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Combinatorial treatment rescues tumour-microenvironment-mediated attenuation of MALT1 inhibitors in B-cell lymphomas

Nature Materials volume 22, pages 511–523 (2023)Cite this article

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Abstract

Activated B-cell-like diffuse large B-cell lymphomas (ABC-DLBCLs) are characterized by constitutive activation of nuclear factor κB driven by the B-cell receptor (BCR) and Toll-like receptor (TLR) pathways. However, BCR-pathway-targeted therapies have limited impact on DLBCLs. Here we used >1,100 DLBCL patient samples to determine immune and extracellular matrix cues in the lymphoid tumour microenvironment (Ly-TME) and built representative synthetic-hydrogel-based B-cell-lymphoma organoids accordingly. We demonstrate that Ly-TME cellular and biophysical factors amplify the BCR–MYD88–TLR9 multiprotein supercomplex and induce cooperative signalling pathways in ABC-DLBCL cells, which reduce the efficacy of compounds targeting the BCR pathway members Bruton tyrosine kinase and mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1). Combinatorial inhibition of multiple aberrant signalling pathways induced higher antitumour efficacy in lymphoid organoids and implanted ABC-DLBCL patient tumours in vivo. Our studies define the complex crosstalk between malignant ABC-DLBCL cells and Ly-TME, and provide rational combinatorial therapies that rescue Ly-TME-mediated attenuation of treatment response to MALT1 inhibitors.

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Fig. 1: CD4 T-cell abundance in ABC-DLBCLs and development of bioengineered lymphoma organoids.
Fig. 2: T-cell signal amplifies BCR pathway and attenuates the therapeutic response to MALT1 inhibition in ABC-DLBCLs.
Fig. 3: Integrin-binding ligands modulate MALT1 expression, activity and inhibition.
Fig. 4: Microenvironment conditions that facilitate MALT1 inhibitor resistance enhance multiple signalling pathways.
Fig. 5: CD40L, ECM and hydrogel stiffness regulate TLR9 and pSRC expression in PDX organoids.
Fig. 6: Combinatorial treatment with cooperative signalling pathway inhibitors rescues MALT1 inhibitors in organoids and in vivo.

Data availability

The raw RNA-seq data on organoids are available in the GEO database under accession code GSE209551. Remaining patient RNA sequencing data are available publicly through the Hematologic Malignancies Research Consortium (HMRC, European Genome–Phenome Archive EGAS00001002606), the National Cancer Institute (NCI) Genomic Data Center (GSE99276) and with approval from the NCBI dbGaP controlled access portal, and Weill Cornell’s Gene Expression Omnibus (GEO) GSE145043. The remaining data are available within the article, supplementary information or source data file. Due to very large file sizes and volumes of data, the remaining raw data supporting the findings of this study are available from the corresponding author on a reasonable request. Source data are provided with this paper.

References

  1. Phelan, J. D. et al. A multiprotein supercomplex controlling oncogenic signalling in lymphoma. Nature 560, 387–391 (2018).

    Article  CAS  Google Scholar 

  2. Chapuy, B. et al. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat. Med. 24, 679–690 (2018).

    Article  CAS  Google Scholar 

  3. Roschewski, M., Staudt, L. M. & Wilson, W. H. Diffuse large B-cell lymphoma-treatment approaches in the molecular era. Nat. Rev. Clin. Oncol. 11, 12–23 (2014).

    Article  CAS  Google Scholar 

  4. Lenz, G. et al. Oncogenic CARD11 mutations in human diffuse large B cell lymphoma. Science 319, 1676–1679 (2008).

    Article  CAS  Google Scholar 

  5. Fontan, L. et al. Identification of MALT1 feedback mechanisms enables rational design of potent antilymphoma regimens for ABC-DLBCL. Blood 137, 788–800 (2021).

    Article  CAS  Google Scholar 

  6. Wilson, W. H. et al. Targeting B cell receptor signaling with ibrutinib in diffuse large B cell lymphoma. Nat. Med. 21, 922–926 (2015).

    Article  CAS  Google Scholar 

  7. Young, R. M. & Staudt, L. M. Targeting pathological B cell receptor signalling in lymphoid malignancies. Nat. Rev. Drug Disco. 12, 229–243 (2013).

