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We collected studies in glioblastoma that included survival thresholds through a PubMed search (search term: glioblastoma short-term prognostic signature). As can be seen from this table, a wide range of survival thresholds is used, with thresholds for short-term survival ranging between 7-24 months, and thresholds for long-term survival between 13-36 months after initial diagnosis. The threshold of 1.7yrs, which we chose for our comparison in the discovery datasets, corresponds to 20 months, which lies right in between these two ranges.

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer.Significance:Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Smooth scatterplot visualizing the correlation between edge weights in the condition-specific network modeled on discovery dataset 1 and the condition-specific network modeled on discovery dataset 2. Pearson correlation coefficient (R) and p-value (p) are indicated in the plot.

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78–100] survival compared with 61% (95% CI, 45–82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538–47. ©2018 AACR.

Extensive clinical features for the dataset included in the study.

Overall survival analysis based on chemotherapy treatment.

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties., Graphical Abstract Graphical AbstractModeling gene regulation across human conditions integrates cancer tissues and cell lines, small molecules and normal tissue networks.

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. Significance: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

Inference and analysis of cellular biological networks requires software tools that integrate multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is an open-source software suite to model biological networks, including context-specific gene regulatory networks and multi-omics partial correlation networks, to conduct differential analyses, estimate community structure, and model the transitions between biological states. The netZoo builds on our ongoing development of network methods, harmonizing the implementations in various computing languages (R, Python, MATLAB, and C) and between methods to allow a better integration of these tools into analytical pipelines. To demonstrate the value of this integrated toolkit, we analyzed the multi-omic data from the Cancer Cell Line Encyclopedia (CCLE) by inferring gene regulatory networks for each cancer cell line and associating network features with other phenotypic attributes such as drug sensitivity. This allowed us to identify transcription factors that play a critical role in both drug resistance and cancer development in melanoma. We also used netZoo to build a pan-cancer, multi-tiered CCLE map and used it to identify known metabolic hallmarks of cancer and to estimate novel context-specific elements that mediate post-transcriptional regulation. Because the netZoo tools are open-source and there is a growing community of both users and developers, we built an ecosystem to support community contributions, share use cases, and visualize networks online. As additional data types become available and our suite of methods grows, we will expand “the zoo” to incorporate an increasingly sophisticated collection of tools for network inference and analysis.

Bipartite network inference is a ubiquitous problem across disciplines. One important example in the field molecular biology is gene regulatory network inference. Gene regulatory networks are an instrumental tool aiding in the discovery of the molecular mechanisms driving diverse diseases, including cancer. However, only noisy observations of the projections of these regulatory networks are typically assayed. In an effort to better estimate regulatory networks from their noisy projections, we formulate a non-convex but analytically tractable optimization problem called OTTER. This problem can be interpreted as relaxed graph matching between the two projections of the bipartite network. OTTER's solutions can be derived explicitly and inspire a spectral algorithm, for which we provide network recovery guarantees. We also provide an alternative approach based on gradient descent that is more robust to noise compared to the spectral algorithm. Interestingly, this gradient descent approach resembles the message passing equations of an established gene regulatory network inference method, PANDA. Using three cancer-related data sets, we show that OTTER outperforms state-of-the-art inference methods in predicting transcription factor binding to gene regulatory regions. To encourage new graph matching applications to this problem, we have made all networks and validation data publicly available.

