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Targeting both death and paracaspase domains of MALT1 with antisense oligonucleotides overcomes resistance to immune-checkpoint inhibitors

Abstract

Targeting MALT1’s paracaspase activity has been explored for B cell lymphoma and solid tumors. While the role of MALT1 in promoting cancer cell proliferation has been investigated, its involvement in immune evasion is unclear. Here we report that MALT1 promotes immune evasion through its paracaspase and death domain. In a paracaspase-dependent manner, MALT1 protects CD274 mRNA from degradation by its cleavage of ROQUIN1 and ROQUIN2. In a death-domain-dependent manner, MALT1 promotes the proliferation and polarization of tumor-associated macrophages to generate an immunosuppressive tumor microenvironment. Targeting MALT1 with antisense oligonucleotides inhibits PD-L1 expression in patient-derived tumor cells and suppresses the proliferation and M2-like polarization of tumor-associated macrophages isolated from patients with cancer. In preclinical models of solid tumors in female mice, treatment with MALT1 antisense oligonucleotides overcomes resistance to immune-checkpoint inhibitors. Together, our study demonstrates that targeting MALT1 is a potential strategy to overcome immune-checkpoint inhibitor resistance.

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Fig. 1: MALT1 promotes cancer cell immune evasion through blocking ROQUIN1/2-mediated Cd274 mRNA degradation.
Fig. 2: MALT1 promotes tumor growth and ICI therapy resistance.
Fig. 3: MALT1 induces proliferation and polarization of TAMs in a paracaspase-independent manner.
Fig. 4: Death domain of MALT1 is required for TAM proliferation and polarization.
Fig. 5: MALT1 ASO suppresses PD-L1 expression and macrophage modulation in mice and in patients.
Fig. 6: Inhibition of MALT1 by ASO overcomes ICI therapy resistance.

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Data availability

RNA-seq data generated in this study were deposited in the Gene Expression Omnibus (GEO) under accession numbers GSE265893 (Fig. 1d), GSE242764 (Fig. 1e) and GSE242878 (Extended Data Fig. 7j). scRNA-seq data were also deposited at the GEO under the accession number GSE242554 (Fig. 3a). The publicly available RNA-seq data of patients who accepted anti-PD-1 treatment used in this study are available in the European Nucleotide Archive database under accession codes PRJEB23709 (ref. 29) and PRJEB25780 (ref. 30). The TNBC publicly available RNA-seq data used in this study are available in the Sequence Read Archive database under accession code SRP157974 (ref. 53). The UCSC Xena platform (https://xenabrowser.net/datapages/) was used to obtain the publicly available RNA-seq data of TCGA-melanoma and TCGA-colon cancer. The TNBC and colon cancer publicly available data used for Kaplan–Meier plots in this study were obtained from the Kaplan–Meier plotter database (https://kmplot.com). The melanoma publicly available data used for Kaplan–Meier plots in this study were obtained from http://gepia2.cancer-pku.cn. Source data for Figs. 16 and Extended Data Figs. 110 have been provided as Source Data files. All remaining data supporting the findings of this study are available within the article, Supplementary Information, Source Data and/or from the corresponding author on reasonable request. Source data are provided with this paper.

References

  1. Kalbasi, A. & Ribas, A. Tumour-intrinsic resistance to immune checkpoint blockade. Nat. Rev. Immunol. 20, 25–39 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to T cell-mediated killing. Science 359, 770–775 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Cassetta, L. & Pollard, J. W. A timeline of tumour-associated macrophage biology. Nat. Rev. Cancer 23, 238–257 (2023).

    Article  CAS  PubMed  Google Scholar 

  5. Yu, J. et al. Liver metastasis restrains immunotherapy efficacy via macrophage-mediated T cell elimination. Nat. Med. 27, 152–164 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Obradovic, A. et al. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages. Cell 184, 2988–3005.e2916 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Argyle, D. & Kitamura, T. Targeting macrophage-recruiting chemokines as a novel therapeutic strategy to prevent the progression of solid tumors. Front. Immunol. 9, 2629 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ruland, J., Duncan, G. S., Wakeham, A. & Mak, T. W. Differential requirement for Malt1 in T and B cell antigen receptor signaling. Immunity 19, 749–758 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. Rebeaud, F. et al. The proteolytic activity of the paracaspase MALT1 is key in T cell activation. Nat. Immunol. 9, 272–281 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Gehring, T. et al. MALT1 phosphorylation controls activation of T lymphocytes and survival of ABC-DLBCL tumor cells. Cell Rep. 29, 873–888.e810 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Blonska, M. & Lin, X. NF-κB signaling pathways regulated by CARMA family of scaffold proteins. Cell Res 21, 55–70 (2011).

