Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

LIMIT is an immunogenic lncRNA in cancer immunity and immunotherapy

Abstract

Major histocompatibility complex-I (MHC-I) presents tumour antigens to CD8+ T cells and triggers anti-tumour immunity. Humans may have 30,000–60,000 long noncoding RNAs (lncRNAs). However, it remains poorly understood whether lncRNAs affect tumour immunity. Here, we identify a lncRNA, lncRNA inducing MHC-I and immunogenicity of tumour (LIMIT), in humans and mice. We found that IFNγ stimulated LIMIT, LIMIT cis-activated the guanylate-binding protein (GBP) gene cluster and GBPs disrupted the association between HSP90 and heat shock factor-1 (HSF1), thereby resulting in HSF1 activation and transcription of MHC-I machinery, but not PD-L1. RNA-guided CRISPR activation of LIMIT boosted GBPs and MHC-I, and potentiated tumour immunogenicity and checkpoint therapy. Silencing LIMIT, GBPs and/or HSF1 diminished MHC-I, impaired antitumour immunity and blunted immunotherapy efficacy. Clinically, LIMIT, GBP- and HSF1-signalling transcripts and proteins correlated with MHC-I, tumour-infiltrating T cells and checkpoint blockade response in patients with cancer. Together, we demonstrate that LIMIT is a cancer immunogenic lncRNA and the LIMIT–GBP–HSF1 axis may be targetable for cancer immunotherapy.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: LIMIT is an immunogenic lncRNA.
Fig. 2: LIMIT augments MHC-I expression.
Fig. 3: LIMIT enhances anti-tumour immunity.
Fig. 4: LIMIT cis-activates GBPs to boost MHC-I and tumour immunity.
Fig. 5: GBPs activate HSF1 to stimulate MHC-I expression and tumour immunity.
Fig. 6: The LIMIT–GBP–HSF1 axis drives MHC-I and tumour immunity.

Data availability

The RNA-seq data (GSE99299) and processed single-cell data (GSE123814) were obtained from Gene Expression Omnibus (GEO). The MS proteomics data (PXD006003) were obtained from PRIDE repository. The TCGA cancer datasets were obtained from UCSC Xena (http://xena.ucsc.edu/). The RNA-seq data and clinical information for immune checkpoint blockade clinical trials were provided by the respective corresponding authors. All raw data supporting the findings of this study are available from the corresponding author on request. Source data are provided with this paper.

References

  1. 1.

    Zou, W., Wolchok, J. D. & Chen, L. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: mechanisms, response biomarkers, and combinations. Sci. Transl. Med. 8, 328rv324 (2016).

    Google Scholar 

  2. 2.

    Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  PubMed  Google Scholar 

  3. 3.

    Garcia-Lora, A., Algarra, I. & Garrido, F. MHC class I antigens, immune surveillance, and tumor immune escape. J. Cell. Physiol. 195, 346–355 (2003).

    CAS  PubMed  Google Scholar 

  4. 4.

    Festenstein, H. & Garrido, F. MHC antigens and malignancy. Nature 322, 502–503 (1986).

    CAS  PubMed  Google Scholar 

  5. 5.

    Garrido, F., Aptsiauri, N., Doorduijn, E. M., Garcia Lora, A. M. & van Hall, T. The urgent need to recover MHC class I in cancers for effective immunotherapy. Curr. Opin. Immunol. 39, 44–51 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Hon, C. C. et al. An atlas of human long non-coding RNAs with accurate 5′ ends. Nature 543, 199–204 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Mercer, T. R., Dinger, M. E. & Mattick, J. S. Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 10, 155–159 (2009).

    CAS  PubMed  Google Scholar 

  8. 8.

    Ponting, C. P., Oliver, P. L. & Reik, W. Evolution and functions of long noncoding RNAs. Cell 136, 629–641 (2009).

    CAS  PubMed  Google Scholar 

  9. 9.

    Wilusz, J. E., Sunwoo, H. & Spector, D. L. Long noncoding RNAs: functional surprises from the RNA world. Genes Dev. 23, 1494–1504 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Kung, J. T., Colognori, D. & Lee, J. T. Long noncoding RNAs: past, present, and future. Genetics 193, 651–669 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Flynn, R. A. & Chang, H. Y. Long noncoding RNAs in cell-fate programming and reprogramming. Cell Stem Cell 14, 752–761 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Huarte, M. The emerging role of lncRNAs in cancer. Nat. Med. 21, 1253–1261 (2015).

