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Keap1 mutation renders lung adenocarcinomas dependent on Slc33a1

Nature Cancer volume 1, pages 589–602 (2020)Cite this article

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A Publisher Correction to this article was published on 21 August 2020

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Abstract

Approximately 20–30% of human lung adenocarcinomas (LUADs) harbor mutations in Kelch-like ECH-associated protein 1 (KEAP1) that hyperactivate the nuclear factor, erythroid 2-like 2 (NFE2L2) antioxidant program. We previously showed that Kras-driven Keap1-mutant LUAD is highly aggressive and dependent on glutaminolysis. Here we performed a druggable genome CRISPR screen and uncovered a Keap1-mutant-specific dependency on solute carrier family 33 member 1 (Slc33a1), as well as several functionally related genes associated with the unfolded protein response. Genetic and biochemical experiments using mouse and human Keap1-mutant tumor lines, as well as preclinical genetically engineered mouse models, validate Slc33a1 as a robust Keap1-mutant-specific dependency. Furthermore, unbiased genome-wide CRISPR screening identified additional genes related to Slc33a1 dependency. Overall, our study provides a rationale for stratification of patients harboring KEAP1-mutant or NRF2-hyperactivated tumors as likely responders to targeted SLC33A1 inhibition and underscores the value of integrating functional genetic approaches with genetically engineered mouse models to identify and validate genotype-specific therapeutic targets.

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Fig. 1: A genetic screen for Keap1-mutant genetic vulnerabilities uncovers Slc33a1 as a robust genotype-specific dependency.
Fig. 2: Loss of Slc33a1 promotes the activation of a UPR.
Fig. 3: Loss of Slc33a1 is rescued by inhibiting glutathione synthesis.
Fig. 4: Slc33a1 loss results in metabolic rewiring.
Fig. 5: Slc33a1 loss validates as a Keap1-mutant-specific vulnerability in transplant models.
Fig. 6: Keap1-mutant tumors harbor an increased dependency for Slc33a1 in an autochthonous model of murine lung adenocarcinoma.
Fig. 7: Genome-wide CRISPR screen identifies suppressors of Slc33a1 deficiency.

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Data availability

RNA-seq data from this study have been deposited in the Gene Expression Omnibus under accession code GSE145945. CRISPR screen and target locus sequence datasets have been submitted to the Sequence Read Archive and are available under BioProject ID PRJNA611102. DepMap datasets analyzed here can be found in elsewhere64. Unedited western blots have been provided as Source Data Figs. 1 and 2. MS data have been deposited in MetaboLights with the primary accession code MTBLS1647. Numerical source data for Main and Extended Data Figures are provided as Source Data Extended Data Figs. 1–6. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Bioinformatic analyses were performed using open-source software, including RSEM v.1.2.12 (ref. 76), JADE v.1.1.0, Annovar v.2016-02-01 (ref. 73), SeqAn v.2.0.1 (ref. 74) and SSW v.0.1.4, as well as in-house scripts in R that are available from the corresponding author on reasonable request.

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Acknowledgements

We thank J. Settleman, D. Stokoe, T. Papagiannakopoulos, I. Harris, L. Sullivan, M. Sullivan, Z. Li, G. DeNicola and M. Hemann for scientific discussions and feedback; T. Tammela and T. Westerling for contributions to Aiforia tumor deep neural network analyses; S. Levine for massively parallel sequencing expertise; M. Griffin, M. Jennings and G. Paradis for FACS support; K. Cormier and the Hope Babette Tang (1983) Histology Facility for histology support; B. Chan and the Whitehead Metabolomics core for LC–MS and metabolite analysis; K. Yee, A. Deconinck and J. Teixeira for administrative support; and the Swanson Biotechnology Center for excellent core facilities. This work was supported by the Howard Hughes Medical Institute, Calico Life Sciences and the Koch Institute Support (core) grant P30-CA14051 from the National Cancer Institute (NCI). R.R. was supported by the National Science Foundation Graduate Research Fellowship and the NCI of the National Institutes of Health under award numbers 1122374 and F31CA224796, respectively. P.P.H. is supported in part by the National Cancer Institute of the National Institutes of Health under award number 2T32CA071345-21A1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. M.G.V.H. is a Howard Hughes Medical Institute Faculty Scholar and acknowledges additional support from SU2C, the Ludwig Center for Molecular Oncology at MIT, the MIT Center for Precision Cancer Medicine and the NCI. T.J. is a Howard Hughes Medical Institute Investigator, David H. Koch Professor of Biology and Daniel K. Ludwig Scholar. We apologize for any relevant citations we have missed due to reference limitations.