An important premise of epidemiology is that individuals with the same disease share similar underlying etiologies and clinical outcomes. In the past few decades, our knowledge of disease pathogenesis has improved, and disease classification systems have evolved to the point where no complex disease processes are considered homogenous. As a result, pathology and epidemiology have been integrated into the single, unified field of molecular pathological epidemiology (MPE). Advancing integrative molecular and population-level health sciences and addressing the unique research challenges specific to the field of MPE necessitates assembling experts in diverse fields, including epidemiology, pathology, biostatistics, computational biology, bioinformatics, genomics, immunology, and nutritional and environmental sciences. Integrating these seemingly divergent fields can lead to a greater understanding of pathogenic processes. The International MPE Meeting Series fosters discussion that addresses the specific research questions and challenges in this emerging field. The purpose of the meeting series is to: discuss novel methods to integrate pathology and epidemiology; discuss studies that provide pathogenic insights into population impact; and, educate next-generation scientists. Herein, we share the proceedings of the Fourth International MPE Meeting, held in Boston, MA, USA on May 30(th) to June 1(st), 2018. Major themes of this meeting included ‘integrated genetic and molecular pathologic epidemiology’, ‘immunology-MPE’, and ‘novel disease phenotyping’. The key priority areas for future research identified by meeting attendees included integration of tumor immunology and cancer disparities into epidemiologic studies, further collaboration between computational and population-level scientists to gain new insight on exposure-disease associations, and future pooling projects of studies with comparable data.

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in expression of particular genes, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms that associate with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas (n=522 and 431). We performed a comparative network analysis between patients with long- and short-term survival, correcting for patient age, sex, and neoadjuvant treatment status. We identified seven pathways associated with survival, all of which were involved in immune system signaling. Differential regulation of PD1 signaling was validated in an independent dataset from the German Glioma Network (n=70). We found that transcriptional repression of genes in this pathway—for which treatment options are available—was lost in short-term survivors and that this was independent of mutation burden and only weakly associated with T-cell infiltrate. These results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival, and identify new potential therapeutic interventions, thus underscoring the value of analyzing gene regulatory networks in individual cancer patients.

Sex differences manifest in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sex differences, we evaluated sex-biased gene regulation by constructing sample-specific gene regulatory networks in 29 human healthy tissues using 8,279 whole-genome expression profiles from the Genotype-Tissue Expression (GTEx) project. We find sex-biased regulatory network structures in each tissue. Even though most transcription factors (TFs) are not differentially expressed between males and females, many have sex-biased regulatory targeting patterns. In each tissue, genes that are differentially targeted by TFs between the sexes are enriched for tissue-related functions and diseases. In brain tissue, for example, genes associated with Parkinson’s disease and Alzheimer’s disease are targeted by different sets of TFs in each sex. Our systems-based analysis identifies a repertoire of TFs that play important roles in sex-specific architecture of gene regulatory networks, and it underlines sex-specific regulatory processes in both health and disease.

Alterations in microRNA (miRNA) processing have been previously linked to aging. Here we used the small molecule enoxacin to pharmacologically interfere with miRNA biogenesis and study how it affects aging in C. elegans. Enoxacin extended worm lifespan and promoted survival under normal and oxidative stress conditions. Enoxacin-induced longevity required the transcription factor SKN-1/Nrf2 and was blunted by the antioxidant N-acetyl-cysteine, suggesting a prooxidant-mediated mitohormetic response. The longevity effects of enoxacin were also dependent on the miRNA pathway, consistent with changes in miRNA expression elicited by the drug. Among these differentially expressed miRNAs, the widely conserved miR-34-5p was found to play an important role in enoxacin-mediated longevity. Enoxacin treatment down-regulated miR-34-5p and did not further extend lifespan of long-lived mir-34 mutants. Moreover, N-acetyl-cysteine abrogated mir-34(gk437)-induced longevity. Evidence also points to double-stranded RNA-specific adenosine deaminases (ADARs) as new targets of enoxacin since ADAR loss-of-function abrogates enoxacin-induced lifespan extension. Thus, enoxacin increases lifespan by reducing miR-34-5p levels, interfering with the redox balance and promoting healthspan., Highlights • Enoxacin extends lifespan and promotes healthspan in C. elegans. • Enoxacin downregulates miR-34-5p to prolong lifespan. • Longevity induced by enoxacin and mir-34 loss of function involves a mitohormetic response. • Double-stranded RNA-specific adenosine deaminases (ADARs) are potential targets of enoxacin.

