Figure S1. Super-enhancers near the KLF5 gene are focally amplified in cancer cell lines;Figure S2. The regulatory potential of the KLF5/KLF12 intervening region;Figure S3. Expression level of KLF12, DIS3 and PIBF1 after repression of KLF5 superenhancers;Figure S4. Genomic analysis of KLF5 binding sites in BICR31 cells;Figure S5. Target genes of KLF5 in head and neck squamous cell carcinomas;Figure S6. The effect of KLF5 silencing on H3K27ac profile;Figure S7. Mutation hotspots in KLF5;Figure S8. Mutation profile in FBXW7 in colorectal cancers; Figure S9. Ectopic expression of KLF5 mutants in HEK293T cells; Figure S10. Ectopic expression of KLF5 E419Q in HCC95 cells

Supplementary Figure S6. Representative example of complex amplified locus showing a CRISPR-CN correlation. Supplementary Figure S7. Comparative analysis of the relationship of CRISPR-Cas9 guide scores to the predicted number of CRISPR-Cas9-induced DNA cuts based on either copy number or total predicted perfect-match on- and off-target alignments. Supplementary Figure S8. Comprehensive analysis of CRISPR-Cas9 sensitivity correlated to the total predicted number of DNA cuts conferred by each sgRNA. Supplementary Figure S9. (A) PANC-1 infection efficiency corresponding to Figure 6 in vitro validation experiment measuring short-term proliferation and viability response of PANC-1 cells transduced at high multiplicity of infection. (B) Immunoblot of protein from PANC-1 cells harvested 48 hours after infection at high MOI with the indicated sgRNAs targeting inside (19q8) or outside (19q4) the PANC-1 19q13 amplicon shown in Figure 6A. Supplementary Figure S10. CRISPR-Cas9 targeting of amplified regions or multiple genomic loci induces DNA damage and a G2 cell cycle arrest in CAL120 cells.

Sample Information: Table with cell line identities and relevant information for this manuscript.

Function enrichment of down regulated genes upon Prmt1 depletion at the translation level using DAVID functional annotation tool (GO BP_direct).

<p>GSEA analysis of differentially expressed genes in the control versus Prmt1 KO mOS at the transcript level using GO gene sets.</p>

Cell Essential Control Genes: Table of cell essential control genes used in this manuscript.

S1: Sources, media, and other information on all cell lines. S2: p53 status annotations for all cell lines. S3: sgRNA sequences for TP53 inactivation. S4: Primers for TP53 allele screening and RT-qPCR. S5: Sources, catalog numbers, and dilutions of all antibodies. S6: RNA-seq data for p53 target genes. S7: GSEA from RNA-seq data.

Characteristics of the 21 lung cancer cell lines assayed in the Project Achilles data set (8). NSCLC = non-small cell lung cancer, LCLC = large cell lung cancer, SCLC = small cell lung cancer. S1. Each data point represents the normalized gene-level scores for ABL1 (ATARiS gene solution 2) in the 30 hematopoietic and lymphoid cell lines tested (8). The three most sensitive lines (indicated by red circles) are the only lines within this data set known to harbor the BCR-ABL1 translocation. S2. (A) Trametinib dose response curve after 6 days. Cell viability normalized to DMSO treated controls. (B) Refametinib dose response curve after 6 days. Cell viability normalized to DMSO treated controls. (C) PD0325901 dose response curve after 6 days. Cell viability normalized to DMSO treated controls. (D) Immunoblot of protein levels after 4-hour treatment with DMSO or 10 nM trametinib in each cancer cell line. S3. Xenograft tumor growth after NCI-H1437 subcutaneous injection in nude mice. Mice were treated daily with either vehicle 5% DMSO or 0.3mg/kg trametinib. For each tumor, the volume was normalized to its size at the start of drug treatment. Supplemental Table 1. Characteristics of the 21 lung cancer cell lines assayed in the Project Achilles data set (8). NSCLC = non-small cell lung cancer, LCLC = large cell lung cancer, SCLC = small cell lung cancer.

Supplementary Figure 1 identifies EGLN1 as a cancer dependency in RNAi and CRISPR datasets and contains all significant associations. Supplementary Figure 2 is a lineage analysis of EGLN1 dependency within RNAi dataset. Supplementary Figure 3 is an immunoblot showing EGLN1 knockout in EGLN1-insensitive cell line. Supplementary Figure 4 shows pan-EGLN inhibition affects proliferation and apoptosis. Supplementary Figure 5 shows specific EGLN1 inhibitor IOX2 and VHL inhibitor VH298 inhibit proliferation. Supplementary Figure 6 shows differentially expressed genes in EGLN1-KO cells. Supplementary Figure 7 shows increased apoptosis in vivo when inhibiting EGLN1 or VHL.

sgRNAs Used in Validation Experiment: Table of sgRNA IDs, sequences and chromosomal locations.

