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Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine malignancy featured by cold tumor immune microenvironment (TIME), limited benefit from immunotherapy, and poor survival. The spatial heterogeneity of TIME significantly associated with anti-tumor immunity has not been systemically studied in SCLC. We performed ultra-high-plex Digital Spatial Profiling on 132 tissue microarray cores from 44 treatment-naive limited-stage SCLC tumors. Incorporating single-cell RNA-sequencing data from a local cohort and published SCLC data, we established a spatial proteo-transcriptomic landscape covering over 18,000 genes and 60 key immuno-oncology proteins that participate in signaling pathways affecting tumorigenesis, immune regulation, and cancer metabolism across 3 pathologically defined spatial compartments (pan-CK-positive tumor nest; CD45/CD3-positive tumor stroma; para-tumor). Our study depicted the spatial transcriptomic and proteomic TIME architecture of SCLC, indicating clear intra-tumor heterogeneity dictated via canonical neuroendocrine subtyping markers; revealed the enrichment of innate immune cells and functionally impaired B cells in tumor nest and suggested potentially important immunoregulatory roles of monocytes/macrophages. We identified RE1 silencing factor (REST) as a potential biomarker for SCLC associated with low neuroendocrine features, more active anti-tumor immunity, and prolonged survival. [ABSTRACT FROM AUTHOR]

Human lung adenosquamous cell carcinoma (LUAS), containing both adenomatous and squamous pathologies, harbors strong plasticity and is significantly associated with poor prognosis. We established an up-to-date comprehensive genomic and transcriptomic landscape of LUAS in 109 Chinese specimens and demonstrated LUAS development via adeno-to-squamous transdifferentiation. Unsupervised transcriptomic clustering and dynamic network biomarker analysis identified an inflammatory subtype as the critical transition stage during LUAS development. Dynamic dysregulation of the counteracting lineage-specific transcription factors (TFs), containing adenomatous TFs NKX2-1 and FOXA2, and squamous TFs TP63 and SOX2, finely tuned the lineage transition via promoting CXCL3/5-mediated neutrophil infiltration. Genomic clustering identified the most malignant subtype featured with STK11-inactivation, and targeting LSD1 through genetic deletion or pharmacological inhibition almost eradicated STK11-deficient lung tumors. These data collectively uncover the comprehensive molecular landscape, oncogenic driver spectrum and therapeutic vulnerability of Chinese LUAS. [ABSTRACT FROM AUTHOR]

RNA-seq technology is widely employed in various research areas related to transcriptome analyses, and the identification of all the expressed transcripts from short sequencing reads presents a considerable computational challenge. In this study, we introduce TransRef, a new computational algorithm for accurate transcriptome assembly by redefining a novel graph model, the neo-splicing graph, and then iteratively applying a constrained dynamic programming to reconstruct all the expressed transcripts for each graph. When TransRef is utilized to analyze both real and simulated datasets, its performance is notably better than those of several state-of-the-art assemblers, including StringTie2, Cufflinks and Scallop. In particular, the performance of TransRef is notably strong in identifying novel transcripts and transcripts with low-expression levels, while the other assemblers are less effective. [ABSTRACT FROM AUTHOR]

Somatic mutations of the chromatin remodeling gene ARID2 are observed in ∼7% of human lung adenocarcinomas (LUADs). However, the role of ARID2 in the pathogenesis of LUADs remains largely unknown. Here we find that ARID2 expression is decreased during the malignant progression of both human and mice LUADs. Using two KrasG12D -based genetically engineered murine models, we demonstrate that ARID2 knockout significantly promotes lung cancer malignant progression and shortens overall survival. Consistently, ARID2 knockdown significantly promotes cell proliferation in human and mice lung cancer cells. Through integrative analyses of ChIP-Seq and RNA-Seq data, we find that Hspa1a is up-regulated by Arid2 loss. Knockdown of Hspa1a specifically inhibits malignant progression of Arid2 -deficient but not Arid2 -wt lung cancers in both cell lines as well as animal models. Treatment with an HSPA1A inhibitor could significantly inhibit the malignant progression of lung cancer with ARID2 deficiency. Together, our findings establish ARID2 as an important tumor suppressor in LUADs with novel mechanistic insights, and further identify HSPA1A as a potential therapeutic target in ARID2-deficient LUADs. [ABSTRACT FROM AUTHOR]

