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:

Functional patient-derived organoid screenings identify MCLA-158 as a therapeutic EGFR × LGR5 bispecific antibody with efficacy in epithelial tumors

Nature Cancer volume 3, pages 418–436 (2022)Cite this article

Subjects

Abstract

Patient-derived organoids (PDOs) recapitulate tumor architecture, contain cancer stem cells and have predictive value supporting personalized medicine. Here we describe a large-scale functional screen of dual-targeting bispecific antibodies (bAbs) on a heterogeneous colorectal cancer PDO biobank and paired healthy colonic mucosa samples. More than 500 therapeutic bAbs generated against Wingless-related integration site (WNT) and receptor tyrosine kinase (RTK) targets were functionally evaluated by high-content imaging to capture the complexity of PDO responses. Our drug discovery strategy resulted in the generation of MCLA-158, a bAb that specifically triggers epidermal growth factor receptor degradation in leucine-rich repeat-containing G-protein-coupled receptor 5-positive (LGR5+) cancer stem cells but shows minimal toxicity toward healthy LGR5+ colon stem cells. MCLA-158 exhibits therapeutic properties such as growth inhibition of KRAS-mutant colorectal cancers, blockade of metastasis initiation and suppression of tumor outgrowth in preclinical models for several epithelial cancer types.

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: bAb generation, PDO biobank and EGF dependency.
Fig. 2: Functional bAb screening on PDOs.
Fig. 3: Characterization of MCLA-158.
Fig. 4: Targeting of LGR5+ cells by MCLA-158.
Fig. 5: Therapeutic effects of MCLA-158 compared to cetuximab.
Fig. 6: Transcriptional changes in PDOs treated with either MCLA-158 or cetuximab.
Fig. 7: Internalization and degradation of EGFR by MCLA-158 treatment.

Similar content being viewed by others

Data availability

Organoid exome sequencing data have been deposited at European Genome–Phenome Archive (study no. EGAS00001004584). RNA-sequencing data of P18T and C55T organoids treated with antibodies are deposited at Gene Expression Omnibus (GSE186531). Microarray data of Fab072+ versus Fab072− tumor cells are deposited at Gene Expression Omnibus (GSE190543). Further information and requests for resources and reagents should be directed to the corresponding authors. All requests for raw and analyzed data and materials will be reviewed promptly by the corresponding author to verify whether the request is subject to any intellectual property or confidentiality obligations. Any data and materials that can be shared will be released via a material transfer agreement. Source data are provided with this paper.

References

  1. Merlos-Suárez, A. et al. The intestinal stem cell signature identifies colorectal cancer stem cells and predicts disease relapse. Cell Stem Cell https://doi.org/10.1016/j.stem.2011.02.020 (2011).

  2. Vermeulen, L. et al. Single-cell cloning of colon cancer stem cells reveals a multi-lineage differentiation capacity. Proc. Natl Acad. Sci. USA 105, 13427–13432 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. de Sousa e Melo, F. et al. A distinct role for Lgr5+ stem cells in primary and metastatic colon cancer. Nature 543, 676–680 (2017).

    Article  PubMed  CAS  Google Scholar 

  4. Schepers, A. G. et al. Lineage tracing reveals Lgr5+ stem cell activity in mouse intestinal adenomas. Science 337, 730–735 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Cortina, C. et al. A genome editing approach to study cancer stem cells in human tumors. EMBO Mol. Med. 9, 869–879 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shimokawa, M. et al. Visualization and targeting of LGR5+ human colon cancer stem cells. Nature 545, 187–192 (2017).

    Article  CAS  PubMed  Google Scholar 

  7. Koo, B. K. et al. Tumour suppressor RNF43 is a stem-cell E3 ligase that induces endocytosis of Wnt receptors. Nature 488, 665–669 (2012).

    Article  CAS  PubMed  Google Scholar 

  8. De Lau, W. et al. Lgr5 homologues associate with Wnt receptors and mediate R-spondin signalling. Nature 476, 293–297 (2011).

