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Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells

Nature Medicine (2024)Cite this article

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Abstract

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. In this study, we investigated functional drivers of post-treatment recurrent GBM through integrative genomic analyses, genome-wide genetic perturbation screens in patient-derived GBM models and independent lines of validation. Specific genetic dependencies were found consistent across recurrent tumor models, accompanied by increased mutational burden and differential transcript and protein expression compared to its primary GBM predecessor. Our observations suggest a multi-layered genetic response to drive tumor recurrence and implicate PTP4A2 (protein tyrosine phosphatase 4A2) as a modulator of self-renewal, proliferation and tumorigenicity in recurrent GBM. Genetic perturbation or small-molecule inhibition of PTP4A2 acts through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and its downstream molecular players, exploiting a functional dependency on ROBO signaling. Because a pan-PTP4A inhibitor was limited by poor penetrance across the blood–brain barrier in vivo, we engineered a second-generation chimeric antigen receptor (CAR) T cell therapy against ROBO1, a cell surface receptor enriched across recurrent GBM specimens. A single dose of ROBO1-targeted CAR T cells doubled median survival in cell-line-derived xenograft (CDX) models of recurrent GBM. Moreover, in CDX models of adult lung-to-brain metastases and pediatric relapsed medulloblastoma, ROBO1 CAR T cells eradicated tumors in 50–100% of mice. Our study identifies a promising multi-targetable PTP4A–ROBO1 signaling axis that drives tumorigenicity in recurrent GBM, with potential in other malignant brain tumors.

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Fig. 1: Genetic vulnerabilities in rGBM.
Fig. 2: PTP4A2 vulnerability is therapeutically actionable in rGBM.
Fig. 3: A PTP4A–ROBO1 dephosphorylation axis drives rGBM.
Fig. 4: ROBO1 CAR T cells exhibit specific and potent anti-tumor response.
Fig. 5: ROBO1 CAR T cells mount potent anti-tumor response in rGBM.
Fig. 6: ROBO1 CAR T cells elicit potent anti-tumor activity in adult lung-to-brain metastases and pediatric medulloblastoma.

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Data availability

All processed data are included in the manuscript and supplementary materials. Raw CRISPR–Cas9 screening data (Gene Expression Omnibus (GEO) GSE240418), bulk RNA sequencing data (GEO GSE240490), scRNA-seq sequencing data (figshare, https://doi.org/10.6084/m9.figshare.25917628)83, bulk proteomics (MassIVE MSV000094876) and phospho-proteomics (figshare, https://doi.org/10.6084/m9.figshare.26042632)84 are available online. Source data are provided with this paper.

Code availability

No custom code was developed in this study.

References

  1. DeVita, V. T. Jr. The evolution of therapeutic research in cancer. N. Engl. J. Med. 298, 907–910 (1978).

    Article  PubMed  Google Scholar 

  2. Ostrom, Q. T. et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2009–2013. Neuro Oncol. 18, v1–v75 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Lapointe, S., Perry, A. & Butowski, N. A. Primary brain tumours in adults. Lancet 392, 432–446 (2018).

    Article  PubMed  Google Scholar 

  4. 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 

  5. Stupp, R. et al. Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol. 10, 459–466 (2009).

    Article  CAS  PubMed  Google Scholar 

  6. Toledo, C. M. et al. Genome-wide CRISPR–Cas9 screens reveal loss of redundancy between PKMYT1 and WEE1 in glioblastoma stem-like cells. Cell Rep. 13, 2425–2439 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. MacLeod, G. et al. Genome-wide CRISPR–Cas9 screens expose genetic vulnerabilities and mechanisms of temozolomide sensitivity in glioblastoma stem cells. Cell Rep. 27, 971–986 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Norden, A. D. et al. A real-world claims analysis of costs and patterns of care in treated patients with glioblastoma multiforme in the United States. J. Manag. Care Spec. Pharm. 25, 428–436 (2019).

    PubMed  Google Scholar 

  9. Orzan, F. et al. Genetic evolution of glioblastoma stem-like cells from primary to recurrent tumor. Stem Cells 35, 2218–2228 (2017).

    Article  CAS  PubMed  Google Scholar 

  10. Wang, J. et al. Clonal evolution of glioblastoma under therapy. Nat. Genet. 48, 768–776 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Korber, V. et al. Evolutionary trajectories of IDHWT glioblastomas reveal a common path of early tumorigenesis instigated years ahead of initial diagnosis. Cancer Cell 35, 692–704 (2019).

