Abstract
Cancer genomics studies have identified thousands of putative cancer driver genes1. Development of high-throughput and accurate models to define the functions of these genes is a major challenge. Here we devised a scalable cancer-spheroid model and performed genome-wide CRISPR screens in 2D monolayers and 3D lung-cancer spheroids. CRISPR phenotypes in 3D more accurately recapitulated those of in vivo tumours, and genes with differential sensitivities between 2D and 3D conditions were highly enriched for genes that are mutated in lung cancers. These analyses also revealed drivers that are essential for cancer growth in 3D and in vivo, but not in 2D. Notably, we found that carboxypeptidase D is responsible for removal of a C-terminal RKRR motif2 from the α-chain of the insulin-like growth factor 1 receptor that is critical for receptor activity. Carboxypeptidase D expression correlates with patient outcomes in patients with lung cancer, and loss of carboxypeptidase D reduced tumour growth. Our results reveal key differences between 2D and 3D cancer models, and establish a generalizable strategy for performing CRISPR screens in spheroids to reveal cancer vulnerabilities.
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Data availability
Sequencing data from all CRISPR screens and RNA-seq experiments are available under BioProject accession number PRJNA535417. All other data are available from the corresponding author upon reasonable request.
Code availability
All screening data were analysed with custom Python scripts (v.2.7) that are available at https://github.com/biohank/CRISPR_screen_analysis. Custom Matlab scripts (v.2015b) were used to quantify signals from all immunofluorescence images and to analyse FACS data: these scripts can be requested from K.H.
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Acknowledgements
We thank J. Sage and members of the Bassik laboratory for discussions and critical reading of the manuscript. This work was supported by the NIH Director’s New Innovator Award Program (1DP2HD084069), NIH/NCI 1U01CA199261 to M.C.B., P.J. and A.S.-C., and NIH/NCI 1U01CA217851 to C.J.K, C.C. and M.C.B. This work was also partly supported by a Stanford SPARK Translational Research Grant. We also thank D. Mochly-Rosen and K. Grimes at Stanford University for their support of this work. K.H. is supported by the Walter V. and Idun Berry award. S.E.P. is supported by National Science Foundation.
Author information
Authors and Affiliations
Contributions
K.H. and M.C.B. conceived and designed the study. K.H. designed the scalable 3D culture system for genome-wide CRISPR screens. K.H., A.L. and K.S. performed the CRISPR screens. K.H. and S.E.P. performed in vivo mouse experiments including the in vivo CRISPR screens and the competition assay. K.H., S.E.P. and M.Y. performed immunohistochemistry on tumour sections from the competition assay. G.R.A., M.D., M.O. and R.A.K. performed the western blots for IGF1R in various cancer cell lines. K.H. designed the 1D4 reporter and performed the immunocytochemistry. A.L. performed the western blot for the 1D4 assay. K.H. performed and analysed the RNA-seq. J.A.S. analysed the TCGA data for patient survival outcomes associated with genes downstream of CPD. M.W. defined co-essential modules from the DepMap CRISPR dataset. K.H. and K.K. generated Cas9-expressing non-small-cell lung carcinoma lines. A.J.G. performed PRECOG analysis. K.H., K.S. and K.L. validated the hits from the CRISPR screens. K.H. performed the drug validations. K.H. wrote the Python and Matlab scripts to analyse the screening data and to quantify the immunofluorescence signals in microscope images. K.H. and M.C.B. wrote the manuscript. All authors discussed the results and the manuscript.
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Competing interests
The authors, through the Office of Technology Licensing at Stanford University, have filed patent applications on methods for inhibiting tumour growth by inhibiting CPD as well as systems and methods for identifying CPD inhibitors and other tumour suppressors and/or oncogenes.
