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:

Connectivity map-based drug repositioning of bortezomib to reverse the metastatic effect of GALNT14 in lung cancer

Oncogene volume 39, pages 4567–4580 (2020)Cite this article

Subjects

A Correction to this article was published on 11 February 2021

This article has been updated

Abstract

Despite the continual discovery of promising new cancer targets, drug discovery is often hampered by the poor druggability of these targets. As such, repurposing FDA-approved drugs based on cancer signatures is a useful alternative to cancer precision medicine. Here, we adopted an in silico approach based on large-scale gene expression signatures to identify drug candidates for lung cancer metastasis. Our clinicogenomic analysis identified GALNT14 as a putative driver of lung cancer metastasis, leading to poor survival. To overcome the poor druggability of GALNT14 in the control of metastasis, we utilized the Connectivity Map and identified bortezomib (BTZ) as a potent metastatic inhibitor, bypassing the direct inhibition of the enzymatic activity of GALNT14. The antimetastatic effect of BTZ was verified both in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGFβ-mediated gene expression and suppressed TGFβ-dependent metastatic genes. These results demonstrate that our in silico approach is a viable strategy for the use of undruggable targets in cancer therapies and for revealing the underlying mechanisms of these targets.

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: GALNT14 as a putative molecular target for lung cancer metastasis.
Fig. 2: Computational repositioning of BTZ to reverse the GALNT14 expression signature.
Fig. 3: The effect of BTZ in relation to proteasome inhibition.
Fig. 4: Attenuation of the TGFβ gene response by BTZ treatment or GALNT14 knockdown.
Fig. 5: In vivo validation of the antimetastatic effect of BTZ.

Similar content being viewed by others

Code availability

All computational codes are available from the authors upon request.

Change history

References

  1. Spear BB, Heath-Chiozzi M, Huff J. Clinical application of pharmacogenetics. Trends Mol Med. 2001;7:201–4.

    CAS  PubMed  Google Scholar 

  2. Olsen D, Jorgensen JT. Companion diagnostics for targeted cancer drugs - clinical and regulatory aspects. Front Oncol. 2014;4:105.

    PubMed  PubMed Central  Google Scholar 

  3. Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR Jr, Tsao A, et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer Disco. 2011;1:44–53.

    CAS  Google Scholar 

  4. Marquart J, Chen EY, Prasad V. Estimation of the percentage of US patients with cancer who benefit from genome-driven oncology. JAMA Oncol. 2018;4:1093–8.

    PubMed  PubMed Central  Google Scholar 

  5. Lazo JS, Sharlow ER. Drugging undruggable molecular cancer targets. Annu Rev Pharmacol Toxicol. 2016;56:23–40.

    CAS  PubMed  Google Scholar 

  6. Dang CV, Reddy EP, Shokat KM, Soucek L. Drugging the ‘undruggable’ cancer targets. Nat Rev Cancer. 2017;17:502–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Gayvert KM, Dardenne E, Cheung C, Boland MR, Lorberbaum T, Wanjala J, et al. A computational drug repositioning approach for targeting oncogenic transcription factors. Cell Rep. 2016;15:2348–56.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Nagaraj AB, Wang QQ, Joseph P, Zheng C, Chen Y, Kovalenko O, et al. Using a novel computational drug-repositioning approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene. 2018;37:403–14.

    CAS  PubMed  Google Scholar 

  9. Kwon OS, Kim W, Cha HJ, Lee H. In silico drug repositioning: from large-scale transcriptome data to therapeutics. Arch Pharm Res. 2019;42:879–89.

    CAS  PubMed  Google Scholar 

  10. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673–83.

    CAS  PubMed  Google Scholar 

  11. Bertolini F, Sukhatme VP, Bouche G. Drug repurposing in oncology-patient and health systems opportunities. Nat Rev Clin Oncol. 2015;12:732–42.

    PubMed  Google Scholar 

  12. Sleire L, Forde HE, Netland IA, Leiss L, Skeie BS, Enger PO. Drug repurposing in cancer. Pharmacol Res. 2017;124:74–91.

    CAS  PubMed  Google Scholar 

  13. van’t Veer LJ, Bernards R. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature. 2008;452:564–70.

