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Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer

Nature Genetics volume 52, pages 408–417 (2020)Cite this article

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Abstract

Local adaptation directs populations towards environment-specific fitness maxima through acquisition of positively selected traits. However, rapid environmental changes can identify hidden fitness trade-offs that turn adaptation into maladaptation, resulting in evolutionary traps. Cancer, a disease that is prone to drug resistance, is in principle susceptible to such traps. We therefore performed pooled CRISPR–Cas9 knockout screens in acute myeloid leukemia (AML) cells treated with various chemotherapies to map the drug-dependent genetic basis of fitness trade-offs, a concept known as antagonistic pleiotropy (AP). We identified a PRC2–NSD2/3-mediated MYC regulatory axis as a drug-induced AP pathway whose ability to confer resistance to bromodomain inhibition and sensitivity to BCL-2 inhibition templates an evolutionary trap. Across diverse AML cell-line and patient-derived xenograft models, we find that acquisition of resistance to bromodomain inhibition through this pathway exposes coincident hypersensitivity to BCL-2 inhibition. Thus, drug-induced AP can be leveraged to design evolutionary traps that selectively target drug resistance in cancer.

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Fig. 1: Loss-of-function CRISPR screens identify drug-induced antagonistic pleiotropy.
Fig. 2: Analysis of drug-induced antagonistic pleiotropy reveals gene–gene and drug–drug interactions.
Fig. 3: MYC and its epigenetic regulators are drug-induced AP genes.
Fig. 4: MYC upregulation enables a drug-induced evolutionary trap.
Fig. 5: JQ-1 treatment primes PDX model of AML for treatment with ABT-199.

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Data availability

The data supporting the findings of this study are found within the paper and supplementary files. Source Data for Figs. 2–5 and Extended Data Figs. 2 and 4–9 are available online.

Code availability

Scripts for analyzing CRISPR–Cas9 screens and calculating API are available on Github (https://github.com/linkvein/).

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Acknowledgements

We thank the members of the K.C.W. laboratory and A. Puissant laboratory for helpful discussions and scientific input. We also thank K. Wood (University of Michigan), G. Blobe and S. Floyd (Duke Pharmacology & Cancer Biology) for providing helpful feedback. This work was supported by Duke University School of Medicine start-up funds and support from the Duke Cancer Institute (K.C.W.), NIH awards (R01CA207083 to K.C.W., F30CA206348 to K.H.L. and F31CA195967 to P.S.W.), National Science Foundation Graduate Research Fellowship awards (DGE-1106401 to G.R.A. and DGF-1106401 to L.C.), the Duke Medical Scientist Training Program (T32 GM007171 to K.H.L.), the Duke Undergraduate Research Support Office (to J.C.R. and A.X.), the ATIP/AVENIR French research program (to A.P.) and the EHA research grant for Non-Clinical Advanced Fellow (to A.P.). A.P. is a recipient of support from the ERC Starting program (758848) and supported by the St Louis Association for leukemia research. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation or the NIH. Finally, we dedicate this work to the memory of our friend, Kimberly Brigati Wang, and her courageous fight against AML.

Author information

Author notes

  1. These authors contributed equally: Kevin H. Lin, Justine C. Rutter.

  2. These authors jointly supervised this work: Kris C. Wood, Alexandre Puissant.

Authors and Affiliations

  1. Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA

    Kevin H. Lin, Justine C. Rutter, Abigail Xie, Yeong-Ran Ahn, Ziwei Dai, Raiyan T. Sobhan, Gray R. Anderson, Katherine R. Singleton, Amy E. Decker, Peter S. Winter, Jason W. Locasale & Kris C. Wood

  2. Université de Paris, Génomes, Biologie Cellulaire et Thérapeutique U944, INSERM, CNRS, Paris, France

    Bryann Pardieu, Reinaldo Dal Bello, Antoine Forget, Raphael Itzykson & Alexandre Puissant

