Abstract 5033: Secondary findings from genome sequencing are prevalent in pediatric cancer patients
Bibliographic record
Abstract
Abstract Purpose: Genome sequencing in cancer care can reveal secondary findings (SFs) - genetic variations unrelated to the primary condition - yielding potential impacts on patient health and reproductive decision-making. While the American College of Medical Genetics (ACMG) recommends investigating a specific list of actionable SFs during clinical diagnostic sequencing, the prevalence of these findings in pediatric patients with cancer remains unknown. Germline genome sequencing is becoming standard in cancer care, yet no evidence exists to guide providers on the frequency and clinical relevance of SFs in pediatrics. Methods: To understand the landscape of SFs in pediatric cancer, we analyzed germline variants from genome sequencing across pediatric patients from the SickKids Cancer Sequencing Program (KiCS) and the Canadian COVID-19 Genomics Network (HostSeq). Germline pathogenic (P) and likely-pathogenic (LP) variants with allele frequencies <0.03 in gnomAD populations were considered SFs (including carrier status). Variants associated with primary clinical indications were removed. We compared the prevalence of SFs (including ACMG SF v3.2) across cancer and non-cancer populations of diverse genetic ancestry to inform clinical decision-making in pediatrics. Results: The median age of KiCS participants (n=396) was 15 years (SD=10.6), compared to 7 years for HostSeq participants (SD=5.3, n=158), with 55% and 56% male participants respectively. Cancer diagnoses for KiCS participants included 51 different tumor types. On average, KiCS patients had 3.96 P or LP SFs from germline genome sequencing, similar to the HostSeq COVID-19 cohort (3.73). Across the combined cohort (n=454), P and LP variants were most common in CFTR (n=39), ABCA4 (n=32), NPHS2 (n=32), FLG (n=31) and BCHE (n=21) genes. Moreover, 5.7% of participants across cohorts had ≥1 clinically actionable ACMG SF (36 patients, n=454). Hereditary breast and ovarian cancer were the most common ACMG conditions identified from ACMG SFs in the Hostseq cohort. Citation Format: Safa Majeed Grant, Brianne Laverty, Stephenie Prokopec, Christian Marshall, Trevor Pugh, Adam Shlien, Anita Villani, Stephen Scherer, Yvonne Bombard, David Malkin. Secondary findings from genome sequencing are prevalent in pediatric cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5033.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".