    Article  CAS  PubMed  Google Scholar 

  12. 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  PubMed  Google Scholar 

  13. Coornaert, B. et al. T cell antigen receptor stimulation induces MALT1 paracaspase–mediated cleavage of the NF-κB inhibitor A20. Nat. Immunol. 9, 263–271 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Staal, J. et al. T-cell receptor-induced JNK activation requires proteolytic inactivation of CYLD by MALT1. EMBO J. 30, 1742–1752 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Jeltsch, K. M. et al. Cleavage of roquin and regnase-1 by the paracaspase MALT1 releases their cooperatively repressed targets to promote TH17 differentiation. Nat. Immunol. 15, 1079–1089 (2014).

    Article  CAS  PubMed  Google Scholar 

  17. Uehata, T. et al. Malt1-induced cleavage of regnase-1 in CD4(+) helper T cells regulates immune activation. Cell 153, 1036–1049 (2013).

    Article  CAS  PubMed  Google Scholar 

  18. Xia, X. et al. Glutaminolysis mediated by MALT1 protease activity facilitates PD-L1 expression on ABC-DLBCLcells and contributes to their immune evasion. Front. Oncol. 8, 632 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Di Pilato, M. et al. Targeting the CBM complex causes Treg cells to prime tumours for immune checkpoint therapy. Nature 570, 112–116 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Cheng, L., Deng, N., Yang, N., Zhao, X. & Lin, X. Malt1 protease is critical in maintaining function of regulatory T cells and may be a therapeutic target for antitumor immunity. J Immunol 202, 3008–3019 (2019).

    Article  CAS  PubMed  Google Scholar 

  21. 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  PubMed  PubMed Central  Google Scholar 

  22. 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  PubMed  Google Scholar 

  23. Di Pilato, M. et al. Translational studies using the MALT1 inhibitor (S)-mepazine to induce Treg fragility and potentiate immune checkpoint therapy in cancer. J. Immunother. Precis. Oncol. 6, 61–73 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Sharma, P., Hu-Lieskovan, S., Wargo, J. A. & Ribas, A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168, 707–723 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Uren, A. G. et al. Identification of paracaspases and metacaspases: two ancient families of caspase-like proteins, one of which plays a key role in MALT lymphoma. Molecular Cell 6, 961–967 (2000).

    CAS  PubMed  Google Scholar 

  26. Lucas, P. C. et al. Bcl10 and MALT1, independent targets of chromosomal translocation in MALT lymphoma, cooperate in a novel NF-κB signaling pathway. J. Biol. Chem. 276, 19012–19019 (2001).

    Article  CAS  PubMed  Google Scholar 

  27. Vogel, K. U. et al. Roquin paralogs 1 and 2 redundantly repress the Icos and Ox40 costimulator mRNAs and control follicular helper T cell differentiation. Immunity 38, 655–668 (2013).

    Article  CAS  PubMed  Google Scholar 

  28. Han, Y., Liu, D. & Li, L. PD-1/PD-L1 pathway: current researches in cancer. Am. J. Cancer Res. 10, 727–742 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Gide, T. N. et al. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/Anti-CTLA-4 combined therapy. Cancer Cell 35, 238–255.e236 (2019).

    Article  CAS  PubMed  Google Scholar 

  30. Kim, S. T. et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat. Med. 24, 1449–1458 (2018).

    Article  CAS  PubMed  Google Scholar 

  31. Quancard, J. et al. Optimization of the in vivo potency of pyrazolopyrimidine MALT1 protease inhibitors by reducing metabolism and increasing potency in whole blood. J. Med. Chem. 63, 14594–14608 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Petty, A. J. et al. Hedgehog signaling promotes tumor-associated macrophage polarization to suppress intratumoral CD8+ T cell recruitment. J. Clin. Investig. 129, 5151–5162 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Benoit, M. E., Clarke, E. V., Morgado, P., Fraser, D. A. & Tenner, A. J. Complement protein C1q directs macrophage polarization and limits inflammasome activity during the uptake of apoptotic cells. J. Immunol. 188, 5682–5693 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Bohlson, S. S., O’Conner, S. D., Hulsebus, H. J., Ho, M. M. & Fraser, D. A. Complement, c1q, and c1q-related molecules regulate macrophage polarization. Front. Immuno.l 5, 402 (2014).

