Primary Adult Retroperitoneal Sarcoma: A Comprehensive Genomic Profiling Study
Bibliographic record
Abstract
Background: Adult primary retroperitoneal sarcomas (RPSs) are a group of heterogeneous tumors with different histological subtypes. Comprehensive genomic profiling (CGP) analyses have recently provided significant insights into the biology of sarcomas by identifying genomic alterations (GAs) which could benefit from targeted therapies. Methods: RPS were evaluated by CGP using next-generation sequencing of up to 406 cancer-related genes. Tumor mutational burden (TMB) was determined on 0.83 to 1.14 mut/Mb of sequenced DNA. Finally, PD-L1 expression was determined. Results: Overall, 296 cases of primary RPS were analyzed. Liposarcoma (LPS) subtype had more GA/tumor than leiomyosarcoma (LMS) subtypes, with follicular dendritic cell sarcomas harboring the highest and synovial sarcomas the lowest. TP53 and Rb1 alterations were the highest in LMS, and CDK4/6 and MDM2 in LPS. However, both the TMB and targetable GA rates were low across subtypes. PD-L1 immunostaining was low positive in 21% and high positive in 5% of patients, respectively. Conclusions: CGP analysis revealed that potentially actionable genomic targets were rare in our cohort of RPS. Moreover, RPSs seem less likely to respond to immune checkpoint inhibitors based on putative biomarkers status. Nevertheless, genomic stratification according to histological subtypes led to description of GAs that can inform future clinical trials design.
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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 | 0.001 |
| 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".