v 2.8%, respectively). Stage at presentation likely accounts for the overall lower incidence within our data set compared with others. Previous studies report lymph node positivity among 40% to 55% of patients with triple-negative breast cancer versus 30% within our complete data set.33,35 Because triple-negative breast cancer is a more aggressive phenotype, our data set likely represent a better prognosis sample of patients with an overall lower likelihood of triple-negative disease. The most remarkable finding of our analysis was the discovery of a common biology unique to breast tumors arising in young women. This shared biology, beyond traditional clinical parameters, included genes regulating immune function, mTOR/rapamycin pathway, hypoxia, BRCA1, stem cell biology, apoptosis, histone deacetylase, and oncogenic signaling pathways including Myc, E2F, Ras, β-catenin, AKT, p53, PTEN, and MAP kinase, many of which hold prognostic and therapeutic implications in breast cancer treatments.17,31,36,37 To our knowledge, with the exception of hereditary cancer syndromes, the contributions of these biologic pathways to the pathogenesis of breast cancer have yet to be explored in an age-specific manner.38-40 Further study to gain a more solid understanding of the biology unifying breast cancer diagnosed at a young age is warranted and will contribute to the development of more effective therapeutic strategies. A strength of our analysis is the number of patients from which both clinicopathologic and genomic expression data are available; however, we recognize limitations to this approach. The interpretation of long-term outcome in our combined data set is limited because patients from individual data sets likely received a number of different adjuvant therapies (hormonal, chemotherapeutic, and local) or no therapy at all. Year of treatment, patients’ geographic location, and overall goal of each study may have influenced adjuvant treatments. For instance, GSE2034 [NCBI GEO] (the Netherlands) was collected from lymph node–negative patients who never received adjuvant chemotherapy, whereas GSE4922 [NCBI GEO] (Sweden and Singapore) included patients who underwent definitive surgery for whom adjuvant therapy data are unavailable. This limitation highlights the need for prospective clinical, pathologic, and genomic data collection, allowing for continued study (eg, supervised analysis of age-specific breast cancer phenotypes; ie, triple negative, luminal A/B, HER-2 positive) to promote development of more effective therapeutics across all ages. In summary, breast cancer arising in young women has been recognized for decades to represent an aggressive phenotype; however, the biology driving this disease process has been largely unknown. The results of this comprehensive genomic analysis illustrate the unique gene sets uniting the biology of breast cancer arising in a younger host and that age at breast cancer diagnosis remains the most important variable in determining outcome. This analysis hopefully provides a clearer understanding into the biologic complexity driving breast cancer arising at a young age and offers hope in providing young women superior preventative and therapeutic options.
Although all authors completed the disclosure declaration, the following author(s) indicated a financial or other interest that is relevant to the subject matter under consideration in this article. Certain relationships marked with a "U" are those for which no compensation was received; those relationships marked with a "C" were compensated. For a detailed description of the disclosure categories, or for more information about ASCO's conflict of interest policy, please refer to the Author Disclosure Declaration and the Disclosures of Potential Conflicts of Interest section in Information for Contributors. Employment or Leadership Position: Yi Zhang, Johnson & Johnson (C); Yixin Wang, Johnson & Johnson (C) Consultant or Advisory Role: None Stock Ownership: Yi Zhang, Johnson & Johnson; Yixin Wang, Johnson & Johnson Honoraria: None Research Funding: None Expert Testimony: None Other Remuneration: None
Conception and design: Carey K. Anders, P. Kelly Marcom, Joseph R. Nevins, Anil Potti, Kimberly L. Blackwell Financial support: Carey K. Anders, Anil Potti, Kimberly L. Blackwell Provision of study materials or patients: John A. Foekens, Yi Zhang, Yixin Wang, Jeffrey R. Marks Collection and assembly of data: Carey K. Anders, Gloria Broadwater, Chaitanya R. Acharya Data analysis and interpretation: Carey K. Anders, David S. Hsu, Gloria Broadwater, Chaitanya R. Acharya, P. Kelly Marcom, Phillip G. Febbo, Joseph R. Nevins, Anil Potti, Kimberly L. Blackwell Manuscript writing: Carey K. Anders, David S. Hsu, Gloria Broadwater, Chaitanya R. Acharya, John A. Foekens, P. Kelly Marcom, Joseph R. Nevins, Anil Potti, Kimberly L. Blackwell Final approval of manuscript: Carey K. Anders, David S. Hsu, Gloria Broadwater, Chaitanya R. Acharya, John A. Foekens, Yi Zhang, Yixin Wang, P. Kelly Marcom, Jeffrey R. Marks, Phillip G. Febbo, Joseph R. Nevins, Anil Potti, Kimberly L. Blackwell
Cross-Platform Affymetrix Gene Chip Comparison To map the probe sets across various generations of Affymetrix GeneChip arrays (Affymtrix, Santa Clara, CA), we used an in-house program, Chip Comparer (http://tenero.duhs.duke.edu/genearray/perl/chip/chipcomparer.pl). First, each probe set identification (ID) in given Affymetrix gene chips were mapped to the corresponding LocusID. This is done by parsing local copies of LocusLink and UniGene databases to identify inherent relationship between the GenBank accession number associated with each probe set sequence and its corresponding LocusID. Second, probe sets from different gene chips are matched by sharing the same LocusID (or orthologous pair of LocusIDs in the case of mapping gene chips across species).
mRNA Expressions Analysis
ComBat Method
Supported by National Institutes of Health Grant No. CA093245-05 (C.K.A.) from the National Cancer Institute. Presented at the 43rd Annual Meeting of the American Society of Clinical Oncology, June 1-5, 2007, Chicago, IL. Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.
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