    Google Scholar 

  35. Zhang, Y. et al. Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer. J. Hepatol. 75, 1128–1141 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Sinha, P., Clements, V. K. & Ostrand-Rosenberg, S. Interleukin-13-regulated M2 macrophages in combination with myeloid suppressor cells block immune surveillance against metastasis. Cancer Res. 65, 11743–11751 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Schlauderer, F. et al. Molecular architecture and regulation of BCL10-MALT1 filaments. Nat. Commun. 9, 4041 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Pixley, F. J. & Stanley, E. R. CSF-1 regulation of the wandering macrophage: complexity in action. Trends Cell Biol. 14, 628–638 (2004).

    Article  CAS  PubMed  Google Scholar 

  39. Hu, J. et al. Regulation of tumor immune suppression and cancer cell survival by CXCL1/2 elevation in glioblastoma multiforme. Sci. Adv. 7, eabc2511 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang, T. et al. PTGES/PGE2 signaling links immunosuppression and lung metastasis in Gprc5a-knockout mouse model. Oncogene 39, 3179–3194 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kole, R., Krainer, A. R. & Altman, S. RNA therapeutics: beyond RNA interference and antisense oligonucleotides. Nat. Rev. Drug Discovery 11, 125–140 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Bennett, C. F. Therapeutic antisense oligonucleotides are coming of age. Ann. Rev. Med. 70, 307–321 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. De Henau, O. et al. Overcoming resistance to checkpoint blockade therapy by targeting PI3Kγ in myeloid cells. Nature 539, 443–447 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Grasselly, C. et al. The antitumor activity of combinations of cytotoxic chemotherapy and immune checkpoint inhibitors is model-dependent. Front. Immunol. 9, 2100 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Pan, D. et al. MALT1 is required for EGFR-induced NF-κB activation and contributes to EGFR-driven lung cancer progression. Oncogene 35, 919–928 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Lee, J.-Y. L. et al. MALT1 is a targetable driver of epithelial-to-mesenchymal transition in claudin-low, triple-negative breast cancer. Mol. Cancer Res. 20, 373–386 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Ekambaram, P. et al. The CARMA3-Bcl10-MALT1 signalosome drives NFκB activation and promotes aggressiveness in angiotensin ii receptor-positive breast cancer. Cancer Res. 78, 1225–1240 (2018).

    Article  CAS  PubMed  Google Scholar 

  48. Butowski, N. et al. Orally administered colony stimulating factor 1 receptor inhibitor PLX3397 in recurrent glioblastoma: an Ivy Foundation early phase clinical trials consortium phase II study. Neuro-oncology 18, 557–564 (2016).

    Article  PubMed  Google Scholar 

  49. Pienta, K. J. et al. Phase 2 study of carlumab (CNTO 888), a human monoclonal antibody against CC-chemokine ligand 2 (CCL2), in metastatic castration-resistant prostate cancer. Invest. New Drugs 31, 760–768 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Noel, M. et al. Phase 1b study of a small molecule antagonist of human chemokine (C-C motif) receptor 2 (PF-04136309) in combination with nab-paclitaxel/gemcitabine in first-line treatment of metastatic pancreatic ductal adenocarcinoma. Invest. New Drugs 38, 800–811 (2020).

    Article  CAS  PubMed  Google Scholar 

  51. Meng, J. et al. Tumor-derived Jagged1 promotes cancer progression through immune evasion. Cell Rep. 38, 110492 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. Qi, S. et al. Supramolecular lipid nanoparticles based on host–guest recognition: a new generation delivery system of mRNA vaccines for cancer immunotherapy. Adv. Mater. 36, 2311574 (2024).

    Article  CAS  Google Scholar 

  53. Jiang, Y.-Z. et al. Genomic and transcriptomic landscape of triple-negative breast cancers: subtypes and treatment strategies. Cancer Cell 35, 428–440.e425 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key Research and Development Program of China (2020YFA0509400 to H. Zheng), National Science Foundation of China (81772981 and 81972462 to H. Zheng) and Tsinghua University Initiative Scientific Research Program. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. We thank C. Chen from Kunming Medical University for supplying the E0771 cells. We acknowledge M. Chen from Tsinghua University for the helpful discussions on mRNA stability assay. We extend our gratitude to all members of Zheng Laboratory for the invaluable contributions and technical support. We acknowledge the Technology Center for Protein Sciences at Tsinghua University for their assistance with FACS. We also appreciate the support provided by the Laboratory Animal Resource Center at Tsinghua University in facilitating animal-related research.

