High-sensitivity sequencing reveals multi-organ somatic mosaicism causing DICER1 syndrome
Bibliographic record
Abstract
BACKGROUND: Somatic mosaicism is being increasingly recognised as an important cause of non-Mendelian presentations of hereditary syndromes. A previous whole-exome sequencing study using DNA derived from peripheral blood identified mosaic mutations in DICER1 in two children with overgrowth and developmental delay as well as more typical phenotypes of germline DICER1 mutation. However, very-low-frequency mosaicism is difficult to detect, and thus, causal mutations can go unnoticed. Highly sensitive, cost-effective approaches are needed to molecularly diagnose these persons. We studied four children with multiple primary tumours known to be associated with the DICER1 syndrome, but in whom germline DICER1 mutations were not detected by conventional mutation detection techniques. METHODS AND RESULTS: We observed the same missense mutation within the DICER1 RNase IIIb domain in multiple tumours from different sites in each patient, raising suspicion of somatic mosaicism. We implemented three different targeted-capture technologies, including the novel HaloPlex(HS) (Agilent Technologies), followed by deep sequencing, and confirmed that the identified mutations are mosaic in origin in three patients, detectable in 0.24-31% of sequencing reads in constitutional DNA. The mosaic origin of patient 4's mutation remains to be unequivocally established. We also discovered likely pathogenic second somatic mutations or loss of heterozygosity (LOH) in tumours from all four patients. CONCLUSIONS: Mosaic DICER1 mutations are an important cause of the DICER1 syndrome in patients with severe phenotypes and often appear to be accompanied by second somatic truncating mutations or LOH in the associated tumours. Furthermore, the molecular barcode-containing HaloPlex(HS) provides the sensitivity required for detection of such low-level mosaic mutations and could have general applicability.
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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 | 0.001 | 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".