Identification of effective combination therapies is critical to address the emergence of drug-resistant cancers, but direct screening of all possible drug combinations is infeasible. Here we introduce a CRISPR-based double knockout (CDKO) system that improves the efficiency of combinatorial genetic screening using an effective strategy for cloning and sequencing paired single guide RNA (sgRNA) libraries and a robust statistical scoring method for calculating genetic interactions (GIs) from CRISPR-deleted gene pairs. We applied CDKO to generate a large-scale human GI map, comprising 490,000 double-sgRNAs directed against 21,321 pairs of drug targets in K562 leukemia cells and identified synthetic lethal drug target pairs for which corresponding drugs exhibit synergistic killing. These included the BCL2L1 and MCL1 combination, which was also effective in imatinib-resistant cells. We further validated this system by identifying known and previously unidentified GIs between modifiers of ricin toxicity. This work provides an effective strategy to screen synergistic drug combinations in high-throughput and a CRISPR-based tool to dissect functional GI networks.
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We thank S. Collins and members of the Bassik laboratory for helpful discussions and critical reading of the manuscript, A. Sockell and L. Tonkin for technical assistance in deep sequencing, and M. Porteus and S. Mantri for CD34+ HSPCs. We thank M. Cleary, J. Duque-Afonso, and P. Jackson for helpful discussions regarding drug combination assays. We thank M. Kampmann, M. Horlbeck, L. Gilbert, and J. Weissman for helpful discussions, and L. Bruhn, C. Carstens, P. Sheffield, and B. Borgo of Agilent technologies for oligonucleotide synthesis and helpful discussions. GM12892 cells were a gift from S.B. Montgomery. The work was funded by the NIH Director's New Innovator Award Program (project no. 1DP2HD084069-01) NIH/NHGRI (training grant T32 HG000044 to D.W.M.), NIH/NCI 1U01CA199261-02, and a seed grant from Stanford ChEM-H. K.H. is supported by the Walter V. and Idun Berry award. E.E.J. was supported by the NIH (2T32CA009302) and a Hubert Shaw and Sandra Lui Stanford Graduate Fellowship.
The authors declare no competing financial interests.
Integrated supplementary information
Supplementary Figure 1 A scalable CDKO system with minimized recombination compatible with deep sequencing.
(a) Generation of PCR amplicons for deep sequencing (see methods). Double-sgRNA cassettes were directly amplified from genomic DNA and adapters were added during two rounds of PCRs. (b) Paired-end sequencing to directly read double-sgRNAs. PCR amplicons (around 640 bp) were clustered efficiently in flow cells and three custom sequencing primers used to read a front sgRNA, a rear sgRNA, and an index in order. (c) Double-sgRNA vectors with either two identical mU6 promoters, or mU6 and hU6 promoters driving expression of mCherry and GFP-targeting sgRNAs, were PCR-amplified using the primer pairs indicated. (d) The double-sgRNA cassettes in panel c were PCR-amplified from either purified plasmids or genomic DNA isolated from K562 cells infected with the corresponding double-sgRNA vectors. Expected size of the PCR amplicons are marked with red arrows and PCR amplicons from recombination-affected vectors are marked with blue arrows. (e) Flow cytometry analysis of GFP and mCherry knockout efficiency in cells infected with the vectors in panel. (f) Sequencing of the recombination-affected PCR amplicon from the double-sgRNA vector with two identical mU6 promoters shows that the recombination happens between two mU6 promoters.
(a) Estimating the minimally required read count for a double-sgRNA. From the representation of single-sgRNAs in the hU6 and mU6 single-sgRNA library, the expected frequency of double-sgRNAs were calculated and compared to the observed frequency of double-sgRNAs in the DrugTarget-CDKO library. Ratios of the two frequencies showed that under ∼50 read counts, the observed frequencies markedly fell below the expected. Based on this data, double-sgRNAs with less than 50 read counts were removed from further analyses. (b) Frequencies of double-sgRNAs were compared between the Plasmid library and the T0 sample. Frequencies of Safe_Safe sgRNAs were slightly enriched in the T0 sample since most double-sgRNAs have negative γ-phenotypes. (c) Minimal positional bias in DrugTarget-CDKO library. γ-phenotypes of gene pairs were compared between both orientations. PCC, Pearson correlation coefficient. (d) High reproducibility of measured γ-phenotypes of gene pairs between two experimental replicates. PCC, Pearson correlation coefficient. (e) Single knockout phenotypes of genes are highly correlated between the DrugTarget-CDKO screen and a previous genome-wide single-sgRNA screen10. PCC, Pearson correlation coefficient. (f) Sequencing depth affects the phenotypic plateau observed in Figure 3a.
