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miR-147b-mediated TCA cycle dysfunction and pseudohypoxia initiate drug tolerance to EGFR inhibitors in lung adenocarcinoma


Drug tolerance is an acute defence response preceding a fully drug-resistant state and tumour relapse; however, there are few therapeutic agents targeting drug tolerance in the clinic. Here we show that miR-147b initiates a reversible state of tolerance to the epidermal growth factor receptor (EGFR) inhibitor osimertinib in non-small-cell lung cancer. With miRNA-seq analysis, we find that miR-147b is the most upregulated microRNA in osimertinib-tolerant and EGFR-mutated lung cancer cells. Whole-transcriptome analysis of single-cell-derived clones reveals a link between osimertinib tolerance and pseudohypoxia responses irrespective of oxygen levels. Further metabolomics and genetic studies demonstrate that osimertinib tolerance is driven by miR-147b-mediated repression of VHL and succinate dehydrogenase, which are linked to the tricarboxylic acid cycle and pseudohypoxia pathways. Finally, pretreatment with a miR-147b inhibitor delays osimertinib-associated drug tolerance in patient-derived 3D structures. This link between miR-147b and the tricarboxylic acid cycle may provide promising targets for preventing tumour relapse.

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Fig. 1: NSCLC cells adopt a tolerance strategy against EGFR TKIs.
Fig. 2: miR-147b initiates drug tolerance.
Fig. 3: A miR-147b–VHL axis mediates drug tolerance through impaired VHL activity.
Fig. 4: A miR-147b–SDHD axis mediates drug tolerance through SDH enzyme activity in the TCA cycle.
Fig. 5: Blocking miR-147b overcomes drug tolerance.

Data availability

Information and data for PDX models from the JAX PDX Resource are publicly available from the PDX Portal hosted by the Mouse Tumor Biology Database ( Data from this study have been deposited in the Gene Expression Omnibus (GEO) under the following accessions: GSE103155 (microarray; single-cell-derived clones) and GSE103352 (miRNA-seq). The results shown in this manuscript were in part based on data generated by the TCGA Research Network32: Analyses for an association between miRNA profiles and EGFR mutations as well as an association between VHL and miR-147b in a cohort of human lung adenocarcinoma cell lines were based on a public RNA-seq dataset31 available in the ArrayExpress database under accession E-MTAB-2706. The heat map for miRNA expression was generated according to the Heatmapper server ( Genetic mutation status was confirmed by canSAR portal (v3.0 beta; and the Catalogue of Somatic Mutations In Cancer (COSMIC; The data that support the findings of this study are available from the corresponding author upon reasonable request.


  1. 1.

    Kobayashi, S. et al. EGFR mutation and resistance of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 352, 786–792 (2005).

    CAS  Article  Google Scholar 

  2. 2.

    Paez, J. G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    CAS  Article  Google Scholar 

  3. 3.

    Niederst, M. J. & Engelman, J. A. Bypass mechanisms of resistance to receptor tyrosine kinase inhibition in lung cancer. Sci. Signal. 6, re6 (2013).

    Article  Google Scholar 

  4. 4.

    Thress, K. S. et al. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non–small cell lung cancer harboring EGFR T790M. Nat. Med. 21, 560–562 (2015).

    CAS  Article  Google Scholar 

  5. 5.

    Pao, W. et al. Acquired resistance of lung adenocarcinomas to gefitinib or erlotinib is associated with a second mutation in the EGFR kinase domain. PLoS Med. 2, e73 (2005).

    Article  Google Scholar 

  6. 6.

    Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016).

    CAS  Article  Google Scholar 

  7. 7.

    Ramirez, M. et al. Diverse drug-resistance mechanisms can emerge from drug-tolerant cancer persister cells. Nat. Commun. 7, 10690 (2016).

    CAS  Article  Google Scholar 

  8. 8.

    Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).

    CAS  Article  Google Scholar 

  9. 9.

    Smith, M. P. et al. Inhibiting drivers of non-mutational drug tolerance is a salvage strategy for targeted melanoma therapy. Cancer Cell 29, 270–284 (2016).

    CAS  Article  Google Scholar 

  10. 10.

    Soria, J. C. et al. Osimertinib in untreated EGFR-mutated advanced non-small-cell lung cancer. N. Engl. J. Med. 378, 113–125 (2017).

