Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

A designer peptide against the EAG2–Kvβ2 potassium channel targets the interaction of cancer cells and neurons to treat glioblastoma

Nature Cancer volume 4, pages 1418–1436 (2023)Cite this article

Subjects

Abstract

Glioblastoma (GBM) is an incurable brain cancer that lacks effective therapies. Here we show that EAG2 and Kvβ2, which are predominantly expressed by GBM cells at the tumor–brain interface, physically interact to form a potassium channel complex due to a GBM-enriched Kvβ2 isoform. In GBM cells, EAG2 localizes at neuron-contacting regions in a Kvβ2-dependent manner. Genetic knockdown of the EAG2–Kvβ2 complex decreases calcium transients of GBM cells, suppresses tumor growth and invasion and extends the survival of tumor-bearing mice. We engineered a designer peptide to disrupt EAG2–Kvβ2 interaction, thereby mitigating tumor growth in patient-derived xenograft and syngeneic mouse models across GBM subtypes without overt toxicity. Neurons upregulate chemoresistant genes in GBM cells in an EAG2–Kvβ2-dependent manner. The designer peptide targets neuron-associated GBM cells and possesses robust efficacy in treating temozolomide-resistant GBM. Our findings may lead to the next-generation therapeutic agent to benefit patients with GBM.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: EAG2 and Kvβ2 are upregulated at the GBM–brain interface.
Fig. 2: EAG2 localizes at GBM cell–neuron contact sites in a Kvβ2-dependent manner and regulates calcium transients of GBM cells.
Fig. 3: EAG2 and Kvβ2 promote GBM growth and invasion.
Fig. 4: Identification of Kvβ2 amino acid sequence that mediates EAG2–Kvβ2 interaction.
Fig. 5: K90-114TAT disrupts EAG2–Kvβ2 interaction and suppresses GBM growth.
Fig. 6: Cluster 7 GBM cells signify aggressive and TMZ-resistant GBM.
Fig. 7: K90-114TAT treatment is effective against TMZ-resistant GBM.
Fig. 8: A designer peptide to treat GBM by disruption of EAG2–Kvβ2 potassium channel complex-mediated cancer–neuron interaction.

Similar content being viewed by others

Data availability

The spatial expression of EAG2, Kvβ2 and other genes in GBM geometrical regions was derived from the Ivy Glioblastoma Atlas Project (https://glioblastoma.alleninstitute.org). The structure of Kvβ2 is available at the Protein Data Bank with accession code 3eau (https://www.ebi.ac.uk/pdbe/entry/pdb/3eau). IlluminaHiseq RNA-seq data of The Cancer Genome Atlas LGGs and GBMs are available at NCI’s Genomic Data Commons (https://gdc.cancer.gov/about-data/publications/lgggbm_2016). The clinical information and gene expression datasets of CGGA (mRNAseq_693, Illumina HiSeq) are available at the Chinese Glioma Genome Atlas (http://www.cgga.org.cn/index.jsp). Previously published microarray data of TMZ-resistant and -sensitive GBM clones that were reanalyzed here are available at ArrayExpress under accession code E-MTAB-2693 (https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2693/). Preprocessed transcript data from the GLASS Consortium are available at Synapse (https://www.synapse.org/glass). Bulk RNA-seq data from GBM cell–neuron coculture, and scRNA-seq data from the PDX GBM model with peptide treatment reported in this manuscript, have been deposited in the NCBI Gene Expression Omnibus under accession code GSE231577. All unique materials such as patient-derived cell cultures are freely available and can be obtained by contacting the corresponding author and with a standard material transfer agreement with The Hospital for Sick Children. Data for all figures can be found within the manuscript, in the accompanying source data or from the corresponding author on reasonable request. Source data are provided with this paper.

Code availability

All codes used in this manuscript are freely available at GitHub (https://github.com/l-magnificence/Sequencing-Data-Analysis-Code-for-EAG2-Project).

References

  1. Kotecha, R., Odia, Y., Khosla, A. A. & Ahluwalia, M. S. Key clinical principles in the management of glioblastoma. JCO Oncol. Pract. 19, 180–189 (2023).