    Article  PubMed  Google Scholar 

  12. Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 7, 2719–2727 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Tatari, N. et al. The proteomic landscape of glioblastoma recurrence reveals novel and targetable immunoregulatory drivers. Acta Neuropathol. 144, 1127–1142 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Meyers, R. M. et al. Computational correction of copy number effect improves specificity of CRISPR–Cas9 essentiality screens in cancer cells. Nat. Genet. 49, 1779–1784 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dempster, J. M. et al. Agreement between two large pan-cancer CRISPR–Cas9 gene dependency data sets. Nat. Commun. 10, 5817 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lenoir, W. F., Lim, T. L. & Hart, T. PICKLES: the database of pooled in-vitro CRISPR knockout library essentiality screens. Nucleic Acids Res. 46, D776–D780 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Gangemi, R. M. et al. SOX2 silencing in glioblastoma tumor-initiating cells causes stop of proliferation and loss of tumorigenicity. Stem Cells 27, 40–48 (2009).

    Article  CAS  PubMed  Google Scholar 

  19. McQueeney, K. E. et al. Targeting ovarian cancer and endothelium with an allosteric PTP4A3 phosphatase inhibitor. Oncotarget 9, 8223–8240 (2018).

    Article  PubMed  Google Scholar 

  20. Salamoun, J. M. et al. Photooxygenation of an amino-thienopyridone yields a more potent PTP4A3 inhibitor. Org. Biomol. Chem. 14, 6398–6402 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Bessette, D. C., Qiu, D. & Pallen, C. J. PRL PTPs: mediators and markers of cancer progression. Cancer Metastasis Rev. 27, 231–252 (2008).

    Article  CAS  PubMed  Google Scholar 

  22. Kobayashi, M. et al. PRL2/PTP4A2 phosphatase is important for hematopoietic stem cell self-renewal. Stem Cells 32, 1956–1967 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Dong, Y. et al. Phosphatase of regenerating liver 2 (PRL2) deficiency impairs Kit signaling and spermatogenesis. J. Biol. Chem. 289, 3799–3810 (2014).

    Article  CAS  PubMed  Google Scholar 

  24. Wong, K. et al. Signal transduction in neuronal migration: roles of GTPase activating proteins and the small GTPase Cdc42 in the Slit-Robo pathway. Cell 107, 209–221 (2001).

    Article  CAS  PubMed  Google Scholar 

  25. Hu, H. et al. Cross GTPase-activating protein (CrossGAP)/Vilse links the Roundabout receptor to Rac to regulate midline repulsion. Proc. Natl Acad. Sci. USA 102, 4613–4618 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Dallol, A. et al. Frequent epigenetic inactivation of the SLIT2 gene in gliomas. Oncogene 22, 4611–4616 (2003).

    Article  CAS  PubMed  Google Scholar 

  27. Liu, L. et al. Slit2 and Robo1 expression as biomarkers for assessing prognosis in brain glioma patients. Surg. Oncol. 25, 405–410 (2016).

    Article  PubMed  Google Scholar 

  28. Rhee, J., Buchan, T., Zukerberg, L., Lilien, J. & Balsamo, J. Cables links Robo-bound Abl kinase to N-cadherin-bound beta-catenin to mediate Slit-induced modulation of adhesion and transcription. Nat. Cell Biol. 9, 883–892 (2007).

    Article  CAS  PubMed  Google Scholar 

  29. Rhee, J. et al. Activation of the repulsive receptor Roundabout inhibits N-cadherin-mediated cell adhesion. Nat. Cell Biol. 4, 798–805 (2002).

    Article  CAS  PubMed  Google Scholar 

  30. Urwyler, O. et al. Branch-restricted localization of phosphatase Prl-1 specifies axonal synaptogenesis domains. Science 364, eaau9952 (2019).

    Article  CAS  PubMed  Google Scholar 

  31. Chokshi, C. R. et al. Advances in immunotherapy for adult glioblastoma. Cancers 13, 3400 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Brown, C. E. et al. Locoregional delivery of IL-13Rα2-targeting CAR-T cells in recurrent high-grade glioma: a phase 1 trial. Nat. Med. 30, 1001–1012 (2024).

  33. Abdelfattah, N. et al. Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target. Nat. Commun. 13, 767 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang, L. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat. Cancer 3, 1534–1552 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ravi, V. M. et al. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. Cancer Cell 40, 639–655 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. Majzner, R. G. et al. GD2-CAR T cell therapy for H3K27M-mutated diffuse midline gliomas. Nature 603, 934–941 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Goswami, S. et al. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat. Med. 26, 39–46 (2020).