Author information

Authors and Affiliations

Authors

Contributions

Y.T. and H. Zheng were responsible for designing and conducting the experiments. C.T., X. Lan and T.Z. provided bioinformatics analysis. S.Q. and G.Y. contributed to the encapsulation of ASO by LNP. Z.J., Z.X., H. Zhou. and J.L. contributed to the recruitment of patients with cancer and the collection of patient samples. X. Lin provided conceptual development of the molecular mechanisms of MALT1 and essential resources for the study. J.M., G.X., H.H., X.W. and H.Y. offered experimental support for molecular and animal studies. H. Zheng conceived the concept, designed the experiments and oversaw the project. Y.T. and H. Zheng drafted and revised the paper.

Corresponding author

Correspondence to Hanqiu Zheng.

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The authors declare no competing interests.

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Nature Cancer thanks Heiko Bruns, Daniel Krappmann, Jérôme Paggetti and Guizhi Zhu for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 CRISPR-a screening reveals MALT1 as a candidate in immune evasion.

a. Diagram of the screening system: E0771 cells expressing dCas9 were transduced with a custom gRNA library consisting of six gRNAs per gene. Edited E0771 cells were co-cultured with activated CD8+ T cells at an effector-to-target cell ratio (E:T ratio) of 5 for 24 h, followed by Illumina sequencing of gRNA representation. b, Rank-Ordered Normalized Z-Score (NormZ Score): Genes are rank-ordered based on their NormZ score from the screening. Resistor genes are highlighted in yellow, while sensitizer genes are highlighted in blue. The experiment containing 1 sample was conducted only once. c, E0771 cells overexpressing individual cDNA of 18 successfully cloned resistor genes were quantified for survival rates after co-culture with activated CD8+ T cells at an E:T ratio of 3. Positive control genes are highlighted in blue, and top candidate genes are highlighted in yellow. n = 7 biological independent samples in FST group and n = 12 biological independent samples in other groups. Data represent mean ± SD. d, Representative immunoblotting image of MALT1 for respective E0771, B16F10, and MC38 cell lines. Cells were either transduced with viruses with vector control (VEC) or Malt1 wild-type isoform (M-WT) overexpression. Cells were also transduced with lentiviruses containing control shRNA (CTRL) or two independent shRNAs against Malt1 (KD1 and KD2). β-actin protein was used as an internal loading control. The experiment was independently repeated three times with similar results. e-j, In vitro cell proliferation rates for respective E0771, B16F10, and MC38 cell lines from experiments in (d). Cell proliferation rates were analyzed by Cell Counting Kit-8 (CCK-8) assay. Data represent mean ± SD. For e-j, n = 4 biological independent samples in B16F10-VEC-Day4, E0771-MALT1-KD2-Day2, and E0771-MALT1-KD2-Day4 group, and n = 5 biological independent samples in other groups. For c, unpaired two-tailed t-test, for e-j, two-way ANOVA with Sidak’s multiple comparisons test.

Source data

Extended Data Fig. 2 MALT1 regulates PD-L1 expression in a paracaspase-dependent manner.

a, MFI of cell surface PD-L1 expression from B16F10 CTRL- and MALT1-KD (KD1 and KD2) cells. Data represent mean ± SD. b, MFI of cell surface PD-L1 expression from B16F10 CTRL- and MALT1-KD cells after CD8+ T cell killing for 12 h at E:T ratio of 3. Data represent mean ± SD. c-d, Quantification of MFI of cell surface PD-L1 expression of the cancer cells isolated from CTRL or MALT1-KD tumor tissues formed by E0771 (c) and B16F10 (d) cell lines respectively. Data represent mean ± SD. e, Representative immunoblotting images of stable E0771 cell lines with VEC, M-WT, and M-PD overexpression. β-actin was used as a loading control. The experiment was independently repeated three times with similar results. f, Representative crystal violet staining images of surviving E0771-VEC, E0771-M-WT, and E0771-M-PD after co-culture with activated CD8+ T cells for 24 h at E:T ratio of 3. The experiment was independently repeated three times with similar results. g, MFI of cell surface PD-L1 expression from B16F10-VEC-, M-WT-, and M-PD- cells. Data represent mean ± SD. h, MFI of cell surface PD-L1 expression from B16F10-VEC-, M-WT-, and M-PD- cells after co-culture with activated CD8+ T cells for 12 h at E:T ratio of 3. Data represent mean ± SD. i, Quantification of MFI of cell surface PD-L1 expression of the cancer cells from tumor tissues generated by B16F10 cells overexpressing VEC, M-WT or M-PD. Data represent mean ± SD. For a, b, g, and h, n = 3 biological independent samples per group. For c, d and i, n = 3 mice per group. For a-d, g-i, unpaired two-tailed t-test.

