Abstract B068: NL-4C: The Dutch Comprehensive Childhood Commons, a resource to tackle pediatric cancer worldwide
Bibliographic record
Abstract
Abstract 1. Introduction. Cancer is one of the primary causes of death among children. Despite recent progress in treatment, the prognosis for patients with high-risk and relapse disease is still very poor, while survivors commonly suffer from adverse treatment-related effects. Identifying the genetic aberrations underlying the different types of pediatric cancer could help understand tumor biology, drive drug development, and improve prognosis overall. We hereby describe the NL-4C: the Dutch Comprehensive Childhood Cancer Commons, a research infrastructure initiative that aims to collect over 4,000 pediatric cancer genomes from the Dutch population to develop new insights into tumor biology. 2. Description. The NL-4C platform is hosted by the Prinses Máxima Center, the Dutch national institute for pediatric oncology that receives, on average 600 new cases every year. At the Máxima, molecular characterization of all consented patients is performed using next generation sequencing techniques including whole genome (WGS), whole exome (WES) and RNA sequencing. The latter two are standard of care, and used in our precision medicine program MaxPM. NL-4C will build up on this, and offer 3 core research infrastructure components: 1) a large collection of pediatric genomes with harmonized downstream computational analyses such as germline and somatic single-nucleotide variants and indels, copy number and larger structural variants. 2) A patient data registry with baseline and clinical annotations; all these data will be made available through, 3) a 3-tiered cloud-based portal tailored for general and scientific audiences to foster international collaboration. Importantly, NL-4C adheres to strict data protection and privacy regulations to guarantee that patient information is safely used in the cloud and patients’ rights are preserved. 3. Results. The NL-4C initiative has taken the first steps to deliver a working platform: on April 2024, the first 100 genomes were placed within the Google Cloud tenant of the Prinses Máxima Center. By September 2024, we anticipate that the number of genomes available in the cloud will surpass the 1,000 mark,we will have released the first version of computational pipelines and resulting data, and provide a minimal data portal.4. Discussion Through the collection of genomic and clinical data of Dutch pediatric cancer patients, we aim to join efforts of similar precision oncology programs worldwide, and develop a large federated, harmonized data resource to drive pediatric cancer research, help characterize tumor subtypes, identify actionable events, and improve treatment outcome overall. NL-4C is a scientific research infrastructure project funded by the NWO (Nederlandse Organisatie voor Wetenschappelijk Onderzoek or Dutch Research Council, 2023-2028). Citation Format: Karina C. Borja Jiménez, Bastiaan B.J. Tops, Jayne H. Hehir-Kwa, Hinri H.D. Kerstens, Harm van Tinteren, Harriët F.A. Zoon, Patrick C.W. Kemmeren. NL-4C: The Dutch Comprehensive Childhood Commons, a resource to tackle pediatric cancer worldwide [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 B068.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.161 | 0.049 |
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".