Identifying Somatic Mosaicism for Tuberous Sclerosis Complex by Targeted Next-Generation Sequencing
Bibliographic record
Abstract
Background: Tuberous sclerosis complex (TSC) is a rare disease typically manifested with hamartomas affecting the skin, heart, brain, liver, and kidney. However, 15-20% of patients display mild clinical features suggestive but not diagnostic of TSC, and often they have no pathogenic TSC1 and TSC2mutation detected (NMD). Here, we report our study of a cohort of patients with mild clinical features suspicious of TSC somatic mosaicism (SM) using Next Generation Sequencing (NGS). Methods: We performed targeted gene panel screen by NGS using DNA samples from blood, buccal mucosa, and urinary epithelial cells (when available) and a minor allele frequency of 1% cut-off to detect mosaicism. Standard algorithms for sequence alignment, base calling, and QC filtering were applied to identify rare (MAF ≤1%) deleterious variants of high and moderate impact as predicted by multiple predictive algorithms. All potential pathogenic mosaic TSC1 and TSC2 variants were validated by a novel in-house assay (Mosaic Detection by Enrichment of Mutant Allele; MODEMA) or droplet digital PCR. Results: From a clinical review of 80 pts with confirmed or possible TSC, 18 patients with mild disease suspicious of TSC SM were sequenced. We found germline missense TSC1/TSC2 mutations in 5 patients, mosaic TSC1/TSC2 mutations in 9 patients in whom 7 were validated by MODEMA and/or digital PCR, and 4 with NMD. Patients with confirmed TSC SM were predominantly young female; all had multiple renal angiomyolipomas and few extra-renal clinical features. Conclusions: Patients with mild clinical features suggestive but not diagnostic of TSC can be caused by missense or mosaic TSC1/TSC2 mutations. The diagnosis of TSC SM has important implications for genetic counselling and clinical prognostication, and can be improved by NGS.
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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 | 0.001 |
| 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".