Abstract A59: Sequencing identifies diagnostically relevant alterations in pediatric solid tumor patients
Bibliographic record
Abstract
Abstract Introduction: Molecular techniques have been incorporated into the diagnostic algorithms for many specific tumors, but the diagnostic role of next-generation sequencing has not been described at a population level. We report diagnostically relevant alterations identified by large-scale sequencing in a prospective cohort of pediatric solid tumors. Methods and Objectives: Patients are eligible for the GAIN / iCat2 study if they have a high-risk, recurrent, or refractory extracranial solid tumor diagnosed at age 30 or less and have an adequate sample for sequencing available. After informed consent, tumor was sequenced using a next-generation sequencing assay that evaluates 447 genes and includes data about sequence variants, copy number alterations, and, in selected genes, translocations. Some cases received additional sequencing via RNASeq or targeted RNA sequencing for further evaluation of fusions. Diagnostic relevance was determined according to AMP/ASCO/CAP standards and guidelines for the reporting of sequence variants in cancer. Results: 349 patients were enrolled as of December 31, 2018, and had tumor tissue successfully sequenced. These patients represent 60 unique diagnoses according to the WHO ICD-O classification. The most common single diagnoses were osteosarcoma (n=64), Ewing sarcoma (n=44), and alveolar rhabdomyosarcoma (n=32). For 349 patients, 184 (53%) had one or more genetic alterations that were diagnostically relevant, of which 159 (86%) were structural variants, 16 (8%) were sequence variants, and 9 (5%) were copy number variations. Alterations of high diagnostic relevance include CIC-DUX4 fusions in sarcoma (n=8), TP53 intron 1 rearrangements in osteosarcoma (n=26), DICER1 sequence variants in various tumors (n=7), and BCOR internal tandem duplications in clear-cell sarcoma of kidney and primitive myxoid mesenchymal tumor of infancy (n=3). Conclusions: Diagnostically relevant alterations were identified in over half of pediatric solid tumor patients evaluated. Gene fusions are particularly prevalent. These results support a role for sequencing that includes robust fusion assessment to inform diagnosis in patients with pediatric solid tumors. Citation Format: Alanna J. Church, Laura B. Corson, Alma Imamovic-Tuco, Gianna R. Strand, Dierdre Reidy, Duong Doan, Robert S. Pinches, Mark A. Applebaum, Rochelle Bagatell, Brian D. Crompton, Steven G. DuBois, Julia L. Glade Bender, Theodore W. Laetsch, Lobin A. Lee, Neal I. Lindeman, Marian H. Harris, Margaret E. Macy, Luke Maese, Navin Pinto, Amit J. Sabnis, Eliezer M. Van Allen, Susan I. Vear, Daniel A. Weiser, Catherine M. Clinton, Katherine A. Janeway. Sequencing identifies diagnostically relevant alterations in pediatric solid tumor patients [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 A59.
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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 | 0.001 |
| 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) | 0.002 | 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".