Ye, X. & Weinberg, R. A. Epithelial-mesenchymal plasticity: a central regulator of cancer progression. Trends Cell Biol. 25, 675–686 (2015).
Google Scholar
Shibue, T. & Weinberg, R. A. EMT, CSCs, and drug resistance: the mechanistic link and clinical implications. Nat. Rev. Clin. Oncol. 14, 611–629 (2017).
Google Scholar
Lambert, A. W. & Weinberg, R. A. Linking EMT programmes to normal and neoplastic epithelial stem cells. Nat Rev. Cancer 21, 325–338 (2021).
Google Scholar
Puisieux, A., Brabletz, T. & Caramel, J. Oncogenic roles of EMT-inducing transcription factors. Nat. Cell. Biol. 16, 488–494 (2014).
Google Scholar
Brabletz, S., Schuhwerk, H., Brabletz, T. & Stemmler, M. P. Dynamic EMT: a multi-tool for tumor progression. EMBO J. 40, e108647 (2021).
Google Scholar
Pastushenko, I. & Blanpain, C. EMT transition states during tumor progression and metastasis. Trends Cell Biol. 29, 212–226 (2019).
Google Scholar
Nieto, M. A., Huang, R. Y., Jackson, R. A. & Thiery, J. P. EMT: 2016. Cell 166, 21–45 (2016).
Google Scholar
Kasai, H., Allen, J. T., Mason, R. M., Kamimura, T. & Zhang, Z. TGF-β1 induces human alveolar epithelial to mesenchymal cell transition (EMT). Respir Res. 6, 56 (2005).
Google Scholar
Kim, J. H. et al. Transforming growth factor β1 induces epithelial-to-mesenchymal transition of A549 cells. J. Korean Med. Sci. 22, 898–904 (2007).
Google Scholar
Voon, D. C., Huang, R. Y., Jackson, R. A. & Thiery, J. P. The EMT spectrum and therapeutic opportunities. Mol. Oncol. 11, 878–891 (2017).
Google Scholar
Tan, T. Z. et al. Epithelial–mesenchymal transition spectrum quantification and its efficacy in deciphering survival and drug responses of cancer patients. EMBO Mol. Med. 6, 1279–1293 (2014).
Google Scholar
Lapouge, G. et al. Skin squamous cell carcinoma propagating cells increase with tumour progression and invasiveness. EMBO J. 31, 4563–4575 (2012).
Google Scholar
Latil, M. et al. Cell-type-specific chromatin states differentially prime squamous cell carcinoma tumor-initiating cells for epithelial to mesenchymal transition. Cell Stem Cell 20, 191–204.e195 (2017).
Google Scholar
Pastushenko, I. et al. Identification of the tumour transition states occurring during EMT. Nature 556, 463–468 (2018).
Google Scholar
Paradisi, A. et al. Combining chemotherapeutic agents and netrin-1 interference potentiates cancer cell death. EMBO Mol. Med. 5, 1821–1834 (2013).
Google Scholar
Paradisi, A. et al. Netrin-1 up-regulation in inflammatory bowel diseases is required for colorectal cancer progression. Proc. Natl Acad. Sci. USA 106, 17146–17151 (2009).
Google Scholar
Paradisi, A. et al. NF-κB regulates netrin-1 expression and affects the conditional tumor suppressive activity of the netrin-1 receptors. Gastroenterology 135, 1248–1257 (2008).
Google Scholar
Fitamant, J. et al. Netrin-1 expression confers a selective advantage for tumor cell survival in metastatic breast cancer. Proc. Natl Acad. Sci. USA 105, 4850–4855 (2008).
Google Scholar
Delloye-Bourgeois, C. et al. Netrin-1 acts as a survival factor for aggressive neuroblastoma. J. Exp. Med. 206, 833–847 (2009).
Google Scholar
Sung, P. J. et al. Cancer-associated fibroblasts produce netrin-1 to control cancer cell plasticity. Cancer Res. 79, 3651–3661 (2019).
Google Scholar
Park, K. W. et al. The axonal attractant Netrin-1 is an angiogenic factor. Proc. Natl Acad. Sci. USA 101, 16210–16215 (2004).
Google Scholar
Arakawa, H. Netrin-1 and its receptors in tumorigenesis. Nat. Rev. Cancer 4, 978–987 (2004).
Google Scholar
Brisset, M., Grandin, M., Bernet, A., Mehlen, P. & Hollande, F. Dependence receptors: new targets for cancer therapy. EMBO Mol. Med. 13, e14495 (2021).
