Aspartate signalling drives lung metastasis via alternative translation – Nature
Gerull, W. D., Puri, V. & Kozower, B. D. The epidemiology and biology of pulmonary metastases. J. Thorac. Dis. 13, 2585–2589 (2021).
Google Scholar
Mohammed, T.-L. H. et al. ACR appropriateness criteria screening for pulmonary metastases. J. Thorac. Imag. 26, W1–W3 (2011).
Google Scholar
Crist, S. B. et al. Unchecked oxidative stress in skeletal muscle prevents outgrowth of disseminated tumour cells. Nat. Cell Biol. 24, 538–553 (2022).
Google Scholar
Peinado, H. et al. Pre-metastatic niches: organ-specific homes for metastases. Nat. Rev. Cancer 17, 302–317 (2017).
Google Scholar
Doglioni, G., Parik, S. & Fendt, S. M. Interactions in the (pre)metastatic niche support metastasis formation. Front. Oncol. 9, 219 (2019).
Google Scholar
Altea-Manzano, P. et al. A palmitate-rich metastatic niche enables metastasis growth via p65 acetylation resulting in pro-metastatic NF-κB signaling. Nat. Cancer 4, 344–364 (2023).
Google Scholar
Pelechano, V. & Alepuz, P. eIF5A facilitates translation termination globally and promotes the elongation of many non polyproline-specific tripeptide sequences. Nucleic Acids Res. 45, 7326–7338 (2017).
Google Scholar
Park, M. H., Nishimura, K., Zanelli, C. F. & Valentini, S. R. Functional significance of eIF5A and its hypusine modification in eukaryotes. Amino Acids 38, 491–500 (2010).
Google Scholar
Elia, I. et al. Proline metabolism supports metastasis formation and could be inhibited to selectively target metastasizing cancer cells. Nat. Commun. 8, 15267 (2017).
Google Scholar
Elia, I. et al. Breast cancer cells rely on environmental pyruvate to shape the metastatic niche. Nature 568, 117–121 (2019).
Google Scholar
Buescher, J. M. et al. A roadmap for interpreting 13C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015).
Google Scholar
Erreger, K. et al. Subunit-specific agonist activity at NR2A-, NR2B-, NR2C-, and NR2D-containing N-methyl-D-aspartate glutamate receptors. Mol. Pharmacol. 72, 907 (2007).
Google Scholar
Song, X. et al. Mechanism of NMDA receptor channel block by MK-801 and memantine. Nature 556, 515–519 (2018).
Google Scholar
Rouillard, A. D. et al. The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database 2016, baw100 (2016).
Google Scholar
West, A. E. et al. Calcium regulation of neuronal gene expression. Proc. Natl Acad. Sci. USA 98, 11024–11031 (2001).
Google Scholar
Xie, F. et al. Identification of a potent inhibitor of CREB-mediated gene transcription with efficacious in vivo anticancer activity. J. Med. Chem. 58, 5075–5087 (2015).
Google Scholar
Verrecchia, F. & Mauviel, A. Transforming growth factor-beta signaling through the Smad pathway: role in extracellular matrix gene expression and regulation. J. Invest. Dermatol. 118, 211–215 (2002).
Google Scholar
Geukens, T. et al. Rapid autopsies to enhance metastatic research: the UPTIDER post-mortem tissue donation program. NPJ Breast Cancer 10, 31 (2024).
Google Scholar
Vettore, L., Westbrook, R. L. & Tennant, D. A. New aspects of amino acid metabolism in cancer. Br. J. Cancer 122, 150–156 (2020).
Google Scholar
North, W. G., Gao, G., Memoli, V. A., Pang, R. H. & Lynch, L. Breast cancer expresses functional NMDA receptors. Breast Cancer Res. Treat. 122, 307–314 (2010).
Google Scholar
Li, L. & Hanahan, D. Hijacking the neuronal NMDAR signaling circuit to promote tumor growth and invasion. Cell 153, 86–100 (2013).
Google Scholar
Zeng, Q. et al. Synaptic proximity enables NMDAR signalling to promote brain metastasis. Nature 573, 526–531 (2019).
Google Scholar
Krieg, S., Fernandes, S. I., Kolliopoulos, C., Liu, M. & Fendt, S.-M. Metabolic signaling in cancer metastasis. Cancer Discov. 14, 934–952 (2024).
Google Scholar
Güth, R. et al. DHPS-dependent hypusination of eIF5A1/2 is necessary for TGFβ/fibronectin-induced breast cancer metastasis and associates with prognostically unfavorable genomic alterations in TP53. Biochem. Biophys. Res. Commun. 519, 838–845 (2019).
Google Scholar
Karras, P., Black, J. R. M., McGranahan, N. & Marine, J. C. Decoding the interplay between genetic and non-genetic drivers of metastasis. Nature 629, 543–554 (2024).
Google Scholar
Fendt, S.-M., Frezza, C. & Erez, A. Targeting metabolic plasticity and flexibility dynamics for cancer therapy. Cancer Discov. 10, 1797–1807 (2020).
Google Scholar
Lee, L. J. et al. Cancer plasticity: the role of mRNA translation. Trends Cancer 7, 134–145 (2021).
Google Scholar
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).
Google Scholar
Schmidt, E. K., Clavarino, G., Ceppi, M. & Pierre, P. SUnSET, a nonradioactive method to monitor protein synthesis. Nat. Methods 6, 275–277 (2009).
Google Scholar
Delaunay, S. et al. Elp3 links tRNA modification to IRES-dependent translation of LEF1 to sustain metastasis in breast cancer. J. Exp. Med. 213, 2503–2523 (2016).
Google Scholar
Liang, S. et al. Polysome-profiling in small tissue samples. Nucleic Acids Res. 46, e3 (2018).
Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Google Scholar
Stephens, M. False discovery rates: a new deal. Biostatistics 18, 275–294 (2017).
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
Korotkevich, G. et al. Fast gene set enrichment analysis. Preprint at bioRxiv https://doi.org/10.1101/060012 (2021).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Google Scholar
Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Google Scholar
Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Google Scholar
Christen, S. et al. Breast cancer-derived lung metastases show increased pyruvate carboxylase-dependent anaplerosis. Cell Rep. 17, 837–848 (2016).
Google Scholar
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 e387 (2021).
Google Scholar
Parik, S. et al. GBM tumors are heterogeneous in their fatty acid metabolism and modulating fatty acid metabolism sensitizes cancer cells derived from recurring GBM tumors to temozolomide. Front. Oncol. 12, 988872 (2022).
Google Scholar
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 e3529 (2021).
Google Scholar
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
Google Scholar
Janssen, P. et al. The effect of background noise and its removal on the analysis of single-cell expression data. Genome Biol. 24, 140 (2023).
Google Scholar
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Google Scholar
McInnes, M., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.03426 (2018).
Blondel V. D., Guillaume J.-L., Lambiotte R. & Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research 10, 979 (2022).
Google Scholar
Denis, J. F. et al. Activation of Smad2 but not Smad3 is required to mediate TGF-β signaling during axolotl limb regeneration. Development 143, 3481–3490 (2016).
Google Scholar
Vennin, C. et al. Transient tissue priming via ROCK inhibition uncouples pancreatic cancer progression, sensitivity to chemotherapy, and metastasis. Sci. Transl. Med. 9, eaai8504 (2017).
Google Scholar
Fridlender, Z. G. et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: “N1” versus “N2” TAN. Cancer Cell 16, 183–194 (2009).
Google Scholar
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).
Google Scholar
