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SUPPLEMENTARY FIGURE 1 ) A % % Explained variation vs. Consensus matrix (4 clusters) 100 number of clusters X ) l 1 2 3 4 Clusters ) 25 l l nul ( n nu ( Consensus o i t 20 a ihood l i hood i r l ke a li ke - i 15 v l g - 0 .2 .4 .6 .8 1 g d lo e lo – ) – n 10 i d ) l e a t l t ul i f f ( p ( x 5 E hood hood i i l l % ke ke 0 i i l l - - g g 1 2 3 4 5 6 o lo l ( Number of clusters B Expanded clusters (including samples w/ missing data) 1 2 3 4 Newly-added samples 4 14 9 Fluke-assoc. Fluke-Pos Fluke-Neg Location Intrahepatic Perihilar Distal Perihilar/Distal MSI/POLE MSI POLE deficiency Viral hepatitis HBV HCV HBV & HCV No data PSC PSC Country Thailand Singapore Korea France Romania Brazil China Italy Sequencing WGS Targeted s n t io t Mutation a oin t P u m r Chr 1 e b m Gain nu y Loss op C Chr 22 n o i High ress p Low x E n o i t a High A l y h DN t Low me C Clustering by gene expression Clustering by copy number alterations Clustering by point mutations Z-score CNA Cluster 4 Cluster 2 Cluster 1 Cluster 4 Cluster 4 Cluster 2 -2 0 2 - 6 0 +6 D Clustering stratified by anatomical site Intrahepatic CCAs Extrahepatic CCAs 92.8% concordant w/ 88% concordant w/ original clusters original clusters Orig. cluster Intrahepatic CCAs Fluke-assoc. Fluke-Pos Fluke-Neg 92.75% concordant w/ Location original clusters Intrahepatic Perihilar Distal Perihilar/Distal s n t io t Mutation a oin t P u m r Chr 1 e b m Gain nu y Loss op C hr 22 n o i High ress p x Low E n o i t a High A l y h DN t Low me SUPPLEMENTARY FIGURE 2 A B CTNNB1 WNT5B AKT1 POLE 500 9.8 10.5 12.0 * * * 10.0 11.5 *** 9.4 100 MSI 9.5 MSI 11.0 9.0 9.0 MSI 8.5 10.5 10 8.6 8.0 10.0 mRNA expression mRNA 7.5 number of nonsynonymous point mutations (log-scaled) 8.2 9.5 7.0 0 7.8 6.5 9.0 Cluster Other Other Other 1 2 3 4 Cluster 2 Cluster 2 Cluster 2 Clusters Clusters Clusters ***p<0.001 *p<0.05 C D ERBB2 3000 13 * 13 *** 13 ** 2500 * 12 12 2000 12 1500 11 11 11 1000 Immune Score mRNA expression mRNA 500 10 10 10 0 9 9 9 -500 Cluster Cluster Fluke-Pos Fluke-Neg Amplified Non−amplified Cluster 1+2 3+4 1 2 3 4 p < 0.05 *p<0.05 **p<0.01 ***p<0.001 * E B A C A PSMB9 CTSK LCP2 B2M HLA PSMB10 PSMC2 HLA TAP2 PSMD12 PSMD7 PSMB1 PSMD1 PSMA5 PSMD2 PSMC6 PSME2 CTSL2 PSMB8 PSMB6 PSMF1 PSME4 HLA TAPBP PSMD6 PSMA6 CTSB TAP1 PSMB2 PSMA3 PSMD14 PSMC4 CTLA4 PDL1 BTL PDL2 PD1 TNFRSF4 CD19 ICOS TNFRSF9 TLR9 IFITM1 CD3E IFNG CD8A PRF1 CD40 CD3D CD247 