Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.

A neural stimulation technique that can inhibit neural activity reversibly and directly without genetic modification is valuable for understating complex brain functions and treating brain diseases. Here, we propose a near-infrared (NIR)-activatable nanoplasmonic technique that can inhibit the electrical activity of neurons by utilizing gold nanorods (GNRs) as photothermal transducers on cellular membranes. The GNRs were bound onto the plasma membrane of neurons and irradiated with NIR light to induce GNR-mediated photothermal heating near the membrane. The electrical activity from the cultured neuronal networks pretreated with GNRs was immediately inhibited upon NIR irradiation, and fully restored when NIR light was removed. The degree of inhibition could be precisely modulated by tuning the laser intensity, thereby enabling restoration of firing of a hyperactive neuronal network with epileptiform activity. This nanotechnological approach to inhibit neural activity provides a powerful therapeutic platform to control cellular functions associated with disordered neural circuits.

Ezrin is a member of the ERM (ezrin, radixin, moesin) protein family and links F-actin to the cell membrane following phosphorylation. Ezrin has been associated with tumor progression and metastasis in several cancers including the pediatric solid tumors, osteosarcoma and rhabdomyosarcoma. In this study, we were surprised to find that ezrin was not constitutively phosphorylated but rather was dynamically regulated during metastatic progression in osteosarcoma. Metastatic osteosarcoma cells expressed phosphorylated ERM early after their arrival in the lung, and then late in progression, only at the invasive front of larger metastatic lesions. To pursue mechanisms for this regulation, we found that inhibitors of PKC (protein kinase C) blocked phosphorylation of ezrin, and that ezrin coimmunoprecipitated in cells with PKCa, PKCi and PKCc. Furthermore, phosphorylated forms of ezrin and PKC had identical expression patterns at the invasive front of pulmonary metastatic lesions in murine and human patient samples. Finally, we showed that the promigratory effects of PKC were linked to ezrin phosphorylation. These data are the first to suggest a dynamic regulation of ezrin phosphorylation during metastasis and to connect the PKC family members with this regulation.

Pulmonary metastasis remains the leading ca use of death for cancer patients. Opportunities to improve treatment outcomes for patients require new methods to study and view the biology of metastatic progression. Here, we describe an ex vivo pulmonary metastasis assay (PuMA) in which the metastatic progression of GFPexpressing cancer cells, from a single cell to the formation of multicellular colonies, in the mouse lung microenvironment was assessed in real time for up to 21 days. The biological validity of this assay was confirmed by its prediction of the in vivo behavior of a variety of high-and low-metastatic human and mouse cancer cell lines and the discrimination of tumor microenvironments in the lung that were most permissive to metastasis. Using this approach, we provide what we believe to be new insights into the importance of tumor cell interactions with the stromal components of the lung microenvironment. Finally, the translational utility of this assay was demonstrated through its use in the evaluation of therapeutics at discrete time points during metastatic progression. We believe that this assay system is uniquely capable of advancing our understanding of both metastasis biology and therapeutic strategies. IntroductionPulmonary metastasis remains a leading cause of death for cancer patients (1, 2). Opportunities to improve outcomes for these patients require a greater understanding of the biology of metastasis. In addition, there is a need to evaluate novel therapeutics, in a timely manner, that specifically target metastases and metastatic progression. Simple in vitro assay systems are not sufficient to model the complex interaction between cancer cells and the surrounding microenvironment that is necessary for metastasis (3). Accordingly, in vivo models of metastasis, largely in mice, have been necessary. For the most part, these models provide end points of metastatic outcome (i.e., yes or no metastasis) and time to late-stage metastatic events.A "black box" exists during which metastatic progression from single cells to gross metastatic lesions at a secondary site occurs. Recent attempts to shed light on this process have included imaging strategies that allow some of the steps of metastatic progression to be followed in vivo (4). However, these approaches often involve sophisticated and expensive imaging techniques that are time consuming and do not easily allow serial assessment of early metastatic progression at secondary sites, particularly in the lung and at the single-cell level. Challenges associated with studying metastasis have resulted in limited opportunities to include the assessment of novel treatment agents against metastatic end points (5). Therefore, an unmet need in the field of cancer research is a simple assay in which the process of metastatic progression at a secondary site can be reproduced and studied over time.