    Article  CAS  Google Scholar 

  8. Young, R. M., Phelan, J. D., Wilson, W. H. & Staudt, L. M. Pathogenic B-cell receptor signaling in lymphoid malignancies: new insights to improve treatment. Immunol. Rev. 291, 190–213 (2019).

    Article  CAS  Google Scholar 

  9. Schmitz, R. et al. Genetics and pathogenesis of diffuse large B-cell lymphoma. N. Engl. J. Med. 378, 1396–1407 (2018).

    Article  CAS  Google Scholar 

  10. Fontan, L. et al. MALT1 small molecule inhibitors specifically suppress ABC-DLBCL in vitro and in vivo. Cancer cell 22, 812–824 (2012).

    Article  CAS  Google Scholar 

  11. Sun, L., Deng, L., Ea, C. K., Xia, Z. P. & Chen, Z. J. The TRAF6 ubiquitin ligase and TAK1 kinase mediate IKK activation by BCL10 and MALT1 in T lymphocytes. Mol. Cell 14, 289–301 (2004).

    Article  CAS  Google Scholar 

  12. Hailfinger, S. et al. Malt1-dependent RelB cleavage promotes canonical NF-kappaB activation in lymphocytes and lymphoma cell lines. Proc. Natl Acad. Sci. USA 108, 14596–14601 (2011).

    Article  CAS  Google Scholar 

  13. Fontan, L. et al. Specific covalent inhibition of MALT1 paracaspase suppresses B cell lymphoma growth. J. Clin. Invest. 128, 4397–4412 (2018).

    Article  Google Scholar 

  14. Nagel, D. et al. Pharmacologic inhibition of MALT1 protease by phenothiazines as a therapeutic approach for the treatment of aggressive ABC-DLBCL. Cancer Cell 22, 825–837 (2012).

    Article  CAS  Google Scholar 

  15. Kotlov, N. et al. Clinical and biological subtypes of B-cell lymphoma revealed by microenvironmental signatures. Cancer Discov. 11, 1468–1489 (2021).

    Article  CAS  Google Scholar 

  16. Wright, G. W. et al. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell 37, 551–568 e514 (2020).

    Article  CAS  Google Scholar 

  17. Scott, D. W. & Gascoyne, R. D. The tumour microenvironment in B cell lymphomas. Nat. Rev. Cancer 14, 517–534 (2014).

    Article  CAS  Google Scholar 

  18. Elgueta, R. et al. Molecular mechanism and function of CD40/CD40L engagement in the immune system. Immunol. Rev. 229, 152–172 (2009).

    Article  CAS  Google Scholar 

  19. Ito, D. et al. CD40 ligand is necessary and sufficient to support primary diffuse large B-cell lymphoma cells in culture: a tool for in vitro preclinical studies with primary B-cell malignancies. Leuk. Lymphoma 53, 1390–1398 (2012).

    Article  CAS  Google Scholar 

  20. Nojima, T. et al. In-vitro derived germinal centre B cells differentially generate memory B or plasma cells in vivo. Nat. Commun. 2, 465 (2011).

    Article  Google Scholar 

  21. Ennishi, D. et al. Molecular and genetic characterization of MHC deficiency identifies EZH2 as therapeutic target for enhancing immune recognition. Cancer Discov. 9, 546–563 (2019).

    Article  Google Scholar 

  22. Reddy, A. et al. Genetic and functional drivers of diffuse large B cell lymphoma. Cell 171, 481–494.e415 (2017).

    Article  CAS  Google Scholar 

  23. Ni, C. Z. et al. Molecular basis for CD40 signaling mediated by TRAF3. Proc. Natl Acad. Sci. USA 97, 10395–10399 (2000).

    Article  CAS  Google Scholar 

  24. Phelps, E. A. et al. Maleimide cross-linked bioactive PEG hydrogel exhibits improved reaction kinetics and cross-linking for cell encapsulation and in situ delivery. Adv. Mater. 24, 64–70 (2012). 62.

    Article  CAS  Google Scholar 

  25. Graney, P. L. et al. Organoid polymer functionality and mode of Klebsiella pneumoniae membrane antigen presentation regulates ex vivo germinal center epigenetics in young and aged B cells. Adv. Funct. Mater. 30, 2001232 (2020).

