Abstract
Malignancy can be suppressed by the immune system. However, the classes of immunosurveillance responses and their mode of tumor sensing remain incompletely understood. Here, we show that although clear cell renal cell carcinoma (ccRCC) was infiltrated by exhaustion-phenotype CD8+ T cells that negatively correlated with patient prognosis, chromophobe RCC (chRCC) had abundant infiltration of granzyme A-expressing intraepithelial type 1 innate lymphoid cells (ILC1s) that positively associated with patient survival. Interleukin-15 (IL-15) promoted ILC1 granzyme A expression and cytotoxicity, and IL-15 expression in chRCC tumor tissue positively tracked with the ILC1 response. An ILC1 gene signature also predicted survival of a subset of breast cancer patients in association with IL-15 expression. Notably, ILC1s directly interacted with cancer cells, and IL-15 produced by cancer cells supported the expansion and anti-tumor function of ILC1s in a murine breast cancer model. Thus, ILC1 sensing of cancer cell IL-15 defines an immunosurveillance mechanism of epithelial malignancies.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
from$1.95
to$39.95
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
All raw and processed single-cell RNA have been deposited in GEO with accession ID GSE199798.
Source data for the following figures are available and listed in the Inventory of Supplemental Information (Figs. 5h–j, 6a and 7c,f and Extended Data Figs. 6g–o, 7d–k and 9c–e). All other correspondence and requests for materials and/or data that support the findings of this study are available from and should be addressed to M.O.L. (lim@mskcc.org).
Change history
15 June 2022
A Correction to this paper has been published: https://doi.org/10.1038/s41590-022-01264-5
References
Burnet, M. Cancer: a biological approach. III. Viruses associated with neoplastic conditions. IV. Practical applications. Br. Med J. 1, 841–847 (1957).
Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).
Sharma, P. & Allison, J. P. Immune checkpoint targeting in cancer therapy: toward combination strategies with curative potential. Cell 161, 205–214 (2015).
Motzer, R. J., McHenry, M. B. & Chen, A. C. Immune checkpoint blockade in advanced renal-cell carcinoma. N. Engl. J. Med. 379, 92–93 (2018).
Chiossone, L., Dumas, P. Y., Vienne, M. & Vivier, E. Natural killer cells and other innate lymphoid cells in cancer. Nat. Rev. Immunol. 18, 671–688 (2018).
Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).
Dadi, S. et al. Cancer immunosurveillance by tissue-resident innate lymphoid cells and innate-like T cells. Cell 164, 365–377 (2016).
Friedrich, C. et al. Effector differentiation downstream of lineage commitment in ILC1s is driven by Hobit across tissues. Nat. Immunol. 22, 1256–1267 (2021).
Di Censo, C. et al. Granzyme A and CD160 expression delineates ILC1 with graded functions in the mouse liver. Eur. J. Immunol. 51, 2568–2575 (2021).
Bjorkstrom, N. K., Ljunggren, H. G. & Michaelsson, J. Emerging insights into natural killer cells in human peripheral tissues. Nat. Rev. Immunol. 16, 310–320 (2016).
Cichocki, F., Grzywacz, B. & Miller, J. S. Human NK cell development: one road or many? Front Immunol. 10, 2078 (2019).
Mackay, L. K. et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352, 459–463 (2016).
Collins, P. L. et al. Gene regulatory programs conferring phenotypic identities to human NK cells. Cell 176, 348–360 e312 (2019).
Yomogida, K. et al. Hobit confers tissue-dependent programs to type 1 innate lymphoid cells. Proc. Natl Acad. Sci. U S A 118, e2117965118 (2021).
Diaz-Montero, C. M., Rini, B. I. & Finke, J. H. The immunology of renal cell carcinoma. Nat. Rev. Nephrol. 16, 721–735 (2020).
McGregor, B. A. et al. Results of a multicenter phase II study of atezolizumab and bevacizumab for patients with metastatic renal cell carcinoma with variant histology and/or sarcomatoid features. J. Clin. Oncol. 38, 63–70 (2020).
Zhang, N. & Bevan, M. J. CD8(+) T cells: foot soldiers of the immune system. Immunity 35, 161–168 (2011).
McFarland, A. P. et al. Multi-tissue single-cell analysis deconstructs the complex programs of mouse natural killer and type 1 innate lymphoid cells in tissues and circulation. Immunity 54, 1320–1337 e1324 (2021).
