Abstract IA012: Generation and Mining of a Pediatric-focused Cancer Cell Line Atlas to Define Druggable Genetic Interactions in Childhood Malignancies
Bibliographic record
Abstract
Abstract Pediatric solid and central nervous system tumors are the primary cause of cancer-related fatalities in children. Discovering novel targeted therapies requires utilizing pediatric cancer models that accurately mirror the patient's illness. However, the creation and evaluation of these models have significantly trailed adult cancer research, emphasizing the pressing demand for pediatric-centric cell line repositories. Here, we establish a centralized collection of over 450 childhood cancer cell lines. We subjected over 250 of these cell lines to comprehensive multi-omics analyses (including DNA sequencing, RNA sequencing, and DNA methylation analysis), while concurrently conducting pharmacological screenings and genetic CRISPR-Cas9 loss-of-function assays to unveil pediatric-specific treatment avenues and biomarkers. Machine learning approaches were then applied to uncover genotype-phenotype relationships and synthetic lethal interactions. Our endeavor sheds light on the specific vulnerabilities of pathways in molecularly characterized pediatric tumor subclasses and reveals clinically relevant therapeutic opportunities linked with biomarkers. We offer access to the cell line data and resources through an open-access portal. Citation Format: Ron Firestein. Generation and Mining of a Pediatric-focused Cancer Cell Line Atlas to Define Druggable Genetic Interactions in Childhood Malignancies [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr IA012.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".