Cooperation between PRMT1 and PRMT6 drives lung cancer health disparities among Black/African American men.

Lung cancer is the third most common cancer with Black/AA men showing higher risk and poorer outcomes than NHW men. Lung cancer disparities are multifactorial, driven by tobacco exposure, inequities in care access, upstream health determinants, and molecular determinants including biological and genetic factors. Elevated expressions of protein arginine methyltransferases (PRMTs) correlating with poorer prognosis have been observed in many cancers. Most importantly, our study shows that PRMT6 displays higher expression in lung cancer tissues of Black/AA men compared to NHW men. In this study, we investigated the underlying mechanism of PRMT6 and its cooperation with PRMT1 to form a heteromer as a driver of lung cancer. Disrupting PRMT1/PRMT6 heteromer by a competitive peptide reduced proliferation in non-small cell lung cancer cell lines and patient-derived organoids, therefore, giving rise to a more strategic approach in the treatment of Black/AA men with lung cancer and to eliminate cancer health disparities.

Results Obtained from a Pivotal Validation Trial of a Microsatellite Analysis (MSA) Assay for Bladder Cancer Detection through a Statistical Approach Using a Four-Stage Pipeline of Modern Machine Learning Techniques.

Several studies have shown that microsatellite changes can be profiled in urine for the detection of bladder cancer. The use of microsatellite analysis (MSA) for bladder cancer detection requires a comprehensive analysis of as many as 15 to 20 markers, based on the amplification and interpretations of many individual MSA markers, and it can be technically challenging. Here, to develop fast, more efficient, standardized, and less costly MSA for the detection of bladder cancer, we developed three multiplex-polymerase-chain-reaction-(PCR)-based MSA assays, all of which were analyzed via a genetic analyzer. First, we selected 16 MSA markers based on 9 selected publications. Based on samples from Johns Hopkins University (the JHU sample, the first set sample), we developed an MSA based on triplet, three-tube-based multiplex PCR (a Triplet MSA assay). The discovery, validation, and translation of biomarkers for the early detection of cancer are the primary focuses of the Early Detection Research Network (EDRN), an initiative of the National Cancer Institute (NCI). A prospective study sponsored by the EDRN was undertaken to determine the efficacy of a novel set of MSA markers for the early detection of bladder cancer. This work and data analysis were performed through a collaboration between academics and industry partners. In the current study, we undertook a re-analysis of the primary data from the Compass study to enhance the predictive power of the dataset in bladder cancer diagnosis. Using a four-stage pipeline of modern machine learning techniques, including outlier removal with a nonlinear model, correcting for majority/minority class imbalance, feature engineering, and the use of a model-derived variable importance measure to select predictors, we were able to increase the utility of the original dataset to predict the occurrence of bladder cancer. The results of this analysis showed an increase in accuracy (85%), sensitivity (82%), and specificity (83%) compared to the original analysis. The re-analysis of the EDRN study results using machine learning statistical analysis proved to achieve an appropriate level of accuracy, sensitivity, and specificity to support the use of the MSA for bladder cancer detection and monitoring. This assay can be a significant addition to the tools urologists use to both detect primary bladder cancers and monitor recurrent bladder cancer.

A Core Regulatory Circuit in Glioblastoma Stem Cells Links MAPK Activation to a Transcriptional Program of Neural Stem Cell Identity.

Glioblastoma, the most common primary malignant brain tumor, harbors a small population of tumor initiating cells (glioblastoma stem cells) that have many properties similar to neural stem cells. To investigate common regulatory networks in both neural and glioblastoma stem cells, we subjected both cell types to in-vitro differentiation conditions and measured global gene-expression changes using gene expression microarrays. Analysis of enriched transcription factor DNA-binding sites in the promoters of differentially expressed genes was used to reconstruct regulatory networks involved in differentiation. Computational predictions, which were biochemically validated, show an extensive overlap of regulatory circuitry between cell types including a network centered on the transcription factor KLF4. We further demonstrate that EGR1, a transcription factor previously shown to be downstream of the MAPK pathway, regulates KLF4 expression and that KLF4 in turn transcriptionally activates NOTCH as well as SOX2. These results demonstrate how known genomic alterations in glioma that induce constitutive activation of MAPK are transcriptionally linked to master regulators essential for neural stem cell identify.

