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Supplementary Figure S1 A 40 20 20 0 TCGA 0 −20 PETACC-3 Agendia (GSE42284) −40 Agendia (ICO) −20 Agendia (VHB) Principal component 2 −60 Principal component 4 Marisa et al. (GSE39582) Schlicker et al. (GSE35896) −80 −40 GSE75316 GSE75315 −60 −40 −20 0 20 40 60 80 −40 −20 0 20 40 Principal component 1 Principal component 3 B 4 3 2 1 0 supplementary Figure S1. Clustering of colorectal cancer BRAF V600E mutant patients. A: Principal component analysis of BRAF mutant patients. The four first principal components which explain the most of data variation are shown. Patients are labeled according to the cohort to which they belong. B: Hierarchical clustering of BRAF mutant patients using the Ward method to compute distance between patients. Supplementary Figure S2 A Row Z−Score - 4 - 2 0 2 4 BM1 BM2 200 150 100 50 BRAF V600E mutant patients 1000 2000 3000 4000 5000 6000 7000 8000 9000 .6 .4 .2 0 BM Subtype Genes B BM1 BM2 NMF clustering after 0 MSI status correction BM2 BM1 0.5 Unsupervised BM1 51 9 Cohen’s kappa = 0.82 Proportion NMF clustering [95%CI = 0.73-0.90] 6 125 BM subtypes BM2 MSI corrected 1 supplementary Figure S2. Subtyping of CRC BRAF mutant patients. A: Heatmap displaying genewise and patientwise double clustering of 218 CRC BRAF mutant patients. Clustering was performed using the Ward method to compute distance between genes and between patients. Gene expression is displayed as standardized z-score. B: NMF clustering after adjustment for MSI status, the BM subtype calls were then compared to the original calls computed without any adjustment. High agreement is shown between the two methods by the Cohen’s kappa coefficient of 0.82. Supplementary Figure S3 EMT signature 0.6 BM Subtype 0.5 0.4 423 0.3 Site 0 0 0 0.2 0 0 53 0 EMT 0 0 8 Enrichment score 0.1 0.0 147 0 0 0 0 0 0 0 0 0 0 0 0 0 0 BM1 BM2 1 0 0 0 48 Ranked 476 genes MSI status Gender KRAS signature 0.30 BM Subtype 0.25 0.20 448 0.15 Site 0 0 0.10 0 0 0 28 0 0.05 KRAS 0 0 8 Enrichment score 0.00 170 0 0 -0.05 0 0 0 0 0 0 0 2 0 0 0 0 1 0 0 0 BM1 BM2 46 Ranked 476 genes MSI status Gender E2F signature 0.0 BM Subtype -0.1 -0.2 -0.3 452 -0.4 Site 0 0 -0.5 0 0 0 -0.6 24 0 E2F 0 0 7 Enrichment score -0.7 -0.8 172 0 0 1 0 0 0 0 0 0 3 0 0 0 0 1 0 0 0 BM1 BM2 45 Ranked 476 genes MSI status Gender G2M signature 0.0 -0.1 BM Subtype -0.2 -0.3 454 -0.4 Site 0 0 -0.5 0 0 0 22 0 -0.6 G2M 0 0 7 Enrichment score -0.7 172 0 0 -0.8 1 0 0 0 0 0 0 4 0 1 0 0 0 0 0 0 BM1 BM2 48 Ranked 476 genes MSI status Gender supplementary Figure S3. Gene Set Enrichment Analysis of BRAF mutant patients. The 476 BRAF mutant specific genes were used to perform a standard GSEA analysis between BM1 and BM2. EMT (MSigDB ref: M5930) and KRAS (MSigDB ref: M5953) signatures were found enriched in BM1 while G2M (MSigDB ref: M5901) and E2F (MSigDB ref: M5925) signatures were enriched in BM2. Venn diagrams on the right side highlight the number of genes that are shared or specific between the respective signatures and the indicated clinical factors. Supplementary Figure S4 BM1 BRAF/KRAS WT (WT2) Positive signatures BM2 KRAS mutant BM subtypes Vs WT2 Negative signatures COMPLEMENT