Source data

Extended Data Fig. 3 MALT1 cleaves ROQUIN1/2 to stabilize Cd274 mRNA.

a, E0771-VEC or E0771-MALT1 cells were transduced with respective shRNA to knock down the indicated gene. Cancer cell survival rates after CD8+ T cells killing were showed. b, Survival rates of E0771 cells transduced to singly express MALT1, REGNASE1 (3X FLAG-tagged), or simultaneously express MALT1 and REGNASE1 after CD8+ T cells killing at E:T ratio of 1. c, Survival rates of E0771 cells transduced with lentiviruses to singly KD endogenous MALT1 or REGNASE1, or simultaneously KD both MALT1 and REGNASE1 after CD8+ T cells killing at E:T ratio of 1. d, e, Similar experiments were performed as in (c), with the KD of ROQUIN1 (d) and ROQUIN2 (e) being tested. f, MFI of PD-L1 from E0771 cells which were transduced with lentiviruses to singly KD endogenous MALT1 or REGNASE1, or simultaneously KD both MALT1 and REGNASE1 after CD8+ T cells killing. g, h, Similar experiments were performed as in (f), with the KD of ROQUIN1 (g) and ROQUIN2 (h) being tested. i, The relative mRNA levels of Tnfrsf4, Tnf, Tnfaip3, and Cd274 in E0771-CTRL-KD, E0771-ROQUIN1-KD, and E0771-ROQUIN2-KD cells were determined by qPCR. Gapdh was control. j, E0771 cells overexpressing VEC or MALT1 were immunoblotted with α-MALT1 and α-ROQUIN1/2 antibodies. β-actin was used as control. The experiment was independently repeated three times with similar results. k, The relative mRNA levels of Tnfrsf4, Tnf, Tnfaip3, and Cd274 in E0771-VEC and E0771-MALT1 cells were determined by qPCR. Gapdh was used as control. l, E0771-VEC or E0771-MALT1 cells were transduced with respective shRNA to knock down the indicated gene. Cancer cell survival rates after CD8+ T cells killing at E:T ratio of 3 were showed. m, The relative protein level of PD-L1 at different time points post actinomycin D treatment were detected by FACS. For a, c–e, and l, n = 6 biological independent samples; for b, n = 12 biological independent samples; for f-i, k, and m, n = 3 biological independent samples. For a-i, and k-m, data represent mean ± SD. For a-i, one-way ANOVA analysis; for k-m, two-way ANOVA analysis.

Source data

Extended Data Fig. 4 The tumor-promoting effect of MALT1 depends on the immune system.

a, 2 × 105 4T1 CTRL- and MALT1-KD cells were orthotopically injected into female syngeneic mice. Tumor volume was measured. Data represent mean ± SEM. b, 1 × 105 E0771-CTRL- and MALT1-KD cells were orthotopically injected into female immunodeficient nude mice. Tumor volume was measured. c, 1 × 105 B16F10 CTRL- and MALT1-KD cells were subcutaneously injected into 6-week-old female immunodeficient nude mice. Tumor volume was measured. d, Similar as in (c), with 1 × 105 MC38 CTRL- and MALT1-KD cells were injected into nude mice. e, Similar as in (b), with 1 × 105 PyMT-A CTRL- and MALT1-KD cells were injected into nude mice. f-h, 180 TNBC (f), 256 colon cancer (g), and 236 melanoma (h) patients equally divided into high and low group by MALT1 level. Boxplots represents the first quartile, median, and third quartile values of CD274 in MALT1-high and MALT1-low groups. i, Percentage of PD-L1high cancer cells from PBS- or α-PD-1-treated tumor tissues formed by 4T1-Control KD and 4T1-MALT1-KD cells. n = 3 mice per group. j, Percentage of PD-L1high cancer cells from 4T1 tumors treated with indicated inhibitor and/or PD-1 antibody treatment. n = 3 mice per group. k, Percentage of IFNγ+ CD8+ T cell from PBS- or α-PD-1-treated tumor tissues formed by 4T1-Control KD and 4T1-MALT1-KD cells. n = 3 mice per group. l, Percentage of IFNγ+ CD8+ T cell from 4T1 tumors treated with indicated inhibitor and/or PD-1 antibody treatment. n = 2 mice in Mepazine with α-PD-1 group, and n = 3 mice in other groups. m, 1 × 105 E0771 cells were orthotopically injected into syngeneic female mice. JNJ-67856633 or MLT-985 treatments started since Day 7 with the dosage of 30 mg/kg per day. α-PD-1 treatment started since Day 7 with intraperitoneal injection with 3 mg/kg of α-PD-1 antibody once a week. Tumor volume was measured. n, Similar experiment as in (m) with orthotopically injection of 2 × 105 4T1 cells. o, 2 × 105 4T1-VEC, 4T1-M-WT, and 4T1-M-PD cells were injected into female syngeneic mice. Tumor volume was measured. For b–e, i-o, data represent mean ± SD. For a-e, m-o, two-way ANOVA; for f-i, k, unpaired two-tailed t-test; for j, l, one-way ANOVA.

