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Published on: February 8, 2013
Cellular senescence predicts breast cancer risk from benign breast disease biopsy images
Indra Heckenbach1, Rita Peila2, Christopher Benz3
1Center for Healthy Aging, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark.
Background:
Each year, millions of women undergo breast biopsies. Of these, 80% are negative for malignancy but some may be at elevated risk of invasive breast cancer (IBC) due to the presence of benign breast disease (BBD). Cellular senescence plays a complex but poorly understood role in breast cancer development and the presence or absence of these cells may have prognostic value.
Methods:
We conducted a case-control study, nested within a cohort of 15,395 women biopsied for BBD at Kaiser Permanente Northwest between 1971 and 2006. Cases (n = 512) were women who developed a subsequent invasive breast cancer (IBC) at least one year after the BBD biopsy; controls (n = 491) did not develop IBC during the same follow-up period. Using H&E-stained biopsy images, we predicted senescence based on deep learning models trained on replicative senescence (RS), ionizing radiation (IR), and various drug treatments. Age-adjusted and multivariable odds ratios (ORs) and 95% confidence intervals (CI) were estimated using unconditional logistic regression.
Results:
The RS- and IR-derived senescence scores for adipose tissue and the RS-derived score for epithelial tissue were positively associated with the risk of IBC (adipose tissue - RS model: ORq4 vs. q1=1.69, 95% CI 1.03-2.77, and IR model: ORq4 vs. q1=1.73, 95%CI 1.06-2.82; epithelial tissue- RS model: ORq4 vs. q1=1.53, 95% CI 1.05-2.22). The results were stronger among postmenopausal women and women with epithelial hyperplasia with/without atypia, and postmenopausal women also showed a positive association for stromal tissue with the RS model (ORq4 vs. q1=1.84, 95%CI 1.12-3.04). There was an elevated risk of IBC in those with higher senescence scores in both epithelial and adipose tissue compared with those with low senescence scores in both (IR epithelium-IR fat: ORq2-4 vs. q1=2.14, 95% CI 1.30-3.51; and IR epithelium-RS fat: ORq2-4 vs. q1= 2.24, 95% CI 1.15-4.35).
Conclusions:
This study suggests that nuclear senescence scores predicted by deep learning models in breast epithelial and adipose tissue can predict the risk of breast cancer development among women with BBD.