GZMA CD86 CD4 CD226 CD80 ICAM1 CD3G ICAM4 FOXP3 Cluster 4 3 2 1 0.5 1.0 Antigen presentation Immune Checkpoint Immune response Z-score gene 1.0 0.5 0.0 expression across 4 Clusters F Validation cohort Fluke association Anatomical location 1.0 Cluster 1 1.0 Fluke−Neg 1.0 Intrahepatic Cluster 2 Fluke−Pos Distal Cluster 4 Perihilar 0.8 0.8 0.8 * 0.6 0.6 0.6 al Probability al Probability al Probability v v v vi 0.4 vi 0.4 vi 0.4 r r *** r Su Su Su 0.2 0.2 0.2 *p < 0.05 Log-Rank test ***p < 0.001 Log-Rank test p = 0.38 Log-Rank test 0 0 0 0 500 1000 1500 2000 0 500 1000 1500 2000 2500 3000 0 500 1000 1500 2000 2500 3000 Time Time Time SUPPLEMENTARY FIGURE 3 A B MAP2K4 CCA_TH_8 MAP2K4 CCA_TH_22 17p 17q 17p 9p o i at r R 2 og l y c e l e ll a - quen e B r f c r i f e i b c e 1N 1N 2N 2N 2N 1N m p u s n - e l e l l opy A c 7Mb 16Mb 26Mb 34Mb 7Mb 16Mb 0 9Mb C D MAP2K4 ERBB2 CCA_TH_120 CEP17 n o i s s e r p x e A N ERBB2 CCA_TH_29 R CEP17 m ** No deletion Homozygous deletion **p < 0.01 (Low SUPPLEMENTARY FIGURE4 SUPPLEMENTARY Fluke-status DNA-repair SV count FBXW7 SMAD4 B A High) TP53 chr15 chr16 chr13 chr13 chr14 chr17 L1 chr18 CCA_CH_6 chr12 chr12 chr19 CCA_TH_12 chr CCA_SG_2 20 chr CCA_TH_6 Somatic L1 chr 21 CCA_CH_8 1 chr 1 1 CCA_IT_2 22 CCA_TH_4 CCA_SG_8 chrX chr10 chr10 CCA_RO_7 CCA_RO_2 CCA_SG_3 retrotranspositions CCA_CH_3 chr CCA_IT_4 Y chr9 CCA_SG_20 CCA_TH_13 CCA_RO_5 CCA_JP_6 chr1 chr1 CCA_JP_3 chr8 CCA_JP_5 CCA_RO_1 CCA_SG_17 CCA_SG_15 CCA_JP_4 chr7 CCA_SG_16 chr2 chr2 CCA_TH_20 CCA_RO_6 CCA_SG_6 chr6 CCA_SG_18 chr3 chr3 CCA_JP_8 CCA_IT_3 chr5 chr4 CCA_TH_2 CCA_JP_10 CCA_SG_14 CCA_SG_4 CCA_CH_1 CCA_RO_3 CCA_RO_4 CCA_SG_19 CCA_SG_5 CCA_IT_1 CCA_SG_7 CCA_JP_2 CCA_CH_2 CCA_CH_7 CCA_TH_1 CCA_TH_11 CCA_CH_4 CCA_SG_10 CCA_JP_7 CCA_SG_11 CCA_JP_1 CCA_TH_5 CCA_TH_22 CCA_JP_9 CCA_CH_5 CCA_TH_21 CCA_TH_14 CCA_TH_8 CCA_TH_15 CCA_SG_12 CCA_TH_9 CCA_SG_1 CCA_TH_10 CCA_SG_13 CCA_TH_17 CCA_TH_7 CCA_SG_9 CCA_TH_18 CCA_TH_3 CCA_TH_16 CCA_TH_19 SUPPLEMENTARY FIGURE 5 A Example non-significant gene sets Fisher’s test Synthetic mutations test Gene expression test GS: Genes in gene set Null distributions of M Tumors w/ binding-change BC: Genes w/ binding-change under synthetic mutations Tumors w/o binding-change mutations p: 0.76 M p: 0.91 p: 0.20 REACTOME_ GS Not GS IMMUNE_ BC M=329 6,394 SYSTEM Not BC 62,321 1,164,986 (895 genes) 300 320 340 360 380 400 -2 0 2 4 MARSON_ p: .75 p: 1 p: 0.13 BOUND_BY_ GS Not GS FOXP3_ BC M=451 6,272 M