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Author notes

  1. Francisco J. Sánchez-Rivera

    Present address: Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

  2. Alexander Muir

    Present address: Ben May Department for Cancer Research, University of Chicago, Chicago, IL, USA

  3. These authors contributed equally: Rodrigo Romero, Francisco J. Sánchez-Rivera.

Authors and Affiliations

  1. Koch Institute for Integrative Cancer Research, Cambridge, MA, USA

    Rodrigo Romero, Francisco J. Sánchez-Rivera, Peter M. K. Westcott, Kim L. Mercer, Arjun Bhutkar, Alexander Muir, Tania J. González Robles, Sheng Rong Ng, Leanne Li, Caterina I. Colón, Santiago Naranjo, Peggy P. Hsu, Matthew G. Vander Heiden & Tyler Jacks

  2. Massachusetts Institute of Technology Department of Biology, Cambridge, MA, USA

    Rodrigo Romero, Francisco J. Sánchez-Rivera, Swanny Lamboy Rodríguez, Laura Z. Liao, Sheng Rong Ng, Santiago Naranjo, Mary Clare Beytagh, Matthew G. Vander Heiden & Tyler Jacks

  3. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Kim L. Mercer & Tyler Jacks

  4. Whitehead Institute, Massachusetts Institute of Technology, Cambridge, MA, USA

    Caroline A. Lewis

  5. Massachusetts General Hospital Cancer Center, Boston, MA, USA

    Peggy P. Hsu

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Peggy P. Hsu & Matthew G. Vander Heiden

  7. Tufts University, Boston, MA, USA

    Roderick T. Bronson

  8. Harvard Medical School, Boston, MA, USA

    Roderick T. Bronson

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Contributions

Author contributions were as follows: R.R., F.J.S.R. and T.J. designed the study; R.R., F.J.S.R., P.M.K.W., K.L.M., T.J.G.R., S.L.R., L.Z.L. and M.C.B. performed experiments; A.B. and P.M.K.W. performed bioinformatic analyses; A.M., P.P.H., C.A.L. and M.G.V.H. provided feedback and interpretation of metabolism data; S.R.N., L.L. and C.I.C. curated and generated the DGL; P.M.K.W. and R.T.B. trained Aiforia deep neural networks for histological assessment of lung tumor burden and grade; S.N. developed the double-sgRNA cloning strategy; R.R., F.J.S.R., P.M.K.W. and T.J. wrote the manuscript with comments from all authors.

Corresponding author

Correspondence to Tyler Jacks.

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Competing interests

M.G.V.H. is a consultant and scientific advisory board member for Agios Pharmaceuticals, Aeglea Biotherapeutics, iTeos and Auron Therapeutics. P.P.H. is a consultant for Auron Therapeutics. T.J. is a member of the Board of Directors of Amgen and Thermo Fisher Scientific. He is also a Co-founder of Dragonfly Therapeutics and T2 Biosystems. T.J. serves on the Scientific Advisory Board of Dragonfly Therapeutics, SQZ Biotech and Skyhawk Therapeutics. None of these affiliations represents a conflict of interest with respect to the design or execution of this study or interpretation of data presented in this manuscript. The laboratory of T.J. currently also receives funding from the Johnson & Johnson Lung Cancer Initiative, but this funding did not support the research described in this manuscript. This work was supported by the Howard Hughes Medical Institute and Calico Life Sciences.

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Extended data

Extended Data Fig. 1 CRISPR screening platform identifies genotype-specific vulnerabilities in Keap1-mutant LUAD cell lines.

a, Number of genes in each category in the druggable genome library (DGL). b, Composition of genes in the DGL by gene category. Bottom rectangle displays the breakdown of the total number of sgRNAs in the DGL. c, Waterfall plots of rank ordered average GSs after 8 doublings (t8) post infection between both KP cell lines (left) and KPK cell lines (right). Genes in red indicate pan-essential genes. Genes in blue indicate tumor suppressor genes. Data derived from n = 2 cell lines per panel. d, Ranked differential gene scores (KPK1-KP1 GS, n = 1 pair) of the top significantly depleted genes that passed all thresholds (see Methods). Genes marked in red indicate commonly depleted genes in the top ranked list between both isogenic cell line pairs. e, Ranked differential gene scores (KPK2-KP2 GS, n = 1 pair) of the top significantly depleted genes that passed all thresholds (see Methods). Genes marked in red indicate commonly depleted genes in the top ranked list between both isogenic cell line pairs.

