title: Novel breast tissue feature strongly associated with risk of breast cancer.
abstract: OBJECTIVE
  Accurate, individualized risk prediction for breast cancer is lacking. Tissue-based features may help to stratify women into different risk levels. Breast lobules are the anatomic sites of origin of breast cancer. As women age, these lobular structures should regress, which results in reduced breast cancer risk. However, this does not occur in all women.
  
  
  METHODS
  We have quantified the extent of lobule regression on a benign breast biopsy in 85 patients who developed breast cancer and 142 age-matched controls from the Mayo Benign Breast Disease Cohort, by determining number of acini per lobule and lobular area. We also calculated Gail model 5-year predicted risks for these women.
  
  
  RESULTS
  There is a step-wise increase in breast cancer risk with increasing numbers of acini per lobule (P = .0004). Adjusting for Gail model score, parity, histology, and family history did not attenuate this association. Lobular area was similarly associated with risk. The Gail model estimates were associated with risk of breast cancer (P = .03). We examined the individual accuracy of these measures using the concordance (c) statistic. The Gail model c statistic was 0.60 (95% CI, 0.50 to 0.70); the acinar count c statistic was 0.65 (95% CI, 0.54 to 0.75). Combining acinar count and lobular area, the c statistic was 0.68 (95% CI, 0.58 to 0.78). Adding the Gail model to these measures did not improve the c statistic.
  
  
  CONCLUSIONS
  Novel, tissue-based features that reflect the status of a woman's normal breast lobules are associated with breast cancer risk. These features may offer a novel strategy for risk prediction.
authors.affiliation: Department of Oncology, Mayo Clinic Cancer Center, Mayo Graduate School of Medical Education, Mayo Clinic College of Medicine, Rochester, MN 55905, USA.
authors.forename: Kevin P
authors.initials: KP
authors.lastname: McKian
authors.forename: Carol A
authors.initials: CA
authors.lastname: Reynolds
authors.forename: Daniel W
authors.initials: DW
authors.lastname: Visscher
authors.forename: Aziza
authors.initials: A
authors.lastname: Nassar
authors.forename: Derek C
authors.initials: DC
authors.lastname: Radisky
authors.forename: Robert A
authors.initials: RA
authors.lastname: Vierkant
authors.forename: Amy C
authors.initials: AC
authors.lastname: Degnim
authors.forename: Judy C
authors.initials: JC
authors.lastname: Boughey
authors.forename: Karthik
authors.initials: K
authors.lastname: Ghosh
authors.forename: Stephanie S
authors.initials: SS
authors.lastname: Anderson
authors.forename: Douglas
authors.initials: D
authors.lastname: Minot
authors.forename: Jill L
authors.initials: JL
authors.lastname: Caudill
authors.forename: Celine M
authors.initials: CM
authors.lastname: Vachon
authors.forename: Marlene H
authors.initials: MH
authors.lastname: Frost
authors.forename: V Shane
authors.initials: VS
authors.lastname: Pankratz
authors.forename: Lynn C
authors.initials: LC
authors.lastname: Hartmann
mesh_terms: D000328:Adult; D000367:Age Factors; D001706:Biopsy; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000235:genetics / Q000473:pathology*; D016022:Case-Control Studies; D049109:Cell Proliferation; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D006965:Hyperplasia; D016015:Logistic Models; D008875:Middle Aged; D010298:Parity; D010375:Pedigree; D011230:Precancerous Conditions / Q000235:genetics / Q000473:pathology*; D011237:Predictive Value of Tests; D011247:Pregnancy; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors