Breast cancers originating from the terminal ductal lobular units: In situ and invasive acinar adenocarcinoma of the breast, AAB

Tabár, Làszló; Dean, Peter B.; Tucker, F. Lee; Yen, Amy Ming‐Fang; Fann, Jean Ching Yuan; Lin, Abbie Ting-Yu; Smith, Robert A.; Duffy, Stephen W.; Chen, Chien-Jen

doi:10.1016/j.ejrad.2022.110323

Cited by 12 publications

(12 citation statements)

References 18 publications

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“…Although much effort has been put into describing the mammary epithelial and stromal cell populations in the human gland by single cell analysis (Nguyen et al, 2018;Chen et al, 2022;Gray et al, 2022), it is not known how these cell types are anatomically distributed within the mammary gland. Previous studies assessing cell localisation in breast tissue sections (Dontu and Ince, 2015;Villadsen et al, 2007) or in micro-collected TDLUs/ducts (Goldhammer et al, 2022;Kohler et al, 2022) also provide limited information of the spatial distribution cell types within TDLUs.…”

Section: Introductionmentioning

confidence: 99%

“…The TDLUs, composed of an extralobular terminal duct (ETD) that branches into lobules with acini at the tips of the ducts ( Fig. 1A-B ), are considered the predominant source of breast cancer (Wellings et al, 1975; Russo and Russo, 1997; Tabár et al, 2022). Upon lactation, milk is produced by the luminal epithelium within the TDLUs, and propelled to the nursing young through oxytocin-dependent basal epithelial contractions (Gimpl and Fahrenholz, 2001; Stevenson et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Volumetric analysis of the terminal ductal lobular unit architecture and cell phenotypes in the human breast

Peurla

Paavolainen

Tammelin

et al. 2023

Preprint

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The major lactiferous ducts of the human breast branch out and ultimately end at terminal ductal lobular units (TDLUs). These glandular structures are the source of milk upon lactation, and also the most common origin of breast cancer. Despite their functional and clinical importance, the three dimensional (3D) architecture of TDLUs has remained undetermined, largely due to their absence in rodent animal models. By utilizing recent technological advances in optical tissue clearing and microscopy, we imaged the 3D structure of healthy human breast tissue to determine whether general branching patterns or cell type specific niches exist in the TDLU. Our data demonstrate that TDLUs have a consistent shape and their branch parameters are largely comparable between different TDLUs and individuals irrespective of age or parity. Simulation of TDLU branching morphogenesis in 3D by mathematical modelling suggests that evolutionarily conserved mechanisms regulate mammary gland branching in humans and mice, despite their anatomical differences. The data also demonstrate a new level of organization within the TDLU structure identifying a main branch that dominates in bifurcation events and exhibits a more duct-like keratin expression pattern. In all, our data provide the first structural insights into 3D human breast anatomy and branching, and exemplify the power of volumetric imaging in deeper understanding of human breast biology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Volumetric analysis of the terminal ductal lobular unit architecture and cell phenotypes in the human breast

Peurla

Paavolainen

Tammelin

et al. 2023

Preprint

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“…We also identified terminal duct lobular units, TDLUs, which are believed to be the primary site for origination of breast malignancies. 25 By automatically recognizing tissue regions in each image, nuclei could be classified by the region where they were found. The NUSP was applied to generate senescence scores for each of the 32,857,482 nuclei recognized using multiple models, and their precise locations in the tissue images were tracked.…”

Section: Resultsmentioning

confidence: 99%

Breast cancer risk based on a deep learning predictor of senescent cells in normal tissue

Heckenbach

Powell

Fuller

et al. 2023

Preprint

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SummaryBackgroundThe ability to predict future risk of cancer development in non-malignant biopsies is poor. Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumor-promoting microenvironmental mechanism that secretes pro-inflammatory paracrine factors. With most work done in non-human models and the heterogenous nature of senescence the precise role of senescent cells in the development of cancer in humans is not well understood. Further, more than one million non-malignant breast biopsies are taken every year that could be a major source of risk-stratification for women.MethodsWe applied single cell deep learning senescence predictors based on nuclear morphology to histological images of 4,411 H&E-stained breast biopsies from healthy female donors. Senescence was predicted in the epithelial, stromal, and adipocyte compartments using predictor models trained on cells induced to senescence by ionizing radiation (IR), replicative exhaustion (RS), or antimycin A, Atv/R and doxorubicin (AAD) exposures. To benchmark our senescence-based prediction results we generated 5-year Gail scores, the current clinical gold standard for breast cancer risk prediction.FindingsWe found significant differences in adipocyte-specific IR and AAD senescence prediction for the 86 out of 4,411 healthy women who developed breast cancer an average 4.8 years after study entry. Risk models demonstrated that individuals in the upper median of scores for the adipocyte IR model had a higher risk (OR=1.71 [1.10-2.68], p=0.019), while the adipocyte AAD model revealed a reduced risk (OR=0.57 [0.36-0.88], p=0.013). Individuals with both adipocyte risk factors had an OR of 3.32 ([1.68-7.03], p<0.001). Alone, 5-year Gail scores yielded an OR of 2.70 ([1.22-6.54], p=0.019). When combining Gail scores with our adipocyte AAD risk model, we found that individuals with both of these risk predictors had an OR of 4.70 ([2.29-10.90], p<0.001).InterpretationAssessment of senescence with deep learning allows considerable prediction of future cancer risk from non-malignant breast biopsies, something that was previously impossible to do. Furthermore, our results suggest an important role for microscope image-based deep learning models in predicting future cancer development. Such models could be incorporated into current breast cancer risk assessment and screening protocols.FundingThis study was funded by the Novo Nordisk Foundation (#NNF17OC0027812), and by the National Institutes of Health (NIH) Common Fund SenNet program (U54AG075932).