The phenotype of primary cells in culture varies according to the donor environmental condition. We recently showed that the time of the day imposes a molecular program linked to the inflammatory response that is heritable in culture. Here we investigated whether microRNAs (miRNAs) would show differential expression according to the time when cells were obtained, namely daytime or nighttime. Cells obtained from explants of cremaster muscle and cultivated until confluence (∼20 days) presented high CD133 expression. Global miRNA expression analysis was performed through deep sequencing in order to compare both cultured cells. A total of 504 mature miRNAs were identified, with a specific miRNA signature being associated to the light versus dark phase of a circadian cycle. miR-1249 and miR-129-2-3p were highly expressed in daytime cells, while miR-182, miR-96-5p, miR-146a-3p, miR-146a-5p, and miR-223-3p were highly expressed in nighttime cells. Nighttime cells are regulated for programs involved in cell processes and development, as well as in the inflammation, cell differentiation and maturation; while daytime cells express miRNAs that control stemness and cytoskeleton remodeling. In summary, the time of the day imposes a differential profile regarding to miRNA signature on CD133(+) cells in culture. Understanding this daily profile in the phenotype of cultured cells is highly relevant for clinical outputs, including cellular therapy approaches. J. Cell. Physiol. 231: 1953-1963, 2016. © 2016 Wiley Periodicals, Inc.

Despite pronounced sex differences in cancer incidence, severity, and response to treatment, most current approaches to clinical management, as well as therapeutics development and selection, are sex-independent. For most cancer types, including liver cancer, males have a higher risk of developing the disease and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We inferred patient-specific regulatory networks of liver hepatocellular carcinoma using data from TCGA. By comparing the female and male networks, we found marked sex differences in transcriptional regulatory processes relevant to disease development, progression, and response to therapy. We found that oncogenes have significantly higher regulatory targeting in males, while tumor suppressor genes have significantly higher targeting in females. Many “hallmark” cancer pathways, including the HEDGEHOG, WNT, and TGF-β signaling pathways, were significantly more highly targeted in males, while drug metabolism and immune related pathways were enriched for genes highly targeted in females. We also evaluated sex-biased somatic mutation patterns using mutation and copy number alteration data in TCGA. By summarizing mutations found in genes into pathway mutation scores, we found sex-biased mutation profiles for many pathways, providing additional support for biological sex differences associated with WNT, NOTCH, TGF-β, and HEDGEHOG signaling pathways. Our analysis uncovered patterns of gene regulation that differentiate male and female liver cancer and may be associated with sex differences in prognosis and treatment response. These findings provide insight into the mechanisms that drive clinically observed sex differences and underscore the importance of considering sex as a factor influencing disease etiology and in developing and prescribing therapies. Our network approach can provide insights into why some therapies have differential effect in males and females, and suggest new ways to optimize drug response in each sex. Citation Format: Camila M. Lopes-Ramos, Marieke Kuijjer, Kimberly Glass, Dawn DeMeo, John Quackenbush. Regulatory networks of liver carcinoma reveal sex specific patterns of gene regulation [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6569.

Significant sex differences are observed in colon cancer, and understanding these differences is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. We used both transcript-based and gene regulatory network methods to analyze RNA-Seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and found no significant sex differences except for sex-chromosome genes. We then inferred patient-specific gene regulatory networks, and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78–100] survival compared with 61% (95% CI, 45–82) for women with lower targeting, respectively (P = 0.034). Our network analysis uncovers patterns of transcriptional regulation that differentiate male and female colon cancer and identifies differences in regulatory processes involving the drug metabolism pathway associated with survival in women who receive adjuvant chemotherapy. This approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases. Significance: A network-based approach reveals that sex-specific patterns of gene targeting by transcriptional regulators are associated with survival outcome in colon cancer. This approach can be used to understand how sex influences progression and response to therapies in other cancers. Cancer Res; 78(19); 5538–47. ©2018 AACR.

