Abstract A03: Prevalence and spectrum of germline mutations in children with high-risk cancer
Bibliographic record
Abstract
Abstract Introduction: Zero Childhood Cancer’s National Precision Medicine for Children with Cancer Study (PRISM) utilizes novel technologies to guide individualized management of children with high-risk cancer (expected overall survival less than 30%). Germline DNA is utilized to distinguish cancer-specific somatic variants from constitutional variants or polymorphisms, allowing identification of clinically relevant germline mutations. The prevalence of cancer predisposition syndromes in pediatric cancer may range from 8.5% to as high as 33%. Method PRISM combines molecular genomic analysis (WGS and RNASeq) with in vitro high-throughput drug screening and patient-derived xenograft drug efficacy testing. A Molecular Tumour Board (MTB) of Oncology and Genetics professionals convenes to determine the significance of genomic analysis as curated by bioinformaticians, molecular scientists, and clinicians. Results: Between September 2017 and June 2019, 218 children aged under 21 years have been recruited in PRISM (37% with central nervous system tumors, 47% with non-CNS solid tumors, and 16% with hematologic malignancies), and results are available for 208 after discussion at MTB meeting. Forty-two reportable germline variants were detected in 35 participants (detection rate: 16.8%), comprising 28 pathogenic and 14 likely pathogenic variants, across 22 cancer predisposition genes. The most frequently affected gene was CHEK2 (n=7), followed by SMARCB1 (n=5) and BRCA2 (n=3) and NF1 (3). In one out of three participants with germline mutations, somatic analysis revealed a double hit in the same gene altered in the germline. Distributions of participants with germline mutation per group were 16% of patients with CNS tumors (12/77), 19% of patients with non-CNS solid tumors (18/96), and 15% of patients with hematologic malignancies (5/34). Conclusion: Germline mutation detection rate in cancer predisposition genes was higher than expected, 16.8%; however, it may result from selection bias (i.e., cohort of high-risk cancers). Although genomic sequencing has expanded our understanding of pediatric cancer predisposition and presented opportunities for genetics-mediated care, identifying underlying germline mutations with potential clinical implications remains a clinical challenge for pediatric oncologists. Citation Format: Paulette Barahona, Alexandra Sherstyuk, Mark Cowley, Paul Ekert, Judy Kirk, Dong-Anh Khuong-Quang, Amit Kumar, Loretta Lau, Chelsea Mayoh, Glenn Marshall, Emily Moud, Tracey O’Brien, Mark Pinese, David Thomas, Vanessa Tyrell, David Ziegler, Michelle Haber, Katherine Tucker, Noemi Auxiliadora Fuentes-Bolanos, Meera Warby. Prevalence and spectrum of germline mutations in children with high-risk cancer [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 A03.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".