    Article  PubMed  Google Scholar 

  2. Stupp, R. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N. Engl. J. Med. 352, 987–996 (2005).

    Article  CAS  PubMed  Google Scholar 

  3. Lee, S. Y. Temozolomide resistance in glioblastoma multiforme. Genes Dis. 3, 198–210 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Johnson, B. E. et al. Mutational analysis reveals the origin and therapy-driven evolution of recurrent glioma. Science 343, 189–193 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. van Thuijl, H. F. et al. Evolution of DNA repair defects during malignant progression of low-grade gliomas after temozolomide treatment. Acta Neuropathol. 129, 597–607 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Pan, Y. et al. NF1 mutation drives neuronal activity-dependent initiation of optic glioma. Nature 594, 277–282 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Chen, P. et al. Olfactory sensory experience regulates gliomagenesis via neuronal IGF1. Nature 606, 550–556 (2022).

    Article  CAS  PubMed  Google Scholar 

  8. Magnon, C. et al. Autonomic nerve development contributes to prostate cancer progression. Science 341, 1236361 (2013).

    Article  PubMed  Google Scholar 

  9. Venkatesh, H. S. et al. Neuronal activity promotes glioma growth through neuroligin-3 secretion. Cell 161, 803–816 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Venkatesh, H. S. et al. Targeting neuronal activity-regulated neuroligin-3 dependency in high-grade glioma. Nature 549, 533–537 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Dolma, S. et al. Inhibition of dopamine receptor D4 impedes autophagic flux, proliferation, and survival of glioblastoma stem cells. Cancer Cell 29, 859–873 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Hayakawa, Y. et al. Nerve growth factor promotes gastric tumorigenesis through aberrant cholinergic signaling. Cancer Cell 31, 21–34 (2017).

    Article  CAS  PubMed  Google Scholar 

  13. Venkataramani, V. et al. Glutamatergic synaptic input to glioma cells drives brain tumour progression. Nature 573, 532–538 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Venkatesh, H. S. et al. Electrical and synaptic integration of glioma into neural circuits. Nature 573, 539–545 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Curry, R. N. et al. Glioma epileptiform activity and progression are driven by IGSF3-mediated potassium dysregulation. Neuron 111, 682–695 (2023).

    Article  CAS  PubMed  Google Scholar 

  16. Zeng, Q. et al. Synaptic proximity enables NMDAR signalling to promote brain metastasis. Nature 573, 526–531 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Anastasaki, C., Gao, Y. & Gutmann, D. H. Neurons as stromal drivers of nervous system cancer formation and progression. Dev. Cell 58, 81–93 (2023).

    Article  CAS  PubMed  Google Scholar 

  18. Winkler, F. et al. Cancer neuroscience: state of the field, emerging directions. Cell 186, 1689–1707 (2023).

    Article  CAS  PubMed  Google Scholar 

  19. Osswald, M. et al. Brain tumour cells interconnect to a functional and resistant network. Nature 528, 93–98 (2015).

    Article  CAS  PubMed  Google Scholar 

  20. Yu, K. et al. PIK3CA variants selectively initiate brain hyperactivity during gliomagenesis. Nature 578, 166–171 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Krishna, S. et al. Glioblastoma remodelling of human neural circuits decreases survival. Nature 617, 599–607 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Hille, B. Ion Channels of Excitable Membranes 3rd edn (Sinauer, 2001).

  23. Bagal, S. K. et al. Ion channels as therapeutic targets: a drug discovery perspective. J. Med. Chem. 56, 593–624 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Hutchings, C. J., Colussi, P. & Clark, T. G. Ion channels as therapeutic antibody targets. MAbs 11, 265–296 (2019).

    Article  CAS  PubMed  Google Scholar 

  25. Pongs, O. & Schwarz, J. R. Ancillary subunits associated with voltage-dependent K+ channels. Physiol. Rev. 90, 755–796 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Liu, X., Chang, Y., Reinhart, P. H., Sontheimer, H. & Chang, Y. Cloning and characterization of glioma BK, a novel BK channel isoform highly expressed in human glioma cells. J. Neurosci. 22, 1840–1849 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sales, T. T. et al. Suppression of the Eag1 potassium channel sensitizes glioblastoma cells to injury caused by temozolomide. Oncol. Lett. 12, 2581–2589 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Venturini, E. et al. Targeting the potassium channel Kv1.3 kills Glioblastoma cells. Neurosignals 25, 26–38 (2017).

