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The stress response regulator HSF1 modulates natural killer cell anti-tumour immunity

Nature Cell Biology (2024)Cite this article

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Abstract

Diverse cellular insults converge on activation of the heat shock factor 1 (HSF1), which regulates the proteotoxic stress response to maintain protein homoeostasis. HSF1 regulates numerous gene programmes beyond the proteotoxic stress response in a cell-type- and context-specific manner to promote malignancy. However, the role(s) of HSF1 in immune populations of the tumour microenvironment remain elusive. Here, we leverage an in vivo model of HSF1 activation and single-cell transcriptomic tumour profiling to show that augmented HSF1 activity in natural killer (NK) cells impairs cytotoxicity, cytokine production and subsequent anti-tumour immunity. Mechanistically, HSF1 directly binds and regulates the expression of key mediators of NK cell effector function. This work demonstrates that HSF1 regulates the immune response under the stress conditions of the tumour microenvironment. These findings have important implications for enhancing the efficacy of adoptive NK cell therapies and for designing combinatorial strategies including modulators of NK cell-mediated tumour killing.

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Fig. 1: Tumour-infiltrating NK cells maintain reduced levels of HSF1 activity.
Fig. 2: HSF1 activation in the tumour microenvironment accelerates tumour progression.
Fig. 3: HSF1 stabilization leads to NK cell inhibitory bias and impairs NK-mediated anti-tumour immunity.
Fig. 4: Augmented HSF1 impairs NK cell IFNγ production and cytotoxicity.
Fig. 5: Inverse relationship between the stress response and effector function in NK cells.
Fig. 6: HSF1 regulates an effector programme in NK cells.

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Data availability

The scRNA-seq, ATAC-seq, ChIP-seq and RNA-seq data that support the findings of this study have been deposited in the GEO under the accession code GSE167552. Previously published data that were re-analysed here are available under the following accession codes: GSE72056 (ref. 17), GSE120575 (ref. 18) and GSE134814 (ref. 19). The mm10/GRCm38 reference genome is available at the NCBI under the RefSeq assembly GCF_000001635.20. All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

No custom code was used for analysis. Analysis of ChIP-seq, ChIPmentation, ATAC-seq and RNA-seq was performed using the SNS pipeline (https://igordot.github.io/sns/) as described in the Methods. scRNA-seq analysis was performed using standard packages as described in the Methods.

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Acknowledgements

We thank members of the Aifantis laboratory for helpful discussions; J. Weber for the B16-F10 cell line and M. Bosenberg for the YUMM1.7 and YUMMER1.7 cell lines; S. Koralov for expertise in Southern blotting technology; the members of the Interdisciplinary Melanoma Cooperative Group at the NYU Perlmutter Cancer Center for scientific input and discussions; A. Heguy and the NYU Genome Technology Center, supported in part by the grant P30CA016087 from the National Institutes of Health (NIH) and the National Cancer Institute (NCI), for expertise with sequencing experiments; the NYU Histology Core (supported in part by the grant P30CA016087 from the NIH, NCI) for assistance; and the NYU Langone Cytometry and Cell Sorting Laboratory (supported in part by grant P30CA016087 from the NIH/NCI) for cell sorting and flow cytometry technologies. This work was supported in part by the Laura and Isaac Perlmutter Cancer Center Support Grant; NIH/NCI P30CA016087 and the NYULH Center for Biospecimen Research and Development, Histology and Immunohistochemistry Laboratory (RRID:SCR_018304). This work used computing resources at the High-Performance Computing Facility at the NYU Medical Center. Figure 2a,c,g and Extended Data Fig. 4d were created with BioRender.com released under an Academic Licence. I.A. is supported by the NCI at the NIH (grant nos. RO1CA202025, RO1CA202027, RO1CA228135 and 1P50CA225450). K.H. is supported by the NIH (training grants MSTP T32 GM136573, T32 CA009161 (Levy) and the Ruth L. Kirschstein Predoctoral NRSA F30 CA232704). N.K. is supported a Human Frontiers Science Program Long Term Fellowship (LT000150/2013-L) and previously by a Charles H. Revson Senior Fellowship in Biomedical Science (15–31) and a European Molecular Biology Organization (EMBO) Long Term Fellowship (ALTF 850-2012). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. Nikos Kourtis

