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LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells

Nature Cancer volume 5, pages 262–282 (2024)Cite this article

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Abstract

The contribution of antitumor immunity to metastatic dormancy is poorly understood. Here we show that the long noncoding RNA Malat1 is required for tumor initiation and metastatic reactivation in mouse models of breast cancer and other tumor types. Malat1 localizes to nuclear speckles to couple transcription, splicing and mRNA maturation. In metastatic cells, Malat1 induces WNT ligands, autocrine loops to promote self-renewal and the expression of Serpin protease inhibitors. Through inhibition of caspase-1 and cathepsin G, SERPINB6B prevents gasdermin D-mediated induction of pyroptosis. In this way, SERPINB6B suppresses immunogenic cell death and confers evasion of T cell-mediated tumor lysis of incipient metastatic cells. On-target inhibition of Malat1 using therapeutic antisense nucleotides suppresses metastasis in a SERPINB6B-dependent manner. These results suggest that Malat1-induced expression of SERPINB6B can titrate pyroptosis and immune recognition at metastatic sites. Thus, Malat1 is at the nexus of tumor initiation, reactivation and immune evasion and represents a tractable and clinically relevant drug target.

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Fig. 1: Malat1 is required for metastatic reactivation of breast cancer.
Fig. 2: Induced silencing of Malat1 indicates a role in both tumor initiation and reinitiation at metastatic sites.
Fig. 3: Malat1 regulates the stemness traits and metastatic potential of colorectal cancer, lung cancer and melanoma models.
Fig. 4: Malat1 suppresses T cell-mediated immunity and induces recruitment of immunosuppressive neutrophils at incipient metastatic sites.
Fig. 5: Malat1 mediates escape from T cell-mediated dormancy.
Fig. 6: Expression of SERPINB6B is required for immune evasion and metastatic outgrowth.
Fig. 7: SERPINB6B inhibits pyroptosis and decreases the sensitivity of incipient metastatic cells to T cell-mediated killing.
Fig. 8: ASO-mediated inhibition of Malat1 induces antitumor immunity and suppresses metastatic outgrowth.

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Data availability

Total RNA-seq, scRNA-seq and ChIRP-seq data that support the findings of this study have been deposited in the Gene Expression Omnibus under accession codes GSE183562, GSE183943 and GSE183596. Constructs and genetically engineered cell lines will be made available upon a material transfer agreement. All data are available in the main text or the Supplementary Information. Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding authors on reasonable request.

Code availability

No custom code was generated for data analysis.

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Acknowledgements

This study is dedicated to the memory of our mentor, teacher and friend Filippo Giancotti, a pioneer in the metastasis field known for his brilliance, rigor and kindness. This work was supported by NIH grant R35 CA197566 and partially by CPRIT Recruitment of Established Investigators Award RR160031 (F.G.G.). Use of the Core Facilities was supported by NIH grants P30CA008748 (MSKCC), P30 CA016672 (MDACC) and P30 CA013696 (Columbia University). The Advanced Technology Genomics Core was also supported by NIH award 1S10OD024977 (MDACC), and the Single Cell Analysis Core was also supported by CPRIT grant RP180684. B.I. is supported by NIH grants R37CA258829, U54CA274506, R01CA280414 and R01CA266446, the Pershing Square Sohn Cancer Research Alliance Award, the Burroughs Wellcome Fund Career Award for Medical Scientists, a Tara Miller Melanoma Research Alliance Young Investigator Award, the Louis V. Gerstner, Jr, Scholars Program and the V Foundation Scholars Award. Graphics in Figs. 2f,h and 8h were created with BioRender.com. We thank P. Bird (Monash University) and K. V. Prasanth (University of Illinois, Chicago) for constructs and A. K. Rustgi and P. Sims (Columbia University) for critically reading the manuscript.