  3. Division of Applied Mathematics, Brown University, Providence, RI, USA

    Emily T. Winn

  4. Department of Hematology, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil

    Reinaldo Dal Bello

  5. Service Hématologie Adultes, AP-HP, Hôpital Saint-Louis, Paris, France

    Raphael Itzykson

  6. Department of Biostatistics, Brown University, Providence, RI, USA

    Lorin Crawford

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Contributions

K.H.L., J.C.R., A.P. and K.C.W. conceptualized the project. K.H.L. and J.C.R. were responsible for methodology. Validation was done by K.H.L. and J.C.R. K.H.L., J.C.R., A.X., Z.D. and E.T.W. performed the formal analysis. The investigation was carried out by K.H.L., J.C.R., A.X., Y.-R.A., B.P., R.D.B., A.F. and R.I. Resources were collected by Y.-R.A., R.T.S., G.R.A., K.R.S., A.E.D., P.S.W., A.P. and K.C.W. Data were curated by K.H.L., J.C.R., A.X. and J.W.L. The original draft was written by K.H.L., J.C.R. and K.C.W. All authors reviewed and edited the paper. K.H.L. and J.C.R. were responsible for visualization. L.C., A.P. and K.C.W. supervised the project. Funding was acquired by K.H.L., G.R.A., P.S.W., A.P. and K.C.W.

Corresponding authors

Correspondence to Alexandre Puissant or Kris C. Wood.

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Competing interests

J.W.L. serves on the scientific advisory board and owns equity in Nanocare Technologies and Raphael Pharmaceuticals. R.I. has received previous funding from Oncoethix SA for work on the bromodomain inhibitor OTX015. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Validation of API using external datasets.

a, API analysis performed on published CRISPR/Cas9-based gene essentiality dataset identifies overrepresented gene ontologies in top 15% of AP genes across 14 AML cell lines. b–f, Exemplar gene networks associated with overrepresented gene ontologies in (a). g, API analysis performed on published shRNA-based gene essentiality dataset identifies overrepresented gene ontologies in top 15% of AP genes across 398 human cancer cell lines. h–j, Exemplar gene networks associated with overrepresented gene ontologies in (g). k, Cell line lineages represented in published shRNA-based gene essentiality dataset plotted according to number of AP genes versus number of cell lines within each lineage. Red dashed line depicts number of AP genes with random sampling of cell lines for a given n. l, Cell line lineages plotted according to fraction of expected AP, defined as the number of AP genes in a given linage divided by number of expected AP genes for a given n. b–f; h–j, Heatmaps generated by unsupervised hierarchical clustering of genes (columns) and cell lines (rows) based on Euclidean distance.

Extended Data Fig. 2 Additional analysis of drug-treated screens using API.

a, Graphical depiction of scoring regions of nine drug modifier screens. Red lines indicate cutoff controlled at p-value 0.05. b, Gene ontology analysis of drug-induced AP genes ranked by fold-change. c, Circos plot displaying data from drug-modifier CRISPR screens as in Fig. 2a. d, PCA analysis of nine drug modifier screens conducted in n = 2 biologically independent experiments. Colors denote different drugs. e, Heatmap representing effect of sgRNAs targeting DCK, UCK2, SLC29A1 on cytarabine, decitabine, and azacitidine; schematic depicts effect of DCK and SLC29A1 on deoxycytidines. f, Correlogram depicting Pearson correlation coefficients of DCK, SLC29A1, and UCK2 depletion across nine drug modifier screens. g,h, Decitabine (g) and cytarabine (h) 8-point drug dilution series following CRISPR/Cas9 knockout of SLC29A1 or DCK versus non-targeting control in OCI-AML2 cells. i,j, Fold-change of SLC29A1 (i) and DCK (j) transcripts following CRISPR/Cas9 knockout of SLC29A1 versus non-targeting control. P-values computed by two-sided two-sample t-Test for equal means. g–j, Data are mean ± SEM for n = 3 biologically independent experiments.

Source data

Extended Data Fig. 3 Comparisons between API, PCA, and correlation.

a, List of 36 drug pairs ranked by greatest PCA distance (left), greatest % of shared AP interactions (middle), and smallest Pearson correlation coefficient (right). Lines match drug pairs in each list. Drug pairs >10 positions lower in percent of shared AP interactions rank joined by red lines; drug pairs >10 positions higher in percent of shared AP interactions rank joined by blue lines. b, Percent of shared AP interactions, Pearson correlation coefficient and PCA distance for 36 drug pairs. a,b, Data from drug-modifier screens conducted in n = 2 biologically independent experiments.