    Article  CAS  PubMed  Google Scholar 

  38. Hoogstrate, Y. et al. Transcriptome analysis reveals tumor microenvironment changes in glioblastoma. Cancer Cell 41, 678–692 (2023).

    Article  CAS  PubMed  Google Scholar 

  39. Al-Dalahmah, O. et al. Re-convolving the compositional landscape of primary and recurrent glioblastoma reveals prognostic and targetable tissue states. Nat. Commun. 14, 2586 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. 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 

  42. Numan, T. et al. Non-invasively measured brain activity and radiological progression in diffuse glioma. Sci. Rep. 11, 18990 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Huang-Hobbs, E. et al. Remote neuronal activity drives glioma progression through SEMA4F. Nature 619, 844–850 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bagley, S. J. et al. Intrathecal bivalent CAR T cells targeting EGFR and IL13Rα2 in recurrent glioblastoma: phase 1 trial interim results. Nat. Med. 30, 1320–1329 (2024).

  45. Choi, B. D. et al. Intraventricular CARv3-TEAM-E T cells in recurrent glioblastoma. N. Engl. J. Med. 390, 1290–1298 (2024).

  46. Qazi, M. A. et al. Cotargeting ephrin receptor tyrosine kinases A2 and A3 in cancer stem cells reduces growth of recurrent glioblastoma. Cancer Res. 78, 5023–5037 (2018).

    Article  CAS  PubMed  Google Scholar 

  47. 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 

  48. Barthel, F. P. et al. Longitudinal molecular trajectories of diffuse glioma in adults. Nature 576, 112–120 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Tang, X. et al. Administration of B7-H3 targeted chimeric antigen receptor-T cells induce regression of glioblastoma. Signal Transduct. Target. Ther. 6, 125 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. O’Rourke, D. M. et al. A single dose of peripherally infused EGFRvIII-directed CAR T cells mediates antigen loss and induces adaptive resistance in patients with recurrent glioblastoma. Sci. Transl. Med. 9, eaaa0984 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Brown, C. E. et al. Regression of glioblastoma after chimeric antigen receptor T-cell therapy. N. Engl. J. Med. 375, 2561–2569 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bielamowicz, K. et al. Trivalent CAR T cells overcome interpatient antigenic variability in glioblastoma. Neuro Oncol. 20, 506–518 (2018).

    Article  CAS  PubMed  Google Scholar 

  53. Hart, T. & Moffat, J. BAGEL: a computational framework for identifying essential genes from pooled library screens. BMC Bioinformatics 17, 164 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Colic, M. et al. Identifying chemogenetic interactions from CRISPR screens with drugZ. Genome Med. 11, 52 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Singh, S. K. et al. Identification of human brain tumour initiating cells. Nature 432, 396–401 (2004).

    Article  CAS  PubMed  Google Scholar 

  56. Gwynne, W. D. et al. Cancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma. Cancer Cell 40, 1488–1502 (2022).

    Article  CAS  PubMed  Google Scholar 

  57. Vora, P. et al. The rational development of CD133-targeting immunotherapies for glioblastoma. Cell Stem Cell 26, 832–844.e6 (2020).

    Article  CAS  PubMed  Google Scholar 

  58. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  60. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).

    Article  PubMed  Google Scholar 

  62. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618, 598–606 (2023).

    Article  CAS  PubMed  Google Scholar 

  64. Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501.e26 (2024).

    Article  CAS  PubMed  Google Scholar 

  65. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Verhaak, R. G. et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell 17, 98–110 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Yuan, H. et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 47, D900–D908 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Zhang, Z. et al. Integrated analysis of single-cell and bulk RNA sequencing data reveals a pan-cancer stemness signature predicting immunotherapy response. Genome Med. 14, 45 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Shats, I. et al. Using a stem cell-based signature to guide therapeutic selection in cancer. Cancer Res. 71, 1772–1780 (2011).

    Article  CAS  PubMed  Google Scholar 

  71. Ben-Porath, I. et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Palmer, N. P., Schmid, P. R., Berger, B. & Kohane, I. S. A gene expression profile of stem cell pluripotentiality and differentiation is conserved across diverse solid and hematopoietic cancers. Genome Biol. 13, R71 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Teschendorff, A. E., Maity, A. K., Hu, X., Weiyan, C. & Lechner, M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data. Bioinformatics 37, 1528–1534 (2021).

