Abstract
Cellular plasticity describes the ability of cells to transition from one set of phenotypes to another. In melanoma, transient fluctuations in the molecular state of tumor cells mark the formation of rare cells primed to survive BRAF inhibition and reprogram into a stably drug-resistant fate. However, the biological processes governing cellular priming remain unknown. We used CRISPR–Cas9 genetic screens to identify genes that affect cell fate decisions by altering cellular plasticity. We found that many factors can independently affect cellular priming and fate decisions. We discovered a new plasticity-based mode of increasing resistance to BRAF inhibition that pushes cells towards a more differentiated state. Manipulating cellular plasticity through inhibition of DOT1L before the addition of the BRAF inhibitor resulted in more therapy resistance than concurrent administration. Our results indicate that modulating cellular plasticity can alter cell fate decisions and may prove useful for treating drug resistance in other cancers.
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Data availability
All data used in this work can be found at https://www.dropbox.com/sh/t08558cl4mepfm6/AABBvbtlTPSNNPoMC9NTro-9a?dl=0/. RNA-seq data are also deposited at the Gene Expression Omnibus (GSE151825 and GSE149280). The gene sets used for analysis were obtained from https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp#C5/.
Code availability
All custom code used in this work is available at https://github.com/edatorre/2020_TorreEtAl_data.git/ and https://www.dropbox.com/sh/t08558cl4mepfm6/AABBvbtlTPSNNPoMC9NTro-9a?dl=0/. The software used for image analysis can be found at https://github.com/arjunrajlaboratory.
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Acknowledgements
We thank M. Herlyn for always providing excellent advice and guidance. We also thank the Flow Cytometry core team, especially F. Tuluc, at the Children’s Hospital of Philadelphia for all their advice and help. We also thank all members of the Raj laboratory, as well as J. Murray, for their comments and suggestions. We thank C. Vakoc for providing the transcription factor, epigenetic regulator and kinase domain-focused sgRNA library. C.L.J. acknowledges the National Institutes of Health (NIH) awards T32 DK007780 and F30 HG010822. B.L.E. acknowledges support from NIH training grants F30 CA236129, T32 GM007170 and T32 HG000046. S.M.S. acknowledges support from DP5 OD028144. I.A.M. acknowledges funding from the NIH and the National Institute of Neurological Disorders (F30NS100595). A.T.W. and M.E.F. are supported by R01CA207935. A.T.W. and G.M.A. are supported by P01 CA114046, and A.T.W. is supported by CA227550, CA232256, the E.V. McCollum Chair and a Bloomberg Distinguished Professorship. Core facilities used in this grant are supported by P30CA010815 and P30CA006973. J.S. acknowledges support from the Linda Pechenik Montague Investigator Award and Cold Spring Harbor Laboratory sponsored research. A.R. acknowledges R01 CA238237, a NIH/National Cancer Institute Physical Science–Oncology Centers award (U54 CA193417), a National Science Foundation CAREER award (1350601), P30 CA016520, SPORE P50 CA174523, NIH U01 CA227550, NIH 4DN U01 HL129998, NIH Center for Photogenomics (RM1 HG007743) and the Tara Miller Foundation.
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E.A.T., J.S. and A.R. designed and supervised the study. E.A.T. performed the experiments and analysis. E.A. and K.A.B. assisted with CRISPR screens. S.B. and C.L.J. assisted with tissue culture, image acquisition and analysis. L.E.B. designed image analysis software. M.E.F. and G.M.A. performed in vivo assays. B.L.E. and S.M.S. assisted with acquisition of transcriptomic data. B.L.E., S.M.S. and I.A.M. assisted with data analysis. A.T.W. provided guidance on interpretation of the data.
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A.R. and S.M.S. receive patent royalty income from LGC/Biosearch Technologies related to Stellaris RNA FISH probes. All other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Technical validation of WM989-A6-G3-Cas9-5a3 cell line.