Hoffmann, J. C. & Schon, M. P. Integrin alphaE(CD103)beta7 in epithelial cancer. Cancers (Basel) 13, 6211 (2021).
Borcherding, N. et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun. Biol. 4, 122 (2021).
Zhou, Z. et al. Granzyme A from cytotoxic lymphocytes cleaves GSDMB to trigger pyroptosis in target cells. Science 368, eaaz7548 (2020).
Lieberman, J. Granzyme A activates another way to die. Immunol. Rev. 235, 93–104 (2010).
Wagner, J. A. et al. CD56bright NK cells exhibit potent antitumor responses following IL-15 priming. J. Clin. Invest. 127, 4042–4058 (2017).
Loibl, S., Poortmans, P., Morrow, M., Denkert, C. & Curigliano, G. Breast cancer. Lancet 397, 1750–1769 (2021).
Mamessier, E. et al. Human breast cancer cells enhance self tolerance by promoting evasion from NK cell antitumor immunity. J. Clin. Invest. 121, 3609–3622 (2011).
Zhang, Y. et al. Single-cell analyses reveal key immune cell subsets associated with response to PD-L1 blockade in triple-negative breast cancer. Cancer Cell 39, 1578–1593 e1578 (2021).
Fluck, M. M. & Schaffhausen, B. S. Lessons in signaling and tumorigenesis from polyomavirus middle T antigen. Microbiol Mol. Biol. Rev. 73, 542–563 (2009).
Sosinowski, T. et al. CD8alpha+ dendritic cell trans presentation of IL-15 to naive CD8+ T cells produces antigen-inexperienced T cells in the periphery with memory phenotype and function. J. Immunol. 190, 1936–1947 (2013).
Mortier, E. et al. Macrophage- and dendritic-cell-derived interleukin-15 receptor alpha supports homeostasis of distinct CD8+ T cell subsets. Immunity 31, 811–822 (2009).
Franklin, R. A. et al. The cellular and molecular origin of tumor-associated macrophages. Science 344, 921–925 (2014).
Swonger, J. M., Liu, J. S., Ivey, M. J. & Tallquist, M. D. Genetic tools for identifying and manipulating fibroblasts in the mouse. Differentiation 92, 66–83 (2016).
Nixon, B. G. et al. Cytotoxic granzyme C-expressing ILC1s contribute to antitumor immunity and neonatal autoimmunity. Sci. Immunol. 7, eabi8642 (2022).
Eckl, J. et al. Transcript signature predicts tissue NK cell content and defines renal cell carcinoma subgroups independent of TNM staging. J. Mol. Med (Berl.) 90, 55–66 (2012).
Simoni, Y. et al. Human innate lymphoid cell subsets possess tissue-type based heterogeneity in phenotype and frequency. Immunity 46, 148–161 (2017).
Gao, Y. et al. Tumor immunoevasion by the conversion of effector NK cells into type 1 innate lymphoid cells. Nat. Immunol. 18, 1004–1015 (2017).
Guan, Y. et al. Renal cell tumors convert natural killer cells to a proangiogenic phenotype. Oncotarget 11, 2571–2585 (2020).
Burkett, P. R. et al. Coordinate expression and trans presentation of interleukin (IL)-15Ralpha and IL-15 supports natural killer cell and memory CD8+ T cell homeostasis. J. Exp. Med. 200, 825–834 (2004).
Dubois, S., Mariner, J., Waldmann, T. A. & Tagaya, Y. IL-15Ralpha recycles and presents IL-15 In trans to neighboring cells. Immunity 17, 537–547 (2002).
Waldmann, T. A. & Tagaya, Y. The multifaceted regulation of interleukin-15 expression and the role of this cytokine in NK cell differentiation and host response to intracellular pathogens. Annu Rev. Immunol. 17, 19–49 (1999).
Mlecnik, B. et al. Functional network pipeline reveals genetic determinants associated with in situ lymphocyte proliferation and survival of cancer patients. Sci. Transl. Med. 6, 228ra237 (2014).
Angelova, M. et al. Evolution of metastases in space and time under immune selection. Cell 175, 751–765 e716 (2018).
Freytag, S., Tian, L., Lonnstedt, I., Ng, M. & Bahlo, M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Res 7, 1297 (2018).
Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).
Lun, A. T., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res 5, 2122 (2016).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Xu, C. & Su, Z. Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics 31, 1974–1980 (2015).
Liu, M. et al. TGF-beta suppresses type 2 immunity to cancer. Nature 587, 115–120 (2020).
Yuan, T. L. & Cantley, L. C. PI3K pathway alterations in cancer: variations on a theme. Oncogene 27, 5497–5510 (2008).
Acknowledgements
We thank members of the Li laboratory for helpful discussions, N. Cheung for providing us with the human IL-15/IL-15Rα complex reagent, M. Huse for help with the single-cell killing microwell assay and K. Hsu for providing us with the K562 cell line. This work was supported by National Institutes of Health grants F31CA210332 (B.G.N.), R01CA243904-01A1 (M.O.L.) and P30 CA008748 (the Memorial Sloan Kettering Cancer Center Support Grant); Department of Defense grant KC19008.e001 (M.O.L.); a Howard Hughes Medical Institute Faculty Scholar Award (M.O.L.); a Cancer Research Institute CLIP grant (M.O.L.); the Ludwig Center for Cancer Immunotherapy (M.O.L.); and the Functional Genomic Initiative (M.O.L.).
Author information
Authors and Affiliations
Contributions
M.O.L., S.D. and E.R.K. conceived the project. E.R.K. performed and analyzed most experiments with input from M.O.L and A.A.H. Bioinformatics and data analysis were conducted by C.K. and F.K. with input from C.S.L. and T.A.C. E.S.G. performed the immunofluorescence and intravital imaging experiments. B.G.N. generated GzmctdT-T2A-iCre mice and assisted with the design and execution of experiments with human tissue. J.Z. and H.Z. characterized IL-15 expression in the mouse models. M.L. discovered differential innate lymphocyte and CD8+ T cell responses in patients with chRCC and ccRCC and made major contributions in generating the bulk RNA-sequencing dataset. K.A.B. and K.W. assisted with collection of human tissues. K.J.C. helped manage the mouse colony. R.M.K. provided the IL-152A-eGFP reporter mouse. K.I. and G.C. provided the Il15fl mouse. E.R.K. wrote and M.O.L. edited the manuscript.
Corresponding author
Ethics declarations
Competing interests
MSKCC has filed a patent application with the U.S. Patent and Trademark Office directed toward targeting ILC1 IL-15 signaling for cancer immunotherapy. M.O.L. is an scientific advisory board member of and holds equity or stock options in Amberstone Biosciences and META Pharmaceuticals. All other authors have no competing interests.
Peer review
Peer review information
Nature Immunology thanks Timotheus Halim and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Zoltan Fehervari was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Cluster-defining marker plots for all clusters and heatmap of differential gene expression analysis among the three CD8+ T cell clusters.
a, Marker plots showing normalized expression of selected common markers for lymphoid and myeloid populations (CD3D – T cells, CD4 – CD4+ T cells, CD8A – CD8+ T cells, KLRB1 – innate lymphocytes, CD14 – myeloid cells). b, Heatmap of expression of the top 30 DEGs by log fold change, [computed using Wilcox test in the FindMarkers() function of Seurat (FDR P < 0.05 and LFC > 0)], across the three CD8+ T cell clusters within the chRCC and ccRCC patients. Each column represents an individual cell. c, Plots showing the back gating strategy for plots shown in Fig. 1e.
Extended Data Fig. 2 Heatmap of differential gene expression analysis between clusters NK and ILC1 and comparison of CD56 expression between CD49a+CD103+ ILC1s and CD49a−CD103− NK cells.
a, Heatmap of expression of the top 30 DEGs by log fold change, [computed using Wilcox test in the FindMarkers() function of Seurat (FDR P < 0.05 and LFC > 0)], across the two innate lymphocyte clusters in the chromophobe renal cell carcinoma (chRCC) and clear cell RCC (ccRCC) patients. Each column represents an individual cell. b, Plots showing the back gating strategy for plots shown in Fig. 2c. c, CD56 MFI in CD49a+CD103+ ILC1s (orange) compared to CD49a−CD103− NK cells (green) in the indicated histology. Each pair of symbols connected by a line denotes an individual patient (chRCC n = 9, ccRCC n = 15). Paired ratio t test was used for statistical analysis, NS = non-significant, ****p < 0.0001.