Background Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. Results We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. Conclusions Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues. Electronic supplementary material The online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

Background Genetic studies have largely concentrated on the impact of somatic mutations found in coding regions, and have neglected mutations outside of these. However, 3' untranslated regions (3' UTR) mutations can also disrupt or create miRNA target sites, and trigger oncogene activation or tumor suppressor inactivation. Methods We used next-generation sequencing to widely screen for genetic alterations within predicted miRNA target sites of oncogenes associated with colorectal cancer, and evaluated the functional impact of a new somatic mutation. Target sequencing of 47 genes was performed for 29 primary colorectal tumor samples. For 71 independent samples, Sanger methodology was used to screen for E2F1 mutations in miRNA predicted target sites, and the functional impact of these mutations was evaluated by luciferase reporter assays. Results We identified germline and somatic alterations in E2F1. Of the 100 samples evaluated, 3 had germline alterations at the MIR205-5p target site, while one had a somatic mutation at MIR136-5p target site. E2F1 gene expression was similar between normal and tumor tissues bearing the germline alteration; however, expression was increased 4-fold in tumor tissue that harbored a somatic mutation compared to that in normal tissue. Luciferase reporter assays revealed both germline and somatic alterations increased E2F1 activity relative to wild-type E2F1. Conclusions We demonstrated that somatic mutation within E2F1:MIR136-5p target site impairs miRNA-mediated regulation and leads to increased gene activity. We conclude that somatic mutations that disrupt miRNA target sites have the potential to impact gene regulation, highlighting an important mechanism of oncogene activation.

Summary Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes., Graphical abstract

In the original version of [this article][1] ([1][2]), in the Disclosure of Potential Conflicts of Interest section, Dr. Charles S. Fuchs did not report consulting work with the biotechnology and pharmaceutical industry focused on cancer drug development. With respect to an analysis of molecular

SummaryExpression quantitative trait locus (eQTL) analysis associates genotype with gene expression, but most eQTL studies only includecis-acting variants and generally examine a single tissue. We used data from 13 tissues obtained by the Genotype-Tissue Expression (GTEx) project v6.0 and, in each tissue, identified bothcis- andtrans-eQTLs. For each tissue, we represented significant associations between single nucleotide polymorphisms (SNPs) and genes as edges in a bipartite network. These networks are organized into dense, highly modular communities often representing coherent biological processes. Global network hubs are enriched in distal gene regulatory regions such as enhancers, but are devoid of disease-associated SNPs from genome wide association studies. In contrast, local, community-specific network hubs (core SNPs) are preferentially located in regulatory regions such as promoters and enhancers and highly enriched for trait and disease associations. These results provide help explain how many weak-effect SNPs might together influence cellular function and phenotype.

Machado-Joseph disease (MJD) or spinocerebellar ataxia type 3 (SCA3) is an autosomal dominant neurodegenerative disorder caused by expansion of the polyglutamine domain of the ataxin-3 (ATX3) protein. MJD/SCA3 is the most frequent autosomal dominant ataxia in many countries. The mechanism underlying MJD/SCA3 is thought to be mainly related to protein misfolding and aggregation leading to neuronal dysfunction followed by cell death. Currently, there are no effective treatments for patients with MJD/SCA3. Here, we report on the potential use of lithium carbonate and coenzyme Q10 to reduce cell death caused by the expanded ATX3 in cell culture. Cell viability and apoptosis were evaluated by MTT assay and by flow cytometry after staining with annexin V-FITC/propidium iodide. Treatment with lithium carbonate and coenzyme Q10 led to a significant increase in viability of cells expressing expanded ATX3 (Q84). In addition, we found that the increase in cell viability resulted from a significant reduction in the proportion of apoptotic cells. Furthermore, there was a significant change in the expanded ATX3 monomer/aggregate ratio after lithium carbonate and coenzyme Q10 treatment, with an increase in the monomer fraction and decrease in aggregates. The safety and tolerance of both drugs are well established; thus, our results indicate that lithium carbonate and coenzyme Q10 are good candidates for further in vivo therapeutic trials.

