Identification of targetable vulnerabilities of PLK1-overexpressing cancers by synthetic dosage lethality
Bibliographic record
Abstract
Summary Tumor heterogeneity poses a significant challenge in combating treatment resistance. Despite Polo-like kinase 1 (PLK1) being universally overexpressed in cancers and contributing to chromosomal instability (CIN), direct PLK1 inhibition hasn’t yielded clinical progress. To address this, we utilized the synthetic dosage lethality (SDL) approach, targeting PLK1’s genetic interactions for selective killing of overexpressed tumor cells while mitigating heterogeneity-associated challenges. Employing computational methods, we conducted a genome-wide shRNA screen, identifying 105 SDL candidates. Further in vivo CRISPR screening in a breast cancer xenograft model and in vitro CRISPR analysis validated these candidates. Employing Perturb-seq revealed IGF2BP2/IMP2 as a key SDL hit eliminating PLK1-overexpressing cells. Suppression of IGF2BP2, genetically or pharmacologically, downregulated PLK1 and limited tumor growth. Our findings strongly propose targeting PLK1’s genetic interactions as a promising therapeutic approach, holding broad implications across multiple cancers where PLK1 is overexpressed.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".