Source data

Extended Data Fig. 5 MALT1 enhances the proliferation and pro-tumoral polarization of macrophages in vitro.

a, The percentage of subtypes of immune cells from E0771-VEC, E0771-M-WT, and E0771-M-PD tumor tissues of experiment in Fig. 3a. 5 tumor samples from 5 mice were contained in each group. b, Quantification of the cell number of different macrophage subtypes from E0771-VEC, E0771-M-WT, and E0771-M-PD tumor tissues of experiment in Fig. 3b. 5 tumor samples from 5 mice were contained in each group. c, Representative images of BMDMs cultured for 48 h with the addition of CM collected from E0771-VEC, E0771-M-WT, and E0771-M-PD cells. d, Quantification of the number of BMDMs in (c) by using hemocytometer for counting. n = 4 biological independent samples. Data represent mean ± SD. e-f, MFI of M1-like macrophage markers MHC-II (e) and CD86 (f) of BMDMs cultured with CM from E0771-VEC, E0771-M-WT, and E0771-M-PD cells. n = 5 biological independent samples. Data represent mean ± SD. g-h, MFI of M2-like macrophage markers CD206 (g) and CD163 (h) of BMDMs cultured with CM of E0771-VEC, E0771-M-WT, and E0771-M-PD cells. n = 5 biological independent samples. Data represent mean ± SD. For d-h, one-way ANOVA with Dunnett’s multiple comparisons test.

Source data

Extended Data Fig. 6 MALT1 promotes the immune-suppressive function of TAMs.

a, Representative FACS analysis plot for CD11b+, F4/80+ macrophages infiltrated in E0771-VEC, E0771-M-WT, and E0771-M-PD tumor samples. b-c, Quantification of the percentages of CD8+ (b) and CD4+ (c) T cell among all living cells from E0771-VEC, E0771-M-WT, and E0771-M-PD tumor samples. n = 5 mice per group. Data represent mean ± SD. d, Quantifications of the percentage of macrophages among CD45+ cells, MFI of MHC-II and CD206 on macrophages, percentage of CD45+CD8+ and CD45+CD4+ T cells among living cells from E0771-CTRL, MALT1-KD1, and -MALT1-KD2 tumor tissues. n = 8 mice per group. Data represent mean ± SD. e, Similar experiments were performed as in (d) for PyMT-A cells. n = 5 mice per group. Data represent mean ± SD. f, Quantification of the survival rates of B16F10-VEC, B16F10-M-WT, and B16F10-M-PD cancer cells in co-culture system with activated CD8+ T cells (- MΦ group) and in tri-culture system ( + MΦ group). The ratio of tumor cells, macrophages and T cells is 1: 4: 4. n = 6 biological independent samples. Data represent mean ± SD. g, FACS analysis of the percentage of macrophage (CD11b+ F4/80+) among CD45+ cells from lungs of mice injected with either PBS (left) or α-CSF1R neutralizing antibody (right). h, FACS analysis of CD8+ T cell (TCRβ+CD8+) percentage among CD45+ cells from spleens of mice injected with PBS (left) or α-CD8 neutralizing antibody (right). For b–f, one-way ANOVA with Dunnett’s multiple comparisons test.