    Article  PubMed  CAS  Google Scholar 

  9. Muzny, D. M. et al. Comprehensive molecular characterization of human colon and rectal cancer. Nature 487, 330–337 (2012).

    Article  CAS  Google Scholar 

  10. Dow, L. E. et al. APC restoration promotes cellular differentiation and reestablishes crypt homeostasis in colorectal cancer. Cell 161, 1539–1552 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Buske, P. et al. A comprehensive model of the spatio-temporal stem cell and tissue organisation in the intestinal crypt. PLoS Comput. Biol. 7, e1001045 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Wong, V. W. Y. et al. Lrig1 controls intestinal stem-cell homeostasis by negative regulation of ErbB signalling. Nat. Cell Biol. 14, 401–408 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Karapetis, C. S. et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N. Engl. J. Med. 359, 1757–1765 (2008).

    Article  CAS  PubMed  Google Scholar 

  14. Douillard, J.-Y. et al. Panitumumab-FOLFOX4 treatment and RAS mutations in colorectal cancer. N. Engl. J. Med. 369, 1023–1034 (2013).

    Article  CAS  PubMed  Google Scholar 

  15. Van Cutsem, E. et al. Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer. J. Clin. Oncol. 33, 692–700 (2015).

    Article  PubMed  CAS  Google Scholar 

  16. Peeters, M. et al. Massively parallel tumor multigene sequencing to evaluate response to panitumumab in a randomized phase III study of metastatic colorectal cancer. Clin. Cancer Res. 19, 1902–1912 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Yaeger, R. et al. Clinical sequencing defines the genomic landscape of metastatic colorectal cancer. Cancer Cell 33, 125–136 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Montagut, C. et al. Identification of a mutation in the extracellular domain of the epidermal growth factor receptor conferring cetuximab resistance in colorectal cancer. Nat. Med. 18, 221–223 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Sato, T. et al. Long-term expansion of epithelial organoids from human colon, adenoma, adenocarcinoma, and Barrett’s epithelium. Gastroenterology 141, 1762–1772 (2011).

    Article  CAS  PubMed  Google Scholar 

  20. van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Fujii, M. et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor requirements during tumorigenesis. Cell Stem Cell 18, 827–838 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Caw, G. et al. Unbiased combinatorial screening identifies a bispecific IgG1 that potently inhibits HER3 signaling via HER2-guided ligand blockade. Cancer Cell https://doi.org/10.1016/j.ccell.2018.04.003 (2018).

  23. Sachs, N. et al. A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172, 373–386 (2018).

    Article  CAS  PubMed  Google Scholar 

  24. Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

    Article  CAS  PubMed  Google Scholar 

  25. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Seshagiri, S. et al. Recurrent R-spondin fusions in colon cancer. Nature 488, 660–664 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Giannakis, M. et al. RNF43 is frequently mutated in colorectal and endometrial cancers. Nat. Genet. 46, 1264–1266 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Picco, G. et al. Loss of AXIN1 drives acquired resistance to WNT pathway blockade in colorectal cancer cells carrying RSPO 3 fusions. EMBO Mol. Med. 9, 293–303 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. De Nardis, C. et al. A new approach for generating bispecific antibodies based on a common light chain format and the stable architecture of human immunoglobulin G1. J. Biol. Chem. 292, 14706–14717 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  30. Davidson, E. & Doranz, B. J. A high-throughput shotgun mutagenesis approach to mapping B-cell antibody epitopes. Immunology 143, 13–20 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Koefoed, K. et al. Rational identification of an optimal antibody mixture for targeting the epidermal growth factor receptor. MAbs 3, 584–595 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Gulli, L. F., Palmer, K. C., Chen, Y. Q. & Reddy, K. B. Epidermal growth factor-induced apoptosis in A431 cells can be reversed by reducing the tyrosine kinase activity. Cell Growth Differ. 7, 173–178 (1996).

    CAS  PubMed  Google Scholar 

  33. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Sato, T. et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Vermeulen, L. et al. Wnt activity defines colon cancer stem cells and is regulated by the microenvironment. Nat. Cell Biol. 12, 468–476 (2010).

