Abstract B009: The Dutch childhood cancer genome project: Data-driven precision medicine and research
Bibliographic record
Abstract
Abstract Despite improvements in cure rates, cancer is still the leading cause of disease-related deaths among children in high-income countries. As childhood cancers are rare in comparison to adult cancers, concerted efforts are needed to advance research and improve patient care. In The Netherlands, all childhood cancer care and most research has been concentrated in a single national center, the Princess Máxima Center for Pediatric Oncology. The institute’s centralized position enables uniform generation of data from patients across the country, that through federation with other initiatives can provide a unique contribution to tackle childhood cancer world-wide. Whole-exome sequencing, whole-genome sequencing and RNA-sequencing are routinely performed for diagnostic and research purposes, leading to a representative national dataset with little batch effects. Currently this data collection consists of ∼1000 patient samples with rich and uniform clinical data. This collection is expected to grow to ∼4,000 patient samples by 2028. Here, I will show how we use this rich, harmonized data resource to improve patient care and aid data-driven research into understanding the role of different types of somatic variation in tumor initiation and progression. First, by performing whole-exome sequencing and RNA-sequencing as standard-of-care we provide individualized diagnosis and treatment plans for precision medicine purposes. Second, by investigating complex genomic rearrangements in pediatric solid tumors we show that these occur in approximately half of the tumor samples and are likely highly pathogenic. Third, by performing integrative tumor driver identification, we identify events that are otherwise likely missed through more targeted approaches. Fourth, through the development of M&M, a pan-cancer RNA-seq based classifier we obtain an accuracy of ∼95% in predicting tumor (sub)types across the breadth of (rare) pediatric tumors. Ultimately, we anticipate that the data collection presented here will further facilitate pediatric cancer research and provide an invaluable resource for precision oncology. Citation Format: Joanna von Berg, Ianthe A.E.M. van Belzen, Fleur S.A. Wallis, Anastasia Spinou, Roula Farag, Victoria M. Cruz, Lennart A. Kester, Marco Koudijs, John L. Baker-Hernandez, Alex Janse, Shashi Badloe, Sam de Vos, Marcel Santoso, Eugene T.P. Verwiel, Mark van Tuil, Hindrik H.D. Kerstens, Jayne Y. Hehir-Kwa, Frank C.P. Holstege, Bastiaan B.J. Tops, Patrick Kemmeren. The Dutch childhood cancer genome project: Data-driven precision medicine and research [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 B009.
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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 | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".