Detection of mosaic<i>RB1</i>mutations in families with retinoblastoma
Bibliographic record
Abstract
The RB1 gene mutation detection rate in 1,020 retinoblastoma families was increased by the use of highly sensitive allele specific-PCR (AS-PCR) to detect low-level mosaicism for 11 recurrent RB1 CGA>TGA nonsense mutations. For bilaterally affected probands, AS-PCR increased the RB1 mutation detection sensitivity from 92.6% to 94.8%. Both RB1 oncogenic changes were detected in 92.7% of sporadic unilateral tumors (357/385); 14.6% (52/357) of unilateral probands with both tumor mutations identified carried one of the tumor mutations in blood. Mosaicism was evident in 5.5% of bilateral probands (23 of 421), in 3.8% of unilateral probands (22 of 572), and in one unaffected mother of a unilateral proband. Half of the mosaic mutations were only detectable by AS-PCR for the 11 recurrent CGA>TGA mutations, and not by standard sequencing. This suggests that significant numbers of low-level mosaics with other classes of RB1 mutations remain unidentified by current technology. We show that the use of linkage analysis in a two-generation retinoblastoma family resulted in the erroneous conclusion that a child carried the parental mutation, because the founder parent was mosaic for the RB1 mutation. Of 142 unaffected parental pairs tested, only one unaffected parent of a proband (0.7%) showed somatic mosaicism for the proband's mutation, in contrast to an overall 4.5% somatic mosaicism rate for retinoblastoma probands, suggesting that mosaicism for an RB1 mutation is highly likely to manifest as 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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".