    Article  CAS  PubMed  Google Scholar 

  36. Todaro, M. et al. CD44v6 is a marker of constitutive and reprogrammed cancer stem cells driving colon cancer metastasis. Cell Stem Cell 14, 342–356 (2014).

    Article  CAS  PubMed  Google Scholar 

  37. Jung, P. et al. Isolation and in vitro expansion of human colonic stem cells. Nat. Med. https://doi.org/10.1038/nm.2470 (2011).

  38. Hofheinz, R. D., Segaert, S., Safont, M. J., Demonty, G. & Prenen, H. Management of adverse events during treatment of gastrointestinal cancers with epidermal growth factor inhibitors. Crit. Rev. Oncol. Hematol. 114, 102–113 (2017).

    Article  PubMed  Google Scholar 

  39. Lupo, B. et al. Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aax8313 (2020).

  40. He, T. C. et al. Identification of c-MYC as a target of the APC pathway. Science 281, 1509–1512 (1998).

    Article  CAS  PubMed  Google Scholar 

  41. van de Wetering, M. et al. The β-catenin/TCF-4 complex imposes a crypt progenitor phenotype on colorectal cancer cells. Cell 111, 241–250 (2002).

    Article  PubMed  Google Scholar 

  42. Sansom, O. J. et al. Myc deletion rescues APC deficiency in the small intestine. Nature 446, 676–679 (2007).

    Article  CAS  PubMed  Google Scholar 

  43. Snyder, J. C., Rochelle, L. K., Lyerly, H. K., Caron, M. G. & Barak, L. S. Constitutive internalization of the leucine-rich G protein-coupled receptor-5 (LGR5) to the trans-Golgi network. J. Biol. Chem. 288, 10286–10297 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26 (2020).

    Article  CAS  PubMed  Google Scholar 

  46. Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat. Med. 25, 1607–1614 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ooft, S. N. et al. Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Sci. Transl. Med. https://doi.org/10.1126/scitranslmed.aay2574 (2019).

  48. Drost, J. & Clevers, H. Organoids in cancer research. Nat. Rev. Cancer 18, 407–418 (2018).

    Article  CAS  PubMed  Google Scholar 

  49. Shaib, W., Mahajan, R. & El-Rayes, B. Markers of resistance to anti-EGFR therapy in colorectal cancer. J. Gastrointest. Oncol. 4, 308–318 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Woolston, A. et al. Genomic and transcriptomic determinants of therapy resistance and immune landscape evolution during anti-EGFR treatment in colorectal cancer. Cancer Cell 36, 35–50 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hobor, S. et al. TGF-α and amphiregulin paracrine network promotes resistance to EGFR blockade in colorectal cancer cells. Clin. Cancer Res. 20, 6429–6438 (2014).

    Article  CAS  PubMed  Google Scholar 

  53. Dienstmann, R. et al. Safety and activity of the first-in-class Sym004 anti-EGFR antibody mixture in patients with refractory colorectal cancer. Cancer Discov. 5, 598–609 (2015).

    Article  CAS  PubMed  Google Scholar 

  54. Kearns, J. D. et al. Enhanced targeting of the EGFR network with MM-151, an oligoclonal anti-EGFRantibody therapeutic. Mol. Cancer Ther. 14, 1625–1636 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Spangler, J. B. et al. Combination antibody treatment down-regulates epidermal growth factor receptor by inhibiting endosomal recycling. Proc. Natl Acad. Sci. USA 107, 13252–13257 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Montagut, C. et al. Efficacy of Sym004 in patients with metastatic colorectal cancer with acquired resistance to anti-EGFR therapy and molecularly selected by circulating tumor DNA analyses: a phase 2 randomized clinical trial. JAMA Oncol. 4, e175245 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Malliri, A. et al. The rac activator Tiam1 is a Wnt-responsive gene that modifies intestinal tumor development. J. Biol. Chem. 281, 543–548 (2006).

