Abstract IA006: Synthetic lethalities for SWI/SNF mutant cancers
Bibliographic record
Abstract
Abstract Genes that encode subunits of SWI/SNF (BAF) chromatin remodeling complexes are mutated in over 20% of cancers. These include recurrent mutations of ARID1A (BAF250a) in ovarian, endometrioid, bladder, stomach, colorectal and pancreatic cancers and neuroblastoma; of the SMARCA4 (BRG1) subunit in medulloblastomas and non-small cell lung cancers; of the PBRM1 subunit in renal carcinomas; of the ARID2 subunit in hepatocellular, lung, and pancreas carcinomas as well as melanomas; of the BRD7 subunit in breast cancers. The SWI/SNF complex includes both core and lineage-specific subunits and utilizes the energy of ATP to modulate chromatin structure and regulate transcription. My laboratory began studying the SWI/SNF complex when SMARCB1 (INI1/SNF5/BAF47) became the first SWI/SNF subunit linked to tumor suppression when it was found to be biallelically inactivated in nearly all cases of a highly aggressive type of pediatric cancer called malignant rhabdoid tumor (MRT). Despite the extremely aggressive and lethal nature of MRT we have shown that these cancers are diploid and have remarkably simple genomes. We leverage this model tumor to identify how SWI/SNF complexes function, to determine how mutation of SWI/SNF subunits drive cancer and other diseases, and to identify therapeutic vulnerabilities that result from SWI/SNF mutations. We have leverage the Cancer Dependency Map, and the Pediatric Cancer Dependencies Accelerator, to systematically identify robust and impactful synthethic lethalities resulting from SWI/SNF mutations. These mechanisms and insights will be presented. Citation Format: Charles W.M. Roberts. Synthetic lethalities for SWI/SNF mutant cancers [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 IA006.
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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".