Ultrasensitive determination of surface proteins on tumor-derived small extracellular vesicles for breast cancer identification based on lanthanide-activated signal amplification strategy

Deciphering the landscape of triple negative breast cancer from microenvironment dynamics and molecular insights to biomarker analysis and therapeutic modalities

Triple negative breast cancer (TNBC) displays a notable challenge in clinical oncology due to its invasive nature which is attributed to the absence of progesterone receptor (PR), estrogen receptor (ER), and human epidermal growth factor receptor (HER-2). The heterogenous tumor microenvironment (TME) of TNBC is composed of diverse constituents that intricately interact to evade immune response and facilitate cancer progression and metastasis. Based on molecular gene expression, TNBC is classified into four molecular subtypes: basal-like (BL1 and BL2), luminal androgen receptor (LAR), immunomodulatory (IM), and mesenchymal. TNBC is an aggressive histological variant with adverse prognosis and poor therapeutic response. The lack of response in most of the TNBC patients could be attributed to the heterogeneity of the disease, highlighting the need for more effective treatments and reliable prognostic biomarkers. Targeting certain signaling pathways and their components has emerged as a promising therapeutic strategy for improving patient outcomes. In this review, we have summarized the interactions among various components of the dynamic TME in TNBC and discussed the classification of its molecular subtypes. Moreover, the purpose of this review is to compile and provide an overview of the most recent data about recently discovered novel TNBC biomarkers and targeted therapeutics that have proven successful in treating metastatic TNBC. The emergence of novel therapeutic strategies such as chemoimmunotherapy, chimeric antigen receptor (CAR)-T cells-based immunotherapy, phytometabolites-mediated natural therapy, photodynamic and photothermal approaches have made a significant positive impact and have paved the way for more effective interventions.

Logic-Measurer: A Multienzyme-Assisted Ultrasensitive Circuit for Logical Detection of Exosomal MicroRNAs

The logic profiling of exosomal microRNAs (miRNAs) offers broad potential applications in the accurate diagnosis and staging of cancer. However, the logical detection of low-abundance exosomal miRNAs in complex clinical samples remains challenging. This study introduces a logic analysis system termed "Measurer" (a multi-enzyme-assisted ultrasensitive circuit) that offers ultrasensitive and versatile method for detecting multiple exosomal miRNAs. The Logic-Measurer comprises three modules: a stem-loop hairpin-enhanced CRISPR/Cas13a, a polymerase-driven primer exchange reaction, and an exonuclease III-mediated fluorescence output. The efficient Logic-Measurer was switched by the faster rate of trans-cleavage activity of Cas13a due to its improved affinity for hairpin RNA structures. The mechanistic model of hairpin-enhanced CRISPR/Cas13a was confirmed by molecular dynamics simulations. The Logic-Measurer accurately detected exosomal miRNA-21 or miRNA-375 down to 2.1 and 4.4 fM, with superior specificity, and enabled in situ detection of miRNA-21 and miRNA-375 in as low as 1.4 × 102 particles/mL exosomes via membrane fusion. In addition, this method demonstrated 87.3 and 82.1% accuracy in the diagnosis and early detection of breast cancer, respectively, among a cohort of 315 individuals. Subsequent subgroup analysis further confirmed the method's ability to accurately differentiate estrogen receptor-positive patients from healthy individuals. Therefore, the Logic-Measurer offers valuable insights into the development of a CRISPR/Cas-based enhanced diagnostic platform and the next generation of diagnostic technology based on enzyme circuits.

Advances of machine learning-assisted small extracellular vesicles detection strategy

Detection of extracellular vesicles (EVs), particularly small EVs (sEVs), is of great significance in exploring their physiological characteristics and clinical applications. The heterogeneity of sEVs plays a crucial role in distinguishing different types of cells and diseases. Machine learning, with its exceptional data processing capabilities, offers a solution to overcome the limitations of conventional detection methods for accurately classifying sEV subtypes and sources. Principal component analysis, linear discriminant analysis, partial least squares discriminant analysis, XGBoost, support vector machine, k-nearest neighbor, and deep learning, along with some combined methods such as principal component-linear discriminant analysis, have been successfully applied in the detection and identification of sEVs. This review focuses on machine learning-assisted detection strategies for cell identification and disease prediction via sEVs, and summarizes the integration of these strategies with surface-enhanced Raman scattering, electrochemistry, inductively coupled plasma mass spectrometry and fluorescence. The performance of different machine learning-based detection strategies is compared, and the advantages and limitations of various machine learning models are also evaluated. Finally, we discuss the merits and limitations of the current approaches and briefly outline the perspective of potential research directions in the field of sEV analysis based on machine learning.