    Article  CAS  PubMed  Google Scholar 

  58. Baker, A.-M., Graham, T. A., Elia, G., Wright, N. A. & Rodriguez-Justo, M. Characterization of LGR5 stem cells in colorectal adenomas and carcinomas. Sci Rep. 5, 8654 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Uchida, H. et al. Overexpression of leucine-rich repeat-containing G protein-coupled receptor 5 in colorectal cancer. Cancer Sci. 101, 1731–1737 (2010).

    Article  CAS  PubMed  Google Scholar 

  60. Cochran, J. R., Kim, Y.-S., Olsen, M. J., Bhandari, R. & Wittrup, K. D. Domain-level antibody epitope mapping through yeast surface display of epidermal growth factor receptor fragments. J. Immunol. Methods 287, 147–158 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. van Uhm, J. I. et al. The ultimate radiochemical nightmare: upon radio-iodination of Botulinum neurotoxin A, the introduced iodine atom itself seems to be fatal for the bioactivity of this macromolecule. EJNMMI Res. 5, 5 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Lindmo, T., Boven, E., Cuttitta, F., Fedorko, J. & Bunn, P. A. Determination of the immunoreactive fraction of radiolabeled monoclonal antibodies by linear extrapolation to binding at infinite antigen excess. J. Immunol. Methods 72, 77–89 (1984).

    Article  CAS  PubMed  Google Scholar 

  63. Raine, K. M. et al. cgpPindel: identifying somatically acquired insertion and deletion events from paired end sequencing. Curr. Protoc. Bioinformatics 52, 15.7.1–15.7.12 (2015).

    Article  Google Scholar 

  64. Di, Z. et al. Ultra high content image analysis and phenotype profiling of 3D cultured micro-tissues. PLoS ONE 9, e109688 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. AM, S. et al. Identification of anti-tumour biologics using primary tumour models, 3D phenotypic screening and image-based multi-parametric profiling. Mol. Cancer https://doi.org/10.1186/s12943-015-0415-0 (2015).

  66. Birmingham, A. et al. Statistical methods for analysis of high-throughput RNA interference screens. Nat. Methods 6, 569–575 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Morral, C. et al. Zonation of ribosomal DNA transcription defines a stem cell hierarchy in colorectal cancer. Cell Stem Cell 26, 845–861 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Rashidi, B. et al. An orthotopic mouse model of remetastasis of human colon cancer liver metastasis. Clin. Cancer Res. 6, 2556–2561 (2000).

    CAS  PubMed  Google Scholar 

  69. Muñoz, J. et al. The Lgr5 intestinal stem cell signature: robust expression of proposed quiescent ‘+4’ cell markers. EMBO J. 31, 3079–3091 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

We thank all patients for donating materials to the organoid biobank and all employees of U-PORT UMC Utrecht, as well as O. Kranenburg at UMC Utrecht and E. Wink van Gestel and N. van Scharrenburg at Meander Medisch Centrum for their assistance with patient inclusion and tissue acquisition. We thank J. Blokker, R. Korporaal and T. Mehraban for their contributions to building the CRC organoid biobank. We also thank R. Fong from Integral Molecular for performing the alanine scanning; L. Kaldenberg for graphically displaying the structural models; H. van der Maaden and W. Bartelink for technical assistance; M. Sevillano, A. Berenguer and staff at IRB facilities for excellent support with flow cytometry, functional genomics and histopathology. This study was funded by the European Union under the Seventh Framework Programme (FP7-HEALTH-2013-INNOVATION-2, SUPPRESSTEM, grant agreement no. 601876). IRB Barcelona is the recipient of a Severo Ochoa Award of Excellence from the Spanish Ministry of Economy and Competitiveness and E.B. received support from AGAUR 2017-SGR-698 (Generalitat de Catalunya). A.V. was supported by grant from the Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III FEDER (PI19/01320). A.C.-S. held an FPU predoctoral fellowship from the Spanish Ministry of Economy and Competitiveness.

