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OmicSoft Corporation rna-seq expression and dna alteration data
Correlation of PROTAC activity with CRBN and <t>VHL</t> <t>RNA</t> expression, <t>DNA</t> copy number, and protein level (A and B) DC50 values from the cell line panel for each compound were used to group the cell lines into the bottom (low) and top (high) quartiles. Low and high quartiles were plotted against ligase mRNA expression or copy number, p values from unpaired two-samples, two-sided Wilcoxon rank sum tests of the groups were calculated. (A) dBET1 activity correlates significantly with CRBN copy number, p = 0.00058, and mRNA expression, p = 0.0048. Cell lines with non-synonymous mutations of CRBN were marked in red. (B) MZ1 activity correlates with VHL RNA expression, p = 0.028 but not VHL copy number, p = 0.059. Cell lines with non-synonymous mutations of VHL were marked in red. (C) Dose-response curves from representative kidney-derived cancer cell lines are shown; 786-O is devoid of both VHL and CRBN activity, 769P is lacking VHL activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (D) Lysates from untreated cells were separated by capillary electrophoresis, VHL and CRBN proteins were immune-detected. Each of the five kidney-derived cancer cell lines is lacking VHL protein, all of the cell lines express appreciable CRBN protein. (E) Dose-response curves from two representative lung cancer cell lines, H23 lacks dBET1-CRBN-associated activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (F) Lung-derived cancer cell lines with low CRBN activity have low or no CRBN protein, all lung cancer cell lines express VHL protein.
Rna Seq Expression And Dna Alteration Data, supplied by OmicSoft Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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1) Product Images from "Profiling of diverse tumor types establishes the broad utility of VHL-based ProTaCs and triages candidate ubiquitin ligases"

Article Title: Profiling of diverse tumor types establishes the broad utility of VHL-based ProTaCs and triages candidate ubiquitin ligases

Journal: iScience

doi: 10.1016/j.isci.2022.103985

Correlation of PROTAC activity with CRBN and VHL RNA expression, DNA copy number, and protein level (A and B) DC50 values from the cell line panel for each compound were used to group the cell lines into the bottom (low) and top (high) quartiles. Low and high quartiles were plotted against ligase mRNA expression or copy number, p values from unpaired two-samples, two-sided Wilcoxon rank sum tests of the groups were calculated. (A) dBET1 activity correlates significantly with CRBN copy number, p = 0.00058, and mRNA expression, p = 0.0048. Cell lines with non-synonymous mutations of CRBN were marked in red. (B) MZ1 activity correlates with VHL RNA expression, p = 0.028 but not VHL copy number, p = 0.059. Cell lines with non-synonymous mutations of VHL were marked in red. (C) Dose-response curves from representative kidney-derived cancer cell lines are shown; 786-O is devoid of both VHL and CRBN activity, 769P is lacking VHL activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (D) Lysates from untreated cells were separated by capillary electrophoresis, VHL and CRBN proteins were immune-detected. Each of the five kidney-derived cancer cell lines is lacking VHL protein, all of the cell lines express appreciable CRBN protein. (E) Dose-response curves from two representative lung cancer cell lines, H23 lacks dBET1-CRBN-associated activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (F) Lung-derived cancer cell lines with low CRBN activity have low or no CRBN protein, all lung cancer cell lines express VHL protein.
Figure Legend Snippet: Correlation of PROTAC activity with CRBN and VHL RNA expression, DNA copy number, and protein level (A and B) DC50 values from the cell line panel for each compound were used to group the cell lines into the bottom (low) and top (high) quartiles. Low and high quartiles were plotted against ligase mRNA expression or copy number, p values from unpaired two-samples, two-sided Wilcoxon rank sum tests of the groups were calculated. (A) dBET1 activity correlates significantly with CRBN copy number, p = 0.00058, and mRNA expression, p = 0.0048. Cell lines with non-synonymous mutations of CRBN were marked in red. (B) MZ1 activity correlates with VHL RNA expression, p = 0.028 but not VHL copy number, p = 0.059. Cell lines with non-synonymous mutations of VHL were marked in red. (C) Dose-response curves from representative kidney-derived cancer cell lines are shown; 786-O is devoid of both VHL and CRBN activity, 769P is lacking VHL activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (D) Lysates from untreated cells were separated by capillary electrophoresis, VHL and CRBN proteins were immune-detected. Each of the five kidney-derived cancer cell lines is lacking VHL protein, all of the cell lines express appreciable CRBN protein. (E) Dose-response curves from two representative lung cancer cell lines, H23 lacks dBET1-CRBN-associated activity. Dose titration curves are derived from n = 2 independent experiments, error bars represent standard error of the mean (SEM). (F) Lung-derived cancer cell lines with low CRBN activity have low or no CRBN protein, all lung cancer cell lines express VHL protein.

