Abstract B003: Identification of novel genes that regulate aneuploidy tolerance by attenuating aneuploidy-induced stresses
Bibliographic record
Abstract
Abstract Aneuploidy, an imbalanced number of chromosomes or chromosome arms, is the most common genetic aberration in cancer and is associated with a worse prognosis and advanced disease. However, in non-transformed cells, aneuploidy has a substantial fitness cost, leading to cell cycle arrest and cell death. This fitness cost is the result of several cellular stresses associated with aneuploidy, including proteotoxic, metabolic, mitotic and replication stress. Therefore, for aneuploidy to become beneficial, tolerance mechanisms that enable cancer cells to cope with aneuploidy-induced cellular stresses must come into play. Such mechanisms may uncover novel synthetic lethalities of aneuploid cancer cells. To this end, we systematically dissected the cellular mechanisms that facilitate aneuploidy tolerance. We combined large-scale analyses of gene expression, as well as genetic and pharmacological dependency datasets from human tumors, cancer cell lines and non-transformed cells, to identify genes and pathways associated with aneuploidy tolerance. We identified 55 genes associated (with high confidence) with aneuploidy tolerance. Gene set enrichment analysis (GSEA) revealed an enrichment for ribosome biogenesis and translation, DNA repair, replication stress and the p53 pathway. Interestingly, these pathways were all previously implicated with the aforementioned aneuploidy-induced cellular stresses. We plan to conduct a dependency mini-screen to validate the top ‘hits’ from our comprehensive bioinformatic analysis, and to explore the role of validated candidates in conferring aneuploidy tolerance. One of the top ‘hits’ of our exploratory analysis was a novel gene with an unclear cellular function. We experimentally confirmed that this gene was over-expressed in aneuploid cells, across several isogenic systems of matched diploid-aneuploid cell lines. Aneuploidy induction using MPS1 inhibitors led to its significant upregulation, demonstrating that its increased expression is a general feature of aneuploid cells. Mechanistically, we found that the novel gene had a role in regulating chromosome segregation, as its knockdown increased the rate of mitotic aberrations – as well as the sensitivity to mitotic checkpoint inhibition – resulting in increased chromosomal instability. We now study the molecular basis for the effect of this novel gene on aneuploidy tolerance. Specifically, we knocked it down in aneuploid cells, and over-expressed it in diploid cells, and we currently explore how these manipulations affect the above-mentioned aneuploidy tolerance pathways, namely replication stress, ribosome biogenesis, DNA damage repair and tp53 activation. In summary, we revealed a novel gene that is a key regulator of aneuploidy tolerance in cancer cells, likely due to its direct involvement in pathways required for the cellular coping with aneuploidy-induced stresses. Citation Format: Yonatan Eliezer, Adi V Tarrab, Tal Ben-Yishay, Hajime Okada, Tom Winkler, Uri Ben-David. Identification of novel genes that regulate aneuploidy tolerance by attenuating aneuploidy-induced stresses [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 B003.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".