Dysfunction of invariant natural killer T (iNKT) cells in tumor microenvironment hinders their anti-tumor efficacy, and the underlying mechanisms remain unclear. Here we report that iNKT cells ...increase lipid biosynthesis after activation, and that is promoted by PPARγ and PLZF synergically through enhancing transcription of Srebf1. Among those lipids, cholesterol is required for the optimal IFN-γ production from iNKT cells. Lactic acid in tumor microenvironment reduces expression of PPARγ in intratumoral iNKT cells and consequently diminishes their cholesterol synthesis and IFN-γ production. Importantly, PPARγ agonist pioglitazone, a thiazolidinedione drug for type 2 diabetes, successfully restores IFN-γ production in tumor-infiltrating iNKT cells from both human patients and mouse models. Combination of pioglitazone and alpha-galactosylceramide treatments significantly enhances iNKT cell-mediated anti-tumor immune responses and prolongs survival of tumor-bearing mice. Our studies provide a strategy to augment the anti-tumor efficacy of iNKT cell-based immunotherapies via promoting their lipid biosynthesis.
Pancreatic ductal adenocarcinoma (PDAC) has a collagen-rich dense extracellular matrix (ECM) that promotes malignancy of cancer cells and presents a barrier for drug delivery. Data analysis of our ...published mass spectrometry (MS)-based studies on enriched ECM from samples of progressive PDAC stages reveal that the C-terminal prodomains of fibrillar collagens are partially uncleaved in PDAC ECM, suggesting reduced procollagen C-proteinase activity. We further show that the enzyme responsible for procollagen C-proteinase activity, bone morphogenetic protein1 (BMP1), selectively suppresses tumor growth and metastasis in cells expressing high levels of COL1A1. Although BMP1, as a secreted proteinase, promotes fibrillar collagen deposition from both cancer cells and stromal cells, only cancer-cell-derived procollagen cleavage and deposition suppresses tumor malignancy. These studies reveal a role for cancer-cell-derived fibrillar collagen in selectively restraining tumor growth and suggest stratification of patients based on their tumor epithelial collagen I expression when considering treatments related to perturbation of fibrillar collagens.
Extracellular matrix (ECM) deposition is a hallmark of many diseases, including cancer and fibroses. To exploit the ECM as an imaging and therapeutic target, we developed alpaca-derived libraries of ..."nanobodies" against disease-associated ECM proteins. We describe here one such nanobody, NJB2, specific for an alternatively spliced domain of fibronectin expressed in disease ECM and neovasculature. We showed by noninvasive in vivo immuno-PET/CT imaging that NJB2 detects primary tumors and metastatic sites with excellent specificity in multiple models of breast cancer, including human and mouse triple-negative breast cancer, and in melanoma. We also imaged mice with pancreatic ductal adenocarcinoma (PDAC) in which NJB2 was able to detect not only PDAC tumors but also early pancreatic lesions called pancreatic intraepithelial neoplasias, which are challenging to detect by any current imaging modalities, with excellent clarity and signal-to-noise ratios that outperformed conventional 2-fluorodeoxyglucose PET/CT imaging. NJB2 also detected pulmonary fibrosis in a bleomycin-induced fibrosis model. We propose NJB2 and similar anti-ECM nanobodies as powerful tools for noninvasive detection of tumors, metastatic lesions, and fibroses. Furthermore, the selective recognition of disease tissues makes NJB2 a promising candidate for nanobody-based therapeutic applications.
The prognosis for pancreatic ductal adenocarcinoma (PDAC) remains poor despite decades of effort. The abundant extracellular matrix (ECM) in PDAC comprises a major fraction of the tumor mass and ...plays various roles in promoting resistance to therapies. However, nonselective depletion of ECM has led to poor patient outcomes. Consistent with that observation, we previously showed that individual matrisome proteins derived from stromal cells correlate with either long or short patient survival. In marked contrast, those derived from cancer cells correlate strongly with poor survival. Here, we studied three cancer cell-derived matrisome proteins that are significantly overrepresented during PDAC progression, AGRN (agrin), SERPINB5 (serine protease inhibitor B5), and CSTB (cystatin B). Using both overexpression and knockdown experiments, we demonstrate that all three are promoters of PDAC metastasis. Furthermore, these proteins operate at different metastatic steps. AGRN promoted epithelial-to-mesenchymal transition in primary tumors, whereas SERPINB5 and CSTB enhanced late steps in the metastatic cascade by elevating invadopodia formation and
extravasation. All three genes were associated with a poor prognosis in human patients and high levels of SERPINB5, secreted by cancer cells and deposited in the ECM, correlated with poor patient prognosis. This study provides strong evidence that cancer cell-derived matrisome proteins can be causal in promoting tumorigenesis and metastasis and lead to poor patient survival. Therefore, compared with the bulk matrix, mostly made by stromal cells, precise interventions targeting cancer cell-derived matrisome proteins, such as AGRN, SERPINB5, and CSTB, may represent preferred potential therapeutic targets. SIGNIFICANCE: This study provides insights into the biological roles of cancer cell-derived matrisome proteins in PDAC and supports the notion that these proteins are protumorigenic and better therapeutic targets.
The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual ...encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples; then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.
