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Steatosis shifts the histopathological heterogeneity of liver metastases towards poor prognosis replacement growth in colorectal cancer patients

GSE292820 Mus musculus Expression profiling by high throughput sequencing 6 samples Submitted 2026/04/22 Platform GPL34475
Summary
Liver metastases are a leading cause of cancer-related deaths, particularly in colorectal cancer. Two distinct histological growth patterns characterize liver metastases: the desmoplastic growth pattern, marked by a stromal encapsulating ring around the metastasis, and the replacement growth pattern, where cancer cells integrate into and replace the native hepatocyte architecture. The replacement pattern is associated with poorer therapeutic responses and worse overall prognosis. Concurrently, obesity, which alters the liver microenvironment, is linked to unfavorable outcomes in colorectal cancer patients. This study explores the interplay between a lipid-rich liver microenvironment and histological growth patterns in metastases. Using resected patient specimens and preclinical mouse models of obesity, we observed a correlation between liver steatosis and a shift toward the more aggressive replacement growth pattern. Mechanistically, lipid-rich environments, both in vitro and in vivo, were found to enhance proline synthesis in colorectal cancer cells, promoting extracellular matrix (ECM) production. This phenotypic switch is driven by fatty acid oxidation, which generates acetyl-coenzyme A, leading to the accumulation of the transcription factor MYC. Inhibiting fatty acid oxidation or silencing MYC reversed the aggressive growth advantage in lipid-rich conditions. Interestingly, cancer cells lose the aggressive phenotype once the lipid abundance is withdrawn. Furthermore, publicly available patient datasets revealed upregulated proline and ECM metabolism in inherent replacement growth patterns. Targeting these pathways significantly reduced liver metastasis outgrowth in preclinical models.
Published in
Steatosis shapes prognosis-defining liver metastasis heterogeneity in CRC
Peng-Winkler Y, Liu XZ, Verheul SML et al. · Nature 2026 · PMID 42386979 · doi:10.1038/s41586-026-10686-2
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Direct links to NCBI, no account and no request form: the whole study as GSE292820_RAW.tar, processed values as the series matrix, the supplementary file directory, and per-sample supplementary files for any of the 6 samples. Raw sequencing reads are also available from ENA.

Also filed as BioProject PRJNA1241311 and SRA study SRP572752. Searching any of these in the dataset finder brings you back here.

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