BACKGROUND:Hundreds of functional genomic screens have been performed across a diverse set of cancer contexts, as part of efforts such as the Cancer Dependency Map, to identify gene dependencies-genes whose loss of function reduces cell viability or fitness. Recently, large-scale screening efforts have shifted from RNAi to CRISPR-Cas9, due to superior efficacy and specificity. However, many effective oncology drugs only partially inhibit their protein targets, leading us to question whether partial suppression of genes using RNAi could reveal cancer vulnerabilities that are missed by complete knockout using CRISPR-Cas9. Here, we compare CRISPR-Cas9 and RNAi dependency profiles of genes across approximately 400 matched cancer cell lines.RESULTS:We find that CRISPR screens accurately identify more gene dependencies per cell line, but the majority of each cell line's dependencies are part of a set of 1867 genes that are shared dependencies across the entire collection (pan-lethals). While RNAi knockdown of about 30% of these genes is also pan-lethal, approximately 50% have selective dependency patterns across cell lines, suggesting they could still be cancer vulnerabilities. The accuracy of the unique RNAi selectivity is supported by associations to multi-omics profiles, drug sensitivity, and other expected co-dependencies.CONCLUSIONS:Incorporating RNAi data for genes that are pan-lethal knockouts facilitates the discovery of a wider range of gene targets than could be detected using the CRISPR dataset alone. This can aid in the interpretation of contrasting results obtained from CRISPR and RNAi screens and reinforce the importance of partial gene suppression methods in building a cancer dependency map.

Table S1: KLF5 E419Q-gained super-enhancers and the nearest genes

Supplementary Figure S1. Genome-scale CRISPR-Cas9 screening identifies a strong correlation between copy number and sensitivity to CRISPR-Cas9 genome editing. Supplementary Figure S2. Global summary of the relationship of CRISPR-Cas9 guide scores to genomic copy number for all 33 cell lines screened. Supplementary Figure S3. Amplified genes represent the strongest perceived dependencies in pooled CRISPR-Cas9 screening data. Figure S4. Evaluation of the influence of copy number on CRISPR-Cas9 dependency scores in published data from Hart et al. (25). Figure S5. Representative examples of structural variations leading to copy number amplification underlying the gene-independent anti-proliferative response to CRISPRCas9 targeting of loci within these regions.

This file contains additional data supporting the role of Prmt1 in human OS maintenance and murine OS tumor initiation, as well as proteomics and RNA-seq data supporting the role of Prmt1 in translation regulation.

<p>GSEA analysis of differentially expressed genes in the control versus Prmt1 KO mOS at the transcript level using hallmark gene sets.</p>

Expanded methodological detail on large-scale data analysis (RNAi, CRISPR-Cas9, p53 status annotations, RNA-seq, and primary tumor expression).

Supplementary Tables 1-6. Supplementary Table 1 contains a list of constructs used in this manuscript. Tab 2 with Supplementary table 2 is a list of all six-sigma genes we identified. Supplementary table 3 contains EGLN1 CRISPR Associations with mutations, gene expression, CRISPR dependencies and copy number. Supplementary table 4 contains EGLN1 RNAi Associations with mutations, gene expression, RNAi dependencies and copy number. Supplementary Table 5 contains all cell line information and annotations used for this manuscript. Supplementary Table 6 contains RNA sequence data from EGLN1 KO cells and EGLN1 WT cells.

Arginine methylated peptides identified by SILAC-proteomics

Supplementary proteomics method

S1: RNAi data and CRISPR-Cas9 data divided by p53 status. S2: Expression of MDM2 and MDM4 in MRT and control cell lines. S3: CRISPR-Cas9 mediated inactivation of TP53 in MRT cell lines. S4: Immunoblots for p53 pathway activation upon MDM2 and MDM2/4 inhibition in MRT and control cell lines. S5: Cell counts, flow cytometry, and senescence assays in MRT cells following MDM2 and MDM2/4 inhibition. S6: Characterization of MRT cells expressing SMARCB1 or p16. S7: MRT xenograft growth characteristics and pharmacodynamic responses.