    CAS  PubMed  Google Scholar 

  13. 13.

    Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Riaz, N. et al. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell 171, 934–949 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hugo, W. et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 168, 542 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Van Allen, E. M. et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science 350, 207–211 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Nathanson, T. et al. Somatic mutations and neoepitope homology in melanomas treated with CTLA-4 blockade. Cancer Immunol. Res 5, 84–91 (2017).

    CAS  PubMed  Google Scholar 

  18. 18.

    Sui, J. et al. Systematic analyses of a novel lncRNA-associated signature as the prognostic biomarker for hepatocellular carcinoma. Cancer Med. https://doi.org/10.1002/cam4.1541 (2018).

  19. 19.

    Kaplan, D. H. et al. Demonstration of an interferon γ-dependent tumor surveillance system in immunocompetent mice. Proc. Natl Acad. Sci. USA 95, 7556–7561 (1998).

    CAS  PubMed  Google Scholar 

  20. 20.

    Fruh, K. & Yang, Y. Antigen presentation by MHC class I and its regulation by interferon γ. Curr. Opin. Immunol. 11, 76–81 (1999).

    CAS  PubMed  Google Scholar 

  21. 21.

    Dong, H. et al. Tumor-associated B7-H1 promotes T-cell apoptosis: a potential mechanism of immune evasion. Nat. Med. 8, 793–800 (2002).

    CAS  PubMed  Google Scholar 

  22. 22.

    Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9-based transcription factors. Nat. Methods 10, 973–976 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Lin, H. et al. Host expression of PD-L1 determines efficacy of PD-L1 pathway blockade-mediated tumor regression. J. Clin. Invest. 128, 805–815 (2018).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Sun, Q., Hao, Q. & Prasanth, K. V. Nuclear long noncoding RNAs: key regulators of gene expression. Trends Genet. 34, 142–157 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Cheng, Y. S., Colonno, R. J. & Yin, F. H. Interferon induction of fibroblast proteins with guanylate binding activity. J. Biol. Chem. 258, 7746–7750 (1983).

    CAS  PubMed  Google Scholar 

  26. 26.

    Kim, B. H. et al. Interferon-induced guanylate-binding proteins in inflammasome activation and host defense. Nat. Immunol. 17, 481–489 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Messeguer, X. et al. PROMO: detection of known transcription regulatory elements using species-tailored searches. Bioinformatics 18, 333–334 (2002).

    CAS  PubMed  Google Scholar 

  28. 28.

    Consortium, E. P. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Google Scholar 

  29. 29.

    Dai, C. & Sampson, S. B. HSF1: guardian of proteostasis in cancer. Trends Cell Biol. 26, 17–28 (2016).

    CAS  PubMed  Google Scholar 

  30. 30.

    West, J. D., Wang, Y. & Morano, K. A. Small molecule activators of the heat shock response: chemical properties, molecular targets, and therapeutic promise. Chem. Res. Toxicol. 25, 2036–2053 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Zou, J., Guo, Y., Guettouche, T., Smith, D. F. & Voellmy, R. Repression of heat shock transcription factor HSF1 activation by HSP90 (HSP90 complex) that forms a stress-sensitive complex with HSF1. Cell 94, 471–480 (1998).

    CAS  Google Scholar 

  32. 32.

    Dayalan Naidu, S. & Dinkova-Kostova, A. T. Regulation of the mammalian heat shock factor 1. FEBS J. 284, 1606–1627 (2017).

    CAS  PubMed  Google Scholar 

  33. 33.

    Whitesell, L. & Lindquist, S. L. HSP90 and the chaperoning of cancer. Nat. Rev. Cancer 5, 761–772 (2005).

    CAS  Google Scholar 

  34. 34.

    Yost, K. E. et al. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25, 1251–1259 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Harel, M. et al. Proteomics of melanoma response to immunotherapy reveals mitochondrial dependence. Cell 179, 236–250 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Heward, J. A. & Lindsay, M. A. Long non-coding RNAs in the regulation of the immune response. Trends Immunol. 35, 408–419 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Flores-Concha, M. & Onate, A. A. Long non-coding RNAs in the regulation of the immune response and trained immunity. Front. Genet. 11, 718 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Schmitt, A. M. & Chang, H. Y. Long noncoding RNAs in cancer pathways. Cancer Cell 29, 452–463 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Sun, T. T. et al. LncRNA GClnc1 promotes gastric carcinogenesis and may act as a modular scaffold of WDR5 and KAT2A complexes to specify the histone modification pattern. Cancer Discov. 6, 784–801 (2016).