Author information

Author notes

  1. Deceased: Filippo G. Giancotti.

Authors and Affiliations

  1. Cancer Metastasis Initiative, Herbert Irving Comprehensive Cancer Center and Department of Genetics and Development, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

    Dhiraj Kumar, Sreeharsha Gurrapu, Yan Wang, Seong-Yeon Bae, Poonam R. Pandey & Filippo G. Giancotti

  2. Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Hong Chen & Jayanta Mondal

  3. The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, USA

    Jayanta Mondal

  4. Department of Urology, Urological Science Institute, Yonsei University College of Medicine, Seoul, Republic of Korea

    Hyunho Han

  5. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Chang-Jiun Wu

  6. Department of Systems Biology, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA

    Spyros Karaiskos

  7. Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Fei Yang, Aysegul Sahin & Ignacio I. Wistuba

  8. Department of Genitourinary Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Jianjun Gao

  9. Department of Breast Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Debasish Tripathy

  10. Shanghai Tenth People’s Hospital, Advanced Institute of Translational Medicine, School of Medicine and Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Hua Gao

  11. Department of Medicine, Division of Hematology and Oncology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York, NY, USA

    Benjamin Izar

  12. Department of Systems Biology, Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY, USA

    Benjamin Izar

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  1. Dhiraj Kumar
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Contributions

F.G.G. designed the overall strategy and directed the project. H.G. performed the initial genetic screen and isolated the prometastatic fragment of Malat1. D.K. designed and performed most of the experiments. S.G. performed IHC staining and assisted with some of the mouse and FACS experiments and data analysis. H.H., Y.W., H.C., S.K. and C.-J.W. performed bioinformatic analyses. P.R.P. helped with the RNA pulldown experiments and data analyses. S.-Y.B. and J.M. helped with some of the RT–qPCR and FACS experiments and data analyses. F.Y., A.S., I.I.W. and D.T. selected breast cancer clinical samples and provided clinical expertise. A.S. analyzed RNA FISH data. J.G. provided immuno-oncology expertise. B.I. participated in the immune cell infiltration IF experimental design and data interpretation. D.K. and F.G.G. wrote the initial manuscript, and D.K, H.G., S.-Y.B., S.G., B.I. and F.G.G. revised the manuscript.

Corresponding authors

Correspondence to Dhiraj Kumar or Benjamin Izar.

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Competing interests

B.I. is a consultant for or received honoraria from Volastra Therapeutics, Johnson & Johnson/Janssen, Novartis, Eisai, AstraZeneca and Merck and has received research funding to Columbia University from Agenus, Alkermes, Arcus Biosciences, Checkmate Pharmaceuticals, Compugen, Immunocore and Synthekine. The other authors declare no competing interests.

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Nature Cancer thanks Judy Lieberman and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Malat1 acts as a pro-metastatic and deletion of Malat1 gene does not affect nearby genes expression.

a, Malat1 RNA FISH in the indicated cell lines. b, Model depicts full length (FL) Malat1 and fragment (Frag, 1040-2137 bp) of Malat1 lncRNA isolated by cDNA screening. c, 4T07 and D2A1-d cells were transduced with full-length (FL) Malat1 or the 1040-2137 bp fragment (Frag) and subjected to RT-qPCR (n = 3 independent experiments). d and e, Malat1 FL or Frag overexpressing 4T07-TGL (d, n = 5 mice/group) or D2A1-d-TGL cells (e, n = 6 mice/group) were inoculated i.v. in syngeneic (Balb/c) mice. Mice were subjected to bioluminescent imaging at the indicated time points. f, Schematics of the mouse chromosomal region encompassing Malat1, location of the sequences complementary to the gRNA pairs (1+2) used for deletion and the primer pairs (F1/R1, F2/R2 and F3/R3) used for genomic PCR. g, Sequences of the fragments of DNA deleted by CRISPR-Cas9. The sequences targeted by the two gRNAs are represented by yellow boxes, and the PAM sequences by shaded gray boxes. The Δ sign indicates deletion. h, RT-qPCR analysis of the expression of genes upstream and downstream of Malat1 in WT and Malat1 KO 4T1 cells (n = 3 independent experiments). i, Localization of Malat1 (RNA-FISH) and SRSF1 (IF) in the indicated cells. Images are representative of three independent experiments. j, Immunoblotting of nuclear splicing factors SRSF1, SRSF2, and SRSF4 in indicated cells. k, RNA-FISH of Malat1 and Neat1. Dotted green staining indicates Neat1. Images are representative of three independent experiments. l, RNA-FISH of Malat1 in 4T1 primary tumors and spontaneous lung metastases (n = 5 mice/group). m, Western blot of Cas9 from WT 4T1 cells and Cas9 mediated Malat1 knockout 4T1 single cell clones (KO#3, KO#33, or KO#43, left). Cas9 expression in KO#33 before and after Cas9 KO using four different Cas9 gRNA (right). n, Balb/c mice were injected with 4T1-TGL WT with or without Cas9 and Malat1 KO#33 cells before and after Cas9 deletion. Lung metastasis was examined using bioluminescence imaging (n = 7 mice/group). P values in c, h, and n were calculated using a one-way ANOVA with multiple comparisons, in d and e using a two-sided Mann-Whitney test, and in l using a two-tailed, unpaired Student’s t-test. Data are presented as mean values +s.e.m.