Association of the Long Non-coding RNA Steroid Receptor RNA Activator (SRA) with TrxG and PRC2 Complexes.

Long non-coding RNAs (lncRNAs) have been recognized as key players in transcriptional regulation. We show that the lncRNA steroid receptor RNA activator (SRA) participates in regulation through complex formation with trithorax group (TrxG) and polycomb repressive complex 2 (PRC2) complexes. Binding of the SRA-associated RNA helicase p68 preferentially stabilizes complex formation between SRA and a TrxG complex but not PRC2. In human pluripotent stem cells NTERA2, SRA binding sites that are also occupied by p68 are significantly enriched for H3K4 trimethylation. Consistent with its ability to interact with TrxG and PRC2 complexes, some SRA binding sites in human pluripotent stem cells overlap with bivalent domains. CTCF sites associated with SRA appear also to be enriched for bivalent modifications. We identify NANOG as a transcription factor directly interacting with SRA and co-localizing with it genome-wide in NTERA2. Further, we show that SRA is important for maintaining the stem cell state and for reprogramming of human fibroblasts to achieve the pluripotent state. Our results suggest a mechanism whereby the lncRNA SRA interacts with either TrxG or PRC2. These complexes may then be recruited by various DNA binding factors to deliver either activating or silencing signals, or both, to establish bivalent domains.

Mapping of long-range INS promoter interactions reveals a role for calcium-activated chloride channel ANO1 in insulin secretion.

We used circular chromatin conformation capture (4C) to identify a physical contact in human pancreatic islets between the region near the insulin (INS) promoter and the ANO1 gene, lying 68 Mb away on human chromosome 11, which encodes a Ca(2+)-dependent chloride ion channel. In response to glucose, this contact was strengthened and ANO1 expression increased, whereas inhibition of INS gene transcription by INS promoter targeting siRNA decreased ANO1 expression, revealing a regulatory effect of INS promoter on ANO1 expression. Knockdown of ANO1 expression caused decreased insulin secretion in human islets, establishing a physical proximity-dependent feedback loop involving INS transcription, ANO1 expression, and insulin secretion. To explore a possible role of ANO1 in insulin metabolism, we carried out experiments in Ano1(+/-) mice. We observed reduced serum insulin levels and insulin-to-glucose ratios in high-fat diet-fed Ano1(+/-) mice relative to Ano1(+/+) mice fed the same diet. Our results show that determination of long-range contacts within the nucleus can be used to detect novel and physiologically relevant mechanisms. They also show that networks of long-range physical contacts are important to the regulation of insulin metabolism.

Micro-environment causes reversible changes in DNA methylation and mRNA expression profiles in patient-derived glioma stem cells.

In vitro and in vivo models are widely used in cancer research. Characterizing the similarities and differences between a patient's tumor and corresponding in vitro and in vivo models is important for understanding the potential clinical relevance of experimental data generated with these models. Towards this aim, we analyzed the genomic aberrations, DNA methylation and transcriptome profiles of five parental tumors and their matched in vitro isolated glioma stem cell (GSC) lines and xenografts generated from these same GSCs using high-resolution platforms. We observed that the methylation and transcriptome profiles of in vitro GSCs were significantly different from their corresponding xenografts, which were actually more similar to their original parental tumors. This points to the potentially critical role of the brain microenvironment in influencing methylation and transcriptional patterns of GSCs. Consistent with this possibility, ex vivo cultured GSCs isolated from xenografts showed a tendency to return to their initial in vitro states even after a short time in culture, supporting a rapid dynamic adaptation to the in vitro microenvironment. These results show that methylation and transcriptome profiles are highly dependent on the microenvironment and growth in orthotopic sites partially reverse the changes caused by in vitro culturing.