    Article  CAS  Google Scholar 

  26. Lutolf, M. P. & Hubbell, J. A. Synthesis and physicochemical characterization of end-linked poly(ethylene glycol)-co-peptide hydrogels formed by Michael-type addition. Biomacromolecules 4, 713–722 (2003).

    Article  CAS  Google Scholar 

  27. Rodda, L. B. et al. Single-cell RNA sequencing of lymph node stromal cells reveals niche-associated heterogeneity. Immunity 48, 1014–1028 e1016 (2018).

    Article  CAS  Google Scholar 

  28. Massia, S. P. & Hubbell, J. A. Vascular endothelial cell adhesion and spreading promoted by the peptide REDV of the IIICS region of plasma fibronectin is mediated by integrin α 4 β 1. J. Biol. Chem. 267, 14019–14026 (1992).

    Article  CAS  Google Scholar 

  29. Rydholm, A. E., Bowman, C. N. & Anseth, K. S. Degradable thiol-acrylate photopolymers: polymerization and degradation behavior of an in situ forming biomaterial. Biomaterials 26, 4495–4506 (2005).

    Article  CAS  Google Scholar 

  30. Quancard, J. et al. An allosteric MALT1 inhibitor is a molecular corrector rescuing function in an immunodeficient patient. Nat. Chem. Biol. 15, 304–313 (2019).

    Article  CAS  Google Scholar 

  31. Richards, K. L. et al. Gene profiling of canine B-cell lymphoma reveals germinal center and postgerminal center subtypes with different survival times, modeling human DLBCL. Cancer Res. 73, 5029–5039 (2013).

    Article  CAS  Google Scholar 

  32. Tolar, P., Hanna, J., Krueger, P. D. & Pierce, S. K. The constant region of the membrane immunoglobulin mediates B cell-receptor clustering and signaling in response to membrane antigens. Immunity 30, 44–55 (2009).

    Article  CAS  Google Scholar 

  33. Clark, A. Y. et al. Integrin-specific hydrogels modulate transplanted human bone marrow-derived mesenchymal stem cell survival, engraftment, and reparative activities. Nat. Commun. 11, 114 (2020).

    Article  CAS  Google Scholar 

  34. Arnaout, M. A., Mahalingam, B. & Xiong, J. P. Integrin structure, allostery, and bidirectional signaling. Annu. Rev. Cell Dev. Biol. 21, 381–410 (2005).

    Article  CAS  Google Scholar 

  35. Bellis, S. L. Advantages of RGD peptides for directing cell association with biomaterials. Biomaterials 32, 4205–4210 (2011).

    Article  CAS  Google Scholar 

  36. Knight, C. G. et al. Identification in collagen type I of an integrin alpha2 beta1-binding site containing an essential GER sequence. J. Biol. Chem. 273, 33287–33294 (1998).

    Article  CAS  Google Scholar 

  37. Zeltz, C. & Gullberg, D. The integrin-collagen connection—a glue for tissue repair? J. Cell Sci. 129, 653–664 (2016).

    Article  CAS  Google Scholar 

  38. Arana, E., Harwood, N. E. & Batista, F. D. Regulation of integrin activation through the B-cell receptor. J. Cell Sci. 121, 2279–2286 (2008).

    Article  CAS  Google Scholar 

  39. Carrasco, Y. R. & Batista, F. D. B‐cell activation by membrane‐bound antigens is facilitated by the interaction of VLA‐4 with VCAM‐1. EMBO J. 25, 889–899 (2006).

    Article  CAS  Google Scholar 

  40. Weekes, C. D., Kuszynski, C. A. & Sharp, J. G. VLA-4 mediated adhesion to bone marrow stromal cells confers chemoresistance to adherent lymphoma cells. Leuk. Lymphoma 40, 631–645 (2001).

    Article  CAS  Google Scholar 

  41. Headen, D. M. et al. Local immunomodulation Fas ligand-engineered biomaterials achieves allogeneic islet graft acceptance. Nat. Mater. 17, 732–739 (2018).

    Article  CAS  Google Scholar 

  42. Landon, A. L. et al. MNKs act as a regulatory switch for eIF4E1 and eIF4E3 driven mRNA translation in DLBCL. Nat. Commun. 5, 5413 (2014).

