Journal: Cancers

Article Title: Combining APR-246 and HDAC-Inhibitors: A Novel Targeted Treatment Option for Neuroblastoma

doi: 10.3390/cancers13174476

Figure Lengend Snippet: TP53 mutation status insufficiently predicts the APR-246/PRIMA-1 response. ( a ): PRIMA-1 sensitivity separated according to TP53 mutation status derived from Cancer Cell Line Encyclopedia and CTD 2 portal ( n = 805); ( b ): Expression of p53 is shown in the Western blot of the whole cell lysates of one TP53 nonsense mutated cell line (KNS-42), three TP53 missense mutated cell lines (SJ-GBM2, SF188, SK-N-BE(2)-C) and one TP53 wild type cell line (IMR-32). GAPDH served as loading control. The ratio indicates normalization of p53 to the GAPDH signal; ( c , d ): RT-PCR measurement of TP53 pathway activation. CDKN1A expression is depicted as log2 transformed fold change to solvent control. Treatment was applied for 24 h with doxorubicin or APR-246, alone or in combination, as indicated; ( e , f ): metabolic activity measurement with CellTiter Glo-assay in neuroblastoma cell lines IMR-32 ( e ) and SK-N-BE(2)-C ( f ) after 72 h treatment. IMR-32 ( e ) were treated with 0–100 nM doxorubicin alone (blue) or in combination with a fixed APR-246 concentration of 1 µM (red). SK-N-BE(2)-C ( f ) were treated with 0–1000 nM doxorubicin alone (blue) or in combination with a fixed APR-246 concentration of 20 µM (red). Luminescence was normalized to the solvent control (DMSO). ( g , h ): metabolic activity measurement with CellTiter Glo-assay in pediatric high-grade glioma cell lines SJ-GBM2 ( g ) and SF188 ( h ) upon 72 h treatment. Both were treated with 0–1000 nM doxorubicin alone (blue) or in combination with a fixed APR-246 concentration of 30 µM (red). Luminescence was normalized to the solvent control (DMSO).

Article Snippet: We then looked for differentially expressed genes in the responsive versus less-responsive cohorts using the Cancer Cell Line Encyclopedia (CCLE, Broad Institute, Cambridge, MA, USA) gene expression data set in R2 (R2: microarray and visualization platform: http://r2.amc.nl , accessed on 5 January 2021).

Techniques: Mutagenesis, Derivative Assay, Expressing, Western Blot, Control, Reverse Transcription Polymerase Chain Reaction, Activation Assay, Transformation Assay, Solvent, Activity Assay, Glo Assay, Concentration Assay

Journal: Cancers

Article Title: Combining APR-246 and HDAC-Inhibitors: A Novel Targeted Treatment Option for Neuroblastoma