Extended Data Fig. 3 Validation of the ILC1 signature.
a, Table outlining the cell surface markers used to define each immune cell population sorted for downstream bulk RNA-sequencing. b, Area under the ROC curve and c, precision recall curve, when using the ILC1 signature to discriminate resident ILC1 populations (n = 12) vs all others (n = 57). The areas under the ROC and PR curves were calculated using the PRROC package in R.
Extended Data Fig. 4 CD49a+CD103+ ILC1s and CD49a−CD103− NK cells are phenotypically distinct in terms of NKG2A expression.
a, Violin plot showing KLRC1 expression in the indicated clusters. b, (Left) Representative histograms of NKG2A expression in CD49a-CD103− NK cells (green) and CD49a+CD103+ ILC1s (orange) from the same patient of the indicated histology. (Center) Quantification of NKG2A+ cells within the indicated cell type and histology. (Right) MFI of NKG2A in NKG2A+CD49a+CD103+ ILC1s compared to NKG2A+CD49a-CD103−NK cells in tumor samples. chRCC n = 6, ccRCC n = 9. Each pair of symbols connected by a line denotes an individual patient. Two-tailed unpaired t test was used for statistical analysis of the percent NKG2A positive, and paired ratio t test was used for statistical analysis of MFI, ***p < 0.001, ****p < 0.0001. c, Violin plot showing log-normalized expression of the HLA-E gene in the TCGA ccRCC and chRCC cohorts. Two-sided Wilcoxon test was used for statistical analysis p < 2.2e-16. d, Correlation between level of HLA-E expression and ILC1 signature in chRCC cases from the TCGA database. Statistical analyses calculated using Spearman’s correlation. Error bands represent the 95% confidence interval. e, Association of HLA-E expression and overall survival across the TCGA chRCC cohort. High represents the top quartile and low represents the bottom 3 quartiles of IL-15 expression level. P value calculated using a Cox regression and log-rank test.
Extended Data Fig. 5 IL2RB expression in clusters NK and ILC1, IL15 expression in chRCC and ccRCC tumors from the TCGA, and IL-15 regulation of CD56 expression in CD49a+CD103+ ILC1s.
a, Violin plot showing log-normalized expression of the IL2RB gene in the indicated innate lymphocyte clusters. b, Violin plot showing level of IL15 expression across chromophobe renal cell carcinoma (chRCC) and clear cell RCC (ccRCC) patients in the TCGA cohort. One-sided Wilcoxon test was used for statistical analysis, *p < 0.05. c, (Left) Representative histograms of CD56 expression in CD49a+CD103+ ILC1s treated with the indicated concentration of IL-15/IL-15Rα complex. (Right) MFI of CD56 in CD49a+CD103+ innate lymphocytes isolated from tumors treated with 100 ng/mL IL-15/IL-15Rα complex compared to 10 ng/mL IL-15/IL-15Rα complex. Each pair of symbols connected by a line denotes cells isolated from an individual patient (n = 5, 1 chRCC and 4 ccRCC), in 5 independent experiments. Paired ratio t test was used for statistical analysis, *p < 0.05. d, Representative images of chRCC tumor tissue that was stained with anti-CD103 (red), anti-E-Cadherin (white), anti-CD3 (green), and DAPI (blue). White arrows denote CD3−CD103+ innate lymphocytes. Scale bar = 20 μM. Quantification is representative of three independent chRCC patient tumor tissues, each dot represents one patient. Error bar represents mean ± SEM.
Extended Data Fig. 6 ILC1s function independently of DC- and macrophage-expressed IL-15.