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Peer review information Nature thanks Charles M. Rudin, Nicola Valeri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data figures and tables
Extended Data Fig. 1 High quality and reproducibility of 2D and 3D genome-wide CRISPR screens and hits with differential effects in the two conditions.
a, H23 cells expressing mCherry were seeded at different densities in ultra-low attachment plates in the presence of 0.75% methylcellulose. Sytox Green was added at 100 nM concentration. Average mCherry signal and Sytox Green signal measured across single cells were used to estimate the total numbers of live cells and dead cells at each seeding density. Cell growth and death rates were then monitored simultaneously on a live-cell microscope for 60 h. We aimed for a cell death rate of about 30% during the initial growth phase of spheroids, and 105 cells per well (1.9 cm2) was the chosen cell seeding density for our genome-wide screens in 3D spheroids. b, Two-dimensional growth phenotypes of 20,463 genes were highly reproducible between experimental replicates (top). Sequencing counts of 208,687 sgRNAs in a T0 sample and a day 21 sample from the 2D genome-wide screens (bottom) show that most negative-control sgRNAs (red dots) are not enriched or disenriched between T0 and day 21 (black dots). This indicates the complexity of the genome-wide library was maintained throughout the 2D screen. In the top plot, the data are fit by a linear regression line (blue dotted line). The grey line marks a 1:1 diagonal. c, The quality and reproducibility of the 3D screens were comparable to those of the 2D screens, suggesting that the scalable 3D spheroid culture system is on a par with traditional 2D culture methods for its performance in genome-scale CRISPR screens. n = 20,463 genes (top); n = 208,687 sgRNAs (bottom). In the top plot, the data are fit by a linear regression line (blue dotted line). The grey line marks a 1:1 diagonal. d, Cumulative distribution of sequencing reads for sgRNAs in the genome-wide CRISPR library. Read counts were normalized by total reads for each sample and the cumulative sums of sgRNAs were plotted as relative percentages of the number of expected sgRNAs. e, Cumulative sums of TSG counts (left) or oncogene counts (right) are plotted against genes sorted by their 2D, 3D or 3D/2D phenotypes (T-score) from the genome-wide screens in H23 cells. TSGs are expected to have positive growth phenotypes when deleted. Therefore, genes are sorted in descending order from the most positive to the most negative phenotypes in the left plot. Oncogenes are expected to have negative or toxic growth phenotypes and genes are sorted in ascending order in the right plot. Black dotted line, randomly sorted genes. The first 4,000 genes are displayed. f, Summary of hits with differential 3D/2D phenotypes. Top positive (red-filled circles) and negative (blue-filled circles) hits from the differential 3D/2D phenotypes reveal many cancer-relevant genes associated with transcriptional regulation, cell motility, cell adhesion and energy metabolism. Cancer-signalling pathways such as Ras–MAPK, TGFβ, MET, Rho, β-catenin and Hippo signalling are highly represented. Sizes of circles are proportional to 3D/2D phenotype scores. g, The 10 most significant pan-lung cancer genes31 and 50 top core essential genes are marked. Genes sorted by absolute phenotype (T-score) in 2D, 3D and 3D/2D (see Methods).
Extended Data Fig. 2 Genome-wide 2D and 3D CRISPR screens in H1975, a lung adenocarcinoma line with EGFRL858R mutation.
a, Distributions of 2D and 3D phenotypes are shown as volcano plots. The y axis represents absolute T-score for each gene, and the x axis represents effect size of each gene. Size of dots represents absolute T-score of genes. b, Prediction of TSGs or oncogenes with 2D, 3D, 3D/2D phenotypes in H1975 cells. Cumulative sums of TSGs counts (top panel) or oncogenes counts (bottom panel) are plotted against genes sorted by their 2D, 3D, or 3D/2D phenotypes (T-score) from the genome-wide screens in H1975 cells. These data indicate 3D or differential 3D/2D phenotypes show marked improvement for prediction of TSGs when compared to the 2D phenotypes, with marginal improvement for predicting oncogenes. In the box plots, centre lines mark median, box limits mark upper and lower quartiles, whiskers show 1.5× interquartile range and points indicate outliers. c, Enriched pathways among the top 1,000 hits from each culture condition were analysed using PANTHER overrepresentation test. Significance of enriched pathways was measured with Fisher’s exact test and the Benjamini–Hochberg FDR was subsequently computed (x axis). The EGFR signalling pathway, a known driver for H1975 cells, is enriched in only 3D or 3D/2D phenotypes. Number of genes for enriched pathways are marked to the right of bars. d, The cumulative sum of the significance of 11,249 pan-lung cancer mutations from 1,144 patients with lung cancer as measured by MutSig2CV is displayed on the y axis, whereas the x axis shows phenotypes for genes sorted by their strength in 2D (solid red line), 3D (solid blue line) or 3D/2D (solid yellow line). Black dotted line, randomly sorted genes. Top 3,000 genes are shown.
