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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".