UNSTIMULATED Not BC 84,879 1,142,428 (1219 genes) 440 460 480 500 520 540 560 580 -2 0 2 4 B C PARD3 PIAS1 AICDA Number of dysregulated gene sets out of 19 randomly-selected gene sets 3 3 3 y t p = 0.11 i 80 v i 2.5 2.5 2.5 t c a 60 e 2 2 2 s p = 0.64 a r e f * p = 0.46 40 Actual data i 1.5 1.5 1.5 c p < 0.01 u l Frequency tant / Wildtype ) e u 1 1 1 20 v i M t a ( l ** e 0.5 * 0.5 0.5 0 R 0 1 2 3 4 0 0 0 H69 EGI1 H69 EGI1 H69 EGI1 Cell line Cell line Cell line ** p < 0.01 * p < 0.05 D MIKKELSEN_ MIKKELSEN_ WONG_ MCV6_HCP_ MEF_ICP_ ENDOMETRIUM_ WITH_H3K27ME3 WITH_H3K27ME3 CANCER_DN 20 10 6 ** ** *** 15 10 5 3 5 Number of genes with binding-change mutations 0 0 0 Other Other Cluster 1 Other Cluster 1 clusters Cluster 1 clusters clusters ** p < 0.01 ***p < 0.01 SUPPLEMENTARY FIGURE 6 A Cluster 1 Cluster 4 2 2 ) 33 ce 82 44 ) ce 359 n Hypermethylated n Hypermethylated e s promoters e s r r r promoters r 1 e e 1 e e t 1 t 4 ff s s r ff r u u e l e No correlation l t di t c di No correlation c 1654 (74%) s s r 881 (69%) u r 0 u l l e 0 e C 1654 h 26 ion C 881 h t 692 ion t O O ess ess -1 r 2 -1 2 r Pos correlation Neg correlation g p Neg correlation g 497 (22%) p 82 (4%) o Pos correlation o x l 349 (27%) l x ( 44 (3%) ( E E 36 349 0 497 -2 -2 -0.4 -0.2 0 0.2 0.4 0.6 -0.4 -0.2 0 0.2 0.4 0.6 Hypomethylated Hypermethylated Hypomethylated Hypermethylated Methylation difference (β) Methylation difference (β) (Cluster 1 – Other clusters) (Cluster 4 – Other clusters) Negative correlation Positive correlation No correlation B C BAP1 mut/loss vs wt (excluding IDH mutants) ** 12 *** n n o o i i 10 ess ess r R r p D p 8 F ex ex 0 2 1 1 6 H g T Z E lo E T - 4 2 Cluster 1 Other clusters Cluster 1 Other clusters 0 -0.4 -0.2 0 0.2 0.4 *** p < 0.001 **p < 0.01 Hypomethylated Hypermethylated Change in β D E Overlap between hypermethylated regions and mutations (in normally unmethylated regions) CpG > TpG mutations b Cluster 1 p<0.001 Cluster 4 p=0.29 M / s Hyper Not Not on Hyper i hyper hyper t methylated a methylated methylated t methylated u m Mutated 68 148 Mutated 4 45 f o Not r Not e mutated 176,141 2,418,391 112,577 2,282,526 b mutated m u N Non C>T mutations Cluster 1 p=0.20 Cluster 4 p=0.83 Not Hyper Not Hyper hyper hyper methylated methylated methylated methylated Cluster 1 Cluster 2 and 3 Cluster 4 Mutated 22 224 Mutated 6 116 Not Not 176,187 2,418,315 112,575 2,282,455 mutated mutated SUPPLEMENTARY FIGURE LEGENDS Supplementary Figure 1.
