PdMo nanoflowers for endogenous/exogenous-stimulated nanocatalytic therapy.

The clinical application of reactive oxygen species (ROS)-mediated tumor treatment has been critically limited by inefficient ROS generation. Herein, we rationally synthesized and constructed the three-dimensional PdMo nanoflowers through a one-pot solvothermal reduction method for elaborately regulated peroxidase-like enzymatic activity and glutathione peroxidase-like enzymatic activity, to promote oxidation ROS evolvement and antioxidation glutathione depletion for achieving intensive ROS-mediated tumor therapy. The three-dimensional superstructure composed of two-dimensional nanosheet subunits can solve the issues by avoiding the appearance of tightly stacked crystalline nanostructures. Significantly, Mo is chosen as a second metal to alloy with Pd because of its more chemical valence and negative ionization energy than Pd for improved electron transfer efficiencies and enhanced enzyme-like activities. In addition, the photothermal effect generated by PdMo nanoflowers could also enhance its enzymatic activities. Thus, this work provides a promising paradigm for achieving highly ROS-mediated tumor therapeutic efficacy by regulating the multi-enzymatic activities of Pd-based nanoalloys.

Osteosarcoma Explorer: A Data Commons With Clinical, Genomic, Protein, and Tissue Imaging Data for Osteosarcoma Research.

PURPOSE: Osteosarcoma research advancement requires enhanced data integration across different modalities and sources. Current osteosarcoma research, encompassing clinical, genomic, protein, and tissue imaging data, is hindered by the siloed landscape of data generation and storage. MATERIALS AND METHODS: Clinical, molecular profiling, and tissue imaging data for 573 patients with pediatric osteosarcoma were collected from four public and institutional sources. A common data model incorporating standardized terminology was created to facilitate the transformation, integration, and load of source data into a relational database. On the basis of this database, a data commons accompanied by a user-friendly web portal was developed, enabling various data exploration and analytics functions. RESULTS: The Osteosarcoma Explorer (OSE) was released to the public in 2021. Leveraging a comprehensive and harmonized data set on the backend, the OSE offers a wide range of functions, including Cohort Discovery, Patient Dashboard, Image Visualization, and Online Analysis. Since its initial release, the OSE has experienced an increasing utilization by the osteosarcoma research community and provided solid, continuous user support. To our knowledge, the OSE is the largest (N = 573) and most comprehensive research data commons for pediatric osteosarcoma, a rare disease. This project demonstrates an effective framework for data integration and data commons development that can be readily applied to other projects sharing similar goals. CONCLUSION: The OSE offers an online exploration and analysis platform for integrated clinical, molecular profiling, and tissue imaging data of osteosarcoma. Its underlying data model, database, and web framework support continuous expansion onto new data modalities and sources.

A Deep Learning Approach for Histology-Based Nucleus Segmentation and Tumor Microenvironment Characterization.

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.

Development of Fmoc-Protected Bis-Amino Acids toward Automated Synthesis of Highly Functionalized Spiroligomers.

We report the fluorenylmethoxycarbonyl (Fmoc) protection of functionalized bis-amino acid building blocks using a temporary Cu2+ complexation strategy, together with an efficient multikilogram-scale synthesis of bis-amino acid precursors. This allows the synthesis of stereochemically and functionally diverse spiroligomers utilizing solid-phase Fmoc/tBu chemistry to facilitate the development of applications. Four tetramers were assembled on a semiautomated microwave peptide synthesizer. We determined their secondary structures with two-dimensional nuclear magnetic resonance spectroscopy.

Using UAV image data to monitor the effects of different nitrogen application rates on tea quality.

