Single-cell multiomic modelling of early metastatic events promoted by the extracellular matrix
Bibliographic record
Abstract
BACKGROUND: Cancer metastasis, primarily driven by epithelial-to-mesenchymal transition (EMT), is responsible for most cancer-related mortalities. Traditional pre-clinical models fail to fully capture mesenchymal characteristics due to the loss of human stroma. The extracellular matrix (ECM) plays a crucial role in EMT, yet conventional in vitro models often rely on defined ECM components, which may not adequately replicate the human physiological ECM niche. METHODS: To mimic the in situ dissemination of cancer cells, we employed a patient-derived extracellular matrix (pdECM). We transitioned the culture matrix for patient-derived colorectal cancer organoids from a basement membrane extract (BME) to a patient-derived ECM (pdECM). We performed single-cell multiomic analyses, integrating transcriptomic and epigenomic data, to investigate changes in organoid phenotypes and reconstruct the EMT trajectory. RESULTS: Organoids cultured in the pdECM exhibited increased tumor cell dissemination and motility, resembling in situ lesions without exogenous ligand treatment. Single-cell multiomic analysis revealed TNF-α signaling as an early metastatic event in the EMT trajectory. Epigenomic changes led to increased accessibility of AP-1 complex target genes, particularly MMP7, which promoted an invasive phenotype. Our multimodal computational approach distinguished early and late EMT states, demonstrating that pdECM-induced EMT occurs independently of traditional EMT master regulators. Notably, pdECM organoids exhibited a partial EMT phenotype, characterized by hybrid epithelial-mesenchymal states. CONCLUSION: This study presents an advanced in vitro model that closely recapitulates in situ tumorigenesis and provides novel insights into the metastatic cascade. The pdECM system enables the reconstruction of EMT dynamics, highlighting the critical role of ECM composition in metastasis and offering a physiologically relevant platform for the development of targeted therapies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".