SMARCB1 Deficiency as a Driver of the Hallmarks of Cancer in Rhabdoid Tumours: Novel Insights into Dysregulated Energy Metabolism, Emerging Targets, and Ongoing Clinical Trials
Bibliographic record
Abstract
Rhabdoid tumours (RTs) are aggressive neoplasms most often characterised by biallelic loss of the SMARCB1 gene, which encodes a core subunit of the SWI/SNF chromatin remodelling complex. Despite their relative genetic stability, RTs exhibit a highly malignant phenotype and poor prognosis. This review aims to explore the mechanisms underlying SMARCB1 aberrations, their role in driving hallmarks of cancer, and emerging therapeutic strategies for RTs. The loss of SMARCB1 drives multiple cancer hallmarks by disrupting key regulatory pathways. It promotes unchecked cell proliferation through alterations in p16INK4a and Myc signalling. SMARCB1-deficient tumours possess immune evading capabilities via PD-L1 overexpression and immune checkpoint activation. SMARCB1 deficiency also alters cellular energetics by various mechanisms. The nucleotide biosynthesis pathway has been demonstrated to be upregulated in RT organoids, as shown by increased levels of pathway metabolites. Enzymes of the mevalonate pathway such as HMG-CoA reductase and mevalonate kinase are also dysregulated. Targeting glutathione metabolism with eprenetapopt may result in oxidative stress and induce apoptosis in RTs. Widespread epigenetic aberrations, including increased EZH2 activity, are being targeted with inhibitors such as tazemetostat. Future horizons in RT treatment include immunotherapies, epigenetic modifiers, and gene therapies. The synergy and optimal timing of targeted therapy with conventional treatment needs to be better characterised in order to proceed to later stage clinical trials and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".