BACKGROUND: Accurate and efficient evaluation of the effect of nitrogen application rate on tea quality is of great significance for nitrogen management in a tea garden. However, previous methods were all through soil or leaf sampling, using biochemical methods for laboratory testing. These methods are not only less one-time detection samples, but also time-consuming, laborious and inefficient. Therefore, the development of fast, efficient and non-destructive diagnostic methods is an important goal in this field. RESULTS: We obtained spectral information on the tea canopy using a multispectral camera carried by an unmanned aerial vehicle (UAV), and extracted the average DN value of the experimental plot by environmental visual imagery (ENVI); we finally obtained 28 spectral parameters. By analyzing the correlation between spectral parameters and ground parameters measured synchronously, five spectral parameters with high correlation were selected. Finally, the prediction models of tea nitrogen, polyphenol and amino acid content were established by using support vector machine (SVM), partial least squares and backpropagation neural network. Through modeling comparison and coefficient verification, the results show that the ground parameters measured in the laboratory were in good agreement with the results estimated by the model. The SVM model had the best performance in predicting nitrogen and tea polyphenol content, with R2  = 0.7583 and 0.7533, root mean square error of prediction (RMSEP) = 0.4086 and 0.3392, and normalized RMSEP (NRMSEP) = 1.23 and 1.28, respectively. The partial least squares regression model had the best performance in predicting amino acid content, with R2  = 0.7597, RMSEP = 0.1176 and NRMSEP = 4.10. CONCLUSION: The results show that the model based on UAV image data and machine learning algorithm can effectively detect the main biochemical components of the tea plant, which provides an important basis for tea garden management. © 2021 Society of Chemical Industry.

A deep learning-based model for screening and staging pneumoconiosis.

This study aims to develop an artificial intelligence (AI)-based model to assist radiologists in pneumoconiosis screening and staging using chest radiographs. The model, based on chest radiographs, was developed using a training cohort and validated using an independent test cohort. Every image in the training and test datasets were labeled by experienced radiologists in a double-blinded fashion. The computational model started by segmenting the lung field into six subregions. Then, convolutional neural network classification model was used to predict the opacity level for each subregion respectively. Finally, the diagnosis for each subject (normal, stage I, II, or III pneumoconiosis) was determined by summarizing the subregion-based prediction results. For the independent test cohort, pneumoconiosis screening accuracy was 0.973, with both sensitivity and specificity greater than 0.97. The accuracy for pneumoconiosis staging was 0.927, better than that achieved by two groups of radiologists (0.87 and 0.84, respectively). This study develops a deep learning-based model for screening and staging of pneumoconiosis using man-annotated chest radiographs. The model outperformed two groups of radiologists in the accuracy of pneumoconiosis staging. This pioneer work demonstrates the feasibility and efficiency of AI-assisted radiography screening and diagnosis in occupational lung diseases.

Developing and Using a Data Commons for Understanding the Molecular Characteristics of Germ Cell Tumors.

Germ cell tumors (GCTs) are a rare disease, but they account for 15% of all malignancies diagnosed during adolescence. The biological mechanisms underpinning their development are only starting to be explored. Current GCT treatment may be associated with significant toxicity. Therefore, there is an urgent need to understand the molecular basis of GCT and identify biomarkers to tailor the therapy for individual patients. However, this research is severely hamstrung by the rarity of GCTs in individual hospitals/institutes. A publicly available genomic data commons with GCT datasets compiled from different institutes/studies would be a valuable resource to facilitate such research. In this study, we first reviewed publicly available web portals containing GCT genomics data, focusing on comparing data availability, data access, and analysis tools, and the limitations of using these resources for GCT molecular studies. Next, we specifically designed a GCT data commons with a web portal, GCT Explorer, to assist the research community to store, manage, search, share, and analyze data. The goal of this work is to facilitate GCT molecular basis exploration and translational research.

Development of a Data Model and Data Commons for Germ Cell Tumors.

Germ cell tumors (GCTs) are considered a rare disease but are the most common solid tumors in adolescents and young adults, accounting for 15% of all malignancies in this age group. The rarity of GCTs in some groups, particularly children, has impeded progress in treatment and biologic understanding. The most effective GCT research will result from the interrogation of data sets from historical and prospective trials across institutions. However, inconsistent use of terminology among groups, different sample-labeling rules, and lack of data standards have hampered researchers' efforts in data sharing and across-study validation. To overcome the low interoperability of data and facilitate future clinical trials, we worked with the Malignant Germ Cell International Consortium (MaGIC) and developed a GCT clinical data model as a uniform standard to curate and harmonize GCT data sets. This data model will also be the standard for prospective data collection in future trials. Using the GCT data model, we developed a GCT data commons with data sets from both MaGIC and public domains as an integrated research platform. The commons supports functions, such as data query, management, sharing, visualization, and analysis of the harmonized data, as well as patient cohort discovery. This GCT data commons will facilitate future collaborative research to advance the biologic understanding and treatment of GCTs. Moreover, the framework of the GCT data model and data commons will provide insights for other rare disease research communities into developing similar collaborative research platforms.

