Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors

IF 13 2区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Small Pub Date : 2024-10-26 DOI:10.1002/smll.202406475
Deeptha Ishwar, Srilakshmi Premachandran, Sunit Das, Krishnan Venkatakrishnan, Bo Tan
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Abstract

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.

Abstract Image

通过肿瘤相关免疫细胞特征和免疫纳米传感器剖析乳腺肿瘤异质性并识别乳腺癌亚型
乳腺癌是一种复杂的异质性疾病,其细胞、遗传、表观遗传和分子表达各不相同。由于乳腺癌具有复杂的多方面特征,因此检测乳腺癌的瘤内异质性是一项重大挑战,然而,识别瘤内异质性对于指导有效的治疗决策和了解疾病的进展情况至关重要。目前,还没有一种方法能够全面捕捉乳腺肿瘤的异质性。本研究旨在利用免疫细胞和超快激光制造的免疫纳米传感器来识别和描述乳腺肿瘤的代谢异质性。结合来自自然杀伤细胞(NK)和T细胞的光谱标记,采用机器学习方法区分癌症和健康样本,识别原发性和转移性肿瘤,并在单细胞水平上确定雌激素受体(ER)/孕激素受体(PR)状态。该平台成功区分了异质性乳腺癌样本和健康人样本,灵敏度达 97.8%,特异度达 92.2%,并准确识别了原发性肿瘤和转移性肿瘤。特征性光谱特征可区分 ER/PR 阳性和阴性肿瘤,灵敏度高达 97.5%。这项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方面的潜力,并为乳腺癌诊断和管理中的微创液体活检方法铺平了道路。
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来源期刊
Small
Small 工程技术-材料科学:综合
CiteScore
17.70
自引率
3.80%
发文量
1830
审稿时长
2.1 months
期刊介绍: Small serves as an exceptional platform for both experimental and theoretical studies in fundamental and applied interdisciplinary research at the nano- and microscale. The journal offers a compelling mix of peer-reviewed Research Articles, Reviews, Perspectives, and Comments. With a remarkable 2022 Journal Impact Factor of 13.3 (Journal Citation Reports from Clarivate Analytics, 2023), Small remains among the top multidisciplinary journals, covering a wide range of topics at the interface of materials science, chemistry, physics, engineering, medicine, and biology. Small's readership includes biochemists, biologists, biomedical scientists, chemists, engineers, information technologists, materials scientists, physicists, and theoreticians alike.
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