Extended Data Fig. 3 Genome-wide 2D and 3D CRISPR screens in H2009, a lung adenocarcinoma line with KRASG12A mutation.
a, Distributions of 2D and 3D phenotypes are shown as volcano plots. The y axis represents absolute T-score for each gene, and the x axis represents effect size of each gene. Size of dots represents absolute T-score of genes. b, Prediction of TSGs or oncogenes with 2D, 3D and 3D/2D phenotypes in H2009 cells. Cumulative sums of TSG counts (top) or oncogene counts (bottom) are plotted against genes sorted by their 2D, 3D or 3D/2D phenotypes (T-score) from the genome-wide screens in H2009 cells. These data indicate that 3D phenotypes, and in particular the differential 3D/2D phenotypes show improved prediction of both TSGs and oncogenes when compared with 2D phenotypes. In the box plots, centre lines mark median, box limits mark upper and lower quartiles, whiskers show 1.5× interquartile range and points indicate outliers. c, Enriched pathways among the top 1,000 hits from each culture condition were analysed using PANTHER overrepresentation test. Significance of enriched pathways was measured with Fisher’s Exact test and the Benjamini–Hochberg FDR was subsequently computed (x axis). The Ras pathway, a known driver for H2009 cells, is enriched in 3D/2D phenotypes. Numbers of genes for enriched pathways are marked to the right of bars. d, The cumulative sum of the significance of 11,249 pan-lung cancer mutations from 1,144 patients with lung cancer as measured by MutSig2CV is displayed on the y axis, while the x axis shows phenotypes for genes sorted by their strength in 2D (solid red line), 3D (solid blue line) or 3D/2D (solid yellow line). Black dotted line, randomly sorted genes. Top 3,000 genes are shown.
Extended Data Fig. 4 High quality and reproducibility of optimized in vivo CRISPR screens and analysis of the CPD co-essential module.
a, A CRISPR sgRNA library targeting 911 hits with differential growth effects in 3D versus 2D (Supplementary Table 4) was introduced into H23 cells, and introduced by subcutaneous injection into NSG mice. After 30 days, tumours were collected and sgRNAs were amplified. In vivo growth phenotypes of 911 genes were highly reproducible between experimental replicates (left). Sequencing counts of T0 samples and day 30 samples from the in vivo batch-retest screens (right). In the left plot, the data are fit by a linear regression line (blue dotted line). b, Cumulative distribution of sequencing reads for sgRNAs in the batch-retest library in H23 cells. Read counts were normalized by total reads for each sample and the cumulative sums of sgRNAs were plotted as relative percentages of the number of expected sgRNAs. c, The 4,034 co-essential gene modules based on the DepMap CRISPR dataset are plotted as volcano plots for KRASi 2D phenotype scores. The y axis shows significance of enrichments of co-essential modules as measured in log P values from the two-sided Mann–Whitney U-test (see Methods); the x axis shows average gene effects of members in CERES modules. d, Genes in the CPD module are indicated among 17,634 genes sorted by their correlations to CPD. Pearson correlation coefficients between CPD and other genes are measured in batch-corrected CERES effects in the DepMap CRISPR dataset. e, CERES effects of CPD, FURIN and IGF1R are shown as correlation plots. CERES effects are batch-corrected before plotting21. Blue lines, regression lines. Blue shaded translucent bands, 95% confidence intervals. f, Lack of correlation between CPD and OR2A25, an olfactory receptor, in their CERES effects across 517 cancer lines.
Extended Data Fig. 5 Analysis of CPD co-essential module with a 145 × 145 gene genetic-interaction map.
a, Cloning of CDKO library. A total of 463 sgRNAs targeting 145 hits from the 3D/2D phenotypes were PCR-amplified from an oligonucleotide array. These 145 hits include members of the CPD co-essential module. sgRNAs were separately cloned into two lentiviral vectors with either a mU6 or a hU6 promoter to generate two CRISPR single-knockout libraries. hU6-sgRNA cassettes were then cut out from one library and ligated into the other library containing the mU6 promoter. This generated a CDKO library with all possible pairwise combinations of the 463 sgRNAs (214,369 double sgRNAs). This CDKO library was used to measure genetic interactions (GIs) of 10,440 gene pairs (145 × 145 combinations). b, The 145 × 145 genetic-interaction map; the 145 × 145 matrix of genetic-interaction scores are shown as a heat map. The 145 genes are clustered by the similarities of their genetic interactions (Pearson correlation coefficients of genetic interactions) in the map. Members of the CPD co-essential module form a cluster (marked with red box) in this genetic-interaction map, consistent with their correlations in the DepMap CRISPR dataset. c, A genetic-interaction map validates the CPD co-essential module in H23. Correlations of genetic interactions are used to sort 145 genes on the basis of their similarities to genetic interactions of CPD. Genes in the CPD module are marked with red dots along the sorted genes.