Source data

Extended Data Fig. 2 In vitro validation of Slc33a1 as a Keap1-mutant-specific dependency.

a, Schematic of lentiviral vectors utilized for fluorescence competition assays. b, Experimental validation pipeline for gene essentiality by fluorescence competition assays. c, Fluorescence competition assays of KP or KPK isogenic pairs targeted with sgCtrl or two independent sgRNAs targeting Arf4. Plot displays day 2 normalized %GFP + (pUSPmNG) cells at day 10 post infection with sgCtrl or sgArf4. Data are representative of n = 3 independent culture wells per cell line per sgRNA. d, Quantified colony formation assay via the integrated pixel density assessed by ImageJ software. Related to Fig. 1f. Data are representative of n = 3 independent culture wells/cell line/sgRNA. e, Schematic of LVt-TSTOP lentiviral vector utilized for doxycycline inducible miR30a-based shRNA expression. TRE, tetracycline-response element (TRE3GS); rtTA, reverse tetracycline-controlled transactivator (Tet-On 3 G); P2A; 2 A self-cleaving peptide; Puro, Puromycin-resistance cassette. f, Colony formation assay of KP1 and KPK1 cells transduced with LVt-TSTOP vectors expressing shRenilla or shSlc33a1. Data are representative of n = 3 independent culture wells per cell line per shRNA. g, Quantified colony formation assay via the integrated pixel density assessed by ImageJ software. Related to (f). Data are representative of n = 3 independent culture wells per cell line per shRNA. h, Slc33a1 expression quantified by qPCR in cells treated with and without doxycycline (10 ng/mL) for 48 hours. Bars represent mRNA expression normalized to Actb then to -dox conditions, and the error bars represent mean ± s.d. from the mean across n = 3 or 4 independent experiments. P values were determined by unpaired two-tailed Student’s t-test. i, Slc33a1 expression quantified by qPCR 48 hours post-transduction with sgRNA-CRISPRi constructs. Bars represent mRNA expression normalized to Actb then to sgCtrl samples, and the error bars represent mean ± s.d. from the mean across n = 4 independent experiments. P values were determined by unpaired two-tailed Student’s t-test. j, Left: Representative microscopy images of cells transduced with sgRNAs mediating transcriptional repression of the Slc33a1 promoter (Scale bar = 100 μm). Right: Colony formation assay of dCas9-KRAB expressing cells in the presence of the indicated promoter-targeting sgRNAs. Data are representative of n = 3 independent culture wells per cell line per sgRNA. k, Quantified colony formation assay via the integrated pixel density assessed by ImageJ software. Related to (j). Data are representative of n = 3 independent culture wells per cell line per sgRNA. All error bars depicted represent mean ± s.d.. Data from a single experiment are shown in c, d, f, g, j, and k are representative of two independent experiments with similar results. Data for experiments c, h, and i are available as source data (Source_Data_Extended_Data_1; Source_Data_Extended_Data_2).

Source data

Extended Data Fig. 3 Slc33a1 dependency is abrogated in Nrf2-deficient cell lines and dependency is recapitulated across large human cancer cell line CRISPR-screens.

