Abstract
T cell infiltration into tumors is a favorable prognostic feature, but most solid tumors lack productive T cell responses. Mechanisms that coordinate T cell exclusion are incompletely understood. Here we identify hepatocyte activation via interleukin-6/STAT3 and secretion of serum amyloid A (SAA) proteins 1 and 2 as important regulators of T cell surveillance of extrahepatic tumors. Loss of STAT3 in hepatocytes or SAA remodeled the tumor microenvironment with infiltration by CD8+ T cells, while interleukin-6 overexpression in hepatocytes and SAA signaling via Toll-like receptor 2 reduced the number of intratumoral dendritic cells and, in doing so, inhibited T cell tumor infiltration. Genetic ablation of SAA enhanced survival after tumor resection in a T cell-dependent manner. Likewise, in individuals with pancreatic ductal adenocarcinoma, long-term survivors after surgery demonstrated lower serum SAA levels than short-term survivors. Taken together, these data define a fundamental link between liver and tumor immunobiology wherein hepatocytes govern productive T cell surveillance in cancer.
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
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Bulk RNA-seq data and scRNA-seq data have been deposited at the Gene Expression Omnibus (GEO) under the accession code GSE232701. All other data are present in the article and Supplementary Information or from the corresponding author upon reasonable request. Source data are provided with this paper.
Code availability
R code used to generate figures can be made available upon reasonable request.
References
Bruni, D., Angell, H. K. & Galon, J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat. Rev. Cancer 20, 662–680 (2020).
Balachandran, V. P. et al. Identification of unique neoantigen qualities in long-term survivors of pancreatic cancer. Nature 551, 512–516 (2017).
Chen, P. L. et al. Analysis of immune signatures in longitudinal tumor samples yields insight into biomarkers of response and mechanisms of resistance to immune checkpoint blockade. Cancer Discov. 6, 827–837 (2016).
Slaney, C. Y., Kershaw, M. H. & Darcy, P. K. Trafficking of T cells into tumors. Cancer Res. 74, 7168–7174 (2014).
Stromnes, I. M. et al. T cells engineered against a native antigen can surmount immunologic and physical barriers to treat pancreatic ductal adenocarcinoma. Cancer Cell 28, 638–652 (2015).
Grunwald, B. T. et al. Spatially confined sub-tumor microenvironments in pancreatic cancer. Cell 184, 5577–5592 (2021).
Steele, N. G. et al. Multimodal mapping of the tumor and peripheral blood immune landscape in human pancreatic cancer. Nat. Cancer 1, 1097–1112 (2020).
Li, J. et al. Tumor cell-intrinsic factors underlie heterogeneity of immune cell infiltration and response to immunotherapy. Immunity 49, 178–193 (2018).
Stone, M. L. & Beatty, G. L. Cellular determinants and therapeutic implications of inflammation in pancreatic cancer. Pharmacol. Ther. 201, 202–213 (2019).
Kortlever, R. M. et al. Myc cooperates with Ras by programming inflammation and immune suppression. Cell 171, 1301–1315 (2017).
Tumeh, P. C. et al. Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC. Cancer Immunol. Res. 5, 417–424 (2017).
Lee, J. C. et al. Regulatory T cell control of systemic immunity and immunotherapy response in liver metastasis. Sci. Immunol. 5, eaba0759 (2020).
Daud, A. I. et al. Tumor immune profiling predicts response to anti-PD-1 therapy in human melanoma. J. Clin. Invest. 126, 3447–3452 (2016).
Beatty, G. L. et al. Exclusion of T Cells from pancreatic carcinomas in mice is regulated by Ly6ClowF4/80+ extratumoral macrophages. Gastroenterology 149, 201–210 (2015).
De Simone, G. et al. Identification of a Kupffer cell subset capable of reverting the T cell dysfunction induced by hepatocellular priming. Immunity 54, 2089–2100 (2021).
Lee, J. W. et al. Hepatocytes direct the formation of a pro-metastatic niche in the liver. Nature 567, 249–252 (2019).
Wattenberg, M. M. et al. Systemic inflammation is a determinant of outcomes of CD40 agonist-based therapy in pancreatic cancer patients. JCI Insight https://doi.org/10.1172/jci.insight.145389 (2021).
Findeisen, P. et al. Serum amyloid A as a prognostic marker in melanoma identified by proteomic profiling. J. Clin. Oncol. 27, 2199–2208 (2009).
Cho, W. C. et al. Identification of serum amyloid a protein as a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling. Clin. Cancer Res. 10, 43–52 (2004).
