Abstract IA003: Genetic predisposition to pediatric cancer: the long way from discovery to surveillance guidelines
Bibliographic record
Abstract
Abstract Pediatric cancers are estimated to be related to a genetic predisposition in about 8 to 10% of cases. This estimation mostly relies on already discovered cancer predisposing genes. However, the numerous new cancer predisposing genes that have been discovered in the past decade thanks to high throughput sequencing in vast cohort of pediatric patients illustrate that new genes may still be unravelled. Moreover, the frequency of early post-zygotic mosaicism is being readdressed thanks to more dedicated sequencing and research. The actual clinical impact of all those discoveries is the main challenge for the community: defining the penetrance, delineating the cancer spectrum and therefore the appropriate surveillance, and setting up innovative follow-up techniques are necessary. Here, we’ll describe recent findings on brain tumor predisposing genes (ELP1 and SMARCB1, among others) to illustrate the increasing recognition of genetic predisposition in pediatric cancers and how the clinical translation of these laboratory findings remain challenging at clinical and ethical levels. Citation Format: Franck Bourdeaut. Genetic predisposition to pediatric cancer: the long way from discovery to surveillance guidelines [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA003.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".