Kow, A. W. C. Hepatic metastasis from colorectal cancer. J. Gastrointest. Oncol. 10, 1274–1298 (2019).
Google Scholar
Morgan, E. et al. Global burden of colorectal cancer in 2020 and 2040: incidence and mortality estimates from GLOBOCAN. Gut 72, 338–344 (2023).
Google Scholar
Siegel, R. L., Wagle, N. S. & Jemal, A. Leading cancer deaths in people younger than 50 years. JAMA 335, 632–634 (2026).
Google Scholar
Nielsen, K., Rolff, H. C., Eefsen, R. L. & Vainer, B. The morphological growth patterns of colorectal liver metastases are prognostic for overall survival. Mod. Pathol. 27, 1641–1648 (2014).
Google Scholar
Höppener, D. J. et al. Histopathological growth patterns and survival after resection of colorectal liver metastasis: an external validation study. JNCI Cancer Spectr. 5, pkab026 (2021).
Google Scholar
Vermeulen, P. B. et al. Liver metastases from colorectal adenocarcinomas grow in three patterns with different angiogenesis and desmoplasia. J. Pathol. 195, 336–342 (2001).
Google Scholar
Latacz, E. et al. Histopathological growth patterns of liver metastasis: updated consensus guidelines for pattern scoring, perspectives and recent mechanistic insights. Br. J. Cancer 127, 988–1013 (2022).
Google Scholar
Höppener, D. J. et al. The relationship between primary colorectal cancer histology and the histopathological growth patterns of corresponding liver metastases. BMC Cancer 22, 911 (2022).
Google Scholar
van Dam, P.-J. et al. International consensus guidelines for scoring the histopathological growth patterns of liver metastasis. Br. J. Cancer 117, 1427–1441 (2017).
Google Scholar
Bhattacharjee, S. et al. Tumor restriction by type I collagen opposes tumor-promoting effects of cancer-associated fibroblasts. J. Clin. Invest. 131, e146987 (2021).
Google Scholar
Bocuk, D. et al. The adaptation of colorectal cancer cells when forming metastases in the liver: expression of associated genes and pathways in a mouse model. BMC Cancer 17, 342 (2017).
Google Scholar
Frentzas, S. et al. Vessel co-option mediates resistance to anti-angiogenic therapy in liver metastases. Nat. Med. 22, 1294–1302 (2016).
Google Scholar
Fleischer, J. R. et al. Molecular differences of angiogenic versus vessel co-opting colorectal cancer liver metastases at single-cell resolution. Mol. Cancer 22, 17 (2023).
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
Pilley, S. E. et al. Loss of attachment promotes proline accumulation and excretion in cancer cells. Sci. Adv. 9, eadh2023 (2023).
Google Scholar
Westbrook, R. L. et al. Proline synthesis through PYCR1 is required to support cancer cell proliferation and survival in oxygen-limiting conditions. Cell Rep. 38, 110320 (2022).
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
Doglioni, G. et al. Aspartate signalling drives lung metastasis via alternative translation. Nature 638, 244–250 (2025).
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
Lachmann, A. et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010).
Google Scholar
Latacz, E. et al. Molecular characterization of the histopathological growth patterns of colorectal cancer liver metastases by RNA sequencing of targeted samples at the tumor-liver interface. Clin. Exp. Metastasis 42, 1 (2025).
Google Scholar
Han, H. et al. Small-molecule MYC inhibitors suppress tumor growth and enhance immunotherapy. Cancer Cell 36, 483–497.e15 (2019).
Google Scholar
Vervoorts, J. et al. Stimulation of c-MYC transcriptional activity and acetylation by recruitment of the cofactor CBP. EMBO Rep. 4, 484–490 (2003).
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
Tabariès, S. et al. Claudin-2 promotes colorectal cancer liver metastasis and is a biomarker of the replacement type growth pattern. Commun. Biol. 4, 657 (2021).
Google Scholar
Palmieri, V. et al. Neutrophils expressing lysyl oxidase-like 4 protein are present in colorectal cancer liver metastases resistant to anti-angiogenic therapy. J. Pathol. 251, 213–223 (2020).
Google Scholar
Garralda, E. et al. MYC targeting by OMO-103 in solid tumors: a phase 1 trial. Nat. Med. 30, 762–771 (2024).
Google Scholar
Schwartz, L. H. et al. RECIST 1.1-Update and clarification: From the RECIST committee. Eur. J. Cancer 62, 132–7 (2016).
Google Scholar
Haghshomar, M. et al. Diagnostic accuracy of CT for the detection of hepatic steatosis: a systematic review and meta-analysis. Radiology 313, e241171 (2024).
Google Scholar
Ophoff, D. et al. Liver fat and clinical outcomes in individuals with stage I-III colon or rectal cancer. J. Natl Cancer Inst. 188, 466–475 (2025).
Google Scholar
Lee, K. S. et al. c-MYC copy-number gain is an independent prognostic factor in patients with colorectal cancer. PLoS ONE 10, e0139727 (2015).
Google Scholar
Planque, M., Igelmann, S., Ferreira Campos, A. M. & Fendt, S.-M. Spatial metabolomics principles and application to cancer research. Curr. Opin. Chem. Biol. 76, 102362 (2023).
Google Scholar
Demicco, M., Liu, X.-Z., Leithner, K. & Fendt, S.-M. Metabolic heterogeneity in cancer. Nat. Metab. 6, 18–38 (2024).
