Systematic over-expression screens for chromosome instability identify conserved dosage chromosome instability genes in yeast and human tumors
Bibliographic record
Abstract
Abstract Somatic copy number amplifications (SCNAs) and gene over-expression are common features of many cancers. To determine the role of gene over-expression on genome stability, we performed functional genomic screens in the budding yeast for chromosome instability, a defining characteristic of cancer that can be targeted by therapeutics. Over-expression of 245 yeast genes increases chromosome instability by influencing processes such as chromosome segregation and DNA damage repair. Testing candidate human homologs, which were highly recurrently altered in tumors lead to the identification of 2 genes, Tdp1 and Taf12 that contribute to CIN in human cells when over-expressed. Rhabdomyosarcoma lines with higher levels of Tdp1 also show chromosome instability and can be partially rescued by siRNA-mediated knockdown of Tdp1. Using synthetic dosage lethality screens in yeast, we identified candidate target genes that will specifically target tumors with high levels of Tdp1. We demonstrate the utility of functional genetic screens in model organisms to broaden the spectrum of CIN genes, to identify novel genes relevant to chromosome instability in humans and to identify candidate gene targets that can be leveraged to selectively kill tumors over-expressing specific genes.
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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".