Ishwar Deeptha, Premachandran Srilakshmi, Das Sunit, Venkatakrishnan Krishnan, Tan Bo
Faculty of Dentistry, Department of Stomatology, University of Montreal, Montreal, Quebec, H3T 1J4, Canada.
Institute for Biomedical Engineering, Science and Technology (I BEST), Partnership between Toronto Metropolitan University and St. Michael's Hospital, Toronto, Ontario, M5B 1W8, Canada.
Small. 2024 Dec;20(52):e2406475. doi: 10.1002/smll.202406475. Epub 2024 Oct 26.
Breast cancer is a complex and heterogeneous disease with varying cellular, genetic, epigenetic, and molecular expressions. The detection of intratumor heterogeneity in breast cancer poses significant challenges due to its complex multifaceted characteristics, yet its identification is crucial for guiding effective treatment decisions and understanding the disease progression. Currently, there exists no method capable of capturing the full extent of breast tumor heterogeneity. In this study, the aim is to identify and characterize metabolic heterogeneity in breast tumors using immune cells and an ultrafast laser-fabricated Immuno Nano Sensor. Combining spectral markers from both Natural Killer (NK) and T cells, a machine-learning approach is implemented to distinguish cancer from healthy samples, identify primary versus metastatic tumors, and determine estrogen receptor (ER)/progesterone receptor (PR) status at the single-cell level. The platform successfully distinguished heterogeneous breast cancer samples from healthy individuals, achieving 97.8% sensitivity and 92.2% specificity, and accurately identified primary tumors from metastatic tumors. Characteristic spectral signatures allow for discrimination between ER/PR-positive and negative tumors with 97.5% sensitivity. This study demonstrates the potential of immune cell-based metabolic profiling in providing a comprehensive assessment of breast tumor heterogeneity and paving the way for minimally invasive liquid biopsy approaches in breast cancer diagnosis and management.
乳腺癌是一种复杂的异质性疾病，具有不同的细胞、遗传、表观遗传和分子表达。由于其复杂的多方面特征，检测乳腺癌中的肿瘤内异质性面临重大挑战，但其识别对于指导有效的治疗决策和理解疾病进展至关重要。目前，尚无能够全面捕捉乳腺肿瘤异质性的方法。在本研究中，目标是使用免疫细胞和超快激光制造的免疫纳米传感器来识别和表征乳腺肿瘤中的代谢异质性。结合自然杀伤（NK）细胞和T细胞的光谱标记，实施一种机器学习方法来区分癌症样本与健康样本，识别原发性肿瘤与转移性肿瘤，并在单细胞水平确定雌激素受体（ER）/孕激素受体（PR）状态。该平台成功地将异质性乳腺癌样本与健康个体区分开来，灵敏度达到97.8%，特异性达到92.2%，并准确地从转移性肿瘤中识别出原发性肿瘤。特征光谱特征能够以97.5%的灵敏度区分ER/PR阳性和阴性肿瘤。本研究证明了基于免疫细胞的代谢谱分析在全面评估乳腺肿瘤异质性以及为乳腺癌诊断和管理中的微创液体活检方法铺平道路方面的潜力。
