Abstract B15: Genomic classification and prognosis in rhabdomyosarcoma: A report from the Children’s Oncology Group, the Institute of Cancer Research, and the National Cancer Institute
Bibliographic record
Abstract
Abstract Purpose: Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma of childhood. Despite aggressive therapy, the 5-year survival rate for patients with metastatic or recurrent disease remains poor and beyond PAX-FOXO1 fusion status, no genomic markers are available for risk stratification. We therefore performed a large-scale study through an international consortium to more accurately determine the incidence of driver mutations and their association with clinical outcome. Patients and Method: Formalin-fixed, paraffin-embedded material was collected from patients enrolled on Children’s Oncology Group trials and UK patients enrolled on MMT trials. Pathology was reviewed centrally and extracted DNA was subjected to targeted capture sequencing using a panel of 39 genes previously associated with RMS. Mutations, indels, deletions, gene amplifications, and copy number variation was called using analysis pipelines developed at the NCI. Results: DNA from six hundred and forty-one patients was suitable for analyses. A median of 1 variant call was found per tumor. Mutation of a RAS isoform was found in 29% of all fusion negative cases, mutation of a RAS pathway member was seen in greater than 50% of cases, and 24% had no putative driver mutation identified. BCOR (15%), NF1 (11%), and TP53 (12%) mutations were found at a higher incidence than previously reported. Interestingly, mutations in HRAS were notable in the infant population whereas those in NRAS were enriched in adolescents. Among infants < 1 year, 71% of cases harbored a mutation in HRAS or KRAS. In contrast, mutation of MYOD1 was associated with an older age and a head and neck primary site. Finally, 29% of the evaluated tumors harbored multiple driver mutations consistent with subclonal variation and tumor heterogeneity in fusion-negative RMS. Conclusion: This is the largest genomic characterization of clinically annotated RMS tumors to date and provides genetic features that refine risk stratification and can be incorporated into prospective trials. Citation Format: Jack Shern, Joanna Selfe, Rajesh Patidar, Young Song, Marielle Yohe, Jun Wei, Xinyu Wen, Erin Rudinski, Donald Barkauskas, David Hall, Corinne Linardic, Meriel Jenney, Julia Chisholm, Rebecca Brown, Anna Kelsey, Susanne Gatz, Stephen Skapek, Douglas Hawkins, Janet Shipley, Javed Khan. Genomic classification and prognosis in rhabdomyosarcoma: A report from the Children’s Oncology Group, the Institute of Cancer Research, and the National Cancer Institute [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr B15.
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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 | 0.001 | 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".