    Article  Google Scholar 

  43. Manning, G., Whyte, D. B., Martinez, R., Hunter, T. & Sudarsanam, S. The protein kinase complement of the human genome. Science 298, 1912–1934 (2002).

    Article  CAS  Google Scholar 

  44. Zheng, M. et al. A mix of S and ΔS variants of STAT3 enable survival of activated B-cell-like diffuse large B-cell lymphoma cells in culture. Oncogenesis 4, e184 (2016).

    Article  CAS  Google Scholar 

  45. Apoorva, F. N. U. et al. Lymph node stiffness-mimicking hydrogels regulate human B-cell lymphoma growth and cell surface receptor expression in a molecular subtype-specific manner. J. Biomed. Mater. Res. A 105, 1833–1844 (2017).

    Article  CAS  Google Scholar 

  46. Ohto, U. et al. Structural basis of CpG and inhibitory DNA recognition by Toll-like receptor 9. Nature 520, 702–705 (2015).

    Article  CAS  Google Scholar 

  47. Scuoppo, C. et al. Repurposing dasatinib for diffuse large B cell lymphoma. Proc. Natl Acad. Sci. USA 116, 16981–16986 (2019).

    Article  CAS  Google Scholar 

  48. Battistello, E. et al. Pan-SRC kinase inhibition blocks B-cell receptor oncogenic signaling in non-Hodgkin lymphoma. Blood 131, 2345–2356 (2018).

    Article  CAS  Google Scholar 

  49. Gopal, A. K. et al. PI3Kδ inhibition by idelalisib in patients with relapsed indolent lymphoma. N. Engl. J. Med. 370, 1008–1018 (2014).

    Article  CAS  Google Scholar 

  50. Kim, S., Shah, S. B., Graney, P. L. & Singh, A. Multiscale engineering of immune cells and lymphoid organs. Nat. Rev. Mater. 4, 355–378 (2019).

    Article  Google Scholar 

  51. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30(7):923–30 (2014).

  52. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139–140 (2010); https://doi.org/10.1093/bioinformatics/btp616

  53. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W. & Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res., 43(7), e47 (2015); https://doi.org/10.1093/nar/gkv007

  54. Kolde, R. pheatmap: Pretty Heatmaps. R package version 1.0.12 (2019).

  55. Shen, Y. et al. Oncogenic role of the SOX9–DHCR24–cholesterol biosynthesis axis in IGH-BCL2+ diffuse large B-cell lymphomas. Blood 139, 73–86 (2022).

    Article  CAS  Google Scholar 

  56. Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100–106 (2021).

    Article  CAS  Google Scholar 

  57. Apoorva, F. et al. How biophysical forces regulate human B cell lymphomas. Cell Rep. 23, 499–511 (2018).

    Article  CAS  Google Scholar 

  58. Purwada, A. et al. Ex vivo engineered immune organoids for controlled germinal center reactions. Biomaterials 63, 24–34 (2015).

    Article  CAS  Google Scholar 

  59. Purwada, A. et al. Ex vivo synthetic immune tissues with T cell signals for differentiating antigen-specific, high affinity germinal center B cells. Biomaterials 198, 27–36 (2019).

    Article  CAS  Google Scholar 

  60. Purwada, A., Shah, S. B., Beguelin, W., Melnick, A. M. & Singh, A. Modular immune organoids with integrin ligand specificity differentially regulate ex vivo B cell activation. ACS Biomater. Sci. Eng. 3, 214–225 (2017).

    Article  CAS  Google Scholar 

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Acknowledgements

We acknowledge financial support from the US National Cancer Institute (5R01CA238745-03 awarded to A.S., 1R01CA266052-01A1 awarded to A.S. and J.L.K), the Wellcome Leap HOPE program awarded to A.S., the US NIH/National Institute of Allergy and Infectious Diseases (NIAID) (5R01AI132738-05 awarded to A.S.), the Innovative Molecular Analysis Technology programme of the US National Cancer Institute (NIH R33-CA212968-03 awarded to A.S.), the US Department of Defense (grant number W81XWH-17-1-0215, awarded to A.S.), and the Terry Fox Research Institute (grant numbers 1023 and 1061, awarded to C.S. and D.W.S.). Research reported in this publication was supported in part by the Bioinformatics and Systems Biology shared resource of the Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292. We thank K. Richards and T. Pierpont for kindly providing canine PDXs for research and J. Cyster for kindly sharing the WEHI cells. The opinions, interpretations, conclusions and recommendations are those of the authors and are not necessarily endorsed by the funding agencies.