Extended Data Fig. 2 The expression MALAT1 correlates with tumor progression and recurrence and is associated with poor prognosis in breast cancer.

a, RT-qPCR analysis of Malat1 expression in WT and Malat1 KO clones of D2A1-m cells (n = 3 independent experiments). b, WT and Malat1 KO D2A1-m-TGL cells were injected i.v. into Balb/c mice and lung metastasis was measured by bioluminescent imaging (left) and quantification of photon flux (right, n = 6 mice for WT, n = 8 mice for KO#13, and n = 7 mice for KO#35 group). c, Malat1 expression in WT or Malat1 KO clones of MMTV-neu cells (n = 3 independent experiments). d, WT and Malat1 KO MMTV-neu-TGL cells were administered i.v. into 5 weeks old MMTV-neu mice and lung metastasis was measured by bioluminescent imaging (left) and quantified (right, n = 5 mice/group). e, High levels of MALAT1 correlate with breast cancer progression in breast cancer datasets from Oncomine. f, The expression of MALAT1 is significantly higher in the samples from patients recurring at 5 years as compared to those from non-recurring patients in breast cancer datasets from Oncomine. g, Higher levels of MALAT1 correlate with breast cancer metastasis in the breast cancer datasets from LnCAR. Box plots for fig e, f and g span the first to third quartiles and whiskers show 1.5 × interquartile range. h, KM plotter analysis of Relapse Free Survival (RFS) of breast cancer patients using all available MALAT1 probe sets. IDBC, Invasive Ductal Breast Cancer; DBC, Ductal Breast Cancer; IBC, Invasive Breast Cancer. P values in a-d were calculated using a one-way ANOVA with multiple comparisons and in e-g using a two-tailed, unpaired Student’s t-test. Data are presented as mean values +s.e.m.

Extended Data Fig. 3 Deletion of Malat1 induces dormancy features in breast cancer.

a, Immunoblotting of WT and Malat1 KO 4T1 cells with antibodies to NR2F1, DEC2, p-p38 and p-ERK1/2. RhoGDI was used as a loading control for immunoblotting. b-d, WT and Malat1 KO 4T1-TGL cells were subjected to assays of cell migration (b, n = 3 independent experiments), cell invasion (c, n = 3 independent experiments), and clonogenicity (d, n = 3 independent experiments). e-g, WT and Malat1 KO D2A1-m cells were subjected to assays of cell growth (e, n = 3 independent experiments), tumor sphere formation (f, n = 3 independent experiments) and clonogenicity (g, n = 3 independent experiments). h, Orthotopic mammary tumor growth of WT and Malat1 KO 4T1-TGL cells was monitored by bioluminescent imaging. Graph represents the total photon flux at the indicated times (n = 8 mice/group). i, 4T1 cells expressing doxycycline (Dox)-inducible control and Malat1 targeting mirE shRNAs were treated with Dox for 48 hours and subjected to RT-qPCR to estimate Malat1 expression (n = 3 independent experiments). j, Malat1 RNA-FISH in 4T1 orthotopic tumors at day 16 after induction with Dox. Dot plots represent the FISH intensity signal/field from the indicated samples (n = 5 mice/group). k-n, IHC analysis of the indicated cell cycle progression, neoangiogenesis, and dormancy markers in WT and Malat1-silenced 4T1 orthotopic tumors. The bar graphs indicate the quantitation of IHC staining data (n = 5 mice/group). o, quantification of GFP+ circulating tumor cells (CTCs) from the peripheral blood of 4T1-TGL mammary tumor-bearing Balb/c mice at 16 days (n = 9 mice for Sh-co and n = 4 mice for Sh#3 and Sh#4). p, Lung sections from the indicated groups of mice were subjected to H&E staining. Images are representative from n = 3 mice/group. P values were calculated using a one-way ANOVA with multiple comparisons. Data are presented as mean values +s.e.m (b-g, i, j, l, n, o) or +s.d. (h).

