Breast cancer subtypes have important implications of treatment responses and clinical outcomes. Exosomes have been considered as promising biomarkers for liquid biopsies, but the utility of exosomes for accurate diagnosis of distinct breast cancer subtypes is a grand challenge due to the difficulty in uncovering the subtle compositional difference in complex clinical settings. Herein, we report an artificial intelligent surface-enhanced Raman spectroscopy (SERS) strategy for labelfree spectroscopic analysis of serum exosomes, allowing for accurate diagnosis of breast cancer and assessment of surgical outcomes. Our deep learning algorithm trained with SERS spectra of cancer cell-derived exosomes is demonstrated with a 100% prediction accuracy for human patients with different breast cancer subtypes who do not undergo surgery using SERS spectra of serum exosomes. Furthermore, when combined with similarity analysis by principal component analysis, our approach is able to evaluate the surgical outcomes of breast cancer of distinct molecular subtypes.

Protein profiles of exosomes (EXOs) in clinical samples of cancer patients have become a promising diagnostic and therapeutic biomarker. However, simultaneous quantitative analysis of multiple exosomal proteins of interest remains challenging. To address the unmet need, we develop a paper-based surface-enhanced Raman spectroscopy (SERS)-vertical flow biosensor, named iREX (integrated Raman spectroscopic EXO) biosensor, for multiplexed quantitative profiling of exosomal proteins in clinical serum samples of patients. Utilizing this iREX biosensor, we are able to quantitatively profile MUC1, HER2 and CEA in EXO samples derived from various breast cancer cell subtypes. The results show discriminative expression profiles of the three exosomal proteins in these cell subtypes, which allows for accurate diagnosis and molecular subtyping of breast cancer. We further validate the clinical utility of the iREX biosensor for simultaneous quantitative analysis of MUC1, HER2 and CEA in patient's blood serums, thereby aiding in noninvasive breast cancer subtyping and longitudinal treatment monitoring. Our iREX biosensor integrating the SERS detection in a vertical flow diagnostic device offers great advantages of high sensitivity, molecular specificity, powerful multiplexing capability, and high diagnostic accuracy. We believe that the iREX biosensor could be a promising clinical tool for comprehensive analysis of exosomal proteins in clinical samples for personalized diagnosis and precise management of breast cancer.

Exosomes (exos) widely existing in body fluids show great potential for noninvasive cancer diagnosis. Quantitative analysis of exos is traditionally performed by targeting specific exosomal surface proteins, but it is often imprecise due to the common expression of exosomal proteins and subtle expression differences between different cancer subtypes. Herein, we report quantitative surface-enhanced Raman spectroscopy (SERS) of serum exos through a combination of a paper-based lateral flow strip (LFS) biosensor with multivariate spectral unmixing analysis rather than simply quantifying exosomal proteins. Our SERS-LFS biosensor enables absolute quantification of two different serum exos with a limit of detection down to ∼106 particles/mL for both exos. We further exemplify the application of this strategy in quantitative dual-plex detection of serum exos from breast cancer patients. We find that human epidermal growth factor receptor 2+ (HER2+) and luminal A breast cancer patients undergoing no surgery are enriched in serum exos derived from SKBR-3 cells and MCF-7 cells (denoted as SKBR and MCF exos), respectively. The surgical treatment of these breast cancer patients accompanies an obvious decrease of either SKBR or MCF exos in the serum. These results suggest the great potential of the combination of the SERS-LFS biosensor and multivariate spectral unmixing for breast cancer subtyping and therapeutic surveillance with the powerful quantitative capability of exos in clinical samples.

The prevalence of fentanyl abuse raises global public health concerns with an unprecedented surge in overdose deaths. Rapid identification and quantification of fentanyl in biofluids is of paramount importance to combat fentanyl abuse for law enforcement agencies and promptly treat patients for medical professionals. Herein, a freestanding surface-enhanced Raman spectroscopy (SERS) biosensor with excellent condensing enrichment capability, termed FrEnSERS biosensor, is reported for quantitative label-free detection of trace fentanyl in biofluids. This biosensor comprises a reduced graphene oxide membrane decorated with high-density hydrophobic Au nanostars. A combination of the high SERS enhancement and the focusing effect for analyte enrichment of the hydrophobic surface accounts for the remarkable SERS performance of the FrEnSERS biosensor. We demonstrate that the FrEnSERS biosensor achieves the sensitive and quantitative detection of fentanyl in both serum and urine over a wide dynamic range spanning more than 4 orders of magnitude, with a limit of detection of 0.47 ng/mL for serum samples and 0.73 ng/mL for urine samples. Our biosensor is sensitive, cost-effective, and reliable for rapid quantitative analysis of fentanyl in biofluids with great promise for forensic analysis and clinical diagnosis.

The expression of human epidermal growth factor receptor-2 (HER2) has important implications for pathogenesis, progression, and therapeutic efficacy of breast cancer. The detection of its variation during the treatment is crucial for therapeutic decision-making but remains a grand challenge, especially at the cellular level. Here, we develop a machine learning-driven surface-enhanced Raman spectroscopy (SERS)-integrated strategy for label-free detection of cellular HER2. Specifically, our method allows the extraction of cell-rich spectral signatures utilized for identification and classification of cancer cells with distinct HER2 expression with a high accuracy of 99.6%. By combining label-free SERS detection and machine learning-driven chemometric analysis, we are able to perform longitudinal monitoring of therapeutic efficacy at the cellular level during the treatment of HER2+ breast cancer, which aids in the subsequent decision-making and management. This work provides a promising technique capable of performing dynamic label-free spectroscopic detection for therapeutic surveillance of diseases.