Mice With Increased Numbers of Polyploid Hepatocytes Maintain Regenerative Capacity But Develop Fewer Hepatocellular Carcinomas Following Chronic Liver Injury.

BACKGROUND & AIMS: Thirty to 90% of hepatocytes contain whole-genome duplications, but little is known about the fates or functions of these polyploid cells or how they affect development of liver disease. We investigated the effects of continuous proliferative pressure, observed in chronically damaged liver tissues, on polyploid cells. METHODS: We studied Rosa-rtTa mice (controls) and Rosa-rtTa;TRE-short hairpin RNA mice, which have reversible knockdown of anillin, actin binding protein (ANLN). Transient administration of doxycycline increases the frequency and degree of hepatocyte polyploidy without permanently altering levels of ANLN. Mice were then given diethylnitrosamine and carbon tetrachloride (CCl4) to induce mutations, chronic liver damage, and carcinogenesis. We performed partial hepatectomies to test liver regeneration and then RNA-sequencing to identify changes in gene expression. Lineage tracing was used to rule out repopulation from non-hepatocyte sources. We imaged dividing hepatocytes to estimate the frequency of mitotic errors during regeneration. We also performed whole-exome sequencing of 54 liver nodules from patients with cirrhosis to quantify aneuploidy, a possible outcome of polyploid cell divisions. RESULTS: Liver tissues from control mice given CCl4 had significant increases in ploidy compared with livers from uninjured mice. Mice with knockdown of ANLN had hepatocyte ploidy above physiologic levels and developed significantly fewer liver tumors after administration of diethylnitrosamine and CCl4 compared with control mice. Increased hepatocyte polyploidy was not associated with altered regenerative capacity or tissue fitness, changes in gene expression, or more mitotic errors. Based on lineage-tracing experiments, non-hepatocytes did not contribute to liver regeneration in mice with increased polyploidy. Despite an equivalent rate of mitosis in hepatocytes of differing ploidies, we found no lagging chromosomes or micronuclei in mitotic polyploid cells. In nodules of human cirrhotic liver tissue, there was no evidence of chromosome-level copy number variations. CONCLUSIONS: Mice with increased polyploid hepatocytes develop fewer liver tumors following chronic liver damage. Remarkably, polyploid hepatocytes maintain the ability to regenerate liver tissues during chronic damage without generating mitotic errors, and aneuploidy is not commonly observed in cirrhotic livers. Strategies to increase numbers of polypoid hepatocytes might be effective in preventing liver cancer.

Computational Staining of Pathology Images to Study the Tumor Microenvironment in Lung Cancer.

The spatial organization of different types of cells in tumor tissues reveals important information about the tumor microenvironment (TME). To facilitate the study of cellular spatial organization and interactions, we developed Histology-based Digital-Staining, a deep learning-based computation model, to segment the nuclei of tumor, stroma, lymphocyte, macrophage, karyorrhexis, and red blood cells from standard hematoxylin and eosin-stained pathology images in lung adenocarcinoma. Using this tool, we identified and classified cell nuclei and extracted 48 cell spatial organization-related features that characterize the TME. Using these features, we developed a prognostic model from the National Lung Screening Trial dataset, and independently validated the model in The Cancer Genome Atlas lung adenocarcinoma dataset, in which the predicted high-risk group showed significantly worse survival than the low-risk group (P = 0.001), with a HR of 2.23 (1.37-3.65) after adjusting for clinical variables. Furthermore, the image-derived TME features significantly correlated with the gene expression of biological pathways. For example, transcriptional activation of both the T-cell receptor and programmed cell death protein 1 pathways positively correlated with the density of detected lymphocytes in tumor tissues, while expression of the extracellular matrix organization pathway positively correlated with the density of stromal cells. In summary, we demonstrate that the spatial organization of different cell types is predictive of patient survival and associated with the gene expression of biological pathways. SIGNIFICANCE: These findings present a deep learning-based analysis tool to study the TME in pathology images and demonstrate that the cell spatial organization is predictive of patient survival and is associated with gene expression.See related commentary by Rodriguez-Antolin, p. 1912.

