Genomics‐led approach to drug testing in models of undifferentiated pleomorphic sarcoma
Bibliographic record
Abstract
Undifferentiated pleomorphic sarcoma (UPS) is a rare cancer with limited systemic treatment options and poor outcomes. To seek novel therapeutic interventions, we undertook mutational analysis of 20 UPS patient tumours, four established UPS cell lines and three patient-derived xenograft (PDX) models. Frequently mutated genes were uncommon; in contrast, copy number (CN) events were common with CN gain frequently observed at genes including JUN, EGFR and CDK6 and loss at WNT8B, RB1 and PTEN. Analysis of overlapping genomic changes between patient tumours and PDX models or cell lines revealed druggable events. A selected panel of drugs targeting these was analysed in in vitro UPS models demonstrating that the mitogen-activated protein kinase kinase (MEK) inhibitor trametinib is synergistic in combination with the fibroblast growth factor receptor (FGFR) inhibitor infigratinib. This was further confirmed to be efficacious in an ex vivo tumour slice model. Taken together, our results demonstrate the rationale for utilising genomic data to identify drug classes targeting druggable events in low-prevalence cancers and indicate that trametinib alone or in combination with infigratinib should be further explored for clinical UPS management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".