Author information

Authors and Affiliations

  1. OcellO BV, Leiden, The Netherlands

    Bram Herpers, Jara García Mateos, Kuan Yan, Lucia Salinaro & Leo S. Price

  2. Crown Bioscience Netherlands BV, Leiden, The Netherlands

    Bram Herpers, Jara García Mateos, Kuan Yan & Leo S. Price

  3. Merus NV, Utrecht, The Netherlands

    Berina Eppink, Rob C. Roovers, Carina Bartelink-Clements, Vanessa Zondag-van der Zande, Abdul Basmeleh, Szabolcs Fatrai, David Maussang, Jeroen J. Lammerts van Bueren, John de Kruif, Ton Logtenberg & Mark Throsby

  4. Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain

    Mark I. James, Carme Cortina, Adrià Cañellas-Socias, Xavier Hernando-Momblona, Camille Stephan-Otto Attolini, Elena Sancho & Eduard Batlle

  5. CIBERONC, Madrid, Spain

    Carme Cortina, Adrià Cañellas-Socias, Xavier Hernando-Momblona, Elena Sancho, Hector G. Palmer, Josep Tabernero & Eduard Batlle

  6. Hubrecht Organoid Technology (HUB), Utrecht, the Netherlands

    Sylvia F. Boj & Robert G. J. Vries

  7. Wellcome Sanger Institute, Hinxton, UK

    Dominik Glodzik & Michael R. Stratton

  8. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Dominik Glodzik

  9. Princess Maxima Center for Pediatric Oncology, Utrecht, The Netherlands

    Marc van de Wetering & Hans Clevers

  10. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences (KNAW) and University Medical Center Utrecht, Utrecht, the Netherlands

    Marc van de Wetering & Hans Clevers

  11. Oncode Institute, Hubrecht Institute, Utrecht, the Netherlands

    Marc van de Wetering & Hans Clevers

  12. Vall d’Hebron Institute of Oncology (VHIO), Barcelona, Spain

    Irene Chicote, Garazi Serna, Laia Cabellos, Lorena Ramírez, Paolo Nuciforo, Hector G. Palmer & Josep Tabernero

  13. Medical Oncology Department, Vall d’Hebron University Hospital (HUVH), Barcelona, Spain

    Laia Cabellos, Lorena Ramírez, Hector G. Palmer & Josep Tabernero

  14. Department of Medical Oncology, Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL)-CIBERONC, L’Hospitalet de Llobregat, Barcelona, Spain

    Ramon Salazar & Cristina Santos

  15. Chemoresistance and Predictive Factors Group, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Oncobell Program, Bellvitge Biomedical Research Institute (IDIBELL), L’Hospitalet del Llobregat, Barcelona, Spain

    Alberto Villanueva

  16. Xenopat SL, Parc Cientific de Barcelona (PCB), Barcelona, Spain

    Alberto Villanueva

  17. Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain

    Eduard Batlle

Authors
  1. Bram Herpers
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Berina Eppink
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark I. James
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Carme Cortina
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Adrià Cañellas-Socias
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sylvia F. Boj
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xavier Hernando-Momblona
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Dominik Glodzik
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rob C. Roovers
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Marc van de Wetering
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Vanessa Zondag-van der Zande
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jara García Mateos
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kuan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Lucia Salinaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Abdul Basmeleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Szabolcs Fatrai
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. David Maussang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jeroen J. Lammerts van Bueren
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Irene Chicote
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Garazi Serna
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Laia Cabellos
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Lorena Ramírez
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Paolo Nuciforo
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Ramon Salazar
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Cristina Santos
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Alberto Villanueva
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Camille Stephan-Otto Attolini
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Elena Sancho
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Hector G. Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Josep Tabernero
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Michael R. Stratton
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. John de Kruif
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Ton Logtenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Hans Clevers
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Leo S. Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Robert G. J. Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Eduard Batlle
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Mark Throsby
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.T., E.B., T.L., H.C. and R.G.J.V. conceived and designed the study. B.H., J.G.M., K.Y., L.S. and L.S.P. performed antibody screens and characterized mechanisms of action. B.E., C.B.-C., V.Z.-v.d.Z., R.C.R., A.B., D.M., J.d.K., T.L. and M.T. generated the bispecific antibody panel. M.I.J., A.C.-S., C.C., S.F., J.J.L.v.B., X.H.-M., E.S. and E.B. characterized the MCLA-158 mode of action and performed xenograft experiments. D.G. and M.R.S. performed genomic analyses of PDOs. C.S.-O.A. performed statistical analysis. L.C., L.R. and P.N. performed in situ hybridization for LGR5. S.F.B., M.v.d.W., R.G.J.V. and H.C. generated the organoid biobank. H.G.P., J.T., I.C., G.S., R.S., C.S. and A.V. performed and analyzed antibody effects on orthotopic PDXs. E.B. and M.T. coordinated the study and wrote the manuscript.

