Abstract IA015: Identifying and targeting synthetic lethalities of aneuploid (cancer) cells
Bibliographic record
Abstract
Abstract Aneuploidy, an imbalanced number of chromosomes or chromosome arms, is a genetic hallmark of cancer cells, yet aneuploidy remains a biological enigma and a missed opportunity for cancer therapy. Aneuploid cells must cope with several types of cellular stresses, potentially creating synthetic lethalities that can be used to target aneuploid cancer cells. Here, I will describe our efforts to identify synthetic lethalities of the aneuploid state (in contrast to synthetic lethalities of specific recurrent aneuploidies. Specifically, I will focus on three recent unpublished studies, where we report that: (1) Aneuploid cells are preferentially sensitive to perturbation of the spindle assembly checkpoint (SAC) and its regulator KIF18A; the expression and activity of CDC20 determine the sensitivity to SAC inhibition. (2) Aneuploid cells depend on MAPK signaling for overcoming aneuploidy-induced DNA damage; targeting MAPK signaling can sensitize aneuploid cells to DNA damage inducing agents and to PARP inhibitors. (3) Aneuploid cells depend on RNA and protein degradation mechanisms to attenuate the cellular consequence of extra chromosomes; this renders aneuploid cells more sensitive to inhibition of nonsense-mediate decay, miRNA-mediated gene silencing, and the proteasome complex. Citation Format: Uri Ben-David. Identifying and targeting synthetic lethalities of aneuploid (cancer) cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA015.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".