Author information

Author notes

  1. Shivem B. Shah

    Present address: Columbia University, New York, USA

  2. Katsuyoshi Takata

    Present address: Niigata University, Niigata, Japan

  3. Lorena Fontan

    Present address: Janssen Pharmaceuticals, Inc., Beerse, Belgium

Authors and Affiliations

  1. Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    Shivem B. Shah, Katherine M. Lee, Benjamin D. Cosgrove & Ankur Singh

  2. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University School of Medicine, Atlanta, GA, USA

    Christopher R. Carlson, Kristine Lai, Zhe Zhong, Grazia Marsico, Mayar Allam, Thomas Hu, Ahmet F. Coskun & Ankur Singh

  3. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Christopher R. Carlson, Kristine Lai, Zhe Zhong, Grazia Marsico, Karen E. Martin, Andrés J. García & Ankur Singh

  4. College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, USA

    Nicole E. Félix Vélez

  5. College of Arts and Sciences, Cornell University, Ithaca, NY, USA

    Elisabeth B. Abeles

  6. Department of Molecular Biology & Genetics, Cornell University, Ithaca, NY, USA

    Lauren D. Walter

  7. Winship Cancer Center, Emory University School of Medicine, Atlanta, GA, USA

    Khanjan Gandhi & Jean L. Koff

  8. College of Veterinary Medicine, Cornell University, Ithaca, NY, USA

    Scott D. Butler, Rishi Puri & Angela L. McCleary-Wheeler

  9. Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA

    Wayne Tam & Giorgio Inghirami

  10. Englander Institute for Precision Medicine, Weill Cornell Medical College, New York, NY, USA

    Olivier Elemento

  11. Centre for Lymphoid Cancer, British Columbia Cancer Center, Vancouver, British Columbia, Canada

    Katsuyoshi Takata, Christian Steidl & David W. Scott

  12. Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada

    Christian Steidl & David W. Scott

  13. Division of Hematology/Oncology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA

    Lorena Fontan & Ari Melnick

  14. Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan

    Hideki Ueno

  15. Petit Institute for Bioengineering and Biosciences, Georgia Institute of Technology, Atlanta, GA, USA

    Andrés J. García & Ankur Singh

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  1. Shivem B. Shah
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Contributions

S.B.S. and A.S. designed the research. S.B.S., C.C., Z.Z., K.L., G.M., K.M.L., N.E.F.V. and E.B.A. assisted with organoid experiments and data collection, along with data analysis. G.I. and A.L.M-W. provided human and canine PDX specimens, respectively, and W.T provided biopsy tumour microarrays. S.D.B. and R.P. performed canine and human PDX xenograft implantation and treatment of mice with implanted human PDXs. K.T., D.W.S., K.T. and C.S. provided sample analysis of the BC cohort. K.G. and J.L.K. analysed the HMRC cohort. L.D.W. and B.D.C. performed RNA sequencing analysis on organoids. M.A., T.H. and A.C. performed and analysed cyclic immunofluorescence studies. K.E.M. and A.J.G. performed rheology experiments and analysis. H.U., L.F., O.E., J.L.K. and A.M. provided critical reagents and/or intellectual input. The manuscript was written by S.B.S. and A.S., revised by A.S., and all authors provided comments.

Corresponding author

Correspondence to Ankur Singh.

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Competing interests

A.S. received research support from 3M. A.M. receives research support from Janssen Pharmaceuticals and serves as a consultant to Epizyme and Constellation. L.F. is currently an employee of Janssen Research & Development, LLC. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Hydrogel functionality modulates the survival of B cell lymphomas.

Left: Flow cytometry gating. Right: Percent and count of live CD19 + WEHI B cell lymphoma cells cultured in hydrogels with PEG-4MAL, PEG-4VS, and PEG-4ACR functionalities after 48 h of culture. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 5, where each dot represents a hydrogel).