Extended Data Fig. 4 Malat1 controls the expression of immunoregulatory and Wnt signaling genes.

a, WT and Malat1 KO 4T1 were subjected to bulk RNA-seq. Heatmap represents the top 50 DEGs (differentially expressed genes) in WT versus Malat1 KO 4T1 cells. b, RT-qPCR analysis of the expression of the indicated genes from the list of top DEGs (n = 3 independent experiments). c, Hallmark gene set enrichment analysis (GSEA) of bulk RNA-seq data from WT versus Malat1 KO 4T1 cells. d, KEGG-DEG relationship network. Data are presented as mean values +s.e.m (b).

Extended Data Fig. 5 Malat1 regulated 4T1 metastasis is antithetically controlled by CD8 T cells and neutrophils.

a, Feature Plot of activation, lytic immune, immune suppressor, and antigen presentation function genes at indicated conditions. b, UMAP of neutrophils isolated from the lungs of mice inoculated 4 weeks earlier with 4T1 cells identifies seven clusters. c, DittoPlots of neutrophil maturation markers, immunosuppressive markers, and gelatinase granules. d, UMAPs represent scRNA-seq analyses of CD45+ cells from 4T1 primary tumors. e, Comparison of immune cells infiltration during the development of spontaneous lung metastases in mice injected with Dox-inducible control or Malat1-targeting mirE shRNAs.

Extended Data Fig. 6 Tumor cell-intrinsic expression of Malat1 regulates antithetically the infiltration of T cells and neutrophils at early metastatic sites.

a and b, Graphs represent the percentage of indicated immune subsets isolated from the lungs of Balb/c mice at 5 days or 4 weeks after i.v. injection of WT and Malat1 KO 4T1 cells. Symbols represent data from individual mice. The horizontal bar indicates the mean value. c, tSNE plots of immune subsets isolated from the lungs of Balb/c mice after i.v. injection of WT and Malat1 KO 4T1 cells at 5 days or 4 weeks. d, Graphs represent the percentage of indicated immune subsets isolated from the blood of Balb/c mice as indicated in (a). Each individual data point was visualized. Gating strategies for flow cytometry data analysis are provided in the Supplementary Fig. 1. e, Mice were inoculated i.v. with WT or Malat1 KO 4T1 cells. Metastatic lungs were isolated 4 weeks after the injection of tumor cells and subjected to IHC staining with the T cell markers CD4 and CD8 and the neutrophil marker MPO. Images are the representative of n = 5 mice/group. f, Mice were inoculated orthotopically with WT or Malat1-silenced 4T1 cells. The IHC staining was performed for T cell and neutrophil markers at 16 days (n = 5 mice/group). g and h, Representative images of spleens (g) of mice and comparison of their weight (h) at 4 weeks after i.v. injection of WT or Malat1 KO 4T1 cells (n = 4 mice for WT and n = 5 mice for KO#3 group). P values in a, b, d, and f were calculated using a one-way ANOVA with multiple comparisons, in g using a two-tailed, unpaired Student’s t-test. Data are presented as mean values +s.e.m.

