Targeting TBP-Associated Factors in Ovarian Cancer
Bibliographic record
Abstract
As ovarian tumors progress, they undergo a process of dedifferentiation, allowing adaptive changes in growth and morphology that promote metastasis and chemoresistance. Herein, we outline a hypothesis that TATA-box binding protein associated factors (TAFs), which compose the RNA Polymerase II initiation factor, TFIID, contribute to regulation of dedifferentiation states in ovarian cancer. Numerous studies demonstrate that TAFs regulate differentiation and proliferation states; their expression is typically high in pluripotent cells and reduced upon differentiation. Strikingly, TAF2 exhibits copy number increases or mRNA overexpression in 73% of high-grade serous ovarian cancers (HGSC). At the biochemical level, TAF2 directs TFIID to TATA-less promoters by contact with an Initiator element, which may lead to the deregulation of the transcriptional output of these tumor cells. TAF4, which is altered in 66% of HGSC, is crucial for the stability of the TFIID complex and helps drive dedifferentiation of mouse embryonic fibroblasts to induced pluripotent stem cells. Its ovary-enriched paralog, TAF4B, is altered in 26% of HGSC. Here, we show that TAF4B mRNA correlates with Cyclin D2 mRNA expression in human granulosa cell tumors. TAF4B may also contribute to regulation of tumor microenvironment due to its estrogen-responsiveness and ability to act as a cofactor for NFκB. Conversely, TAF9, a cofactor for p53 in regulating apoptosis, may act as a tumor suppressor in ovarian cancer, since it is downregulated or deleted in 98% of HGSC. We conclude that a greater understanding of mechanisms of transcriptional regulation that execute signals from oncogenic signaling cascades is needed in order to expand our understanding of the etiology and progression of ovarian cancer, and most importantly to identify novel targets for therapeutic intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".