Abstract
Cancer-associated systemic inflammation is strongly linked to poor disease outcome in patients with cancer1,2. For most human epithelial tumour types, high systemic neutrophil-to-lymphocyte ratios are associated with poor overall survival3, and experimental studies have demonstrated a causal relationship between neutrophils and metastasis4,5. However, the cancer-cell-intrinsic mechanisms that dictate the substantial heterogeneity in systemic neutrophilic inflammation between tumour-bearing hosts are largely unresolved. Here, using a panel of 16 distinct genetically engineered mouse models for breast cancer, we uncover a role for cancer-cell-intrinsic p53 as a key regulator of pro-metastatic neutrophils. Mechanistically, loss of p53 in cancer cells induced the secretion of WNT ligands that stimulate tumour-associated macrophages to produce IL-1β, thus driving systemic inflammation. Pharmacological and genetic blockade of WNT secretion in p53-null cancer cells reverses macrophage production of IL-1β and subsequent neutrophilic inflammation, resulting in reduced metastasis formation. Collectively, we demonstrate a mechanistic link between the loss of p53 in cancer cells, secretion of WNT ligands and systemic neutrophilia that potentiates metastatic progression. These insights illustrate the importance of the genetic makeup of breast tumours in dictating pro-metastatic systemic inflammation, and set the stage for personalized immune intervention strategies for patients with cancer.
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Data availability
The RNA-sequencing data have been deposited in the Gene Expression Omnibus (GEO, NCBI) repository under accession number GSE112665. All other data are found in the Source Data, Supplementary Information or available from the authors on reasonable request.
References
Diakos, C. I., Charles, K. A., McMillan, D. C. & Clarke, S. J. Cancer-related inflammation and treatment effectiveness. Lancet Oncol. 15, e493–e503 (2014).
McAllister, S. S. & Weinberg, R. A. The tumour-induced systemic environment as a critical regulator of cancer progression and metastasis. Nat. Cell Biol. 16, 717–727 (2014).
Templeton, A. J. et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J. Natl. Cancer Inst. 106, dju124 (2014).
Coffelt, S. B., Wellenstein, M. D. & de Visser, K. E. Neutrophils in cancer: neutral no more. Nat. Rev. Cancer 16, 431–446 (2016).
Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).
Kowanetz, M. et al. Granulocyte-colony stimulating factor promotes lung metastasis through mobilization of Ly6G+Ly6C+ granulocytes. Proc. Natl Acad. Sci. USA 107, 21248–21255 (2010).
Bald, T. et al. Ultraviolet-radiation-induced inflammation promotes angiotropism and metastasis in melanoma. Nature 507, 109–113 (2014).
Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 528, 413–417 (2015).
Park, J. et al. Cancer cells induce metastasis-supporting neutrophil extracellular DNA traps. Sci. Transl. Med. 8, 361ra138 (2016).
Steele, C. W. et al. CXCR2 inhibition profoundly suppresses metastases and augments immunotherapy in pancreatic ductal adenocarcinoma. Cancer Cell 29, 832–845 (2016).
Ethier, J. L., Desautels, D., Templeton, A., Shah, P. S. & Amir, E. Prognostic role of neutrophil-to-lymphocyte ratio in breast cancer: a systematic review and meta-analysis. Breast Cancer Res. 19, 2 (2017).
Cooks, T. et al. Mutant p53 prolongs NF-κB activation and promotes chronic inflammation and inflammation-associated colorectal cancer. Cancer Cell 23, 634–646 (2013).
Schwitalla, S. et al. Loss of p53 in enterocytes generates an inflammatory microenvironment enabling invasion and lymph node metastasis of carcinogen-induced colorectal tumors. Cancer Cell 23, 93–106 (2013).
Stodden, G. R. et al. Loss of Cdh1 and Trp53 in the uterus induces chronic inflammation with modification of tumor microenvironment. Oncogene 34, 2471–2482 (2015).
Wörmann, S. M. et al. Loss of p53 function activates JAK2–STAT3 signaling to promote pancreatic tumor growth, stroma modification, and gemcitabine resistance in mice and is associated with patient survival. Gastroenterology 151, 180–193 (2016).
Bezzi, M. et al. Diverse genetic-driven immune landscapes dictate tumor progression through distinct mechanisms. Nat. Med. 24, 165–175 (2018).
Kersten, K. et al. Mammary tumor-derived CCL2 enhances pro-metastatic systemic inflammation through upregulation of IL1β in tumor-associated macrophages. OncoImmunology 6, e1334744 (2017).