a, Slc33a1 expression quantified by qPCR in cells transduced with sgRNA-resistant cDNA (*) overexpression constructs. Bars represent mRNA expression normalized to Actb then to PGK Ctrl samples, and the error bars represent mean ± s.d. from the mean across n = 4 independent experiments. P values were determined by unpaired two-tailed Student’s t-test. b, Fluorescence competition assay plot displays %RFP + cells marking sgCtrl or sgSlc33a1 infected cells over time. This experiment was performed once with n = 4 biologically independent samples obtained from two independent cell lines per genotype that were tested with two independent sgRNAs. P values were determined by two-way ANOVA with Tukey’s post-hoc multiple comparisons test. c, NRF2 western blot of KPK2 and a mixed population of pUSEPR-sgNrf2 KPK cells (KPKN2). Histone H3 was used as a loading control. Gel has been cropped to remove non-relevant cell lines and nonspecific NRF2 bands. d, Fluorescence competition assay plot displays day 2 normalized %RFP + cells marking sgCtrl or sgSlc33a1 infected cells over time. Data are representative of n = 3 independent culture wells per cell line per sgRNA. e, Colony formation assay of the indicated cell lines infected with sgCtrl or sgSlc33a1. Data from n = 3 independent culture wells per cell line per sgRNA. f, Quantified colony formation assay via the integrated pixel density assessed by ImageJ software. Related to (e). Data are representative of n = 3 independent culture wells per cell line per sgRNA. g, CERES normalized scores obtained across all cell lines. Dotted red line represents the median CERES scores of all core-essential genes defined by the DepMap consortium (19Q2, n = 563 cell lines). h, CERES normalized scores obtained across KEAP1/NRF2-mutant or wild-type cancer cell lines (n = 563 total cells). Values above comparison denote the fold change relative to the average CERES score of the wild-type group. P values were determined by unpaired two-tailed Student’s t-test. i, Co-dependencies arising from the DepMap dataset cluster NRF2 with SLC33A1, SUCO, TAPT1, DNAJBL11 and ADPGK. Each node depicts a gene. Connecting lines indicate a direct co-dependency shared between each node (ref. 28). j,k, Waterfall plot of the rank ordered Pearson correlation coefficient between (j) SLC33A1 CERES scores versus or (k) ATF4 CERES scores versus all genes screened in the DepMap consortium (19Q2, n = 563 total cells). l,m, Fluorescence competition assays of sgRNA infected cells. Plot displays day 2 normalized %GFP + cells marking (l) sgCtrl or sgSuco edited or (m) sgCtrl or sgTapt1 edited cells. Data are representative of n = 3 independent culture wells per cell line per sgRNA and representative of three independent experiments with similar results. All error bars depicted represent mean ± s.d. unless otherwise stated. Data from a single experiment are shown in c, d, e, and f are representative of two independent experiments with similar results. Data for experiments a, b, d, l and m are available as source data (Source_Data_Extended_Data_1; Source_Data_Extended_Data_2).

Extended Data Fig. 4 NRF2 transcriptional states positively correlate with SLC33A1 dependency, independently of KEAP1 or NRF2 mutation status.

a, Differential CERES scores between KEAP1-mutant signature high-ranking cell lines (n = 42) versus low-ranking cell lines (n = 32) from ref. 64. Horizontal dotted line represents P value significance cut-off (P < 0.05; unpaired two-sided Student’s t-test). Each dot represents the differential gene score per gene. Blue dots represent genes that pass all set threshold values. b, Graph representing the median CERES score of the indicated genes across 558 cell lines ranked by the NRF2 core gene set high-ranking cell lines (n = 31) versus low-ranking cell lines (n = 45) from the DepMap that did not have annotated NRF2/KEAP1 mutations or the remaining cells that were in either high- or low-ranking category and annotated as NRF2/KEAP1-wild-type (n = 424) or mutant (n = 63). Each dot represents the CERES score observed (ref. 64). Dotted line represents the median CERES scores of all core-essential genes defined by the DepMap consortium. OR12D2, P = 0.5599; RPA3, P = 0.0504. c, Fold change of the data plotted in (b). Values are relative to the average of CERES score of the NRF2/KEAP1-wild-type cohort for each gene. OR12D2, P = 0.5599; RPA3 P = 0.0504. P-values in (b) and (c) were determined by Kruskal-Wallis test with Dunn’s multiple comparisons testing. All error bars represent mean ± s.d. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, n.s. = not significant.

Source data

Extended Data Fig. 5 Slc33a1 loss promotes UPR transcriptional signatures and GSH synthesis inhibitors rescue Slc33a1-dependency.

a-d, GSEA enrichment plots of conserved ER stress response signatures (ER chaperones FDR = 0.0; Aminoacyl tRNA synthetase FDR = 0.0; ER/Golgi traffic FDR = 0.0146; ERAD FDR = 0.025). Analysis derived from a single experiment from KP-sgCtrl, KP-sgSlc33a1, KPK-sgCtrl, KPK-sgSlc33a1 samples. e, GSH concentrations measured in cells treated with or without 48 hours of (25 μM) BSO or (780 nM) Erastin. Data are representative of n = 3 independent culture wells per cell line per sgRNA. f, Viability of cells infected with sgRNAs and treated with or without 48 hours of BSO and Erastin as in (e). Viability was measured using cell-titer Glo. Data are representative of n = 3 independent culture wells per cell line per sgRNA per treatment condition. g, Colony formation assay of KPK1 cells transduced with LVt-TSTOP vectors expressing shRenilla or shSlc33a1. h, Day 2 normalized fluorescence competition assays of 50 μM BSO or 1 μM Erastin treated pUSEPR-infected KP or KPK isogenic cell lines expressing sgCtrl or sgRpa3. Data are representative of n = 3 independent culture wells per cell line per sgRNA. i, Total cell counts of KPK-Slc33a1 knockout cells treated with indicated conditions 7 days post infection. Data are representative of n = 3 independent experiments. P values were determined by ANOVA with Dunnet’s multiple comparisons testing. All error bars depicted represent mean ± s.d. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, n.s. = not significant. Data from a single experiment are shown in e, f, g, and h are representative of two independent experiments with similar results. Data for experiments e, f, h and i are available as source data (Source_Data_Extended_Data_1; Source_Data_Extended_Data_3).

