<i>BARD1</i>germline variants induce haploinsufficiency and DNA repair defects in neuroblastoma
Bibliographic record
Abstract
BACKGROUND: High-risk neuroblastoma is a complex genetic disease that is lethal in more than 50% of patients despite intense multimodal therapy. Through genome-wide association studies (GWAS) and next-generation sequencing, we have identified common single nucleotide polymorphisms and rare, pathogenic or likely pathogenic germline loss-of-function variants in BARD1 enriched in neuroblastoma patients. The functional implications of these findings remain poorly understood. METHODS: We correlated BARD1 genotype with expression in normal tissues and neuroblastomas, along with the burden of DNA damage in tumors. To validate the functional consequences of germline pathogenic or likely pathogenic BARD1 variants, we used CRISPR-Cas9 to generate isogenic neuroblastoma (IMR-5) and control (RPE1) cellular models harboring heterozygous BARD1 loss-of-function variants (R112*, R150*, E287fs, and Q564*) and quantified genomic instability in these cells via next-generation sequencing and with functional assays measuring the efficiency of DNA repair. RESULTS: Both common and rare neuroblastoma-associated BARD1 germline variants were associated with lower levels of BARD1 mRNA and an increased burden of DNA damage. Using isogenic heterozygous BARD1 loss-of-function variant cellular models, we functionally validated this association with inefficient DNA repair. BARD1 loss-of-function variant isogenic cells exhibited reduced efficiency in repairing Cas9-induced DNA damage, ineffective RAD51 focus formation at DNA double-strand break sites, and enhanced sensitivity to cisplatin and poly (ADP-ribose) polymerase (PARP) inhibition both in vitro and in vivo. CONCLUSIONS: Taken together, we demonstrate that germline BARD1 variants disrupt DNA repair fidelity. This is a fundamental molecular mechanism contributing to neuroblastoma initiation that may have important therapeutic implications.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".