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BackgroundDesmoplastic small round cell tumor (DSRCT) was a rare and aggressive malignant tumor mostly occurring in the abdominal and pelvic cavity of young patients. However, few cases had been reported concerning DSRCT located in the head and neck region. Case presentationA 25-year-old man was referred to our hospital due to the enlargement of the right submandibular mass. Magnetic resonance imaging revealed a 3 cm×2 cm solid nodule located in the right submandibular, and physical examination showed no other occupying lesion elsewhere. Histologically, the tumor was composed of various-sized small round cell nests, embedded in an abundant desmoplastic stroma. Immunohistochemically, the tumor cells were typically positive for epithelial (CK and EMA), mesenchymal (Vimentin and Desmin), and neuroendocrine marker (CD56, NSE, Syn and CgA), but negative for WT-1. Fluorescence in situ hybridization (FISH) revealed the presence of a break-apart involving Ewing sarcoma region 1 (EWSR1) gene. This tumor was finally diagnosed as DSRCT in the right submandibular gland. The patient received chemotherapy and radiotherapy, and showed recurrence after 17 months of follow-up.ConclusionsDSRCT of the submandibular gland is rare, the diagnosis of this tumor in uncommon location relies on the histomorphology, immunophenotype and EWSR1 gene detection. Differential diagnosis including primary salivary gland tumors and the other small round cell tumors needs to be excluded.

Chemotherapy is the most common clinical treatment for non-small cell lung cancer (NSCLC), but low efficiency and high toxicity of current chemotherapy drugs limit their clinical application. Therefore, it is urgent to develop hypotoxic and efficient chemotherapy drugs. Theophylline, a natural compound, is safe and easy to get, and it can be used as a modified scaffold structure and hold huge potential for developing safe and efficient antitumor drugs. Herein, we linked theophylline with different azide compounds to synthesize a new type of 1,2,3-triazole ring-containing theophylline derivatives. We found that some theophylline1,2,3-triazole compounds showed a good tumor-suppressive efficacy. Especially, derivative d17 showed strong antiproliferative activity against a variety of cancer cells in vitro, including H460, A549, A2780, LOVO, MB-231, MCF-7, OVCAR3, SW480, and PC-9. It is worth noting that the two NSCLC cell lines H460 H and A549 are sensitive to compound d17 particularly, with IC50 of 5.929 ± 0.97 μM and 6.76 ± 0.25 μM, respectively. Compound d17 can significantly induce cell apoptosis by increasing the ratio of apoptotic protein Bax/Bcl-2 by downregulating the expression of phosphorylated Akt protein, and it has little toxicity to normal hepatocyte cells LO2 at therapeutic concentrations. These data indicate that these theophylline acetic acid-1,2,3-triazole derivatives may be potential drug candidates for anti-NSCLC and are worthy of further study.

Type 2 diabetes (T2D) is a rapidly growing epidemic, which leads to increased mortality rates and health care costs. Nutrients (namely, carbohydrates, fat, protein, mineral substances, and vitamin), sensing, and management are central to metabolic homeostasis, therefore presenting a leading factor contributing to T2D. Understanding the comprehensive effects and the underlying mechanisms of nutrition in regulating glucose metabolism and the interactions of diet with genetics, epigenetics, and gut microbiota is helpful for developing new strategies to prevent and treat T2D. In this review, we discuss different mechanistic pathways contributing to T2D and then summarize the current researches concerning associations between different nutrients intake and glucose homeostasis. We also explore the possible relationship between nutrients and genetic background, epigenetics, and metagenomics in terms of the susceptibility and treatment of T2D. For the specificity of individual, precision nutrition depends on the person’s genotype, and microbiota is vital to the prevention and intervention of T2D.