    Present address: Regeneron Pharmaceuticals, Tarrytown, NY, USA

Authors and Affiliations

  1. Department of Pathology, NYU Grossman School of Medicine, New York, NY, USA

    Kathryn Hockemeyer, Theodore Sakellaropoulos, Xufeng Chen, Olha Ivashkiv, Maria Sirenko, Giovanni Gambi, Elena Battistello, Kleopatra Avrampou, Zhengxi Sun, Maria Guillamot, Luis Chiriboga, George Jour, Igor Dolgalev, Kate Corrigan, Kamala Bhatt, Aristotelis Tsirigos, Nikos Kourtis & Iannis Aifantis

  2. Laura & Isaac Perlmutter Cancer Center, NYU Grossman School of Medicine, New York, NY, USA

    Kathryn Hockemeyer, Theodore Sakellaropoulos, Xufeng Chen, Olha Ivashkiv, Maria Sirenko, Giovanni Gambi, Elena Battistello, Kleopatra Avrampou, Zhengxi Sun, Maria Guillamot, Igor Dolgalev, Kate Corrigan, Kamala Bhatt, Aristotelis Tsirigos, Nikos Kourtis & Iannis Aifantis

  3. Applied Bioinformatics Laboratories, NYU Langone Medical Center, New York, NY, USA

    Theodore Sakellaropoulos, Hua Zhou, Igor Dolgalev & Aristotelis Tsirigos

  4. Ronald O. Perelman Department of Dermatology, NYU Grossman School of Medicine, New York, NY, USA

    George Jour & Iman Osman

  5. Department of Urology, NYU Grossman School of Medicine, New York, NY, USA

    Iman Osman

  6. Interdisciplinary Melanoma Cooperative Group, NYU Langone Medical Center, New York, NY, USA

    Iman Osman

  7. Department of Medicine, NYU Grossman School of Medicine, New York, NY, USA

    Iman Osman & Aristotelis Tsirigos

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Contributions

I.A., N.K. and K.H. conceptualized the project. K.H., N.K., G.G. and L.C. designed and performed biological experiments with the assistance of E.B., O.I., G.G., M.G., L.C., K.A., X.C., K.C., K.B. and Z.S. G.J. performed analysis of immunohistochemistry experiments. T.S., H.Z. and I.D. developed software and data curation for computational analysis. K.H., T.S., E.B., G.J., I.D., H.Z. and M.S. performed computational analyses. A.T. and I.A. provided access to instrumentation, computing resources, analysis tools and supervision. K.H., T.S., N.K. and M.S. prepared data and schematics for visualization. L.C. provided resources for multiplex immunohistochemistry experiments. I.O. provided patient samples from the IMCG database. K.H., N.K. and I.A. were responsible for funding acquisition. K.H., N.K. and I.A. wrote the original draft. K.H., N.K., T.S., E.B., M.G., I.O., A.T. and I.A. reviewed the manuscript. K.H., N.K. and I.A. edited the manuscript.

Corresponding authors

Correspondence to Nikos Kourtis or Iannis Aifantis.

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Extended data

Extended Data Fig. 1 Variability of HSF1 activity and regulation between populations of the tumour microenvironment.

a, Heatmap of the HSF1 signature in distinct populations in tumours of patients with melanoma16. b, Violin plots and boxplots of the stress score across tumour-infiltrating immune populations in patients with melanoma17. One-way ANOVA. The n values for each cell type are as follows: MF 1267, pDC 282, Treg 3947, CD8T 6744, gdT 948, B 1729, NK 724. c, Heatmap of the HSF1 signature in distinct populations in murine melanoma tumours in wild-type mice18. d, Violin plots and boxplots of the stress score across tumour-infiltrating immune populations in murine melanoma18. One-way ANOVA. The n values for each cell type are as follows: CD8T 474, Treg 182, NK 398, DC 202, MF 2437. e, Boxplots of HSF1 (left) and FBXW7 (right) expression in individual cells from tumour-infiltrating immune populations assessed by scRNA-seq16. The n values for each cell type are as follows: CD8T 1044, MF 125, Treg 57, NK 52, B 515, CD4T 35, T 504, Th 428. b, d, e, Boxplots represent the bottom quartile to top quartile with median indicated. Whiskers represent −1.5 x IQR to 1.5 x IQR. Dots represent outliers from the minima to the maxima of the data.