Google Scholar
Hao, W. et al. The pan-cancer landscape of netrin family reveals potential oncogenic biomarkers. Sci. Rep. 10, 5224 (2020).
Google Scholar
Dumartin, L. et al. Netrin-1 mediates early events in pancreatic adenocarcinoma progression, acting on tumor and endothelial cells. Gastroenterology 138, 1595–1606 (2010). 1606 e1591-1598.
Google Scholar
Kefeli, U. et al. Netrin-1 in cancer: potential biomarker and therapeutic target? Tumour Biol. 39, 1010428317698388 (2017).
Google Scholar
Haerinck, J. & Berx, G. Partial EMT takes the lead in cancer metastasis. Dev. Cell 56, 3174–3176 (2021).
Google Scholar
Simeonov, K. P. et al. Single-cell lineage tracing of metastatic cancer reveals selection of hybrid EMT states. Cancer Cell 39, 1150–1162.e1159 (2021).
Google Scholar
Yang, J. et al. Guidelines and definitions for research on epithelial–mesenchymal transition. Nat. Rev. Mol. Cell Biol. 21, 341–352 (2020).
Google Scholar
Jin, X. et al. Netrin-1 interference potentiates epithelial-to-mesenchymal transition through the PI3K/AKT pathway under the hypoxic microenvironment conditions of non-small cell lung cancer. Int. J. Oncol. 54, 1457–1465 (2019).
Zhang, X. et al. Netrin-1 elicits metastatic potential of non-small cell lung carcinoma cell by enhancing cell invasion, migration and vasculogenic mimicry via EMT induction. Cancer Gene Ther. 25, 18–26 (2018).
Google Scholar
Yan, W. et al. Netrin-1 induces epithelial-mesenchymal transition and promotes hepatocellular carcinoma invasiveness. Dig. Dis. Sci. 59, 1213–1221 (2014).
Google Scholar
Han, P. et al. Netrin-1 promotes cell migration and invasion by down-regulation of BVES expression in human hepatocellular carcinoma. Am. J. Cancer Res. 5, 1396–1409 (2015).
Google Scholar
Revenco, T. et al. Context dependency of epithelial-to-mesenchymal transition for metastasis. Cell Rep. 29, 1458–1468.e1453 (2019).
Google Scholar
DeConti, R. C. Chemotherapy of squamous cell carcinoma of the skin. Semin. Oncol. 39, 145–149 (2012).
Google Scholar
Khansur, T. & Kennedy, A. Cisplatin and 5-fluorouracil for advanced locoregional and metastatic squamous cell carcinoma of the skin. Cancer 67, 2030–2032 (1991).
Google Scholar
Chen, Q. Y. et al. miR-206 regulates cisplatin resistance and EMT in human lung adenocarcinoma cells partly by targeting MET. Oncotarget 7, 24510–24526 (2016).
Google Scholar
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst 1, 417–425 (2015).
Google Scholar
Grandin, M. et al. Structural decoding of the Netrin-1/UNC5 interaction and its therapeutical implications in cancers. Cancer Cell 29, 173–185 (2016).
Google Scholar
Mak, M. P. et al. A patient-derived, pan-cancer EMT signature identifies global molecular alterations and immune target enrichment following epithelial-to-mesenchymal transition. Clin. Cancer Res. 22, 609–620 (2016).
Google Scholar
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Google Scholar
Srinivas, S. et al. Cre reporter strains produced by targeted insertion of EYFP and ECFP into the ROSA26 locus. BMC Dev. Biol. 1, 4 (2001).
Google Scholar
Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).
Google Scholar
Tuveson, D. A. et al. Endogenous oncogenic K-ras(G12D) stimulates proliferation and widespread neoplastic and developmental defects. Cancer Cell 5, 375–387 (2004).
Google Scholar
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).
Google Scholar
Boussouar, A. et al. Netrin-1 and its receptor DCC are causally implicated in melanoma progression. Cancer Res. 80, 747–756 (2020).
Google Scholar
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Google Scholar
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
Google Scholar
Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. Gigascience 7, giy083 (2018).
Google Scholar
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Google Scholar
Franzen, O., Gan, L. M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database 2019, baz046 (2019).
Google Scholar
Buttner, M., Ostner, J., Muller, C. L., Theis, F. J. & Schubert, B. scCODA is a Bayesian model for compositional single-cell data analysis. Nat. Commun. 12, 6876 (2021).
Google Scholar
Elyada, E. et al. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts. Cancer Discov. 9, 1102–1123 (2019).
Google Scholar
Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).
Google Scholar
Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).
Google Scholar