Source data

Extended Data Fig. 2 Characterization of hydrogel-based organoid cultures.

a, Loss modulus of hydrogels with and without cells for different macromer densities. Two-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 5). b, Expression of CD40L on mitomycin-treated CD40L-stromal cells over 4 days of hydrogel culture. Two-tailed unpaired t-test (mean ± s.e.m., n = 5). c, Percentage of Ki67 + HBL1 and OCI-LY10 grown for 7 days in REDV, REDV + CD40L, or REVD-functionalized organoids. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 5). d-e, Long-term passaging. (d) Flow cytometry tracked viability at each passage (mean ± s.e.m., n = 4). (e) Left: pBTK histogram at passages 2, 3, 4, 5, and 10 gated for live cells. Right: pBTK MFI value at passages 2, 3, 4, 5, and 10. Organoids passaged after 96 h of culture. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 4). Each dot in a–e represents a hydrogel-based organoid.

Source data

Extended Data Fig. 3 T cell signal attenuates the therapeutic response to MALT1 inhibition in human and canine ABC-DLBCLs.

a, Survival (normalized to vehicle-treated) of human ABC-DLBCL PDXs cultured in organoids with and without CD40L after 48-h culture, followed by 48-h treatment with 500 or 2000 nM MI2 treatment or 1000 nM MLT-748 MALT1 inhibitor compound. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 4, PDX#4; n = 5, PDX#5, where each dot is a hydrogel-based organoid). b-c, Survival (normalized to vehicle-treated) human ABC-DLBCL PDX cells cultured in organoids with and without CD40L after 48-h culture, followed by 48-h treatment with MALT1 inhibitor MI2 at 500 nM (b) and 2000 nM (c) treatment. One-way ANOVA with Tukey’s multiple-comparison test (b) and two-tailed unpaired t-test (c), (mean ± s.e.m., n = 3 for (b), n = 5 for (c), where each dot is a hydrogel-based organoid). d, Survival (normalized to vehicle-treated) of HBL1 cells cultured in organoids with baseline stromal cell and CD40L-transduced stromal cell after 48-h culture, followed by 48-h treatment with 500 nM MI2 treatment. Two-tailed unpaired t-test (mean ± s.e.m., n = 7). e, Survival (normalized to vehicle-treated) of canine PDX cells cultured in organoids with and without CD40L-stromal cells after 48-h culture, followed by 48-h treatment with 2000 nM MI2 treatment. Two-tailed unpaired t-test (mean ± s.e.m., n = 5 for -CD40L and n = 6 for +CD40L).

Source data

Extended Data Fig. 4 Characterization of hydrogel-based organoids.

a, BCR (magenta) puncta expression at the single-cell level on CD20 (green) expressing OCI-LY10 in the presence or absence of CD40L-stromal cells, with DAPI (blue). Data representative of n = 5 hydrogels for each condition. b, Median fluorescent intensity (MFI) of MALT1 in human ABC-DLBCL PDX cells cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test between two groups within a cell line (mean ± s.e.m., n = 3, where each dot is a hydrogel-based organoid). c, MFI of MALT1 in human ABC-DLBCL cell lines cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test between two groups within a cell line (mean ± s.e.m., n = 4, where each dot is a hydrogel-based organoid).

Source data

Extended Data Fig. 5 Expression of BCR pathway proteins in ABC-DLBCL organoids.

a, Left: Representative flow cytometry histograms. Right: Median fluorescent intensity (MFI) of BCL10 in human PDX cells cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 4 for PDX#4; n = 5 for PDX#5; where each dot is a hydrogel-based organoid). b, MFI of BCL10 in human ABC-DLBCL PDX cells cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test between two groups within a cell line (mean ± s.e.m., n = 3, where each dot is a hydrogel-based organoid). c, Ratio of MFI of pNF-κB/NF-κB in human ABC-DLBCL PDX cells cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 5 for PDX#2). d, CD40 (green) spatial localization relative to IgM BCR (magenta) puncta in HBL1 ABC-DLBCLs grown in REDV-functionalized hydrogels in the presence of CD40L-stromal cells. Left: 3D projection of IgM BCR, DAPI (blue), and CD40. Right: Orthogonal projection with IgM BCR, DAPI, and actin (orange).Data representative of n = 3 organoids. e, 3D projection of spatial expression of TRAF3 (green) in HBL1 ABC-DLBCLs grown in REDV-functionalized hydrogels in the presence or absence of CD40L-stromal cells, with DAPI (blue) and actin (magenta). Data representative of n = 3 organoids.