    CAS  PubMed  Google Scholar 

  40. 40.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Peng, D. et al. Epigenetic silencing of TH1-type chemokines shapes tumour immunity and immunotherapy. Nature 527, 249–253 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Wang, W. et al. CD8+ T cells regulate tumour ferroptosis during cancer immunotherapy. Nature 569, 270–274 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Zaretsky, J. M. et al. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375, 819–829 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Shin, D. S. et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations. Cancer Discov. 7, 188–201 (2017).

    CAS  PubMed  Google Scholar 

  47. 47.

    Sucker, A. et al. Acquired IFNγ resistance impairs anti-tumor immunity and gives rise to T-cell-resistant melanoma lesions. Nat. Commun. 8, 15440 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Li, J. et al. Epigenetic driver mutations in ARID1A shape cancer immune phenotype and immunotherapy. J. Clin. Invest. https://doi.org/10.1172/JCI134402 (2020).

  49. 49.

    Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Arun, G., Diermeier, S. D. & Spector, D. L. Therapeutic targeting of long non-coding RNAs in cancer. Trends Mol. Med. 24, 257–277 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Gil, N. & Ulitsky, I. Regulation of gene expression by cis-acting long non-coding RNAs. Nat. Rev. Genet. 21, 102–117 (2020).

    CAS  PubMed  Google Scholar 

  52. 52.

    Jones, A. N. & Sattler, M. Challenges and perspectives for structural biology of lncRNAs-the example of the Xist lncRNA A-repeats. J. Mol. Cell. Biol. 11, 845–859 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Shenoy, A. R. et al. GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals. Science 336, 481–485 (2012).

    CAS  PubMed  Google Scholar 

  54. 54.

    Tretina, K., Park, E. S., Maminska, A. & MacMicking, J. D. Interferon-induced guanylate-binding proteins: guardians of host defense in health and disease. J. Exp. Med. 216, 482–500 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Yamamoto, M. et al. A cluster of interferon-γ-inducible p65 GTPases plays a critical role in host defense against Toxoplasma gondii. Immunity 37, 302–313 (2012).

    CAS  PubMed  Google Scholar 

  56. 56.

    Mbofung, R. M. et al. HSP90 inhibition enhances cancer immunotherapy by upregulating interferon response genes. Nat. Commun. 8, 451 (2017).

    PubMed  PubMed Central  Google Scholar 

  57. 57.

    Proia, D. A. & Kaufmann, G. F. Targeting heat-shock protein 90 (HSP90) as a complementary strategy to immune checkpoint blockade for cancer therapy. Cancer Immunol. Res. 3, 583–589 (2015).

    CAS  PubMed  Google Scholar 

  58. 58.

    Yuno, A. et al. Clinical evaluation and biomarker profiling of Hsp90 inhibitors. Methods Mol. Biol. 1709, 423–441 (2018).

    CAS  PubMed  Google Scholar 

  59. 59.

    Charo, J. et al. Bcl-2 overexpression enhances tumor-specific T-cell survival. Cancer Res. 65, 2001–2008 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Owaki, H. et al. Raf-1 is required for T cell IL2 production. EMBO J. 12, 4367–4373 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Zou laboratory for intellectual input. This work was supported in part by the research grants from the US NIH/NCI R01 grants (CA217648, CA123088, CA099985, CA193136, CA152470, to W.Z.; and CA216919, CA213566, CA120458; to M. Cohen), and the NIH through the University of Michigan Rogel Cancer Center Grant (CA46592).

Author information

Affiliations

Authors

Contributions

G.L. and W.Z. conceived the idea, designed the experiments and composed the paper. G.L. conducted experiments. I.K. assisted in FACS analysis. J.N., S.W., S.G. and L.V. assisted in animal experiments. X.L., S.L. and J.L. assisted in bioinformatics analysis. J.Z., W.D., H.L., T.W., C.S., J.J.M., M. Cieslik and M. Cohen contributed to the interpretation of the results. W.Z. supervised the project.

Corresponding author

Correspondence to Weiping Zou.

Ethics declarations

Competing interests

W.Z. has served as a scientific advisor or consultant for NGM, Cstone, Oncopia and Hengenix. All of the other authors declare no competing interests.