Extended Data Fig. 6 Validation of individual sgRNAs targeting top hits with differential 3D/2D growth effects.
a, A schematic showing the competitive growth assay used to validate individual sgRNAs in 2D and 3D conditions. Cells expressing a gene-targeting sgRNA (mCherry) are mixed with cells expressing a control sgRNA (safe sgRNA, encoding GFP). Relative changes of mCherry to GFP ratios are monitored to compute growth phenotypes of gene-targeting sgRNAs. b, Genes within the CPD module and selected top hits with differential effects in 3D versus 2D growth were targeted with individual sgRNAs and subjected to competitive growth assays in both 2D and 3D culture. Relative 2D and 3D growth phenotypes of individual sgRNAs were measured by tracking changes in ratios of mCherry (gene-targeting sgRNAs) to GFP (control sgRNA) in the assays by automated fluorescence microscopy. (n = 3 wells in a 24-well plate, mean ± s.e.m.). c, Binary masks of H23 spheroids with the indicated gene knockouts. H23 knockout cell lines expressing sgRNAs against top hits from the 3D/2D phenotypes were seeded at equal density on ultra-low attachment plates. 3D spheroids generated from the knockout lines were imaged in a fluorescent microscope 72 h after seeding. For each knockout line, 48 images were taken from three wells in a 24-well plate using a 10× objective. Binary masks were then generated from mCherry signals of 3D spheroids. Forty-eight images were then stitched together to be shown as one large image for each knockout. d, Relative colony masses of H23 spheroids with gene knockouts are quantified and displayed in bar graphs (n = 3 wells in a 24-well plate, mean ± s.e.m.). e, Genes in the CPD module and KRAS were targeted with corresponding small-molecule inhibitors. Cells were seeded in 96-well plates in 2D (blue line) and 3D (red line) conditions, and grown in the presence of titrating doses of inhibitors for 72 h. Live cells were quantified with alamar blue assays. Relative growth of treated cells compared with the untreated samples are plotted in the drug titration curves. n = 3 wells in a 96-well plate for linsitinib and n = 4 for all other drugs; mean ± s.e.m.
Extended Data Fig. 7 Induced CPD knockdown in established H23 spheroids slows growth.
a, Doxycycline (Dox; 0.2 μg ml−1) was added to established spheroids at 48 h after initial seeding. Spheroids were expressing both mCherry and KRAB–dCas9 separated by a T2A sequence under the same doxycycline-inducible promoter. Addition of doxycycline rapidly induced KRAB-dCas9-T2A-mCherry expression in spheroids. (n = 3 wells in a 24-well plate, mean ± s.e.m.). b, Immunofluorescence staining of CPD (green) showed that CPD sgRNAs 1 and 3 robustly reduced CPD levels in H23 cells expressing the inducible KRAB–dCas9 upon doxycycline addition. CPD sgRNA 2 was less effective. Mean intensities of CPD immunofluorescence of two biological replicates were measured in the bottom bar plot. c, Immunostaining of KRAS (green) by western blot showed that KRAS sgRNAs 1 and 3 robustly reduced KRAS levels in H23 cells expressing the inducible KRAB–dCas9 upon doxycycline addition. KRAS sgRNA 2 was less effective. These experiments were repeated twice to confirm the result. d, Relative spheroid growth, five days after doxycycline addition, comparing doxycycline-treated and untreated samples, measured in control cells and cells expressing CPD and KRAS sgRNA cells. H23 cells with inducible KRAB–dCas9–T2A–mCherry were first transduced with gene-targeting sgRNAs using a lentivirus that also expressed a GFP marker. Cells were seeded and allowed to form spheroids for 48 h. Doxycycline was then added and growth of spheroids in doxycycline-treated or untreated samples was monitored by GFP signal for another five days. Spheroids expressing CPD sgRNAs 1 or 3 and spheroids expressing KRAS sgRNAs 1 or 3 showed markedly reduced growth upon doxycycline addition, whereas spheroids expressing control sgRNA did not show any difference between doxycycline-treated and untreated samples (n = 3 wells in a 24-well plate. mean ± s.e.m.). e, Growth of spheroids expressing control sgRNA, CPD sgRNA 3 or KRAS sgRNA 3 were monitored after doxycycline addition. Cells were seeded to form spheroids in the first 48 h and growth of spheroids was monitored by GFP fluorescence for the next 5 days (n = 3 wells in a 24-well plate, mean ± s.e.m.).
