A Systematic Analysis of Negative Growth Control Implicates the DREAM Complex in Cancer Cell Dormancy
Bibliographic record
Abstract
Abstract Epithelial ovarian cancer (EOC) generates multicellular aggregates called spheroids that detach from the primary tumor and disseminate through ascites. Spheroids possess a number of characteristics of tumor dormancy including withdrawal from the cell cycle and resistance to chemotherapeutics. This report systematically analyzes the effects of RNAi depletion of 21 genes that are known to contribute to negative regulation of the cell cycle in 10 ovarian cancer cell lines. Interestingly, spheroid cell viability was compromised by loss of some cyclin-dependent kinase inhibitors such as p57Kip2, as well as Dyrk1A, Lin52, and E2F5 in most cell lines tested. Many genes essential for EOC spheroid viability are pertinent to the mammalian DREAM repressor complex. Mechanistically, the data demonstrate that DREAM is assembled upon the induction of spheroid formation, which is dependent upon Dyrk1A. Loss of Dyrk1A results in retention of the b-Myb–MuvB complex, elevated expression of DREAM target genes, and increased DNA synthesis that is coincident with cell death. Inhibition of Dyrk1A activity using pharmacologic agents Harmine and INDY compromises viability of spheroids and blocks DREAM assembly. In addition, INDY treatment improves the response to carboplatin, suggesting this is a therapeutic target for EOC treatment. Implications: Loss of negative growth control mechanisms in cancer dormancy lead to cell death and not proliferation, suggesting they are an attractive therapeutic approach. Mol Cancer Res; 15(4); 371–81. ©2016 AACR.
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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) | 0.001 | 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".