Abstract A022: Decoding aneuploidy: Identifying drivers and therapeutic targets in recurrent breast cancer copy number alterations
Bibliographic record
Abstract
Abstract Chromosome instability is highly prevalent in cancer and drives large scale chromosomal imbalances, known as aneuploidies. However, how aneuploidy contributes to tumorigenesis remains difficult to study due to the vast numbers of genes affected. To address these limitations, we have developed a CRISPR-Knock Out and Activation Linked Assay (CRISPR-KOALA), which enables systematic high-throughput bidirectional genetic screens in immune-competent mouse models of cancer. Using CRISPR-KOALA we screened the mouse orthologs of all 3,752 genes residing on the ten most frequently altered human chromosome arms in basal-like breast cancer (BLBC), which to date is the largest bidirectional screen performed in vivo. Our screen identified 90 cancer driver genes, the vast majority of which have hitherto unknown functions in cancer. These genes drive distinct signalling pathways, reflecting the high degree of BLBC heterogeneity. Individual manipulation of the identified cancer driver genes completely overcomes the need for copy number alterations (CNAs) in p53-mutant BLBC mouse models. Mechanistically, we uncover PLGRKT as a potent oncogene and show that its tumor-promoting activity is associated with the creation of highly stress-resistant mitochondria that promote tumor cell survival, rather than through its canonical role of regulating the extracellular matrix. Overall, our work reveals that arm-level CNAs can function to select specific driver genes to promote heterogenous biological processes. Citation Format: Khalid N Al-Zahrani, Ellen R Langille, Andreea Obersterescu, Christopher Lowden, Katie Teng, Lauren Caldwell, David Cook, Miguel Pérez-Castro, Cynthia Chiu, Alexander Bahcheli2, Ricky Tsai, Jacob Berman, Kin Chan, Linkang Zhang, K.W. Annie Bang, Michael Parsons, Adele Lopes, Jocelyn Nurtanto, E. Idil Temel, Iosifina Fotiadou, Julien Dessapt, Hartland Jackson, Sean Egan, Jüri Reimand, Jeffrey Wrana, Daniel Schramek. Decoding aneuploidy: Identifying drivers and therapeutic targets in recurrent breast cancer copy number alterations [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A022.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".