Abstract B028: Deep mutational scanning of SMARCB1 identifies missense mutants that destabilize SWI/SNF complex stability and diminish remodeling activity
Bibliographic record
Abstract
Abstract SMARCB1-deficient cancers are aggressive and highly lethal pediatric malignancies. Loss of SMARCB1 protein expression, a subunit within the SWI/SNF chromatin remodeling complex, remains the key diagnostic feature of these cancers. This can occur through large deletions, balanced translocations, frameshift mutations, or truncating nonsense mutations. Here, we sought to understand the effect of missense mutations on the tumor suppressor function of SMARCB1 through deep mutational scanning (DMS).Specifically, we developed and introduced a library containing >99% of all possible SMARCB1 amino acid substitutions, including frameshift and nonsense mutants, into three pediatric SMARCB1-deficient cell lines (G401 - malignant rhabdoid tumor of the kidney, BT16 - atypical teratoid/rhabdoid tumor, and PEDS0005T - renal medullary carcinoma) and assessed cell fitness after 8-12 days. We observed broad mutational intolerance in three SMARCB1 domains: the winged-helix domain, the intrinsically disordered region, and the RPT2 domain. Following our high-throughput study, we then focused on two highly enriched residues predicted to closely interact within the RPT2 domain of SMARCB1.We validated that specific missense mutations in these two residues mimic loss of function while retaining protein expression. Mechanistic studies revealed that these mutations destabilize the SWI/SNF complex, notably resulting in decreased affinity for SWI/SNF subunits known to be associated with cancer pathogenesis. This complex instability leads to diminished nucleosome remodeling and subsequent transcriptional deregulation.These findings challenge our current understanding of what a loss-of-function mutation means in the context of SMARCB1, suggesting that the absence of SMARCB1 protein expression may not be the sole indicator of SMARCB1 deficiency. Furthermore, this dataset provides a valuable resource for researchers to investigate key residues of SMARCB1 that may drive critical intermolecular interactions necessary for proper SWI/SNF complex assembly and function. Citation Format: Garrett Cooper, Benjamin Lee, Won Kim, Eliseo Salas, Yongdong Su, Victor Chen, Xiaoping Yang, Robert Lintner, Federica Piccioni, Andrew Giacomelli, Thomas Howard, Karen Conneely, David Root, William Hahn, David Gorkin, Bo Liang, Jaclyn Biegel, Susan Chi, Andrew Hong. Deep mutational scanning of SMARCB1 identifies missense mutants that destabilize SWI/SNF complex stability and diminish remodeling activity [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr B028.
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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 | 0.001 | 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".