    Article  Google Scholar 

  11. 11.

    Go, M. K., Zhang, W. C., Lim, B. & Yew, W. S. Glycine decarboxylase is an unusual amino acid decarboxylase involved in tumorigenesis. Biochemistry 53, 947–956 (2014).

    CAS  Article  Google Scholar 

  12. 12.

    Ward, P. S. & Thompson, C. B. Metabolic reprogramming: a cancer hallmark even Warburg did not anticipate. Cancer Cell 21, 297–308 (2012).

    CAS  Article  Google Scholar 

  13. 13.

    Zhang, W. C. et al. Glycine decarboxylase activity drives non–small cell lung cancer tumor-initiating cells and tumorigenesis. Cell 148, 259–272 (2012).

    CAS  Article  Google Scholar 

  14. 14.

    Jain, M. et al. Metabolite profiling identifies a key role for glycine in rapid cancer cell proliferation. Science 336, 1040–1044 (2012).

    CAS  Article  Google Scholar 

  15. 15.

    Vander Heiden, M. G., Cantley, L. C. & Thompson, C. B. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science 324, 1029–1033 (2009).

    Article  Google Scholar 

  16. 16.

    Raimundo, N., Baysal, B. E. & Shadel, G. S. Revisiting the TCA cycle: signaling to tumor formation. Trends Mol. Med. 17, 641–649 (2011).

    CAS  Article  Google Scholar 

  17. 17.

    Vyas, S., Zaganjor, E. & Haigis, M. C. Mitochondria and cancer. Cell 166, 555–566 (2016).

    CAS  Article  Google Scholar 

  18. 18.

    Sabharwal, S. S. & Schumacker, P. T. Mitochondrial ROS in cancer: initiators, amplifiers or an Achilles’ heel? Nat. Rev. Cancer 14, 709–721 (2014).

    CAS  Article  Google Scholar 

  19. 19.

    MacKenzie, E. D. et al. Cell-permeating α-ketoglutarate derivatives alleviate pseudohypoxia in succinate dehydrogenase–deficient cells. Mol. Cell Biol. 27, 3282–3289 (2007).

    CAS  Article  Google Scholar 

  20. 20.

    Selak, M. A. et al. Succinate links TCA cycle dysfunction to oncogenesis by inhibiting HIF-α prolyl hydroxylase. Cancer Cell 7, 77–85 (2005).

    CAS  Article  Google Scholar 

  21. 21.

    Nowicki, S. & Gottlieb, E. Oncometabolites: tailoring our genes. FEBS J. 282, 2796–2805 (2015).

    CAS  Article  Google Scholar 

  22. 22.

    Kaelin, W. G. Jr. Molecular basis of the VHL hereditary cancer syndrome. Nat. Rev. Cancer 2, 673–682 (2002).

    CAS  Article  Google Scholar 

  23. 23.

    Alvarez, S. W. et al. NFS1 undergoes positive selection in lung tumours and protects cells from ferroptosis. Nature 551, 639–643 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    Hangauer, M. J. et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature 551, 247–250 (2017).

    CAS  Article  Google Scholar 

  25. 25.

    Dye, B. R. et al. In vitro generation of human pluripotent stem cell derived lung organoids. eLife 4, e05098 (2015).

    Article  Google Scholar 

  26. 26.

    Zhan, T., Rindtorff, N. & Boutros, M. Wnt signaling in cancer. Oncogene 36, 1461–1473 (2017).

    CAS  Article  Google Scholar 

  27. 27.

    Li, H. et al. MicroRNA-181a regulates epithelial–mesenchymal transition by targeting PTEN in drug-resistant lung adenocarcinoma cells. Int. J. Oncol. 47, 1379–1392 (2015).

    CAS  Article  Google Scholar 

  28. 28.

    Sun, F. D., Wang, P. C., Luan, R. L., Zou, S. H. & Du, X. MicroRNA-574 enhances doxorubicin resistance through down-regulating SMAD4 in breast cancer cells. Eur. Rev. Med. Pharmacol. Sci. 22, 1342–1350 (2018).

    PubMed  Google Scholar 

  29. 29.