SummaryCell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell line’s transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models.HighlightsCell lines differ from their source tissues in gene expression and regulationDistinct cell lines share altered patterns of cell cycle regulationCell cycle genes are less strongly targeted by repressive TFs in cell linesCell lines share expression with their source tissue, but at reduced levels

SummarySexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks.HighlightsSexual dimorphism manifests in both gene expression and gene regulatory networksSubstantial sexual dimorphism in regulatory networks was found in several tissuesMany differentially regulated genes are not differentially expressedSex hormone receptors do not exhibit sexually dimorphic expression in most tissues

Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. We find analysis of large RNA-Seq data sets requires both careful quality control and that one account for sparsity due to the heterogeneity intrinsic in multi-group studies. An R package instantiating our method for large-scale RNA-Seq normalization and preprocessing, YARN, is available at bioconductor.org/packages/yarn.HighlightsOverview of assumptions used in preprocessing and normalizationPipeline for preprocessing, quality control, and normalization of large heterogeneous dataA Bioconductor package for the YARN pipeline and easy manipulation of count dataPreprocessed GTEx data set using the YARN pipeline available as a resource

Resistance to therapy is the major obstacle to a favorable outcome in cancer treatment. Neoadjuvant chemoradiotherapy (nCRT) can lead to complete tumor regression in a significant proportion of patients with rectal cancer (up to 40%). The possibility of avoiding radical surgery and its associated functional consequences for those who present complete response has become increasingly attractive, but approximately 60% of patients have only partial or no tumor remission after nCRT. In order to identify molecular mechanisms determining poor response to nCRT and to uncover optimized treatment strategies, we have conducted global gene expression analysis followed by pathway enrichment analysis in pretreatment biopsies from patients showing complete or poor clinical response to nCRT. We show that mitochondrial oxidative phosphorylation and phosphatidylinositol signaling pathways are consistently altered in rectal tumors comparatively. Both pathways have already been associated with resistance to radiation and chemotherapy in solid tumors and appear to converge on the activation of the Akt pathway in nonresponding tumors. To further address the role of Akt activation in response to nCRT, we evaluated by immunohistochemistry, Akt activation in an independent set of pretreatment biopsies from rectal cancer patients and observed not only high pAkt levels in those presenting poor response to nCRT but also that nuclear pAkt can also discriminate resistant tumors. We also evaluated the combination of an allosteric Akt-inhibitor MK2206 to chemoradiation in a radioresistant colorectal cancer cell line SW480 either in vitro and in vivo and observed an improvement of 50% of tumoricidal effect. Altogether, our results indicate that activation of the Akt pathway is a key event affecting response to nCRT and that combining chemoradiotherapy and Akt inhibitors such as MK2206 may significantly improve response rates to neoadjuvant therapy in rectal cancer. Citation Format: Fernanda C. Koyama, Camila M. Lopes Ramos, Jennifer M. Fernandes, Fernanda C. Ledesma, Venancio A F Alves, Fernanda C. Vailati, Angelita Habr-Gama, Rodrigo O. Perez, Anamaria A. Camargo. Akt inhibitior MK2206 combination to neoadjuvant radiotherapy: Improving the rectal cancer treatment [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A53.

