1. Volume 29, Issue 3, Pages 394-406 (March 2016)
Genome-Wide Profiles of Extra-cranial Malignant Rhabdoid Tumors Reveal Heterogeneity and Dysregulated Developmental Pathways 
Hye-Jung E. Chun, Emilia L. Lim, Alireza Heravi-Moussavi, Saeed Saberi, Karen L. Mungall, Mikhail Bilenky, Annaick Carles, Kane Tse, Inna Shlafman, Kelsey Zhu, Jenny Q. Qian, Diana L. Palmquist, An He, William Long, Rodrigo Goya, Michelle Ng, Veronique G. LeBlanc, Erin Pleasance, Nina Thiessen, Tina Wong, Eric Chuah, Yong-Jun Zhao, Jacquie E. Schein, Daniela S. Gerhard, Michael D. Taylor, Andrew J. Mungall, Richard A. Moore, Yussanne Ma, Steven J.M. Jones, Elizabeth J. Perlman, Martin Hirst, Marco A. Marra 
Cancer Cell 
Volume 29, Issue 3, Pages (March 2016)
DOI: /j.ccell
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2. Figure 1 Mutations Identified in 40 MRT Cases
The figure shows selected alterations detected using genome analysis (top; CNNLOH, copy number neutral LOH), the number and types of somatic SNVs (middle; median of mutations is indicated by a dashed line), and clinical characteristics of 40 MRT cases (bottom). Each column represents a sample. See also Figure S1 and Tables S1 and S2.
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3. Figure 2
Clustering of miRNA Expression Profiles from MRT and Other Tumor and Normal Tissue Types
MRT samples are represented individually, while non-MRT tumor and normal tissue types are represented by the medians of miRNA expression of all samples of that type. Orange, MRT cases clustered with normal cerebellum samples (MD_NORM and GBM_NORM) and pheochromocytomas and paragangliomas (PCPG_TUM); green, MRT cases clustered with synovial sarcomas (SARC). See also Figure S2 and Table S3.
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4. Figure 3 NMF Clustering of miRNA Expression Profiles
NMF consensus heatmap (top), clinical characteristics of MRT cases in each group (middle), and miRNAs that were differentially expressed between subgroups 1 and 2 (n = 29; log2FC > 1, FDR < 0.05) (bottom). Pan-cancer miRNA cluster, see Figure 2. The labels, “Cerebellum” and “Synovial sarcoma” indicate MRT cases that clustered with normal cerebellum and pheochromocytomas and paragangliomas, and with synovial sarcomas, respectively. The silhouette width represents the robustness of the clustering solution of two groups by NMF. The yellow dashed line separates miRNA subgroups. Each column represents a sample. See also Figure S3 and Table S3.
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5. Figure 4 Analysis of mRNA Expression Profiles
(A and B) Bar graphs show adjusted p values and numbers of genes (in brackets) mapped to top enriched GO terms for overexpressed genes (A) and underexpressed genes (B) in MRTs compared with fetal cerebellum and hESCs (gray dashed line at p = 0.05).
(C) NMF consensus heatmap and clinical characteristics of 40 MRT samples (top); a heatmap showing overexpressed genes in subgroups 1 and 2, which were enriched for pathways (∗pathways with BH-corrected enrichment p < 0.05; pathways are assigned to genes as indicated by the black and gray bars on the right of the heatmap) (middle); and mutations, differential expression, and differential CGI promoter methylation in genes that regulate various stages of neural crest development (Simões-Costa and Bronner, 2015), including neural plate border formation, neural crest specification and migration, and differentiation of neural crest cells into various cell types including cardiac neural crest (Card), myoblast (Myo), and sympathetic neurons (SN) (bottom). Each column represents a sample.
(D and E) Hierarchical clustering (D) and principal component analysis (E) of 40 MRT samples. Blue and red colors indicate samples in NMF-derived subgroups 1 and 2, respectively.