Extended Data Fig. 7 Malat1 interacts directly with DNA sequences near subsets of differentially expressed genes.

a, RNA retrieval displayed as percentage of Gapdh (negative control) and Malat1 in Odd, Even, and LacZ pulldown samples (n = 3 independent experiments). Probe sequences are listed in Supplementary Table 6. b, GREAT MSigDB pathway analysis of genes identified by Malat1 ChIRP. c, Venn diagram showing the substantial overlap between genes identified by Malat1- ChIRP and genes differentially expressed genes upon Malat1 KO. d, RT-qPCR analysis of the expression of Serpins in 4T1 cells (n = 3 independent experiments). e, RT-qPCR analysis of ChIRP DNA shows retrieval of Serpinb6b DNA with a Malat1-specific probe, but not the LacZ probe. Gapdh served as a negative control (n = 3 independent experiments). f, The expression of Wnts and Serpins was analyzed RT-qPCR in WT or Malat1 KO 4T1 cells (n = 3 independent experiments). g, Malat1 ChIRP-seq peaks in WT 4T1 cells were mapped to the genome tracks of Serpinb6b, Wnt3, Wnt6, and Wnt9b by using the UCSC genome browser. h, An enrichment map of proteins identified by Malat1 ChIRP-MS was generated by using g:Profiler. Clusters of nodes were automatically labeled by using the AutoAnnotate application. i, Immunofluorescence staining of YAP under the indicated conditions. Nuclei were counter stained with DAPI. Images are the representative of three independent experiments. j and k, Western blot (j) and RT-qPCR (k) analysis of YAP signaling-associated proteins and mRNAs (n = 3 independent experiments). P values in a and k were calculated using a two-way ANOVA with multiple comparisons, in d using a two-tailed, unpaired Student’s t-test and in e and f using a one-way ANOVA with multiple comparisons. Data are presented as mean values +s.e.m.

Extended Data Fig. 8 Contribution of SERPINB6B and WNT signaling to stemness and metastasis.

a and b, Levels of expressions of Serpina3g (S3g), Serpinb6b (S6b), Wnt3, Wnt6, and Wnt9b were assessed by RT-qPCR in the indicated cells (n = 3 independent experiments). c, 4T1 cells were transduced with tandem miRE-shRNAs targeting S3g and S6b and subjected to RT-qPCR analysis (n = 3 independent experiments). d, 4T1-TGL cells were transduced with an inducible control mirE shRNA or tandem mirE shRNAs targeting S3g and S6b and inoculated i.v. in Balb/c mice. Mice were treated from day 0 onwards with doxycycline. Lung metastasis was measured by bioluminescence imaging. The panels show representative images (left) and the graph represents the normalized photon flux (right, n = 5 mice/group). e, Malat1 KO 4T1 cells were transduced with cDNA vectors encoding S3g, S6b or Wnt3 and subjected to RT-qPCR analysis. (n = 3 independent experiments). f, Malat1 KO 4T1 cells overexpressing or not Wnt3 were subjected to sphere formation assay (n = 3 independent experiments). g, 4T07 cells were transduced with cDNA vectors encoding S6b or Wnt3 and subjected to RT-qPCR analysis (n = 3 independent experiments). h, IF staining of β-catenin in 4T07 cells overexpressing or not Wnt3 in 4T07. Images are the representative of n = 3 biological replicates. i, Bar graph represents the tumor sphere formation capacity of 4T07 cells overexpressing or not Wnt3 and Malat1 WT or KO 4T1 cells (n = 4 independent experiments). j, RT-qPCR analysis of 4T1 cells expressing control, S6b-, and Porcn-targeted Dox inducible miRE shRNAs (n = 3 independent experiments). k, Control, S6b- and Porcn-silenced 4T1 cells were subjected to immunoblotting with antibodies to Porcn, Wnt3, Wnt6, and β-catenin. Actin served as a loading control. l, Control, S6b- and Porcn-silenced 4T1 cells were subjected to tumor sphere assay. Bar graph represents the percentage of tumor spheres over the control value (n = 3 independent experiments). ns, not significant; S3g, Serpina3g; S6b, Serpinb6b; Porcn, Porcupine. P values in a-g were calculated using a two-tailed, unpaired Student’s t-test, in i, j, and l using a one-way ANOVA with multiple comparisons. Data are presented as mean values +s.e.m.