Annunziato, S. et al. Modeling invasive lobular breast carcinoma by CRISPR/Cas9-mediated somatic genome editing of the mammary gland. Genes Dev. 30, 1470–1480 (2016).
Song, X. et al. CD11b+/Gr-1+ immature myeloid cells mediate suppression of T cells in mice bearing tumors of IL-1β-secreting cells. J. Immunol. 175, 8200–8208 (2005).
Singh, V., Holla, S., Ramachandra, S. G. & Balaji, K. N. WNT-inflammasome signaling mediates NOD2-induced development of acute arthritis in mice. J. Immunol. 194, 3351–3360 (2015).
Spranger, S., Bao, R. & Gajewski, T. F. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature 523, 231–235 (2015).
Avgustinova, A. et al. Tumour cell-derived Wnt7a recruits and activates fibroblasts to promote tumour aggressiveness. Nat. Commun. 7, 10305 (2016).
Luke, J. J., Bao, R., Sweis, R. F., Spranger, S. & Gajewski, T. F. WNT/β-catenin pathway activation correlates with immune exclusion across human cancers. Clin. Cancer Res. 25, 3074–3083 (2019).
Kim, N. H. et al. p53 and microRNA-34 are suppressors of canonical Wnt signaling. Sci. Signal. 4, ra71 (2011).
Nusse, R. & Clevers, H. Wnt/β-catenin signaling, disease, and emerging therapeutic modalities. Cell 169, 985–999 (2017).
Wellenstein, M. D. & de Visser, K. E. Cancer-cell-intrinsic mechanisms shaping the tumor immune landscape. Immunity 48, 399–416 (2018).
Boggio, K. et al. Interleukin 12-mediated prevention of spontaneous mammary adenocarcinomas in two lines of Her-2/neu transgenic mice. J. Exp. Med. 188, 589–596 (1998).
Jonkers, J. et al. Synergistic tumor suppressor activity of BRCA2 and p53 in a conditional mouse model for breast cancer. Nat. Genet. 29, 418–425 (2001).
Derksen, P. W. et al. Somatic inactivation of E-cadherin and p53 in mice leads to metastatic lobular mammary carcinoma through induction of anoikis resistance and angiogenesis. Cancer Cell 10, 437–449 (2006).
Liu, X. et al. Somatic loss of BRCA1 and p53 in mice induces mammary tumors with features of human BRCA1-mutated basal-like breast cancer. Proc. Natl Acad. Sci. USA 104, 12111–12116 (2007).
Henneman, L. et al. Selective resistance to the PARP inhibitor olaparib in a mouse model for BRCA1-deficient metaplastic breast cancer. Proc. Natl Acad. Sci. USA 112, 8409–8414 (2015).
Huijbers, I. J. et al. Using the GEMM-ESC strategy to study gene function in mouse models. Nat. Protocols 10, 1755–1785 (2015).
Kas, S. M. et al. Insertional mutagenesis identifies drivers of a novel oncogenic pathway in invasive lobular breast carcinoma. Nat. Genet. 49, 1219–1230 (2017).
Annunziato, S. et al. Comparative oncogenomics identifies combinations of driver genes and drug targets in BRCA1-mutated breast cancer. Nat. Commun. 10, 397 (2019).
Liu, J. et al. Targeting Wnt-driven cancer through the inhibition of Porcupine by LGK974. Proc. Natl Acad. Sci. USA 110, 20224–20229 (2013).
Doornebal, C. W. et al. A preclinical mouse model of invasive lobular breast cancer metastasis. Cancer Res. 73, 353–363 (2013).
Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).
Brinkman, E. K., Chen, T., Amendola, M. & van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).
Schmidt, D. et al. ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions. Methods 48, 240–248 (2009).
Lerdrup, M., Johansen, J. V., Agrawal-Singh, S. & Hansen, K. An interactive environment for agile analysis and visualization of ChIP-sequencing data. Nat. Struct. Mol. Biol. 23, 349–357 (2016).
Okada, N. et al. A positive feedback between p53 and miR-34 miRNAs mediates tumor suppression. Genes Dev. 28, 438–450 (2014).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Bouaoun, L. et al. TP53 variations in human cancers: new lessons from the IARC TP53 database and genomics data. Hum. Mutat. 37, 865–876 (2016).