Luo, G. et al. Blood neutrophil–lymphocyte ratio predicts survival in patients with advanced pancreatic cancer treated with chemotherapy. Ann. Surg. Oncol. 22, 670–676 (2015).
Farren, M. R. et al. Systemic immune activity predicts overall survival in treatment-naive patients with metastatic pancreatic cancer. Clin. Cancer Res. 22, 2565–2574 (2016).
Liu, M., Kalbasi, A. & Beatty, G. L. Functio Laesa: cancer inflammation and therapeutic resistance. J. Oncol. Pract. 13, 173–180 (2017).
Pastorino, U. et al. Baseline and postoperative C-reactive protein levels predict mortality in operable lung cancer. Eur. J. Cancer 79, 90–97 (2017).
Weber, J. S. et al. A serum protein signature associated with outcome after Anti-PD-1 therapy in metastatic melanoma. Cancer Immunol. Res. 6, 79–86 (2018).
Laino, A. S. et al. Serum interleukin-6 and C-reactive protein are associated with survival in melanoma patients receiving immune checkpoint inhibition. J. Immunother. Cancer 8, e000842 (2020).
He, L. N. et al. Baseline and early changes in circulating serum amyloid A (SAA) predict survival outcomes in advanced non-small cell lung cancer patients treated with anti-PD-1/PD-L1 monotherapy. Lung Cancer 158, 1–8 (2021).
Goldinger, S. M. et al. Correlation between metastatic site and response to anti-programmed death-1 (PD-1) agents in melanoma. J. Clin. Oncol. 34, 9549–9549 (2016).
Nishino, M. et al. Immune-related response assessment during PD-1 inhibitor therapy in advanced non-small-cell lung cancer patients. J. Immunother. Cancer 4, 84 (2016).
Langlet, C. et al. CD64 expression distinguishes monocyte-derived and conventional dendritic cells and reveals their distinct role during intramuscular immunization. J. Immunol. 188, 1751–1760 (2012).
Sheikh, N. A. & Jones, L. A. CD54 is a surrogate marker of antigen presenting cell activation. Cancer Immunol. Immunother. 57, 1381–1390 (2008).
Lee, J. & Beatty, G. L. Serum amyloid A proteins and their impact on metastasis and immune biology in cancer. Cancers 13, 3179 (2021).
Duhen, T. et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat. Commun. 9, 2724 (2018).
Kim, J. C., Jung, Y. S., Lee, H. Y., Park, J. S. & Bae, Y. S. Serum amyloid A inhibits dendritic cell differentiation by suppressing GM-CSF receptor expression and signaling. Exp. Mol. Med 49, e369 (2017).
Tang, M. et al. Toll-like receptor 2 activation promotes tumor dendritic cell dysfunction by regulating IL-6 and IL-10 receptor signaling. Cell Rep. 13, 2851–2864 (2015).
Gabrilovich, D. Mechanisms and functional significance of tumour-induced dendritic-cell defects. Nat. Rev. Immunol. 4, 941–952 (2004).
Lin, J. H. et al. Type 1 conventional dendritic cells are systemically dysregulated early in pancreatic carcinogenesis. J. Exp. Med. 217, e20190673 (2020).
Spranger, S., Dai, D., Horton, B. & Gajewski, T. F. Tumor-residing Batf3 dendritic cells are required for effector T cell trafficking and adoptive T cell therapy. Cancer Cell 31, 711–723 (2017).
Zhang, Q. et al. Gut microbiome directs hepatocytes to recruit MDSCs and promote cholangiocarcinoma. Cancer Discov. 11, 1248–1267 (2021).
Migita, K. et al. Lipopolysaccharide signaling induces serum amyloid A (SAA) synthesis in human hepatocytes in vitro. FEBS Lett. 569, 235–239 (2004).
Yoshida, T. et al. C reactive protein impairs adaptive immunity in immune cells of patients with melanoma. J. Immunother. Cancer https://doi.org/10.1136/jitc-2019-000234 (2020).
de Beer, M. C. et al. Impact of serum amyloid A on high density lipoprotein composition and levels. J. Lipid Res. 51, 3117–3125 (2010).
Hingorani, S. R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).
Rhim, A. D. et al. EMT and dissemination precede pancreatic tumor formation. Cell 148, 349–361 (2012).
Long, K. B. et al. IFNgamma and CCL2 cooperate to redirect tumor-infiltrating monocytes to degrade fibrosis and enhance chemotherapy efficacy in pancreatic carcinoma. Cancer Discov. 6, 400–413 (2016).
Evans, R. A. et al. Lack of immunoediting in murine pancreatic cancer reversed with neoantigen. JCI Insight 1, e88328 (2016).
