Gupta, P. B., Pastushenko, I., Skibinski, A., Blanpain, C. & Kuperwasser, C. Phenotypic plasticity: driver of cancer initiation, progression, and therapy resistance. Cell Stem Cell 24, 65–78 (2019).
Vitale, I., Shema, E., Loi, S. & Galluzzi, L. Intratumoral heterogeneity in cancer progression and response to immunotherapy. Nat. Med. 27, 212–224 (2021).
Lüönd, F., Tiede, S. & Christofori, G. Breast cancer as an example of tumour heterogeneity and tumour cell plasticity during malignant progression. Br. J. Cancer 125, 164–175 (2021).
Bergers, G. & Fendt, S. M. The metabolism of cancer cells during metastasis. Nat. Rev. Cancer 21, 162–180 (2021).
Prasetyanti, P. R. & Medema, J. P. Intra-tumor heterogeneity from a cancer stem cell perspective. Mol. Cancer 16, 41 (2017).
Pastushenko, I. & Blanpain, C. EMT transition states during tumor progression and metastasis. Trends Cell Biol. 29, 212–226 (2019).
Locasale, J. W. et al. Phosphoglycerate dehydrogenase diverts glycolytic flux and contributes to oncogenesis. Nat. Genet. 43, 869–874 (2011).
Possemato, R. et al. Functional genomics reveal that the serine synthesis pathway is essential in breast cancer. Nature 476, 346–350 (2011).
Rinaldi, G. et al. In vivo evidence for serine biosynthesis-defined sensitivity of lung metastasis, but not of primary breast tumors, to mTORC1 inhibition. Mol. Cell 81, 386–397 (2021).
Ngo, B. et al. Limited environmental serine and glycine confer brain metastasis sensitivity to PHGDH inhibition. Cancer Discov. 10, 1352–1373 (2020).
Geeraerts, S. L. et al. Repurposing the antidepressant sertraline as SHMT inhibitor to suppress serine/glycine synthesis addicted breast tumor growth. Mol. Cancer Ther. 20, 50–63 (2020).
Pacold, M. E. et al. A PHGDH inhibitor reveals coordination of serine synthesis and one-carbon unit fate. Nat. Chem. Biol. 12, 452–458 (2016).
Schmidt, J. M. et al. Stem-cell-like properties and epithelial plasticity arise as stable traits after transient Twist1 activation. Cell Rep. 10, 131–139 (2015).
Noh, S., Kim, D. H., Jung, W. H. & Koo, J. S. Expression levels of serine/glycine metabolism-related proteins in triple negative breast cancer tissues. Tumour Biol. 35, 4457–4468 (2014).
Pascual, G. et al. Targeting metastasis-initiating cells through the fatty acid receptor CD36. Nature 541, 41–45 (2017).
Oshimori, N., Oristian, D. & Fuchs, E. TGF-β promotes heterogeneity and drug resistance in squamous cell carcinoma. Cell 160, 963–976 (2015).
Margarido, A. S., Bornes, L., Vennin, C. & van Rheenen, J. Cellular plasticity during metastasis: new insights provided by intravital microscopy. Cold Spring Harb. Perspect. Med. 10, a037267 (2020).
Beerling, E., Oosterom, I., Voest, E., Lolkema, M. & van Rheenen, J. Intravital characterization of tumor cell migration in pancreatic cancer. IntraVital 5, e1261773 (2016).
Kariya, Y., Oyama, M., Suzuki, T. & Kariya, Y. αvβ3 Integrin induces partial EMT independent of TGF-β signaling. Commun. Biol. 4, 490 (2021).
Mori, S. et al. Enhanced expression of integrin αvβ3 induced by TGF-β is required for the enhancing effect of fibroblast growth factor 1 (FGF1) in TGF-β-induced epithelial-mesenchymal transition (EMT) in mammary epithelial cells. PLoS ONE 10, e0137486 (2015).
Seguin, L. et al. An integrin β3–KRAS–RalB complex drives tumour stemness and resistance to EGFR inhibition. Nat. Cell Biol. 16, 457–468 (2014).
Bellahcène, A., Castronovo, V., Ogbureke, K. U., Fisher, L. W. & Fedarko, N. S. Small integrin-binding ligand N-linked glycoproteins (SIBLINGs): multifunctional proteins in cancer. Nat. Rev. Cancer 8, 212–226 (2008).
Janik, M. E., Lityńska, A. & Vereecken, P. Cell migration—the role of integrin glycosylation. Biochim. Biophys. Acta 1800, 545–555 (2010).
Pocheć, E. et al. Aberrant glycosylation of αvβ3 integrin is associated with melanoma progression. Anticancer Res. 35, 2093–2103 (2015).