Acknowledgements
Research in the De Visser laboratory is funded by a European Research Council Consolidator award (ERC InflaMet 615300), the Netherlands Organization for Scientific Research (NWO-VICI 91819616), Oncode Institute, the Dutch Cancer Society (KWF10083; KWF10623) and the Beug Foundation for Metastasis Research. K.E.d.V. is an EMBO Young Investigator. Research in the Jonkers laboratory is funded by ERC Synergy grant 319661. We thank members of the De Visser and Jonkers laboratories and R. Mezzadra for fruitful discussion during the preparation of the manuscript. We thank O. van Tellingen, the Mouse Clinic for Cancer and Aging (MCCA) intervention Unit, flow cytometry facility, mouse transgenic facility, genomics core facility, animal laboratory facility and animal pathology facility of the Netherlands Cancer Institute for technical assistance.
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Contributions
M.D.W., S.B.C., J.J. and K.E.d.V. conceived the ideas and designed the experiments. M.D.W., S.B.C., D.E.M.D. and M.H.v.M. performed the animal experiments, flow cytometry, RT–qPCR, serum analyses, western blot, immunohistochemistry and other experiments and analysed the data. C.-S.H., K.V., A.P.D., E.S. and R.d.K.-G. provided technical support and performed animal experiments. M.H.v.M., L.H., S.M.K. and J.J. generated mouse models. M.D.W. and R.d.K.-G. performed mouse intervention experiments. I.v.d.H. generated the GEMM-derived cell lines. S.P., M.D.W. and W.Z. performed and analysed the ChIP–seq experiments. M.S., I.d.R., M.D.W., L.F.A.W. and T.N.S. performed the bioinformatics analyses on mouse and human RNA-sequencing datasets. M.D.W., S.B.C. and K.E.d.V. wrote the paper and prepared the figures, with input from all authors.
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M.D.W., S.B.C., D.E.M.D., M.H.v.M., M.S., I.d.R., L.H., S.M.K., S.P., C.-S.H., K.V., A.P.D., R.d.K.-G., E.S., I.v.d.H., W.Z. and J.J. report no competing interests. L.F.A.W. reports research funding from Genmab. T.N.S. is a consultant for Adaptive Biotechnologies, AIMM Therapeutics, Allogene Therapeutics, Amgen, Merus, Neon Therapeutics, Scenic Biotech and Third Rock Ventures, reports research support from Merck, Bristol-Myers Squibb, Merck KGaA, and is stockholder in AIMM Therapeutics, Allogene Therapeutics, Merus, Neogene Therapeutics, Neon Therapeutics and Scenic Biotech, all outside the scope of this work. K.E.d.V. reports research funding from Roche and is consultant for Third Rock Ventures, outside the scope of this work.
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Extended data figures and tables
Extended Data Fig. 1 Neutrophil expansion in p53-deficient tumour-bearing GEMMs.
a, Representative plots of flow cytometry analysis on blood of end-stage (cumulative tumour size 1,500 mm3) mammary tumour-bearing mice. Neutrophils were defined as CD11b+Ly6G+Ly6C+. cKIT expression on gated total neutrophils in blood is shown (gating was based on blood of wild-type mice). Quantification and statistical analysis of these data are found in Fig. 1a, b.
Extended Data Fig. 2 CRISPR–Cas9-mediated gene disruption of Trp53 in WEA and WEP cancer cell lines.
a, Insertion and deletion (indel) spectrum of bulk Wap-cre;Cdh1F/F;Akt1E17K (WEA) cancer cell lines after transfection with two individual sgRNAs against Trp53 and puromycin selection, as determined by the TIDE algorithm and compared to the sequence of target region of control cells. P values associated with the estimated abundance of each indel are calculated by a two-tailed t-test of the variance–covariance matrix of the s.e.m. b, Western blot analysis showing p53 levels of control and p53-knockout WEA cell lines. Inactivation of the p53 pathway is shown by loss of p21 staining after 10 Gy irradiation. KO1 (sgRNA1) resulted in a truncated p53 protein, and KO2 (sgRNA2) shows absence of p53 protein. For all subsequent experiments, KO2 was used. Blot is representative of two independent experiments. For uncropped images, see Supplementary Fig. 1. c, In vitro growth kinetics of WEA control and p53-knockout cells, as determined by IncuCyte (n = 7 technical replicates per group). d, In vivo growth kinetics of orthotopically transplanted WEA;Trp53+/+ (n = 4 mice) and WEA;Trp53−/− (n = 6) cancer cell lines, with t = 0 being the first day tumours were palpable. e, Indel spectrum of bulk Wap-cre;Cdh1F/F;Pik3caE545K (WEP) cancer cell lines after transfection with sgRNA2 against Trp53 and puromycin selection, as determined by the TIDE algorithm. f, In vitro growth kinetics of WEP control and p53-knockout cells, as determined by IncuCyte (n = 7 technical replicates per group). g, In vivo growth kinetics of orthotopically transplanted WEP;Trp53+/+ (n = 5) and WEP;Trp53−/− (n = 5) cell lines, with t = 0 being the first day tumours were palpable. h, Gating strategy to identify circulating neutrophils and their cKIT expression. i, Gating strategy to identify neutrophils in the lung. j, Representative images of spleens from mice bearing WEA;Trp53+/+ and WEA;Trp53−/− tumours and quantification of spleen area (length × width) at end stage (tumour volume 1,500 mm3) of mice bearing p53-proficient (n = 4) and p53-deficient WEA (n = 6) and WEP (n = 5 per group) tumours. All data are mean ± s.e.m. P values were determined by area under the curve (AUC) analysis followed by two-tailed Welch’s t-test (c, d, f, g) or two-tailed Mann–Whitney U-test (j). ns, not significant.
