Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study

Abstract

Background: Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors.

Methods: In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir-ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores-the current clinical gold standard for breast cancer risk prediction-for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer).

Findings: 4382 female donors (median age at donation 45 years [IQR 34-57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7-11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10-2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36-0·88]; p=0·013). For the other tissue compartments and the RS model, no significant associations were found (except for stromal tissue via the IR model, had higher odds of developing breast cancer [OR 1·59, 1·03-2·49]). Individuals with both of the adipose risk factors had an OR of 3·32 (1·68-7·03; p=0·0009). Participants with 5-year Gail scores above the median had an OR for development of cancer of 2·33 (1·46-3·82; p=0·0012) compared with those with scores below the median. When combining Gail scores with our adipose AAD risk model, we found that individuals with both of these predictors had an OR of 4·70 (2·29-10·90; p<0·0001). When combining the Gail score with our adipose IR model, we found that individuals with both predictors had an OR of 3·45 (1·77-7·24; p=0·0002).

Interpretation: Assessment of senescence-associated nuclear morphologies with deep learning allows prediction of future cancer risk from normal breast biopsy samples. The combination of multiple models improved prediction of future breast cancer compared with the current clinical benchmark, the Gail model. 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.

Funding: Novo Nordisk Foundation, Danish Cancer Society, and the US National Institutes of Health.