Novel breast tissue feature strongly associated with risk of breast cancer - PubMed (original) (raw)

. 2009 Dec 10;27(35):5893-8.

doi: 10.1200/JCO.2008.21.5079. Epub 2009 Oct 5.

Carol A Reynolds, Daniel W Visscher, Aziza Nassar, Derek C Radisky, Robert A Vierkant, Amy C Degnim, Judy C Boughey, Karthik Ghosh, Stephanie S Anderson, Douglas Minot, Jill L Caudill, Celine M Vachon, Marlene H Frost, V Shane Pankratz, Lynn C Hartmann

Affiliations

Novel breast tissue feature strongly associated with risk of breast cancer

Kevin P McKian et al. J Clin Oncol. 2009.

Abstract

Purpose: 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.

Conclusion: 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.

PubMed Disclaimer

Conflict of interest statement

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Figures

Fig 1.

Fig 1.

(A) Shown is a field of normal lobules (terminal duct lobular units), each composed of multiple acini. (B) Complete regression (involution) of these lobules has occurred, leaving small residual structures largely depleted of acini. Reprinted with permission.

Fig 2.

Fig 2.

(A) We subdivided an intact lobule to facilitate counting of individual acini. (B) Delineation of the circumference of the lobule for calculation of lobule area by the computer software is demonstrated.

Fig 3.

Fig 3.

Whole-breast mounts from (A) preinvolutional and (B) postinvolutional women. Reprinted with permission.

Similar articles

Cited by

References

    1. Freedman AN, Seminara D, Gail MH, et al. Cancer risk prediction models: A workshop on development, evaluation, and application. J Natl Cancer Inst. 2005;97:715–723. - PubMed
    1. Elmore JG, Fletcher SW. The risk of cancer risk prediction: “What is my risk of getting breast cancer?”. J Natl Cancer Inst. 2006;98:1673–1675. - PubMed
    1. Pankratz VS, Hartmann LC, Degnim AC, et al. Assessment of the accuracy of the Gail model in women with atypical hyperplasia. J Clin Oncol. 2008;26:5374–5379. - PMC - PubMed
    1. Hartmann LC, Sellers TA, Frost MH, et al. Benign breast disease and the risk of breast cancer. N Engl J Med. 2005;353:229–237. - PubMed
    1. Dupont WD, Page DL. Risk factors for breast cancer in women with proliferative breast disease. N Engl J Med. 1985;312:146–151. - PubMed

Publication types

MeSH terms

LinkOut - more resources