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Extended Data Fig. 6 Single-cell MALT1 fluorescent intensity and protein count.

a, Distribution of fluorescent intensity of HBL1 cells from six separate organoids for MALT1 after 48 h of culture in indicated conditions and subsequent 48 h of MI2 treatment. Total number of cells is indicated in red. Organoids without (left) and with CD40L-stromal cells (right) were respectively treated with 250 nM and 2000 nM MI2. b, Distribution of fluorescent intensity of ABC-DLBCL PDXs from six separate organoids for MALT1 after 48 h of culture in indicated conditions and subsequent 48 h of MI2 treatment. Organoids without and with CD40L-stromal cells were respectively treated with 2000 nM MI2. c, Protein expression of the six highest expressed proteins, from NanoString analysis in Fig. 4, in cells cultured in GFOGER-functionalized organoids and REDV-functionalized organoids with CD40L-stromal cells. Results indicate mean ± s.e.m. of two replicates, with each replicate including at least 60 organoids.

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Extended Data Fig. 7 CD40L, ECM, and hydrogel stiffness regulate TLR9 and pSRC expression in PDX organoids.

a, Gene expression of MYD88 in ABC-DLBCL (n = 242) and GCB-DLBCL (n = 264) patients from the HMRC cohort. Data were quantile normalized and log2 transformed. Two-tailed unpaired t-test. b, Pearson’s correlations for the genes of interest in the ABC-DLBCL (n = 242) samples. c, TLR9 median fluorescent intensity (MFI) for OCI-LY10 and OCI-LY3 cells cultured in ± CD40L-stromal cells conditions for 96 h. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 3). d, TLR9 MFI for human ABC-DLBCL HBL1 cells cultured in REDV or GFOGER-functionalized organoids without CD40L-stromal cells for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 4). e, Effect of stiffness, modulated by indicated PEG-4MAL macromer densities (w/v%) at 4:3:3 REDV to VPM and DTT crosslinkers, on TLR9 in human PDX cells after 96-h culture Two-tailed unpaired t-test (mean ± s.e.m., n = 5) f, TLR9 MFI for human ABC-DLBCL cells cultured in REDV-presenting hydrogels for 96 h with different cell seeding density. In indicated groups (red), MI2 was added at 2000 nM for the last 48 h of culture. Two-tailed unpaired t-test for each cell line (mean ± s.e.m., n = 5). g-h, Effect of 1 μM CpG addition on MALT1 (g) and TLR9 (h) expression in cells cultured in REDV-functionalized hydrogel-based organoids for 96 h. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 3). i, Survival (normalized to vehicle-treated) of OCI-LY10 and OCI-LY3 cells cultured in REDV-functionalized hydrogel-based organoids without and with 1 μM CpG for 96 h, including 48 h of 2000 nM MI2 treatment. Two-tailed unpaired t-test (mean ± s.e.m., n = 3). j, Protein expression of the six highest expressed proteins measured via NanoString nCounter SPRINT Profiler in HBL1 cells after 96-h culture in REDV-functionalized organoids with 1 μM CpG. mean ± s.e.m., n = 3 replicates each representing 60 organoids.

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Extended Data Fig. 8 pSRC median fluorescent intensity for human ABC-DLBCL cell lines and PDX cells cultured in indicated conditions for 96 h.

a, pSRC median fluorescent intensity (MFI) in HBL1 (n = 7), OCI-LY3 (n = 4), and OCI-LY10 (n = 4) cell lines grown in REDV-functionalized hydrogel-based organoids with or without CD40L-expressing stromal cells. Two-tailed unpaired t-test (mean ± s.e.m.), where each dot is a hydrogel-based organoid. b, pSRC MFI in human ABC-DLBCL PDX cells grown in REDV-functionalized hydrogel-based organoids with CD40L-expressing stromal cells at different hydrogel polymer densities (stiffnesses). Two-tailed unpaired t-test (mean ± s.e.m., n = 3, where each dot is a hydrogel-based organoid). c, Effect of 1 μM CpG addition on pSRC MFI in OCI-LY3 and OCI-LY10 cells cultured for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 3, where each dot is a hydrogel-based organoid).