Extended Data Fig. 4 KDM1A functions as a drug-induced AP gene by regulating differentiation.

a, Correlogram depicting Pearson correlation coefficients of KDM1A and RCOR1/2/3 depletion across nine drug modifier screens. Data from drug-modifier screens conducted in n = 2 biologically independent experiments. b, Immunoblot analysis of LSD1, MYC, H3K4me1 and H3K9me2 following CRISPR/Cas9 knockout of KDM1A in OCI-AML2 cells. Representative immunoblot of n = 3 independent experiments. Uncropped blots in Source Data. c, BH3 profiling of OCI-AML2 cells following CRISPR/Cas9 knockout of KDM1A versus non-targeting control. BCL2 priming defined as percent depolarization from HRK peptide (10μM) subtracted from percent depolarization from BAD (10μM) peptide. d,e, Flow-cytometry analysis of CD11b expression distribution (d) and median signal (e) in OCI-AML2 cells following CRISPR/Cas9 knockout of KDM1A versus non-targeting control. Median signal normalized to non-targeting control sgRNA. Data are mean ± SEM for n = 2 biologically independent experiments. f–i, BCL2 (f, h) and CD11b (encoded by ITGAM) (g, i) expression in hematopoietic stem cells (HSC; n = 11), common myeloid progenitors (CMP, n = 3), megakaryocyte-erythroid progenitor cell (MEP, n = 3), granulocyte monocyte progenitors (GMP, n = 3), CD11c+ myeloid dendritic cells (mDC, n = 5), CD123+ plasmacytoid dendritic cells (pDC, n = 5), and CD14+ monocytes (n = 13) from BloodSpot using HemaExplorer dataset. Sample size refers to biologically independent samples. Data are log2 expression of highest intensity microarray probe. Boxplot elements defined in Methods. c,f,g; P-values computed by two-sided two-sample t-Test for equal means. c,e,f,g; Data are mean ± SEM for n = 3 biologically independent experiments.

Source data

Extended Data Fig. 5 MYC is a drug-induced AP gene.

a, Correlogram depicting Pearson correlation coefficients of MYC, NSD3, NSD2, EED and EZH2 depletion values across nine drug modifier screens. Data from drug-modifier screens conducted in n = 2 biologically independent experiments. b,c, Confirmation of MYC shRNA knockdown in OCI-AML2 by transcript (b) and protein (c). Representative immunoblot of n = 3 independent experiments. d,e, JQ-1 (d) and ABT-199 (e) 8-point drug dilution series following shRNA knockdown of MYC in OCI-AML2 cells. f,g, Confirmation of MYC overexpression in OCI-AML2 by transcript (f) and protein (g). Representative immunoblot of n = 3 independent experiments. h,i, JQ-1 (h) and ABT-199 (i) 8-point drug dilution series following overexpression of MYC in OCI-AML2 cells. b,f; P-values computed by two-sided two-sample t-Test for equal means. b,d–f,h,i, Data are mean ± SEM for n = 3 biologically independent experiments. Uncropped blots in Source Data.

Source data

Extended Data Fig. 6 EZH2/EED/NSD2/NSD3 modulate JQ-1 and ABT-199 sensitivity through MYC.

a,b; d,e, Relative expression of NSD2 (a), NSD3 (b), EZH2 (d), and EED (e) transcripts in cells with CRISPR/Cas9 knockout. P-values computed by two-sided two-sample t-Test for equal means. Data are presented as mean ± SEM for n = 3 biologically independent experiments. c,f, 8-point dose-response curves of JQ-1 (c) and ABT-199 (f) following CRISPR/Cas9 knockout of NSD2/3 (c) and EZH2 or EED (f). Data are presented as mean ± SEM for n = 3 biologically independent experiments.

