Abstract 6599: 3D genomic analysis reveals novel enhancer hijacking mechanisms caused by complex structural alterations that drive oncogene overexpression
Bibliographic record
Abstract
Abstract Enhancer hijacking, caused by structural alterations on chromosomes as well as extrachromosomal DNA (ecDNA), is a common cancer driver event. The complexity and ubiquity of structural alterations in cancer genomes make it difficult to identify enhancer hijacking with genome sequencing alone. Here we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking caused by various types of structural alterations. Applying HAPI analysis to HiChIP data from 34 cancer cell lines, we identified novel enhancers hijacked through chromosomal rearrangements to activate both known and potentially novel oncogenes such as MYC, CCND1, ETV1, CRKL, and ID4, which we validated using CRISPRi assays and RNA-seq analysis. Furthermore, we found that ecDNAs often contain multiple oncogenes from different chromosomes, which cause nested enhancer hijacking among them. For instance, we found that MYC ecDNAs relocate additional oncogenes from other chromosomes such as CDX2, ERBB2, or CD44 near the MYC locus, co-opting MYC’s enhancers for their overexpression, which we validated using dual-color DNA FISH and CRISPRi assays. This multiple oncogenes-involved enhancer hijacking mechanism may suggest novel therapeutic strategies such as targeting either the co-opting oncogenes or the hijacked enhancers for ecDNAs. Our study provides a robust strategy to detect enhancer hijacking events using our publicly available HAPI analysis tool and reveals novel mechanisms underlying oncogene activation caused by chromosomal and extrachromosomal structural alterations. Citation Format: Katelyn L. Mortenson, Courtney Dawes, Emily R. Wilson, Nathan E. Patchen, Hailey Johnson, Jason Gertz, Swneke D. Bailey, Yan Liu, Katherine E. Varley, Xiaoyang Zhang. 3D genomic analysis reveals novel enhancer hijacking mechanisms caused by complex structural alterations that drive oncogene overexpression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6599.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".