Techniques Used: Activity Assay, RNA Expression, Expressing, Derivative Assay, Titration, Electrophoresis

Comparison of genomic features for seven PROTAC Ub-ligases (A) Boxplot showing the distribution of log transformed fold change of the expression of seven Ub-ligases in cancer tissue compared to its paired non-cancer control tissue. Only cancer types with at least 50 non-cancer control tissue samples were included. (B) DNA alternation landscape of seven Ub-ligases across cancer types in TCGA. Bar graph showing percentage of samples harboring each Ub-ligase mutations across tumor types, the number of samples altered are labeled in the right side of each bar. Different mutation types are color labeled, with red representing amplification, blue representing homozygous deletion, green representing non-synonymous mutations, and gray representing a mixture of the above type of mutations. (C) Genome-scale CRISPR-Cas9 essentiality screen results for genes in across different cancer cell lines performed by the Broad Institute were characterized by dependency score (CERES) to reflect the functional importance of genes in certain cancer types. Boxplots summarized the distribution of CERES score of each ligase receptor in cell lines of representative tumor types. A lower score means that a gene is more likely to be essential for the cancer cell line survival and proliferation. A score of −1 corresponds to the median of all common essential genes, used as a cutoff indicator here.
Figure Legend Snippet: Comparison of genomic features for seven PROTAC Ub-ligases (A) Boxplot showing the distribution of log transformed fold change of the expression of seven Ub-ligases in cancer tissue compared to its paired non-cancer control tissue. Only cancer types with at least 50 non-cancer control tissue samples were included. (B) DNA alternation landscape of seven Ub-ligases across cancer types in TCGA. Bar graph showing percentage of samples harboring each Ub-ligase mutations across tumor types, the number of samples altered are labeled in the right side of each bar. Different mutation types are color labeled, with red representing amplification, blue representing homozygous deletion, green representing non-synonymous mutations, and gray representing a mixture of the above type of mutations. (C) Genome-scale CRISPR-Cas9 essentiality screen results for genes in across different cancer cell lines performed by the Broad Institute were characterized by dependency score (CERES) to reflect the functional importance of genes in certain cancer types. Boxplots summarized the distribution of CERES score of each ligase receptor in cell lines of representative tumor types. A lower score means that a gene is more likely to be essential for the cancer cell line survival and proliferation. A score of −1 corresponds to the median of all common essential genes, used as a cutoff indicator here.

Techniques Used: Comparison, Transformation Assay, Expressing, Control, Labeling, Mutagenesis, Amplification, CRISPR, Functional Assay



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Image Search Results


Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Association of BEND2 fusions with poor clinical outcomes (A) Diagram of sample composition for the pooled BEND2 cohort of pNET. (B) Kaplan-Meier curve of disease-specific survival rate regarding BEND2 rearrangement status. (C) Forest plot showing the hazard ratio (95% CI) in the univariate Cox regression and multivariate regression after adjusting for major clinicopathological features and the corresponding p values. The total number for the cohort, the number of cases per variable category, and the number of events (disease-specific deaths) for each level were also indicated.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques:

Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Inter-tumor heterogeneity at single-nuclei level (A) UMAP representation of 43,619 nuclei isolated from nine pNET samples with tumor purity annotated at the top left corner. (B) UMAP showing a separation between tumor cells and non-tumor cells. Tumor cells were grouped according to the five-subtype bulk classification, referred as pseudo-bulk sn-clusters. (C) Dot plot of canonical marker genes across all identified cell populations. Dot size indicates the proportion of cells expressing a gene, and color intensity reflects mean expression levels. (D) Heatmap of the Hallmark signaling pathways specific for each of the five pseudo-bulk sn-clusters based on GSVA enrichment scores. (E) Regulon specificity plot showing the top six regulons identified for each of the five pseudo-bulk sn-clusters. The x axis of the plot represents the genes within the regulon, while the y axis represents the specificity score. (F) Validation of bulk subtype-specific regulon on the bulk pNET cohort using GSVA based on a set of target genes within a regulon.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Isolation, Marker, Expressing, Protein-Protein interactions, Biomarker Discovery

Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: Cell-cell communication network and in silico drug sensitivity prediction in pNET subtypes (A) Cell-cell communication network visualized in Cytoscape, depicting the number and strength of interactions between tumor and microenvironmental cell populations. Nodes represent cell types, and edges represent intercellular interactions, with edge width and color reflecting interaction strength and frequency. (B) Bubble plot of selected ligand-receptor pairs between tumor subtypes and non-tumor microenvironmental cell populations. (C) Correlation plots showing associations between gene expression and deconvoluted cell-type proportions in bulk pNET samples. (D) Kaplan-Meier survival curves showing different DSS rates between pNET patients with high and low expression of genes of interest, including NOTCH3 (left), CD74 (middle), and the geometric mean value of these two genes (right). Patients were categorized into different groups according to the optimal thresholding. (E) Mechanisms of action of the 10 compounds predicted by Connectivity Map (CMap) analysis to preferentially target the BEND2 fusion. (F) Predicted sensitivities to 12 HDAC inhibitors across pNET subtypes based on GDSC database analyses. Statistical comparisons were performed using the Kruskal-Wallis test. p < 0.1; ∗ p < 0.05; ∗∗ p < 0.01; ∗∗∗ p < 0.001.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: In Silico, Gene Expression, Expressing

BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Journal: Cell Reports Medicine

Article Title: Molecular taxonomy of pancreatic neuroendocrine tumors reveals BEND2 -fusions-driven transcriptional plasticity and therapeutic vulnerabilities

doi: 10.1016/j.xcrm.2026.102642

Figure Lengend Snippet: BEND2 fusions induce transcriptional reprogramming and morphological plasticity in pNET tumor cells (A) Phase-contrast microscopy images of BON1 cells at 48 h post-induction showing morphological changes following overexpression of BEND2 -only, CHD7 - BEND2 , EWSR1 - BEND2 , or mCherry control. (B) Bar plot showing the number of differentially expressed genes at 12 and 48 h across all BON1 cell line models, including EWSR1 (E), BEND2 (B), CHD7 - BEND2 (CB), and EWSR1 - BEND2 (EB), compared to the mCherry (M) control. (C) Heatmap illustrating transcriptomic clustering of BON1 cell lines at 48 h (D) Venn diagram showing overlapping significantly upregulated transcription factors (TFs) in CHD7 - BEND2 and EWSR1 - BEND2 models compared to controls at 12 h (E) Bar plot of ASCL1 expression across BON1 cell line models at 12 and 48 h. Data are represented as mean ± SEM. (F) Heatmap showing transcriptional activation of neurodevelopmental, mesenchymal, and immune-related TFs in fusion-expressing BON1 cells at 48 h, accompanied by ASCL1 downregulation, activation of GAST-high subtype-specific regulons, and upregulation of immune checkpoint genes PDCD1 and CD274 . (G) Heatmap illustrating temporal transcriptomic shifts in BON1 fusion models using a 50-gene classifier (25 neuroendocrine and 25 non-neuroendocrine genes) derived from human SCLC lines. (H) GSEA results showing transcriptional reprogramming at 48 h in BEND2 fusion lines compared to 12 h (I) Bar plot showing GATA6 expression uniquely and robustly upregulated in BEND2 fusion lines at both 12 and 48 h. Data are represented as mean ± SEM. (J) Bar plot showing specific upregulation of POMC in BON1 cells expressing EWSR1 - BEND2 at 48 h. Data are represented as mean ± SEM. (K) Bar plot showing markedly higher POMC expression in the EWSR1 - BEND2 -positive tumor compared to the CHD7 - BEND2 -positive tumor in the clinical cohort.

Article Snippet: Pre-processed bulk RNA-seq data from pNET clinical samples and cell lines (this study) , Mendeley Data , https://data.mendeley.com/datasets/r9m66rjtxy/1.

Techniques: Microscopy, Over Expression, Control, Expressing, Activation Assay, Derivative Assay