Diversity and unpredictability of artifacts potentially presented to an iris sensor calls for presentation attack detection methods that are agnostic to specificity of presentation attack instruments. This article proposes a method that combines two-dimensional and three-dimensional properties of the observed iris to address the problem of spoof detection in case when some properties of artifacts are unknown. The 2D (textural) iris features are extracted by a state-of-the-art method employing Binary Statistical Image Features (BSIF) and an ensemble of classifiers is used to deliver 2D modality-related decision. The 3D (shape) iris features are reconstructed by a photometric stereo method from only two images captured under near-infrared illumination placed at two different angles, as in many current commercial iris recognition sensors. The map of normal vectors is used to assess the convexity of the observed iris surface. The combination of these two approaches has been applied to detect whether a subject is wearing a textured contact lens to disguise their identity. Extensive experiments with NDCLD’15 dataset, and a newly collected NDIris3D dataset show that the proposed method is highly robust under various open-set testing scenarios, and that it outperforms all available open-source iris PAD methods tested in identical scenarios. The source code and the newly prepared benchmark are made available along with this article. [ABSTRACT FROM AUTHOR]

Accumulating evidence indicates that the alternative splicing program undergoes extensive changes during cancer development and progression. The RNA-binding protein QKI-5 is frequently downregulated and exhibits anti-tumor activity in lung cancer. Howeve-r, little is known about the functional targets and regulatory mechanism of QKI-5. Here, we report that upregulation of exon 14 inclusion of cytoskeletal gene Adducin 3 (ADD3) significantly correlates with a poor prognosis in lung cancer. QKI-5 inhibits cell proliferation and migration in part through suppressing the splicing of ADD3 exon 14. Through genome-wide mapping of QKI-5 binding sites in vivo at nucleotide resolution by iCLIP-seq analysis, we found that QKI-5 regulates alternative splicing of its target mRNAs in a binding position-dependent manner. By binding to multiple sites in an upstream intron region, QKI-5 represses the splicing of ADD3 exon 14. We also identified several QKI mutations in tumors, which cause dysregulation of the splicing of QKI targets ADD3 and NUMB. Taken together, our results reveal that QKI-mediated alternative splicing of ADD3 is a key lung cancer-associated splicing event, which underlies in part the tumor suppressor function of QKI. [ABSTRACT FROM AUTHOR]

The problem of noninstrusive load monitoring (NILM) is usually formulated as a single-channel blind source separation task, whose successful solution enable fast and convenient load identification and energy disaggregation. When applied at test time, NILM algorithms aim to identify the operating characteristics of individual appliances from an aggregate power measurement of the entire house. Recent advances in deep learning gave rise to many methods that mostly focus on learning a direct mapping from aggregate measurement to individual appliance power. However, these methods are not only computationally expensive, but they often suffer from overfitting and do not generalize very well. In this article, we propose a novel NILM method that leverages advances in statistical learning that have not been properly applied in this domain before. The proposed method consists of three stages: first, a Bayesian nonparametric learning-based approach for appliance state extraction; second, synthetic minority oversampling technique for data augmentation and mitigating the heavy imbalance in switching events; and third, appliance-specific lightweight long short-term memory networks for status classification for each appliance. We adopt a “differential” input (the difference before and after the switching event) to reduce the complexity of network training and make the proposed method robust to multiappliance switching events. Experiments are conducted to demonstrate the effectiveness of the proposed method, achieving superior performance when compared to recent methods. An ablation study is conducted to demonstrate the effectiveness of each module of our method. Finally, we investigate the quality of generated synthetic samples. [ABSTRACT FROM AUTHOR]