Sparse representation based classification (SRC) achieves good results by addressing recognition problem with sufficient training samples per subject. Tumor classification, however, is a typical ...small sample problem. In this paper, an inverse projection group sparse representation (IPGSR) model is presented for tumor classification based on constructing a low rank variation dictionary (LRVD), for short, LRVD-IPGSR model. Firstly, an IPGSR model is constructed based on making full use of existing training and test samples, and group sparsity effect of genetic data. Furthermore, from a new viewpoint, a LRVD is constructed for improving the performance of IPGSR-based tumor classification. The LRVD can be independently constructed by detecting and utilizing variations of normals and typical patients, rather than directly using and changed with the genetic data or their corresponding feature data. And the LRVD can be automatic updated and extended to fit the case of new types of diseases. Finally, the LRVD-IPGSR model is fully analyzed from feasibility, stability, optimization and convergence. The performance of the LRVD-IPGSR model-based tumor classification framework is verified on eight microarray gene expression datasets, which contain early diagnosis, tumor type recognition and postoperative metastasis.
The aging microenvironment serves important roles in cancers. However, most studies focus on circumscribed hot spots such as immunity and metabolism. Thus, it is well ignored that the aging ...microenvironment contributes to the proliferation of tumor. Herein, we established three prognosis-distinctive aging microenvironment subtypes, including AME1, AME2, and AME3, based on aging-related genes and characterized them with “Immune Exclusion,” “Immune Infiltration,” and “Immune Intermediate” features separately. AME2-subtype tumors were characterized by specific activation of immune cells and were most likely to be sensitive to immunotherapy. AME1-subtype tumors were characterized by inhibition of immune cells with high proportion of Catenin Beta 1 (CTNNB1) mutation, which was more likely to be insensitive to immunotherapy. Furthermore, we found that CTNNB1 may inhibit the expression of C-C Motif Chemokine Ligand 19 (
CCL19
), thus restraining immune cells and attenuating the sensitivity to immunotherapy. Finally, we also established a robust aging prognostic model to predict the prognosis of patients with hepatocellular carcinoma. Overall, this research promotes a comprehensive understanding about the aging microenvironment and immunity in hepatocellular carcinoma and may provide potential therapeutic targets for immunotherapy.
Repulsive guidance molecules (RGMs) are evolutionarily conserved proteins implicated in repulsive axon guidance. Here we report the function of the Caenorhabditis elegans ortholog DRAG-1 in axon ...branching. The axons of hermaphrodite-specific neurons (HSNs) extend dorsal branches at the region abutting the vulval muscles. The drag-1 mutants exhibited defects in HSN axon branching in addition to a small body size phenotype. DRAG-1 expression in the hypodermal cells was required for the branching of the axons. Although DRAG-1 is normally expressed in the ventral hypodermis excepting the vulval region, its ectopic expression in vulval precursor cells was sufficient to induce the branching. The C-terminal glycosylphosphatidylinositol anchor of DRAG-1 was important for its function, suggesting that DRAG-1 should be anchored to the cell surface. Genetic analyses suggested that the membrane receptor UNC-40 acts in the same pathway with DRAG-1 in HSN branching. We propose that DRAG-1 expressed in the ventral hypodermis signals via the UNC-40 receptor expressed in HSNs to elicit branching activity of HSN axons.
Dysfunction of invariant natural killer T (iNKT) cells contributes to immune resistance of tumors. Most mechanistic studies focus on their static functional status before or after activation, not ...considering motility as an important characteristic for antigen scanning and thus anti-tumor capability. Here we show via intravital imaging, that impaired motility of iNKT cells and their exclusion from tumors both contribute to the diminished anti-tumor iNKT cell response. Mechanistically, CD1d, expressed on macrophages, interferes with tumor infiltration of iNKT cells and iNKT-DC interactions but does not influence their intratumoral motility. VCAM1, expressed by cancer cells, restricts iNKT cell motility and inhibits their antigen scanning and activation by DCs via reducing CDC42 expression. Blocking VCAM1-CD49d signaling improves motility and activation of intratumoral iNKT cells, and consequently augments their anti-tumor function. Interference with macrophage-iNKT cell interactions further enhances the anti-tumor capability of iNKT cells. Thus, our findings provide a direction to enhance the efficacy of iNKT cell-based immunotherapy via motility regulation.
The latest industrial revolution, Industry 4.0, is driven by the emergence of digital manufacturing and, most notably, additive manufacturing (AM) technologies. The simultaneous material and ...structure forming in AM broadens the material and structural design space. This expanded design space holds a great potential in creating improved engineering materials and products that attract growing interests from both academia and industry. A major aspect of this growing interest is reflected in the increased adaptation of data‐driven tools that accelerate the exploration of the vast design space in AM. Herein, the integration of data‐driven tools in various aspects of AM is reviewed, from materials design in AM (i.e., homogeneous and composite material design) to structure design for AM (i.e., topology optimization). The optimization of AM tool path using machine learning for producing best‐quality AM products with optimal material and structure is also discussed. Finally, the perspectives on the future development of holistically integrated frameworks of AM and data‐driven methods are provided.
Additive manufacturing (AM) emerges as a digital manufacturing method with introduction of Industry 4.0, enables not only realization of more novel designs, but also advantageous integration with artificial intelligence. Herein, showcasing examples of successful integration of AI and data‐driven approaches in making AM a smarter manufacturing method is focused on and opportunities for future development are discussed.
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