(a) GIT scores were calculated based on Raw-GIs and compared between two experimental replicates. PCC, Pearson correlation coefficient. (b) GIM scores were calculated and compared between two experimental replicates (see methods). GIM scores of gene pairs comprised of two same genes are marked in pink. The 5 most synergistic gene pairs are marked in orange. PCC, Pearson correlation coefficient. (c) Histograms of GIT scores showed that most gene pairs comprised of two same genes are buffering (red solid line). GIT score of Safe_Safe pair (purple dotted line). (d) Norm GIs of double-sgRNA combinations targeting the most synergistic (AKT1_AKT2) and most buffering (SKP2_SKP2) gene pairs by GIT score were plotted against the expected γ-phenotypes. These two pairs showed distinct GI distributions that were well-separated in opposite directions from 0. Safe_Safe double-sgRNAs are marked as yellow dots and all other double-sgRNAs are marked as grey dots. (e) Expected and observed γ-phenotypes of gene pairs were plotted and color-coded by their GIT score. The most synergistic (AKT1_AKT2) and buffering (SKP2_SKP2) pairs are highlighted.
Supplementary Figure 4 Two different sgRNAs targeting the same gene are more synergistic than identical sgRNAs.
(a-c) Norm-GIs were measured for three groups - all double-sgRNAs, pairs of same guides, and pairs of two different sgRNAs targeting the same genes. Two sgRNAs targeting the same gene tend to be buffering. However, two different sgRNAs targeting the same gene tend to be less buffering than two identical sgRNAs. (a) Norm-GIs were plotted against the expected γ-phenotypes for all three groups (black: all double-sgRNAs, pink: double-sgRNAs comprised of two same guides, blue: double-sgRNAs comprised of two different guides targeting the same gene) (b) Plots in a were binned against the expected phenotype of double-sgRNAs. Data represent mean ± s.e.m. Data were binned in a way that each range includes at least 30 data points for any given group among the three. (c) Distribution of Norm-GIs for the three groups. Only double-sgRNAs with absolute γ-phenotypes greater than 3.7 were analyzed. The pink line indicates the mean, the blue box represents the 25th-75th percentile, and the dotted bar indicates the 10th-90th percentile. (d) The Norm-GI patterns in 3 x 3 double-sgRNA combinations of pairs targeting the same gene. Top panel shows the distribution of GI scores for pairs targeting the same gene. The blue dotted lines mark the 5 most synergistic pairs in the distribution. The brown dotted lines represent the 35th-39th buffering pairs, and the yellow dotted lines mark the 5 most buffering pairs in the distribution. All double-sgRNA combinations for each group were averaged over the 3 x 3 grid and color-coded by their Norm-GIs. (e) In the dual sgRNA validation assay, two different sgRNAs targeting TK1 showed synergistic GI whereas two identical sgRNAs did not show significant GI. Data represent mean ± s.d. (n=3) from replicate cultures.
(a) Correlations of GI profiles between two sgRNAs were compared in two experimental replicates: sgRNAs targeting the same gene are marked in pink. Due to the low GI frequencies, correlations of GI profiles between sgRNA pairs were very low. PCC, Pearson correlation coefficient. (b) The distributions of correlations of GI profiles for all sgRNA pairs (blue) and for sgRNAs pairs targeting the same gene (orange). Medians of the distributions are marked by dotted lines. (c) Sparse genetic interactions in the DrugTarget-CDKO GI map. GIM scores of all gene pairs were calculated and color-coded by a yellow-cyan heatmap. Genes were hierarchically clustered by their correlation of GI profiles. γ-phenotypes of individual genes are marked in sidebars with a red-blue heatmap.