Background: Isocitrate dehydrogenase wild-type (WT) glioblastoma (GBM) accounts for 90% of all GBMs, yet only 27% of isocitrate dehydrogenase WT-GBMs have p53 mutations. However, the tumor surveillance function of WT-p53 in GBM is subverted by mechanisms that are not fully understood.Methods: We investigated the proteolytic inactivation of WT-p53 by asparaginyl endopeptidase (AEP) and its effects on GBM progression in cancer cells, murine models, and patients' specimens using biochemical and functional assays. The sera of healthy donors (n = 48) and GBM patients (n = 20) were examined by enzyme-linked immunosorbent assay. Furthermore, effects of AEP inhibitors on GBM progression were evaluated in murine models (n = 6-8 per group). The statistical significance between groups was determined using two-tailed Student t tests.Results: We demonstrate that AEP binds to and directly cleaves WT-p53, resulting in the inhibition of WT-p53-mediated tumor suppressor function in both tumor cells and stromal cells via extracellular vesicle communication. High expression of uncleavable p53-N311A-mutant rescue AEP-induced tumorigenesis, proliferation, and anti-apoptotic abilities. Knock down or pharmacological inhibition of AEP reduced tumorigenesis and prolonged survival in murine models. However, overexpression of AEP promoted tumorigenesis and shortened the survival time. Moreover, high AEP levels in GBM tissues were associated with a poor prognosis of GBM patients (n = 83; hazard ratio = 3.94, 95% confidence interval = 1.87 to 8.28; P < .001). A correlation was found between high plasma AEP levels and a larger tumor size in GBM patients (r = 0.6, P = .03), which decreased dramatically after surgery.Conclusions: Our results indicate that AEP promotes GBM progression via inactivation of WT-p53 and may serve as a prognostic and therapeutic target for GBM. [ABSTRACT FROM AUTHOR]

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1 , HLA-DQB1 , MMP2 , and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives. [ABSTRACT FROM AUTHOR]

Methylation of cytosine to 5-methylcytosine (5mC) is a prevalent DNA modification found in many organisms. Sequential oxidation of 5mC by ten-eleven translocation (TET) dioxygenases results in a cascade of additional epigenetic marks and promotes demethylation of DNA in mammals1,2. However, the enzymatic activity and function of TET homologues in other eukaryotes remains largely unexplored. Here we show that the green alga Chlamydomonas reinhardtii contains a 5mC-modifying enzyme (CMD1) that is a TET homologue and catalyses the conjugation of a glyceryl moiety to the methyl group of 5mC through a carbon–carbon bond, resulting in two stereoisomeric nucleobase products. The catalytic activity of CMD1 requires Fe(ii) and the integrity of its binding motif His-X-Asp, which is conserved in Fe-dependent dioxygenases3. However, unlike previously described TET enzymes, which use 2-oxoglutarate as a co-substrate4, CMD1 uses l-ascorbic acid (vitamin C) as an essential co-substrate. Vitamin C donates the glyceryl moiety to 5mC with concurrent formation of glyoxylic acid and CO2. The vitamin-C-derived DNA modification is present in the genome of wild-type C. reinhardtii but at a substantially lower level in a CMD1 mutant strain. The fitness of CMD1 mutant cells during exposure to high light levels is reduced. LHCSR3, a gene that is critical for the protection of C. reinhardtii from photo-oxidative damage under high light conditions, is hypermethylated and downregulated in CMD1 mutant cells compared to wild-type cells, causing a reduced capacity for photoprotective non-photochemical quenching. Our study thus identifies a eukaryotic DNA base modification that is catalysed by a divergent TET homologue and unexpectedly derived from vitamin C, and describes its role as a potential epigenetic mark that may counteract DNA methylation in the regulation of photosynthesis. An algal TET dioxygenase homologue, CMD1, uses vitamin C as a glycerol donor to modify 5-methylcytosine and helps to regulate gene transcription in response to high light levels. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is a very heterogeneous disease that can be pathologically classified into different subtypes including small-cell lung carcinoma (SCLC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large-cell carcinoma (LCC). Although much progress has been made towards the oncogenic mechanism of each subtype, transcriptional circuits mediating the upstream signaling pathways and downstream functional consequences remain to be systematically studied. Results: Here we trained a one-class support vector machine (OC-SVM) model to establish a general transcription factor (TF) regulatory network containing 325 TFs and 18724 target genes. We then applied this network to lung cancer subtypes and identified those deregulated TFs and downstream targets. We found that the TP63/SOX2/DMRT3 module was specific to LUSC, corresponding to squamous epithelial differentiation and/or survival. Moreover, the LEF1/MSC module was specifically activated in LUAD and likely to confer epithelial-to-mesenchymal transition, known important for cancer malignant progression and metastasis. The proneural factor, ASCL1, was specifically up-regulated in SCLC which is known to have a neuroendocrine phenotype. Also, ID2 was differentially regulated between SCLC and LUSC, with its up-regulation in SCLC linking to energy supply for fast mitosis and its down-regulation in LUSC linking to the attenuation of immune response. We further described the landscape of TF regulation among the three major subtypes of lung cancer, highlighting their functional commonalities and specificities. Conclusions: Our approach uncovered the landscape of transcriptional deregulation in lung cancer, and provided a useful resource of TF regulatory network for future studies. [ABSTRACT FROM AUTHOR]