    PubMed  Google Scholar 

  14. Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171:1437–e1417.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. van Noort V, Scholch S, Iskar M, Zeller G, Ostertag K, Schweitzer C, et al. Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling. Cancer Res. 2014;74:5690–9.

    PubMed  Google Scholar 

  16. Chen B, Ma L, Paik H, Sirota M, Wei W, Chua MS, et al. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nature Commun. 2017;8:16022.

  17. Lee H, Kang S, Kim W. Drug repositioning for cancer therapy based on large-scale drug-induced transcriptional signatures. PloS One. 2016;11:e0150460.

    PubMed  PubMed Central  Google Scholar 

  18. Ten Hagen KG, Fritz TA, Tabak LA. All in the family: the UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferases. Glycobiology. 2003;13:1R–16R.

    PubMed  Google Scholar 

  19. Wagner KW, Punnoose EA, Januario T, Lawrence DA, Pitti RM, Lancaster K, et al. Death-receptor O-glycosylation controls tumor-cell sensitivity to the proapoptotic ligand Apo2L/TRAIL. Nat Med. 2007;13:1070–7.

    CAS  PubMed  Google Scholar 

  20. Wu C, Shan Y, Liu X, Song W, Wang J, Zou M, et al. GalNAc-T14 may be involved in regulating the apoptotic action of IGFBP-3. J Biosci (Res Support, Non-U S Gov’t). 2009;34:389–95.

    CAS  Google Scholar 

  21. Song KH, Park MS, Nandu TS, Gadad S, Kim SC, Kim MY. GALNT14 promotes lung-specific breast cancer metastasis by modulating self-renewal and interaction with the lung microenvironment. Nat Commun. 2016;7:13796.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Huanna T, Tao Z, Xiangfei W, Longfei A, Yuanyuan X, Jianhua W, et al. GALNT14 mediates tumor invasion and migration in breast cancer cell MCF-7. Mol Carcinog. 2015;54:1159–71.

    PubMed  Google Scholar 

  23. Wang ZQ, Bachvarova M, Morin C, Plante M, Gregoire J, Renaud MC, et al. Role of the polypeptide N-acetylgalactosaminyltransferase 3 in ovarian cancer progression: possible implications in abnormal mucin O-glycosylation. Oncotarget. 2014;5:544–60.

    PubMed  PubMed Central  Google Scholar 

  24. Kwon OS, Oh E, Park JR, Lee JS, Bae GY, Koo JH, et al. GalNAc-T14 promotes metastasis through Wnt dependent HOXB9 expression in lung adenocarcinoma. Oncotarget. 2015;6:41916–28. https://doi.org/10.18632/oncotarget.6019.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Shan J, Liu Y, Wang Y, Li Y, Yu X, Wu C. GALNT14 involves the regulation of multidrug resistance in breast cancer cells. Transl Oncol. 2018;11:786–93.

    PubMed  PubMed Central  Google Scholar 

  26. De Mariano M, Gallesio R, Chierici M, Furlanello C, Conte M, Garaventa A, et al. Identification of GALNT14 as a novel neuroblastoma predisposition gene. Oncotarget. 2015;6:26335–46.

    PubMed  PubMed Central  Google Scholar 

  27. Soria JC, Mark Z, Zatloukal P, Szima B, Albert I, Juhasz E, et al. Randomized phase II study of dulanermin in combination with paclitaxel, carboplatin, and bevacizumab in advanced non-small-cell lung cancer. J Clin Oncol. 2011;29:4442–51.

    CAS  PubMed  Google Scholar 

  28. Yeh CT, Liang KH, Lin CC, Chang ML, Hsu CL, Hung CF. A single nucleotide polymorphism on the GALNT14 gene as an effective predictor of response to chemotherapy in advanced hepatocellular carcinoma. Int J Cancer. 2014;134:1214–24.

    CAS  PubMed  Google Scholar 

  29. Liang KH, Lin CL, Chen SF, Chiu CW, Yang PC, Chang ML, et al. GALNT14 genotype effectively predicts the therapeutic response in unresectable hepatocellular carcinoma treated with transcatheter arterial chemoembolization. Pharmacogenomics. 2016;17:353–66.

