Volume 22, Issue 1, Pages 286-298 (January 2018) Comprehensive Genomic Characterization of RNA-Binding Proteins across Human Cancers  Ze-Lin Wang, Bin Li, Yu-Xia Luo, Qiao Lin, Shu-Rong Liu, Xiao-Qin Zhang, Hui Zhou, Jian-Hua Yang, Liang-Hu Qu  Cell Reports  Volume 22, Issue 1, Pages 286-298 (January 2018) DOI: 10.1016/j.celrep.2017.12.035 Copyright © 2017 The Author(s) Terms and Conditions

Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 1 Transcription Pattern Analysis of RBPs in Each of 15 Cancer Types (A) The number of significantly dysregulated RBPs in each cancer type. (B) The distributions of dysregulated RBPs across 15 cancer types. (C) A conserved RBP signature shared by two-thirds of the 15 cancer types. Unsupervised clustering analysis was performed using the pheatmap function using average and correlation as metrics in R based on log2-fold change values. See also Figures S1 and S2 and Table S1. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 2 Identification of Potential Driver RBPs with SCNAs (A) Chromosome plot displays the distributions of RBPs with SCNAs in the genome. Combine type indicates that RBPs display copy number gains or losses in different cancer types. (B) The number of RBP drivers with SCNAs in each cancer type. (C) Heat map depicting SCNA profile of driver RBPs across 15 cancer types. Orange represents amplification, and green represents deletion. (D) Comparison of SCNAs of RBPs and TFs as well as ERs across 15 cancer types. The y axis shows the ratios of the number of RBPs, TFs, and ERs with SCNAs and total RBPs, TFs, and ERs in each of 15 cancer types. The x axis shows the type of observed factors. amp, amplification; del, deletion; RBP, RNA-binding protein; TF, transcription factor; ER, epigenetic regulator. See also Table S3. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 3 Somatic Mutation Landscape of RBPs (A) A summary of mutation types within RBP loci. “In_Frame” means the insert and deletion mutations in frame. (B) The number of non-silent coding-mutated RBPs in individual samples for each of the 15 cancer types. The values were preprocessed via log2-based transformation. (C) Mutation spectrum of the 6 possible transition (Ti) and transversion (Tv) categories for each cancer type. (D) Similarity of sequence context for 6 SNV types among 15 cancers. The values represent Pearson correlation coefficients between any 2 types. Unsupervised clustering was performed using the pheatmap package in R. “Sequence context” means the mutation ratios of 6 SNV type (A > C, A > G, A > T, C > A, C > G, and C > T). Mutation ratio of each SNV type was calculated as its number divided by the total number of 6 SNV types. Pearson correlation coefficients based on these 6 ratios between any 2 cancer types were estimated. The larger r value represents higher similarity between the two cancer types compared. See also Figure S3. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 4 Identification of Significantly Mutated RBPs (A) The number of mutated drivers in each cancer type. OG, oncogene; TSG, tumor suppressor gene. (B) Venn diagram depicts a small number of overlapping RBPs between SCNA drivers and mutational drivers. RBPs marked in red represent the same functional influence results (loss-of-function) via SCNAs or mutations. (C) Heat map depicts the mutation landscape of drivers across the 15 cancer types. See also Table S4. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 5 Properties of Potential Driver RBPs in Cancer Cell Lines Gene silencing effects of 132 driver candidates with available shRNA data in all 216 cancer cell lines and matched cancer cell lines. The distributions of log2 ratios of the shRNA concentrations in the final cell population and the initial DNA pool were compared between known oncogenes, tumor suppressors, putative onco-RBPs, and tumor-suppressive RBPs identified by us, non-driver RBPs, and the non-cancer genes using the Wilcoxon test. The list of known tumor suppressors and oncogenes was downloaded from the COSMIC database (version 74). See also Table S5. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions

Figure 6 Functional Validation of 6 RBP Candidates in Colorectal and Liver Cancer Cell Lines (A–C) CCK-8 (A) and colony formation assay (B) and the corresponding quantification (C) show the effects of ELAC1 overexpression on the proliferation of HCT116 and SW480 cells. (D–F) CCK-8 (D) and colony formation assay (E) and the corresponding quantification (F) show the effects of BYSL and ZC3H13 knockdown on the proliferation of HCT116 and SW480 cells. (G–I) CCK-8 (G) and colony formation assay (H) and the corresponding quantification (I) show the effects of NSUN6, RBMS3 and ZGPAT overexpression on the proliferation of Huh-7 and SK-hep-1 cells. CCK-8 assays measure short time interval, and colony formation assays measure at least 10 days. The quantification of colony formation assays is shown as the means ± SEM from 3 independent experiments. ∗p < 0.05 (Student’s t test). See also Figures S4–S6. Cell Reports 2018 22, 286-298DOI: (10.1016/j.celrep.2017.12.035) Copyright © 2017 The Author(s) Terms and Conditions