Extended Data Fig. 2 An in vivo model of augmented HSF1 exhibiting normal hematopoiesis.

a, Schematic of knock-in targeting strategy. AG to GC mutations are indicated by the asterisk. Length in bp: long homology arm (LA) – 6714, point mutation – 14, middle arm (MA) – 404, short homology arm (SA) – 2014, targeted region – 418. b, Identification of embryonic stem cell clones with stable incorporation of the targeting vector. The wild-type (WT, 8591 bp) and knock-in (MUT or S303/7 A, 7567 bp) bands as indicated; the clone (#8) selected for microinjection as indicated by the asterisk. c, Sanger sequencing of the nucleotides corresponding to amino acids 302-308 of the HSF1 protein in mice wild-type or homozygous for the knock-in allele. Positions 303 and 307 are underlined. d, Kaplan-Meier survival analysis of the lifespan of WT or S303/7 A mice (n = 12). Log-rank (Mantel-Cox) test. e, Immunohistochemistry staining with anti-HSF1 antibody of the indicated tissue types from WT (left) or S303/7 A (right) mice. Representative of 3 biological replicates. f, Gating strategy for myeloid populations. g, Gating strategy for lymphoid populations. h, Frequency of myeloid populations in S303/7 A or WT spleen. i, Total count of myeloid populations in S303/7 A or WT spleen. j, Frequency of lymphoid populations in S303/7 A or WT spleen. k, Total count of lymphoid populations in S303/7 A or WT spleen. l, Gating strategy for multipotent progenitors (MPPs) and lineage-biased progenitors. Lineage includes B220, CD11b, CD3e, CD4, CD8a, Ly-6C/Ly-6G, NK1.1, Ter-119. m, Frequency of MPPs in S303/7 A or WT bone marrow. n, Total count of MPPs in S303/7 A or WT bone marrow. o, Frequency of lineage-biased progenitors in S303/7 A or WT bone marrow. p, Total count of lineage-biased progenitors in S303/7 A or WT bone marrow. h-k, m-p, n = 4 biological replicates. p calculated by two-tailed t-test, bars represent the mean and error bars represent the SEM.

Source data

Extended Data Fig. 3 Expression of NK cell ligands and receptors.

a, Tumour volumes over time of 1 × 105 YUMMER1.7 cells injected into WT (n = 4) or S303/7 A (n = 5) mice. b, Tumour volumes over time of 2.5 × 105 YUMMER1.7 cells injected into WT (n = 4) or S303/7 A (n = 5) mice. c, Tumour volumes over time of 5 × 105 YUMMER1.7 cells injected into WT (n = 4) or S303/7 A (n = 5) mice. e, Representative histograms of NK cell receptor ligands and isotype controls in B16-F10 and YUMMER1.7 cells. f, quantification of expression of NK cell receptor ligands normalized to isotype controls in B16-F10 and YUMMER1.7. n = 3 technical replicates. bars represent the mean for visualization. g, Immunoblot analysis of HSF1 in total lysate of thymocytes from Hsf1+/+ or Hsf1−/− mice. n = 3 biological replicates. h, Frequency of Hsf1+/+ or Hsf1−/− NK cells expressing surface activating receptors. i, Frequency of Hsf1+/+ or Hsf1−/− NK cells expressing surface inhibitory receptors. j, Frequency of Hsf1+/+ or Hsf1−/− NK cells across the maturation stages. h – j, n = 3 biological replicates, bars represent the mean and error bars represent the SEM, p calculated by two-tailed t-test.

Source data

Extended Data Fig. 4 HSF1-stabilized NK cells exhibit decreased IFN-γ production and proliferation.

