Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition

Abstract

KRAS GTPases are activated in one-third of cancers, and KRAS(G12C) is one of the most common activating alterations in lung adenocarcinoma1,2. KRAS(G12C) inhibitors3,4 are in phase-I clinical trials and early data show partial responses in nearly half of patients with lung cancer. How cancer cells bypass inhibition to prevent maximal response to therapy is not understood. Because KRAS(G12C) cycles between an active and inactive conformation4,5,6, and the inhibitors bind only to the latter, we tested whether isogenic cell populations respond in a non-uniform manner by studying the effect of treatment at a single-cell resolution. Here we report that, shortly after treatment, some cancer cells are sequestered in a quiescent state with low KRAS activity, whereas others bypass this effect to resume proliferation. This rapid divergent response occurs because some quiescent cells produce new KRAS(G12C) in response to suppressed mitogen-activated protein kinase output. New KRAS(G12C) is maintained in its active, drug-insensitive state by epidermal growth factor receptor and aurora kinase signalling. Cells without these adaptive changes—or cells in which these changes are pharmacologically inhibited—remain sensitive to drug treatment, because new KRAS(G12C) is either not available or exists in its inactive, drug-sensitive state. The direct targeting of KRAS oncoproteins has been a longstanding objective in precision oncology. Our study uncovers a flexible non-uniform fitness mechanism that enables groups of cells within a population to rapidly bypass the effect of treatment. This adaptive process must be overcome if we are to achieve complete and durable responses in the clinic.

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Fig. 1: Divergent single-cell fates after conformation-specific KRAS(G12C) inhibition.
Fig. 2: Adaptation to the G12Ci treatment is dependent on EGFR signalling.
Fig. 3: AURKA is involved in the adaptive reactivation of KRAS and escape from drug-induced quiescence.
Fig. 4: Newly synthesized KRAS(G12C) escapes trapping by the drug.

Data availability

The data that support the findings of this study are available within the paper and its Supplementary Information files. Source Data for Figs. 14 and Extended Data Figs. 29 are provided with the paper. The scRNA-seq data have been deposited in the Gene Expression Omnibus (GEO) with the accession code GSE137912. Data or other materials are available from the corresponding author upon reasonable request.

Code availability

The analysis was performed using standard protocols with previously described computational tools. The scripts, along with the processed files described in the Methods, are available from the corresponding author upon reasonable request.

References

  1. 1.

    Pylayeva-Gupta, Y., Grabocka, E. & Bar-Sagi, D. RAS oncogenes: weaving a tumorigenic web. Nat. Rev. Cancer 11, 761–774 (2011).

  2. 2.

    Li, S., Balmain, A. & Counter, C. M. A model for RAS mutation patterns in cancers: finding the sweet spot. Nat. Rev. Cancer 18, 767–777 (2018).

  3. 3.

    Ostrem, J. M., Peters, U., Sos, M. L., Wells, J. A. & Shokat, K. M. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 503, 548–551 (2013).

  4. 4.

    Patricelli, M. P. et al. Selective inhibition of oncogenic KRAS output with small molecules targeting the inactive state. Cancer Discov. 6, 316–329 (2016).

  5. 5.

    Lito, P., Solomon, M., Li, L. S., Hansen, R. & Rosen, N. Allele-specific inhibitors inactivate mutant KRAS G12C by a trapping mechanism. Science 351, 604–608 (2016).

  6. 6.

    Simanshu, D. K., Nissley, D. V. & McCormick, F. RAS proteins and their regulators in human disease. Cell 170, 17–33 (2017).

  7. 7.

    Hunter, J. C. et al. Biochemical and structural analysis of common cancer-associated KRAS mutations. Mol. Cancer Res. 13, 1325–1335 (2015).

  8. 8.

    Zeng, M. et al. Potent and selective covalent quinazoline inhibitors of KRAS G12C. Cell Chem. Biol. 24, 1005–1016 (2017).

  9. 9.

    Janes, M. R. et al. Targeting KRAS mutant cancers with a covalent G12C-specific inhibitor. Cell 172, 578–589 (2018).

  10. 10.

    Corcoran, R. B. et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2, 227–235 (2012).

  11. 11.

    Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).

  12. 12.

    Lito, P., Rosen, N. & Solit, D. B. Tumor adaptation and resistance to RAF inhibitors. Nat. Med. 19, 1401–1409 (2013).

  13. 13.

    Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).

  14. 14.

    Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

  15. 15.

    Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J. P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).

  16. 16.

    Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

  17. 17.

    Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

  18. 18.

    Cheung, T. H. & Rando, T. A. Molecular regulation of stem cell quiescence. Nat. Rev. Mol. Cell Biol. 14, 329–340 (2013).

  19. 19.

    Oki, T. et al. A novel cell-cycle-indicator, mVenus–p27K, identifies quiescent cells and visualizes G0–G1 transition. Sci. Rep. 4, 4012 (2014).

  20. 20.

    Lemmon, M. A. & Schlessinger, J. Cell signaling by receptor tyrosine kinases. Cell 141, 1117–1134 (2010).

  21. 21.

    Drosten, M. et al. Genetic analysis of Ras signalling pathways in cell proliferation, migration and survival. EMBO J. 29, 1091–1104 (2010).

  22. 22.

    Katayama, H. et al. Phosphorylation by aurora kinase A induces Mdm2-mediated destabilization and inhibition of p53. Nat. Genet. 36, 55–62 (2004).

  23. 23.

    Lim, K. H. et al. Aurora-A phosphorylates, activates, and relocalizes the small GTPase RalA. Mol. Cell. Biol. 30, 508–523 (2010).

  24. 24.

    Umstead, M., Xiong, J., Qi, Q., Du, Y. & Fu, H. Aurora kinase A interacts with H-Ras and potentiates Ras–MAPK signaling. Oncotarget 8, 28359–28372 (2017).

  25. 25.

    Gong, X. et al. Aurora A kinase inhibition is synthetic lethal with loss of the RB1 tumor suppressor gene. Cancer Discov. 9, 248–263 (2019).

  26. 26.

    Donnella, H. J. et al. Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer. Nat. Chem. Biol. 14, 768–777 (2018).

  27. 27.

    Shah, K. N. et al. Aurora kinase A drives the evolution of resistance to third-generation EGFR inhibitors in lung cancer. Nat. Med. 25, 111–118 (2019).

  28. 28.

    Keen, N. & Taylor, S. Aurora-kinase inhibitors as anticancer agents. Nat. Rev. Cancer 4, 927–936 (2004).

  29. 29.

    Sunaga, N. et al. Knockdown of oncogenic KRAS in non-small cell lung cancers suppresses tumor growth and sensitizes tumor cells to targeted therapy. Mol. Cancer Ther. 10, 336–346 (2011).

  30. 30.

    Fakih, M. et al. Phase 1 study evaluating the safety, tolerability, pharmacokinetics (PK), and efficacy of AMG 510, a novel small molecule KRAS G12C inhibitor, in advanced solid tumors. J. Clin. Oncol. 37, 3003 (2019).

  31. 31.

    Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

  32. 32.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  33. 33.

    Azizi, E. et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174, 1293–1308 (2018).

  34. 34.

    van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

  35. 35.

    Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).

  36. 36.

    McCarthy, D. J., Campbell, K. R., Lun, A. T. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).

  37. 37.

    Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S. & Marioni, J. C. Normalizing single-cell RNA sequencing data: challenges and opportunities. Nat. Methods 14, 565–571 (2017).

  38. 38.

    Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).

  39. 39.

    Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

  40. 40.

    Rodriguez, A. & Laio, A. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

  41. 41.

    Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496–2506 (2016).

  42. 42.

    Van den Berge, K. et al. Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol. 19, 24 (2018).

  43. 43.

    Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

  44. 44.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

  45. 45.

    Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902 (2014).

  46. 46.

    Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).

  47. 47.

    Sanson, K. R. et al. Optimized libraries for CRISPR–Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).

  48. 48.

    Li, W. et al. MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biol. 15, 554 (2014).

  49. 49.

    Lito, P. et al. Disruption of CRAF-mediated MEK activation is required for effective MEK inhibition in KRAS mutant tumors. Cancer Cell 25, 697–710 (2014).

  50. 50.

    Lito, P. et al. Relief of profound feedback inhibition of mitogenic signaling by RAF inhibitors attenuates their activity in BRAFV600E melanomas. Cancer Cell 22, 668–682 (2012).

  51. 51.

    Xue, Y. et al. An approach to suppress the evolution of resistance in BRAFV600E-mutant cancer. Nat. Med. 23, 929–937 (2017).

  52. 52.

    He, L. et al. Methods for high-throughput drug combination screening and synergy scoring. Methods Mol. Biol. 1711, 351–398 (2018).