Background High-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate the immune response of the T cell. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response. Methods Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Based on our previous discoveries, we propose to investigate the distribution of neoantigens with different immunogenicity abilities in 3D genome and propose to adopt this important information into neoantigen prediction. Results We retrospect the DNA origins of the immuno-positive and immuno-negative neoantigens in the context of 3D genome and discovered that DNA loci of the immuno-positive neoantigens and immuno-negative neoantigens have very different distribution pattern. Specifically, comparing to the background 3D genome, DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We thus used this information into neoantigen prediction and demonstrated the effectiveness of this approach. Conclusion We believe that the 3D genome information will help to increase the precision of neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

Metabolism plays crucial roles in the fate decision of human embryonic stem cells (hESCs). Here, we show that the depletion of p53 in hESCs enhances glycolysis and reduces oxidative phosphorylation, and delays mesendoderm differentiation of hESCs. More intriguingly, the disruption of p53 in hESCs leads to dramatic upregulation of phosphatidylcholine and decrease of total choline in both pluripotent and differentiated state of hESCs, suggesting abnormal choline metabolism in the absence of p53. Collectively, our study reveals the indispensable role of p53 in orchestrating both glucose and lipid metabolism to maintain proper hESC identity.

The metabolic switch from oxidative phosphorylation to glycolysis is required for tumorigenesis in order to provide cancer cells with energy and substrates of biosynthesis. Therefore, it is important to elucidate mechanisms controlling the cancer metabolic switch. MTR4 is a RNA helicase associated with a nuclear exosome that plays key roles in RNA processing and surveillance. We demonstrate that MTR4 is frequently overexpressed in hepatocellular carcinoma (HCC) and is an independent diagnostic marker predicting the poor prognosis of HCC patients. MTR4 drives cancer metabolism by ensuring correct alternative splicing of pre-mRNAs of critical glycolytic genes such as GLUT1 and PKM2. c-Myc binds to the promoter of the MTR4 gene and is important for MTR4 expression in HCC cells, indicating that MTR4 is a mediator of the functions of c-Myc in cancer metabolism. These findings reveal important roles of MTR4 in the cancer metabolic switch and present MTR4 as a promising therapeutic target for treating HCC., Aberrant alternative splicing has been shown to contribute to the tumorigenic processes. Here, the authors show that MTR4 is overexpressed in hepatocellular carcinoma and has a role in tumorigenesis through the modulation of the splicing of glycolytic genes PKM2 and GLUT1.

Ovarian cancer is one of the most serious female malignancies worldwide. Despite intensive efforts being made to overcome ovarian cancer, there still remain limited optional treatments for this disease. Nobiletin, a prospective food-derived phytochemical extracted from citrus fruits, has recently been reported to suppress ovarian cancer cells, but the role of pyroptosis in ovarian carcinoma with nobiletin still remains unknown. In this study, we aim to explore the effect of nobiletin on ovarian carcinoma and further expound the underlying mechanisms of nobiletin-induced ovarian cancer cell death. Our results showed that nobiletin could significantly inhibit cell proliferation, induce DNA damage, and also lead to apoptosis by increasing the cleaved poly (ADP-ribose) polymerase (PARP) level of human ovarian cancer cells (HOCCs) in a dose-dependent manner. Moreover, we revealed that nobiletin decreased mitochondrial membrane potential and induced reactive oxygen species (ROS) generation and autophagy of HOCCs, contributing to gasdermin D-/gasdermin E-mediated pyroptosis. Taken together, nobiletin as a functional food ingredient represents a promising new anti-ovarian cancer candidate that could induce apoptosis and trigger ROS-mediated pyroptosis through regulating autophagy in ovarian cancer cells.

Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: the low fraction of tumor DNA in cfDNA, the molecular heterogeneity of cancer, and sample sizes that are too small to reflect the heterogeneous patient population. We have developed an integrated cancer detection system, CancerRadar, that addresses all three challenges. It consists of (1) a cost-effective experimental assay, cfMethyl-Seq, for genome-wide methylation profiling of cfDNA, which provides >12-fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in CpG islands; and (2) a computational platform to extract information from cfMethyl-Seq data and diagnose the patient. The platform derives cfDNA methylations, cfDNA fragment sizes, copy number variations (CNV), and microbial composition from the raw cfMethyl-Seq data, and performs multi-feature ensemble learning. We demonstrate the power of CancerRadar by detecting and locating cancer in a cohort of 275 colon, liver, lung, and stomach cancer patients and 204 non-cancer individuals. For cancer detection, we achieve a sensitivity of 85.6%± 6.7% across all stages and 80.6%±9.1% for early stages (I and II), with a specificity of 99% in both cases. These metrics are derived using leave-one-out cross-validation. During independent validation on a reserved subsample, it achieves a sensitivity of 89.1%±11.3% across all stages and 85.7%±14.2% for early stages, with a specificity of 97% (one false positive). For locating a tumor's tissue of origin (TOO), CancerRadar achieved an accuracy of 91.5%±5.0% for all stages and 89.1%±7.3% for early stages, on an independent subsample. This study is the first to integrate cfDNA methylation, cfDNA fragment size, CNV, and microbial composition analyses for cancer detection on the same patient cohort. cfDNA methylation was the most useful for detecting cancer, but including features from other categories significantly increased the performance, especially for early-stage cancer. In contrast, with respect to TOO prediction, methylation-derived features were overwhelmingly important while including other features did not further improve performance. To fully exploit the power of cfDNA methylation, we identified four types of methylation markers with different characteristics. We have also improved our previous read-level deconvolution algorithm to more accurately identify trace tumor signals. Finally, our data show that as training sample sizes increase, the detection power of CancerRadar continues to increase. Although all existing cancer detection studies are limited by training sample sizes, the CancerRadar system uniquely and cost-effectively retains the genome-wide epigenetic and genetic profiles of cancer abnormalities, thereby permitting the classification models to learn and exploit newly significant features as training cohorts grow, as well as expanding their scope to other cancer types. Citation Format: Mary Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin Jung Chen, Paul Winograd, Shize Li, Zorawar Noor, Edward Garon, Samuel French, Clara Magyar, Sarah Dry, Clara Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven Dubinett, Denise Aberle, Vatche Agopian, Steven-Huy Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou. Multi-feature ensemble learning on cell-free dna for accurately detecting and locating cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 24.

Circulating cell-free DNAs (cfDNAs) are fragmented DNA molecules released into the blood by cells. Previous studies have suggested that mitochondria-originated cfDNA fragments (mt-cfDNAs) in cancer patients are more fragmented than those from healthy controls. However, it is still unknown where these short mt-cfDNAs originate, and whether the length of mt-cfDNAs can be correlated with tumor burden and cancer progression. In this study, we first performed whole-genome sequencing analysis (WGS) of cfDNAs from a human tumor cell line-xenotransplantation mouse model and found that mt-cfDNAs released from transplanted tumor cells were shorter than the mouse counterpart. We next analyzed blood cfDNA samples from hepatocellular carcinoma and prostate cancer patients and found that mt-cfDNA lengths were inversely related to tumor size as well as the concentration of circulating tumor DNA. Our study suggested that monitoring the size of mt-cfDNAs in cancer patients would be a useful way to estimate tumor burden and cancer progression.

The high-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate immune response of the T cells. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response. Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Here we retrospect the DNA origins of the immune-positive and non-negative neoantigens in the context of 3D genome and discovered that 1) DNA loci of the immuno-positive neoantigens tend to cluster genome-wise. 2) DNA loci of the immuno-positive neoantigens tend to belong to active chromosomal compartment (compartment A) in some chromosomes. 3). DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We believe that the 3D genome information will help more precise neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

BackgroundGenome structures are dynamic and non-randomly organized in the nucleus of higher eukaryotes. To maximize the accuracy and coverage of three-dimensional genome structural models, it is important to integrate all available sources of experimental information about a genome’s organization. It remains a major challenge to integrate such data from various complementary experimental methods. Here, we present an approach for data integration to determine a population of complete three-dimensional genome structures that are statistically consistent with data from both genome-wide chromosome conformation capture (Hi-C) and lamina-DamID experiments.ResultsOur structures resolve the genome at the resolution of topological domains, and reproduce simultaneously both sets of experimental data. Importantly, this data deconvolution framework allows for structural heterogeneity between cells, and hence accounts for the expected plasticity of genome structures. As a case study we chooseDrosophila melanogasterembryonic cells, for which both data types are available. Our three-dimensional genome structures have strong predictive power for structural features not directly visible in the initial data sets, and reproduce experimental hallmarks of theD. melanogastergenome organization from independent and our own imaging experiments. Also they reveal a number of new insights about genome organization and its functional relevance, including the preferred locations of heterochromatic satellites of different chromosomes, and observations about homologous pairing that cannot be directly observed in the original Hi-C or lamina-DamID data.ConclusionsOur approach allows systematic integration of Hi-C and lamina-DamID data for complete three-dimensional genome structure calculation, while also explicitly considering genome structural variability.

