US incidence of breast cancer subtypes defined by joint hormone receptor and HER2 status.

By N. Howlader, S. Altekruse, Christopher I. Li, V. Chen, C. Clarke, L. Ries, Kathleen A. Cronin. Journal of the National Cancer Institute, 2014

1,438 citations recorded by the source corpus.

BACKGROUND In 2010, Surveillance, Epidemiology, and End Results (SEER) registries began collecting human epidermal growth factor 2 (HER2) receptor status for breast cancer cases. METHODS Breast cancer subtypes defined by joint hormone receptor (HR; estrogen receptor [ER] and progesterone receptor [PR]) and HER2 status were assessed across the 28% of the US population that is covered by SEER registries. Age-specific incidence rates by subtype were calculated for non-Hispanic (NH) white, NH black, NH Asian Pacific Islander (API), and Hispanic women. Joint HR/HER2 status distributions by age, race/ethnicity, county-level poverty, registry, stage, Bloom-Richardson grade, tumor size, and nodal status were evaluated using multivariable adjusted polytomous logistic regression. All statistical tests were two-sided. RESULTS Among case patients with known HR/HER2 status, 36810 (72.7%) were found to be HR(+)/HER2(-), 6193 (12.2%) were triple-negative (HR(-)/HER2(-)), 5240 (10.3%) were HR(+)/HER2(+), and 2328 (4.6%) were HR(-)/HER2(+); 6912 (12%) had unknown HR/HER2 status. NH white women had the highest incidence rate of the HR(+)/HER2(-) subtype, and NH black women had the highest rate of the triple-negative subtype. Compared with women with the HR(+)/HER2(-) subtype, triple-negative patients were more likely to be NH black and Hispanic; HR(+)/HER2(+) patients were more likely to be NH API; and HR(-)/HER2(+) patients were more likely to be NH black, NH API, and Hispanic. Patients with triple-negative, HR(+)/HER2(+), and HR(-)/HER2(+) breast cancer were 10% to 30% less likely to be diagnosed at older ages compared with HR(+)/HER2(-) patients and 6.4-fold to 20.0-fold more likely to present with high-grade disease. CONCLUSIONS In the future, SEER data can be used to monitor clinical outcomes in women diagnosed with different molecular subtypes of breast cancer for a large portion (approximately 28%) of the US population.

Subfields: Cancer Research, Oncology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/cb899faff89c794cb9509309360d7d471ab3fd03

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

  1. Balázs Győrffy
  2. Mitchell Dowsett
  3. J. Reis-Filho
  4. A. Jemal
  5. A. Stanisławek
  6. Marcin Czeczelewski
  7. Sergiusz Łukasiewicz
  8. Robert Sitarz

In Oncology

  1. Balázs Győrffy
  2. A. Jemal
  3. J. Reis-Filho
  4. Masakazu Toi
  5. S. Tolaney
  6. R. Siegel
  7. K. Miller

In Pulmonary and Respiratory Medicine

  1. Balázs Győrffy
  2. A. Jemal

Citations in the corpus