Extended Data Fig. 3 Haematopoiesis in p53-null tumour-bearing mice is skewed towards the development of neutrophils.
a, Schematic representation of neutrophil development in the bone marrow. b, Gating strategy of neutrophil progenitor populations in the bone marrow. Dot plot indicates the cKIT expression levels in promyelocytes compared with mature neutrophils (n = 20 mice). MFI, median fluorescence intensity. c, Frequency of bone marrow progenitor populations in mice bearing end-stage Wap-cre;Cdh1F/F;Akt1E17K;Trp53+/+ (n = 9) and Wap-cre;Cdh1F/F;Akt1E17K;Trp53−/− (n = 11) tumours, as determined by flow cytometry. d, Total live cells and total live progenitor population numbers per hindleg of mice bearing WEA;Trp53+/+ and WEA;Trp53−/− tumours (n = 5 per group). All data are ± s.e.m. P values are determined by two-tailed Mann–Whitney U-test. LSK, Lin–Sca1+cKIT+, which contain the LT-HSC (long-term haematopoietic stem cells), ST-HSC (short-term haematopoietic stem cells) and MPP (multipotent progenitors). CMP, common myeloid progenitors; GMP, granulocytic and monocytic progenitors; MEP, megakaryocyte and erythrocyte progenitors.
Extended Data Fig. 4 Macrophages are differentially activated by Trp53−/− mouse and human breast cancer cell lines.
a, Expression of CCR2, CCR6, CD206, CSF-1R, CXCR4 and MHC-II on live CD11b+F4/80+ BMDMs after exposure to control medium or conditioned medium (CM) of Wap-cre;Cdh1F/F;Akt1E17K;Trp53+/+ or Wap-cre;Cdh1F/F;Akt1E17K;Trp53−/− cell lines, as determined by flow cytometry (n = 4 biological replicates per group). b, TIDE analysis of bulk MCF-7 cells after transfection with TP53-targeting sgRNAs and puromycin selection. For subsequent experiments, sgRNA1 was used. c, Expression of CD206, CD163 and HLA-DR on human CD11b+CD14+CD68+ monocyte-derived macrophages (MDMs) after exposure to conditioned medium of TP53+/+ MCF-7 or TP53−/− (sgRNA1) MCF-7 cancer cells (n = 3 biological replicates per group). d, RT–qPCR analysis showing IL1B expression in human CD11b+CD14+CD68+ MDMs after exposure to control medium (n = 4 biological replicates) conditioned medium of TP53+/+ MCF-7 or TP53−/− MCF-7 cancer cells (n = 5 biological replicates per group). Data are normalized to normal medium control. Plots are representative of three separate experiments and average of two technical replicates. All data are mean ± s.e.m. P values were determined by two-tailed one-way ANOVA with Tukey’s multiple-testing correction.
