Exploring the relationship between CHD1 and MYC in cancer and development
Bibliographic record
Abstract
Phenotype arises from genotype as a result of gene expression during embryonic development. Transcription is a tightly regulated phenomenon in biology and can be tuned to different levels depending on the cellular and environmental state. To allow for optimal transcription, a permissive chromatin landscape is necessary. One essential modulator of this open chromatin organization in mouse embryonic stem cells (mESCs) is the chromatin remodeler Chd1. Genomic binding of Chd1 overlaps with the activating histone mark H3K4me3, the transcription factor c-Myc (Myc), and RNA Polymerase II. Chd1 controls transcriptional output in part by nucleosome eviction, allowing for optimal RNA Polymerase activity. One transcription factor often associated with maintaining physiological levels of transcription is Myc, an oncogene commonly activated in various types of human cancer. As such, I hypothesize that Chd1 may play a key role in Myc-driven cancer. In Chapter 2, I explore this epistatic interaction in a model of human breast cancer. I found that the knockdown of CHD1 suppresses tumorigenesis in a MYC-driven breast cancer model. Through RNA-sequencing and ATAC-sequencing, I determined that the anti-tumour effects of CHD1 knockdown were attributable to p53 activation. This leads to the disruption of the cell cycle and increased nucleolar stress. This work provides evidence that CHD1 may be a promising therapeutic target in MYC-driven breast cancer. In Chapter 3, I investigate the role of Chd1 using a Myc- driven murine blood cancer model. In this study, Myc-activation drives an increase in white blood cell populations in the hematopoietic compartment that is rescued by the genetic deletion of Chd1. Taken together, this work indicates that CHD1 and MYC genetically interact in the regulation of transcription and that CHD1 may be a promising target in at least breast cancer and even beyond.
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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 | 0.001 |
| 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".