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潜伏于人体全身各处的衰老细胞又被称为僵尸细胞，既不继续分裂繁殖，亦未完全死亡，仍然具有代谢活性，既可抑制肿瘤细胞无限增殖，又可分泌促炎旁分泌因子削弱抗肿瘤免疫系统从而促进肿瘤生长。由于绝大多数衰老细胞研究都在体外细胞或者动物体内完成，而且衰老具有异质性，因此衰老细胞对人类癌症发展的具体作用尚不明确。此外，全世界每年进行的健康乳腺活检超过100万次，可能成为乳腺癌风险分层的主要资源。
2024年9月25日，英国《柳叶刀》数字医疗分册在线发表丹麦哥本哈根大学、美国巴克衰老研究所、伯克利加利福尼亚大学、印第安纳大学医学院、埃塞俄比亚默克莱大学的研究报告，通过人工智能深度学习定量分析健康女性志愿者乳腺组织衰老相关细胞核形态，探讨衰老标志物对乳腺癌发展的临床意义，预测将来发生乳腺癌的风险。该研究由诺和诺德基金会、丹麦癌症学会、美国国家卫生研究院提供资助。
该队列回顾研究首先根据细胞核形态对2009年至2019年印第安纳大学西蒙癌症中心科曼组织库4382例健康女性志愿者（中位年龄45岁、四分位34至57岁）乳腺粗针活检标本苏木精伊红染色组织学图像进行单细胞人工智能深度学习，确定衰老预测因素。随后采用经过验证的模型预测终末导管小叶单位和非终末导管小叶单位上皮、基质和脂肪组织区域的衰老，该模型已经利用电离辐射、复制性衰竭（复制性衰老）或药物（抗霉素A、阿扎那韦-利托那韦和多柔比星）诱发衰老的细胞进行训练。为了对根据衰老预测癌症的结果进行基准测试，该研究根据组织捐献时的特征，利用目前乳腺癌风险临床预测金标准盖尔模型，对年龄≥35岁志愿者进行5年乳腺癌风险盖尔评分。通过逻辑回归模型根据病例组（数据截至2022年7月31日已被诊断乳腺癌的参与者）与对照组（未被诊断乳腺癌的参与者）不同组织区域预测衰老评分估计乳腺癌发生概率。
结果，截至2022年7月31日，中位随访10年（四分位7～11），其中86例（2.0%）活检后平均4.8±2.84年已被诊断乳腺癌，其余4296例（98.0%）未被诊断乳腺癌。
86例患者与对照组相比：
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脂肪组织电离辐射模型评分高于中位个体乳腺癌发生概率显著较高（概率比：1.71，95%置信区间：1.10～2.68；P=0.019）
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脂肪组织药物诱发模型评分高于中位个体乳腺癌发生概率显著较低（概率比：0.57，95%置信区间：0.36～0.88；P=0.013）
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基质组织电离辐射模型评分高于中位个体乳腺癌发生概率显著较高（概率比：1.59，95%置信区间：1.03～2.49；P=0.038）
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具有两种脂肪风险因素个体乳腺癌发生概率显著较高（概率比：3.32，95%置信区间：1.68～7.03；P=0.0009）
5年乳腺癌风险盖尔评分高于中位与低于中位相比，个体乳腺癌发生概率显著较高（概率比：2.33，95%置信区间：1.46～3.82；P=0.0012）。
将盖尔评分结合：
因此，该研究结果表明，采用人工智能深度学习定量分析结合衰老相关细胞核形态，可以从正常乳腺活检标本预测将来的乳腺癌风险。多种模型组合与目前临床金标准盖尔模型相比，可显著改善对将来乳腺癌风险的预测。显微镜图像人工智能深度学习模型对于预测将来癌症发展可以发挥重要作用，此类模型可以纳入当前的乳腺癌风险定量分析和筛查方案，对于高风险女性可以加强乳腺钼靶监测和活检，对于低风险女性可以避免乳腺钼靶监测和活检。
Lancet Digit Health. 2024 Sep 25;6(10):e681-e690. IF: 23.8
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.
Indra Heckenbach, Mark Powell, Sophia Fuller, Jill Henry, Sam Rysdyk, Jenny Cui, Amanuel Abraha Teklu, Eric Verdin, Christopher Benz, Morten Scheibye-Knudsen.
University of Copenhagen, Copenhagen, Denmark; Buck Institute for Research on Aging, Novato, CA, USA; Zero Breast Cancer, San Rafael, CA, USA; University of California, Berkeley, Berkeley, CA, USA; Indiana University School of Medicine, Indianapolis, IN, USA; Mekelle University, Mekelle, Ethiopia.
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.
DOI: 10.1016/S2589-7500(24)00150-X
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