a, To compare the frequency of drug resistance between WM989-A6-G3 and its daughter cell line WM989-A6-G3-Cas9-5a3, we cultured an equal number of cells from each cell line in 1 μM vemurafenib for 3 weeks. Then, we counted the number of colonies that resulted from each cell line. Each dot represents the number of resistant colonies normalized to 10,000 cells alive in each sample before the addition of vemurafenib. The bars represent the mean number of colonies over triplicates, normalized to 10,000 cells alive in each sample before the addition of vemurafenib. Error bars represent the standard error of the mean. b, To compare the frequency of NGFRHIGH/EGFRHIGH cells between WM989-A6-G3 and its daughter cell line WM989-A6-G3-Cas9-5a3, we looked at the distribution of expression of NGFR and EGFR using immunofluorescence. c, WM989-A6-G3-Cas9-5a3 cells expressing NGFR, EGFR, and both NGFR and EGFR are more likely to survive and proliferate in the presence of vemurafenib14. Here, we show the number of colonies that grew upon vemurafenib exposure in a mixed population of WM989-A6-G3-Cas9-5a3 or in the same population but enriched for EGFRHIGH cells, NGFRHIGH cells, or NGFRHIGH/EGFRHIGH cells. d, In this plot, we show the single guide RNA representation (as percent GFP-positive cells) of controls over time in WM989-A6-G3 cells with or without Cas9 expression. Negative controls (black) are single guide RNAs aimed at ROSA26, a non-expressing gene in human melanoma. Positive controls (red) target proteins necessary for cell viability. Only cells expressing both Cas9 and a positive control single guide RNA should disappear from the population over time.
Extended Data Fig. 2 Effect of negative and positive control single RNAs in the CRISPR screens.
Our pooled CRISPR screen included non-targeting single guide RNAs as negative controls (gray bars, 50+ single guide RNAs) as well as single guide RNAs affecting cell viability as positive controls (red bars, 25+ single guide RNAs). We quantified the change in representation of these single guide RNAs over time and report the log2 fold change in representation from 6 days after transfection to right before selection (vemurafenib exposure or selection by NGFR and EGFR expression). We expect positive controls to lose representation over time more often than negative controls. Our screening scheme utilized three separate pooled single guide RNA libraries, one targeting kinases (top), another targeting epigenetic domains (middle), and a final one targeting transcription factors (bottom).
Extended Data Fig. 3 Secondary validation of hits across multiple cell lines by secondary targeted CRISPR screening.
We assessed the robustness and generality of the effect of hits identified in the priming and resistance screens (WM989-A6-G3-Cas9-5a3, black bars) by carrying out secondary priming (left) and resistance screens (right) targeting 86 different proteins in WM989-A6-G3-Cas9 (orange bars) as well as in another BRAFV600E melanoma cell line (451Lu-Cas9, blue). On the left, we plot the log2 fold change in frequency of NGFRHIGH/EGFRHIGH cells (normalized by non-targeting controls) for each sgRNA (dots) targeting 34 of the high confidence hits (Tiers 1 and 2) we identified in the priming screen. We found that 25 of the 34 high confidence hits showed at least a two fold change (as a median across sgRNA triplicates; see Supplementary Table 4) in the frequency of NGFRHIGH/EGFRHIGH cells concordant with the effects detected in the original screening clonal cell line (WM989-A6-G3-Cas9-5a3). In 451Lu-Cas9 cells, 20 of the 34 targets also showed a change in the frequency of NGFRHIGH/EGFRHIGH cells, with 11 of those exhibiting at least a two-fold change (as a median across sgRNA triplicates; see Supplementary Table 4). On the right, we plot the log2 fold change in frequency of cells resistant to vemurafenib (normalized by non-targeting controls) for each sgRNA (dots) targeting 9 of the high confidence hits (Tiers 1 and 2) identified through the resistance screen. In WM989-A6-G3-Cas9, we found that 7 of the 9 targets replicated the effect we observed originally. For 451Lu-Cas9, the same 7 factors showed similar effects. Within each plot, the color of the target label indicates the effect observed in the primary screens. See Supplementary Table 4 for the results of Tier 3 and Tier 4 targets.
Extended Data Fig. 4 Screen for factors modulating number of resistant colonies upon BRAFV600E inhibition.