Additional information

Peer review information Nature Cell Biology thanks Weiyi Peng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 LIMIT correlates to effector immune genes across multiple cancer types.

a-l, Correlation of LIMIT with IFNG, MHC-I, or CD8 in human patients with sarcoma (SARC) (a-c), colon cancer (COAD) (d-f), breast cancer (BRCA) (g-i), and kidney cancer (KIRC) (j-l). P value by 2 sided linear regression. Source data are provided.

Source data

Extended Data Fig. 2 Genetic loci and sequences of human LIMIT and mouse Limit.

a-b, Genetic locus and genomic sequence of human LIMIT (a) or mouse Limit (b). c, Blast alignment of human LIMIT with GBP1P1 or GBP1.

Extended Data Fig. 3 LIMIT augments MHC-I expression.

a, Schematic diagram showing the alignment among LIMIT, GBP, and shLIMIT. The shLIMIT target sequences are not present in GBP coding genes. b, A375 shFluc, shLIMIT a, and shLIMIT b cells were treated with IFNγ for 48 hours. Surface expression of PD-L1 was determined by flow cytometry (FACS). mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. c, A375 shFluc and shLIMIT cells were treated with IFNγ for 24 hours. RNA levels of indicated genes were determined. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. d, Schematic diagram of the LIMIT promoter. The locations of 5 STAT1/IRF1-binding motifs and 4 sgRNAs capable of deleting the STAT1/IRF1 binding sites are indicated. Source data are provided.

Source data

Extended Data Fig. 4 LIMIT augments MHC-I expression.

a, Schematic diagram of CRISPR activation targeting Limit. The transcriptional activator VPR was directed to the promoter of Limit by the interaction between guide RNAs and dCas9. b-c, B16 cells were transfected with dCas9-VPR alone or together with 4 sgRNAs. Subsequently, RNA levels of Limit (b) and surface expression of MHC-I (H2-Db) (c) were detected 24 and 48 hours post transfection, respectively. mean ± SD, n = 4 biological independent samples, P value by 2-sided t-test. d, B16-OVA cells stably expressing shFluc, shB2m a, and shB2m b were treated with IFNγ for 48 hours. Surface expression of OVA-H2Kb were determined by FACS. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. e, B16-OVA cells were co-cultured with OT-I cell for 48 hours. Dot plots show the CD45 tumor cells. Tumor cell death was determined by PI staining. f-g, B16-OVA cells carrying shFluc or shB2m were co-cultured with OT-I cells at a 1:4 ratio. Cell killing was determined by PI+ in CD45 tumor cells. Dot plots (f) and statistical results (g) are shown. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. h-i, Bone marrow derived dendritic cells (BMDC) (h) or macrophages (BMDM) (i) were treated with IFNγ for 24 hours. RNA levels of Limit were determined by qRT-PCR. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. j, BMDM were transfected with 5’FAM-labbled siRNA targeting Fluc or Limit. Dot plots show FSC vs. SSC and FITC vs. SSC gating. The FITC gating indicates the cells with positive siRNA transfection. 1 of 3 experiments is shown. k, BMDM were transfected with 5’FAM-labbled siRNA targeting Fluc or Limit, and treated with IFNγ for 48 hours. Surface expression of MHC-I (H2-Db) were determined by FACS. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. Source data are provided.

Source data

Extended Data Fig. 5 LIMIT augments antigen-loaded MHC-I expression in vivo.

a, YUMM1.7-OVA cells carrying shFluc or shLimit were treated with IFNγ for 48 hours. Surface expression of OVA-H2Kb were determined by FACS. mean ± SD, n = 5 biological independent samples, P value by 2-sided t-test for end point tumor volume. b, Dot plot showing the CD45 gating of YUMM1.7-OVA tumor cells. c-d, Representative histogram showing the expression of H2Db (c) or OVA-H2Kb (d) in YUMM1.7-OVA shFluc or shLimit tumor cells. e-f, Statistical results of H2Db expression (e) or OVA-H2Kb expression (f) in YUMM1.7-OVA shFluc or shLimit tumor cells. mean ± SD, n = 5 biological independent samples, P value by 2-sided t-test. Source data are provided.

