Colorectal cancer hot spot mutations attenuate the ASXL-MLL4 interaction
Bibliographic record
Abstract
Human additional sex combs like (ASXL) proteins are involved in the maintenance of both transcriptional activation and repression through their ability to bind multiple chromatin regulators, including two tumor suppressors: deubiquitinase BAP1 and methyltransferase MLL4 (KMT2D). The ASXL genes are often altered in colorectal cancer (CRC), and ASXL1 is one of the four hub genes related to the pathogenesis of CRC. Here, we show that MLL4 and BAP1 interdependently target specific genomic regions and positively or negatively regulate expression of a subset of genes in the human colon carcinoma HCT116 cells. MLL4 and BAP1 colocalize on a subset of enhancers and promoters in an interdependent manner. Genomic distribution of BAP1 in CRC cells differs from that in ESCs, with substantially more BAP1 binding sites identified on enhancers and promoters in HCT116 cells. MLL4 occupancy on MLL4 + BAP1 + genomic regions depends on functional ASXLs that interact with both MLL4 and BAP1, and CRC-relevant mutations attenuate the formation of the MLL4-ASXL complex. Mutational analysis and binding assays identified CRC hot spot mutations in ASXLs. Our findings suggest that alterations in the genomic distribution of the MLL4-ASXL-BAP1 axis and CRC hot spot mutations in ASXLs perturb normal transcriptional programs and may trigger pathogenic events in colon cancer.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".