Download references

Acknowledgements

The authors thank C. Sawyers and M. Mroczkowski for their insight on the manuscript; R. Garippa for his advice on the CRISPR screen; and P. Jallepalli for his help with the interpretation of the AURKA findings. This work has been supported in part by the NIH/NCI (1R01CA23074501 to P.L., 1R01CA23026701A1 to P.L., K08CA191082-01A1 to P.L. and 1F30CA232549-01 to J.Y.X.). P.L. is also supported in part by The Pew Charitable Trusts, the Damon Runyon Cancer Research Foundation and the American Lung Association. E.d.S. is supported in part by the MSKCC Pilot Center for Precision Disease Modeling program (U54 OD020355). D.R. is supported by Programma per Giovani Ricercatori Rita Levi Montalcini granted by the Italian Ministry of Education, University and Research. The authors acknowledge the Josie Robertson Investigator Program at MSKCC, a Medical Scientist Training Program grant to the Weill Cornell–Rockefeller–Sloan Kettering Tri-Institutional MD-PhD Program (T32GM007739) and the MSKCC Support Grant-Core Grant program (P30 CA008748).

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Contributions

J.Y.X., Y.Z. and P.L. designed the study and analysed data. J.Y.X., Y.Z., J.A., A.V., T.T.M., D.K. and C.L. performed experiments. B.Q. and E.d.S. helped to perform in vivo studies. L.M. and D.R. helped to carry out the scRNA-seq experiment and performed statistical data analysis, respectively. J.Y.X., Y.Z. and P.L. were the main writers of the manuscript, with considerable help from D.R. All other authors reviewed the manuscript and contributed to writing it. P.L. conceived and supervised the study.

Corresponding author

Correspondence to Piro Lito.

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Competing interests

MSKCC has received research funds from companies developing G12C inhibitors and has confidentiality agreements with these companies. A part of these funds is allocated for research to be conducted under the supervision of P.L. These funds were not used to support the work in this paper. The experiments in this paper were performed with commercially available inhibitors. P.L. has not received honoraria, consultation fees, stock options or travel reimbursement from such companies.

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Peer review information Nature thanks Frank McCormick, Arjun Raj and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 The effect of the G12Ci treatment on KRAS signalling across lung cancer cell lines.

The indicated models were treated with increasing concentrations of the G12Ci (ARS1620) for 2 h (top panels) or with 10 μM over time (bottom panels), and immunoblotted to determine the effect on KRAS signalling intermediates. Key genetic alterations found at baseline in the KRAS(G12C)-mutant cell lines used in this study are listed. A representative of two independent experiments for each cell line is shown.

Extended Data Fig. 2 Quality assessment and processing of scRNA-seq data.

a, b, Gene counts as a function of UMI count. Cells are grouped by length of G12Ci treatment (a) or tumour model (b). c, The number of cells expressing a gene, as a function of its average count across the dataset. d, Variance as a function of mean expression. Technical variance (that is, variability attributed to technical factors) was calculated by the expression of ribosomal genes. n = 10,177 single cells in ad. e, The per cent of variance explained by various experimental factors. A number of variables had a meaningful contribution to the variance of the dataset (that is, they accounted for greater than 1% of the variation), suggesting the need to correct for these potentially confounding factors in downstream analysis. f, Dimensionality reduction and covariate regression using the ZINB-WaVE algorithm. The K parameter of 2 was chosen, as this minimizes batch and other covariate effects. g, t-SNE projection showing single cells coloured by length of inhibitor treatment. h, Parameters used to cluster cells by using the Density Cluster algorithm. i, Cluster distribution in the indicated projections (top) and cell line composition of each cluster (bottom), showing a similar representation of cells from different tumour models in each cluster. j, Silhouette-width analysis to assess the appropriateness of clustering. Negative values indicate cells that have been inappropriately assigned. k, t-SNE projection of KRAS(G12C) single cells with the three inhibitory trajectories identified by the Slingshot algorithm. Source data

Extended Data Fig. 3 KRAS(G12C)-dependent transcriptional output score in 10,177 lung cancer cells.