Kalbasi, A. et al. Tumor-derived CCL2 mediates resistance to radiotherapy in pancreatic ductal adenocarcinoma. Clin. Cancer Res. 23, 137–148 (2017).
Untergasser, A. et al. Primer3–new capabilities and interfaces. Nucleic Acids Res. 40, e115 (2012).
Amend, S. R., Valkenburg, K. C. & Pienta, K. J. Murine hind limb long bone dissection and bone marrow isolation. J. Vis. Exp. https://doi.org/10.3791/53936 (2016).
Kimura, T. et al. Production of adeno-associated virus vectors for in vitro and in vivo applications. Sci. Rep. 9, 13601 (2019).
Acknowledgements
We thank M. C. de Beer, F. C. de Beer, N. R. Webb, L. Tannock and P. Shridas for helpful discussions. We thank E. P. Wileyto from the ACC Biostatistics and Bioinformatics Core Facility at the University of Pennsylvania for statistical support. M.L.S. is a Cancer Research Institute/Samuel and Ruth Engelberg Fellow. M.M.W. is a Damon Runyon Physician-Scientist supported (in part) by the Damon Runyon Cancer Research Foundation (PST-34-21). This work was supported by National Institutes of Health grants T32 CA009140 (to M.L.S.), T32-HL007439-41 (to M.M.W.), K12-CA076931-21 (to M.M.W.), R01-CA197916 (to G.L.B.), R01CA245323 (to G.L.B.), U01 CA224193 (to G.L.B.) and U01 CA224175, PacMen Consortium (to V.P.B.); Department of Defense grants W81XWH2110622 (to M.L.S.) and W81XWH2110621 (to G.L.B.); Stand Up to Cancer (SU2C) Innovative Research Grant SU2C-AACR-IRG 13-17 (to G.L.B.); the Robert L. Fine Cancer Research Foundation (to G.L.B.); the SPORE in Skin Cancer, P50 CA261608 (to G.L.B.); the 2020 AACR-The Mark Foundation for Cancer Research ‘Science of the Patient’ (SOP) Grants, grant number 20-60-51-BEAT (to G.L.B.); and the 2017 Pancreatic Cancer Action Network Precision Medicine Targeted Grant 17-85-BEAT (to G.L.B.).
Author information
Authors and Affiliations
Contributions
Experiments and data analysis were performed by M.L.S., J.L., J.W.L, H. Coho., M.T., M.M.W., V.M.H., H. Choi and S.C.-B.; design and preparation of AAVs by J.L.; RNA-seq analysis by M.L.S. and J.L.; scRNA-seq analysis by M.T.; immunohistochemistry by M.L.S., Y.X., S.K.Z., D.P. and K.M.; RNA in situ hybridization by M.L.S.; tumor cell culture by M.L.S.; animal studies by M.L.S., J.W.L. and H. Coho; flow cytometry by M.L.S.; real-time qPCR by J.W.L.; cytokine bead array by M.L.S.; ELISAs by H. Choi and V.M.H.; CyTOF by M.L.S. and M.W.; V.P.B. provided patient serum samples; G.L.B. and D.D. provided experimental advice; M.L.S. and G.L.B. designed the study; M.L.S., J.L. and G.L.B. prepared and wrote the paper.
Corresponding author
Ethics declarations
Competing interests
J.L. has received compensation for prior consulting work from Anchor Molecular and Prescient Medicine unrelated to this work. G.L.B. reports prior or active roles as a consultant/advisory board member for Adicet Bio, Aduro Biotech, Alligator Biosciences, AstraZeneca, BiolineRx, BioMarin Pharmaceuticals, Boehinger Ingelheim, Bristol-Myers Squibb, Cantargia, Cour Pharmaceuticals, Genmab, HotSpot Therapeutics, Incyte, Janssen, Legend Biotech, Merck, Molecular Partners, Monopteros, Nano Ghosts, Opsona, Pancreatic Cancer Action Network, Seagen, Shattuck Laboratories and Verastem; reports receiving commercial research grants from Alligator Biosciences, Gilead Sciences, Incyte, Bristol-Myers Squibb, Verastem, Halozyme, Biothera, Hibercell, Newlink, Novartis, Arcus and Janssen. G.L.B. is an inventor of intellectual property (US patent numbers 10,640,569 and 10,577,417) and recipient of royalties related to CAR T cells that is licensed by the University of Pennsylvania to Novartis and Tmunity Therapeutics. M.M.W. reports prior or active roles as a consultant for Nanology. The other authors declare no competing interests.
Peer review
Peer review information
Nature Immunology thanks Venu Pillarisetty and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard, in collaboration with the Nature Immunology team.