KRAS SIGNALING COAGULATION EPITHELIAL MESENCHYMAL TRANSITION w Z−Score INFLAMMATORY RESPONSE o R UV RESPONSE 3 APICAL JUNCTION 2 1 MYOGENESIS 0 TNFA SIGNALING VIA NFKB −1 GLYCOLYSIS −2 E2F TARGETS −3 G2M CHECKPOINT MITOTIC SPINDLE ESTROGEN RESPONSE LATE G2M CHECKPOINT MTORC1 SIGNALING MITOTIC SPINDLE E2F TARGETS ALLOGRAFT REJECTION GLYCOLYSIS IL2 STAT5 SIGNALING MYOGENESIS EPITHELIAL MESENCHYMAL TRANSITION APICAL JUNCTION ESTROGEN RESPONSE EARLY XENOBIOTIC METABOLISM COAGULATION UV RESPONSE APOPTOSIS BM subtypes Vs KRAS mutant COMPLEMENT INFLAMMATORY RESPONSE KRAS SIGNALING EPITHELIAL MESENCHYMAL TRANSITION COAGULATION UV RESPONSE MYOGENESIS GLYCOLYSIS E2F TARGETS ESTROGEN RESPONSE LATE MITOTIC SPINDLE G2M CHECKPOINT G2M CHECKPOINT MITOTIC SPINDLE E2F TARGETS MTORC1 SIGNALING ALLOGRAFT REJECTION GLYCOLYSIS MYOGENESIS ESTROGEN RESPONSE EARLY APICAL JUNCTION EPITHELIAL MESENCHYMAL TRANSITION APOPTOSIS XENOBIOTIC METABOLISM supplementary Figure S4. Pathway analysis comparison between BM subtypes, KRAS/BRAF wild-type and KRAS mutant CRCs. Single sample gene set enrichment analysis (ssGSEA) was performed for BM1 versus KRAS/BRAF wild-type (WT2) patients (top panel, top heatmap, 69 patients per group) or versus KRAS mutant patients (bottom panel, top heatmap, 69 patients per group) and similarly for BM2 versus WT2 patients (top panel, bottom heatmap, 149 patients per group) or versus KRAS mutant patients (bottom panel, bottom heatmap, 149 patients per group). These analyses were performed using the hallmark gene signature collection from MSigDB and using the only the 476 BM-specific genes. Supplementary Figure S5 Signature scores FDR Row scaled average signature score -1 -0.5 0 0.5 1 BM1 WT2 KRAS mut BM2 All genes 476 BM-specific genes MITOTIC SPINDLE MYC TARGETS CHOLESTEROL HOMEOSTASIS Enriched OXIDATIVE PHOSPHORYLATION in UV RESPONSE (DOWN) GLYCOLYSIS BM2 UNFOLDED PROTEIN RESPONSE 40 E2F TARGETS MTORC1 SIGNALING 30 DNA REPAIR G2M CHECKPOINT 20 BILE ACID METABOLISM PEROXISOME 10 MYC TARGETS FATTY ACID METABOLISM 0 PI3K/AKT/MTOR SIGNALING -log10(FDR) 10 SPERMATOGENESIS PROTEIN SECRETION 20 UV RESPONSE (UP) HYPOXIA Enriched APICAL_SURFACE in IL2/STAT5 SIGNALING TNFA SIGNALING VIA NFKB BM1 APOPTOSIS P53 PATHWAY COMPLEMENT INFLAMMATORY RESPONSE KRAS SIGNALING IL6/JAK/STAT3 SIGNALING ALLOGRAFT REJECTION INTERFERON ALPHA RESPONSE INTERFERON GAMMA RESPONSE PANCREAS BETA CELLS REACTIVE OXIGEN SPECIES PATHWAY HEME METABOLISM XENOBIOTIC METABOLISM WNT/BETA-CATENIN SIGNALING ESTROGENE RESPONSE LATE ANDROGEN RESPONSE LATE ANGIOGENESIS COAGULATION EPITHELIAL MESENCHYMAL TRANSITION KRAS SIGNALING (UP) TGF-B SIGNALING MYOGENESIS UV RESPONSE (DOWN) APICAL JUNCTION NOTCH SIGNALING HEDGEHOG SIGNALING ESTROGEN RESPONSE EARLY ADIPOGENESIS KRAS SIGNALING (DOWN) supplementary Figure S5. Molecular characterization of BM subtypes. Heatmap displaying the average ssGSEA hallmark signature scores for BM1, BM2, KRAS/BRAF wild-type (WT2) and KRAS mutant using all gene profiles (compared to Figure 3 where only the 476 BM-specific genes were used). False discovery rates adjusted p-values (FDR) for BM1 versus BM2 statistical analyses appear on the right under the form of a heatmap