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Extended Data Fig. 9 Combinatorial treatment with cooperative signalling pathway inhibitors rescues MALT1 inhibitors in organoids.

a, Survival (normalized to vehicle-treated) of canine PDX cells cultured with CD40L-stromal cells in REDV-functionalized organoids for 96 h, with 48 h of treatment with vehicle, 2000 nM MI2, 1 μM idelalisib, or both drugs. One-way ANOVA with Dunnett’s multiple-comparison test against the idelalisib+MI2 group (mean ± s.e.m., n = 5, where each dot is a hydrogel-based organoid). b, Representative flow cytometry gating depicting survival of GCB-DLBCL OCI-LY7 cells cultured with CD40L-stromal cells in REDV-functionalized organoids for 96 h, with 48 h of treatment with vehicle, 2000 nM MI2, idelalisib, or both MI2 and idelalisib. Cells were treated with 40 μM idelalisib. Representative of n = 6 hydrogel-based organoid). c, Survival (normalized to vehicle-treated) of OCI-LY10 cells and OCI-LY3 cells after 96-h culture in REDV-functionalized organoids with CD40L-stromal cells, where + /- indicate 48-h 2000 nM MI2 treatment, 48-h 40 μM idelalisib treatment, and/or 96-h 1 μM CpG treatment. One-way ANOVA with Dunnett’s multiple-comparison test with reference to combination MI2 and idelalisib treated culture (orange), (mean ± s.e.m., n = 3, where each dot is a hydrogel-based organoid).

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Extended Data Fig. 10 Ly-TME attenuates BTK inhibitor response and combinatorial treatment with cooperative signalling pathway inhibitors rescues BTK inhibitor response.

a, Survival (normalized to vehicle-treated) of HBL1 cells cultured in indicated conditions after 48 h of culture and subsequent 48 h of treatment with increasing concentration of BTK inhibitor, ibrutinib. Results indicate the mean ± s.e.m. of six replicates. b, Median fluorescent intensity of pBTK in HBL1 cells cultured in either REDV- (blue) or GFOGER (orange)-functionalized organoids for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 3). c, Median fluorescent intensity of pBTK in HBL1 cells cultured in REDV-functionalized hydrogels-based organoids with or without CD40L expressing stromal cells, for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 3). d, Ratio of median fluorescent intensity (right) of pBTK/BTK in human PDX cells cultured in ±CD40L-stromal cell conditions for 96 h. Two-tailed unpaired t-test (mean ± s.e.m., n = 4 for PDX#4; n = 5 for PDX#5). e, Survival (normalized to vehicle-treated) for HBL1 (left) and OCI-LY10 (right) cells after 96-h culture in REDV-functionalized organoids with CD40L-stromal cells, treated for 48-h with 1000 nM BTK inhibitor ibrutinib and 40 μM PI3K inhibitor idelalisib. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 8 treatment group, n = 16 vehicle group). f, Survival (normalized to vehicle-treated) for HBL1 (left) and OCI-LY10 (right) cells after 96-h culture in REDV-functionalized organoids with CD40L-stromal cells, treated for 48-h with 1000 nM ibrutinib and 40 μM idelalisib, and/or treated with 1 μM CpG for 96-h. One-way ANOVA with Tukey’s multiple-comparison test (mean ± s.e.m., n = 7 treatment group, n = 16 vehicle group).

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Supplementary information

Supplementary Information

Supplementary figs. 1–12 and discussion.

Reporting Summary

Supplementary Table

Supplementary table 1. GSEA comparing HBL1 cells cultured in REDV-functionalized hydrogels with and without CD40L cells.

Supplementary Table

Supplementary table 2. GSEA comparing HBL1 cells cultured in REDV versus GFOGER-functionalized hydrogels without CD40L cells.

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Shah, S.B., Carlson, C.R., Lai, K. et al. Combinatorial treatment rescues tumour-microenvironment-mediated attenuation of MALT1 inhibitors in B-cell lymphomas. Nat. Mater. 22, 511–523 (2023). https://doi.org/10.1038/s41563-023-01495-3

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