Extended Data Fig. 5 Transcriptome profile and composition of the local tumour immune landscape in breast cancer GEMMs.
a, Unsupervised clustering of the top 200 most differentially expressed genes (P < 0.01, log-transformed fold change >3 or <−3) in mammary GEMM tumours as determined by RNA sequencing (n = 145 tumours). Red bars indicate Trp53+/+ tumours, blue bars indicate Trp53−/− tumours. Full tumour genotype is displayed in legend and shown by indicated colours. b, Number of Ly6G+ neutrophils in the tumour (n = 1, 4, 10, 2, 4, 3, 6, 13, 4, 22, 4 and 5 mice, top to bottom). c, Macrophage score as indicative of F4/80+ macrophage abundance in the tumour (n = 2, 2, 4, 4, 4, 2, 3, 5, 4, 9, 5 and 4 mice, top to bottom). d, Number of CD8+ cytotoxic T cells in the tumour (n = 3, 2, 5, 5, 7, 3, 7, 3, 5, 4, 4 and 5 mice, top to bottom). e, Number of CD4+ T cells in the tumour (n = 3, 2, 5, 5, 7, 3, 7, 3, 5, 4, 4 and 5 mice, top to bottom). f, Number of FOXP3+ regulatory T cells in the tumour (n = 3, 2, 5, 5, 7, 3, 7, 3, 5, 4, 4 and 5 mice, top to bottom). g, Ratio of CD8/FOXP3 cells in the tumour (n = 3, 2, 5, 5, 7, 3, 7, 2, 5, 4, 4 and 5 mice, top to bottom). All data are the mean of five microscopic fields of view (FOV) per mouse as determined by immunohistochemistry. Inserts show data combined according to p53 status of the tumour. Each symbol represents an individual mouse. All data are mean ± s.e.m. P values are determined by two-tailed one-way ANOVA with FDR multiple-testing correction (a) or two-tailed Mann–Whitney U-test (b–g).
Extended Data Fig. 6 WNT-related gene activation correlates with loss of p53 in mouse and human breast tumours.
a, b, Heat maps showing that Trp53−/− (KO) GEMM tumours (n = 77) cluster away from Trp53+/+ (WT) tumours (n = 68) based on analysis of the Hallmark p53 pathway (represents positive control) (a) and analysis of the Hallmark WNT and β-catenin pathway (b). Analysis was performed on all tumours of Extended Data Fig. 5a. c, The log-transformed fold change in expression of genes involved in WNT signalling (P < 0.05) in Trp53−/− (n = 77) and Trp53+/+ (n = 68) GEMM tumours depicted in Extended Data Fig. 5a. Black bars indicate genes that positively regulate or are generally increased with active WNT signalling. Red bars indicate genes that negatively regulate or are downregulated with active WNT signalling. d, Gene set enrichment analysis (GSEA) for Hallmark pathways in TCGA wild-type TP53 breast tumours (n = 643) versus mutant TP53 (n = 351) human tumours (any TP53 mutation) or TP53 loss (based on the IARC TP53 database; see Methods). Normalized enrichment score is shown with the FDR indicated. e, Correlation coefficient (R) of all genes involved in WNT signalling that correlate significantly (P < 0.05) with mutant TP53 (n = 351) versus wild-type TP53 (n = 643) in TCGA breast tumours. Black bars indicate genes that positively regulate or are generally increased with active WNT signalling. Red bars indicate genes that negatively regulate or are downregulated with active WNT signalling. P values were determined by two-tailed ANOVA with FDR multiple-testing correction (c, e).
Extended Data Fig. 7 p53 does not bind the regulatory regions of WNT ligands directly.
a, ChIP–seq profile of p53 binding to DNA demonstrating enrichment on the Cdkn1a (p21) locus in Trp53+/+ WEA and WEP cell lines (three cell lines from three independent tumours per GEMM). b, Absence of p53 binding to Wnt1, Wnt6 or Wnt7a loci. c, Enrichment of p53 on the miR-34a (miR-34a) locus. d, RT–qPCR analysis of Wnt ligand expression in WEA;Trp53+/+ and WEA;Trp53−/− cell lines after overexpression (OE) of miR-34a in WEA;Trp53−/−cells (n = 3 technical replicates per group). Plots are representative of three separate experiments with three technical replicates. All data are mean ± s.e.m. P values were determined by two-tailed one-way ANOVA with Tukey multiple-testing correction (d).