a, Schematic describing the IL-152A-eGFP reporter mouse strain. b, Types of cancer cells and stromal cells with the potential for IL-15 expression in PyMT tumors. c, Table listing the Cre recombinase lines used to delete IL-15 in the listed target cell populations. d, Schematic of an Il15 floxed allele. e, Gating strategy for determining eGFP expression in the indicated myeloid cell populations isolated from pooled tumors of a 20-week-old IL-152A-eGFPPyMT mouse. f, Flow cytometric analysis of eGFP expression in the indicated myeloid cell populations from pooled tumors of a 20-week-old IL-152A-eGFPPyMT (colored) or PyMT mouse (gray). g, qPCR analysis of Il15 mRNA expression in sorted DCs from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 3) or Itgax-CreIl15fl/flPyMT (n = 3) mice. h, qPCR analysis of Il15 mRNA expression in sorted TAMs from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 3) or Itgax-CreIl15fl/flPyMT (n = 3) mice. i, Representative plot and quantification of NK1.1+ cells out of total CD45+CD3− cells isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 6) or Itgax-CreIl15fl/flPyMT mice (n = 6). j, Representative plot and quantification of percentage of CD49a+CD103+ ILC1s out of total CD45+CD3−NK1.1+ cells isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 6) or Itgax-CreIl15fl/flPyMT (n = 6) mice. k, Representative histogram and quantification of granzyme B (GzmB) expression in CD49a+CD103+ ILC1s from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 5) or Itgax-CreIl15fl/flPyMT (n = 5) mice. l, (Left) Representative histogram and quantification of granzyme C (GzmC) expression in CD49a+CD103+ ILC1s from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 8) or Itgax-CreIl15fl/flPyMT (n = 5) mice. m, Total tumor burden of Il15fl/flPyMT (11 weeks n = 6, 20 weeks n = 11) and Itgax-CreIl15fl/flPyMT (11 weeks n = 6, 20 weeks n = 11) mice monitored between 11 and 20 weeks of age. n, Representative histograms of CD49b (DX5) expression among total CD3-NK1.1+ cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+ NK cells quantified out of total splenic CD45+ immune cells (Il15fl/flPyMT n = 7, Itgax-CreIl15fl/flPyMT n = 5). o, (Left) Representative plots of CD27 and CD11b expression among total CD3-NK1.1+DX5+ NK cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+CD11b+CD27− cells quantified out of total splenic CD45+ cells (Il15fl/flPyMT n = 7, Itgax-CreIl15fl/flPyMT n = 5). g-o, Each dot represents an individual mouse. Data are pooled from 3 or more independent experiments. All error bars represent the mean ± SEM. Two-tailed unpaired t test was used for statistical analysis, NS = non-significant, *p < 0.05, **p < 0.01, ***p < 0.001.
Extended Data Fig. 7 ILC1s function independently of hematopoietic and stromal cell sources of IL-15.
a, Gating strategy and YFP expression in the indicated populations from pooled tumors of a 20-week-old S100a4-CreRosa26LSL-YFPPyMT mouse. b, Flow cytometric analysis of eGFP expression in the indicated populations from pooled tumors of 20-week-old IL-152A-eGFPPyMT (colored) or control PyMT (gray) mice. c, qPCR analysis of Il15 mRNA expression in sorted live CD45+ immune cells from 20–24-week-old Il15fl/flPyMT (n = 3) or S100a4-CreIl15fl/flPyMT (n = 2) mice. d, qPCR analysis of Il15 mRNA expression in sorted live CD45−CD31−Ter119−CD24−CD29+EpCAM− stromal cells from 20–24-week-old Il15fl/flPyMT (n = 3) or S100a4-CreIl15fl/flPyMT mice (n = 3). e, Representative plot and quantification of percentage of NK1.1+ cells out of total CD3− cells isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 7) or S100a4-CreIl15fl/flPyMT mice (n = 9). f, Representative plot and quantification of percentage of CD49a+CD103+ ILC1s out of total CD45+CD3−NK1.1+ cells isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 7) or S100a4-CreIl15fl/flPyMT (n = 9) mice. g, Representative histogram and quantification of granzyme B (GzmB) expression in CD49a+CD103+ ILC1s isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 6) or S100a4-CreIl15fl/flPyMT (n = 7) mice. h, Representative histogram and quantification of granzyme C (GzmC) expression in CD49a+CD103+ ILC1s isolated from pooled tumors of 20–24-week-old Il15fl/flPyMT (n = 5) or S100a4-CreIl15fl/flPyMT (n = 5) mice. i, Total tumor burden of Il15fl/flPyMT (11 weeks n = 5, 20 weeks n = 7) and S100a4-CreIl15fl/flPyMT (11 weeks n = 7, 20 weeks n = 8) mice monitored between 11 and 20 weeks of age. j, Representative histograms of CD49b (DX5) expression among total CD3−NK1.1+ cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+ NK cells quantified out of total splenic CD45+ cells (Il15fl/flPyMT n = 7, S100a4-CreIl15fl/flPyMT n = 6). k, (Left) Representative plots of CD27 and CD11b expression among total CD3−NK1.1+DX5+ NK cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+CD11b+CD27− cells quantified out of total splenic CD45+ cells (Il15fl/flPyMT n = 7, S100a4-CreIl15fl/flPyMT n = 6). c-k, Each dot represents an individual mouse. Data are pooled from 3 or more independent experiments. All error bars represent the mean ± SEM. Two-tailed unpaired t test was used for statistical analysis, NS = non-significant, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Extended Data Fig. 8 Mouse models utilized for characterization of tissue-resident ILC1 responses in PyMT tumors.
