The genomic landscape of epithelioid sarcoma cell lines and tumours
Bibliographic record
Abstract
We carried out whole genome and transcriptome sequencing on four tumour/normal pairs of epithelioid sarcoma. These index cases were supplemented with whole transcriptome sequencing of three additional tumours and three cell lines. Unlike rhabdoid tumour (the other major group of SMARCB1-negative cancers), epithelioid sarcoma shows a complex genome with a higher mutational rate, comparable to that of ovarian carcinoma. Despite this mutational burden, SMARCB1 mutations remain the most frequently recurring event and are probably critical drivers of tumour formation. Several cases show heterozygous SMARCB1 mutations without inactivation of the second allele, and we explore this further in vitro. Finding CDKN2A deletions in our discovery cohort, we evaluated CDKN2A protein expression in a tissue microarray. Six out of 16 cases had lost CDKN2A in greater than or equal to 90% of cells, while the remaining cases had retained the protein. Expression analysis of epithelioid sarcoma cell lines by transcriptome sequencing shows a unique profile that does not cluster with any particular tissue type or with other SWI/SNF-aberrant lines. Evaluation of the levels of members of the SWI/SNF complex other than SMARCB1 revealed that these proteins are expressed as part of a residual complex, similarly to previously studied rhabdoid tumour lines. This residual SWI/SNF is susceptible to synthetic lethality and may therefore indicate a therapeutic opportunity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".