Identifying actionable variants using next generation sequencing in patients with a historical diagnosis of undifferentiated pleomorphic sarcoma
Bibliographic record
Abstract
There are limited data regarding the molecular characterization of undifferentiated pleomorphic sarcomas (UPS; formerly malignant fibrous histiocytoma). This study aimed to investigate the utility of next generation sequencing (NGS) in UPS to identify subsets of patients who harbour actionable mutations. Patients diagnosed with UPS underwent pathological re-evaluation by a pathologist specializing in sarcoma. Tumor DNA was isolated from archived fresh frozen tissue samples and genotyped using NGS with the Illumina MiSeq TruSeq Amplicon Cancer Panel (48 genes, 212 amplicons). In total, 95 patients initially classified with UPS were identified. Following pathology re-review the histological subtypes were reclassified to include: Myxofibrosarcoma (MFS, N = 44); UPS(N = 18); and Others (N = 27; including undifferentiated spindle cell sarcoma (N = 15) and dedifferentiated liposarcoma (N = 6)). Seven cases were excluded from further analysis for other reasons. Baseline demographics of the finalized cohort (N = 88) showed a median age of 66 years (32-95), primarily with stage I-III disease (92%) and high-grade (86%) lesions. Somatic mutations were identified in 31 cases (35%)(Total mutations = 36: solitary mutation(n = 27); two mutations( =n = 3); three mutations(n = 1)). The most commonly identified mutations were in TP53 (n = 24), ATM (n = 3) and PIK3CA (n = 2). Three of 43 patients with MFS and one of 18 patients with UPS had clinically relevant mutations, mainly related to biomarkers of prediction of response; however few had targetable driver mutations. Somatic mutation status did not influence disease free or overall survival. Based on the small number of clinically relevant mutations, these data do not support the routine use of targeted NGS panels outside of research protocols in UPS.
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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 | 0.002 |
| 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".