a, The distribution of KRAS(G12C)-specific gene-expression output score across single cells in the three tumour models under study. The arrows denote cohorts of cells with high output despite treatment. b, Density plots showing the effect of G12Ci treatment on the KRAS(G12C) output score (n = 2,565 single cells from 0 h, n = 3,259 single cells from 4 h, n = 1,006 single cells from 24 h and n = 3,347 single cells from 72 h). At 72 h the cells assume an asymmetric distribution, suggesting that a subpopulation of KRAS(G12C) cells has adapted to treatment by reactivating KRAS(G12C)-dependent output (arrow). c, The KRAS(G12C) output score as a function of pseudotime (which was adjusted to allow comparisons between trajectories). The trend line was derived by fitting a spline to the G12C output score for each cell (n = 4,759 single cells in path 1, n = 8,653 single cells in path 2 and n = 4,050 single cells in path 3). d, e, The indicated variables are plotted for each cell in a two-dimensional t-SNE (d) or diffusion component (e) space. For simplicity, only the key clusters delineating each trajectory are shown in e. Source data

Extended Data Fig. 4 G12Ci treatment induces quiescence in a subpopulation of cancer cells.

a, Single cells were analysed to determine gene-expression signatures that correlated with the inhibitory fates. The top 20 signatures in each direction are shown. b, The overlap in the cycle-specific gene-expression signatures used to classify cells along their cell-cycle phase. The G0 and G1 phase signatures comprise mostly non-overlapping genes. c, A heat map of cell-cycle-specific gene-expression scores across each cell. Values were scaled across columns. d, Effect of G12Ci on cell-cycle distribution across treatment time and tumour models. e, The cell lines were treated as shown, to determine the level of p27 expression by immunoblotting. A representative of two independent experiments is shown for all except H2030, which was assayed once. f, KRAS(G12C)-mutant cells (H358) were synchronized with double thymidine treatment and then released in the presence or absence of G12Ci treatment, followed by cell-cycle analysis using propidium iodide staining. This assay cannot distinguish G0 from G1. TT, double thymidine. A representative of two independent experiments is shown. Source data

Extended Data Fig. 5 Biosensor validation of the divergent response to G12Ci treatment.

a, Cells expressing the quiescence biosensor (H358, p27K) were treated and rechallenged with the G12Ci to determine the effect on quiescence (that is, p27K high peak) at the indicated times. b, c, The cells were treated with the indicated inhibitors for 72 h to determine the effect on cell number (b, n = 2 biological replicates) or the distribution of biosensor expression (c). d, Comparison of the KRAS(G12C) output score between proliferating and quiescent cells. The transcriptional output signature score derived from scRNA-seq analysis is similar to the KRAS–GTP levels determined by RBD pull-down in Fig. 1j. e, The cells were exposed to a single treatment (0–72 h) or drug rechallenge (+) for 2 h or 24 h. Cell extracts were evaluated by immunoblotting. f, The indicated KRAS(G12C)-mutant lung cancer cell lines were treated with the G12Ci for 72 h followed by drug rechallenge for 4 h. The percentage inhibition in KRAS–GTP/total was determined by comparing baseline versus 4-h G12Ci and 72-h G12Ci versus 72-h + 4-h G12Ci. A representative of two experimental repeats is shown in a, e. Source data

Extended Data Fig. 6 Genes with trajectory-specific expression profiles.

a, Single cells were analysed to identify differentially expressed genes by contrasting paths 1 and 2. The top 50 genes are shown. The teal dots indicate genes that were validated in subsequent experiments. b, A CRISPR–Cas9 screen was carried out in H358 cells to help to narrow down the list of genes with trajectory-specific expression (by identifying and focusing on genes modulating the antiproliferative effect of the G12Ci). The schematic is not drawn to scale. Preference was given to genes with two or more sgRNAs that were downregulated by at least twofold in the G12Ci versus t0 comparison and that were also identified as having trajectory-specific expression in the scRNA-seq analysis. Pathways with several intermediates represented were prioritized. The number of gene-specific sgRNAs that were depleted during G12Ci treatment is also shown. NT, non-targeting control. c, The trend in expression for the indicated genes as a function of pseudotime was established by fitting a spline to single-cell data. The 95% confidence interval is shown. The pseudotime was adjusted to compare between trajectories. d, The expression of the indicated genes in proliferating or quiescent cells. Only cells collected during the adaptive phase (24–72 h) of G12Ci treatment are shown. e, The gene false discovery rate (FDR) in the indicated comparisons across either the entire cohort of cells or the subset of cells collected at the 72-h time point only (n = 4,759 single cells in path 1, n = 8,653 single cells in path 2, n = 4,050 single cells in path 3, n = 6,599 single cells in G1S, S, G2M, M or MG1 (proliferating) and n = 3,578 single cells in G0 (quiescent). Source data

