Spectrum of gross deletions and insertions in theRB1 gene in patients with retinoblastoma and association with phenotypic expression
Bibliographic record
Abstract
Quantitative multiplex PCR and genomic real-time PCR were used to complete an RB1 mutation analysis in 57 of 433 and 72 of 262 patients with hereditary and isolated unilateral retinoblastoma, respectively. These patients were selected because in previous analyses, which focused mainly on the identification of point mutations, no RB1 mutation was found. We identified gross deletions and insertions in peripheral blood DNA from 26 of 57 patients (46%) with hereditary retinoblastoma, and in six of 72 patients (8.3%) with isolated unilateral disease. In addition, we identified 32 somatic mutations in tumor DNA from 31 of 72 patients (43%) with isolated unilateral retinoblastoma. Together with our previous results, we found that gross RB1 alterations were present in the peripheral blood DNA from 65 of 433 (15%) and 17 of 262 (6.5%) patients with bilateral or familial and isolated unilateral retinoblastoma, respectively. Including reported gross deletions, an analysis of the frequency of breakpoints per intron length shows higher densities in introns 13, 16, 23, and 24. Genotype-phenotype analyses showed that on the whole, carriers of gross deletions develop fewer retinoblastomas compared to patients who are heterozygous for other types of RB1 null mutations. Specifically, carriers of cytogenetic and submicroscopic whole gene deletions often have unilateral tumors only. By contrast, almost all patients with gross deletions with one breakpoint in RB1 have bilateral retinoblastoma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".