(a) Selection of genes modulating ricin-sensitivity and resulting Ricin-CDKO library. (b) Schematic of the Ricin-CDKO screen. Infected cells were split into two and one group was treated with 4 pulses of ricin for 14 days, while the other was cultured for 14 days without ricin treatment. (c) Cumulative distribution of sequencing reads for double-sgRNAs. (d) Histogram plotting the number of double-sgRNAs per gene pair. 99.6% of the 6,063 detected gene pairs have more than 6 double-sgRNA combinations. (e) Minimal positional bias in Ricin-CDKO library for ρ-phenotype. PCC, Pearson correlation coefficient. (f) Measuring Norm-GIs of double-sgRNAs. Expected and observed ρ-phenotypes of double-sgRNAs were plotted and deviations from the median line (blue) were processed from Raw-GIs into Norm-GIs as previously done for DrugTarget-CDKO screen. (g) t-value-based GIT scores calculated for ρ-phenotypes were plotted between two experimental replicates. The 5 most synergistic and buffering pairs by rank-sum of GIT scores of two replicates are marked in orange dots. The Pearson correlation after same-gene targeting pairs were removed is reported in parentheses. PCC, Pearson correlation coefficient.
Supplementary Figure 7 Comparison of a previous shRNA-based ricin GI map with the Ricin-CDKO GI map.
(a) Measurement of correlation of genetic interactions between the previously published shRNA-based Ricin GI map11 and the Ricin-CDKO map for the 91 common interactions present in both maps. GIs are scaled according to the system described in the corresponding manuscript. Two cases of genetic interactions which have opposite signs in the two maps are labeled in the plot. PCC, Pearson correlation coefficient. (b) Genetic interactions of the common protein complexes and genes between the two maps are used to generate two compact GI maps for comparison. Essential genes are marked in red.
Supplementary Figure 8 Gene pairs with similar GI profiles are enriched for known protein interactions (Online Methods).
(a) Data in Figure 4d were plotted as AUC (Area Under the Curve). Gene pairs were sorted by the different features of GI map and cumulative sums of the number of STRING interactions identified in the sorted gene pairs were plotted. (b) A genetic interaction network of the 66 most correlated gene pairs (GI correlation > 0.5) in terms of GI patterns was generated. Genes are grouped and colored by their biological functions and their known PPIs. Each edge between two genes indicates that they have a correlation of GI patterns over 0.5. A red edge indicates that this gene pair also has known protein interactions reported in STRING. 40% of the 66 gene pairs have reported protein interactions whereas on average, only 5% of 66 randomly selected gene pairs from the Ricin-CDKO map have reported protein interactions in STRING.
(a) Predicted synergistic gene pairs were validated using individual sgRNAs (using two separate vectors). Data represent mean ± s.d. from 3 replicate cultures. (b) 3 of the synergistic gene pairs were validated using a second pair of sgRNAs. Data represent mean ± s.d. from 3 replicate cultures. (c) Example of GI calculation for individual sgRNA validations using double-sgRNA vector. Double-sgRNA vectors were cloned containing two safe-sgRNAs, one safe-sgRNA and one gene-targeting sgRNA (PIM1_Safe and Safe_PIM2), or two gene-targeting sgRNAs (PIM1_PIM2) and infected into Cas9-expressing K562 cells. Growth phenotypes of single and double gene knockouts are calculated by measuring the depletion of GFP+ cells relative to uninfected cells (PIM1_Safe and Safe_PIM2 for single knockout phenotypes and PIM1_PIM2 for double knockout) from T0 to T7, normalized to Safe_Safe cells. GIs are determined by comparing the observed double knockout phenotype to the expected from the single knockout phenotypes. (d) Using the double-sgRNA vector system, synergy was validated for sgRNA pairs predicted to be synergistic (PIM1_PIM2, BCL2L1_MCL1, PRKDC_ATM, PRKDC_TSPO) while sgRNA pairs not predicted to be synergistic did not show synergy in dual-sgRNA retests (PRKDC_PIM2, PRKDC_MCL1, PIM1_TSPO, BCL2L1_TSPO, BCL2L1_ATM, PIM1_MCL1, BCL2L1_PIM2). Data represent mean ± s.d. from 3 replicate cultures. (e) TIDE indel analysis for sgRNAs against indicated genes.