Genome structures are dynamic and non-randomly organized in the nucleus of higher eukaryotes. To maximize the accuracy and coverage of 3D genome structural models, it is important to integrate all available sources of experimental information about a genome's organization. It remains a major challenge to integrate such data from various complementary experimental methods. Here, we present an approach for data integration to determine a population of complete 3D genome structures that are statistically consistent with data from both genome-wide chromosome conformation capture (Hi-C) and lamina-DamID experiments. Our structures resolve the genome at the resolution of topological domains, and reproduce simultaneously both sets of experimental data. Importantly, this framework allows for structural heterogeneity between cells, and hence accounts for the expected plasticity of genome structures. As a case study we choose Drosophila melanogaster embryonic cells, for which both data types are available. Our 3D geome structures have strong predictive power for structural features not directly visible in the initial data sets, and reproduce experimental hallmarks of the D. melanogaster genome organization from independent and our own imaging experiments. Also they reveal a number of new insights about the genome organization and its functional relevance, including the preferred locations of heterochromatic satellites of differnet chromosomes, and observations about homologous pairing that cannot be directly observed in the original Hi-C or lamina-DamID data. To our knowledge our approach is the first that allows systematic integration of Hi-C and lamina DamID data for complete 3D genome structure calculation, while also explicitly considering genome structural variability. Keywords: 3D genome structure, higher order genome organization, population-based modeling, data integration, Hi-C, lamina-DamID, homologous pairing, Drosophila melanogaster.

Supplementary methods Aâ D and Supplementary Figures S1â S14. (DOCX 1764Â kb)

Supplementary information. A PDF file including Figures S1 and S2, Tables S1 and S2, as well as the details of background bias estimation of CpG read counts, the RF and SVM methods, and CancerLocatorâ s prediction results on simulation data with different levels of CNA events. (PDF 558 kb)

Three-dimensional (3D) genome structures vary from cell to cell even in an isogenic sample. Unlike protein structures, genome structures are highly plastic, posing a significant challenge for structure-function mapping. Here we report an approach to comprehensively identify 3D chromatin clusters that each occurs frequently across a population of genome structures, either deconvoluted from ensemble-averaged Hi-C data or from a collection of single-cell Hi-C data. Applying our method to a population of genome structures (at the macrodomain resolution) of lymphoblastoid cells, we identify an atlas of stable inter-chromosomal chromatin clusters. A large number of these clusters are enriched in binding of specific regulatory factors and are therefore defined as ‘Regulatory Communities.' We reveal two major factors, centromere clustering and transcription factor binding, which significantly stabilize such communities. Finally, we show that the regulatory communities differ substantially from cell to cell, indicating that expression variability could be impacted by genome structures., 3D genome structures are plastic and vary from cell to cell even in an isogenic sample. Here, the authors present an approach to identify frequent 3D chromatin clusters across a population of genome structures, either deconvoluted from ensemble-averaged Hi-C data or from a collection of single-cell Hi-C data.

In order to improve the compatibility of polycarbonate (PC) and acrylonitrile– butadiene–styrene (ABS), a new type of reactive compatibilizer, methyl methacrylate graft acrylonitrile–butadiene–styrene (MABS) tetramer, was synthesized. The structure and properties of PC/ABS (70/30) blend with various MABS ratios were studied in terms of their mechanical and morphological properties. The results indicated that with the addition of MABS, the glass transition temperature (Tg) of the PC and ABS phases were closer to each other. Addition of MABS decreased the domain size of the ABS dispersed phase, making the dispersed phase well distributed, and the interfacial cohesiveness was enhanced. Notched impact strength and elongation at break of the PC/ABS (70/30) blend increased remarkably with the addition of MABS, with a small drop in the tensile strength.