Since the original version of the manuscript was published the lab has moved and the authors were only able to present raw data for one mouse for the 12 week data shown in Supplementary Figure S1g; this is also shown in the appended Raw Data file. The raw data for 4 mice following 8 weeks of treatment from Supplementary Figure S1c are also shown in the appended Raw Data file. [Extracted from the article]

Metastasis is the major cause for high mortality of lung cancer with the underlying mechanisms poorly understood. The scaffolding protein neural precursor cell expressed, developmentally down-regulated 9 (NEDD9) has been identified as a pro-metastasis gene in several types of cancers including melanoma and breast cancer. However, the exact role and related mechanism of NEDD9 in regulating lung cancer metastasis still remain largely unknown. Here, we demonstrate that NEDD9 knockdown significantly inhibits migration, invasion and metastasis of lung cancer cells in vitro and in vivo. The pro-metastasis role of Nedd9 in lung cancer is further supported by studies in mice models of spontaneous cancer metastasis. Moreover, we find that NEDD9 promotes lung cancer cell migration and invasion through the induction of epithelial-mesenchymal transition (EMT) potentially via focal adhesion kinase activation. More importantly, NEDD9 expression inversely correlates with E-cadherin expression in human lung cancer specimens, consistent with the findings from in vitro studies. Taken together, this study highlights that NEDD9 is an important mediator promotes lung cancer metastasis via EMT. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related death worldwide. Aberrant splicing has been implicated in lung tumorigenesis. However, the functional links between splicing regulation and lung cancer are not well understood. Here we identify the RNA-binding protein QKI as a key regulator of alternative splicing in lung cancer. We show that QKI is frequently down-regulated in lung cancer, and its down-regulation is significantly associated with a poorer prognosis. QKI-5 inhibits the proliferation and transformation of lung cancer cells both in vitro and in vivo. Our results demonstrate that QKI-5 regulates the alternative splicing of NUMB via binding to two RNA elements in its pre-mRNA, which in turn suppresses cell proliferation and prevents the activation of the Notch signaling pathway. We further show that QKI-5 inhibits splicing by selectively competing with a core splicing factor SF1 for binding to the branchpoint sequence. Taken together, our data reveal QKI as a critical regulator of splicing in lung cancer and suggest a novel tumor suppression mechanism involving QKI-mediated regulation of the Notch signaling pathway. [ABSTRACT FROM AUTHOR]

