Journal: Nature Cancer

Article Title: The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities

doi: 10.1038/s43018-024-00784-3

Figure Lengend Snippet: a , Global protein levels in newly diagnosed MM cases with t(11;14) ( n = 27) were compared against cases without t(11;14) ( n = 87) with a two-sided, moderated two-sample t -test. The log 2 fold change (FC) of each protein is plotted against its P value. P values were adjusted with the Benjamini–Hochberg method and the significance threshold of 0.05 FDR is indicated. b , The heat map displays the normalized expression of RB1, CDK4, CDK6, CCND1, CCND2 and CCND3 on RNA and protein level and RB1 phosphopeptides. Phosphopeptides are annotated with protein name, position, amino acid and number of phosphorylations. c , Global protein levels in cases with t(4;14) ( n = 19) were compared against other MM cases ( n = 95) with a two-sided, moderated two-sample t -test. The log 2 FC of each protein is plotted against its P value. P values were adjusted with the Benjamini–Hochberg method and the significance threshold of 0.05 FDR is indicated. d , Protein, phosphoprotein and RNA expression levels of FGFR3 and NSD2 in samples with ( n = 19) or without t(4;14) ( n = 95). For phosphopeptide data, the peptide with the least missing values was selected for a graphical representation (FGFR3.S.425; NSD2.S.618). FDRs of the comparison between the two groups are indicated. Box plots show median (middle line), 25th and 75th percentiles, whiskers extend to minimum and maximum excluding outliers (values greater than 1.5× interquartile range (IQR)). e , FGFR3 protein levels in MM samples are plotted against the ssGSEA normalized enrichment score of the Reactome gene set ‘Downstream signaling of activated FGFR3 in phosphoproteomic data’. Normalized TMT ratios in each sample were used as input for ssGSEA. f , FGFR3 and NSD2 RNA expression and CRISPR–Cas9 KO screening data in MM cell lines were extracted from the depmap portal (depmap.org). RNA expression is plotted against the CRISPR KO gene effect. g , Cell viability of MM cell lines after treatment with FGFR3 inhibitor erdafitinib for 96 h at indicated concentrations ( n = 3, independent replicates). Data are plotted as mean ± s.d. Drug treatments of each cell line were compared to respective DMSO controls with a Dunnett’s test. **** P value < 0.0001. Exact P values listed in the source table.

Article Snippet: The human Calabrese CRISPR activation pooled library set A was a gift from David Root and John Doench (Addgene #92379) .

Techniques: Expressing, RNA Expression, Phospho-proteomics, Comparison, CRISPR

Journal: Nature Cancer

Article Title: The proteogenomic landscape of multiple myeloma reveals insights into disease biology and therapeutic opportunities

doi: 10.1038/s43018-024-00784-3

Figure Lengend Snippet: a , Hematopoietic cell populations were sorted using MACS enrichment for the surface markers CD34 (hematopoietic stem and progenitor cells (HSCs)), CD19 (B cells) and CD138 (plasma cells) from bone marrow of individuals without hematologic malignancy ( n = 3). Proteins were quantified via TMT with a booster channel approach. Booster and equal loading control were identical to the internal standard used for TMT analysis of cohort samples. b , Protein levels of cell lineage-specific markers in healthy samples. z -scored TMT ratios are displayed. c , Proteins in MACS sorted healthy bone marrow and CD138 + sorted MM samples were compared with a two-sided, moderated two-sample t -test. P values were adjusted with the Benjamini–Hochberg method. The total number of regulated proteins is indicated, the Venn diagrams show overlap of up- and downregulated proteins in MM samples compared with healthy samples (FDR < 0.1). d , Data analysis workflow to identify potential therapeutic candidates from myeloma upregulated or specifically expressed proteins. e , Gene dependency scores from CRISPR–Cas9 KO screening data from the depmap portal. The gene effect of potential therapeutic targets in myeloma ( n = 18) and other cell lines ( n = 1,082) is displayed. The RNA to protein correlation in myeloma cohort is indicated for each candidate gene. Box plot shows median (middle line), 25th and 75th percentiles, whiskers extend to minimum and maximum excluding outliers (values greater than 1.5× IQR). f , The workflow for a genome-wide CRISPR–Cas9 activation screen using the Calabrese library performed in the MM cell line MM.1S. g , Gene effect on proliferation ranked by beta score. A higher beta score indicates expansion of cells carrying the indicated sgRNAs. The MAGeCK MLE algorithm was applied for the analysis of beta scores and P values. Potential targets identified by proteomic analysis are marked in purple. h , Protein levels of IRS1 and POU2AF1 across healthy and malignant cell populations. Healthy CD138: n = 3; healthy CD19: n = 3, healthy CD34: n = 3; MGUS: n = 7; MM: n = 114; PCL: n = 17. Box plot shows median (middle line), 25th and 75th percentiles, whiskers extend to minimum and maximum excluding outliers (values greater than 1.5× IQR).

