A shower of second hit events as the cause of multifocal renal cell carcinoma in tuberous sclerosis complex
Bibliographic record
Abstract
Tuberous sclerosis complex (TSC) is a genetic disorder characterized by seizures and tumor formation in multiple organs, mainly in the brain, skin, kidney, lung and heart. Renal cell carcinoma (RCC) occurs in ∼3% of TSC patients, and typically develops at age <50. Here we describe genetic findings in two TSC patients with multiple renal tumors, each of whom had the germline mutation TSC2 p.R905Q. The first (female) TSC patient had a left followed by a right nephrectomy at ages 24 and 27. Both kidneys showed multifocal TSC-associated papillary RCC (PRCC). Targeted, next-generation sequencing (NGS) analysis of TSC2 in five tumors (four from the left kidney, one from the right) showed loss of heterozygosity in one tumor, and four different TSC2 point mutations (p.E1351*, p.R1032*, p.R1713H, c.4178_4179delCT) in the other four samples. Only one of the 11 other tumors available from this patient had one of the TSC2 second hit mutations identified. Whole-exome analysis of the five tumors identified a very small number of additional mutated genes, with an average of 3.4 nonsilent coding, somatic mutations per tumor, none of which were seen in >1 tumor. The second (male) TSC patient had bilateral partial nephrectomies (both at age 36), with similar findings of multifocal PRCC. NGS analysis of TSC2 in two of these tumors identified a second hit mutation c.2355+1G>T in one sample that was not seen in other tumors. In conclusion, we report the first detailed genetic analysis of RCCs in TSC patients. Molecular studies indicate that tumors developed independently due to various second hit events, suggesting that these patients experienced a 'shower' of second hit mutations in TSC2 during kidney development leading to this severe phenotype.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".