a, We performed a pooled CRISPR screen to detect modulators of the number of drug-resistant cells that grow in the presence of the BRAFV600E inhibitor vemurafenib. After transducing a library of single guide RNAs and expanding the population, we exposed the cells to the BRAFV600E inhibitor vemurafenib (1 µM) for 3 weeks, after which we sequenced the single guide RNAs in the surviving population. Changes in the frequency of detection of a given single guide RNA indicates that its target may affect the ability of a cell to survive and proliferate upon BRAFV600E inhibition. b, After transfecting a population of melanoma cells, we exposed them to vemurafenib (BRAFV600E inhibitor, 1 μM) for 3 weeks to grow resistant colonies. We then sequenced the DNA to quantify the single guide RNA representation of each target in the resulting population, using the same libraries as in Fig. 1. As before, we ranked the targets into tiers based on the percent of single guide RNAs that exhibited at least a two-fold change in representation throughout the screen (Tier 1, ≥ 75%; Tier 2, ≥ 66%; Tier 3, ≥ 50%; Tier 4, < 50%), thus reflecting the degree of confidence we have in the hit (High confidence hits: Tiers 1 and 2; Low confidence hits: Tiers 3 and 4). In this screen, we identified 24 high confidence factors. For a more detailed description, see the Methods section.
Extended Data Fig. 5 Validation of effects of hits from priming and resistance screens by via NGFR immunofluorescence and resistant colony formation.
a, Frequency of NGFRHIGH cells following the knockout of select targets. Dots represent the change in number of NGFRHIGH cells (as the log2 fold change over non-targeting sgRNA controls). A star indicates targets where, after excluding samples with low cell numbers (< 500 cells), n = 1. Tier refers to the degree of confidence we have in each particular hit (see Methods). We performed this analysis for hits from both the priming screen (top) and the resistance screen (bottom). 21 of 34 high confidence showed at least a 50% increase or decrease in the frequency of NGFRHIGH cells (see Supplementary Table 4). 21 of 49 targets from Tiers 3 and 4 increased or decreased the frequency of NGFRHIGH cells by ≥ 50%. b, Resistance phenotype of melanoma cells following the knockout of hits. Bars represent the log2 fold change over non-targeting control in the number colonies able to grow in vemurafenib. The number of colonies is normalized to the number of cells present before BRAFV600E inhibition (see Methods). In the left panel, we labeled in green and gray the effect a given target has on the frequency of NGFRHIGH/EGFRHIGH cells (based on the initial priming screen). In the right panel, we labeled in red and gray the effect a given target has on the number of cells that resist BRAFV600E inhibition (based on the initial resistance screen). Each bar represents one experimental replicate (see Extended Data Fig. 6b for replicates). c, These images show the effect of CSK knockout on a cell’s ability to develop resistance to BRAFV600E inhibition. We exposed CSK-knockout WM989-A6-G3-Cas9-5a3 cells to 1 μM vemurafenib for 3 weeks and counted the number of resulting colonies. The number of resistant cells is too large to accurately identify individual colonies; thus, the number of colonies reported is an underestimate.
Extended Data Fig. 6 Validation of effects of hits by resistant colony formation.
a, Effect of vemurafenib concentration on the formation of drug-resistant colonies. The dots represent the number of resistant colonies that grow after 3 weeks of treatment with 1 μM, 2 μM, or 4 μM of PLX4032 (vemurafenib). The bars represent the mean over three biological replicates. Error bars represent the standard error. At each concentration, we treated cells that contain either a non-targeting sgRNA, or a sgRNA targeting DOT1L, LATS2, or BRD2. b, Resistance phenotype of melanoma cells following the knockout of hits from the initial screens. Each bar represents the log2 fold change over non-targeting control in the number of colonies able to grow following knockout of the gene indicated. The number of colonies for each target is normalized to the number of cells present in culture before BRAFV600E inhibition, reported as number of colonies per every 10,000 pre-treatment cells (see Methods). As before, the different tiers represent the percent of single guide RNAs against a given target exhibiting at least a two-fold change throughout the initial (top) priming or (bottom) resistance screens. In the top panel, we labeled in green and gray the effect a given target has in the frequency of NGFRHIGH/EGFRHIGH cells (based on the initial priming screen). In the bottom panel, we labeled in red and gray the effect a given target has in the number of cells that resist BRAFV600E inhibition (based on the resistance screen). In this plot, each bar represents one experimental replicate (distinct from the one in Extended Data Fig. 5b).