a, Diagram denoting the Gzmc gene locus of GzmctdT-T2A-iCre mice. b, Flow cytometric analysis of GzmCtdT reporter expression and granzyme C protein expression among the indicated CD3−NK1.1+ innate lymphocyte populations in PyMT tumors. c, Diagram denoting the Cdh1 gene locus of Cdh1mCFP mice. d, Diagram denoting the Polr2a gene locus harboring an expression cassette for a GCaMP5 calcium indicator and a tdT reporter. e, Expected fluorescent phenotype that results when calcium signaling is sensed in GCaMP5−expressing innate lymphocytes.
Extended Data Fig. 9 S100a8-Cre targets cancer cells, and splenic NK cells are unaffected in S100a8-CreIl15fl/flPyMT mice.
a, Gating strategy for determining eGFP expression in CD24+CD29+EpCAM+ epithelial cells from non-reporter mammary gland, IL-152A-eGFP mammary gland, PyMT tumors, and IL-152A-eGFPPyMT tumors. b, Gating strategy and YFP expression in CD24+CD29+ cancer cells from pooled tumors of a 20-week-old S100a8-CreRosa26LSL-YFPPyMT mouse. c, qPCR analysis of Il15 mRNA expression in sorted live CD45-CD24+CD29+EpCAM+ cancer cells from 20–24-week-old Il15fl/flPyMT (n = 3) or S100a8-CreIl15fl/flPyMT mice (n = 3). d, (Left) Representative histograms of CD49b (DX5) expression among total CD3−NK1.1+ cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+ NK cells quantified out of total splenic CD45+ cells (n = 4 for each genotype). e, (Left) Representative plots of CD27 and CD11b expression among total CD3−NK1.1+DX5+ cells in spleens of the indicated mouse genotype. (Right) Percentage of DX5+CD11b+CD27− cells quantified out of total splenic CD45+ cells (n = 4 for each genotype). c-e, Each dot represents an individual mouse. Data are pooled from 3 independent experiments. All error bars represent the mean ± SEM. Two-tailed unpaired t test was used for statistical analysis, NS = non-significant.
Supplementary information
Supplementary Table 1
DEGs for innate and adaptive lymphocytes in human and murine tumors.
Supplementary Video 1
Live imaging depicting GzmCtdT-expressing cells sensing E-cadherinmCFP-expressing cancer cells in association with calcium signaling.
Source data
Source Data Fig. 5
Statistical Source Data.
Source Data Fig. 6
Statistical Source Data.
Source Data Fig. 6
Source Data Images.
Source Data Fig. 7
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. 9
Statistical Source Data.
Rights and permissions
About this article
Cite this article
Kansler, E.R., Dadi, S., Krishna, C. et al. Cytotoxic innate lymphoid cells sense cancer cell-expressed interleukin-15 to suppress human and murine malignancies. Nat Immunol 23, 904–915 (2022). https://doi.org/10.1038/s41590-022-01213-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41590-022-01213-2
This article is cited by
-
Natural killer cell therapies
Nature (2024)
-
Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing
Nature Biotechnology (2024)
-
NK cell-triggered CCL5/IFNγ-CXCL9/10 axis underlies the clinical efficacy of neoadjuvant anti-HER2 antibodies in breast cancer
Journal of Experimental & Clinical Cancer Research (2024)
-
Comprehensive analysis of cuproptosis-related lncRNAs in immune infiltration and prognosis in hepatocellular carcinoma
BMC Bioinformatics (2023)
-
Innate lymphoid cells and innate-like T cells in cancer — at the crossroads of innate and adaptive immunity
Nature Reviews Cancer (2023)