Immune cells are increased in normal breast tissues of BRCA1/2 mutation carriers.

Purpose: Breast cancer risk is elevated in pathogenic germline BRCA 1/2 mutation carriers due to compromised DNA quality control. We hypothesized that if immunosurveillance promotes tumor suppression, then normal/benign breast lobules from BRCA carriers may demonstrate higher immune cell densities.

Methods: We assessed immune cell composition in normal/benign breast lobules from age-matched women with progressively increased breast cancer risk, including (1) low risk: 19 women who donated normal breast tissue to the Komen Tissue Bank (KTB) at Indiana University Simon Cancer Center, (2) intermediate risk: 15 women with biopsy-identified benign breast disease (BBD), and (3) high risk: 19 prophylactic mastectomies from women with germline mutations in BRCA1/2 genes.

Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence.

Background: Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described.

Serum hormone levels and normal breast histology among premenopausal women.

Purpose: Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations.

Automated quantification of levels of breast terminal duct lobular (TDLU) involution using deep learning.

Convolutional neural networks (CNNs) offer the potential to generate comprehensive quantitative analysis of histologic features. Diagnostic reporting of benign breast disease (BBD) biopsies is usually limited to subjective assessment of the most severe lesion in a sample, while ignoring the vast majority of tissue features, including involution of background terminal duct lobular units (TDLUs), the structures from which breast cancers arise. Studies indicate that increased levels of age-related TDLU involution in BBD biopsies predict lower breast cancer risk, and therefore its assessment may have potential value in risk assessment and management.