Background: AAA+ nuclear coregulator cancer associated (ANCCA) is found to be overexpressed in various cancer types and could play a role in common and fundamental cellular processes. A recent study suggested that ANCCA was a likely driver whose expression explained the behavior of differentially expressed proliferation-related genes in lung adenocarcinoma. However, protein expression of ANCCA in lung adenocarcinoma and its association with clinicopathologic parameters and commonly reported driver mutations remains unexplored. Methods: ANCCA expression was evaluated by immunohistochemistry in 143 surgically resected lung adenocarcinomas and was correlated with clinicopathologic and molecular variables including adenocarcinoma histologic subtypes, tumor, node, metastasis status, relapse-free survival, overall survival, EGFR mutations, KRAS mutations, HER2 mutations and ALK fusions. Results: Positive ANCCA expression was significantly associated with male sex, smokers, poorly differentiated tumors, nonlepidic predominant subtype, more advanced T stage, lymph nodal metastasis and late disease stage. Cox multivariate analysis revealed that ANCCA-positive expression was an independent predictor of worse relapse-free survival [hazard ratio (HR) 1.736, 95 % confidence interval (CI) 1.075–2.804; P = .024) and overall survival (HR 7.758, 95 % CI 2.955–20.370; P < .001). The addition of ANCCA protein expression to the prognostic model using pathologic stage markedly improved the prognostic accuracy; the concordance index increased from .692 to .788, and the Akaike information criterion decreased from 354.20 to 336.11. Conclusions: We have identified ANCCA protein expression as a novel independent poor prognostic indicator in lung adenocarcinoma. Prospective studies are warranted to validate its potential prognostic value in combination with the current staging system. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented that discusses the identification of RET gene fusion by exon array analyses in lung cancer patients in East Asia who are never smokers.

Classical algorithms aiming at identifying biological pathways significantly related to studying conditions frequently reduced pathways to gene sets, with an obvious ignorance of the constitutive non-equivalence of various genes within a defined pathway. We here designed a network-based method to determine such non-equivalence in terms of gene weights. The gene weights determined are biologically consistent and robust to network perturbations. By integrating the gene weights into the classical gene set analysis, with a subsequent correction for the 'over-counting' bias associated with multi-subunit proteins, we have developed a novel gene-weighed pathway analysis approach, as implemented in an R package called 'Gene Associaqtion Network-based Pathway Analysis' (GANPA). Through analysis of several microarray datasets, including the p53 dataset, asthma dataset and three breast cancer datasets, we demonstrated that our approach is biologically reliable and reproducible, and therefore helpful for microarray data interpretation and hypothesis generation. [ABSTRACT FROM AUTHOR]

A correction notice to article "Nonintrusive Appliance Identification With Appliance-Specific Networks" is presented which appear in the previous issue of the periodical.

Background: In traditional Chinese medicine (TCM) clinical practice, TCM syndromes help to understand human homeostasis and guide individualized treatment. However, the TCM syndrome changes with disease progression, of which the scientific basis and mechanism remain unclear. Methods: To demonstrate the underlying mechanism of dynamic changes in the TCM syndrome, we applied a dynamic network biomarker (DNB) algorithm to obtain the DNBs of changes in the TCM syndrome, based on the transcriptomic data of patients with chronic hepatitis B and typical TCM syndromes, including healthy controls and patients with liver-gallbladder dampness-heat syndrome (LGDHS), liver-depression spleen-deficiency syndrome (LDSDS), and liver-kidney yin-deficiency syndrome (LKYDS). The DNB model exploits collective fluctuations and correlations of the observed genes, then diagnoses the critical state. Results: Our results showed that the DNBs of TCM syndromes were comprised of 52 genes and the tipping point occurred at the LDSDS stage. Meanwhile, there were numerous differentially expressed genes between LGDHS and LKYDS, which highlighted the drastic changes before and after the tipping point, implying the 52 DNBs could serve as early-warning signals of the upcoming change in the TCM syndrome. Next, we validated DNBs by cytokine profiling and isobaric tags for relative and absolute quantitation (iTRAQ). The results showed that PLG (plasminogen) and coagulation factor XII (F12) were significantly expressed during the progression of TCM syndrome from LGDHS to LKYDS. Conclusions: This study provides a scientific understanding of changes in the TCM syndrome. During this process, the cytokine system was involved all the time. The DNBs PLG and F12 were confirmed to significantly change during TCM-syndrome progression and indicated a potential value of DNBs in auxiliary diagnosis of TCM syndrome in CHB. Trial registration Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (http://www.clinicaltrials.gov) [ABSTRACT FROM AUTHOR]

Keywords: alternative splicing; splicing network; splicing modules; cancer splicing; prognosis 3 This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics Alternative splicing, splicing network, splicing modules, cancer splicing, prognosis. [Extracted from the article]