Abstract 149: Modeling patient-specific <i>CHEK2</i> genetic variants associated with high-risk neuroblastoma
Bibliographic record
Abstract
Abstract Background: Neuroblastoma (NB) is the most common extra-cranial solid tumor in children and accounts for approximately 10% of childhood cancer deaths. Recently, next-generation sequencing of patients enrolled in the SickKids Cancer Sequencing (KiCS) program revealed somatic and germline alterations in genes involved in the DNA damage response (DDR). Multiple germline and somatic pathogenic variants in CHEK2 were detected in patients with NB. Aims and Methods: To better understand potential roles for CHEK2 in NB pathogenesis, we are modeling patient-derived CHEK2 variants in an established transgenic zebrafish model of MYCN-driven NB. Using a CRISPR/Cas9 “knock-in” approach, we have generated stable zebrafish lines that are homozygous and heterozygous for variants of interest with either null, heterozygous, or wild-type tp53 functional status. We aim to assess tumor incidence, growth, and metastasis using direct visualization of disseminated EGFP+ tumor cells, as compared to MYCN over-expression only control fish. Given potential drug sensitivities, we also plan to assess the efficacy of various DDR pathway inhibitors in combination with frontline chemotherapies, which are effective in the treatment of DDR-deficient adult cancers. Results: Targeting chek2 in a tp53 null background had no significant effect on tumor incidence and growth compared to tp53 mutant control animals (p=0.1570 log-rank test). This contrasts with other patient-specific DDR models including gene targeting brca2 (p=0.0235, log-rank test), atm (p<0.001, log-rank test), and bard1 (p<0.001, log-rank test), potentially highlighting Tp53-dependent roles for Chek2 in NB formation in vivo. Through functional characterization of patient-specific CHEK2 variants in NB progression, our work will provide important preclinical information that will inform future diagnostics and therapeutic strategies for patients with high-risk NB. Citation Format: Xueting Xiong, Sarah Cohen-Gogo, Anita Villani, Adam Shlien, Meredith S. Irwin, Madeline Hayes. Modeling patient-specific CHEK2 genetic variants associated with high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 149.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".