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 Liver inflammation correlates with low T cell infiltration.
a) Gating strategy used for neutrophil-to-lymphocyte ratio analysis in peripheral blood. b) Mice were implanted with orthotopic PDAC tumors as described in Fig. 1a. FOXP3+ and CD8+ cells in tumors were quantified by immunohistochemistry. CD4+ T cells in tumors and Ly6G+ cells in livers were quantified by flow cytometry. IL-6 serum levels were measured by cytokine bead array. CD3+ and Ly6G+ cells/uL in the peripheral blood (PB) were measured by flow cytometry. Significance was determined by Mann Whitney test with correction for multiple comparisons. n = 19-20 mice per group. c) Six clonal PDAC cell lines (n = 3 T cellhigh and n = 3 T celllow) were chosen based on their similar tumor burden. Mice were analyzed as described in Fig. 1a. d) Tumor weight 20 days post-orthotopic implantation. Mann Whitney test performed. e) Individual mice were clustered based on the variables listed in Fig. 1a. f) Correlation matrix of the variables described in Fig. 1a. g) Percent area of metastatic lesions in livers of mice described in Fig. 1a. Lesions were measured by H&E staining. Mann Whitney test performed. h) Transcriptional expression of tumor markers (Pdx1 and Krt19) in livers of C57BL/6 mice with an orthotopic T cellhigh (655c3, n = 6 mice) or T celllow (6419c1, n = 4 mice) tumor measured by RNA sequencing analysis. Mann Whitney test performed. i-j) Wild-type mice were intraportally injected with 2e5 PDA cells. n = 2 mice per cell line. 14 days later, livers were formalin fixed, paraffin embedded, and stained with hematoxylin & eosin. Metastatic lesions were quantified. Significance was determined by Mann-Whitney test. For violin plots, all data points are shown with lines at the median and quartiles. For h and j, data are presented as mean values +/− SD.
Extended Data Fig. 2 IL-6/STAT3 signaling in hepatocytes remodels the tumor immune microenvironment.
a-c). Il6+/+ or Il6−/− mice were orthotopically (OT) injected with 5e5 PDA.69 cells. Twenty days later, tumors were harvested. n = 10 Il6+/+ mice and n = 9 Il6−/− mice were examined over 2 biological replicates. b) Tumors were formalin fixed, paraffin embedded, and stained for CD8 (brown), FOXP3 (purple), CK19 (yellow), and nuclei (blue, hematoxylin). Representative images are shown. Scale bars=100 μm. c) Quantification of CD8+ or FOXP3+ cells from (b). Mann Whitney tests were performed. d-g). C57BL/6 or AlbCre+/+Stat3fl/fl mice were orthotopically injected with 5e5 PDA.69 cells or received sham surgery. Twenty days later, livers and tumors were harvested for RNA sequencing. n = 6 mice per group. Data are representative of 1 biological replicate. d) Heatmap of differentially expressed genes (DEGs) in livers. e) Volcano plot showing DEGs in tumors of C57BL/6 or AlbCre+/+Stat3fl/fl mice. Genes enriched in C57/BL6 shown in blue. Genes enriched in the AlbCre+/+Stat3fl/fl group shown in red. Significance determined by log2FoldChange < −0.6 or > 0.6 and adjusted p value < 0.05. f) PCA plot showing clustering of tumors. g) Gene set enrichment analysis of DEGs. The ten most highly enriched gene sets in tumors of AlbCre+/+Stat3fl/fl mice are shown. h-k) C57BL/6 mice were OT injected with 5e5 T celllow (PDA.69) or T cellhigh (2838.c3) PDAC cells. Twenty days later, tumors were harvested. n = 5 mice per group. Data shown are representative of n = 1 biological replicate. i) Tumors were formalin fixed, paraffin embedded, and stained for CD8 (brown), FOXP3 (purple), CK19 (yellow), and nuclei (blue, hematoxylin). Representative images are shown. Scale bars=200µm. j) Quantification of CD8+ or FOXP3+ cells from (i). k) CD3+ T cell infiltration in tumors was analyzed by flow cytometry. l) C57BL/6 mice were treated with GFP.AAV or IL6.AAV as described in Fig. 2h, then injected with 5e5 T cellhigh (2838.c3) PDAC cells. IL-6 serum levels were measured at day 15 by cytokine bead array. Data are representative of 3 biological replicates. For j-l, Mann Whitney tests performed. For c, j, k, and l, data are presented as mean values +/- SD.