    Article  PubMed  Google Scholar 

  29. Puchalski, R. B. et al. An anatomic transcriptional atlas of human glioblastoma. Science 360,660–663 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Varn, F. S. et al. Glioma progression is shaped by genetic evolution and microenvironment interactions. Cell 185, 2184–2199 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Weil, S. et al. Tumor microtubes convey resistance to surgical lesions and chemotherapy in gliomas. Neurol. Oncol. 19, 1316–1326 (2017).

    Article  CAS  Google Scholar 

  32. Park, N. I. et al. ASCL1 reorganizes chromatin to direct neuronal fate and suppress tumorigenicity of glioblastoma stem cells. Cell Stem Cell 21, 209–224 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Hamed, A. A. et al. A brain precursor atlas reveals the acquisition of developmental-like states in adult cerebral tumours. Nat. Commun. 13, 4178 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Pan, Y. et al. Cortisone dissociates the Shaker family K+ channels from their beta subunits. Nat. Chem. Biol. 4, 708–714 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Magill, S. T., Choy, W., Nguyen, M. P. & McDermott, M. W. Ommaya reservoir insertion: a technical note. Cureus 12, e7731 (2020).

    PubMed  PubMed Central  Google Scholar 

  36. Uetani, N., Chagnon, M. J., Kennedy, T. E., Iwakura, Y. & Tremblay, M. L. Mammalian motoneuron axon targeting requires receptor protein tyrosine phosphatases sigma and delta. J. Neurosci. 26, 5872–5880 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Suto, F. et al. Plexin-a4 mediates axon-repulsive activities of both secreted and transmembrane semaphorins and plays roles in nerve fiber guidance. J. Neurosci. 25, 3628–3637 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rosenstein, J. M., Krum, J. M. & Ruhrberg, C. VEGF in the nervous system. Organogenesis 6, 107–114 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. McGregor, C. E. & English, A. W. The role of BDNF in peripheral nerve regeneration: activity-dependent treatments and Val66Met. Front. Cell Neurosci. 12, 522 (2018).

    Article  CAS  PubMed  Google Scholar 

  40. Yoon, C. et al. Low-density lipoprotein receptor-related protein 1 (LRP1)-dependent cell signaling promotes axonal regeneration. J. Biol. Chem. 288, 26557–26568 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Goldshmit, Y. et al. EphA4 blockers promote axonal regeneration and functional recovery following spinal cord injury in mice. PLoS ONE 6, e24636 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Sanchez-Huertas, C. et al. The +TIP Navigator-1 is an actin-microtubule crosslinker that regulates axonal growth cone motility. J. Cell Biol. https://doi.org/10.1083/jcb.201905199 (2020).

  43. Li, H. et al. Transcription factor MEF2C influences neural stem/progenitor cell differentiation and maturation in vivo. Proc. Natl Acad. Sci. USA 105, 9397–9402 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ohkubo, Y., Uchida, A. O., Shin, D., Partanen, J. & Vaccarino, F. M. Fibroblast growth factor receptor 1 is required for the proliferation of hippocampal progenitor cells and for hippocampal growth in mouse. J. Neurosci. 24, 6057–6069 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu, X. et al. Effect of Spp1 on nerve degeneration and regeneration after rat sciatic nerve injury. BMC Neurosci. 18, 30 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Kim, Y. et al. Perspective of mesenchymal transformation in glioblastoma. Acta Neuropathol. Commun. 9, 50 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Meyer, M. et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc. Natl Acad. Sci. USA 112, 851–856 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Wang, H. et al. LGALS3 promotes treatment resistance in glioblastoma and is associated with tumor risk and prognosis. Cancer Epidemiol. Biomarkers Prev. 28, 760–769 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Venkataramani, V. et al. Disconnecting multicellular networks in brain tumours. Nat. Rev. Cancer 22, 481–491 (2022).