for both all genes and for the 476 BM-specific genes. Supplementary Figure S6 M2882 M1417 44 182 M5930 M5953 M11513 M2878 26 1 8 1 42 6 0 0 1 1 0 15 163 0 132 130 0 0 0 2 0 0 1 2 19 0 7 4 0 0 0 0 0 0 4 0 0 0 0 3 0 6 1 0 0 0 0 0 0 0 5 0 5 0 1 1 170 25 194 79 EMT signatures KRAS signatures M8191 M5956 M10573 M5915 APICAL JUNCTION M12829 43 M1484 M1396 M2632 M5925 4 20 M8627 151 116 M5901 0 4 1 3 65 8 136 0 2 9 0 60 1 11 0 0 37 1 0 0 12 113 0 0 12 le signatures 12 0 0 0 0 c 1 0 y 11 0 c 10 339 3 9 1 1 4 Cell M543 M14894 MTORC1 HALLMARK M7561 M16909 M5924 46 535 M1919 1 3 15 176 1 1 329 0 11 0 mTOR-related signatures 1 0 10 supplementary Figure S6. MSigDB gene set overlap. Venn diagram showing the number of genes that are overlap between collections of signatures tested in Figure 3. Supplementary Figure S7 100 Neutrophils Eosinophils Mast cells resting Dendritic cells activated Dendritic cells resting 75 Macrophages M2 Macrophages M1 Macrophages M0 Monocytes NK cells activated 50 NK cells resting T cells gamma delta T cells regulatory (Tregs) T cells follicular helper Relative percentage T cells CD4 memory activated 25 T cells CD4 memory resting T cells CD4 naive T cells CD8 Plasma cells B cells memory B cells naive 0 BM1 BM2 Mast cells activated Dendritic cells resting Macrophages M2 Macrophages M1 Monocytes Plasma cells 40 40 20 30 15 30 20 30 15 20 20 10 20 10 10 10 5 10 10 5 Relative percentage 0 0 0 0 0 0 FDR 5.1.10-3 3.5.10-2 2.8.10-6 3.8.10-2 5.5.10-3 5.5.10-3 Macrophage-associated genes 4 CYBB 2 CD163 CD84 0 MS4A4A GM2A row z-score −2 CD68 MARCO −4 APOE CHIT1 CHI3L1 BCAT1 Subtype FN1 RAI14 BM1 COLEC12 BM2 COL8A2 SCARB2 ATG7 MSI status CXCL5 MSS SCG5 GPC4 MSI ME1 Not available FDX1 supplementary Figure S7. Immune profiles of BM subtypes. A CIBERSORT analysis that reveals the immune landscape of BM subtypes in terms of relative percentage for each immune subset (top panel). The bottom panel displays the relative percentage of immune subsets that were found significantly enriched (FDR < 0.01) for each BM subtypes. Bottom heatmap displays the expression of macrophage-associated genes according to BM subtypes. MSI status is displayed as well. Supplementary Figure S8 β-value A TCGA patients 0 0.2 0.4 0.6 0.8 Illumina 450k 1 BRAF WT BRAF Mutant Methylated Not methylated Probes more methylated in BRAF mutant 15’591 / 374’801 (4.2 %) Probes more methylated in BRAF WT 196 / 374’801 (0.05 %) Differentially methylated Differentially probes (15’591) Probes not significantly different 359’014 / 374’801 (95.8 %) B TCGA patients Illumina 27k Illumina 450k BM1 BM2 MSS All DNA methylation probes All DNA in common between the two platforms (22’333) MSI C Illumina 27k Illumina 450k methylation probes MLH1 D Illumina 27k Illumina 450k SLC16A8 CLDN15 RPS6KA2 SPAG6 C16orf95 NGEF KLK8 TIMP2 METRN DMRT2 NGEF TBC1D13 PLCH2 PSD KIF17 AHRR RHOT1 FAM124B NLRP2 CCBP2 COL18A1 C9orf47 NCRNA00176 DNAJB6 MAPK15 MTERF QSOX2 PSTPIP1 MTERF MCAM PCDHB12 SSTR5 TSSC1 SLC27A6 RAMP1 TRIM29 CD4 SECTM1 HIST1H3H PSMG3 GRAMD2 HSD17B7P2 GDPD5 HLA−DPB2 HSD17B7 GRIN2A AGK LINC00982 CD40 NR5A2 FBXO34 Differentially methylated probes Differentially VPS18 GRB14 SOGA1 supplementary Figure S8.