Extended Data Fig. 3 SAA inhibit T cell infiltration in primary and metastatic tumors.
a) Mice were treated as described in Fig. 3d. CD8+ and FOXP3+ staining in Fig. 3f was quantified. Data shown are representative of n = 3 biological replicates. b-d) Saa+/+ or Saa−/− mice were orthotopically injected with 5e5 PDAC.69 cells, then challenged with 8572.YFP cells via intraportal (I.Po.) injection 10 days later. Tissues were harvested on day 20. Data shown are representative of 3 biological replicates. c) Livers were formalin fixed and paraffin embedded and stained for CD3 (purple), CK19 (yellow) and nuclei (blue, hematoxylin). Representative images area shown. Scale bars = 100μm. d) Quantification of CD3+ cells in metastatic lesions. e-j) Saa+/+ or Saa−/− mice were subcutaneously (SC) injected with T celllow tumors (5e5 7940B (e-g); or 6694.c2 (h-j). At 28-33 days post-tumor implantation, tumors were analyzed. n = 5 mice/group. Data shown are representative of n = 1 biological replicate. f, i) Tumors were formalin fixed and paraffin embedded, then stained for CD8 (brown), FOXP3 (purple), CK19 (yellow) and nuclei (blue, hematoxylin). Representative images are shown. Scale bars=100μm (f) or 200 μm (i). CD8+ and FOXP3+ cells were quantified. g, j) Tumor infiltrating T cells were analyzed by flow cytometry. k) Study schematic for (l-m). 1.5e5 B16F10 cells were subcutaneously (SC) injected on Day 0. 21 days later, analyses were performed. n = 6 mice/group. l) SAA serum levels at necropsy were analyzed by ELISA. Wilcoxon matched-pairs signed rank test was performed. m) Tumors were analyzed by flow cytometry. Mann Whitney tests were performed for panels comparing two groups. For a, d, f, g, i, j, and m, data are presented as mean values +/− SD.
Extended Data Fig. 4 SAA regulate the composition of the tumor microenvironment.
a) Saa+/+ or Saa−/− mice were orthotopically injected with 5e5 PDAC.69 cells. Eighteen days later, tumors were harvested, and single cell RNA-sequencing was performed using the 10x genomics platform. n = 4 mice/group were pooled to n = 2 samples per group. Bulk tumor cells were clustered and visualized using UMAP in R software. Sixteen distinct cell populations were identified. b) Bubble plot displaying expression of genes used to label clusters. c) T cells were subsetted and furthered clustered and visualized using UMAP. Bubble plot showing expression of genes used to label individual T cell clusters. d) Interpolated polar line plot of raw counts of cytotoxic molecules (Gzmb, Prf1, Ifng, and Tnf) and activation markers (Pdcd1, Ctla4, Lag3, Tox, and Havcr2) in CD8+ CTL population of orthotopic tumors harvested from Saa+/+ or Saa−/− mice.
Extended Data Fig. 5 SAA regulate the phenotype of tumor-infiltrating T cells.
a) Saa+/+ or Saa−/− mice were orthotopically injected with 5e5 PDAC.69 cells. Twenty days later, tumors were harvested and analyzed by CyTOF. tSNE plots from 5000 downsampled CD45+ cells were generated and clusters identified using R software. b) Heat map displaying marker expression for each cluster. c) tSNE plots were subsetted on CD3+ T cell clusters. d) Expression of immunoregulatory markers on T cell clusters. e) Quantification of CD8+Perforin+ cells by CyTOF. Data are representative of n = 1 experiment. n = 4 Saa+/+ and n = 5 Saa−/− mice. Mann Whitney test performed. f) Quantification of CD3+CD8+PD1+TIM3+LAG3+ cells by flow cytometry in PDA.69, 7940B, and 6694.c2 subcutaneous tumors at 27-33 days post tumor injection into Saa+/+ (n = 7 for PDA.69, n = 5 for 7940B, 6694c2) or Saa−/− (n = 5) mice. n = 1 biological replicate. Mann Whitney tests were performed. g) Experimental schematic for (h-i). C57BL/6 mice (n = 4-6/group) were injected subcutaneously with 1e6 PDA.69 cells on Day 0. T cells were depleted with 0.2mg anti-CD4 and anti-CD8 antibodies on Day −1 and repeated weekly. On Day 28, tumors were formalin fixed, paraffin embedded, and stained for pSTAT1 (yellow), F4/80 (purple), and CK19 (teal). Data shown are from n = 1 biological replicate. h) Quantification of pSTAT1+ cells was performed in Visiopharm software. Ordinary one-way ANOVA with Sidak’s multiple comparisons test was performed. i) Representative images of tumors. Scale bars = 100µM. j) Saa+/+ or Saa−/− mice were subcutaneously (SC) injected with T celllow tumors (5e5 PDA.69, 7940B, or 6694.c2. Mean tumor growth curves are shown. Mann Whitney tests were performed. n = 5 mice/group. Data shown are representative of n = 5 biological replicates for PDA.69, n = 1 biological replicate for 7940B and 6694.c2. For e, f, and h, data are presented as mean values +/− SD.
