<scp>SMARCB1</scp> loss induces druggable cyclin <scp>D1</scp> deficiency via upregulation of <scp><i>MIR17HG</i></scp> in atypical teratoid rhabdoid tumors
Bibliographic record
Abstract
Atypical teratoid rhabdoid tumor (ATRT) is a fatal pediatric malignancy of the central neural system lacking effective treatment options. It belongs to the rhabdoid tumor family and is usually caused by biallelic inactivation of SMARCB1, encoding a key subunit of SWI/SNF chromatin remodeling complexes. Previous studies proposed that SMARCB1 loss drives rhabdoid tumor by promoting cell cycle through activating transcription of cyclin D1 while suppressing p16. However, low cyclin D1 protein expression is observed in most ATRT patient tumors. The underlying mechanism and therapeutic implication of this molecular trait remain unknown. Here, we show that SMARCB1 loss in ATRT leads to the reduction of cyclin D1 expression by upregulating MIR17HG, a microRNA (miRNA) cluster known to generate multiple miRNAs targeting CCND1. Furthermore, we find that this cyclin D1 deficiency in ATRT results in marked in vitro and in vivo sensitivity to the CDK4/6 inhibitor palbociclib as a single agent. Our study identifies a novel genetic interaction between SMARCB1 and MIR17HG in regulating cyclin D1 in ATRT and suggests a rationale to treat ATRT patients with FDA-approved CDK4/6 inhibitors. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".