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  • GEP Analysis Validates High Risk MDS and Acute Myeloid Leukemia Post

    GEP Analysis Validates High Risk MDS and Acute Myeloid Leukemia Post

    Guerenne et al. Journal of Hematology & Oncology (2016) 9:5 DOI 10.1186/s13045-016-0235-8 RESEARCH Open Access GEP analysis validates high risk MDS and acute myeloid leukemia post MDS mice models and highlights novel dysregulated pathways Laura Guerenne1,2, Stéphanie Beurlet1,2, Mohamed Said3, Petra Gorombei1,2, Carole Le Pogam1,2, Fabien Guidez1,2, Pierre de la Grange4, Nader Omidvar5, Valérie Vanneaux6, Ken Mills7, Ghulam J Mufti3, Laure Sarda-Mantel8,9, Maria Elena Noguera10, Marika Pla1,2,11, Pierre Fenaux1,2,10, Rose Ann Padua1,2,10†, Christine Chomienne1,2,10† and Patricia Krief1,2* Abstract Background: In spite of the recent discovery of genetic mutations in most myelodysplasic (MDS) patients, the pathophysiology of these disorders still remains poorly understood, and only few in vivo models are available to help unravel the disease. Methods: We performed global specific gene expression profiling and functional pathway analysis in purified Sca1+ cells of two MDS transgenic mouse models that mimic human high-risk MDS (HR-MDS) and acute myeloid leukemia (AML) post MDS, with NRASD12 and BCL2 transgenes under the control of different promoters MRP8NRASD12/tethBCL-2 or MRP8[NRASD12/hBCL-2], respectively. Results: Analysis of dysregulated genes that were unique to the diseased HR-MDS and AML post MDS mice and not their founder mice pointed first to pathways that had previously been reported in MDS patients, including DNA replication/damage/repair, cell cycle, apoptosis, immune responses, and canonical Wnt pathways, further validating these models at the gene expression level. Interestingly, pathways not previously reported in MDS were discovered.
  • Advancing the Role of Gamma-Tocotrienol As Proteasomes Inhibitor: a Quantitative Proteomic Analysis of MDA-MB-231 Human Breast Cancer Cells

    Advancing the Role of Gamma-Tocotrienol As Proteasomes Inhibitor: a Quantitative Proteomic Analysis of MDA-MB-231 Human Breast Cancer Cells

    biomolecules Article Advancing the Role of Gamma-Tocotrienol as Proteasomes Inhibitor: A Quantitative Proteomic Analysis of MDA-MB-231 Human Breast Cancer Cells Premdass Ramdas 1,2, Ammu Kutty Radhakrishnan 3 , Asmahani Azira Abdu Sani 4 , Mangala Kumari 5, Jeya Seela Anandha Rao 6 and Puteri Shafinaz Abdul-Rahman 1,7,* 1 Department of Molecular Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; [email protected] 2 Department of Medical Biotechnology, School of Health Sciences, International Medical University, 57000 Kuala Lumpur, Malaysia 3 Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Bandar Sunway, 47500 Selangor, Malaysia; [email protected] 4 Malaysian Genome Institute, National Institute of Biotechnology, 43000 Bangi, Malaysia; [email protected] 5 Division of Human Biology, International Medical University, 57000 Kuala Lumpur, Malaysia; [email protected] 6 Division of Pathology, International Medical University, 57000 Kuala Lumpur, Malaysia; [email protected] 7 University of Malaya Centre of Proteomics Research (UMCPR), University of Malaya, 50603 Kuala Lumpur, Malaysia * Correspondence: [email protected] Received: 27 November 2019; Accepted: 14 December 2019; Published: 21 December 2019 Abstract: Tocotrienol, an analogue of vitamin E has been known for its numerous health benefits and anti-cancer effects. Of the four isoforms of tocotrienols, gamma-tocotrienol (γT3) has been frequently reported for their superior anti-tumorigenic activity in both in vitro and in vivo studies, when compared to its counterparts. In this study, the effect of γT3 treatment in the cytoplasmic and nuclear fraction of MDA-MB-231 human breast cancer cells were assessed using the label-free quantitative proteomics analysis.