Extended Data Fig. 6 SAA regulate dendritic cell infiltration into primary tumors.
a) Saa+/+ (n = 3) or Saa−/− (n = 4) mice were orthotopically injected with 5e5 PDA.69 cells. Twenty days later, tumors were harvested and analyzed by CyTOF. tSNE plots from 5000 CD45+ downsampled cells were generated and clusters identified using R software. b) Heat map displaying marker expression for each cluster. c) Expression of dendritic cell markers on all clusters. d) DC frequency measured by manual gating. For a-d, data are representative of n = 1 experiment. Unpaired t test performed. e) Mice were treated as described in Fig. 2h. n = 5-8 mice per group. Data shown are from n = 2 biological replicates. Tumors were analyzed and dendritic cells quantified by flow cytometry. Significance was determined by Brown Forsythe and Welch test with Dunnett’s T3 correction. For d and e, data are presented as mean values +/− SD.
Extended Data Fig. 7 Inhibition of T cell surveillance by SAA depends on TLR2.
a) Study design for b. Saa+/+ or Saa−/− mice received sham surgery or orthotopic (OT) injection of 5e5 PDA.69 cells. n = 5 mice/group. Bulk liver tissue was collected in Trizol and RNA was extracted. b) Expression of known SAA receptors measured by qRT-PCR. Ordinary one-way ANOVA with Dunnett’s multiple comparisons test was performed. c-d) Twenty days after sham surgery or OT injection of 5e5 PDA.69 cells, livers were analyzed by flow cytometry. n = 5 mice per group. c) Percent of live cells expressing TLR2. d) tSNE plots were created from 5000 downsampled live cells and cell marker expression is displayed. For a-d, data are representative of n = 1 biological replicate. e-j) Tlr2+/+ or Tlr2−/− mice were OT injected with 5e5 PDA.69 cells. Twenty days later, tumors were harvested. Data are representative of 1 biological replicate for (f-g, j) and 2 biological replicates for (h-i). f) SAA serum levels at necropsy were analyzed by ELISA. n = 5 non-tumor bearing Tlr2+/+ mice, n = 9 Tlr2+/+ mice, and n = 10 Tlr2−/− mice. g) Livers were formalin fixed, paraffin embedded, and stained by RNA-ISH for Saa. h-i) Tumors were formalin fixed, paraffin embedded, and stained for CD8 (brown), FOXP3 (purple), CK19 (yellow) and nuclei (blue, hematoxylin). Representative images are shown. Scale bars=100µm. n = 11 mice/group. j) T cell and dendritic cell (DC) infiltration into tumors was analyzed by flow cytometry. n = 8 Tlr2+/+ mice, n = 11 Tlr2−/− mice. For (c-j), significance was determined by Mann Whitney test. For b, c, f, h, and j, data are presented as mean values +/− SD.
Extended Data Fig. 8 A TLR2 agonist is sufficient to inhibit dendritic cell and T cell infiltration into tumors.
a) Experiment schematic for (b-f). C57BL/6 mice were orthotopically injected with 5e5 2838.c3 cells (n = 8 mice per group). Two days later, mice began treatment with 100 µg of Pam3CSK4 q.o.d. Twenty days after tumor implantation, tumors were analyzed b-f) T cells and dendritic cells quantified by flow cytometry. Mann Whitney tests (two sided) were performed. Data are representative of 1 biological replicate. g) Mice of the indicated genotypes were orthotopically implanted with tumor cells as described in Fig. 2h and Extended Data Figure 9e. Left panel: n = 9, 7, 10, and 9 mice were examined for groups 1, 2, 3, and 4 respectively from left to right, over n = 2 biological replicates. Right panel: n = 9 Tlr2+/+ mice and n = 11 Tlr2−/− mice in n = 1 biological replicate. Tumors were analyzed by flow cytometry. Significance was determined by Brown Forsythe and Welch test with Dunnett’s T3 correction (left), and by unpaired t test (right). h) Representative histograms of expression of tumor-reactive T cell markers in Tlr2+/+ or Tlr2−/− mice. For b-g, data are presented as mean values +/− SD.
