Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains

Abstract

CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases. Current screening strategies target CRISPR-Cas9-induced mutations to the 5' exons of candidate genes, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies. Here we overcome this limitation by targeting CRISPR-Cas9 mutagenesis to exons encoding functional protein domains. This generates a higher proportion of null mutations and substantially increases the potency of negative selection. We also show that the magnitude of negative selection can be used to infer the functional importance of individual protein domains of interest. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies. A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting.

Figure 1. Negative selection CRISPR experiments in murine MLL-AF9/Nras^G12D acute myeloid leukemia cells

(a) Experimental strategy. (top) Vectors used to derive clonal MLL-AF9/Nras^G12D leukemia RN2c cells that express a human codon-optimized Cas9 (hCas9) and vectors used for sgRNA transduction. GFP or mCherry reporters were used where indicated to track sgRNA negative selection. LTR: long terminal repeat promoter, PGK: phosphoglycerate kinase 1 promoter, Puro: puromycin resistance gene, U6: a Pol III-driven promoter, sgRNA: chimeric single guide RNA, EFS: EF1α promoter, GFP: green fluorescent protein. (b) Analysis of CRISPR editing efficiency at the ROSA26 locus in RN2c cells. Illumina sequencing was used to quantify PCR-amplified genomic regions corresponding to the ROSA26 sgRNA cut site. (c) Negative selection competition assay that plots the percentage of GFP+/mCherry+ cells over time following transduction of RN2c with the indicated sgRNAs. Experiments were performed in RN2c cells transduced with either an empty murine stem cell virus (MSCV) vector or MSCV expressing human RPA3, which are linked with a GFP reporter. The mCherry/GFP double positive percentage is normalized to the day 2 measurement. e1 labeling of sgRNAs refers to targeting of exon 1. n = 3. (d) Comparison of mouse Rpa3 and human RPA3 sequences at the indicated sgRNA recognition sites. Location of protospacer adjacent motif (PAM) is indicated. Red color indicates mismatches. (e) Summary of negative selection experiments with sgRNAs targeting the indicated genes. Negative selection is plotted as the fold change of GFP positivity (d2/d10) during 8 days in culture. Each bar represents an independent sgRNA targeting a 5’ exon of the indicated gene. The dashed line indicates a two-fold change. The fold change for two Brd4 sgRNAs was >50, but the axis was limited to a 20-fold maximum for visualization purposes. The data shown are the mean value of 3 independent replicates. (f–i) Negative selection timecourse experiments, as described in (c), except a GFP reporter was used exclusively. n=3. All error bars in this figure represent SEM.

Figure 2. CRISPR mutagenesis of functional protein domains leads to a higher proportion of null mutations and an enhanced severity of negative selection

(a) Systematic evaluation of 64 Brd4 sgRNAs in negative selection experiments, targeting each Brd4 exon. The location of each sgRNA relative to the Brd4 protein is indicated along the x-axis. Location of Brd4 sgRNAs used in Figure 1 is indicated. BD1: bromodomain 1, BD2: bromodomain 2, ET: extra-terminal domain, CTM: C-terminal motif. Plotted is the average of three biological replicates. (b) Systematic evaluation of 88 Smarca4 sgRNAs in negative selection experiments, targeting each Smarca4 exon. The relative location of each sgRNA relative to the Smarca4 protein is indicated along the x-axis. Location of Smarca4 sgRNAs used in Figure 1 is indicated. Indicated domains were obtained from the NCBI database. SNF_N and HELIC constitute the ATPase domain. BD: bromodomain. Plotted is the average of three biological replicates. (c–h) Negative selection experiments evaluating sgRNAs targeting 5’ coding exons and domain locations for the indicated proteins. In a-h, the proteins are not drawn to the same scale. WH DBD: winged helix DNA binding domain. Plotted is the average of three biological replicates. (i–k) Deep sequencing analysis of mutation abundance following CRISPR-targeting of different Brd4 regions. This analysis was performed on PCR-amplified genomic regions corresponding to the sgRNA cut site at the indicated timepoints. Indel mutations were categorized into two groups: in-frame (3n) or frameshift (3n+1, 3n+2). Nonsense mutations induced by CRISPR mutagenesis were included in the frameshift category, however such mutations were rare. Green and red numbers indicate the number of distinct in-frame and frameshift mutants that were tracked, respectively. Dots of the same color indicate the median normalized abundance at the indicated time point for all mutations within each group; shaded regions indicate the interquartile range of normalized abundance values. Significant differences between the enrichment values of the in-frame and frameshift mutations were assessed using a Mann-Whitney-Wilcoxon test; ** indicates p < 0.01, and *** indicates p < 0.005. The normalized abundance of each tracked mutation was defined as the ratio of the number of observed mutant sequences divided by the number of wild-type sequences, normalized by the value of this same quantity at day 3.

Figure 3. A chromatin regulatory domain-focused CRISPR screen in MLL-AF9 leukemia validates known drug targets and reveals additional dependencies

(a–f) Summary of negative selection experiments with sgRNAs targeting the indicated domains plotted as fold-change in GFP-positivity. Each bar represents the mean value of three independent biological replicates for an independent sgRNA targeting the indicated domain. Red coloring indicates domains for which prior pharmacological validation of the dependency has been performed. A 20-fold cutoff was applied for visualization purposes. Different timepoints of GFP measurements were chosen based on the severity of the strongest hit in the screen.

Figure 4. CRISPR targeting of enzymatic domains consistently outperforms targeting of 5’ coding exons in negative selection experiments

(a) Evaluation of 32 Ezh2 sgRNAs in negative selection experiments, targeting each Ezh2 exon. The relative location of each sgRNA relative to the Ezh2 protein is indicated along the x-axis. (b–j) Evaluation of 5’ coding exon and enzymatic domain-focused sgRNAs in negative selection experiments. The relative location of each sgRNA relative to the protein is indicated along the x-axis. Owing to the large size of MLL4/KMT2D, we have cropped out amino acids 2000 to 15,000 for visualization purposes. For a–j, plotted is the average fold change in GFP% of three biological replicates. KMT: lysine methyltransferase domain. The proteins are not drawn to the same scale. (k–o) Deep sequencing analysis of mutation abundance following CRISPR-targeting of different Ezh2 and Dot1l regions. This analysis was performed on PCR-amplified genomic regions corresponding to the sgRNA cut site at the indicated timepoints. Indel mutations were categorized into two groups: in-frame (3n) or frameshift (3n+1, 3n+2). Nonsense mutations induced by CRISPR mutagenesis were also included in the frameshift category, however such mutations were rare. Green and red numbers indicate the number of in-frame and frameshift mutants that were tracked, respectively. Dots of the same color indicate the median normalized abundance at the indicated time point for all mutations within each group; shaded regions indicate the interquartile range of normalized abundance values. Significant differences between the enrichment values of the in-frame and frameshift mutations were assessed using a Mann-Whitney-Wilcoxon test; ** indicates p < 0.01, and *** indicates p < 0.005. The normalized abundance of each tracked mutation was defined as the ratio of the number of observed mutant sequences divided by the number of wild-type sequences, normalized by the value of this same quantity at day 3.