Extended Data Fig. 8 CPD deletion inhibits the IGF1R pathway in H322, A549 and H358 cells.
a, Representative immunofluorescence images of IGF1R α-chain (green) in control and CPD-knockout H23 spheroids. b, Quantification of immunofluorescence in a. IGF1R α-chain intensities averaged across nine spheroids per condition. *P = 2.2 × 10−3 (n = 9, two-sided t-test; mean ± s.e.m.). c–e, IGF1R and phosphorylated AKT levels were quantified from immunofluorecence images for H322 (c), A549 (d) and H358 (e) cells. The dotted grey line marks a 100% level (P values calculated using two-sided t-test; mean ± s.e.m.).
Extended Data Fig. 9 CPD deletion acts through the IGF1R pathway to inhibit 3D growth in H23 cells and CPD removes the FURIN -recognition motif from the C terminus of IGF1R and MET α-chain.
a, The growth phenotype observed upon CPD deletion in H23 cells is rescued by addition of excess IGF1 (50 ng ml−1) to the growth medium. A CPD- or IGF1R-targeting sgRNA with mCherry cDNA and a safe sgRNA with GFP cDNA were transduced into H23 cells separately, mixed in 50:50 ratio, and cultured in 3D spheroids for 72 h. Ratios of mCherry to GFP at 72 h, normalized to the ration at T0, were plotted in the bar graphs. Deletion of either CPD or IGF1R reduced 3D growth of spheroids, as reflected in the reduced mCherry-to-GFP ratios compared with control. Treating cells with excess IGF1 ligand (50 ng ml−1) rescued CPD-deletion phenotypes, whereas addition of EGF or HGF did not. This suggests that partial inhibition of the IGF1R pathway by CPD deletion can be compensated by over-activation of the pathway with the excess IGF1 ligand. IGF1 could not rescue the IGF1R deletion phenotype (n = 2 wells in a 24 well plate; mean ± s.e.m.). b, Control, CPD-knockout and IGF1R-knockout spheroids were treated with the indicated growth factors. Sixteen images of mCherry fluorescence in spheroids expressing a gene-targeting sgRNA vector with mCherry marker were stitched together to create the images shown. c, d, IGF1R–1D4 reporters (see Fig. 4b) showed that removal of the FURIN-recognition site RKRR from the C terminus of IGF1R α-chain after FURIN cleavage is severely impaired by CPD deletion in H322 (c) and A549 (d) cells. P values calculated using two-sided t-test; mean ± s.e.m. e, A MET–1D4–KRKKR reporter (with 1D4 epitope inserted upstream of the FURIN -recognition site KRKKR in MET, as with IGF1R in Fig. 4b) showed that removal of KRKKR from the C terminus of MET α-chain is severely impaired by CPD deletion in H322 cells. Total MET-reporter levels were measured using an antibody against MET and ratios of 1D4 to MET signal were used to assess the degree of the KRKKR processing in control and CPD-null background. Error bars show s.e.m. of biological replicates in a 96-well plate. P values calculated using two-sided t-test; mean ± s.e.m.