    Article  CAS  PubMed  Google Scholar 

  74. Zheng, H. et al. StemSC: a cross-dataset human stemness index for single-cell samples. Stem Cell Res. Ther. 13, 115 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Cogger, K. F. et al. Glycoprotein 2 is a specific cell surface marker of human pancreatic progenitors. Nat. Commun. 8, 331 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Baral, T. N., MacKenzie, R. & Arbabi Ghahroudi, M. Single-domain antibodies and their utility. Curr. Protoc. Immunol. 103, 2.17.1–2.17.57 (2013).

    Article  PubMed  Google Scholar 

  77. Rossotti, M. A. et al. Arsenal of nanobodies shows broad-spectrum neutralization against SARS-CoV-2 variants of concern in vitro and in vivo in hamster models. Commun. Biol. 5, 933 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Durocher, Y., Perret, S. & Kamen, A. High-level and high-throughput recombinant protein production by transient transfection of suspension-growing human 293-EBNA1 cells. Nucleic Acids Res. 30, E9 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Zhang, J. et al. Transient expression and purification of chimeric heavy chain antibodies. Protein Expr. Purif. 65, 77–82 (2009).

    Article  CAS  PubMed  Google Scholar 

  80. Rossotti, M. A. et al. Camelid single-domain antibodies raised by DNA immunization are potent inhibitors of EGFR signaling. Biochem. J. 476, 39–50 (2019).

    Article  CAS  PubMed  Google Scholar 

  81. Rossotti, M. et al. Streamlined method for parallel identification of single domain antibodies to membrane receptors on whole cells. Biochim. Biophys. Acta 1850, 1397–1404 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Keren, L. et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell 174, 1373–1387.e19 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Mikolajewicz, N. snRNA-seq, primary-recurrent GBM (Mikolajewicz cohort). figshare https://doi.org/10.6084/m9.figshare.25917628 (2024).

  84. Chokshi, C. Effect of pan-PTP4A inhibition on patient-matched primary and recurrent glioblastoma models using phosphoproteomics. figshare https://doi.org/10.6084/m9.figshare.26042632 (2024).

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Acknowledgements

We thank all members of laboratories led by S. K. Singh, J. Moffat, K. A. Henry, Y. Lu, T. Kislinger, J. S. Lazo and E. R. Sharlow. We thank K. Hope and T. Graham for their guidance and support. We thank G. Hussack, H. van Faassen and D. Callaghan for their support of antibody engineering. This research was supported by a Terry Fox Research Institute Program project grant (1065) to S.K.S., J.M. and K.A.H.; a Canadian Institutes for Health Research project grant to J.M. (PJT-438323); a Brain Tumour Foundation of Canada grant to S.K.S.; and a Brain Cancer Canada grant to S.K.S. C.R.C. was supported by Mitacs Accelerate scholarships (IT15477 and IT11823) and the Cindy Lee Graham Memorial Award. S.K.S. holds a Senior Canadian Research Chair in Human Cancer Stem Cell Biology at McMaster University, and J.M. holds the GlaxoSmithKline Chair in Genetics & Genome Biology at the Hospital for Sick Children and the University of Toronto. J.S.L. is supported by grants from the National Institutes of Health National Cancer Institute (R43 CA228774 and P30 CA044579). This work is dedicated to the memory of Cindy Lee Graham, Mackenzie Rigg and all patients with brain tumors who donated tissues and cells to this research program.

Author information

Author notes

  1. These authors contributed equally: Muhammad Vaseem Shaikh, Benjamin Brakel.

Authors and Affiliations

  1. Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON, Canada

    Chirayu R. Chokshi, Benjamin Brakel, William Maich, Alisha Anand, Yujin Suk, Agata M. Kieliszek, Lina Liu, Daniel Mobilio, Nazanin Tatari, Neil Savage, Nikoo Aghaei, Shan Grewal, Anish Puri & Sheila K. Singh

  2. Centre for Discovery in Cancer Research (CDCR), McMaster University, Hamilton, ON, Canada

    Chirayu R. Chokshi, Muhammad Vaseem Shaikh, Benjamin Brakel, William Maich, Alisha Anand, Shawn C. Chafe, Kui Zhai, Yujin Suk, Agata M. Kieliszek, Petar Miletic, Zahra Alizada, Daniel Mobilio, Nazanin Tatari, Neil Savage, Nikoo Aghaei, Shan Grewal, Anish Puri, Dillon McKenna, Yu Lu, Chitra Venugopal & Sheila K. Singh

  3. Department of Surgery, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

    Muhammad Vaseem Shaikh, Shawn C. Chafe, Kui Zhai, Petar Miletic, Zahra Alizada, Dillon McKenna, Chitra Venugopal & Sheila K. Singh