Extended Data Fig. 7 Effect of pharmacological inhibition of DOT1L on resistance to BRAFV600E and MEK inhibition.
a, Resistance phenotype of melanoma cells following pharmacological inhibition of DOT1L. We pre-treated melanoma cells for seven days with either DMSO, or various concentrations of the DOT1L inhibitor pinometostat (EPZ5676). Then, we exposed the cells to 1 μM vemurafenib for 3 weeks. b, To assess the effect of DOT1L inhibition on cellular proliferation, we compared the population size of WM989-A6-G3 cells over time treated with either 4 μM of pinometostat (DOT1L inhibitor) or DMSO. The population size is estimated by the amount of nucleic acids present in the population using a CyQuant GR dye. The values represent mean fluorescent signal over triplicates. Error bars represent standard error of the mean. c, Resistance phenotype of melanoma cells to BRAFV600E and MEK inhibitors following pharmacological inhibition of DOT1L. We pre-treated melanoma cells for seven days with either DMSO or 4 μM of pinometostat. We then exposed the cells to one of two BRAFV600E inhibitors (vemurafenib and dabrafenib, left panels), to one of two MEK inhibitors (cobimetinib and trametinib, middle panels), or to a combination of a BRAFV600E and MEK inhibitor (vemurafenib + cobimetinib; dabrafenib + trametinib, right panels). White arrows point to a few of the many colonies that grew under each condition.
Extended Data Fig. 8 Percent of targets from the priming screen that validate.
To assess the sensitivity of our screen, we validated the effect observed in the initial priming screen for a select group of targets via NGFR immunofluorescence. Here, each dot represents an individual single guide RNA, and we plot the change in single guide RNA representation between NGFRHIGH/EGFRHIGH cells and controls (as measured in the priming screen). We then organize all sgRNAs into tiers (y-axis, Tiers 1 through 4) based on the percent of single guide RNAs against a target showing at least a two-fold change in representation on NGFRHIGH/EGFRHIGH cells. In red, we labeled targets that when tested again produced at least a 50% change in the frequency of NGFRHIGH cells. In black, we labeled targets that we tested but did not validate, and in gray we show targets we did not test. We display the percent of genes tested and validated at each tier on the right.
Extended Data Fig. 9 Percent of targets from the resistance screen that validate.
To assess the sensitivity of our screen, we validated the effect observed in the initial resistance screen for a select group of targets via colony formation assays. Here, each dot represents an individual single guide RNA, and we plot the change in single guide RNA representation between cells resistant to vemurafenib and cells that have never been exposed to the drug (as measured in the resistance screen). We then organize all single guide RNAs into tiers (y-axis, Tiers 1 through 4) based on the percent of single guide RNAs against a target showing at least a two-fold change in representation on drug resistant cells. In red, we labeled targets that when tested again produced at least a 50% change in the frequency colonies resistant to BRAFV600E inhibition. In black, we labeled targets that we tested but did not validate, and in gray we show targets we did not test. We display the percent of genes tested and validated at each tier on the right.
Extended Data Fig. 10 Transcriptional effects induced by knockout of select screen targets.
a, The heatmap represents the biclustering analysis of different screen targets (rows) based on the change in expression of all genes differentially expressed in at least one knockout (columns). Within the heatmap, red indicates an increase in expression following the knockout, while blue indicates a decrease in gene expression (see heatmap color key). Each target (rows) represents transcriptomes of biological triplicates (unless otherwise stated in Supplementary Table 4). Target labels (rows) in green indicate genes whose knockout increased the frequency of NGFRHIGH/EGFRHIGH cells in the initial screen. In red are those whose knockout increased the number of cells resistant to vemurafenib, and in gray are those that decreased the frequency of either NGFRHIGH/EGFRHIGH cells or of cells resistant to vemurafenib. As before, we organized targets into confidence tiers indicated by the number of asterisks, based on the percent of single guide RNAs against that target that showed an effect in the initial screen (see knockout color key). b, We performed principal component analysis of the transcriptional effects induced by the knockout of select screen targets. We used as input the gene set enrichment scores from Fig. 5a to identify primary axes that account for the greatest degree of transcriptome variability across knockout cell lines. The color indicates the effect of the knockout in the initial priming screen. The size of the dot indicates the degree of confidence we have in each particular hit based on the percent of the single guide RNAs against a target that passed a threshold of two-fold change in the initial priming screen. In black, we labeled melanoma cells where we did not knockout any targets but either enriched for EGFRHIGH cells, NGFRHIGH cells, EGFRHIGH/NGFRHIGH cells, or for cells resistant to vemurafenib.
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Torre, E.A., Arai, E., Bayatpour, S. et al. Genetic screening for single-cell variability modulators driving therapy resistance. Nat Genet 53, 76–85 (2021). https://doi.org/10.1038/s41588-020-00749-z
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DOI: https://doi.org/10.1038/s41588-020-00749-z
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