The molecular landscape of well differentiated retroperitoneal liposarcoma
Bibliographic record
Abstract
Well differentiated liposarcoma (WD-LPS) is a relatively rare tumour, with fewer than 50 cases occurring per year in the UK. These tumours are both chemotherapy- and radiotherapy-resistant and present a significant treatment challenge requiring radical surgery. Little is known of the molecular landscape of these tumours and no current targets for molecular therapy exist. We aimed to carry out a comprehensive molecular characterisation of WD-LPS via whole genome sequencing, RNA sequencing, and methylation array analysis. A recurrent mutation within exon 1 of FOXD4L3 was observed (chr9:70,918,189A>T; c.322A>T; p.Lys108Ter). Recurrent mutations were also observed in Wnt signalling, immunity, DNA repair, and hypoxia-associated genes. Recurrent amplification of HGMA2 was observed, although this was in fact part of a general amplification of the region around this gene. Recurrent gene fusions in HGMA2, SDHA, TSPAN31, and MDM2 were also observed as well as consistent rearrangements between chromosome 6 and chromosome 12. Our study has demonstrated a recurrent mutation within FOXD4L3, which shows evidence of interaction with the PAX pathway to promote tumourigenesis. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.
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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 | 0.001 | 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) | 0.001 | 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".