  4. Human Health Therapeutics Research Centre, Life Sciences Division, National Research Council Canada, Ottawa, ON, Canada

    Martin A. Rossotti & Kevin A. Henry

  5. Donnelly Centre, University of Toronto, Toronto, ON, Canada

    David Tieu & Amy H. Y. Tong

  6. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada

    David Tieu & Jason Moffat

  7. Program for Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Toronto, ON, Canada

    Nicholas Mikolajewicz, David Chen, Katherine Chan, Kevin R. Brown & Jason Moffat

  8. McMaster Immunology Research Centre, McMaster University, Hamilton, ON, Canada

    Jamie D. McNicol

  9. Department of Medicine, McMaster University, Hamilton, ON, Canada

    Jamie D. McNicol & Minomi Subapanditha

  10. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada

    Laura Kuhlmann, Vladimir Ignatchenko & Thomas Kislinger

  11. Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA

    Joseph M. Salamoun & Peter Wipf

  12. Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada

    Jacek M. Kwiecien, John P. Provias & Jian-Qiang Lu

  13. Department of Pharmacology, Fiske Drug Discovery Laboratory, University of Virginia, Charlottesville, VA, USA

    Elizabeth R. Sharlow & John S. Lazo

  14. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada

    Thomas Kislinger

  15. Department of Biochemistry, Microbiology and Immunology, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada

    Kevin A. Henry

  16. Institute for Biomaterials and Biomedical Engineering, University of Toronto, Toronto, ON, Canada

    Jason Moffat

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  1. Chirayu R. Chokshi
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Contributions

Conceptualization: S.K.S., J.M., C.R.C. and D.T. Resources: J.P.P., J.-Q.L. and S.K.S. Methodology, investigation and validation: C.R.C., M.V.S., B.B., M.A.R., D.T., W.M., A.A., S.C.C., K.Z., Y.S., A.M.K., P.M., N.M., D.C., J.D.M., K.C., A.H.Y.T., L.K., L.L., Z.A., D. Mobilio, N.T., N.S., N.A., S.G., A.P., M.S., D. McKenna, V.I., J.M.S., J.M.K., P.W., E.R.S., J.P.P., J.-Q.L., J.S.L., K.R.B. and C.V. Software and formal analysis: C.R.C., B.B., M.V.S., D.T., M.A.R., W.M., P.M., N.M., D.C., J.D.M., M.S., V.I., Y.L., K.R.B., C.V., S.K.S. and J.M. Visualization: C.R.C., M.A.R. and N.M. Writing—original draft preparation: C.R.C. Writing—review and editing: C.R.C., M.V.S., C.V., B.B., K.R.B., J.M. and S.K.S. with input from other authors. Project administration and supervision: S.K.S. and J.M. Funding acquisition: S.K.S., J.M. and K.A.H. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Jason Moffat or Sheila K. Singh.

Ethics declarations

Competing interests

J.S.L., P.W., J.M.S. and E.R.S. are co-inventors of US patent no. 10,308,663, which describes the chemical synthesis, composition of matter and use of JMS-053 for the treatment of cancer. J.S.L., P.W. and E.R.S. are co-founders of KeViRx, Inc., which has licensed the above-mentioned intellectual property from the University of Virginia and the University of Pittsburgh for further development. All other authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Denis Migliorini and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Ulrike Harjes, in collaboration with the Nature Medicine team.

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 Quality control analysis of CRISPR- Cas9 screens in GBM models.

(a) Gene-level LFC of core essential genes (CEGs) and non- essential genes across screens in pGBM (BT594) and rGBM (BT972) cells. All data presented from n = 3 biologically independent replicates; P values are from unpaired Student’s t-test; Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (b) Precision-recall plots of BT594 and BT972 screens. (c) Number of essential genes (FDR <0.05) identified in pGBM (BT594) and rGBM (BT972) cells. (d) Venn diagram of essential genes (FDR <0.05) in pGBM (BT594) and rGBM (BT972) cells after exclusion of CEGs. (e) Gene-level LFC of CEGs and non-essential genes from CRISPR screen in BT241 rGBM cells. All data presented as means from three biologically independent replicates. P values are from unpaired Student’s t-test. Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (f) Precision-recall plots of BT241 screen.

Source data

Extended Data Fig. 2 Treatment-related conditional genetic interactions in pGBM.