    Galluzzi, L. et al. miR-181a and miR-630 regulate cisplatin-induced cancer cell death. Cancer Res. 70, 1793–1803 (2010).

    CAS  Article  Google Scholar 

  30. 30.

    Pao, W. et al. KRAS mutations and primary resistance of lung adenocarcinomas to gefitinib or erlotinib. PLoS Med. 2, e17 (2005).

    Article  Google Scholar 

  31. 31.

    Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).

    CAS  Article  Google Scholar 

  32. 32.

    Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511, 543–550 (2014).

    Article  Google Scholar 

  33. 33.

    Anaya, J. OncoRank: a pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs. Peer J. Prepr. 4, e2574v2571 (2016).

    Google Scholar 

  34. 34.

    Asad, M. et al. FZD7 drives in vitro aggressiveness in Stem-A subtype of ovarian cancer via regulation of non-canonical Wnt/PCP pathway. Cell Death Dis. 5, e1346 (2014).

    CAS  Article  Google Scholar 

  35. 35.

    Ivan, M. et al. HIFα targeted for VHL-mediated destruction by proline hydroxylation: implications for O2 sensing. Science 292, 464–468 (2001).

    CAS  Article  Google Scholar 

  36. 36.

    Frew, I. J. & Krek, W. pVHL: a multipurpose adaptor protein. Sci. Signal. 1, pe30 (2008).

    Article  Google Scholar 

  37. 37.

    Gomes, A. P. et al. Declining NAD+ induces a pseudohypoxic state disrupting nuclear–mitochondrial communication during aging. Cell 155, 1624–1638 (2013).

    CAS  Article  Google Scholar 

  38. 38.

    DeBerardinis, R. J. & Chandel, N. S. Fundamentals of cancer metabolism. Sci. Adv. 2, e1600200 (2016).

    Article  Google Scholar 

  39. 39.

    Hensley, C. T. et al. Metabolic heterogeneity in human lung tumors. Cell 164, 681–694 (2016).

    CAS  Article  Google Scholar 

  40. 40.

    Calvert, A. E. et al. Cancer-associated IDH1 promotes growth and resistance to targeted therapies in the absence of mutation. Cell Rep. 19, 1858–1873 (2017).

    CAS  Article  Google Scholar 

  41. 41.

    Han, L., Dong, Z., Liu, N., Xie, F. & Wang, N. Maternally expressed gene 3 (MEG3) enhances PC12 cell hypoxia injury by targeting miR-147. Cell Physiol. Biochem. 43, 2457–2469 (2017).

    CAS  Article  Google Scholar 

  42. 42.

    Keith, B., Johnson, R. S. & Simon, M. C. HIF1α and HIF2α: sibling rivalry in hypoxic tumour growth and progression. Nat. Rev. Cancer 12, 9–22 (2011).

    Article  Google Scholar 

  43. 43.

    Samanta, D., Gilkes, D. M., Chaturvedi, P., Xiang, L. & Semenza, G. L. Hypoxia-inducible factors are required for chemotherapy resistance of breast cancer stem cells. Proc. Natl Acad. Sci. USA 111, E5429–E5438 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Lim, J. H. et al. Sirtuin 1 modulates cellular responses to hypoxia by deacetylating hypoxia-inducible factor 1α. Mol. Cell 38, 864–878 (2010).

    CAS  Article  Google Scholar 

  45. 45.

    Kaelin, W. G. Jr. The von Hippel–Lindau tumour suppressor protein: O2 sensing and cancer. Nat. Rev. Cancer 8, 865–873 (2008).

    CAS  Article  Google Scholar 

  46. 46.

    Rupaimoole, R. & Slack, F. J. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases. Nat. Rev. Drug Discov. 16, 203–222 (2017).

    CAS  Article  Google Scholar 

  47. 47.

    Esquela-Kerscher, A. & Slack, F. J. OncomiRs—microRNAs with a role in cancer. Nat. Rev. Cancer 6, 259–269 (2006).

    CAS  Article  Google Scholar 

  48. 48.

    Zhang, W. C. et al. Tumour-initiating cell–specific miR-1246 and miR-1290 expression converge to promote non–small cell lung cancer progression. Nat. Commun. 7, 11702 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    Adams, B. D., Parsons, C., Walker, L., Zhang, W. C. & Slack, F. J. Targeting noncoding RNAs in disease. J. Clin. Invest. 127, 761–771 (2017).