doi: 10.3390/cancers13174476

Figure Lengend Snippet: APR-246 response depends on the ROS levels. ( a ): The percentage of ROS positive IMR-32 cells after treatment with 50 µM APR-246 for 5 h compared to solvent control (DMSO) detected after DCFDA staining by FACS; ( b ): Log2 fold change of BODIPY positive IMR-32 cells after treatment with 50 µM APR-246 for 5 h compared to solvent control (DMSO) detected by FACS; ( c ): Basal ROS level in IMR-32, SK-N-BE(2)-C and SJ-GBM2 detected after DCFDA staining by FACS. Data were normalized to IMR-32 signal; ( d ): Log2 fold change of basal ROS level in vincristine resistant SK-N-BE(2)-C compared to control SK-N-BE(2)-C detected after DCFDA staining by FACS; ( e ): Metabolic activity measurement with CellTiter Glo-assay in vincristine resistant SK-N-BE(2)-C (red) and control SK-N-BE(2)-C (blue) after 72 h treatment with APR-246 0–30 µM. Luminescence was normalized to the respective solvent control (DMSO); ( f ): Log2 SLC7A11 expression of PRIMA-1 less responsive cell lines (LowRes, IC50 < 20 µM) compared to responsive cell lines (Res, IC50 > 20 µM) from the Cancer Cell Line Encyclopedia ( n = 744); ( g ): Combination of log2 SLC7A11 expression and PRIMA-1 sensitivity stratified by cancer cell line entities with data obtained from Cancer Cell Line Encyclopedia and CTD 2 portal ( n = 229); ( h ): Plot of the mean log2 SLC7A11 expression of entities ( n = 26) from the CCLE against the mean PRIMA-1 IC50 of the respective entities. Pearson correlation coefficient was calculated using R software; ( i ) Z-scores of SLC7A11 log2 expression from INFORM patient samples stratified by entities and analyzed with R2 ( n = 1643). ACC: adrenocortical carcinoma, ALL: acute lymphoblastic leukemia, AML: acute myeloid leukemia, ATRT: atypical teratoid/rhabdoid tumor, BRA: Brain cancer, EBR: embryonal brain tumor, EPEN: ependymoma, GCT: germ cell tumor, HCC: hepatocellular carcinoma, HBL: hepatoblastoma, HCC: Liver cancer, HGG: high-grade glioma, HNC: Head and Neck Cancer, KID: Kidney Cancer, LYM: Lymphoma, MED: medulloblastoma, MRT: malignant rhabdoid tumor, NB: neuroblastoma, ND: not defined, NHL: non-Hodgkin lymphoma, EWS: Ewing sarcoma, OST: osteosarcoma, RBD: Rhabdoid, RMS: rhabdomyosarcoma, OTHS: other sarcoma, WILMS: nephroblastoma, OTH: other, OTHB: other brain tumor. * p < 0.05, ** p < 0.01, *** p < 0.001.

Article Snippet: We then looked for differentially expressed genes in the responsive versus less-responsive cohorts using the Cancer Cell Line Encyclopedia (CCLE, Broad Institute, Cambridge, MA, USA) gene expression data set in R2 (R2: microarray and visualization platform: http://r2.amc.nl , accessed on 5 January 2021).

Techniques: Solvent, Control, Staining, Activity Assay, Glo Assay, Expressing, Software

Journal: Cancers

Article Title: Combining APR-246 and HDAC-Inhibitors: A Novel Targeted Treatment Option for Neuroblastoma

doi: 10.3390/cancers13174476

Figure Lengend Snippet: Identification of a biomarker predicting APR-246 responsiveness ( a ): in silico analysis of log2 SLC7A11 and PRIMA-1 sensitivity (IC50 in µM) with data obtained from Cancer Cell Line Encyclopedia and CTD 2 portal for available neuroblastoma cell lines. The Pearson correlation coefficient (0.145) was calculated using R software ( n = 12); ( b ): volcano plot of differentially expressed genes between less responsive cell lines (PRIMA-1 IC50 app. > 40 µM, n = 303) and responsive cell lines (PRIMA-1 IC50 app. < 10 µM, n = 102) calculated using R2 software. Except for YAP1 and MIR142 (most significantly down and up-regulated gene respectively) all red dots indicate genes that are part of the genetic signature; ( c , d ): boxplot of z-score of in responsive cell lines that were the most significantly downregulated ( c , YAP1 ) and upregulated ( d , MIR142 ) genes plotted with R2; ( e , f ): receiver operating characteristic (ROC) curve calculated with ROCR package in R software for the genetic signature score to discriminate between low-responders (PRIMA-1 IC50 app. > 40 µM, n = 303) and responders (PRIMA-1 IC50 app. < 10 µM, n = 102) ( e ) or to discriminate the IC50s less than or greater than 20 µM ( f ). Responder and low-responder status ( n = 405) ( e ) or the whole CCLE cell lines ( n = 711) with known PRIMA-1 sensitivity data were used as test cohort ( f ); ( g ): in silico analysis of genetic signature score predicting PRIMA-1 responsiveness in neuroblastoma sub-cohort from CCLE ( n = 12). PRIMA-1 sensitivity cut-off 20 µM was chosen. Ten out of twelve cell lines were predicted correctly. SK-N-SH and SK-N-DZ were falsely predicted to be low-responders. LowRes: low-responder ( n = 303), Res: responder ( n = 102).