(a) Schematic for identification of conditional genetic interactions (cGIs) in pGBM BT594. Tumor cells were infected with genome-wide CRISPR-Cas9 gene knockout library (TKOv3) and exposed to DMSO, sublethal radiation therapy (RT), sublethal TMZ or combination treatment (RT & TMZ) over 15-18 doublings. Illumina sequencing and subsequent analyses (gRNA fold change and Z score computation) of cellular populations at beginning and end of each screen identified cGIs. (b) Gene-level LFC of CEGs and non-essential genes across screens in pGBM BT594 cells. All data presented from n = 3 biologically independent replicates. P values are from unpaired Student’s t tests. Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (c) Precision-recall plots of BT594 screens. (d) Comparison of cGI Z scores computed by measuring gene-level LFC from screens in rGBM BT972 or drug-treated pGBM cells compared to untreated pGBM cells. Pearson correlation coefficients and P values shown for each pairwise comparison with (black) and without (grey) filtering for genes that meet FDR <0.05 in at least one screen.

Extended Data Fig. 3 Pan-PTP4A inhibition on rGBM invasion and JMS-053 permeability.

(a) rGBM (BT972, MBT289, MBT298) invasive spheroid growth following treatment with DMSO (Control) or JMS-053 (4 µM or 8 µM). Data represented as mean ± s.e.; n = 3 experimental replicates. (b) Bi-directional Caco-2 permeability assessment of JMS-053 as compared to high (metoprolol) and low (nadolol and digoxin) permeability control compounds. Low permeability: Papp(A-B) ≤ 0.500 (x 106 cm/s); Moderate permeability: 0.500 <Papp(A-B) <2.50 (x 106 cm/s); High permeability: Papp(A-B) ≥ 2.50 (x 106 cm/s); average of 4 experimental replicates. (c) Parallel artificial membrane permeability assay (PAMPA) to evaluate blood brain barrier (BBB) permeability of JMS-053 as compared to high (propranolol) and low (atenolol) permeability controls. Asterisks indicate samples below the level of detection. Low permeability: Pe <1.00 nm/s; Moderate permeability: 1.00 <Pe <10.0 nm/s; High permeability: Pe > 10.0 nm/s.; average of 4 experimental replicates. ND, not determined.

Extended Data Fig. 4 Phospho-proteomic analysis of JMS-053- treated tumor cells.

(a) Violin plot depicting the percent coefficient of variance (%CV) in phospho-peptide intensities from replicates of patient-matched pGBM (BT594) and rGBM (BT972) cells treated with JMS-053 for 5 or 30 min, as compared to JMS-038 treatment. Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (b) Phospho-peptide (PP) intensity ratios (JMS-053/JMS-038) from BT594 and BT972 cells treated with JMS-053 for 5 or 30 min, as compared to JMS-038 treatment. Data presented for phospho-peptides with ratio > 1.5 and %CV <50; number of enriched phospho-peptides indicated for each sample. Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (c, d) Venn diagrams of phospho-peptides (c) and corresponding phospho-proteins (d) enriched in JMS-053- treated tumor cells (5 or 30 min) as compared to JMS-038 treatment. A JMS-053/JMS-038 phospho-peptide intensity ratio > 1.5 is used to classify enrichment. (e) Network map of Gene Ontology (GO) gene sets enriched in differentially-phosphorylated proteins post-PTP4A inhibition in pGBM and rGBM GBM models. Top six differentially- phosphorylated proteins post-PTP4A inhibition in rGBM models are listed for each set of related gene sets. Network is designed using the Enrichment Map plugin in Cytoscape. Nodes represent enriched pathways, while edges connect related pathways.

Extended Data Fig. 5 Transcriptomic analysis of primary and rGBM post-PTP4A2 knockout.

(a) Row-normalized mRNA expression from pGBM (BT594) or pGBM (BT972) cells with PTP4A2 (2 gRNAs, A or B) or AAVS1 knockout (1 gRNA, control). Data from three independent biological replicates are shown. Rows and columns are sorted by hierarchical clustering. On bottom left, Venn diagram of differentially expressed genes is presented (DEGs; limma LFC| > 2 and adjusted P <0.05). (b) Gene Set Enrichment Analysis (GSEA) enrichment of axon guidance pathway gene expression in rGBM cells following PTP4A2 knockout, as compared to gRNA targeting AAVS1 (control). Hypergeometric test P values after FDR correction for multiple testing. ES, enrichment score. (c) Phospho-peptide levels of all members belonging to ROBO1-SRGAP-CDC42 signaling axis in rGBM following PTP4A-inhibition for 5 or 30 minutes. Identified dephosphorylation sites are indicated for each member. (d) GSEA enrichment plots of ROBO signaling pathway genes in CRISPR/Cas9 screens in rGBM models (BT241 and BT972). Hypergeometric test P values after FDR correction for multiple testing. (e) Immunoblotting of Myc tag in rGBM (BT972) cells electroporated with plasmids over expressing wildtype CDC42 and constitutively active CDC42 Q61L. Proteins linked with Myc tag. (f) Phospho-peptide levels of WNT signaling regulators APC, CTNNB1 (β-Catenin), and LBH in rGBM following PTP4A-inhibition for 5 or 30 minutes.

