Current state of diagnostic genetic testing in pediatric sarcoma: Survey and review by the Cancer Genomics Consortium Sarcoma Working Group
Bibliographic record
Abstract
The genomic landscape of pediatric sarcomas is constantly expanding, and the utilization of integrated cytogenetic and molecular evaluation of these tumors for diagnosis, prognostication, and therapeutic implications continues to evolve. As a result, there are diverse approaches to the clinical practice of genomic testing in pediatric sarcomas. The Cancer Genomics Consortium Sarcoma Working Group conducted a survey to understand the current state of diagnostic genetic testing for pediatric sarcomas and assess the challenges faced in the field. Among 46 respondents across the United States and Canada, most utilized conventional karyotyping (61 %) and/or fluorescence in situ hybridization (87 %). Molecular methodologies, such as chromosomal microarray (30 %), targeted RT-PCR (22 %), gene fusion sequencing panels (35 %), pan-cancer sequencing panels (37 %), exome sequencing (11 %), and genome sequencing (7 %) were less frequently implemented clinically. When asked about challenges in the field of pediatric sarcoma, a scarcity of standard practice testing guidelines was noted most commonly, especially in the setting of limited tissue availability. Systematic evidence reviews and guidelines are needed for pediatric sarcomas with a consideration for multidisciplinary and international collaboration of individuals representing both high- and low-resource settings. As a resource in the interim, three case-based testing workflow scenarios are presented based on working group member experience to illustrate how differing technologies could be applied during evaluation considering diagnostic, prognostic and/or therapeutic needs. Finally, emerging technologies that are being applied to the diagnostic genetic evaluation of pediatric sarcomas are described, which upon implementation, may serve to streamline the work-up and further optimize patient care.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".