    CAS  PubMed  Google Scholar 

  30. Tsherniak A, Vazquez F, Montgomery PG, Weir BA, Kryukov G, Cowley GS, et al. Defining a cancer dependency map. Cell. 2017;170:564–76 e516.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Stern HM, Padilla M, Wagner K, Amler L, Ashkenazi A. Development of immunohistochemistry assays to assess GALNT14 and FUT3/6 in clinical trials of dulanermin and drozitumab. Clin Cancer Res (Valid Stud). 2010;16:1587–96.

    CAS  Google Scholar 

  32. Gross BJ, Swoboda JG, Walker S. A strategy to discover inhibitors of O-linked glycosylation. J Am Chem Soc. 2008;130:440–1.

    CAS  PubMed  Google Scholar 

  33. Hang HC, Yu C, Ten Hagen KG, Tian E, Winans KA, Tabak LA, et al. Small molecule inhibitors of mucin-type O-linked glycosylation from a uridine-based library. Chem Biol. 2004;11:337–45.

    CAS  PubMed  Google Scholar 

  34. Lee JS, Park JR, Kwon OS, Lee TH, Nakano I, Miyoshi H, et al. SIRT1 is required for oncogenic transformation of neural stem cells and for the survival of “cancer cells with neural stemness” in a p53-dependent manner. Neuro Oncol. 2015;17:95–106.

    CAS  PubMed  Google Scholar 

  35. Gandolfi S, Laubach JP, Hideshima T, Chauhan D, Anderson KC, Richardson PG. The proteasome and proteasome inhibitors in multiple myeloma. Cancer Metastasis Rev. 2017;36:561–84.

    CAS  PubMed  Google Scholar 

  36. Maggiora G, Vogt M, Stumpfe D, Bajorath J. Molecular similarity in medicinal chemistry. J Med Chem. 2014;57:3186–204.

    CAS  PubMed  Google Scholar 

  37. Groll M, Berkers CR, Ploegh HL, Ovaa H. Crystal structure of the boronic acid-based proteasome inhibitor bortezomib in complex with the yeast 20S proteasome. Structure. 2006;14:451–6.

    CAS  PubMed  Google Scholar 

  38. Massague J. How cells read TGF-beta signals. Nat Rev Mol Cell Biol (Rev). 2000;1:169–78.

    CAS  Google Scholar 

  39. Wakefield LM, Roberts AB. TGF-beta signaling: positive and negative effects on tumorigenesis. Curr Opin Genet Dev. 2002;12:22–9.

    CAS  PubMed  Google Scholar 

  40. Padua D, Massague J. Roles of TGFbeta in metastasis. Cell Res. 2009;19:89–102.

    CAS  PubMed  Google Scholar 

  41. Levy L, Hill CS. Smad4 dependency defines two classes of transforming growth factor {beta} (TGF-{beta}) target genes and distinguishes TGF-{beta}-induced epithelial-mesenchymal transition from its antiproliferative and migratory responses. Mol Cell Biol. 2005;25:8108–25.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Li AM, Tian AX, Zhang RX, Ge J, Sun X, Cao XC. Protocadherin-7 induces bone metastasis of breast cancer. Biochem Biophys Res Commun. 2013;436:486–90.

    CAS  PubMed  Google Scholar 

  43. Moon YW, Rao G, Kim JJ, Shim HS, Park KS, An SS, et al. LAMC2 enhances the metastatic potential of lung adenocarcinoma. Cell Death Differ (Res Support, N. I H, Extramural). 2015;22:1341–52.

    CAS  Google Scholar 

  44. Zhou X, Updegraff BL, Guo Y, Peyton M, Girard L, Larsen JE, et al. Dissecting the Role of PCDH7, an Oncogenic Cell Surface Receptor, in Non–Small Cell Lung Cancer. J Thorac Oncol. 2017;12:S1542.

    Google Scholar 

  45. Huang D, Du C, Ji D, Xi J, Gu J. Overexpression of LAMC2 predicts poor prognosis in colorectal cancer patients and promotes cancer cell proliferation, migration, and invasion. Tumour Biol. 2017;39:1010428317705849.

    PubMed  Google Scholar 

  46. Gupta GP, Massague J. Cancer metastasis: building a framework. Cell. 2006;127:679–95.

    CAS  PubMed  Google Scholar 

  47. Jahchan NS, Dudley JT, Mazur PK, Flores N, Yang D, Palmerton A, et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Disco. 2013;3:1364–77.