Extended Data Fig. 6 Development of single domain antibodies against human ROBO1.

(a) Single cycle kinetic analysis of XRo VHH (1.25-20 nM) binding to human ROBO1 extracellular domain by surface plasmon resonance. Kinetic and affinity constants are shown inset. Black line shows data and red line show fit to a 1:1 binding model. (b) Binding of XRo VHH (500 nM) and anti-ROBO1 pAb (10 nM) to human ROBO1-4, rat ROBO1 and human VEGFR2 by surface plasmon resonance. (c) SEC profile (Superdex 75 Increase 10/300 GL) of XRo VHH. (d) Flow cytometry analysis of XRo-Fc binding to transiently transfected HEK293-6E cells displaying HA-tagged human ROBO1 ECD fused to the CD4 transmembrane domain or an irrelevant protein-CD4 fusion. A PLAUR-specific VHH-Fc XUp10 served as negative control. (e) Mirrorball® microplate cytometry analysis of XRo-Fc binding to BT428 pGBM cells and ROBO1-negative MCF7 cells. The C. difficile toxin A-specific VHH-Fc A20.1 served as negative control. (f) Immunoprecipitation assay using HEK293-6E cells displaying HA-tagged ROBO1 ECD fused to the CD4 transmembrane domain compared with untransfected HEK293-6E cells. Cleared and filtered lysates from approximately 5×105 cells per lane were applied to streptavidin magnetic beads loaded with the indicated VHH. After washing, immunoprecipitates were eluted and subjected to SDS-PAGE and western blotting (WB) using HRP-conjugated anti-HA antibody. Abbreviations: R1 = ROBO1, A = A20.1 VHH (anti-C. difficile toxin A), M = MKRo20 VHH (anti-ROBO1), X = XRo VHH (anti-ROBO1), 9 = Ig09 VHH (anti-ROBO1). MKRo20 and Ig09 are human ROBO1-specific VHHs with distinct epitopes compared with XRo.

Extended Data Fig. 7 ROBO1 protein expression in GBM.

(a-b) Human tissue specimens from pGBM (n = 41) and rGBM (n = 74) were stained with anti-ROBO1 XRo VHH-Fc antibody and scored for staining intensity. Representative scoring for each tissue type (a) and score distribution by tissue type (b) are shown. (c-d) Immunohistochemical analysis of ROBO1 expression in five spatially distinct regions of a rGBM specimen (MBT352). Each sample is spatially annotated on magnetic resonance (MR) image (c) and was stained for H&E and ROBO1 (d). (e) Flow cytometric analysis of ROBO1 from tumor region derived cell lines.

Extended Data Fig. 8 Single-cell analysis of ROBO, PTP4A, and SLIT gene families in GBM.

(a) ROBO, PTP4A, and SLIT gene family expression was compared across Neftel Subtypes using scRNA-seq tumor profiles from Abdelfattah (N = 30), Wang (N = 58) and in-house (N = 17) human GBM cohorts. Significance was determined by one-way ANOVA. (b) Spearman correlations (rho) between curated gene programs and ROBO1 (x-axis) or PTP4A2 (y-axis) were evaluated intratumorally and averaged across all tumors (N = 105). Gene programs were colored by phenotypic similarity. Significance (FDR <0.05) was determined by Wilcoxon test and post-hoc Benjamini-Hochberg correction. (c) Boxplots of tumor-level ROBO, PTP4A, and SLIT gene family scRNA-seq expression across three independents human GBM cohorts [Abdelfattah (N = 30), Wang (N = 58) and in-house (N = 17)]. Gene expression profiles are compared across Neftel GBM subtypes. (d-e) Spatial correlations between ROBO, PTP4A, and SLIT gene family and IVY spatial gene signatures as evaluated using stRNA-seq data from Ravi human GBM cohort. Heatmap visualizes spatial correlation between individual genes and spatial IVY signatures (d) and representative examples are shown (e). Significant spatial correlations (FDR<0.05*, <0.01**, and <0.001***) were determined by Wilcoxon test and post-hoc Benjamini-Hochberg correction, and spatial heterogeneity was approximated by one-way ANOVA across spatial correlates. CT; cellular tumor, CTmvp; microvascular proliferation; CTpan; pseudopalisading cells around necrosis, FDR; false discovery rate, H&E; hematoxylin and eosin stain, LE; leading edge, scRNA-seq; single cell RNA sequencing, stRNA-seq; spatial transcriptome RNA sequencing.