Article Snippet: We then looked for differentially expressed genes in the responsive versus less-responsive cohorts using the Cancer Cell Line Encyclopedia (CCLE, Broad Institute, Cambridge, MA, USA) gene expression data set in R2 (R2: microarray and visualization platform: http://r2.amc.nl , accessed on 5 January 2021).

Techniques: Biomarker Discovery, In Silico, Software

Journal: Frontiers in Oncology

Article Title: A tumor cell specific Zona Pellucida glycoprotein 3 RNA transcript encodes an intracellular cancer antigen

doi: 10.3389/fonc.2023.1233039

Figure Lengend Snippet: ZP3-Cancer is the dominant transcript in tumor cells and is differentially distributed among cancer types. (A) Expression of the seven annotated ZP3 transcripts in CCls (n=1339). Bars represent Tukey box plots (boxes are median+IQR). Median transcript expression levels (from left to right) are 2.47, 0.22, 0.0, 0.04, 0.42, 0.0 and 0.0 TPM. ZP3-Cancer expression is significantly higher compared to all other transcripts (Kruskal-Wallis test, p < 0.0001). NC = Non-protein coding. (B) Relative expression of ZP3-cancer and ZP3-Oocyte in the CCLs MCF7 and HeLa, and in healthy ovarian tissue containing follicles, as determined by qPCR. ZP3-Cancer expression is set to 1 for MCF7 and HeLa, ZP3-Oocyte expression is set to 1 for ovarian tissue. ‘ZP3-both’ served as a positive control and used primers designed to detect ZP3-Cancer and ZP3-Oocyte simultaneously. Bars represent mean+SD for the triplicate qPCR reactions. (C) Differential expression of ZP3-Cancer in the cancer types covered by the CCLs. Cancer types represented by less than 5 CCLs were not included. Bars represent Tukey box plots (boxes are median+IQR). Number of samples are indicated between brackets. The shaded area indicates CCLs with a ZP3-Cancer expression level below the overall median (2.54 TPM).

Article Snippet: Publicly available ZP3 transcript-level RNA-seq data of 1339 CCLs of the Cancer Cell Line Encyclopedia (CCLE; The Broad Institute) was downloaded from the DepMap portal ( https://depmap.org/portal/ ; file ‘CCLE_RNAseq_transcripts.csv’, version 22Q2).

Techniques: Expressing, Positive Control, Quantitative Proteomics

Journal: eLife

Article Title: Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes

doi: 10.7554/eLife.72879

Figure Lengend Snippet: The functional impact of DUB loss was investigated via transcriptomic profiling of cells following CRISPR-Cas9 knockout (purple box); this was compared to signatures for genetic perturbations in the Broad Connectivity Map database of RNA-seq signatures (green). The impact of DUB knockout on cancer cell line proliferation was analyzed using the Broad Dependency Map (red) and compared to other gene knockouts in the dataset; this identified co-dependent genes (sets of genes whose knockout had similar effects on cell proliferation across cell lines). Multiple protein-protein interaction databases and co-expression (correlation in protein abundance across cell lines) in baseline proteomics in Cancer Cell Line Encyclopedia (CCLE; yellow) cell lines were used to provide support for physical or functional interactions among DUBs exhibiting co-dependency. We then used these analyses to explore the impact of DUB knockout on the transcriptome, determine the impact of DUBs on proliferation, and propose E3 ligase interactors and essential functions of DUBs. Acronyms used in figure: knockout (KO), loss of function (LOF), Cancer Cell Line Encyclopedia (CCLE).

Article Snippet: Cancer Cell Line Encyclopedia (CCLE) Proteomics , A Broad Institute database of baseline shotgun proteomics collected from CCLE cell lines. Data from ~375 cell lines and 12,000 proteins per line are available. , Protein co-expression across cell line panels provides insight into functional interactions: proteins in the same complex are often co-expressed to a significant degree across CCLE cell clines. , Co-expressed genes : correlation in protein abundance across cell lines with FDR <0.01 and |z-score|>2. These thresholds are set based on previous publications ( ). .

