Background: Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Methods: Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach. Results: We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population. Conclusions: Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.

African American breast cancer patients have lower frequency of hormone receptor–positive (HR+)/human epidermal growth factor receptor 2 (HER2)–negative disease and higher subtype-specific mortality. Racial differences in molecular subtype within clinically defined subgroups are not well understood. Using data and biospecimens from the population-based Carolina Breast Cancer Study (CBCS) Phase 3 (2008–2013), we classified 980 invasive breast cancers using RNA expression-based PAM50 subtype and recurrence (ROR) score that reflects proliferation and tumor size. Molecular subtypes (Luminal A, Luminal B, HER2-enriched, and Basal-like) and ROR scores (high vs low/medium) were compared by race (blacks vs whites) and age (≤50 years vs > 50 years) using chi-square tests and analysis of variance tests. Black women of all ages had a statistically significantly lower frequency of Luminal A breast cancer (25.4% and 33.6% in blacks vs 42.8% and 52.1% in whites; younger and older, respectively). All other subtype frequencies were higher in black women (case-only odds ratio [OR] = 3.11, 95% confidence interval [CI] = 2.22 to 4.37, for Basal-like; OR = 1.45, 95% CI = 1.02 to 2.06, for Luminal B; OR = 2.04, 95% CI = 1.33 to 3.13, for HER2-enriched). Among clinically HR+/HER2- cases, Luminal A subtype was less common and ROR scores were statistically significantly higher among black women. Multigene assays highlight racial disparities in tumor subtype distribution that persist even in clinically defined subgroups. Differences in tumor biology (eg, HER2-enriched status) may be targetable to reduce disparities among clinically ER+/HER2- cases.

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString’s commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

Purpose: Cancer progression is mediated by processes that are also important in wound repair. As a result, cancers have been conceptualized as overhealing wounds or “wounds that do not heal,” and gene expression signatures reflective of wound repair have shown value as predictors of breast cancer survival. Despite the widespread acknowledgment of commonalities between host responses to wounds and host responses to cancer, the gene expression responses of normal tissue adjacent to cancers have not been well characterized. Experimental Design: Using RNA extracted from histologically normal breast tissue from 107 patients, including 60 reduction mammoplasty patients and 47 cancer patients, we measured whole genome expression profiles and identified a gene expression signature that is induced in response to breast cancer. Results: This signature represents an in vivo “wound response” signature that is differentially expressed in the normal tissue of breast cancer patients compared with those without disease and is highly accurate (at least 92% sensitivity and 98% specificity) in distinguishing diseased and nondiseased. The in vivo wound response signature is highly prognostic of breast cancer survival, and there is a strong association between the groups identified by this signature and those identified using serum-treated fibroblasts and other microenvironment-derived or microenvironment-related signatures. Conclusions: The prevalence of the wound response signature in histologically normal tissue adjacent to breast cancer suggests that microenvironment response is an important variable in breast cancer progression and may be an important target for clinical interventions. (Clin Cancer Res 2009;15(22):7020–8)

Young-onset breast cancer (<40 years) is associated with worse prognosis and higher mortality. Breast cancer risk factors may contribute to distinct tumor biology and distinct age at onset, but understanding of these relationships has been hampered by limited representation of young women in epidemiologic studies and may be confounded by menopausal status. We examined tumor characteristics and epidemiologic risk factors associated with premenopausal women’s and young women’s breast cancer in phases I–III of the Carolina Breast Cancer Study (5309 cases, 2022 control subjects). Unconditional logistic regression was used to assess heterogeneity by age (<40 vs. ≥40 years) and menopausal status. In both premenopausal and postmenopausal strata, younger women had more aggressive disease, including higher stage, hormone receptor-negative, disease as well as increased frequency of basal-like subtypes, lymph node positivity, and larger tumors. Higher waist-to-hip ratio was associated with reduced breast cancer risk among young women but with elevated risk among older women. Parity was associated with increased risk among young women and reduced risk among older women, while breastfeeding was more strongly protective for young women. Longer time since last birth was protective for older women but not for young women. In comparison, when we stratified by age, menopausal status was not associated with distinct risk factor or tumor characteristic profiles, except for progesterone receptor status, which was more commonly positive among premenopausal women. Age is a key predictor of breast cancer biologic and etiologic heterogeneity and may be a stronger determinant of heterogeneity than menopausal status. Young women’s breast cancer appears to be etiologically and biologically distinct from that among older women.