Google Scholar
Santos, A. A. et al. Spatial metabolomics and its application in the liver. Hepatology 79, 1158–1179 (2024).
Google Scholar
Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N. & Werb, Z. Tumour heterogeneity and metastasis at single-cell resolution. Nat. Cell Biol. 20, 1349–1360 (2018).
Google Scholar
Leduc, S. et al. Transcriptomic characterization of the histopathological growth patterns in breast cancer liver metastases. Clin. Exp. Metastasis 41, 699–705 (2024).
Google Scholar
Calderaro, J. et al. Histological subtypes of hepatocellular carcinoma are related to gene mutations and molecular tumour classification. J. Hepatol. 67, 727–738 (2017).
Google Scholar
Terayama, N., Terada, T. & Nakanuma, Y. Histologic growth patterns of metastatic carcinomas of the liver. Jpn J. Clin. Oncol. 26, 24–29 (1996).
Google Scholar
Camarda, R. et al. Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat. Med. 22, 427–432 (2016).
Google Scholar
Casciano, J. C. et al. MYC regulates fatty acid metabolism through a multigenic program in claudin-low triple negative breast cancer. Br. J. Cancer 122, 868–884 (2020).
Google Scholar
Yan, X. et al. Eugenol inhibits oxidative phosphorylation and fatty acid oxidation via downregulation of c-Myc/PGC-1β/ERRα signaling pathway in MCF10A-ras cells. Sci. Rep. 7, 12920 (2017).
Google Scholar
Zhu, J. et al. Mitochondrial NADP(H) generation is essential for proline biosynthesis. Science 372, 968–972 (2021).
Google Scholar
Schwörer, S. et al. Fibroblast pyruvate carboxylase is required for collagen production in the tumour microenvironment. Nat. Metab. 3, 1484–1499 (2021).
Google Scholar
Schwörer, S. et al. Proline biosynthesis is a vent for TGFβ-induced mitochondrial redox stress. EMBO J. 39, EMBJ2019103334 (2020).
Google Scholar
He, J., Fang, B., Shan, S. & Li, Q. Mechanical stiffness promotes skin fibrosis through Piezo1-mediated arginine and proline metabolism. Cell Death Discov. 9, 354 (2023).
Google Scholar
Wu, J. et al. Glutamyl-prolyl-tRNA synthetase regulates proline-rich pro-fibrotic protein synthesis during cardiac fibrosis. Circ. Res. 127, 827–846 (2020).
Google Scholar
Querejeta, R. et al. Increased collagen type I synthesis in patients with heart failure of hypertensive origin. Circulation 110, 1263–1268 (2004).
Google Scholar
Staab-Weijnitz, C. A. Fighting the fiber: targeting collagen in lung fibrosis. Am. J. Respir. Cell Mol. Biol. 66, 363–381 (2022).
Google Scholar
Leamy, A. K. et al. Enhanced synthesis of saturated phospholipids is associated with ER stress and lipotoxicity in palmitate treated hepatic cells. J. Lipid Res. 55, 1478–1488 (2014).
Google Scholar
Subtil, B. et al. Dendritic cell phenotype and function in a 3D co-culture model of patient-derived metastatic colorectal cancer organoids. Front. Immunol. 14, 1105244 (2023).
Google Scholar
Iyer, K. K. et al. High-dose short-term osimertinib treatment is effective in patient-derived metastatic colorectal cancer organoids. BJC Rep. 2, 29 (2024).
Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Google Scholar
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
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
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 (2016).
Kanehisa, M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).
Google Scholar
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Google Scholar
van Gorsel, M., Elia, I. & Fendt, S.-M. in Metabolic Signaling: Methods and Protocols 1st edn (eds Fendt, S.-M. & Lunt, S. Y.) 53–66 (Humana Press, 2019).
Lorendeau, D. et al. Dual loss of succinate dehydrogenase (SDH) and complex I activity is necessary to recapitulate the metabolic phenotype of SDH mutant tumors. Metab. Eng. 43, 187–197 (2017).
Google Scholar
Vriens, K. et al. Evidence for an alternative fatty acid desaturation pathway increasing cancer plasticity. Nature 566, 403–406 (2019).
Google Scholar
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).
Google Scholar
Stegen, S. et al. De novo serine synthesis regulates chondrocyte proliferation during bone development and repair. Bone Res. 10, 14 (2022).
Google Scholar
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Bankhead, P. et al. QuPath: open source software for digital pathology image analysis. Sci. Rep. 7, 16878 (2017).
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
Mehlem, A., Hagberg, C. E., Muhl, L., Eriksson, U. & Falkevall, A. Imaging of neutral lipids by oil red O for analyzing the metabolic status in health and disease. Nat. Protoc. 8, 1149–1154 (2013).
Google Scholar
Neil, D. A. H. et al. Banff consensus recommendations for steatosis assessment in donor livers. Hepatology 75, 1014–1025 (2022).
Google Scholar
van Buuren, S. & Groothuis-Oudshoorn, K. mice: multivariate imputation by chained equations in R. J. Stat. Softw. 45, 1–67 (2011).
Sjoberg, D. D., Whiting, K., Curry, M., Lavery, J. A. & Larmarange, J. Reproducible summary tables with the gtsummary package. R J. 13, 570–580 (2021).
Google Scholar
Wasserthal, J. et al. TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol. Artif. Intell. https://doi.org/10.1148/ryai.230024 (2023).

