Volume 24, Issue 1, Pages 238-251 (July 2018) Integrative Bayesian Analysis Identifies Rhabdomyosarcoma Disease Genes  Lin Xu, Yanbin Zheng, Jing Liu, Dinesh Rakheja, Sydney Singleterry, Theodore W. Laetsch, Jack F. Shern, Javed Khan, Timothy J. Triche, Douglas S. Hawkins, James F. Amatruda, Stephen X. Skapek  Cell Reports  Volume 24, Issue 1, Pages 238-251 (July 2018) DOI: 10.1016/j.celrep.2018.06.006 Copyright © 2018 The Authors Terms and Conditions

Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 1 Integrative Genomic Analysis Identifies Few Recurrent SNVs and Frequent Copy-Number Alterations in RMS (A) Venn diagrams display the number of RMS cases and type of genomic data used in this analysis. (B) Analysis of somatic protein-altering single nucleotide variants (SNVs) in 134 pairs of RMS tumor-normal specimens with whole-genome sequencing (WGS) data, whole-exome sequencing (WES) data, or both reveals that the vast majority of >3,827 mutated genes were found only in single RMS cases. (C) Charts show statistically significant regions displaying copy-number gains (left) and losses (right) identified by applying the Genomic Identification of Significant Targets in Cancer 2.0 (GISTIC2) algorithm in RMS cases with PAX3/7-FOXO1 fusion genes (FP cases) or without fusion genes (FN cases). See also Tables S1, S2, S3, and S4. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 2 iExCN Analysis Identifies RMS Oncogenic Drivers and Tumor Suppressors (A) Map shows 29 candidate RMS oncogenic drivers and tumor suppressors (★) within Gene Ontology terms (shaded ovals). Physical and genetic interactions from the GeneMANIA database are indicated by solid and dashed lines, respectively. Cell-cycle and nucleic acid binding are the only categories with statistically significant enrichment. (B) Chart displays the number of FN RMS cases (%) in which each of 29 candidate RMS drivers or tumor suppressors has an expression level (fragments per kilobase of transcript per million mapped reads [FPKM]) of ≥1 based on RNA sequencing. (C) Charts display the average copy-number level for copy-number gain events of iExCN-predicted oncogenes and copy-number loss events of iExCN-predicted tumor suppressor genes (TSGs). (D) Charts display the number among 209 FN RMS cases with copy-number gain (red) and loss (blue) events on 29 iExCN-defined RMS disease genes. Data presented as mean ± SEM. See also Figure S1. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 3 iExCN-Predicted Disease Genes Are Validated by CRISPR/Cas9 Mini-pool Screen (A) Schematic diagram shows experimental protocol in which RMS cells are transduced with lentiviral vectors affecting CRISPR/Cas9-based targeting of candidate oncogenes and tumors suppressors as well as randomly chosen genes. Relative changes in lentivirus representation are measured by targeted deep- sequencing at the beginning and the end of the experiment. (B and C) Charts display the number of FN RMS cases (%) in which each gene has an expression level (FPKM) of ≥1 based on RNA sequencing for negative (B) and positive (C) control genes. (D–F) Charts display average fold change of read counts of six sgRNAs per gene from days 0–14 for negative control (D), positive control (E), and iExCN genes (F). Asterisks represent FDR-corrected p < 0.05 by two-tailed Student’s t test. Black lines represent 20% increase or decrease. Data are presented for two RMS cell lines, JR1 and RD, as indicated. (G) Summary statistics for significantly altered genes targeted by depleted and enriched lentiviral vectors in the screen. (H) Venn diagram shows genes targeted by vectors that are significantly depleted or enriched (orange text) in the two FN RMS lines (JR1 and RD). Data presented as mean ± SD from six replicates. See also Figures S2 and S3 and Tables S5, S6, and S7. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 4 Expression of iExCN Genes Falls with Skeletal Muscle Differentiation (A) Charts show gene expression for muscle differentiation markers (red), iExCN-derived oncogenic drivers (blue), and iExCN-derived TSGs (green) in human myoblasts cultured in growth medium (day 0) or differentiation medium for the indicated number of days (1–6). (B) Chart displays the number of iExCN genes (blue bars) or genes randomly chosen from all of those expressed in FN RMS (gray bars), with the indicated correlation to the degree of skeletal muscle differentiation. Pearson correlation coefficient near 0 indicates no correlation, whereas correlation coefficients of −1 or 1 indicate repression or induction of individual genes with muscle differentiation. (C) Charts display ENCODE ChIP-seq data of H3K4me1 and H3K9ac, both of which are histone markers of active promoters, and H3K27ac, which is a histone marker for active promoters and enhancers, in human myoblast and myotube on promoter regions of EZH2, PTN, SNAI2, or RIPK2. (D) Chart demonstrates that ectopically expressed MyoD augments the expression of a muscle creatine kinase (MCK) enhancer or promoter reporter in 10T1/2 fibroblasts, whereas co-transfection of EZH2, PTN, SNAI2, or RIPK2 blunts MyoD activity. Data are average values from replicate samples, normalized to a co-transfected Renilla luciferase. ∗p < 0.05 by two-tailed Student’s t test. (E) Charts show transient transfection of siRNA targeting EZH2, PTN, SNAI2, or RIPK2 or a scrambled control siRNA (+ or −, respectively) influences the expression of MYOG in the indicated fusion-positive (FP) and fusion-negative (FN) cell lines. ∗p < 0.05 by two-tailed Student’s t test. Data presented as mean ± SD from six replicates. See also Figures S4 and S5. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 5 siRNA and Pharmacological Inhibition of iExCN-Defined Oncogenes Decreases RMS Cell Accumulation (A–F) siRNA knockdown in three iExCN-predicted oncogenes (ZFHX4, SNAI2, and RIPK2) in three FN (JR1, RD, and RH18) and two FP (RH28 and RH30) cell lines. Charts display knockdown efficiency (A, C, and E) and inhibition of cell accumulation (B, D, and F) for siRNA knockdown. p values were calculated based on the two-tailed Student’s t test. KD, knockdown. (G and H) Representative western blots (G) and qRT-PCR data (H) demonstrate that RIPK2 suppression by transient transfection of targeting (+) or scrambled (−) siRNA represses CDK4-dependent phosphorylation of RB and enhances expression of CDKN1A (p21Cip1) in the indicated fusion-positive (FP) and fusion-negative (FN) RMS lines. (I) Charts demonstrate that exposure of FP and FN RMS cells to RIPK2 inhibitors PP1 and ponatinib decreases RMS cell accumulation. ∗p < 0.05 by two-tailed Student’s t test. Data presented as mean ± SD from six replicates. See also Figures S3, S6, and S7. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions

Figure 6 Increased Number of iExCN Genes with CNVs Correlates with Survival in Two Independent FN RMS Cohorts (A–D) Kaplan-Meier plots display (A and B) failure-free and (C and D) overall survival in two independent cohorts with different numbers of iExCN genes with CNVs, as indicated. Cohorts I and II are described in the Supplemental Experimental Procedures. (E and F) Chart shows that the chromosomal instability index score in FN RMS cases with CNVs involving no more than 10 iExCN genes is not lower than in cases with CNVs in >10 iExCN genes, analyzed separately in cohort I (E) and II (F). (G and H) Chart shows that the average relative expression of 25 iExCN oncogenes is higher in FN RMS cases with CNVs in >10 iExCN genes in cohort I (G) and II (H). Data presented as mean ± SEM. See also Figure S7. Cell Reports 2018 24, 238-251DOI: (10.1016/j.celrep.2018.06.006) Copyright © 2018 The Authors Terms and Conditions