Techniques: Functional Assay, CRISPR, Knock-Out, RNA Sequencing, Expressing, Quantitative Proteomics

Journal: eLife

Article Title: Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes

doi: 10.7554/eLife.72879

Figure Lengend Snippet: ( A ) The fraction of cancer cell lines present in the DepMap that are strongly dependent on each DUB (using recommended threshold CERES <–0.5). Bars are colored coded based on knockout mouse phenotype data from the IMPC and MGI datasets. ( B ) The strongest co-dependent genes for each DUB (n=7 but similar results were obtained for n=5–10). For visualization purposes, only DUBs that either had a significantly enriched GO term, a co-dependent gene supported by PPID or CCLE proteomics co-expression, or scored as essential in at least 20% of DepMap cell lines are displayed (see for complete codependency results). The inner ring (ring 1) contains the top GO term for the co-dependent genes for each DUB (highly similar GO terms are grouped to aid in viewing the data, see for all GO results). The second ring contains the DUB gene name. The third ring contains the fraction of cell lines strongly dependent on the DUB. The fourth ring contains the co-dependent gene name (green if DUB – co-dependent gene pair exists in a protein-protein interaction database). The fifth ring contains the Pearson correlation value for the DUB-co-dependent gene pair (red represents a positive correlation and blue represents a negative correlation). The sixth ring designates which co-dependent genes had similar transcriptomic profiles in CMap or were co-expressed in baseline proteomics with the respective DUB.

Article Snippet: Cancer Cell Line Encyclopedia (CCLE) Proteomics , A Broad Institute database of baseline shotgun proteomics collected from CCLE cell lines. Data from ~375 cell lines and 12,000 proteins per line are available. , Protein co-expression across cell line panels provides insight into functional interactions: proteins in the same complex are often co-expressed to a significant degree across CCLE cell clines. , Co-expressed genes : correlation in protein abundance across cell lines with FDR <0.01 and |z-score|>2. These thresholds are set based on previous publications ( ). .

Techniques: Knock-Out, Expressing

Journal: eLife

Article Title: Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes

doi: 10.7554/eLife.72879

Figure Lengend Snippet: ( A ) The significant GO terms from the DepMap co-dependent gene analysis were compared to GO terms enriched in the interactors for each DUB and the co-expressed proteins in the Cancer Cell Line Encyclopedia proteomics dataset. ( B ) The most significant GO term from DepMap analysis (smallest FDR <0.05) for each DUB as well as the GO term from DepMap analysis with the largest sum of -log(p-value) across datasets (if different). DUBs that were not present in the dataset are distinguished with a black dot.

Article Snippet: Cancer Cell Line Encyclopedia (CCLE) Proteomics , A Broad Institute database of baseline shotgun proteomics collected from CCLE cell lines. Data from ~375 cell lines and 12,000 proteins per line are available. , Protein co-expression across cell line panels provides insight into functional interactions: proteins in the same complex are often co-expressed to a significant degree across CCLE cell clines. , Co-expressed genes : correlation in protein abundance across cell lines with FDR <0.01 and |z-score|>2. These thresholds are set based on previous publications ( ). .

Techniques:

Journal: eLife

Article Title: Integrating multi-omics data reveals function and therapeutic potential of deubiquitinating enzymes

doi: 10.7554/eLife.72879

Figure Lengend Snippet: : Ubiquitin or ubiquitin-like transferases whose co-dependency relationships correlated with DUBs in the DepMap. DUBs are colored blue and ubiquitin transferases are colored grey. Red lines represent correlations in the top seven co-dependent genes. Green lines represent similarity by CMap (tau similarity score >90). Yellow lines represent co-expression in proteomics (FDR <0.01 and |z-score|>2). Blue lines represent interaction in protein-protein interaction databases.

Article Snippet: Cancer Cell Line Encyclopedia (CCLE) Proteomics , A Broad Institute database of baseline shotgun proteomics collected from CCLE cell lines. Data from ~375 cell lines and 12,000 proteins per line are available. , Protein co-expression across cell line panels provides insight into functional interactions: proteins in the same complex are often co-expressed to a significant degree across CCLE cell clines. , Co-expressed genes : correlation in protein abundance across cell lines with FDR <0.01 and |z-score|>2. These thresholds are set based on previous publications ( ). .

Techniques: Ubiquitin Proteomics, Expressing