Deeptha Ishwar, Srilakshmi Premachandran, Sunit Das, Krishnan Venkatakrishnan, Bo Tan
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Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor-Associated Immune Cell Signatures and Immuno Nano Sensors
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.
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.