Extended Data Fig. 9 SAA regulate a tumor-specific T cell response dependent on dendritic cells.
a) Experiment schematic for treating hematopoietic stem cells (HSCs) harvested from Tlr2+/+ or Tlr2−/− mice with GM-CSF (0.2µg/mL) and Pam3CSK4 (100ng/mL). b) Quantification of CD11c+ DCs by flow cytometry. Significance was determined by ordinary one-way ANOVA with Sidak’s multiple comparisons correction. n = 3 technical replicates per n = 2 biological replicates. c) Study design for (d). HSCs were harvested from Tlr2+/+ or Tlr2−/− mice and treated with GM-CSF (0.2µg/mL) for eight days to differentiate them into bone marrow derived dendritic cells (BMDCs). BMDCs were treated with SAA (1µg/mL) for 24 hours. d) IL-6 and IL-10 were detected in supernatant by cytokine bead array. Significance was determined by one-way ANOVA with Sidak’s multiple comparisons correction. Data are representative of n = 3 biological replicates. e) Mice were treated as described in Fig. 3o, and CD8+tetramer+ cells were quantified by flow cytometry. Significance was determined by Brown-Forsythe and Welch ANOVA test with Dunn’s T3 multiple comparisons test. Data are representative of n = 3 biological replicates. n = 5 mice per group, except for tumor bearing Saa+/+ mice (n = 3) and tumor bearing Saa−/− mice (n = 4). f) Representative flow plots of tetramer expression on CD8+ T cells. g) Study schematic. Saa+/+ (n = 7/group) or Saa−/− mice (n = 8/group) were orthotopically (OT) injected with 5e5 PDA.69 cells on day 0. DC depleting antibody (aDC) was administered on day −2, −1 and twice weekly after tumor implantation. Analysis was performed on Day 20. h) Spleens were analyzed by flow cytometry for DC depletion. n = 6/group for aDC. Mann Whitney tests were performed. i) Tumors were analyzed by flow cytometry for total DC and T cell infiltration. Mann Whitney tests were performed. For b, d, e, h, and i, data are presented as mean values +/- SD.
Extended Data Fig. 10 SAA suppress immunological memory after tumor resection and disrupt systemic immune biology.
a) Study design for b-c. Cured Saa−/− mice after surgical resection of tumor (compared to tumor-naïve mice) were rechallenged with 1e6 7940B tumor cells injected subcutaneously. n = 3 mice per group. b) Overall survival. Significance was determined by Mantel Cox test. c) Tumor growth curves after rechallenge. d) Study design for e-f. Cured mice after tumor rechallenge from a-c (compared to tumor-naïve mice) were depleted of T cells weekly starting one day before challenge with 1e6 subcutaneous 7940B cells. e) Overall survival. Mantel Cox test was performed. f) Mean tumor growth curves after rechallenge. Data shown are representative of 1 biological replicate. g) Ten patients with advanced untreated pancreatic cancer were divided into two groups of n = 5 each by median serum SAA level (215 µg/mL) measured by ELISA. h-i) Saa+/+ or Saa−/− mice (n = 5/group) were orthotopically injected with 5e5 PDA.69 cells. Twenty days later, spleens were collected and analyzed by CyTOF. h) Representative flow plots of CD14+ monocytes and CD3+ T cells. i) Quantification of CD14+ monocytes and CD3+ T cells. Unpaired t tests were performed. j) SAA serum levels detected by ELISA for stage-matched cohorts of treatment-naive, surgically resected short- and long-term survivors (STS, n = 9, and LTS, n = 16). All outliers are shown. Significance was determined by Mann Whitney t-test (two sided). For g, i, and j, data are presented as mean values +/−SD.
Supplementary information
Supplementary Information
Supplementary Tables 1–5 and Figs. 1–4.
Numerical Source Data 1
Supplementary Fig. 1 source data.
Numerical Source Data 2
Supplementary Fig. 2 source data.
Numerical Source Data 3
Supplementary Fig. 3 source data.
Supplementary Data 1
Supplementary Fig. 1f image 2838c3.
Supplementary Data 2
Supplementary Fig. 1f image 6419c5.
Supplementary Data 3
Supplementary Fig. 1f image 6421c2.
Supplementary Data 4
Supplementary Fig. 1f image 6422c1.
Supplementary Data 5
Supplementary Fig. 1f image 6499c4.
Supplementary Data 6
Supplementary Fig. 1f image 6620c1.
Supplementary Data 7
Supplementary Fig. 1f image 6694c2.
Supplementary Data 8
Supplementary Fig. 1f image 6699c3.
Supplementary Data 9
Supplementary Fig. 1g image T cellhi.
Supplementary Data 10
Supplementary Fig. 1g image T celllo.
Supplementary Data 11
Supplementary Fig. 3a LTS.
Supplementary Data 12
Supplementary Fig. 3a STS.