    Article  Google Scholar 

  50. 50.

    Zhang, W. C. & Slack, F. J. ADARs edit microRNAs to promote leukemic stem cell activity. Cell Stem Cell 19, 141–142 (2016).

    CAS  Article  Google Scholar 

  51. 51.

    Wilson, T. R. et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 487, 505–509 (2012).

    CAS  Article  Google Scholar 

  52. 52.

    Lee, C. G., McCarthy, S., Gruidl, M., Timme, C. & Yeatman, T. J. MicroRNA-147 induces a mesenchymal-to-epithelial transition (MET) and reverses EGFR inhibitor resistance. PLoS One 9, e84597 (2014).

    Article  Google Scholar 

  53. 53.

    Guo, J. et al. pVHL suppresses kinase activity of Akt in a proline-hydroxylation-dependent manner. Science 353, 929–932 (2016).

    CAS  Article  Google Scholar 

  54. 54.

    Lee, S. B. et al. An ID2-dependent mechanism for VHL inactivation in cancer. Nature 529, 172–177 (2016).

    CAS  Article  Google Scholar 

  55. 55.

    Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).

    CAS  Article  Google Scholar 

  56. 56.

    Curtis, S. J. et al. Primary tumor genotype is an important determinant in identification of lung cancer propagating cells. Cell Stem Cell 7, 127–133 (2010).

    CAS  Article  Google Scholar 

  57. 57.

    Lundberg, A. S. et al. Immortalization and transformation of primary human airway epithelial cells by gene transfer. Oncogene 21, 4577–4586 (2002).

    CAS  Article  Google Scholar 

  58. 58.

    Shultz, L. D. et al. Human cancer growth and therapy in immunodeficient mouse models. Cold Spring Harb. Protoc. 2014, 694–708 (2014).

    PubMed  PubMed Central  Google Scholar 

  59. 59.

    Krupke, D. M. et al. The Mouse Tumor Biology database: a comprehensive resource for mouse models of human cancer. Cancer Res. 77, e67–e70 (2017).

    CAS  Article  Google Scholar 

  60. 60.

    Watanabe, K. et al. A ROCK inhibitor permits survival of dissociated human embryonic stem cells. Nat. Biotechnol. 25, 681–686 (2007).

    CAS  Article  Google Scholar 

  61. 61.

    Padela, S. et al. A critical role for fibroblast growth factor-7 during early alveolar formation in the neonatal rat. Pediatr. Res. 63, 232–238 (2008).

    CAS  Article  Google Scholar 

  62. 62.

    Bostrom, H. et al. PDGF-A signaling is a critical event in lung alveolar myofibroblast development and alveogenesis. Cell 85, 863–873 (1996).

    CAS  Article  Google Scholar 

  63. 63.

    Sekine, K. et al. Fgf10 is essential for limb and lung formation. Nat. Genet. 21, 138–141 (1999).

    CAS  Article  Google Scholar 

  64. 64.

    Krause, C., Guzman, A. & Knaus, P. Noggin. Int. J. Biochem. Cell Biol. 43, 478–481 (2011).

    CAS  Article  Google Scholar 

  65. 65.

    Jaakkola, P. et al. Targeting of HIF-α to the von Hippel–Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science 292, 468–472 (2001).

    CAS  Article  Google Scholar 

  66. 66.

    Temes, E. et al. Activation of HIF-prolyl hydroxylases by R59949, an inhibitor of the diacylglycerol kinase. J. Biol. Chem. 280, 24238–24244 (2005).

    CAS  Article  Google Scholar 

  67. 67.

    Mills, E. L. et al. Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages. Cell 167, 457–470 (2016).

    CAS  Article  Google Scholar 

  68. 68.

    Dervartanian, D. V. & Veeger, C. Studies on succinate dehydrogenase. I. Spectral properties of the purified enzyme and formation of enzyme-competitive inhibitor complexes. Biochim. Biophys. Acta 92, 233–247 (1964).

    CAS  PubMed  Google Scholar 

  69. 69.

    Gong, M. et al. Pyrosequencing enhancement for better detection limit and sequencing homopolymers. Biochem. Biophys. Res. Commun. 401, 117–123 (2010).