The phenotype of primary cells in culture varies according to the donor environmental condition. We recently showed that the time of the day imposes a molecular program linked to the inflammatory response that is heritable in culture. Here we investigated whether microRNAs (miRNAs) would show differential expression according to the time when cells were obtained, namely daytime or nighttime. Cells obtained from explants of cremaster muscle and cultivated until confluence (∼20 days) presented high CD133 expression. Global miRNA expression analysis was performed through deep sequencing in order to compare both cultured cells. A total of 504 mature miRNAs were identified, with a specific miRNA signature being associated to the light versus dark phase of a circadian cycle. miR-1249 and miR-129-2-3p were highly expressed in daytime cells, while miR-182, miR-96-5p, miR-146a-3p, miR-146a-5p, and miR-223-3p were highly expressed in nighttime cells. Nighttime cells are regulated for programs involved in cell processes and development, as well as in the inflammation, cell differentiation and maturation; while daytime cells express miRNAs that control stemness and cytoskeleton remodeling. In summary, the time of the day imposes a differential profile regarding to miRNA signature on CD133(+) cells in culture. Understanding this daily profile in the phenotype of cultured cells is highly relevant for clinical outputs, including cellular therapy approaches. J. Cell. Physiol. 231: 1953-1963, 2016. © 2016 Wiley Periodicals, Inc.

Neoadjuvant chemoradiotherapy (nCRT) may lead to complete tumor regression in rectal cancer patients. Prediction of complete response to nCRT may allow a personalized management of rectal cancer and spare patients from unnecessary radical total mesorectal excision with or without sphincter preservation. To identify a gene expression signature capable of predicting complete pathological response (pCR) to nCRT, we performed a gene expression analysis in 25 pretreatment biopsies from patients who underwent 5FU-based nCRT using RNA-Seq. A supervised learning algorithm was used to identify expression signatures capable of predicting pCR, and the predictive value of these signatures was validated using independent samples. We also evaluated the utility of previously published signatures in predicting complete response in our cohort. We identified 27 differentially expressed genes between patients with pCR and patients with incomplete responses to nCRT. Predictive gene signatures using subsets of these 27 differentially expressed genes peaked at 81.8% accuracy. However, signatures with the highest sensitivity showed poor specificity, and vice-versa, when applied in an independent set of patients. Testing previously published signatures on our cohort also showed poor predictive value. Our results indicate that currently available predictive signatures are highly dependent on the sample set from which they are derived, and their accuracy is not superior to current imaging and clinical parameters used to assess response to nCRT and guide surgical intervention.

Background Neoadjuvant chemoradiotherapy (nCRT) followed by radical surgery is the preferred treatment strategy for locally advanced rectal cancer. However, complete tumor regression is observed in a significant proportion of patients after nCRT, making them ideal candidates for alternative treatment strategies to this considerably morbid procedure. Identification of such patients based on clinical findings (complete clinical response - cCR) is difficult mainly because it relies on subjective clinical and imaging studies. Our goal was to identify biomarkers capable of predicting complete response to nCRT. Methods We analyzed miRNA expression profile using deep sequencing in rectal tumor biopsies prior to nCRT. Differential expression was investigated by EdgeR for a training (n = 27) and a validation (n = 16) set of patients to identify miRNAs associated with treatment response (complete vs. incomplete). In vitro experiments with two cancer cell lines were also performed in order to evaluate the possible role of miRNAs on response to nCRT. Results We found 4 miRNAs differentially expressed between complete and incomplete responders to nCRT. In addition, validation was performed using an independent group of patients and miR-21-5p was confirmed as being overexpressed in complete responders. Overall sensitivity and specificity of miR-21-5p expression in predicting complete response to nCRT was 78% and 86% respectively. Interestingly, in a subset of patients with cCR followed by early local recurrence, the expression level of miR-21-5p was considerably low, similarly to incomplete responders. We also found SATB1, a miR-21-5p target gene and known multidrug resistance gene, whose expression was inversely correlated with miR-21-5p expression. Finally, we performed functional experiments and showed that miR-21-5p and SATB1 may be directly involved with poor response to nCRT in rectal cancer patients. Conclusions This study suggests miR-21-5p as a promising predictive biomarker, which should aid in the selection of patients with cCR to nCRT that potentially could be spared from radical surgery. Electronic supplementary material The online version of this article (doi:10.1186/s12920-014-0068-7) contains supplementary material, which is available to authorized users.