Age-specific signatures of glioblastoma at the genomic, genetic, and epigenetic levels.

Age is a powerful predictor of survival in glioblastoma multiforme (GBM) yet the biological basis for the difference in clinical outcome is mostly unknown. Discovering genes and pathways that would explain age-specific survival difference could generate opportunities for novel therapeutics for GBM. Here we have integrated gene expression, exon expression, microRNA expression, copy number alteration, SNP, whole exome sequence, and DNA methylation data sets of a cohort of GBM patients in The Cancer Genome Atlas (TCGA) project to discover age-specific signatures at the transcriptional, genetic, and epigenetic levels and validated our findings on the REMBRANDT data set. We found major age-specific signatures at all levels including age-specific hypermethylation in polycomb group protein target genes and the upregulation of angiogenesis-related genes in older GBMs. These age-specific differences in GBM, which are independent of molecular subtypes, may in part explain the preferential effects of anti-angiogenic agents in older GBM and pave the way to a better understanding of the unique biology and clinical behavior of older versus younger GBMs.

Histone demethylase Jumonji D3 (JMJD3) as a tumor suppressor by regulating p53 protein nuclear stabilization.

Histone methylation regulates normal stem cell fate decisions through a coordinated interplay between histone methyltransferases and demethylases at lineage specific genes. Malignant transformation is associated with aberrant accumulation of repressive histone modifications, such as polycomb mediated histone 3 lysine 27 (H3K27me3) resulting in a histone methylation mediated block to differentiation. The relevance, however, of histone demethylases in cancer remains less clear. We report that JMJD3, a H3K27me3 demethylase, is induced during differentiation of glioblastoma stem cells (GSCs), where it promotes a differentiation-like phenotype via chromatin dependent (INK4A/ARF locus activation) and chromatin independent (nuclear p53 protein stabilization) mechanisms. Our findings indicate that deregulation of JMJD3 may contribute to gliomagenesis via inhibition of the p53 pathway resulting in a block to terminal differentiation.

Integration and analysis of genome-scale data from gliomas.

Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.

Predicting in vitro drug sensitivity using Random Forests.

MOTIVATION: Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART). RESULTS: This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable. IMPLEMENTATION: Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.

A systems analysis of importin-{alpha}-{beta} mediated nuclear protein import.

Importin-beta (Impbeta) is a major transport receptor for Ran-dependent import of nuclear cargo. Impbeta can bind cargo directly or through an adaptor such as Importin-alpha (Impalpha). Factors involved in nuclear transport have been well studied, but systems analysis can offer further insight into regulatory mechanisms. We used computer simulation and real-time assays in intact cells to examine Impalpha-beta-mediated import. The model reflects experimentally determined rates for cargo import and correctly predicts that import is limited principally by Impalpha and Ran, but is also sensitive to NTF2. The model predicts that CAS is not limiting for the initial rate of cargo import and, surprisingly, that increased concentrations of Impbeta and the exchange factor, RCC1, actually inhibit rather than stimulate import. These unexpected predictions were all validated experimentally. The model revealed that inhibition by RCC1 is caused by sequestration of nuclear Ran. Inhibition by Impbeta results from depletion nuclear RanGTP, and, in support of this mechanism, expression of mRFP-Ran reversed the inhibition.

Comparing cDNA and oligonucleotide array data: concordance of gene expression across platforms for the NCI-60 cancer cells.

Microarray gene-expression profiles are generally validated one gene at a time by real-time RT-PCR. We describe here a different approach based on simultaneous mutual validation of large numbers of genes using two different expression-profiling platforms. The result described here for the NCI-60 cancer cell lines is a consensus set of genes that give similar profiles on spotted cDNA arrays and Affymetrix oligonucleotide chips. Global concordance is parameterized by a 'correlation of correlations' coefficient.