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Extended Data Fig. 7 Additional characterization of ABT-199 and JQ-1-resistant AML cells.

a, 8-point dose-response curves of ABT-199 in parental and ABT-199-resistant OCI-AML2 cells. b, Immunoblot analysis of EZH2 and MYC in parental and ABT-199-resistant OCI-AML2 cells. c, Fold-change of MYC transcripts across matched parental and JQ-1-resistant AML cell lines. d, Immunoblot analysis of NSD2 and NSD3 across matched parental and JQ-1 resistant AML cell lines. e,f, ARV771 GI50 values of parental and JQ-1-resistant OCI-AML2 (e) and MOLM-13 (f). g, Immunoblot analysis of phosphorylated EZH2 at T487 and S21 in parental and JQ-1 resistant OCI-AML2 cells. h, Immunoblot analysis of ubiquitin and EZH2 following immunoprecipitation of EZH2 in OCI-AML2 cells. i, Immunoblot analysis of EZH2 following treatment of JQ-1 resistant OCI-AML2 cells with CDK1 inhibitor (CDK1i) or proteasome inhibitor (Bortezomib) for 24 hours. a,c,e,f; Data are mean ± SEM for n = 3 biologically independent experiments. c,e,f; P-values computed by two-sided two-sample t-Test for equal means. b,d,g–i; Representative immunoblots of n = 3 independent experiments. Uncropped blots in Source Data.

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Extended Data Fig. 8 MYC upregulation in JQ-1-resistant cells can be driven by AKT/ERK.

a,b, Immunoblot analysis of MYC (a) in matched parental and JQ-1 resistant AML cells following treatment with 20μg/mL cycloheximide (CHX) for indicated times. Quantification by densitometry (b) normalized to time zero signal. c, Immunoblot analysis of phosphorylated ERK at T202/204 and phosphorylated AKT at S437 in parental and JQ-1 resistant AML cells relative to total proteins. d, Immunoblot analysis of MYC in parental and JQ-1 resistant OCI-AML2 and MV4;11 cells treated with VX11E for 24 hours. OCI-AML2 cells treated with 500nM VX11E and MV4;11 cells treated with 2μM VX11E. e,f, GI50 value of VX11E in combination with 100nM JQ-1 normalized to VX11E alone in parental and JQ-1 resistant OCI-AML2 (e) and MV4;11 (f) cells. g,h, Immunoblot analysis of MYC, NSD2 (g) and NSD3 (h) in OCI-AML2 cells following overexpression of pCDH-MYC in combination with sgRNAs targeting NSD2 (g) and NSD3 (h). i, ABT-199 8-point drug dilution series following shRNA knockdown of MYC in combination with GSK-126. e,f,i; Data are mean ± SEM for n = 3 biologically independent experiments. e,f; P-values computed by two-sided two-sample t-Test for equal means. a,c,d,g,h; Representative immunoblots of n = 3 independent experiments. Uncropped blots in Source Data.

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Extended Data Fig. 9 JQ-1-resistant AML cells harbor widespread BIM-related collateral sensitivities.

a, 8-point dose-response curves of JQ-1 in parental and ABT-199-resistant OCI-AML2 cells. b, 8-point dose-response curves of ABT-199 in JQ-1-resistant OCI-AML2 cells following shRNA knockdown of MYC. c,d, Effect of 72-hour, 200nM JQ-1 treatment on cell viability of parental and JQ-1 resistant OCI-AML2 cultured continuously in JQ-1 (c) or taken off JQ-1 for 10 days (d), normalized to effect of vehicle treatment. e,f, Effect of 72-hour, 2nM ABT-199 treatment on cell viability of parental and JQ-1-resistant OCI-AML2 cells cultured continuously in JQ-1 (e) or taken off JQ-1 for 10 days (f), normalized to effect of vehicle treatment. g, Fold-change of BIM transcripts across matched parental and JQ-1 resistant AML cell lines. h, Immunoblot of MYC and BIM following overexpression of pCDH-MYC in OCI-AML2; representative of n = 1 independent experiments. Uncropped blots in Source Data. i, ABT-199 GI50 in parental and JQ-1 resistant MOLM-13 cells following CRISPR/Cas9 knockout of BIM or non-targeting control. j, Specification of 40 compound drug screen in JQ-1 resistant OCI-AML2 cells relative to parental. k–p, 8-point dose-response curves in parental and drug resistant cell line derivatives. q, Gating strategy for flow cytometric analysis of murine bone marrow aspirate. c–g; i, P-values computed by two-sided two-sample t-Test for equal means. a–g; i–p, Data are mean ± SEM for n = 3 biologically independent experiments.

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Lin, K.H., Rutter, J.C., Xie, A. et al. Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer. Nat Genet 52, 408–417 (2020). https://doi.org/10.1038/s41588-020-0590-9

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