Emerging liquid biopsy methods for investigating biomarkers in bodily fluids such as blood, saliva, or urine can be used to perform noninvasive cancer detection. However, the complexity and heterogeneity of exosomes require improved methods to achieve the desired sensitivity and accuracy. Herein, we report our study on developing a breast cancer liquid biopsy system, including a fluorescence sensor array and deep learning (DL) tool AggMapNet. In particular, we used a 12-unit sensor array composed of conjugated polyelectrolytes, fluorophore-labeled peptides, and monosaccharides or glycans to collect fluorescence signals from cells and exosomes. Linear discriminant analysis (LDA) processed the fluorescence spectral data of cells and cell-derived exosomes, demonstrating successful discrimination between normal and different cancerous cells and 100% accurate classification of different BC cells. For heterogeneous plasma-derived exosome analysis, CNN-based DL tool AggMapNet was applied to transform the unordered fluorescence spectra into feature maps (Fmaps), which gave a straightforward visual demonstration of the difference between healthy donors and BC patients with 100% prediction accuracy. Our work indicates that our fluorescent sensor array and DL model can be used as a promising noninvasive method for BC diagnosis.

中文翻译：

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使用传感器阵列和深度学习对循环外泌体进行乳腺癌诊断的荧光分析

用于研究血液、唾液或尿液等体液中的生物标志物的新兴液体活检方法可用于进行无创癌症检测。然而，外泌体的复杂性和异质性需要改进的方法来实现所需的灵敏度和准确性。在此，我们报告了我们开发乳腺癌液体活检系统的研究，包括荧光传感器阵列和深度学习 (DL) 工具 AggMapNet。特别是，我们使用由共轭聚电解质、荧光团标记的肽和单糖或聚糖组成的 12 单元传感器阵列来收集来自细胞和外泌体的荧光信号。线性判别分析 (LDA) 处理细胞和细胞衍生外泌体的荧光光谱数据，证明了正常和不同癌细胞之间的成功区分以及不同 BC 细胞的 100% 准确分类。对于异质血浆来源的外泌体分析，基于 CNN 的深度学习工具 AggMapNet 用于将无序荧光光谱转换为特征图 (Fmaps)，以 100% 的预测准确度直观地展示了健康供体和 BC 患者之间的差异。我们的工作表明，我们的荧光传感器阵列和 DL 模型可用作 BC 诊断的有前途的无创方法。它以 100% 的预测准确度直观地展示了健康供体和 BC 患者之间的差异。我们的工作表明，我们的荧光传感器阵列和 DL 模型可用作 BC 诊断的有前途的无创方法。它以 100% 的预测准确度直观地展示了健康供体和 BC 患者之间的差异。我们的工作表明，我们的荧光传感器阵列和 DL 模型可用作 BC 诊断的有前途的无创方法。