(F) 880 genes differentially expressed between subgroup 1 and subgroup 2 (FDR < 0.05). Blue shades indicate overexpressed genes in subgroup 1, while red shades indicate overexpressed genes in subgroup 2. Lighter shades indicate genes detected at a FDR threshold of 0.05, while the darker shades indicate genes detected at a FDR threshold of Labels indicate the 20 most differentially expressed genes in each subgroup.
See also Figure S4 and Table S4.
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6. Figure 5 Analysis of DNA Methylation Profiles
(A) Correlation between unsupervised clustering of promoter CpG island (CGI) methylation levels and patient age at diagnosis (1,000 tests based on all promoter CGIs; approximately unbiased p = 0.03). The heatmap displays promoter CGIs with at least 15% difference in methylation (row means) between the two subgroups.
(B) Boxplots show the distribution of CpG methylation levels outside CGIs (left) and the average methylation levels within CGIs (right) for MRT cases in subgroups A (n = 10) and B (n = 30), MRT cell lines (n = 3), NPCs (n = 4), and hESCs (n = 3). The box indicates the interquartile range (IQR), with the line within the box indicating the median level. The lines below and above the box indicate values within 1.5 IQR from the first and the third quartile, respectively.
(C) Pathway analysis of genes with hypermethylated promoter CGIs in subgroup A compared to subgroup B (BH-adjusted p < 0.05). The bar graph shows adjusted p values and numbers of genes (in brackets) mapped to top enriched terms from Swiss-Prot Protein Information Resource (SP), InterPro (I), protein annotation from the Simple Modular Architecture Research Tool (SMART; SM), UniProt sequence annotation (UP), and biological processes and molecular function from Gene Ontology (GO-B and GO-M, respectively; gray dashed line at p = 0.05).
(D) Heatmap shows tumor suppressor promoter CGIs with methylation gain in MRT subgroups compared with hESCs (BH-adjusted Welch's t test p < 0.05).
See also Table S5.
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7. Figure 6
Comparison of Global H3K27me3 Marks between MRTs and SMARCB1-Intact Samples
(A) Clustering of H3K27me3 promoter density of MRTs and normal cell types. Note that case PAKLYZ that clustered with normal cell types expressed both SMARCB1 and SMARCA4 (mRNA 48.8 and 37.7 RPKM, respectively).
(B) Pathway analysis of gene promoters with lower H3K27me3 levels in MRTs compared with normal samples. The bar graph shows the adjusted p values and numbers of genes (in brackets) mapped to top enriched terms from InterPro (I) and biological processes from Gene Ontology (GO-B; gray dashed line at p = 0.05).
See also Figure S5 and Table S6.
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8. Figure 7 MRT-Specific Super-enhancers
(A) Genome-wide H3K27ac profiles were used to define enhancer (right bars in lighter shade) and super-enhancer states (left bars in darker shades) in MRT, fetal brain, and hESC cell types. Bar colors indicate different sample types: fetal brain (blue; BrainHu04), hESC (orange; NAAEMA, NAAEMB, NAAEMC), and MRT (red).
(B) Significantly enriched functional terms for MRT-specific super-enhancer-associated genes (BH-adjusted p < 0.05). Bar graph shows adjusted p values and numbers of genes (in brackets) mapped to top enriched terms from InterPro using DAVID (gray dashed line at p = 0.05).
(C) UCSC browser screen shot showing H3K27ac peaks at the HOXC locus in MRT samples (black), hESCs (orange), and in fetal brain (blue). The Y axis scale represents read density.
(D and E) The distribution of expression levels of HOTAIR (D) and all HOXC gene family members (E) in MRT, hESC, and normal cerebellum tissues from adult (Adult Cere), and fetus (Fetal Cere). The box indicates the IQR, with the line within the box indicating the median level. The lines below and above the box indicate values within 1.5 IQR from the first and the third quartile, respectively.
See also Figure S6 and Table S7.
Cancer Cell  , DOI: ( /j.ccell )
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