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“…Further, tumours presenting as calcification alone or in combination with density or asymmetric density might have been missed or dismissed when the tumour was smaller (< 5 mm) [ 33 , 34 ]. Tumours only identifiable because of calcification alone could be missed at prior screening if the suspicious calcification component was not formed yet [ 11 , 35 , 36 ]. High mammographic density might have obscured tumours presenting as density with calcification or asymmetric density in the prior screening round [ 18 , 19 , 34 , 37 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, fragmented or dotted casting calcifications types might be associated with an invasive component [ 42 ], and we assume that these cases were included in the groups of calcification alone and density with calcification. Furthermore, studies have reported casting calcifications to be common in high-grade DCIS, while small clusters of punctate or granular calcifications to be more common in low-grade DCIS, and both types could be associated with an invasive component [ 6 , 28 , 35 ]. Unfortunately, we were not able to distinguish between the different calcification types.…”

Section: Discussionmentioning

confidence: 99%

Mammographic features and risk of breast cancer death among women with invasive screen-detected cancer in BreastScreen Norway 1996–2020

Moshina,

Backmann,

Skaane

et al. 2023

Eur Radiol

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Objectives We explored associations between mammographic features and risk of breast cancer death among women with small (<15 mm) and large (≥15 mm) invasive screen-detected breast cancer. Methods We included data from 17,614 women diagnosed with invasive breast cancer as a result of participation in BreastScreen Norway, 1996–2020. Data on mammographic features (mass, spiculated mass, architectural distortion, asymmetric density, density with calcification and calcification alone), tumour diameter and cause of death was obtained from the Cancer Registry of Norway. Cox regression was used to estimate hazard ratios (HR) with 95% confidence intervals (CI) for breast cancer death by mammographic features using spiculated mass as reference, adjusting for age, tumour diameter and lymph node status. All analyses were dichotomised by tumour diameter (small versus large). Results Mean age at diagnosis was 60.8 (standard deviation, SD=5.8) for 10,160 women with small tumours and 60.0 (SD=5.8) years for 7454 women with large tumours. The number of breast cancer deaths was 299 and 634, respectively. Mean time from diagnosis to death was 8.7 (SD=5.0) years for women with small tumours and 7.2 (4.6) years for women with large tumours. Using spiculated mass as reference, adjusted HR for breast cancer death among women with small tumours was 2.48 (95% CI 1.67–3.68) for calcification alone, while HR for women with large tumours was 1.30 (95% CI 1.02–1.66) for density with calcification. Conclusions Small screen-detected invasive cancers presenting as calcification and large screen-detected cancers presenting as density with calcification were associated with the highest risk of breast cancer death. Clinical relevance statement Small tumours (<15 mm) presented as calcification alone and large tumours (≥ 15 mm) presented as density with calcification were associated with the highest risk of breast cancer death among women with screen-detected invasive breast cancer diagnosed 1996–2020. Key Points • Women diagnosed with invasive screen-detected breast cancer 1996–2020 were analysed. • Small screen-detected cancers presenting as calcification alone resulted in the highest risk of breast cancer death. • Large screen-detected cancers presenting as density with calcification resulted in the highest risk of breast cancer death.

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Breast cancers originating from the terminal ductal lobular units: In situ and invasive acinar adenocarcinoma of the breast, AAB

Cited by 12 publications

References 18 publications

Volumetric analysis of the terminal ductal lobular unit architecture and cell phenotypes in the human breast

Volumetric analysis of the terminal ductal lobular unit architecture and cell phenotypes in the human breast

Breast cancer risk based on a deep learning predictor of senescent cells in normal tissue

Mammographic features and risk of breast cancer death among women with invasive screen-detected cancer in BreastScreen Norway 1996–2020

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