Extended Data Fig. 8 Macrophages are activated by Trp53−/− cancer cells via FZD7 and FZD9 receptors in vitro.
a, The log2-transformed fold change in expression of WNT receptors Fzd7 and Fzd9 in bulk tumours comparing Trp53−/− (n = 77) and Trp53+/+ (n = 68) GEMM tumours using RNA-sequencing analysis. b, Expression of FZD7 and FZD9 in TP53 wild-type (n = 643) and TP53 mutant (n = 351) human breast tumours of the TCGA dataset. c, Silencing of Fzd7 and Fzd9 in BMDMs after transfection with siRNA pools against both receptors, as determined by RT–qPCR (n = 6 biological replicates per group). d, Expression of Il1b in BMDMs after exposure to conditioned medium of Trp53+/+ and Trp53−/− Wap-cre;Cdh1F/F;Akt1E17K cell lines (n = 6 biological replicates per group), as determined by RT–qPCR. Where indicated, BMDMs were transfected with control siRNA or Fzd7 and Fzd9 siRNA pools. Data in a, c, d are mean ± s.e.m. Box plots are as described in Fig. 3e. P values were determined by two-tailed one-way ANOVA with FDR multiple-testing correction (a), two-tailed Mann–Whitney U-test (b) or two-tailed one-way ANOVA with Tukey multiple-testing correction (d).
Extended Data Fig. 9 Pharmacological and genetic targeting of PORCN in p53-deficient tumours reduces systemic inflammation.
a, Total and cKIT+ neutrophil frequencies in lungs of vehicle-treated (n = 7) or LGK974-treated (n = 4) K14-cre;Cdh1F/F;Trp53F/F (KEP) mice using indicated 5-day short-term treatment schedule. Representative flow cytometry plots are shown. b, Frequency of IL-17A-producing γδ T cells in lungs of vehicle-treated (n = 6) or LGK974-treated (n = 4) KEP mice. Representative flow cytometry plots are shown. c, Kinetics of circulating neutrophils in vehicle- or LGK974-treated KEP mice using indicated long-term treatment schedule, shown as frequency at indicated tumour volumes (n = 8 per group). d, RT–qPCR analysis of Porcn expression in end-stage bulk tumour (n = 5 per group). Data are normalized to control shRNA (shControl) and represents an average of two technical replicates. e, Correlation of total neutrophil levels in the circulation with the expression of Porcn in WEA;Trp53−/−;shControl and WEA;Trp53−/−;shPorcn whole tumour lysate (n = 5 per group). f, Correlation of cKIT+ neutrophil levels in circulation with expression of Porcn in WEA;Trp53−/−;shControl and WEA;Trp53−/−;shPorcn whole tumour lysate (n = 5 per group). g, Correlation of Porcn expression and Il1b expression in bulk WEA;Trp53−/−;shControl (blue) and WEA;Trp53−/−;shPorcn tumours (grey) (n = 5 per group). Data represent an average of two technical replicates. h, Spleen area in mice with WEA;Trp53−/−;shControl (blue) and WEA;Trp53−/−;shPorcn tumours (grey) tumours at end stage (n = 5 per group). i, Growth kinetics of orthotopically transplanted KEP mammary tumours, treated with vehicle (n = 12) or LGK974 (n = 15). Each line represents an individual mouse. j, Growth kinetics of orthotopically injected Trp53+/+ and Trp53−/− WEP cells, treated with vehicle or LGK974. Each line represents an individual mouse (n = 9 per group). k, Schematic representation of the findings of this study: loss of p53 in breast cancer cells triggers secretion of WNT ligands to activate tumour-associated macrophages. This stimulates systemic expansion and activation of neutrophils, which we have previously shown to be immunosuppressive5, thus driving metastasis. All data are mean ± s.e.m. P values are determined by two-tailed Mann–Whitney U-test (a–d, h), linear regression analysis (e–g) and area under the curve of average growth curves, followed by two-tailed Welch’s t-test (i, j).
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Supplementary Information
Supplementary Tables 1-2 and Supplementary Figure 1. Supplementary Table 1 shows a list of antibodies used for flow cytometry, western blotting, immunohistochemistry and chromatin immunoprecipitation. It contains information on the fluorochrome, clone, company, catalogue number and dilution used for the experiments. Supplementary Table 2 lists RT-qPCR primers. The sequences of the forward and reverse primers of mouse and human target genes are shown. Supplementary Figure 1 shows images of uncropped western blot scans with marker size indications. The corresponding figures are indicated. Images that were obtained from the same membrane are indicated by dashed line. The red box indicates the cropped image used in the figures. Fluorescently labelled secondary antibodies were used and scanned at either 700 nm or 800 nm, as indicated on the images.
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Wellenstein, M.D., Coffelt, S.B., Duits, D.E.M. et al. Loss of p53 triggers WNT-dependent systemic inflammation to drive breast cancer metastasis. Nature 572, 538–542 (2019). https://doi.org/10.1038/s41586-019-1450-6
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DOI: https://doi.org/10.1038/s41586-019-1450-6
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