Abstract IA17: From functional genomics to new targets in pediatric oncology
Bibliographic record
Abstract
Abstract The sequencing of human genomes harkened a new era of pediatric cancer investigations. Indeed, genomic discoveries, such as the identification of ALK mutations in neuroblastoma, Philadelphia-like fusions in acute B-cell acute lymphoblastic leukemia (B-ALL), and NTRK fusions in infantile fibrosarcoma, have led to early successes in the application of genomically informed, targeted therapy for children with cancer. While these new drugs have become an important part of the pediatric cancer armamentarium, many children with cancer still lack targeted therapies. Thus, new therapeutic approaches are still needed. Approaches such as RNA interference and CRISPR-Cas9 have transformed the functionalizing of the genome and offer promise for identifying new cancer therapeutic targets. We have applied genome-scale CRISPR-CAS9 screening to identify novel therapeutic targets in pediatric malignancies through the development of a Pediatric Cancer Dependency Map. We screened the Broad Institute’s Avana library in over 100 pediatric cancer cell lines and compared their dependencies to over 600 nonpediatric cell lines. Novel targets emerging from these efforts will be discussed. Citation Format: Kimberly Stegmaier. From functional genomics to new targets in pediatric oncology [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr IA17.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".