Article Snippet: The human Calabrese CRISPR activation pooled library set A was a gift from David Root and John Doench (Addgene #92379) .

Techniques: Clinical Proteomics, Control, CRISPR, Biomarker Discovery, Genome Wide, Activation Assay

Journal: Nature communications

Article Title: Identification of regulators of poly-ADP-ribose polymerase inhibitor response through complementary CRISPR knockout and activation screens.

doi: 10.1038/s41467-020-19961-w

Figure Lengend Snippet: Fig. 1 Complementary CRISPR knockout and activation screens identify determinants of PARPi response in parental or BRCA2-knockout HeLa cells. a Schematic representation of the CRISPR knockout screen for olaparib sensitivity in wildtype cells. HeLa cells were infected with the Brunello CRISPR knockout library. Infected cells were divided into PARP inhibitor (olaparib)-treated or control (DMSO) arms. Genomic DNA was extracted from cells surviving the drug treatment and single-guide RNAs (sgRNAs) were identified using Illumina sequencing. b Scatterplot showing the results of this screen. Each gene targeted by the library was ranked based on the MAGeCK negative selection score. Several biologically interesting hits are highlighted. c Schematic representation of the CRISPR knockout screen for olaparib resistance in BRCA2KO cells. HeLa BRCA2KO cells were infected with the Brunello CRISPR knockout library. Infected cells were divided into PARP inhibitor (olaparib)-treated or control (DMSO) arms. d Scatterplot showing the results of this screen, with several biologically interesting hits highlighted. Each gene targeted by the library was ranked based on the MAGeCK positive selection score. e Schematic representation of the CRISPR activation screen for olaparib resistance in BRCA2KO cells. HeLa BRCA2KO cells stably expressing the modified dCas9 enzyme were infected with the Calabrese CRISPR activation library. Infected cells were divided into PARP inhibitor (olaparib)-treated or control (DMSO) arms. f Scatterplot showing the results of this screen, with several biologically interesting hits highlighted. Each gene targeted by the library was ranked based on the MAGeCK positive selection score. Source data are provided as a Source Data file.

Article Snippet: For the CRISPR activation screens, HeLa BRCA2-knockout cells were infected with dCas9 (Addgene, 61425-LV) and selected with blasticidin (3 μg/ml). dCas9expressing cells were then transduced with the Calabrese Human CRISPR Activation Pooled Library (Set A, Addgene, 92379-LV) using enough cells to obtain a library coverage of 500 cells per sgRNA at an MOI of 0.418.

Techniques: CRISPR, Knock-Out, Activation Assay, Infection, Control, Illumina Sequencing, Selection, Stable Transfection, Expressing

Journal: bioRxiv

Article Title: A whole-genome CRISPR screen identifies the spindle accessory checkpoint as a locus of nab-paclitaxel resistance in pancreatic cancer cells

doi: 10.1101/2024.02.15.580539

Figure Lengend Snippet: Genome-wide CRISPR interference and knockout screens identify BUB1B, TTK, and BUB3 as drivers for nab-paclitaxel resistance in PANC-1 cells. (A, B) The Rank plot of genes generated by differential RRA score identifies BUB1B and TTK as the most significant genes in the CRISPR knockdown screen and BUB3 as the most significant gene in the CRISPR knockout screen. (C, D) The sgRNAs targeting BUB1B, TTK, and BUB3 were consistently enriched in nab-paclitaxel-treated cells. Source data are provided as a Source Data file.

Article Snippet: The human CRISPR Brunello lentiviral pooled library (Addgene # 73178-LV) and human CRISPR Dolcetto (Set A) inhibition library (Addgene # 92386-LV) were used to identify genes responsible for enhanced survival of PANC-1 cells treated with nab-paclitaxel.

Techniques: Genome Wide, CRISPR, Knock-Out, Generated, Knockdown