    CAS  Article  Google Scholar 

  70. 70.

    Kim, H. J. et al. Clinical investigation of EGFR mutation detection by pyrosequencing in lung cancer patients. Oncol. Lett. 5, 271–276 (2013).

    CAS  Article  Google Scholar 

  71. 71.

    Shi, J. et al. Deep RNA sequencing reveals a repertoire of human fibroblast circular RNAs associated with cellular responses to herpes simplex virus 1 infection. Cell Physiol. Biochem. 47, 2031–2045 (2018).

    CAS  Article  Google Scholar 

  72. 72.

    Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  73. 73.

    Robinson, M. D. & Oshlack, A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 11, R25 (2010).

    Article  Google Scholar 

  74. 74.

    Trapnell, C. et al. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat. Biotechnol. 31, 46–53 (2013).

    CAS  Article  Google Scholar 

  75. 75.

    Chua, S. W. et al. A novel normalization method for effective removal of systematic variation in microarray data. Nucleic Acids Res. 34, e38 (2006).

    Article  Google Scholar 

  76. 76.

    Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry–based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).

    CAS  Article  Google Scholar 

  77. 77.

    Xia, J. & Wishart, D. S. Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Curr. Protoc. Bioinformatics 55, 14.10.11–14.10.91 (2016).

    Article  Google Scholar 

  78. 78.

    Hu, Y. & Smyth, G. K. ELDA: extreme limiting dilution analysis for comparing depleted and enriched populations in stem cell and other assays. J. Immunol. Methods 347, 70–78 (2009).

    CAS  Article  Google Scholar 

  79. 79.

    Babicki, S. et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res. 44, W147–W153 (2016).

    CAS  Article  Google Scholar 

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We thank S. Kobayashi for discussion and providing EGFR-mutated lung cancer cell lines. We thank members of the Slack laboratory for comments and sharing materials, the Yale SPORE in Lung Cancer team for feedback, A. Jiao, D. Foster and S. M. Lee for reading and providing feedback on the manuscript, and H. Yang, M. Gong, D. Cook and E. Poulin for technical support. F.J.S. and K.P. acknowledge NIH-YALE SPORE in Lung Cancer grant P50CA196530. Additional support to F.J.S. was provided by grant P50CA196530-03S1 and the BIDMC–JAX collaboration project and by support from the Ludwig Center at Harvard. C.J.B. acknowledges support from NIH grant P30CA034196. J.M.A. acknowledges support from NIH grants 5P01CA120964 and 5P30CA006516. W.C.Z. acknowledges an NIH-YALE SPORE in Lung Cancer Career Development Program Award and NRSA grant 5T32HL007893-20. D.B.C. acknowledges NIH 5R37CA218707 as a funding source.

Author information




W.C.Z. and F.J.S. directed the project; W.C.Z., J.M.W., K.-H.C., H.H., C.J.B. and F.J.S. wrote the manuscript; W.C.Z. performed miRNA-seq and microarray analyses, 3D structure derivation, cell culture experiments and in vivo experiments; J.M.W., K.-H.C., C.J.B., M.A.M. and K.P. established the PDX; H.H., M.Y. and J.M.A. performed metabolomics profiling; T.S. assisted with the small-molecule treatment experiments; W.C.Z. directed miRNA-147b target prediction and validation by genetic and metabolic approaches; and D.B.C. provided advice and project support.

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Correspondence to Frank J. Slack.

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Most authors declare no competing interests. DBC reports personal fees (consulting fees) and non-financial support (institutional research support) from Takeda/Millennium Pharmaceuticals, personal fees (consulting fees) and non-financial support (institutional research support) from Astrazeneca, personal fees (honoraria) and non-financial support (institutional research support) from Pfizer, non-financial support (institutional research support) from Merck Sharp & Dohme Corporation and non-financial support (institutional research support) from Merrimack Pharmaceuticals, all outside the submitted work.

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Whole-transcriptome analysis for single-cell-derived gefitinib-tolerant clones in PC9

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Zhang, W.C., Wells, J.M., Chow, KH. et al. miR-147b-mediated TCA cycle dysfunction and pseudohypoxia initiate drug tolerance to EGFR inhibitors in lung adenocarcinoma. Nat Metab 1, 460–474 (2019).

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