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Extended Data Fig. 7 Signaling initiated by MALT1’s DD domain induces the expression of paracrine factors to polarize TAMs.

a, BCL10, CARD10, or RELA knockdown was performed in E0771-VEC, -M-WT, and -M-PD cell lines, respectively. The cell number of BMDMs cultured by CM of these cells was determined. n = 3 biological independent samples. b, Immunoblot analysis of p-IκBα in E0771 cells expressing indicated MALT1 mutants. β-actin was control. c, Immunoblot analysis of REGNASE1 and β-actin in MALT1-KD E0771 cells overexpressing indicated MALT1 mutant. d, Immunoblot analysis of ROQUIN1/2 and β-actin in MALT1-KD E0771 cells overexpressing indicated MALT1 mutant. For c, d, arrowhead (top) indicates full-length protein, and hollow arrowhead indicates cleaved products. For b–d, the experiments were independently repeated three times with similar results. e, Percentages of macrophages among CD45+ cells from tumor tissues generated by E0771 cells with the indicated MALT1 mutant expression. n = 3 mice per group. f-g, MFI of MHC-II (f) and CD206 (g) on TAMs isolated from tumor tissues generated by E0771 cells with the indicated MALT1 mutant expression. n = 3 mice per group. Data represent mean ± SD. h-i, B16F10-MALT1-KD1 cells were overexpressed with the indicated MALT1 mutants. 1 × 105 cells were subcutaneously injected into C57BL/6 mice. Tumor volume was measured. Data represent mean ± SEM. Note that all mutants in (h) contain PD mutation. j, Volcano plots of differentially expressed genes comparing E0771 cells expressing either M-WT or different MALT1 mutants v.s. vector control cells. k, Venn diagram showing differentially upregulated genes only in E0771 cells expressing M-WT and M-PD, but not in cells expressing M-V87R and M-L88D. For j and k, n = 3 biological independent samples per group. l-n, 180 TNBC (l), 256 colon cancer (m), and 236 melanoma (n) patients were equally divided into high and low group by MALT1 level. Boxplots showed the distributions of average scores of 133 genes in MALT1-high and MALT1-low groups. o, Average mRNA expression (Z-score) of 133 genes in tumors from responder (R, n = 11) versus non-responder (NR, n = 32) of gastric cancer patients (PRJEB25780) who received anti-PD-1 antibody therapy. p, Quantification of absolute gene expression changes of E0771-M-WT versus E0771-VEC (Y-scale) and E0771-M-PD versus E0771-VEC (X-scale). n = 3 biological independent samples per group. For a, e-g, data represent mean ± SD. For l-o, box represents the first quartile, median, and third quartile values. For a, h-i, two-way ANOVA; for e-g, j, l-o, unpaired two-tailed t-test.

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Extended Data Fig. 8 The DD domain of MALT1 is essential for the induction of CSF1, PGE2 and CXCL1 expression in cancer cells.

a-c, Enzyme-linked immunosorbent assay (ELISA) analyses of CSF1 (a), PGE2 (b), and CXCL1(c) levels in the CM from E0771 cells expressing the indicated MALT1 mutants. Data represent mean ± SD. d-f, ELISA analyses of CSF1 (d), PGE2 (e), and CXCL1(f) levels in the CM from B16F10 cells expressing the indicated MALT1 mutants. Data represent mean ± SD. g-I, The relative mRNA levels of Csf1 (g), Ptges (h), and Cxcl1 (i) in 4T1 cells with the indicated MALT1 mutant expression. Data represent mean ± SD. j-l, The relative mRNA level of Csf1 (j), Ptges (k), and Cxcl1 (l) in MC38 cells with the indicated MALT1 mutant expression. Data represent mean ± SD. For a-l, n = 3 biological independent samples, one-way ANOVA with Dunnett’s multiple comparisons test.

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Extended Data Fig. 9 MALT1 ASO treatment leads to PD-L1 suppression and M1-like macrophage polarization in vitro.

a-b, Quantification of the survival rates of DMSO-treated, MI-2-treated, Mepazine-treated, and Thioridazine-treated E0771 (a) and B16F10 (b) cancer cells in co-culture system (- MΦ group) and in tri-culture system ( + MΦ group). The ratio of cancer cells, macrophages and T cells is 1: 2: 2. For a, n = 5 biological independent samples in DMSO + MΦ and MI-2 + MΦ group, and n = 6 biological independent samples in other groups. For b, n = 5 biological independent samples per group. Data represent mean ± SD. c, Quantification of cancer cell survival rates of ASONT, -24, or -25 treated B16F10 cancer cells in co-culture (- MΦ group) and in tri-culture system ( + MΦ group). The ratio of cancer cells, macrophages and T cells is 1: 2: 2. n = 6 biological independent samples. Data represent mean ± SD. d, Quantification of the cell number of BMDMs cultured with CM collected from ASO-treated B16F10 cells. n = 4 biological independent samples. Data represent mean ± SD. e-f, MFI of MHC-II (e) and CD206 (f) expression on macrophages cultured with CM collected from ASONT, -24, or -25 treated B16F10 cancer cells in vitro. n = 3 biological independent samples. Data represent mean ± SD. g-i, ELISA analyses of CSF1 (g), PGE2 (h), and CXCL1(i) levels in CM collected from ASONT and ASO24 treated E0771 cancer cells. Data represent mean ± SD. j-l. ELISA analyses of CSF1 (j), PGE2 (k), and CXCL1(l) levels in CM collected from ASONT and ASO24-treated B16F10 cancer cells. Data represent mean ± SD. m-o, The relative mRNA levels of Csf1 (m), Ptges (n), and Cxcl1 (o) in ASONT and ASO24-treated 4T1 cells. Data represent mean ± SD. p-r, The relative mRNA levels of Csf1 (p), Ptges (q), and Cxcl1 (r) in ASONT and ASO24-treated MC38 cells. Data represent mean ± SD. For g-r, n = 3 biological independent samples. For a-f, one-way ANOVA with Dunnett’s multiple comparisons test; for g-r, unpaired two-tailed t-test.

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Extended Data Fig. 10 The effect of MALT1 ASO in human cancer cells and in mice.

a, b, Representative FACS plots (a) and MFI (b) of PD-L1 from MDA-MB-231 cells treated by CTRL, ASONT, MI-2 inhibitor and MALT1 ASO24, and followed by human CD8+ T cells killing. n = 3 biological independent samples. c, Similar as in (a), MFI of PD-L1 on SUM159 cells was shown. n = 3 biological independent samples. d, 1 × 106 mature human macrophages derived from hPBMC were cultured with CM from CTRL, ASONT, MI-2 or MALT1 ASO24 treated MDA-MB-231 cells. 2 days later, the number of macrophages were quantified. n = 5 biological independent samples. e, f, Representative FACS plots (e) and MFI (f) of MHC-II on PBMC similarly treated as in (d). n = 3 biological independent samples. g, h, Representative FACS plots (g) and MFI (h) of CD206 on PBMC similarly treated as in (d). n = 3 biological independent samples. i, k, Survival rates of CTRL-treated and MI-2-treated SUM159 (i) and MDA-MB-231 (k) cells in co-culture (- MΦ) and tri-culture ( + MΦ) system. j, l, Survival rates of ASONT-treated and MALT1-ASO24-treated SUM159 (j) and MDA-MB-231 (l) cells in co-culture and tri-culture system. For i-l, the ratio of cancer cells, macrophages and T cells is 1: 2: 2. For i-l, n = 6 biological independent samples. m, Intratumoral ASO treatment was performed in E0771 tumor-bearing nude mice with the dosage of 5 nmol/injection, twice a week. Tumor volume was measured. n, Percentage of CD25+CD8+ T cell, CD44+CD8+ T cell, CD69+CD8+ T cell within CD45+CD8+ T cells from tumor tissues treated by intratumoral ASO injection. n = 3 mice per group. o, Fluorescence images of the tumor-bearing mice at different time points post injection of LNP-ASO24 (6.5 nmol per mouse) labeled with a fluorescence dye. One mouse per group. p, Total radiant efficiency for in vivo fluorescence intensity in the tumor area of (o). n = 2 tumors per group. q, Percentages of IFNγ+ Tregs among Tregs from LNP-ASONT- or LNP-ASO24-treated E0771 tumor tissues. n = 3 mice per group. r-t, Percentages of B cell (r), NK cell (s), and neutrophil (t) in LNP-ASONT- or LNP-ASO24-treated E0771 tumor tissues, n = 3 mice per group. For b–d, f, h, i-n, q-t, data represent mean ± SD. For b–d, f, h, i-l, q-t, unpaired two-tailed t-test; for m, two-way ANOVA.

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Tao, Y., Tian, C., Qi, S. et al. Targeting both death and paracaspase domains of MALT1 with antisense oligonucleotides overcomes resistance to immune-checkpoint inhibitors. Nat Cancer 6, 702–717 (2025). https://doi.org/10.1038/s43018-025-00930-5

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