a, Quantification of CD62L on naïve splenic NK cells in WT or S303/7 A mice. Representative of 2 independent experiments, n = 4 biological replicates. b, Histograms displaying the expression level of GZMB in NK cells cultured with B16-F10 (1:1) or PMA/Ionomycin for 4 h. c, Quantification of GZMB in WT (n = 5) or S303/7 A (n = 4) NK cells following stimulation (4 h) with the conditions indicated. d, Schematic of adoptive transfer of WT or S303/7 A NK cells into Rag2−/−Il2rg−/− mice followed by metastatic challenge with B16-F10 through tail vein injection. e, Representative H&E staining of lung from S303/7 A mice (n = 5) or WT mice (n = 4). Scale bars as indicated. f, Quantification of metastatic lung nodules from H&E images (left) and quantification of tumour as a proportion of total lung tissue (right). p calculated by two-tailed t-test. Boxplots represent the bottom quartile to top quartile with median indicated. Whiskers represent the minima to the maxima of the data. n = 5 for S303/7 A mice and n = 4 for WT mice g, Immunoblot analysis of HSF1 in nuclear lysate from WT or S303/7 A NK cells with or without heat shock as indicated. h, Cell number over time of NK cells from WT (n = 5) or S303/7 A (n = 4) mice expanded with IL-15. p calculated by two-way ANOVA, data presented as mean values, error bars represent the SEM. a, c, bars represent the mean, error bars represent the SEM. p calculated using two-tailed t-test.

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Extended Data Fig. 5 Single-cell transcriptomic characterization of the HSF1-stabilized microenvironment.

a, Schematic of experiment approach. b, Gating strategy for enrichment of CD45+ cells. Live, viable, singlet cells are shown. c, Uniform Manifold Approximation and Projection (UMAP) representation depicting the integration of 6654 HSF1WT (WT) and 4802 HSF1S303/7A (SS03/7 A) cells. d, UMAP representation of colour-coded clustering of the melanoma tumour microenvironment (11,456 cells). e, Normalized expression level of Ptprc (CD45) overlaid on UMAP representation. f, Copy-number variant (CNV) analysis to identify melanoma cells. Heatmap representation of CNVs by chromosome. g, CNV clusters from analysis in F overlaid on UMAP representation. h, A heatmap representation of population-specific markers of immune populations. i, Cell type identification (left) and population proportions (right) based on transcriptional signatures from the Immunological Genome Project. Immune cell types were called based on similarity from bulk transcriptomic signatures of FACS-sorted immune populations. j, Bar graphs of cell cycle status within each cluster by HSF1 status. k, Log2 UMI score of the NK effector signature across CD56brightCD16lo and CD56dimCD16hi NK cell from blood, normal tissue, and tumour, separated by high (>1 standard deviation above the mean) and low (<1 standard deviation below the mean) ‘stress’ score. The n values for each category/cell type are as follows: CD56brightCD16lo: tumour (high) 2081, tumour (low) 1643, normal (high) 532, normal (low) 499, blood (high) 876, blood (low) 932; CD56dimCD16hi: tumour (high) 3351, tumour (low) 2673, normal (high) 2497, normal (low) 2224, blood (high) 6273, blood (low) 7083. p calculated by Kruskal-Wallis t-test. Boxplots represent the bottom quartile to top quartile with median indicated. Whiskers represent −1.5 x IQR to 1.5 x IQR. Dots represent outliers from the minima to the maxima of the data.

Extended Data Fig. 6 Enrichment analysis of genes directly bound by HSF1 in NK cells.

a, Gene set enrichment analysis (GSEA) of HSF1 peaks ranked by adjusted p-value. Top 10 enriching gene sets from the REACTOME database are shown, with those comprised of molecular chaperones indicated in red. b, GSEA of HSF1 peaks ranked by adjusted p-value. Top 10 enriching gene sets from the KEGG database are shown, with natural killer cell-mediated cytotoxicity indicated in red. c, Venn diagram of overlapping peaks in HSF1S303/7A (S303/7 A) and wild-type (WT) NK cells. d, Pie chart of genomic distribution of regions of HSF1 occupancy (3′ UTR, 5′ UTR, distal intergenic, downstream, exon, intron, promoter) in primary NK cells. a, b, p estimation using an adaptive multi-level split Monte-Carlo scheme using Benjamini–Hochberg adjustment for multiple comparisons.

Supplementary information

Reporting Summary

Supplementary Table 1

Table of sequences for primers and oligonucleotides used in the manuscript.

Supplementary Table 2

Table of fluorescence cytometry antibody information.

Supplementary Table 3

Table of buffer recipes for ChIP-seq and ChIPmentation protocols.

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Hockemeyer, K., Sakellaropoulos, T., Chen, X. et al. The stress response regulator HSF1 modulates natural killer cell anti-tumour immunity. Nat Cell Biol (2024). https://doi.org/10.1038/s41556-024-01490-z

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