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    RESEARCH ARTICLE elife.elifesciences.org ERK8 is a negative regulator of O-GalNAc glycosylation and cell migration Joanne Chia1, Keit Min Tham1, David James Gill1, Emilie Anne Bard-Chapeau1†, Frederic A Bard1,2* 1Institute of Molecular and Cell Biology, Singapore, Singapore; 2Department of Biochemistry, National University of Singapore, Singapore, Singapore Abstract ER O-glycosylation can be induced through relocalisation GalNAc-Transferases from the Golgi. This process markedly stimulates cell migration and is constitutively activated in more than 60% of breast carcinomas. How this activation is achieved remains unclear. Here, we screened 948 signalling genes using RNAi and imaging. We identified 12 negative regulators of O-glycosylation that all control GalNAc-T sub-cellular localisation. ERK8, an atypical MAPK with high basal kinase activity, is a strong hit and is partially localised at the Golgi. Its inhibition induces the relocation of GalNAc-Ts, but not of KDEL receptors, revealing the existence of two separate COPI-dependent pathways. ERK8 down-regulation, in turn, activates cell motility. In human breast and lung carcinomas, ERK8 expression is reduced while ER O-glycosylation initiation is hyperactivated. In sum, ERK8 appears as a constitutive brake on GalNAc-T relocalisation, and the loss of its expression could drive cancer aggressivity through increased cell motility. DOI: 10.7554/eLife.01828.001 *For correspondence: Introduction [email protected] GalNAc-type O-linked glycans are polysaccharides present on secreted and membrane-inserted pro- †Present address: Novartis teins (Tran and Ten Hagen, 2013). Traditionally associated with mucin-like proteins, recent advances in Institutes for BioMedical mass spectrometric analysis have revealed O-glycosylation on hundreds of different proteins (Steentoft Research, Basel, Switzerland et al., 2013).
  • Nanostring®: Product Data Sheet | Ncounter® GX Human Kinase

    Nanostring®: Product Data Sheet | Ncounter® GX Human Kinase

    PRODUCT DATA SHEET nCounter® GX Human Kinase Kit nCounter® GX Human Kinase Kit Product Highlights Highly Curated • Our expert bio-informaticists use a very rigorous process in selecting the most meaningful set of genes Efficient • Multiplexed assay profiles 522 human kinase genes in a single reaction Cost-effective • Gold-standard data at a fraction of the cost Quick Turnaround Time • Complete kit with all consumables ready to ship next day nCounter® GX Human Kinase Kit The nCounter GX Human Kinase Kit is a comprehensive list of 522 human The nCounter Human Kinase Kit represents 99% of the KinBase content for genes known to be differentially expressed in the kinome. Human. With the nCounter GX Human Kinase Kit, scientists can leverage a pre-designed The final nCounter GX Human Kinase Kit consists of 522 protein kinase- panel to accelerate their research and quickly generate expression data for a related genes and 14 internal reference genes. For the gene list and additional large panel of protein kinase-related genes. information about this gene set, visit the nCounter Pre-built Panels product page at: www.nanostring.com. The gene list was compiled using the KinBase database at www.kinase.com. Home > Products > nCounter Gene Expression CodeSets > Pre-built Panels The database is based on the publication, The Protein Kinase Complement of the Human Genome, by G Manning, DB Whyte, R Martinez, T Hunter, S Sudarsanam (2002). Science 298:1912-1934. Molecules That Count® Translational Research Gene Expression miRNA Expression Copy Number Variation 1 PRODUCT DATA SHEET nCounter® GX Human Kinase Kit nCounter® Analysis System Overview The nCounter Analysis System from NanoString offers a cost-effective way to easily profile hundreds of gene transcripts simultaneously with high sensitivity and precision.