Extended Data Fig. 7 The adaptive reactivation of KRAS during G12Ci treatment is dependent on EGFR signalling.

a, The cells were treated with the G12Ci over time to determine the effect on HBEGF expression. mRNA expression was determined by scRNA-seq (mean, n > 1,000 single cells per time point, see Fig. 1b) or by quantitative PCR (qPCR, mean ± s.e.m., n = 3). The amount of protein secreted in the medium was quantified by enzyme-linked immunosorbent assay (mean ± s.e.m., n = 3). Norm., normalized (minimum–maximum). b, Cells transfected with HBEGF-specific siRNAs were treated with increasing concentrations of G12Ci for 72 h to determine the effect on viability (mean ± s.e.m., n = 5). c, Cells treated with the G12Ci for 72 h were stimulated with EGF for 10 min, alone or in combination with the indicated inhibitors. Quiescent cells (p27K high) were isolated by FACS and their extracts were assayed for active KRAS by RBD pull-down. Immunoblots were quantified by densitometry and reported as fold change relative to unstimulated. d, e, Untreated or G12Ci-treated (24 h) H358 cells were stimulated with EGF (200 ng ml−1) for 10 min alone or in combination with the indicated inhibitors. Cell extracts were analysed by immunoblotting (d). The effect of EGF stimulation at baseline (lanes 2–4 versus lane 1) or after G12Ci treatment (lanes 6–8 versus lane 5) was quantified by densitometry (e). fi, The indicated KRASG12C-mutant lung cancer cells (f, g) or HA–KRAS(G12C)-expressing RASless mouse embryonic fibroblasts (h, i) were treated with the G12Ci alone or in combination with EGFR or SHP2 inhibitors, as shown. Cell extracts were subjected to RBD pull-down to determine the level of active (GTP-bound) and total KRAS. The HA tag was used to determine the specific effect on KRAS(G12C) (h, i). j, H358 cells were treated with the G12Ci alongside the EGFR inhibitor gefitinib (EGFRi), the pan-HER inhibitor afatinib (panHERi) or the SHP2 inhibitor SHP099 (SHP2i) to determine the effect on cancer cell growth (top) and the presence of treatment synergy (bottom), by using the Bliss index. Red denotes synergy. The mean of three biological replicates is shown on top. k, The indicated KRAS(G12C)-mutant cells were treated with increasing concentration of the G12Ci in the presence of 10%, 2% or 0% serum to determine the effect on cell viability (mean ± s.e.m., n = 3). A representative of two independent experiments is shown in d, fi. Unless otherwise indicated, n denotes biological replicates. Source data

Extended Data Fig. 8 AURKA is involved in the reactivation of KRAS–GTP during G12Ci treatment.

a, KRAS(G12C)-mutant lung cancer cells were treated with the G12Ci alone or in combination with AURKA inhibitor (AURKAi, alisertib, 10 μM) or panAURK inhibitor (tozasertib, 10 μM) to determine the effect on KRAS–GTP levels over time. There is no effect on KRAS–GTP levels with the AURKAi treatment in the absence of the G12Ci treatment. b, RASless mouse embryonic fibroblasts expressing KRAS(G12C) were treated as shown with the indicated concentrations of AURKAi (μM) to determine the effect on KRAS(G12C)–GTP. c, H358 cells stably transfected with doxycycline-inducible AURKA (dox AURKA) were treated with the G12Ci in the presence or absence of doxycycline (2 μg ml−1). Extracts from cells were analysed by immunoblotting to determine the effect on the indicated intermediates. d, H358 doxycycline-inducible AURKA cells were treated as shown and assayed to determine the effect on cell viability (mean + s.e.m, n = 5). A two-tailed t-test P value is shown. eg, H358 cells stably expressing HA-tagged KRAS G12C under a doxycycline-inducible promoter were treated with doxycycline for 24 h alone (e) or with the indicated inhibitors (f, g). Cell extracts were immunoprecipitated and immunoblotted as indicated. h, KRAS(G12C)-mutant cell lines were treated as shown to determine the effect on cancer cell growth (top) and the presence of treatment synergy (bottom), by using the Bliss index. Red denotes synergy. The mean of three biological replicates is shown on top. i, j, Mice bearing SW1573 (i) or H2122 (j) xenografts were treated with the indicated inhibitors to determine the effect on tumour growth (mean + s.e.m, n = 6 in SW1573, n = 5 in H2122). A two-tailed t-test P value is shown. A representative of at least two independent experiments is shown in ag. Unless otherwise indicated, n denotes biological replicates. Source data

