Gene Networks created by Pairs with high PCC (greater than 0.7) and high hypergeometric p-value yield less experimentally verified interactions.

<p>The number of connections identified was calculated for gene pairs with high PCC and high hypergeometric p-values. These connections were then compared to those identified in the literature. Note that few connections were found to be experimentally validated.</p

Correlation networks identified that intersect epigenetic pathways/signaling pathways with patient specific DE genes.

<p>Connections were calculated for gene-gene pairs emanating from epigenetic pathways or genes in the Notch, SHH, or WNT pathways and genes that were Differentially Expressed in each patient.</p

Pipeline for identifying patient-specific gene association in GBM.

<p>Our first step in our pipeline is to identify Differentially Expressed (DE) genes that are represented in 3 out of 4 algorithms. Next, we filter this DE gene list for those genes that overlapped with DE genes in the TCGA GBM Database. We then calculate the Correlation Coefficient and a hypergeometric p-value for every gene pair. Finally, by selecting the gene pairs with the highest correlation values we create a patient specific gene correlation network, which can be experimentally verified. As a starting point for our experiments, we can use the sub-networks in which, already verified connections exist in the literature.</p

Integrative representation and analysis of the LINCS cell lines using the Cell Line Ontology

Cell lines are crucial to study molecular signatures and pathways, and are widely used in the NIH Common Fund LINCS project. The Cell Line Ontology (CLO) is a community-based ontology representing and classifying cell lines from different resources. To better serve the LINCS research community, from the LINCS Data Portal and ChEMBL we identified 1,097 LINCS cell lines among which 717 cell lines were associated with 121 cancer types. 352 cell line terms did not exist in CLO. To harmonize LINCS cell line representation and CLO, CLO design patterns were slightly updated to add new information of the LINCS cell lines including different database cross-reference IDs. A new shortcut relation was generated to directly link a cell line to the disease of the patient from whom the cell line was originated. After new LINCS cell lines and related information were added to CLO, a CLO subset/view (LINCS-CLOview) of LINCS cell lines was generated and analyzed to identify scientific insights into these LINCS cell lines. Furthermore, we used the LINCS-CLOview to link and analyze various data generated from different LINCS cell lines. This study provides a first time use case on how CLO can be updated and applied to support cell line research from a specific research community or project initiative

Correlation networks created by using the top gene pairs for each patient.

<p>The number of connections we identified were compared to those previously described in the literature (red). Yellow indicates connections, which were identified in protein-protein interaction databases.</p

The LINCS Data Portal and FAIR LINCS Dataset Landing Pages

<p>The LINCS Data Portal (LDP) presents a unified interface to access LINCS datasets and metadata with mappings to several external resources. LDP provides various options to explore, query, and download LINCS dataset packages and reagents that have been described using the LINCS metadata standards.</p><p>We recently introduced LINCS Dataset Landing Pages to provide integrated access to important content for each LINCS dataset. The landing pages provide deep metadata for each LINCS dataset including description of the assays, authors, data analysis pipelines, and standardized reagents such as small molecules cell lines, antibodies, etc, with rich annotations. The landing pages are a key component to make LINCS data persistent and reusable, by integrating LINCS datasets, data processing pipelines, analytes, perturbations, model systems and related concepts as uniquely identifiable digital research objects.</p><p>LDP supports ontology-driven concept search, free text search, facet filtering, logical intersection of filters (AND, OR), and list, table, and matrix views. LDP enables download of LINCS dataset packages, which consist of released datasets and associated metadata. LDP also provides several specialized apps including small molecule compounds and cell lines. A landing page facilitates interactive exploration of all LINCS datasets via several classifications.</p>LDP is built on a robust API and is integrated with the MetaData Registry and interfaces with other components of the Integrated Knowledge Environment (IKE) developed in our Center. All LINCS datasets are also indexed in bioCADDIE DataMed

FIGURE 5 from The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways

Effects of CABIN1 deletion and mutations on the NFAT and MEF2B transcriptional activities. A, Western blot analysis of CABIN1 expression in WT SSK41 cells or in SSK41 cells transduced with lentiviral vectors expressing either a control shRNA or CABIN1-specific shRNAs. The expression of the housekeeping gene GAPDH was used as a loading control. B, Luciferase reporter assay for NFAT (top) and MEF2 (bottom) transcriptional activity in SSK41 cells transduced with lentiviral vectors expressing either a control shRNA or CABIN1-specific shRNAs. Where indicated, cells were stimulated for 4 hours with α-IgM F(ab’)2. *, P = 0.001; **, P C, Western blot analysis of CABIN1 expression in WT SSK41 cells or in SSK41 cells in which CABIN1 was initially knocked down using 3′-UTR targeting shRNA followed by expression of HA-tagged CABIN1 WT or mutants. CABIN1 was detected using anti-CABIN1 and anti-HA antibodies, while expression of the housekeeping gene GAPDH was used as a loading control. D, Luciferase reporter assay for NFAT (top) and MEF2 (bottom) transcriptional activity in SSK41 CABIN1 KD cells and cells expressing indicated CABIN1 constructs as shown in C. Where indicated, cells were stimulated for 4 hours with α-IgM F(ab’)2, in comparison to stimulated HA-WT: *, P P P = 0.00006. E, Representative immunoprecipitation (IP) assays with anti-HA antibodies using whole-cell protein extracts from unstimulated and α-IgM F(ab’)2–stimulated SSK141 cells expressing different CABIN1 mutants, as shown in C followed by Western blotting using indicated antibodies. Also shown mean and SD of relative SIN3A densitometry adjusted to immunoprecipitated CABIN1 in each cell type versus WT cells from three independent experiments. Statistical analyses of relative densitometry in mutants versus WT cells. *, P P < 0.01.</p

FIGURE 6 from The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways

Effects of CABIN1 alterations on gene expression and NFAT activation. A, Heat maps showing FPKM values of genes that are differentially expressed in WT versus CABIN1 KD SSK41 cells after α-IgM F(ab’)2 stimulation. These genes are significantly enriched for leukocyte activation signature, NFAT and MAF2B targets. B, IHC analysis of NFAT expression and localization in OAMZL tumors with WT CABIN 1 (top left) and CABIN1 CN losses (bottom left). For control, NFAT antibody staining showed cytosolic expression in germinal center of normal lymph nodes (top right), while no staining detectable in the uterus (bottom right). Magnification: top left and right and bottom left, 40×; bottom right, 10×. Insets, 100×. C, NFATc1 (green) immunofluorescence in OAMZL tissue sections with WT and mutated CABIN1. Image analysis of mean nuclear fluorescence signal intensities were done using ImageJ software. ****, P D, Cell viability assay of SSK41 WT, shRNA control, and CABIN1 KD cells treated for 72 hours with increasing concentrations of cyclosporin A. HEK293 cells are not sensitive to cyclosporin A and were used as control.</p

Supplementary Table 5 from The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways

CABIN1 expression in Mutants.</p

FIGURE 2 from The Genetic Landscape of Ocular Adnexa MALT Lymphoma Reveals Frequent Aberrations in NFAT and MEF2B Signaling Pathways

Characterization of mutational processes. A, Heat map showing the contribution of each of the 47 COSMIC mutation signatures to each individual tumor. B, Number (top) and proportion (bottom) of mutations in each tumor that are attributed to the top 12 most prevalent signatures. C, Relative enrichment of the top 12 most prevalent signatures in the 20 most recurrently mutated genes.</p