Association of Parity and Time since Last Birth with Breast Cancer Prognosis by Intrinsic Subtype.

BACKGROUND: Parity and time since last birth influence breast cancer risk and vary by intrinsic tumor subtype, but the independent effects of these factors on prognosis have received limited attention. METHODS: Study participants were 1,140 invasive breast cancer patients from phases I and II of the population-based Carolina Breast Cancer Study, with tissue blocks available for subtyping using immunohistochemical markers. Breast cancer risk factors, including pregnancy history, were collected via in-person interviews administered shortly after diagnosis. Vital status was determined using the National Death Index. The association of parity and birth recency with breast cancer-specific and overall survival was assessed using Cox proportional hazards models. RESULTS: During follow-up (median = 13.5 years), 450 patients died, 61% due to breast cancer (n = 276). High parity (3+ births) and recent birth (<5 years before diagnosis) were positively associated with breast cancer-specific mortality, independent of age, race, and selected socioeconomic factors [parity, reference = nulliparous, adjusted HR = 1.76; 95% confidence interval (CI) = 1.13-2.73; birth recency, reference = 10+ years, adjusted HR = 1.29; 95% CI, 0.79-2.11]. The associations were stronger among patients with luminal tumors and those surviving longer than 5 years. CONCLUSIONS: Parity and recent birth are associated with worse survival among breast cancer patients, particularly among luminal breast cancers and long-term survivors. IMPACT: The biologic effects of parity and birth recency may extend from etiology to tumor promotion and progression.

Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium.

BACKGROUND: Classification of breast cancer into intrinsic subtypes has clinical and epidemiologic importance. To examine accuracy of IHC-based methods for identifying intrinsic subtypes, a three-biomarker IHC panel was compared with the clinical record and RNA-based intrinsic (PAM50) subtypes. METHODS: Automated scoring of estrogen receptor (ER), progesterone receptor (PR), and HER2 was performed on IHC-stained tissue microarrays comprising 1,920 cases from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Multiple cores (1-6/case) were collapsed to classify cases, and automated scoring was compared with the clinical record and to RNA-based subtyping. RESULTS: Automated analysis of the three-biomarker IHC panel produced high agreement with the clinical record (93% for ER and HER2, and 88% for PR). Cases with low tumor cellularity and smaller core size had reduced agreement with the clinical record. IHC-based definitions had high agreement with the clinical record regardless of hormone receptor positivity threshold (1% vs. 10%), but a 10% threshold produced highest agreement with RNA-based intrinsic subtypes. Using a 10% threshold, IHC-based definitions identified the basal-like intrinsic subtype with high sensitivity (86%), although sensitivity was lower for luminal A, luminal B, and HER2-enriched subtypes (76%, 40%, and 37%, respectively). CONCLUSION: Three-biomarker IHC-based subtyping has reasonable accuracy for distinguishing basal-like from nonbasal-like, although additional biomarkers are required for accurate classification of luminal A, luminal B, and HER2-enriched cancers. IMPACT: Epidemiologic studies relying on three-biomarker IHC status for subtype classification should use caution when distinguishing luminal A from luminal B and when interpreting findings for HER2-enriched cancers.