Abstract IA022: Mechanisms of splicing dysregulation and dependency in cancer
Bibliographic record
Abstract
Abstract Dysregulated RNA splicing is a hallmark feature of cancer. Much like mutational signatures in tumors, RNA mis-splicing patterns are widely heterogeneous and segregate tumors into clades, suggesting there are distinct mechanisms underlying these splicing aberrations. However, the mechanisms driving such distinct RNA splicing dysregulation remain largely unknown. Herein, we discovered that copy-number variation of essential splicing factors is a pervasive cause of splicing dysregulation, and in some tumor contexts, confers deep dependencies on RNA-binding proteins (RBPs) that converge on these mechanisms. Using a systematic chemical biology approach, we delineate how splicing RBPs participate in distinct types of RNA quality control in cells, and when disrupted, produce surprisingly unique patterns of RNA mis-splicing. These RBP-specific mis-splicing patters are common in distinct cancer types. Notably, unbiased genome-wide analysis revealed that RBP-specific mis-splicing patterns found in tumors were significantly associated with copy number loss of a network of functionally linked splicing factors. These splicing factors often reside on tumor suppressor loci that are frequently deleted in breast and other cancers, suggesting that copy number loss of essential splicing RBPs is a collateral event with selected loss of tumor suppressors. Importantly, RBP-specific mis-splicing signatures also segregate tumor models that are dependent on the concordant RBP, suggesting that these tumors evolve defects in RBP function that predispose them to further perturbation. Together, our work suggests that deletions of tumor suppressor loci may drive collateral dysregulation of RNA splicing through partial loss of splicing RBPs and provokes the hypothesis that these mis-splicing signatures may predict actionable dependencies in the cancers that harbor them. Citation Format: Kristen Karlin. Mechanisms of splicing dysregulation and dependency in cancer [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 IA022.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".