Motivation: It is well known that microRNAs (miRNAs) and genes work cooperatively to form the key part of gene regulatory networks. However, the specific functional roles of most miRNAs and their combinatorial effects in cellular processes are still unclear. The availability of multiple types of functional genomic data provides unprecedented opportunities to study the miRNA–gene regulation. A major challenge is how to integrate the diverse genomic data to identify the regulatory modules of miRNAs and genes. Results: Here we propose an effective data integration framework to identify the miRNA–gene regulatory comodules. The miRNA and gene expression profiles are jointly analyzed in a multiple non-negative matrix factorization framework, and additional network data are simultaneously integrated in a regularized manner. Meanwhile, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply the proposed method to integrate a set of heterogeneous data sources including the expression profiles of miRNAs and genes on 385 human ovarian cancer samples, computationally predicted miRNA–gene interactions, and gene–gene interactions. We demonstrate that the miRNAs and genes in 69% of the regulatory comodules are significantly associated. Moreover, the comodules are significantly enriched in known functional sets such as miRNA clusters, GO biological processes and KEGG pathways, respectively. Furthermore, many miRNAs and genes in the comodules are related with various cancers including ovarian cancer. Finally, we show that comodules can stratify patients (samples) into groups with significant clinical characteristics. Availability: The program and supplementary materials are available at http://zhoulab.usc.edu/SNMNMF/. Contact: xjzhou@usc.edu; zsh@amss.ac.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Genome-wide proximity ligation assays, e.g. Hi-C and its variant TCC, have recently become important tools to study spatial genome organization. Removing biases from chromatin contact matrices generated by such techniques is a critical preprocessing step of subsequent analyses. The continuing decline of sequencing costs has led to an ever-improving resolution of the Hi-C data, resulting in very large matrices of chromatin contacts. Such large-size matrices, however, pose a great challenge on the memory usage and speed of its normalization. Therefore, there is an urgent need for fast and memory-efficient methods for normalization of Hi-C data. We developed Hi-Corrector, an easy-to-use, open source implementation of the Hi-C data normalization algorithm. Its salient features are (i) scalability—the software is capable of normalizing Hi-C data of any size in reasonable times; (ii) memory efficiency—the sequential version can run on any single computer with very limited memory, no matter how little; (iii) fast speed—the parallel version can run very fast on multiple computing nodes with limited local memory. Availability and implementation: The sequential version is implemented in ANSI C and can be easily compiled on any system; the parallel version is implemented in ANSI C with the MPI library (a standardized and portable parallel environment designed for solving large-scale scientific problems). The package is freely available at http://zhoulab.usc.edu/Hi-Corrector/. Contact: alber@usc.edu or xjzhou@usc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such an approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed ‘CancerDetector’, using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation states of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor fraction. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor cfDNAs. In addition, the predicted tumor fraction showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1× to 10×). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

The identification of linear B-cell epitopes is important for developing epitope-based vaccines. Recently, machine learning techniques have been used in the epitope prediction, but the existing encoding schemes usually neglected valuable discriminative information. In this paper, we proposed a novel encoding scheme which combines several groups of sequence-derived structural and physicochemical features, and support vector machine was used to construct the prediction models. When applied to the benchmark dataset, our proposed method demonstrated better results than benchmark methods. Moreover, the study indicated incorporating more discriminative features may contribute to the higher prediction performance.

In the pathway of helper T-cell immunity, the exogenous antigen is firstly degraded into short peptides, and then some peptides can be bond to certain major histocompatibility class II (MHC-II) molecule to develop peptide-MHC complex, finally immune response is activated once receptors on helper T-cell recognize the complex. These binding peptides is named as ‘helper T-cell epitopes’, and they can be useful for the epitope-based vaccine design. Proteases play an important in MHC-II ligand generation and thus in the regulation of specific immune responses, therefore modeling the process of protease cleavage is the preliminary step for epitope recognition. To our knowledge, this work is the first effort for the prediction of cleavage sites in exogenous antigens involved in MHC-II antigen presentation. In this paper, we collect the peptides sequences from IEDB database, and then obtain their parent exogenous antigens from Uniprot database. We use sliding windows to move along the antigens, and transform original task into a problem of unbalanced data classification. The weighted support vector machine is used to construct the prediction models, and computational experiments demonstrate the satisfying results in terms of both independent test and cross-validation.