Kremser, M. E. et al. Characterisation of α3β1 and αvβ3 integrin N-oligosaccharides in metastatic melanoma WM9 and WM239 cell lines. Biochim. Biophys. Acta 1780, 1421–1431 (2008).
Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).
Elbein, A. D. in Cell Surface and Extracellular Glycoconjugates (eds Roberts, D. D. and Mecham, R. P.) 119–180 (Academic Press, 1993); https://doi.org/10.1016/B978-0-12-589630-6.50009-5
Sakai, N., Insolera, R., Sillitoe, R. V., Shi, S.-H. & Kaprielian, Z. Axon sorting within the spinal cord marginal zone via Robo-mediated inhibition of N-cadherin controls spinocerebellar tract formation. J. Neurosci. 32, 15377–15387 (2012).
Chen, J. Y. et al. A novel sialyltransferase inhibitor suppresses FAK/paxillin signaling and cancer angiogenesis and metastasis pathways. Cancer Res. 71, 473–483 (2011).
Sola-Penna, M., Da Silva, D., Coelho, W. S., Marinho-Carvalho, M. M. & Zancan, P. Regulation of mammalian muscle type 6-phosphofructo-1-kinase and its implication for the control of the metabolism. IUBMB Life 62, 791–796 (2010).
Rodriguez, A. E. et al. Serine metabolism supports macrophage IL-1β production. Cell Metab. 29, 1003–1011 (2019).
Zhao, X., Fu, J., Du, J. & Xu, W. The role of d-3-phosphoglycerate dehydrogenase in cancer. Int. J. Biol. Sci. 16, 1495–1506 (2020).
Ma, C. et al. The alternative activity of nuclear PHGDH contributes to tumour growth under nutrient stress. Nat. Metab. 3, 1357–1371 (2021).
Baksh, S. C. et al. Extracellular serine controls epidermal stem cell fate and tumour initiation. Nat. Cell Biol. 22, 779–790 (2020).
Liu, J. et al. Phosphoglycerate dehydrogenase induces glioma cells proliferation and invasion by stabilizing forkhead box M1. J. Neurooncol. 111, 245–255 (2013).
Ma, X., Li, B., Liu, J., Fu, Y. & Luo, Y. Phosphoglycerate dehydrogenase promotes pancreatic cancer development by interacting with eIF4A1 and eIF4E. J. Exp. Clin. Cancer Res. 38, 66 (2019).
Teoh, S. T., Ogrodzinski, M. P., Ross, C., Hunter, K. W. & Lunt, S. Y. Sialic acid metabolism: a key player in breast cancer metastasis revealed by metabolomics. Front. Oncol. 8, 174 (2018).
Vandekeere, S. et al. Serine synthesis via PHGDH is essential for heme production in endothelial cells. Cell Metab. 28, 573–587 (2018).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Wright, G. W. & Simon, R. M. A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics 19, 2448––2455 (2003).
Paulo, J. A. & Gygi, S. P. Nicotine-induced protein expression profiling reveals mutually altered proteins across four human cell lines. Proteomics 17, 1600319 (2017).
Bassez, A. et al. A single-cell map of intratumoral changes during anti-PD1 treatment of patients with breast cancer. Nat. Med. 27, 820–832 (2021).
Zhang, X. et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 73, 4885 (2013).
Lv, X. et al. Orthotopic transplantation of breast tumors as preclinical models for breast cancer. J. Vis. Exp. 159, e61173 (2020).
Quintana, E. et al. Human melanoma metastasis in NSG mice correlates with clinical outcome in patients. Sci. Transl. Med. 4, 159ra149 (2012).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Carpenter, A. E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).
Zanotelli, V. R. T. & Bodenmiller, B. ImcSegmentationPipeline: a pixelclassification based multiplexed image segmentation pipeline. Zenodo https://doi.org/10.5281/zenodo.3841961 (2020).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Rueden, C. T. et al. ImageJ2: ImageJ for the next generation of scientific image data. BMC Bioinform. 18, 529 (2017).
van Gorsel, M., Elia, I. & Fendt, S.-M. 13C tracer analysis and metabolomics in 3D cultured cancer cells. Methods Mol. Biol. 1862, 53–66 (2019).
Young, J. D., Walther, J. L., Antoniewicz, M. R., Yoo, H. & Stephanopoulos, G. An elementary metabolite unit (EMU) based method of isotopically nonstationary flux analysis. Biotechnol. Bioeng. 99, 686–699 (2008).
Fernandez, C. A., Des Rosiers, C., Previs, S. F., David, F. & Brunengraber, H. Correction of 13C mass isotopomer distributions for natural stable isotope abundance. J. Mass Spectrom. 31, 255–262 (1996).