Corresponding authors

Correspondence to Eduard Batlle or Mark Throsby.

Ethics declarations

Competing interests

S.F.B. and R.G.J.V. are employed by the Foundation Hubrecht Organoid Technology, which holds the exclusive license to the Organoid Technology. R.G.J.V. and H.C. are inventors on patents for Organoid Technology. B.E., R.C.R., C.B.-C., V.Z.-v.d.Z., A.B., S.F., J.J.L.v.B., J.d.K., T.L. and M.T. are employees of Merus NV. B.H., J.G.M., K.Y. and L.S.P. are employees of Crown Bioscience Netherlands BV. M.T., J.d.K., T.L., H.C., R.G.J.V., E.B. and B.H. are inventors on intellectual property related to this work. J.T. is paid advisor and H.C. has been paid advisor to Merus NV. H.C. is non-executive Board of Directors-member of Roche, Basel. The other authors declare no competing interests.

Peer review

Peer review information

Nature Cancer thanks Byoung Chul Cho, Livio Trusolino and Ömer Yilmaz 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 Genomic alteration of PDOs.

a. Frequency of mutations in common driver genes among the PDO biobank and the TCGA CRC dataset9. b. Graphs indicate % of PDOs in the biobank with amplifications, deletions and loss of heterozygosity segments across chromosome regions. Upper side of the graphs correspond to PDO biobank and bottom part to the TCGA CRC dataset9. c. Number of single base substitutions in each PDO. d. Number of small indels in each PDO. e. Frequencies of mutational signatures25 in each PDO.

Source data

Extended Data Fig. 2 Molecular characterization of MCLA-158.

a. Examples of KRAS mutant PDOs cultured without EGF or with EGF (5 ng/mL) from the experiment shown in Fig. 1d. DNA is labeled in blue using Hoechst, and actin in red using phalloidin. The pictures are taken from day 7 cultures. Scale bars are 200 μm. b. Examples of KRAS wild-type PDOs cultured without EGF or with EGF (5 ng/mL) from the experiment in Fig. 1e. Scale bars are 200 μm. c. Alanine scanning epitope mapping of MCLA-158 on EGFR. For each clone, the mean binding value with Ab MCLA-158 is plotted as a function of the clone’s mean EGFR expression value (gray circles). Critical residues contributing to the binding of MCLA-158 to EGFR are depicted in blue. d. Alanine scanning epitope mapping of MCLA-158 on LGR5. For each clone, the mean binding value with Ab MCLA-158 is plotted as a function of the clone’s mean LGR5 expression value (gray circles). Critical residues contributing to the binding of MCLA-158 to LGR5 are depicted in blue. e. Flow cytometry histograms of representative P18T PDOs stained with Fab072 bivalent antibody or control TT antibody. Gates used to isolate Fab072+ and Fab072- cells for subsequent experiments are indicated. f. LGR5 mRNA levels by RT-qPCR on FACS-purified cell populations. Mean of n=3 technical replicates of one representative PDO +/- SD. g. Flow cytometry analysis of PDO biobank samples using Fab072 or Fab266 (irrelevant TT control) antibodies. Mean fluorescent intensity (MFI) values are shown.

