Abstract 215: A novel framework for deciphering 3D genome structure in colorectal cancer
Bibliographic record
Abstract
Abstract Background: The spatial organization of chromatin is a fundamental determinant of epigenomic landscape which is widely investigated using high-throughput chromosome conformation capture (HiC) technique. To address challenges associated with the resolution and variability of HiC technique, we applied a novel workflow, HiC-ECC (HiC Enhance, Compare, and Call). This approach not only enhances HiC data quality but also enables the identification of biologically significant chromatin interactions and genome structural features. By integrating advanced computational tools with a focus on colorectal cancer (CRC) biology, this study sheds light on the 3D genome architecture of CRC organoids and its implications for patient stratification and therapeutic targeting. Methods: We applied the HiC-ECC workflow to a cohort of 15 patient-derived CRC organoid models, including samples derived from primary tumors and metastatic sites in the lung and liver. The workflow involved initial data preprocessing to generate high-quality chromatin contact matrices and ensure rigorous quality control. To enhance the resolution of HiC datasets, we utilized deep learning-based methods, enabling more accurate identification of chromatin interactions. Comparative analyses were conducted to identify differential chromatin interaction patterns and conserved structural features across the cohort. Chromatin architecture was further characterized by delineating topologically associating domains (TADs) and clustering them to reveal common and sample-specific interaction networks, offering insights into the hierarchical organization of 3D genome structures. Results: Our analysis revealed four distinct subtypes of CRC organoids based on their 3D genome architecture, revealing a novel approach to interrogate heterogeneity within CRC. This subtyping was driven by differential chromatin interaction patterns and structural features, including unique TADs and conserved regulatory elements. Comparative analysis highlighted subtype-specific chromatin interaction networks, suggesting distinct epigenomic landscapes that correlate with tumor progression and metastatic potential. Conclusion: This study enhances our understanding of CRC epigenomics and lays the groundwork for integrating chromatin architecture into patient stratification and precision therapy. Citation Format: Amin Nooranikhojasteh, Jakob Zerbs, Ghazaleh Tavallaee, Elias Orouji. A novel framework for deciphering 3D genome structure in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 215.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".