Correction: LCE: an open web portal to explore gene expression and clinical associations in lung cancer.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

SCINA: A Semi-Supervised Subtyping Algorithm of Single Cells and Bulk Samples.

Advances in single-cell RNA sequencing (scRNA-Seq) have allowed for comprehensive analyses of single cell data. However, current analyses of scRNA-Seq data usually start from unsupervised clustering or visualization. These methods ignore prior knowledge of transcriptomes and the probable structures of the data. Moreover, cell identification heavily relies on subjective and possibly inaccurate human inspection afterwards. To address these analytical challenges, we developed SCINA (Semi-supervised Category Identification and Assignment), a semi-supervised model that exploits previously established gene signatures using an expectation-maximization (EM) algorithm. SCINA is applicable to scRNA-Seq and flow cytometry/CyTOF data, as well as other data of similar format. We applied SCINA to a wide range of datasets, and showed its accuracy, stability and efficiency, which exceeded most popular unsupervised approaches. SCINA discovered an intermediate stage of oligodendrocytes from mouse brain scRNA-Seq data. SCINA also detected immune cell population changes in cytometry data in a genetically-engineered mouse model. Furthermore, SCINA performed well with bulk gene expression data. Specifically, we identified a new kidney tumor clade with similarity to FH-deficient tumors (FHD), which we refer to as FHD-like tumors (FHDL). Overall, SCINA provides both methodological advances and biological insights from perspectives different from traditional analytical methods.

Systematic Analysis of Gene Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma with a Case Study of FAM83A and FAM83B.

INTRODUCTION: In our previous study, we constructed a Lung Cancer Explorer (LCE) database housing lung cancer-specific expression data and clinical data from over 6700 patients in 56 studies. METHODS: Using this dataset of the largest collection of lung cancer gene expression along with our meta-analysis method, we systematically interrogated the association between gene expression and overall survival as well as the expression difference between tumor and normal (adjacent non-malignant tissue) samples in lung adenocarcinoma (ADC) and lung squamous cell carcinoma (SQCC). A case study for FAM83A and FAM83B was performed as a demonstration for hypothesis testing with our database. RESULTS: We showed that the reproducibility of results across studies varied by histological subtype and analysis type. Genes and pathways unique or common to the two histological subtypes were identified and the results were integrated into LCE to facilitate user exploration. In our case study, we verified the findings from a previous study on FAM83A and FAM83B in non-small cell lung cancer. CONCLUSIONS: This study used gene expression data from a large cohort of patients to explore the molecular differences between lung ADC and SQCC.

LCE: an open web portal to explore gene expression and clinical associations in lung cancer.

We constructed a lung cancer-specific database housing expression data and clinical data from over 6700 patients in 56 studies. Expression data from 23 genome-wide platforms were carefully processed and quality controlled, whereas clinical data were standardized and rigorously curated. Empowered by this lung cancer database, we created an open access web resource-the Lung Cancer Explorer (LCE), which enables researchers and clinicians to explore these data and perform analyses. Users can perform meta-analyses on LCE to gain a quick overview of the results on tumor vs non-malignant tissue (normal) differential gene expression and expression-survival association. Individual dataset-based survival analysis, comparative analysis, and correlation analysis are also provided with flexible options to allow for customized analyses from the user.

Development of novel affinity reagents for detecting protein tyrosine phosphorylation based on superbinder SH2 domain in tumor cells.

Tyrosine phosphorylation, as a hallmark in cellular signal transduction, is important for a diverse array of cellular processes, such as proliferation, metabolism, motility, and survival. Aberrant tyrosine phosphorylation plays a causal role in many diseases, especially the cancer. Detecting protein phosphorylation status in the cancer cells or tissues is vital for assessing the pathological phase, discovering the cancer biomarkers, and identifying the drug targets. However, the common biochemical detection methods remain through anti-pTyr antibodies, which are known to have limited sensitivity, poor reproducibility and high cost. Recent studies have proved that superbinder SH2 domain is a good replacement of anti-pTyr antibodies for the specific enrichment of pTyr peptides in phosphoproteomics analysis. In this work, we exploited a series of affinity reagents based on superbinder SH2 derived from Src protein for detecting the pTyr-containing proteins to replace anti-pY antibodies in immunoblotting and immunofluorescence techniques. The excellent performance of HRP-sSH2 and EGFP-sSH2 was verified by the analysis of several different tumor cell samples and was compared with most commonly used commercial antibodies. EGFP-sSH2-(Arg)9 might be applied as the probe for direct fluorescence imaging in live cells via efficiently penetrating cell membranes and specifically binding with pTyr proteins. In summary, we have developed three novel, convenient, sensitive, and cost-effective affinity reagents that would have wide applications in protein tyrosine phosphorylation analysis for the tumor research and clinical diagnosis.