Supplementary Figure 10 Combination APEX1_ATM drug treatment synergistically induces DSBs and apoptosis.
(a-c) K562 cells were treated with APEX1 (CRT0044876) and ATM (KU-60019) inhibitors for 48 h, fixed, and stained for γH2AX. Cells were analyzed by flow cytometry and representative histograms from each sample are plotted in a. Median FL-1 ± s.d. from 3 replicate cultures are plotted in b. Cells were additionally stained with Hoescht and representative images are shown in c. Scale bars,10 μm. (d,e) K562 cells were treated with indicated drugs for 48 h and assessed by flow cytometry for Annexin V-FITC and propidium iodide (PI) staining. Plots in d are representative of three independent experiments and the percentages of Annexin V-positive cells are quantified in e (mean ± s.d., n = 3).
(a-k) Cell viability and Bliss drug synergy plots for drug pairs in K562 cells (a-j) and MV4;11 cells (k). Additional true positives are shown in a-c: (a) ATM (KU-60019) and PRKDC (NU7441), (b) APEX1 (CRT0044876) and PRKDC, (c) TSPO (PK-11195) and PRKDC. Examples of true negatives are shown in d-g: (d) TXN (PX-12) and XPO1 (KPT-330), (e) MCL1 (A-1210477) and PRKDC, (f) TSPO and XPO1, (g) CARM1 (1-benzyl-3,5-bis-(3-bromo-4-hydroxybenzylidene)piperidin-4-one) and XPO1. (h) NAMPT (FK866) and XPO1 (false positive). (i) BCL2L1 (A-1155463) and XPO1 (false negative). (j) The BCL2L1 and MCL1 pair was tested using a different MCL1 inhibitor (UMI-77) and (k) in the MV4;11 AML cell line (using A-1155463 and A-1210477).
Supplementary Figure 12 Normalized GI improves reproducibility of genetic interactions between replicates.
(a) GIT scores calculated from Raw-GI and Norm-GI are compared for the ρ-phenotype-based Ricin-CDKO map. GIT scores calculated from Norm-GI show higher correlation between replicates than those from Raw-GI. PCC, Pearson correlation coefficient. (b) GIT scores calculated from Raw-GI and Norm-GI are compared for the γ-phenotype-based DrugTarget-CDKO map. GIT scores calculated from Norm-GI show slightly higher correlation between replicates than those from RawGI. PCC, Pearson correlation coefficient. (c) Normalization of GIs improves the uniformity of variance across the range of expected γ-phenotypes. Variance of Raw-GIs and Norm-GIs for the same guide pairs across two experimental replicates in DrugTarget-CDKO map was measured with respect to the expected γ-phenotype. Data are binned across the expected γ-phenotypes (bin number = 22, bin size = 1 pZ) and average variance on each bin is calculated and plotted in the graphs.
Supplementary Figures 1–13 and Supplementary Text (PDF 6026 kb)
Selected 207 genes for DrugTarget-CDKO library (XLSX 29 kb)
700 sgRNAs for DrugTarget-CDKO library (XLSX 29 kb)
Distribution of double-sgRNAs per gene pair after filtering (XLSX 23 kb)
GI scores of DrugTarget-CDKO screen (XLSX 2925 kb)
Selected 79 genes for Ricin-CDKO library (XLSX 11 kb)
284 sgRNAs for Ricin-CDKO library (XLSX 17 kb)
GI scores of Ricin-CDKO screen (XLSX 482 kb)
246 STRING interactions between 79 Ricin hits (XLSX 30 kb)
STRING interactions in the 66 most correlated gene pairs (XLSX 13 kb)
Selected 79 genes for DrugTarget Batch retest (XLSX 9 kb)
287 sgRNAs for DrugTarget Batch retest (XLSX 17 kb)
sgRNAs used for the validation of individual sgRNA pairs (XLSX 12 kb)
Summary of sgRNA and drug validations (XLSX 11 kb)
30 most synergistic DrugTarget pairs (XLSX 11 kb)
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Han, K., Jeng, E., Hess, G. et al. Synergistic drug combinations for cancer identified in a CRISPR screen for pairwise genetic interactions. Nat Biotechnol 35, 463–474 (2017). https://doi.org/10.1038/nbt.3834
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