Extended Data Fig. 9 Inhibition of MAPK signalling stimulates new KRAS synthesis.

a, The cells were treated with the indicated inhibitors and analysed to determine the level of KRAS mRNA or KRAS protein expression (mean ± s.e.m., n = 3). LFC, log2-transformed fold change, relative to 0 h. The indicated P values were determined by ANOVA (P = 0.001) followed by pairwise comparisons versus baseline, while correcting for multiple hypotheses (using Dunnett’s test in Prism). b, SW1573 (KRASG12C+/+) cells were transfected with non-targeting (NT) or KRAS-specific siRNAs followed by treatment with the G12Ci and immunoblotting. c, H358 cells engineered to express HA–KRAS(G12C) under a doxycycline-inducible promoter were treated with the G12Ci, alone or in the presence of doxycycline, to determine the effect on cell viability at 72 h (mean ± s.e.m., n = 3). d, H358 p27K cells were stably transfected with doxycycline-inducible siRes-G12C. The cells were transfected with KRASG12C siRNA (siG12C) followed by doxycycline (2 μg ml−1) induction. The effect on cell viability is shown as mean ± s.e.m. (n = 5 without doxycycline, n = 4 with doxycycline). A two-tailed t-test P value is shown. e, H358 cells with doxycycline-inducible HA–KRAS(G12C) were treated with doxycycline (2 μg ml−1) for 24 h in serum-free medium. Then, the cells were exposed to either EGF (200 ng ml−1) followed by the G12Ci (10 μM), or vice versa. Cell extracts were analysed by RBD pull-down and immunoblotting. The specific effect on KRAS(G12C) was determined by the HA tag. A representative of at least two independent experiments is shown in b, d, e. Unless otherwise indicated, n denotes biological replicates. Source data

Extended Data Fig. 10 Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition.

Left, at baseline, KRAS(G12C) cycles between its active (GTP-bound) and inactive (GDP-bound) conformations. Active KRAS(G12C) engages effector signalling, which regulates a transcriptional repertoire (that is, KRAS output) that is responsible for controlling various cellular functions. Middle, shortly after exposure to G12Ci treatment, KRAS(G12C) is trapped in its inactive state, and eventually the cancer cell population is sequestered in a low-KRAS output state. These cells stop proliferating and enter quiescence (G0). Right, over time, some cells undergo cell death and others adapt to the G12Ci to reactivate KRAS transcriptional output, bypassing drug-induced quiescence to resume proliferation. Our model suggests that this occurs because cells with low-KRAS output produce new KRAS(G12C) protein, which is not bound by the drug. Then, upstream signals operating in distinct cancer cell subpopulations—such as those mediated by EGFR or AURKA—maintain the new protein in its active, drug-insensitive state. By comparison, in cells in which these upstream signals are not active (or in cells in which these signals are pharmacologically inactivated), the new KRAS(G12C) spends a longer time in its inactive conformation, in which it can be bound by the drug and inhibited. This multifactorial process gives rise to a non-uniform treatment response with diverging effects across the cancer cell population.

Supplementary information

Supplementary Information

This file contains the Supplementary Discussion, Supplementary References, Supplementary Table 1 and Supplementary Figure 1.

Reporting Summary

Supplementary Data 1.Logcount matrix. Expression matrix of normalized log-transformed values where rows represent genes and columns represent single-cells.

Supplementary Data 2.Cell annotation. Annotation of single-cells comprising the expression matrix by various parameters.

Supplementary Data 3.Gene annotation. Annotation of genes comprising the expression matrix by various parameters, including FDR from various comparisons.

Supplementary Data 4.Pseudotime by trajectory. The pseudotime value for each single-cells in each of the three trajectories identified.

Supplementary Data 5.G12C-dependent genes from bulk RNAseq. A list of KRAS G12C induced or suppressed genes identified by bulk RNA sequencing.

Supplementary Data 6.G12C-dependent genes present in scRNAseq dataset. KRAS G12C induced or suppressed genes that were found to be expressed in the single-cell RNA sequencing dataset.

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Xue, J.Y., Zhao, Y., Aronowitz, J. et al. Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition. Nature 577, 421–425 (2020). https://doi.org/10.1038/s41586-019-1884-x

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