Extended Data Fig. 9 ROBO1 expression and CAR T activity in pGBM.

(a) Proteomic profiling of rGBM tumor specimens to query protein expression of ROBO1, PTP4A2 and CAR T therapeutic targets under pre-clinical and clinical development, including HER2 (ERBB2), B7-H3, CD70, EPHA2, B4GALNT1 (GD2), MMP2 and CA9. n = 43 biological replicates. Median value at centre with lower and upper hinges at 1st (25th) and 3rd (75th) quartiles, respectively. Whiskers extend from hinge to 1.5*IQR. (b) Flow cytometric analysis of ROBO1 expression in 10 patient-derived GBM cell lines. (c) Representative flow cytometry analysis of ROBO1 CAR T expression in CD3+ human T cells. CAR T expression is assayed by truncated EGFR tag (tEGFR). (d) Percent specific lysis of ROBO1-expressing pGBM (MBT103 and GBM8) target cells co-cultured with ROBO1 CAR T or UTD T cells at various effector:target (E:T) ratios for 24 hours. Data normalized against number of viable tumor cells when cultured in absence of effector cells. Shown are means +/- s.e. of % cell killing in triplicate. Student’s t test was conducted for indicated comparisons (*P <0.05; **P <0.01; ***P <0.001; and ****P <0.0001). (e) Assessment of T cell exhaustion state (PD1+/TIM3+/LAG3+) by immunostaining co-cultures of UTD T or ROBO1 CAR T cells with ROBO1-expressing rGBM (BT241) target cells at an E:T ratio of 1:1 for 24 hours. Data represented as mean ± s.e.; n = 3 experimental replicates. (f) Flow cytometric analysis of ROBO1 expression in patient- derived lung-to-brain metastases (n = 2) and relapsed medulloblastoma (n = 2).

Extended Data Fig. 10 Analysis of tumor microenvironment and toxicity in ROBO1 CAR T treated mice.

(a) Immunohistochemistry analysis of rGBM (BT241) CDX brains post treatment with ROBO1 CAR T or UTD T cells to evaluate macrophage/microglia (IBA1+) infiltration. (b) Analysis of rGBM (BT241) CDX brains post treatment with ROBO1 CAR T or UTD T cells using multiplexed ion beam imaging by time of flight (MIBI-TOF) to evaluate vasculature (mSMA, mCD31, mVWF). (c) Brain, spleen, kidney, heart, liver and lung were collected from ROBO1 CAR T treated mice (n = 2). Organs were paraffin embedded and H&E stained. Pathological investigation revealed no signs of CAR T-related cytotoxicity. (d) Immunohistochemistry analysis of rGBM (BT241) CDX brains post treatment with ROBO1 CAR T or UTD T cells to characterize axonal guidance using neurofilament light chain (NEFL).

Supplementary information

Supplementary Table 1 and Fig. 1 with legend.

Reporting Summary

Supplementary Table 2

Mutational analysis of pGBM (BT594) and rGBM (BT972) models.

Supplementary Table 3

Differential expression analysis at transcript and protein levels in pGBM (BT594) and rGBM (BT972) models.

Supplementary Table 4

Normalized gRNA read counts for genome-wide CRISPR–Cas9 screens.

Supplementary Table 5

BF scores of gRNAs for all untreated pGBM and rGBM models.

Supplementary Table 6

z-score analysis of untreated rGBM BT972 or drug-treated pGBM BT594 as compared to untreated pGBM BT594 screens.

Supplementary Table 7

Phospho-proteomic analyses of pGBM (BT594) and rGBM (BT972) treated with JMS-038 or JMS-053.

Supplementary Table 8

Transcript-level differential expression analyses in pGBM (BT594) and rGBM (BT972) cells with knockout of AAVS1 (control) and PTP4A2.

Source data

Source Data Fig. 1

Unprocessed western blots related to Fig. 3c,d and Extended Data Fig. 5e.

Source Data Extended Data Fig./Table 1

Source data for Figs. 1–6 and Extended Data Figs. 1–6 and 9.

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Chokshi, C.R., Shaikh, M.V., Brakel, B. et al. Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells. Nat Med (2024). https://doi.org/10.1038/s41591-024-03138-9

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