Supplementary Data 13
Supplementary Fig. 3c resected tumor.
Supplementary Data 14
Supplementary Fig. 3c wound clips.
Supplementary Data 15
Supplementary Fig. 3c exteriorization.
Supplementary Data 16
Supplementary Fig. 3c removal.
Supplementary Data 17
Supplementary Fig. 3d T cellhi.
Supplementary Data 18
Supplementary Fig. 3d T celllo.
Supplementary Data 19
Supplementary Fig. 3g SAAKO.
Supplementary Data 20
Supplementary Fig. 3g WT.
Supplementary Data 21
Supplementary Fig. 4f classified image.
Supplementary Data 22
Supplementary Fig. 4f raw image.
Source data
Source Data Fig. 1
Statistical source data Fig. 1.
Source Data Fig. 2
Statistical source data Fig. 2.
Source Data Fig. 3
Statistical source data Fig. 3.
Source Data Fig. 4
Statistical source data Fig. 4.
Source Data Extended Data Fig. 1
Statistical source data Extended Data Fig. 1.
Source Data Extended Data Fig. 2
Statistical source data Extended Data Fig. 2.
Source Data Extended Data Fig. 3
Statistical source data Extended Data Fig. 3.
Source Data Extended Data Fig. 4
Statistical source data Extended Data Fig. 4.
Source Data Extended Data Fig. 5
Statistical source data Extended Data Fig. 5.
Source Data Extended Data Fig. 6
Statistical source data Extended Data Fig. 6.
Source Data Extended Data Fig. 7
Statistical source data Extended Data Fig. 7.
Source Data Extended Data Fig. 8
Statistical source data Extended Data Fig. 8.
Source Data Extended Data Fig. 9
Statistical source data Extended Data Fig. 9.
Source Data Extended Data Fig. 10
Statistical source data Extended Data Fig. 10.
Source Data Fig. 1
Fig. 1j T cellhi.
Source Data Fig. 1
Fig. 1j T cell low.
Source Data Fig. 1
Fig. 1l T cellhi.
Source Data Fig. 1
Fig. 1l T celllo.
Source Data Fig. 2
Fig. 2e AlbCre.
Source Data Fig. 2
Fig. 2e AlbCreStat3ff.
Source Data Fig. 2
Fig. 2i AlbCre GFP.AAV.
Source Data Fig. 2
Fig. 2i AlbCre IL6.AAV.
Source Data Fig. 2
Fig. 2i AlbCreStat3ff GFP.AAV.
Source Data Fig. 2
Fig. 2i AlbCreStat3ff IL6.AAV.
Source Data Fig. 3
Fig. 3f C57BL/6.
Source Data Fig. 3
Fig. 3f SAAKO.
Source Data Fig. 4
Fig. 4a LTS.
Source Data Fig. 4
Fig. 4a STS.
Source Data Extended Data Fig. 2
Extended Data Fig. 2b IL6KO.
Source Data Extended Data Fig. 2
Extended Data Fig. 2b WT.
Source Data Extended Data Fig. 2
Extended Data Fig. 2i 2838.c3.
Source Data Extended Data Fig. 2
Extended Data Fig. 2i PDA.69.
Source Data Extended Data Fig. 3
Extended Data Fig. 3c SAAKO.
Source Data Extended Data Fig. 3
Extended Data Fig. 3c WT.
Source Data Extended Data Fig. 3
Extended Data Fig. 3f SAAKO.
Source Data Extended Data Fig. 3
Extended Data Fig. 3f WT.
Source Data Extended Data Fig. 3
Extended Data Fig. 3i SAAKO.
Source Data Extended Data Fig. 3
Extended Data Fig. 3i WT.
Source Data Extended Data Fig. 5
Extended Data Fig. 5i SAAKO control.
Source Data Extended Data Fig. 5
Extended Data Fig. 5i SAAKO depletion.
Source Data Extended Data Fig. 5
Extended Data Fig. 5i WT control.
Source Data Extended Data Fig. 5
Extended Data Fig. 5i WT depletion.
Source Data Extended Data Fig. 7
Extended Data Fig. 7g TLR2KO.
Source Data Extended Data Fig. 7
Extended Data Fig. 7g WT.
Source Data Extended Data Fig. 7
Extended Data Fig. 7i TLR2KO.
Source Data Extended Data Fig. 7
Extended Data Fig. 7i WT.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Stone, M.L., Lee, J., Lee, J.W. et al. Hepatocytes coordinate immune evasion in cancer via release of serum amyloid A proteins. Nat Immunol 25, 755–763 (2024). https://doi.org/10.1038/s41590-024-01820-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41590-024-01820-1