(Arg)9-SH2 superbinder: a novel promising anticancer therapy to melanoma by blocking phosphotyrosine signaling.

BACKGROUND: Melanoma is a malignant tumor with high misdiagnosis rate and poor prognosis. The bio-targeted therapy is a prevailing method in the treatment of melanoma; however, the accompanying drug resistance is inevitable. SH2 superbinder, a triple-mutant of the Src Homology 2 (SH2) domain, shows potent antitumor ability by replacing natural SH2-containing proteins and blocking multiple pY-based signaling pathways. Polyarginine (Arg)9, a powerful vector for intracellular delivery of large molecules, could transport therapeutic agents across cell membrane. The purpose of this study is to construct (Arg)9-SH2 superbinder and investigate its effects on melanoma cells, expecting to provide potential new approaches for anti-cancer therapy and overcoming the unavoidable drug resistance of single-targeted antitumor agents. METHODS: (Arg)9 and SH2 superbinder were fused to form (Arg)9-SH2 superbinder via genetic engineering. Pull down assay was performed to identify that (Arg)9-SH2 superbinder could capture a wide variety of pY proteins. Immunofluorescence was used to detect the efficiency of (Arg)9-SH2 superbinder entering cells. The proliferation ability was assessed by MTT and colony formation assay. In addition, wound healing and transwell assay were performed to evaluate migration of B16F10, A375 and A375/DDP cells. Moreover, apoptosis caused by (Arg)9-SH2 superbinder was analyzed by flow cytometry-based Annexin V/PI. Furthermore, western blot revealed that (Arg)9-SH2 superbinder influenced some pY-related signaling pathways. Finally, B16F10 xenograft model was established to confirm whether (Arg)9-SH2 superbinder could restrain the growth of tumor. RESULTS: Our data showed that (Arg)9-SH2 superbinder had the ability to enter melanoma cells effectively and displayed strong affinities for various pY proteins. Furthermore, (Arg)9-SH2 superbinder could repress proliferation, migration and induce apoptosis of melanoma cells by regulating PI3K/AKT, MAPK/ERK and JAK/STAT signaling pathways. Importantly, (Arg)9-SH2 superbinder could significantly inhibit the growth of tumor in mice. CONCLUSIONS: (Arg)9-SH2 superbinder exhibited high affinities for pY proteins, which showed effective anticancer ability by replacing SH2-containing proteins and blocking diverse pY-based pathways. The remarkable ability of (Arg)9-SH2 superbinder to inhibit cancer cell proliferation and tumor growth might open the door to explore the SH2 superbinder as a therapeutic agent for cancer treatment.

An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors.

By leveraging tumorgraft (patient-derived xenograft) RNA-sequencing data, we developed an empirical approach, DisHet, to dissect the tumor microenvironment (eTME). We found that 65% of previously defined immune signature genes are not abundantly expressed in renal cell carcinoma (RCC) and identified 610 novel immune/stromal transcripts. Using eTME, genomics, pathology, and medical record data involving >1,000 patients, we established an inflamed pan-RCC subtype (IS) enriched for regulatory T cells, natural killer cells, TH1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. IS is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. The IS subtype correlated with systemic manifestations of inflammation such as thrombocytosis and anemia, which are enigmatic predictors of poor prognosis. Furthermore, IS was a strong predictor of poor survival. Our analyses suggest that tumor cells drive the stromal immune response. These data provide a missing link between tumor cells, the TME, and systemic factors.Significance: We undertook a novel empirical approach to dissect the renal cell carcinoma TME by leveraging tumorgrafts. The dissection and downstream analyses uncovered missing links between tumor cells, the TME, systemic manifestations of inflammation, and poor prognosis. Cancer Discov; 8(9); 1142-55. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 1047.