    Article  CAS  PubMed  Google Scholar 

  50. Huang, X. et al. Voltage-gated potassium channel EAG2 controls mitotic entry and tumor growth in medulloblastoma via regulating cell volume dynamics. Genes Dev. 26, 1780–1796 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Monje, M. et al. Roadmap for the emerging field of cancer neuroscience. Cell 181, 219–222 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Venkataramani, V., Tanev, D. I., Kuner, T., Wick, W. & Winkler, F. Synaptic input to brain tumors: clinical implications. Neurol. Oncol. 23, 23–33 (2021).

    Article  CAS  Google Scholar 

  53. Huang, X. et al. EAG2 potassium channel with evolutionarily conserved function as a brain tumor target. Nat. Neurosci. 18, 1236–1246 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Francisco, M. A. et al. Chloride intracellular channel 1 cooperates with potassium channel EAG2 to promote medulloblastoma growth. J. Exp. Med. https://doi.org/10.1084/jem.20190971 (2020).

  55. Serrano-Heras, G. et al. Involvement of N-methylpurine DNA glycosylase in resistance to temozolomide in patient-derived glioma cells. Sci. Rep. 10, 22185 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Huang, L. et al. Inhibition of intermedin (adrenomedullin 2) suppresses the growth of glioblastoma and increases the antitumor activity of temozolomide. Mol. Cancer Ther. 20, 284–295 (2021).

    Article  CAS  PubMed  Google Scholar 

  57. Aarts, M. et al. Treatment of ischemic brain damage by perturbing NMDA receptor–PSD-95 protein interactions. Science 298,846–850 (2002).

    Article  CAS  PubMed  Google Scholar 

  58. Cook, D. J., Teves, L. & Tymianski, M. A translational paradigm for the preclinical evaluation of the stroke neuroprotectant Tat-NR2B9c in gyrencephalic nonhuman primates. Sci. Transl. Med. 4, 154ra133 (2012).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work is supported by the SickKids Foundation, Arthur and Sonia Labatt Brain Tumour Research Centre, Garron Family Cancer Centre, Canadian Cancer Society Challenge Grant, Concern Foundation Conquer Cancer Now Award, b.r.a.i.n.child, Sontag Foundation Distinguished Scientist Award, Meagan’s HUG, Natural Sciences and Engineering Research Council Discovery Grant, Ontario Institute for Cancer Research Translational Research Initiative, Early Researcher Award and Canadian Institute of Health Research Project Grants (to X.H.), a Garron Family Cancer Centre Pitblado Discovery Grant (to L.-Y.W. and X.H.), Canadian Institute of Health Research Project Grants PJT-156034 and PJT-156439 and a Natural Sciences and Engineering Research Council grant RGPIN-2017-06665 (to L.-Y.W.). Xin C. is supported by SickKids Restracomp Scholarship. S.B. is supported by an International Postdoc Grant from the Swedish Research Council (Vetenskapsrådet) and Powered by Pablove Research Grant from Pablove Foundation. We thank J. Lathia for sharing GL261 cells, S. O. Kelley for sharing PC9 and H1975 cells and J. Ellis for sharing HEK293T cells, human astrocytes and lentivirus packaging plasmids. We thank P. Paroutis and K. Lau at SickKids Imaging Facility for help with confocal imaging and image analysis. We thank C. Simpson at SickKids SPARC BioCentre Molecular Analysis for assistance with mass spectrometry experiments. We thank the staff at The Centre for Phenogenomic for their help on animal works. We thank W. Wang for technical assistance and Huang laboratory members for comments on the manuscript. Schematics in Figs. 2a, 5c,e, 7a and 8 created with BioRender.com. X.H. is a Catalyst Scholar at The Hospital for Sick Children and Canada Research Chair in Cancer Biophysics.

