Cell lines are essential tools to standardize and compare experimental findings in basic and translational cancer research. The current dogma states that cancer stem cells feature an increased tumor initiation capacity and are also chemoresistant. Here, we identified and comprehensively characterized three morphologically distinct cellular subtypes in the non–small cell lung cancer cell line A549 and challenge the current cancer stem cell dogma. Subtype-specific cellular morphology is maintained during short-term culturing, resulting in the formation of holoclonal, meroclonal, and paraclonal colonies. A549 holoclone cells were characterized by an epithelial and stem-like phenotype, paraclone cells featured a mesenchymal phenotype, whereas meroclone cells were phenotypically intermediate. Cell-surface marker expression of subpopulations changed over time, indicating an active epithelial-to-mesenchymal transition (EMT), in vitro and in vivo. EMT has been associated with the overexpression of the immunomodulators PD-L1 and PD-L2, which were 37- and 235-fold overexpressed in para- versus holoclone cells, respectively. We found that DNA methylation is involved in epigenetic regulation of marker expression. Holoclone cells were extremely sensitive to cisplatin and radiotherapy in vitro, whereas paraclone cells were highly resistant. However, inhibition of the receptor tyrosine kinase AXL, whose expression is associated with an EMT, specifically targeted the otherwise highly resistant paraclone cells. Xenograft tumor formation capacity was 24- and 269-fold higher in holo- than mero- and paraclone cells, respectively. Our results show that A549 subpopulations might serve as a unique system to explore the network of stemness, cellular plasticity, tumor initiation capacity, invasive and metastatic potential, and chemo/radiotherapy resistance.

Gold nanoparticles (AuNPs) have been widely used in catalysis, photothermal therapy, and targeted drug delivery. Carrageenan oligosaccharide (CAO) derived from marine red algae was used as a reducing and capping agent to obtain AuNPs by an eco-friendly, efficient, and simple synthetic route for the first time. The synthetic conditions of AuNPs were optimized by response surface methodology (RSM), and the CAO-AuNPs obtained were demonstrated to be ellipsoidal, stable and crystalline by means of transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The CAO-AuNPs showed localized surface plasmon resonance (LSPR) oscillation at about 530 nm with a mean diameter of 35 ± 8 nm. The zeta potential of CAO-AuNPs was around −20 mV, which was related to the negatively charged CAO around AuNPs. The CAO-AuNPs exhibited significant cytotoxic activities to HCT-116 and MDA-MB-231 cells, which could be a promising nanomaterial for drug delivery.

To reduce the increasingly congestion in cities, it is essential for intelligent transportation system (ITS) to accurately forecast the short-term traffic flow to identify the potential congestion sites. In recent years, the emerging deep learning method has been introduced to design traffic flow predictors, such as recurrent neural network (RNN) and long short-term memory (LSTM), which has demonstrated its promising results. In this paper, different from existing work, we study the temporal convolutional network (TCN) and propose a deep learning framework based on TCN model for short-term city-wide traffic forecast to accurately capture the temporal and spatial evolution of traffic flow. Moreover, we design the model with the Taguchi method to develop an optimized structure of the TCN model, which not only reduces the number of experiments, but also yields high accuracy of forecasting results. With the real-world traffic flow data collected from highways in Birmingham City of U.K., we compare our model with four deep learning based models including LSTM models, GRU models, SAE models, DeepTrend and CNN-LSTM models in terms of the mean absolute error (MAE) and mean relative error (MRE) regarding the actual flow data. The experimental results demonstrate that our framework achieves the state-of-art performance with superior accuracy in short-term traffic flow forecasting. INDEX TERMS Deep learning, temporal convolutional networks, short-term forecasting.

Drug resistance and tumor heterogeneity are formidable challenges in cancer medicine, which is particularly relevant for KRAS -mutant cancers, the epitome of malignant tumors recalcitrant to targeted therapy efforts and first-line chemotherapy. In this study, we delineate that KRAS -mutant lung cancer cells resistant to pemetrexed (MTA) and anti-MEK drug trametinib acquire an exquisite dependency on endoplasmic reticulum (ER) stress signaling, rendering resistant cancer cells selectively susceptible to blockage of HSP90, the receptor tyrosine kinase AXL, the eukaryotic translation initiation factor 4E (eIF4E), and the unfolded protein response (UPR). Mechanistically, acquisition of drug resistance enables KRAS -mutant lung cancer cells to bypass canonical KRAS effectors but entail hyperactive AXL/eIF4E, increased protein turnover in the ER, and adaptive activation of an ER stress-relief UPR survival pathway whose integrity is maintained by HSP90. Notably, the unique dependency and sensitivity induced by drug resistance are applicable to KRAS -mutant lung cancer cells undergoing de novo intratumor heterogeneity. In line with these findings, HSP90 inhibitors synergistically enhance antitumor effects of MTA and trametinib, validating a rational combination strategy to treat KRAS -mutant lung cancer. Collectively, these results uncover collateral vulnerabilities co-occurring with drug resistance and tumor heterogeneity, informing novel therapeutic avenues for KRAS -mutant lung cancer.