Source data

Extended Data Fig. 6 Keap1-mutant cells are sensitized to chemical inducers of the UPR.

a, Left: Dose response curve of KP and KPK isogenic pairs with tunicamycin for 48 hours. Right: Dose response curve of KP and KPK isogenic pairs with thapsigargin for 48 hours. Data are representative of n = 3 independent culture wells per cell line per sgRNA. b, Area under the curve (AUC) analysis of tunicamycin treated cells as in (a). Data are representative of n = 3 independent culture wells per cell line per sgRNA. c, AUC analysis of thapsigargin treated cells as in (a). Data are representative of n = 3 independent culture wells per cell line per sgRNA. d, Dose response curve of BSO co-treated (50 μM) or untreated KP cell lines with thapsigargin for 48 hours. Data are representative of n = 3 independent culture wells per cell line per sgRNA. e, Dose response curve of BSO co-treated (50 μM) or untreated KPK cell lines with thapsigargin for 48 hours. Data are representative of n = 3 independent culture wells per cell line per sgRNA. f, AUC analysis of thapsigargin treated cells pretreated with 50 μM BSO as in (h) and (i). Data are representative of n = 3 independent culture wells per cell line per sgRNA. All experiments denoted above are representative of n = 3 parallel infections unless otherwise stated. All error bars depicted represent mean ± s.d. Data from a single experiment are shown in a-f are representative of two independent experiments with similar results. Data for experiments a, d and e are available as source data (Source_Data_Extended_Data_5).

Source data

Extended Data Fig. 7 Slc33a1 loss results in broad metabolic changes that are rescued with BSO treatment.

a, Relative abundance of detected TCA cycle-related metabolites across the depicted cell lines. Data is normalized by cell counts for each cell line. Data are representative of n = 3 independent culture wells per cell line per sgRNA. b, LC/MS detected GSH:GSSG ratios. Data are representative of n = 3 independent culture wells per cell line per sgRNA. c, GC/MS glutamine, cystine, and glutamate secretion in KP and KPK isogenic pairs. Data are representative of n = 3 independent culture wells per cell line per sgRNA. d, Unsupervised clustering of all detectable polar metabolites in KP1 cells transduced with sgCtrl or sgSlc33a1 in either vehicle or BSO treated conditions. Each row represents a different metabolite. Each column represents a different sample. Each cell line condition was completed across independent culture wells per cell line per sgRNA. Data are normalized by cell counts for each cell line. e, Unsupervised clustering of all detectable polar metabolites in KPK1 cells transduced with sgCtrl or sgSlc33a1 in either vehicle or BSO treated conditions. Data collected as in (d). Each column represents a different sample. Data is normalized by cell counts for each cell line. All error bars depicted represent mean ± s.d. All data shown above are from a single mass-spectrometry experiment.

Extended Data Fig. 8 In vivo validation of Slc33a1 as a Keap1-mutant-specific vulnerability in a Kras-driven-Keap1-mutant GEMM of LUAD.