Author information

Authors and Affiliations

  1. Developmental and Stem Cell Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    Weifan Dong, Xiaodi Chen, Xin Chen, Shahrzad Bahrampour, Yi Xiong, Hongyu Zhao, Ji-Eun Kim, Peter B. Dirks, Tae-Hee Kim, Siyi Wanggou & Xi Huang

  2. Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada

    Weifan Dong, Xiaodi Chen, Xin Chen, Shahrzad Bahrampour, Yi Xiong, Hongyu Zhao, Peter B. Dirks, Siyi Wanggou & Xi Huang

  3. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Weifan Dong, Xiaodi Chen, Daniel Schramek, Peter B. Dirks, Tae-Hee Kim & Xi Huang

  4. Neurosciences and Mental Health Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    Adam Fekete, Tian Kong & Lu-Yang Wang

  5. Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China

    Hongwei Liu, Yi Xiong, Qi Yang, Hongyu Zhao, Siyi Wanggou & Xuejun Li

  6. Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China

    Hongwei Liu, Yi Xiong, Qi Yang, Hongyu Zhao, Siyi Wanggou & Xuejun Li

  7. Molecular Medicine Program, The Hospital for Sick Children, Toronto, Ontario, Canada

    Greg L. Beilhartz & Roman A. Melnyk

  8. Department of Physiology, University of Toronto, Toronto, Ontario, Canada

    Tian Kong & Lu-Yang Wang

  9. Macaulay Honors College, City College of New York, New York, NY, USA

    Malia S. Morioka & Ye He

  10. Advanced Science Research Center at the Graduate Center, City University of New York, New York, NY, USA

    Malia S. Morioka & Ye He

  11. The Raymond G. Perelman Center for Cellular and Molecular Therapeutics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Geena Jung & Yuanquan Song

  12. Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Geena Jung & Yuanquan Song

  13. Centre for Molecular and Systems Biology, Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada

    Daniel Schramek

  14. Department of Biochemistry, University of Toronto, Toronto, Ontario, Canada

    Roman A. Melnyk

Authors
  1. Weifan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam Fekete
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaodi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hongwei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Greg L. Beilhartz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shahrzad Bahrampour
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yi Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hongyu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tian Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Malia S. Morioka
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Geena Jung
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Ji-Eun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Daniel Schramek
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Peter B. Dirks
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yuanquan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Tae-Hee Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Ye He
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Siyi Wanggou
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Xuejun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Roman A. Melnyk
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Lu-Yang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Xi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

X.H. directed the study. Experimental design, data acquisition and data analysis were carried out by W.D., A.F., Xiaodi C., H.L., G.L.B., Xin C., S.B., Y.X., Q.Y., H.Z., T.K., M.S.M., G.J., J.-E.K., G.J., Y.S., T.-H.K., Y.H., S.W., X.L., R.A.M. and L.-Y.W. Reagent contributions were provided by D.S. and P.B.D. W.D. and X.H. wrote the manuscript.

Corresponding author

Correspondence to Xi Huang.

Ethics declarations

Competing interests

W.D. and X.H. have filed a PCT patent for the composition of matter and use of designer peptides to treat cancer. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Justin Lathia, Stephen Robbins and Harald Sontheimer for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 EAG2 and Kvβ2 expression in xenograft GBM tumors, EAG2 localization in GBM cells, EAG2 and Kvβ2 knockdown validation, and tumor growth comparison between control tumor and tumor with knockdown of EAG2, Kvβ2, or both.

a. EAG2, Kvβ2, GFP, and DAPI expression in G411 xenograft tumor. EAG2, Tau, and GFAP expression in G489, G523, and G532 xenograft tumors. b. Representative images and quantification of EAG2 localization in GBM cells with or without contact of astrocytes. n = 24 cells examined over 3 independent experiments. P values, two-sided unpaired t-test. Error bars, mean ± s.e.m. c. Validation of Dox-induced knockdown of EAG2 and Kvβ2. d. Bioluminescence images show tumor burdens of mice bearing G411 tumors before and after Dox treatment. Kaplan-Meier survival curves are shown. P values, log-rank test. All experiments were performed 3 times using biologically independent samples.

