Abstract 6545: Investigating secondary findings in a pediatric cancer cohort: preliminary findings
Bibliographic record
Abstract
Abstract Purpose: An expected outcome following germline genome sequencing in oncology is the discovery of ‘secondary findings’ (SFs). SFs comprise pathogenic(P)/likely P (LP) germline variants in cancer genes not typically associated with the presenting cancer, in addition to germline variants of uncertain significance (VUS) to the patient’s cancer. Due to the rarity of childhood cancers and a dearth of studies analyzing SFs, many pediatric SFs are categorized as VUS without clinical interpretation. Interpreting SFs poses significant challenges: VUSs and other SFs are frequently not included in clinical molecular reports, and even when reported (often through research), their clinical utility and long-term impact on patient health are unclear. However, we know VUSs can have clinical importance because some VUSs, when investigated thoroughly, have been reclassified as pathogenic predictors of significant health conditions in children. We hypothesize that an in-depth characterization of the landscape of germline SFs/VUSs across a diverse pediatric cancer cohort will reveal new roles of these genes and mutations in pediatric cancers. Methods: To explore germline SFs in pediatric cancer patients, we analyzed germline whole-genome sequencing (WGS) data for patients with rare, relapsed, refractory, and metastatic childhood cancers enrolled in the SickKids Cancer Sequencing Program (KiCS). We developed a custom analysis pipeline to identify germline single-nucleotide variants and indels deemed SFs, auto-classify their pathogenicity (ex. P, LP, or VUS) using CharGer, filter for PanCanAtlas-indicated cancer predisposition genes, and sort the remaining variants by cancer and non-cancer associations. Results: The KiCS cohort (n = 511) encompassed over 133 different tumor types; the median age of participants was 14 years (SD = 10.27) and 55% of patients were male. Ongoing work in our lab will catalogue the frequency and distribution of SFs in KiCS and analyze germline variants by subgroup (gene, tumor subtype, stage, demographics, gene function). We will also compare SF prevalence in KiCS to the general population using the gnomAD dataset. Results from preliminary analyses of this cohort will be presented. Significance: SFs/VUSs are under-utilized in cancer management. This work advances the holistic understanding of germline genomics in pediatric oncology and the roles of SFs in disease. Future studies will evaluate SFs by patient ancestry and validate cancer associations through the evaluation of allelic imbalance/loss of heterozygosity in matched tumor genomes. Citation Format: Safa Majeed, Stephenie Prokopec, Brianne Laverty, Vallijah Subasri, Michael Taylor, Yvonne Bombard, Trevor Pugh, Adam Shlien, Anita Villani, David Malkin. Investigating secondary findings in a pediatric cancer cohort: preliminary findings. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6545.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".