MicroRNAs (miRNAs) are a class of small (∼22 nucleotides) non-coding RNAs that post-transcriptionally regulate gene expression by interacting with target mRNAs. A majority of miRNAs is located within intronic or exonic regions of protein-coding genes (host genes), and increasing evidence suggests a functional relationship between these miRNAs and their host genes. Here, we introduce miRIAD, a web-service to facilitate the analysis of genomic and structural features of intragenic miRNAs and their host genes for five species (human, rhesus monkey, mouse, chicken and opossum). miRIAD contains the genomic classification of all miRNAs (inter- and intragenic), as well as classification of all protein-coding genes into host or non-host genes (depending on whether they contain an intragenic miRNA or not). We collected and processed public data from several sources to provide a clear visualization of relevant knowledge related to intragenic miRNAs, such as host gene function, genomic context, names of and references to intragenic miRNAs, miRNA binding sites, clusters of intragenic miRNAs, miRNA and host gene expression across different tissues and expression correlation for intragenic miRNAs and their host genes. Protein-protein interaction data are also presented for functional network analysis of host genes. In summary, miRIAD was designed to help the research community to explore, in a user-friendly environment, intragenic miRNAs, their host genes and functional annotations with minimal effort, facilitating hypothesis generation and in-silico validations. Database URL: http://www.miriad-database.org.

Resistance to therapy is a major obstacle to a favorable outcome in cancer treatment. For rectal cancer, neoadjuvant chemoradiotherapy (nCRT) can lead to complete tumor regression in a significant proportion of patients (up to 40%) but approximately 60% of patients have only partial or no tumor remission after nCRT. A number of works searching for a differential gene expression signature has failed. As those gene signatures relies on the sample set, there is an increasing agreement that biological understanding is necessary to produce a signature in terms of pathways and biological processes. In this regard we applied pathway enrichment analysis to RNA-Seq data in order to identify biological pathways involved in tumor resistance and discriminate rectal cancer patients that is refractory to neoadjuvant treatment. Mitochondrial oxidative phosphorylation and phosphatidylinositol signaling were identified as the main pathways involved in resistance to nCRT in patients with rectal cancer. Both pathways have been reported as involved in resistance to radiotherapy and chemotherapy in solid tumors but according to the status of gene regulation observed and the literature, there is evidence that Akt pathway could be central in promoting resistance.Therefore, we evaluated the status of Akt activation by immunohistochemistry of pre-treatment biopsies of rectal tumors as well as by immunoblot of colorectal cancer cell lines utilizing antibodies against phosphorylated Akt (pS473). As a result, we observed an increased proportion of Akt phosphorylation in the resistant cell line, SW480, and in patients with TRG 0-2 in comparison to those with TRG 3-4 and in the sensitive cell line, SW48. To evaluate the implication of Akt in promoting nCRT resistance, we performed colony formation assays using Akt inhibitors as radiosensitizers. We observed an additive effect using MK2206 at sub-doses in combination to radiotherapy (2 Gy) in the resistant cell line. In fact, Akt inhibition improved in approximately 40% the tumoricidal effects of radiotherapy. Moreover, at IC50 dose this effect is even higher, reaching 60% the improvement of radiotherapy. Furthermore, the treatment of MK2206 with radiotherapy alone or in combination with 5-FU have similar effectiveness. In this setting, as Akt is involved in a range of cellular functions that promote tumor progression and metastasis, we propose that combination of Akt inhibitor and radiotherapy as neoadjuvant treatment would maximize tumor remission and improve the rectal cancer treatment. Citation Format: Fernanda C. Koyama, Camila Ramos, Angelita Habr-Gama, Venâncio Avancini Ferreira Alves, Rodrigo O. Perez, Anamaria Aranha Camargo. Implications of Akt inhibition for neoadjuvant radiotherapy: improving the rectal cancer treatment. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 391.