Extended Data Fig. 10 Targeting CPD may have therapeutic effects in patients with lung cancer.
a, Meta-Z scores of genes in the CPD module across different cancer types, from PRECOG analysis43. Positive Z score predicts that high expression of a given gene is associated with poor prognosis of disease. Pink bars show that high CPD expression predicts poor prognosis of lung adenocarcinoma (ADENO) (Z score = 5.59, PRECOG meta-FDR = 3.23 × 10−6). b, Forest plot showing hazard ratios (HR) of CPD measured from different datasets (authors and PubMed IDs for the datasets are indicated on the y axis). The HR is the increase in risk of death for each unit increase in expression of CPD (see Methods). Blue error bars indicate 95% confidence intervals. Number of patient samples used for each study is listed to the left of the plot. c, Forest plot showing the hazard ratios from an adjusted two-sided Cox proportional-hazard model, using the CPD GSVA score as a continuous variable adjusted by age, TP53, KRAS, stage and gender. d, Kaplan–Meier plots for patients with lung cancer with wild-type (left) or mutant (right) KRAS. Variation of a gene set downregulated by CPD deletion in H23 spheroids was first scored by GSVA (CPD GSVA score) in patients with lung cancer. Differences in survival among patients with lung cancer with high versus low CPD GSVA score are illustrated in Kaplan–Meier plots. High CPD GSVA scores are significantly associated with poor prognosis in both wild-type and mutant KRAS patient groups. However, the separation between high and low CPD GSVA groups is larger among KRAS-mutant patients than wild-type patients, suggesting an interaction between CPD and KRAS mutations in patients with lung cancer. P values calculated using a two-sided log-rank test. e, Hazard plots illustrating the two-sided Cox proportional log relative hazard by expression levels of CPD in KRAS-mutant versus KRAS wild-type samples. Grey shading corresponds to 95% confidence intervals. f, CPD deletion sensitizes H358 cells to ARS-853, a KRAS inhibitor. H358 cells with control safe sgRNA (blue line) or CPD sgRNA (red line) were treated with increasing doses of ARS-853 for 72 h in both 2D (top) and 3D (bottom). Live cells were then quantified using alamar blue assay. Relative growth of treated cells compared with the untreated cells is plotted against ARS-853 concentration. n = 4 wells in a 96-well plate, mean ± s.e.m. g, CPD deletion does not show synergy with ARS-853 in H1792 cells. Similar plots as in f were generated for H1792 cells (n = 4 wells in a 96-well plate, mean ± s.e.m.). h, IGF1R was quantified from immunofluorescence images of IGF1R staining across six lung cancer cell lines. H1792 cells show very low IGF1R expression compared with the other five cell lines. n = 4 for H1437, n = 5 for all other cell lines, mean ± s.e.m.
Supplementary information
Supplementary Information
This file contains Supplementary Discussion: Further discussion about genes showing differential phenotypes between 3D and in vivo. Highly enriched/disenriched functional modules are also discussed. Supplementary Figure 1: Uncropped western blots are shown here.
Supplementary Table
Supplementary Table 1: Summary of results from the genome-wide CRISPR screens in 2D and 3D for NCI-H23, NCI-H1975, and NCI-2009 cell lines.
Supplementary Table
Supplementary Table 2: 50 core-essential genes with the strongest negative average CERES effects.
Supplementary Table
Supplementary Table 3: Pan lung cancer mutations with MutSig2CV FDR values, absolute 2D, absolute 3D, absolute 3D/2D phenotype scores and absolute CERES Lung avg effect, absolute CERES LungDiff effect.
Supplementary Table
Supplementary Table 4: 911 hits selected for the batch-retest CRISPR screens and sgRNAs used to target the 911 genes.
Supplementary Table
Supplementary Table 5: An excel file describing 10 lung cancer cell lines with their representative background mutations, and results from CRISPR batch-retest screens in these lines.
Supplementary Table
Supplementary Table 6: 145 genes selected for the GI map and sgRNAs used for this map.
Supplementary Table
Supplementary Table 7: An excel file containing GI map data; GI-t-score matrix and GI correlation matrix.
Supplementary Table
Supplementary Table 8: Genes downregulated/upregulated in CPD-deleted 3D spheroids compared to control spheroids.
Supplementary Table
Supplementary Table 9: 50 genes with differential phenotypes between the batch-retest H23 3D screen and the batch-retest in vivo screen.
Supplementary Table
Supplementary Table 10: Sequences of sgRNAs used for individual validation experiments.
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Han, K., Pierce, S.E., Li, A. et al. CRISPR screens in cancer spheroids identify 3D growth-specific vulnerabilities. Nature 580, 136–141 (2020). https://doi.org/10.1038/s41586-020-2099-x
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DOI: https://doi.org/10.1038/s41586-020-2099-x
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