Source data

Extended Data Fig. 6 LIMIT cis-activates GBPs to boost MHC-I and tumor immunity.

a-b, Fold changes of Limit expression upon IFNγ treatment in YUMM1.7 cells (a) or CT26 cells (b) stably carrying shFluc, shLimit a, or shLimit b. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. c, A375 cells were transfected with LIMIT cDNA for 24 hours. RNA levels of indicated genes were determined by qRT-PCR. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. d, RPKM of Gbp family members upon IFNγ treatment in B16 cells (GSE99299). mean ± SD, n = 3 biological independent samples. e, A375 shFluc or shLIMIT cells were overexpressed with GBP1 (GBP1OE), and treated with IFNγ for 48 hours. Surface expression of HLA-ABC or PD-L1 were determined by FACS. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. f, A375 shFluc or shLIMIT cells were overexpressed with GBP1 (GBP1OE), and treated with IFNγ for 24 hours. RNA levels of IRF1 were determined by qRT-PCR. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. g, Dot plots of intral-tumoral CD8+ T cell infiltration and activation in the YUMM1.7 tumors carrying shFluc, shLimit, shGbp2, or shLimit plus shGbp2. h-i, Correlations between GBP1-5 and LIMIT (h) or MHC-I (i) in human melanoma datasets. P value by 2 sided linear regression. j, Cancer patients having received ICB were divided into low and high GBP groups (bottom 15% vs top 15%). The response rates to ICB were calculated as the percentages of partial response (PR) plus complete response (CR). P value by Chi-square test. Patients were from 4 cohorts. k, Survival plot of patients with melanoma. Based on the expression levels of GBP1-5, patients were divided into high (top 50%) and low (bottom 50%) groups. P value by 2 sided log-rank test. Source data are provided.

Source data

Extended Data Fig. 7 GBPs activate HSF1 to stimulate MHC-I expression.

a, Prediction of potential transcription factors targeting HLA-ABC, TAP1, HSPA5 and CALR. 8 shared transcription factors were altered by IFNγ in A375 cells (GSE99299). b, ChIP-seq results of STAT1 (Hela-S3 cells treated with IFNγ) or HSF1 (HepG2 cells in basal condition) derived from ENCODE at UCSC. The enrichment of STAT1 or HSF1 in the promoters of MHC-I related genes are shown. c, A375 cells were treated with multiple proteostasis stressors and KRIBB11. Surface expression of HLA-ABC was determined 48 hours after treatment. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. d, Dot plots of IFNγ+CD8+ T cells or TNFα+CD8+ T cells in MC38 tumors carrying shFluc, shGbp2, shHsf1, and shGbp2 plus shHsf1. Source data are provided.

Source data

Extended Data Fig. 8 HSF1 drives MHC-I expression and tumor immunity.

a, B16-OVA cells were treated with IFNγ in the presence or absence of KRIBB11 for 48 hours. Cell surface expression of OVA-H2-Kb was determined by FACS. mean ± SD, n = 4 biological independent samples, P value by 2-sided t-test. b, B16-OVA cells were pretreated with IFNγ in the presence or absence of KRIBB11 for 48 hours, then cultured with OT-1 T cells. Cell death was determined by 7-AAD staining in the CD45- tumor cells. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. c, MHC-I (H2-Dd) surface staining of CT26 shFluc or shHsf1 cells treated with IFNγ for 48 hours. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. d, YUMM1.7 shFluc or shHsf1 cells were treated with IFNγ in the presence or absence of KRIBB11 for 48 hours. Surface expression of MHC-I (H2-Db) was determined by FACS. mean ± SD, n = 3 biological independent samples, P value by 2-sided t-test. e, Dot plots of CD3+, Ki67+, IFNγ+, and TNFα+ T cells in YUMM1.7 shFluc or shHsf1 tumors. f, Tumor growth curve of CT26 shFluc or shHsf1 tumors in BALB/c mice. mean ± SD, n = 6 animals, P value by 2-sided t-test for end point tumor volume. g, Percentages of CD3+, IFNγ+, TNFα+, and Ki67+ T cells in CT26 shFluc or shHsf1 tumors. mean ± SD, n = 5 biological independent samples, P value by 2-sided t-test. h, Dot plots of CD3+, Ki67+, IFNγ+, and TNFα+ T cells in CT26 shFluc or shHsf1 tumors. Source data are provided.