Source data

Extended Data Fig. 3 Effects of MCLA-158 on PDOs.

a. Representative Ki67 IHC staining of the quantifications shown in Fig.4b. Lower panels are magnifications. Bar in upper panel is 10 μm and in lower panels is 30 μm. b. Representative cell cycle plots of P18T PDOs treated with the indicated antibodies at a concentration of 2 μg ml-1. % of cells in each cell cycle phase are indicated. c. Nucleus size in PDO P18T cultures treated with indicated antibodies. Each dot indicates average nucleus area (pixels) of all organoids in an independent section (n=2 for cetuximab, n=7 for MCLA-158, 4 for EGFRxTT and 5 for TTxTT). Line labels the mean. Two-tailed Wald test of a linear model. d. P18T cultures were treated with the indicated antibodies for 4 days at a concentration of 20 μg ml-1 and then antibodies were washed away (arrow). Organoid size was monitored and y-axis shows growth relative to the day the treatment started. Each data point is mean +/- standard error (n=3 independent wells). Two-tailed unpaired t-test. e. MCLA-158+ cell distributions analyzed by flow cytometry in parental non-infected, shControl and shLGR5 P18T PDO culture. As a negative control we stained with TT-TT Ab. Cell frequencies were normalized to the mode of every sample.

Source data

Extended Data Fig. 4 Effects of MCLA-158 in PDX and orthotopic xenografts.

a. Kaplan-Meier plot displaying mice survival for the experiment shown in Fig. 4j. Long-rank P value between MCLA-158 and cetuximab using Log-rank Mantel-Cox test. b. Volume of individual P18T xenografts in experiment shown in Fig. 4j. c. Mice bearing PDO C31M-derived subcutaneous xenografts were treated with indicated antibodies or PBS. Tumor volumes relative to day 1 of treatment are shown. Each data point is mean of tumor volumes +/- SEM. n=21 tumors for CET; n=21 for MCLA-158 and n=17 for vehicle at day=0. Two-tailed unpaired t-test. d-f. Organ-specific metastases for the experiment shown in Fig. 5j,k. g. LGR5 mRNA (density) analyzed in tissue sections by RNAscope on peritoneal metastases found in M005 model.

Source data

Extended Data Fig. 5 Subcutaneous CRC PDX growth corresponding to experiment in Fig. 5e.

Panels show growth of individual mice. Two-tailed unpaired t-test was used to calculate p values.

Source data

Extended Data Fig. 6 Localization of MCLA-158 in normal colon mucosa and CRC-derived organoids.

a. Representative confocal images of P18T and C0M PDOs treated with MCLA-158, or with EGFR (Fab232) or LGR5 (Fab072) combined with a control TT arm. PDOs were treated for 24 hours. Antibody localization was detected by immunofluorescence. Arrows indicate localization of antibodies (green) at basolateral membranes. Arrowheads indicate localization of antibodies in the cytoplasm. Red staining is actin labeled with phalloidin. Nuclei (blue) are stained with Hoechst. Scale bar is 50 μm. b. Representative confocal images of normal colon mucosa (C82N and C110N) and CRC (C55T and C47T) PDOs treated with MCLA-158 for 24 hours. Antibodies were added at 1μg ml-1. MCLA-158 (red) and EGFR (green) localization was detected by immunofluorescence. Arrows indicate localization of MCLA-158 at basolateral membranes. Arrowheads indicate localization of MCLA-158 in intracellular structures in the cytoplasm. Scale bar is 100 μm. c. Virtual image of a capillary western blot measuring EGFR levels on P18T, C55T or C82N (normal mucosa PDO) protein extracts. Antibodies were added at 1 μg ml-1 for the indicated time points.

Source data

Supplementary information

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. 3

Unprocessed gels.

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 Fig. 7

Unprocessed western blots.

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 Extended Data Fig. 6

Unprocessed western blots.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Herpers, B., Eppink, B., James, M.I. et al. Functional patient-derived organoid screenings identify MCLA-158 as a therapeutic EGFR × LGR5 bispecific antibody with efficacy in epithelial tumors. Nat Cancer 3, 418–436 (2022). https://doi.org/10.1038/s43018-022-00359-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43018-022-00359-0

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