    CAS  Google Scholar 

  48. Zeniya M, Mori T, Yui N, Nomura N, Mandai S, Isobe K, et al. The proteasome inhibitor bortezomib attenuates renal fibrosis in mice via the suppression of TGF-beta1. Sci Rep. 2017;7:13086.

    PubMed  PubMed Central  Google Scholar 

  49. Chang TP, Poltoratsky V, Vancurova I. Bortezomib inhibits expression of TGF-beta1, IL-10, and CXCR4, resulting in decreased survival and migration of cutaneous T cell lymphoma cells. J Immunol. 2015;194:2942–53.

    CAS  PubMed  Google Scholar 

  50. Padua D, Zhang XH, Wang Q, Nadal C, Gerald WL, Gomis RR, et al. TGFbeta primes breast tumors for lung metastasis seeding through angiopoietin-like 4. Cell. 2008;133:66–77.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Wagle MC, Kirouac D, Klijn C, Liu B, Mahajan S, Junttila M, et al. A transcriptional MAPK Pathway Activity Score (MPAS) is a clinically relevant biomarker in multiple cancer types. Npj Precis Oncol. 2018;2:7.

  52. Argyriou AA, Iconomou G, Kalofonos HP. Bortezomib-induced peripheral neuropathy in multiple myeloma: a comprehensive review of the literature. Blood. 2008;112:1593–9.

    CAS  PubMed  Google Scholar 

  53. Oprea TI, Overington JP. Computational and practical aspects of drug repositioning. Assay Drug Dev Technol. 2015;13:299–306.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Disco Today. 2013;18:350–7.

    CAS  Google Scholar 

Download references

Acknowledgements

We appreciate Jeong-Hwan Kim, Seon-Young Kim and Dong-Uk Kim at Korea Research Institute of Bioscience and Biotechnology (KRIBB) for helpful discussions. This work was supported by a grant from the National Research Foundation of Korea (NRF-2017M3C9A5028691 from HJC, NRF-2019R1C1C1008710 from OSK and NRF- 2017M3C9A5028690 from WK).

Author information

Author notes

  1. Ok-Seon Kwon

    Present address: Stem Cell Convergence Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea

  2. Haeseung Lee

    Present address: Intellectual Information Team, Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, 34054, Republic of Korea

  3. These authors contributed equally: Ok-Seon Kwon, Haeseung Lee

Authors and Affiliations

  1. College of Pharmacy, Seoul National University, Seoul, 08826, Republic of Korea

    Ok-Seon Kwon, Eun-Ji Kwon, Ji Eun Park, Wooin Lee & Hyuk-Jin Cha

  2. Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, 08826, Republic of Korea

    Ok-Seon Kwon & Hyuk-Jin Cha

  3. Department of Life Sciences, College of Natural Science, Ewha Womans University, Seoul, 03760, Republic of Korea

    Haeseung Lee & Wankyu Kim

  4. Department of Life Sciences, College of Natural Sciences, Sogang University, Seoul, 04107, Republic of Korea

    Hyeon-Joon Kong

  5. KaiPharm, Ewha Womans University, Seoul, 03760, Republic of Korea

    Seungmin Kang

  6. Personalized Genomic Medicine Research Center, Korea Research Institute of Bioscience and Biotechnology (KRIBB), Daejeon, 34141, Republic of Korea

    Mirang Kim

Authors
  1. Ok-Seon Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haeseung Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyeon-Joon Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eun-Ji Kwon
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ji Eun Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Wooin Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Seungmin Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mirang Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wankyu Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hyuk-Jin Cha
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HJC and WK conceived the overall study design and led the experiments. OSK and HL mainly conducted the experiments, data analysis, and critical discussion of the results. HJK, JEP, EJK and WL conducted the mouse xenograft experiments. SK, JHK and MK generated and analyzed RNAseq data. All authors contributed to paper writing and revising, and endorsed the final paper.

Corresponding authors

Correspondence to Wankyu Kim or Hyuk-Jin Cha.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kwon, OS., Lee, H., Kong, HJ. et al. Connectivity map-based drug repositioning of bortezomib to reverse the metastatic effect of GALNT14 in lung cancer. Oncogene 39, 4567–4580 (2020). https://doi.org/10.1038/s41388-020-1316-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-020-1316-2

This article is cited by

Search

Advanced search

Quick links