Extended Data Fig. 9 SERPINB6 is associated with poor prognosis in breast cancer.

a, Expression of SERPINB6 in normal (N) and invasive breast cancer (IBC) samples (source Finak breast dataset, Oncomine). b, Expression of SERPINB1, B6, and B9 in breast cancer samples (source http://gepia2.cancer-pku.cn/#analysis). Box plots for fig a and b span the first to third quartiles and whiskers show 1.5 × interquartile range. c, Heatmap shows the level of expression of Serpins genes from differentially expressed in control versus Malat1 KO MMTV-neu cells. Highlighted Serpin genes are the common downregulated Serpins family from DEGs list upon Malat1 KO in MMTV-neu and 4T1 cells. d, KM plotter analysis of Overall Survival (OS) based on expression level of SERPINA3, SERPINB1, and SERPINB6 from RNA-seq data of breast cancer patients. e and f, Overall Survival correlation of indicated Serpins from Liu_2014 and Tang_2018 proteomic datasets68. P values in a and b were calculated using a two-tailed, unpaired Student’s t-test.

Extended Data Fig. 10 SERPINB6B controls antigen presentation and blocks CTSG and Caspase-1 activity.

a, Flow cytometry data of B2m, H-2Kd and H-2Dd at indicated condition (n = 3 independent experiments). b, Percentage of LDH release over control value in control, Malat1 KO, and Malat1 KO 4T1 cells overexpressing S6b exposed to Nigericin and ATP. LDH release was measured by using the CytoTox 96 assay (n = 3 independent experiments). c, RT-qPCR analysis of Gsdma, Gsdmc, Gsdmd, and Gsdme in 4T1-TGL cells (n = 3 independent experiments). d, Immunoblotting of GSDMD and GSDME in WT and Malat1 KO 4T1 cells. e, Percentage of LDH release over control value in WT, Malat1 KO, and Malat1 KO 4T1 cells overexpressing S6b exposed to raptinal for 4 hours (n = 5 independent experiments). f, HEK-293 cells were transfected with mouse Flag-Gsdmd and lysed without protease inhibitors. 10 μg of total proteins was incubated with the indicated concentrations of the indicated proteases for 1 h at 37 °C in 25 μl of reaction buffer. Samples were resolved on SDS-PAGE and subjected to immunoblotting with anti-Flag antibodies. g, HEK-293 cells were transfected with mouse Flag-Gsdmd in combination with HA-S6b or HA-S3g and lysed without protease inhibitors. Total proteins (10 μg) were incubated with increasing concentrations of proteases (25, 50, 100, 200 ng of CTSG; 0.002, 0.02, 0.2, 2 U of Casp-1; 0.05, 0.5, 5, 50 U of Casp-11) for 1 h at 37 °C, resolved on SDS PAGE, and subjected to immunoblotting with anti-Flag antibodies. h, RT-qPCR analysis of CtsG expression in 4T1-TGL cells overexpressing CtsG (n = 3 independent experiments). i, RT-qPCR analysis of CtsG expression in Malat1 KO 4T1 cells overexpressing Serpinb6b or not (n = 3 independent experiments). j, Malat1 KO 4T1 cells were transduced with mirE-shRNAs targeting Gsdmd or control shRNA and subjected to immunoblotting as indicated. P values in a,b,f, and i were calculated using a one-way ANOVA with multiple comparisons, and in c and h using a two-tailed, unpaired Student’s t-test. Data are presented as mean values +s.e.m (a, b, f, i) or +s.d. (c, h).

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Kumar, D., Gurrapu, S., Wang, Y. et al. LncRNA Malat1 suppresses pyroptosis and T cell-mediated killing of incipient metastatic cells. Nat Cancer 5, 262–282 (2024). https://doi.org/10.1038/s43018-023-00695-9

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