Source data

Extended Data Fig. 9 LIMIT-GBP-HSF1 axis drives MHC-I and tumor immunity and immunotherapy.

a-b, HSF1 signaling genes correlated with MHC-I expression (a) or CD8+ T cell infiltration (b) in Pan-Cancer (TCGA, PANCAN), melanoma (TCGA, SKCM) or sarcoma (TCGA, SARC). P value by 2-sided t-test. The minima, 25% percentile, median, 75% percentile, maxima for each blot are (a) (31.53, 40.0075, 42.87, 45.4925, 53.58), (32.02, 41.1175, 43.66, 46.205, 55), (31.82, 39.785, 43.16, 46.31, 50.39), (33.35, 42.215, 44.53, 47.55, 53.41), (32.9, 38.88, 41.19, 43.21, 47.55), (39.33, 44.75, 46.57, 48.4, 51.3); (b) (3.75, 15.6825, 23.6, 30.48, 49.23), (5.47, 19.5875, 24, 29.1525, 45.42), (7.711, 15.7925, 23.415, 29.7395, 39.722), (4.9254, 18.95, 27.857, 34.862, 47.79), (7.7582, 14.727, 18.553, 22.16, 36.084), (15.024, 22.149, 29.1, 35.582, 43.432). c, Survival plots of human melanoma patients (TCGA, SKCM). Based on the expression of HSF1 signaling genes, patients were divided into high (n = 150 patients) and low (n = 150 patients) groups. P value by 2-sided log-rank test. d, Single cell RNA-seq derived cell clusters pre- or post- anti-PD-1 therapy in human skin basal cell carcinoma. Two malignant clusters are denoted by color and show different sensitivities to PD-1 blockade. e, Expression of HSF1 signaling genes and MHC-I related genes in the single cell clusters prior to PD-1 blockade therapy. The therapy sensitive tumor cell cluster exhibited higher levels of HSF1 signaling genes and MHC-I related genes as compared to the therapy insensitive tumor cell cluster. f, Proteomics analysis in melanoma patients having received ICB. Protein expression of GBPs, HSF1 signaling genes, and HLA-ABC were compared in responders (R, n = 40 patients) and non-responders (NR, n = 27 patients). P value by 2-sided t-test. g, Based on the transcript levels of GBP1, human Pan-Cancers were divided into high (top 10%) and low (bottom 10%) groups. HSF1 target gene transcripts were plotted. mean ± SD, n = 1106 patients, P value by 2-sided t-test. The minima, 25% percentile, median, 75% percentile, maxima for each blot are (9.12, 12.29, 13.01, 13.8075, 18.2), (9.24, 12.73, 13.47, 14.19, 19.16), (5.32, 9.64, 10.625, 11.39, 15.74), (6.98, 10.365, 11.11, 11.78, 16.96), (10.86, 12.83, 13.59, 14.16, 16.68), (11.75, 13.77, 14.16, 14.5, 16.71), (10.57, 13.11, 13.84, 14.48, 16.33), (12.15, 13.96, 14.3, 14.71, 17.76). Source data are provided.

Source data

Extended Data Fig. 10 Scheme showing how LIMIT-GBP-HSF1 axis affects MHC-I and tumor immunity.

Cancer cells (or APCs) express LIMIT in response to IFNγ, thereby locally promoting the transcription of GBPs. GBPs interact with HSP90 and release HSP90-decoyed HSF1, resulting in HSF1 activation. Activated HSF1 stimulates the transcription of MHC-I and MHC-I related genes. MHC-I machinery mediates TAA-recognition and T cell activation, eliciting antitumor immune response.

Supplementary information

Reporting Summary

Peer Review File

Supplementary Tables

Supplementary Table 1: lncRNA expression in hot versus cold melanoma. Supplementary Table 2: sequence alignment between LIMIT and GBP1P1. Supplementary Table 3: predictive targets of shLIMIT a by NCBI BLAST. Supplementary Table 4: predictive targets of shLIMIT b by NCBI BLAST. Supplementary Table 5: predictive targets of shLimit a by NCBI BLAST. Supplementary Table 6: predictive targets of shLimit b by NCBI BLAST. Supplementary Table 7: target sequences for linker DNA, sgRNA, shRNA or probes. Supplementary Table 8: primer sequences for RACE, Clone or RT–PCR

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 1

Unprocessed gel/blots.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 2

Unprocessed gel/blots.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 4

Unprocessed gel/blots.

Source Data Fig. 5

Statistical source data.

Source Data Fig. 5

Unprocessed gel/blots.

Source Data Fig. 6

Statistical source data.

Source Data Fig. 6

Unprocessed gel/blots.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Li, G., Kryczek, I., Nam, J. et al. LIMIT is an immunogenic lncRNA in cancer immunity and immunotherapy. Nat Cell Biol 23, 526–537 (2021). https://doi.org/10.1038/s41556-021-00672-3

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing