Deciphering colorectal cancer progression through integrative epigenomic analysis for precision therapeutics.
Bibliographic record
Abstract
e15561 Background: Colorectal cancer (CRC), the third most prevalent cancer globally, remains a major cause of cancer-related mortality. Genomic and epigenomic plasticity during CRC progression, particularly metastasis, poses significant challenges to effective treatment. Patient-derived organoids (PDOs) reflect the heterogeneous genetic and morphological composition of cancer cells, enabling ex vivo disease modeling. This study investigates the epigenomic makeup of human primary and metastatic CRC to advance precision epigenetic therapies. Methods: We analyzed 45 organoid models, including 40 CRC organoids (from primary tumors and liver/lung metastases) and 5 generated from healthy colonic tissue. Using integrative epigenomic profiling, including chromatin accessibility, chromatin state mapping (via six histone modifications), and 3D genome conformation, we constructed a comprehensive CRC epigenomic landscape. Results: Our analysis of CRC patient-derived organoids revealed a core set of accessible genomic regions shared across all organoids, irrespective of tumor location or metastatic status. This conserved chromatin signature highlights common regulatory pathways in CRC during disease progression. Unsupervised clustering based on epigenomic features revealed four distinct CRC subtypes, encompassing both primary and metastatic models. Notably, organoids derived from liver and lung metastases did not cluster by metastatic site, underscoring epigenomic convergence. Subtype D comprised 57.1% (8/14) of primary tumors, while subtypes B and C contained 47.3% (9/19) of liver metastases and 42.8% (3/7) of lung metastases, respectively. KRAS mutation status further stratified subtypes, with wild-type KRAS observed exclusively in subtypes A and B, while KRAS-mutant variants were enriched in subtypes C (6/7) and D (4/10). Histone modification patterns, defined by six marks (H3K4me1, H3K4me3, H3K27me3, H3K27ac, H3K36me3, and H3K9me3) revealed subtype-specific regulatory chromatin landscapes. Three-dimensional chromatin interactions were mapped, and genome-wide interaction contacts are used to identify various chromatin structures that may influence metastatic capacity. Conclusions: This study highlights distinct and shared epigenomic signatures in CRC PDOs, offering novel insights into tumor progression and metastasis. Integrative chromatin analysis provides a framework for identifying epigenetic biomarkers and epigenome targeting strategies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".