Source data

Extended Data Fig. 2 Characterization of peptide treatment on GBM cell growth in vitro and tumor growth in vivo.

a. Co-IP shows EAG2-Kvβ2 interaction in G411 GBM cells treated with TAT, K59-78TAT, K90-114, or K90-114TAT. 3 biologically independent experiments were performed. b. Cell number, microtube number per cell, and microtube length of G489 GBM cells, which are co-cultured with neurons, after TAT, K59-78TAT, K90-114, or K90-114TAT treatment. Sample size (from left to right): n = 31, 42, 31, 43, 50, 52, 33, 41, 78, 146, 116, 49 cells examined over 3 independent experiments. Adjusted P value was calculated by Tukey’s multiple comparisons test (n.s. means not significant, P > 0.05). Error bars, mean ± s.e.m. Cell number and microtube length of K59-78TAT and K90-114TAT treated cells are also shown in Fig. 5b for easier data interpretation. c. Representative images and quantification of tumor areas of G411 xenograft tumors treated for 7 days with K59-78TAT or K90-114TAT. n = 15 samples examined from 5 biologically independent animals. P values, two-sided unpaired t-test. Error bars, mean ± s.e.m. d. Bioluminescence images and survival comparison of G411 GBM-bearing mice treated with K59-78TAT or K90-114TAT at various dosages. P values, log-rank test. Each P value was generated individually by comparing to K59-78TAT. e. Bioluminescence images and survival comparison of G411 GBM-bearing mice treated with TAT, K90-114, or randomized K90-114 TAT. K59-78TAT survival curve is derived from Fig. 5d. P values, log-rank test.

Source data

Extended Data Fig. 3 K90-114TAT treatment alters proliferation and apoptosis of GBM tumors.

Representative images showing proliferation and apoptosis of G489 xenograft tumors (pHis3: n = 54, cleaved caspase 3: n = 53) or GL261 syngeneic tumors (pHis3: n = 53 and 48, cleaved caspase 3: n = 54) treated with K59-78TAT or K90-114TAT. STEM121 labels G489 tumor cells. GFP labels GL261 tumor cells. pHis3 labels mitotic cells. cleaved caspase 3 labels apoptotic cells. Samples were evenly and independently collected from 3 animals. P values, two-sided unpaired t-test. Error bars, mean ± s.e.m.

Source data

Extended Data Fig. 4 K90-114TAT treatment does not affect mouse body weight, survival, and internal organs.

a. Body weight of K59-78TAT- and K90-114TAT-treated tumor-free NSG mice (n = 9 mice in each group). b. Survival of K59-78TAT- and K90-114TAT-treated tumor-free NSG mice (n = 9 mice in each group). c. Pathological analysis of hearts, kidneys, livers, and lungs harvested from control, K59-78TAT and K90-114TAT-treated tumor-bearing NSG mice (n = 3 mice in each group).

Source data

Extended Data Fig. 5 K90-114TAT treatment decreases proliferation and increases apoptosis of TMZ-resistant GBM.

Representative images showing STEM121+ tumor cells, pHis3+ mitotic cells, and cleaved caspase 3+ apoptotic cells in TMZ-resistant xenograft tumors treated with TMZ, K59-78TAT and K90-114TAT. Sample size (from left to right): G411-TMZr: n = 24, 25, 21, 24, 24, 24; G532-TMZr: n = 7, 15, 21, 17, 17, 21. Samples were evenly and independently collected from 3 animals. P values, two-sided unpaired t-test. Error bars, mean ± s.e.m.

Source data

Supplementary information

Reporting Summary

Supplementary Table 1

Sequences of peptides, shRNAs and PCR primers.

Source data

Source Data Fig. 1

Statistical Source Data.

Source Data Fig. 2

Statistical Source Data.

Source Data Fig. 3

Statistical Source Data.

Source Data Fig. 4

Statistical Source Data.

Source Data Fig. 5

Statistical Source Data.

Source Data Fig. 6

Statistical Source Data.

Source Data Fig. 7

Statistical Source Data.

Source Data Extended Data Fig. 1.

Statistical Source Data.

Source Data Extended Data Fig. 2.

Statistical Source Data.

Source Data Extended Data Fig. 3.

Statistical Source Data.

Source Data Extended Data Fig. 4.

Statistical Source Data.

Source Data Extended Data Fig. 5.

Statistical Source Data.

Source Data

Unprocessed immunoblots and gels.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, W., Fekete, A., Chen, X. et al. A designer peptide against the EAG2–Kvβ2 potassium channel targets the interaction of cancer cells and neurons to treat glioblastoma. Nat Cancer 4, 1418–1436 (2023). https://doi.org/10.1038/s43018-023-00626-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-023-00626-8

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer