GENE-50. SWI/SNF COMPLEX HETEROGENEITY RELATES WITH POLYPHENOTYPIC DIFFERENTIATION, PROGNOSIS AND IMMUNE RESPONSE IN RHABDOID TUMORS
Bibliographic record
Abstract
Abstract PURPOSE Rhabdoid tumors (RTs) arise within (Atypical Teratoid/Rhabdoid Tumor-AT/RT) or outside the brain (extraCNS-RT-eCNS-RT) and are driven mainly by inactivation of the SWI/SNF complex subunit SMARCB1. A pathognomonic hallmark of RTs is heterogeneity characterized by multilineage differentiation of tumor cells including anomalous neuronal differentiation in a subset of eCNS-RT. The mechanisms that regulate heterogeneity in RTs are unknown. Because remodeling of the SWI/SNF complex regulates differentiation, we hypothesized that SWI/SNF-BAF and PBAF complex heterogeneity correlates with both multilineage differentiation and clinical outcome. EXPERIMENTAL DESIGN: We performed an integrated analysis of SWI/SNF complex subunit alterations in the developing kidney and cerebellum (most common regions of origin for RT) in comparison to eCNS-RT (n=14) and AT/RT (n=25) tumors. RT samples were interrogated using immunohistochemistry, DNA methylation and gene expression analyses. RESULTS (A) The SWI/SNF-BAF paralogs ACTL6A/ACTL6B were expressed in a mutually exclusive manner in the developing cerebellum and kidney. In contrast, a subset of eCNS-RTs lost mutual exclusivity and co-expressed both subunits. These tumors showed aberrant DNA methylation of genes that regulate neuronal and renal development and demonstrated immunohistochemical evidence of neuronal differentiation. (B) Low expression of the PBAF subunit-PBRM1 identified a group of AT/RTs in younger children with better overall prognosis. PBRM1-low AT/RT and eCNS-RTs showed altered DNA methylation and gene expression in immune-related genes accompanied by increased CD8 cytotoxic T-cell infiltration. CONCLUSIONS Heterogeneity in SWI/SNF BAF (ACTL6A/ACTL6B) and PBAF (PBRM1) subunits correlates with histogenesis, contributes to the immune microenvironment and prognosis in RTs and may inform opportunities to develop immunotherapies.
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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) | 0.000 | 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) | 0.001 | 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 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".