a,Schematic representing sgRNA genomic binding sites for Slc33a1 (top) and Olfr102 (bottom; sgCtrl). Distance between predicted cut-sites is denoted below the locus map. b,Representative images from pHH3 stained tumor bearing lung sections of the indicated samples. Scale bars indicate 100 μm. Number of mice analyzed by IHC equivalent to Fig. 6b. Tumor number across mice varies. Related to Fig. 6e. c,Fraction of mutant and wild-type reads within individual pUSEC-sgSlc33a1 targeted tumors (KPC n = 15; KPKC n = 14) obtained 20 weeks post infection. FS = frameshift; NFS = non-frameshift. d,Representative alleles obtained from KPC (top) or KPKC (bottom) from plucked tumors 20 weeks post infection with pUSEC dual sgRNA lentiviruses expressing sgSlc33a1.2 and sgSlc33a1.3. Plot summarizes the mutational analyses of locus-specific deep sequencing showing the Slc33a1 wild-type locus containing the sgSlc33a1.2 or sgSlc33a1.3 binding site (black) and protospacer-adjacent motif (PAM) sequence (orange) along with representative mutant alleles. Dashes indicate deletion events and red arrows indicate insertion event. e, Mean number of wild type and non-frameshift (NFS) reads from pUSEC-sgSlc33a1 lentiviral-infected KPC (mean of n = 15 tumors from 7 mice) and KPKC mice (mean of n = 14 tumors from 7 mice). P values were determined by unpaired two-tailed Student’s t-test. f, IHC of the NRF2 target, NQO1, from tumor bearing lung sections of the indicated samples. Stacked bar chart represents the average distribution of NQO1 positive, mixed, or negative tumors. Representative images of NQO1 staining (right). Number of mice analyzed by IHC equivalent to Fig. 6b. Tumor number across mice varies. Related to Fig. 6h (Scale bar = 100 um). All error bars depicted represent mean ± s.d.

Extended Data Fig. 9 Genome-wide CRISPR screen identifies multiple suppressors of Slc33a1 dependency.

a, Colony formation assay of the indicated cell lines grown in the presence or absence of 50 μM BSO for 7 days. Results representative of n = 3 independent culture wells per cell line per treatment condition across KPK1 Slc33a1 KO clones (n = 5) or KPK1 Suco (n = 1) or KPK1 Tapt1 KO (n = 1) clones infected in parallel. b, Western blot validation of Slc33a1 knockout. 293Ts overexpressing HA-tagged SLC33A1 serve as a positive control for antibody detection and validation. Results representative of n = 2 independent experiments. Gel has been cropped to separate KP/KPK cells away from 293T samples run on the same gel. c, Cumulative population doublings of Slc33a1 knockout cell pools infected with Brie library in replicates and placed in either BSO (n = 6 independent culture wells per cell line per condition) or vehicle-treated (n = 3 independent culture wells per cell line per condition) conditions infected in parallel. d, Median GSs of the top enriched genes from Brie library-infected KPK-Slc33a1 knockout cells. Error bars represent the range of the data from a single experiment for detected sgRNAs across n = 3 to 4 independent sgRNAs per gene per sample (Exception: n = 1 for sgRnf7 per condition). e, Median differential gene scores of the top enriched genes from Brie library-infected KPK-Slc33a1 knockout cells. Error bars represent the range of the data from a single experiment for detected sgRNAs across n = 3 to 4 independent sgRNAs per gene per sample (Exception: n = 1 for Rnf7). f, Validation of the top enriched gene candidates by colony formation assay. KPK-Slc33a1, -Suco, or -Tapt1 knockout cells were infected with two top-scoring sgRNAs targeting the genes listed in vehicle treated conditions. Representative images from n = 3 independent culture wells per cell line per sgRNA. Related to Fig. 7e. Data from a single experiment are shown in a, b, and f are representative of two independent experiments with similar results. Unmodified gel images for b are available as source data (Source_Data_Fig_2).

Extended Data Fig. 10 Genes targeting ER-resident genes rescue Slc33a1 knockout cellular phenotypes.

a, Microscopy of KPK-Slc33a1 knockout cells infected with the top scoring sgRNAs targeting the listed genes. sgRNAs listed in red denote cells in which the blebbing phenotype associated with Slc33a1 loss was rescued relative to sgCtrl cells. Scale bar = 100 μm. Representative images from n = 3 independent experiments. Related to Fig. 7e and Extended Data Fig. 9f.

Supplementary information

Source data

Source Data Fig. 1

Unprocessed western blot.

Source Data Fig. 2

Unprocessed western blot.

Source Data Extended Data Fig. 1.

Fluorescence-based competition assays, day 2 normalized data (%GFP or %RFP).

Source Data Extended Data Fig. 2.

Real-time qPCR data.

Source Data Extended Data Fig. 3.

GSH measurements (GSH-Glo and CTG).

Source Data Extended Data Fig. 4.

In vivo measurements.

Source Data Extended Data Fig. 5.

Dose–response curves.

Source Data Extended Data Fig. 6.

Cell count data.

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Romero, R., Sánchez-Rivera, F.J., Westcott, P.M.K. et al. Keap1 mutation renders lung adenocarcinomas dependent on Slc33a1. Nat Cancer 1, 589–602 (2020). https://doi.org/10.1038/s43018-020-0071-1

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