Frequency of Germline Mutations in Cancer Susceptibility Genes in Malignant Mesothelioma
Bibliographic record
Abstract
PURPOSE: The aim of the current study was to determine the prevalence and clinical predictors of germline cancer susceptibility mutations in patients with malignant mesothelioma (MM). METHODS: We performed targeted capture and next-generation sequencing of 85 cancer susceptibility genes on germline DNA from 198 patients with pleural, peritoneal, and tunica vaginalis MM. RESULTS: Twenty-four germline mutations were identified in 13 genes in 23 (12%) of 198 patients. BAP1 mutations were the most common (n = 6; 25%). The remaining were in genes involved in DNA damage sensing and repair (n = 14), oxygen sensing (n = 2), endosome trafficking (n = 1), and cell growth (n = 1). Pleural site (odds ratio [OR], 0.23; 95% CI, 0.10 to 0.58; P < .01), asbestos exposure (OR, 0.28; 95% CI, 0.11 to 0.72; P < .01), and older age (OR, 0.95; 95% CI, 0.92 to 0.99; P = .01) were associated with decreased odds of carrying a germline mutation, whereas having a second cancer diagnosis (OR, 3.33; 95% CI, 1.22 to 9.07; P = .02) significantly increased the odds. The odds of carrying a mutation in BAP1 (OR, 1,658; 95% CI, 199 to 76,224; P < .001), BRCA2 (OR, 5; 95% CI, 1.0 to 14.7; P = .03), CDKN2A (OR, 53; 95% CI, 6 to 249; P < .001), TMEM127 (OR, 88; 95% CI, 1.7 to 1,105; P = .01), VHL (OR, 51; 95% CI, 1.1 to 453; P = .02), and WT1 (OR, 20; 95% CI, 0.5 to 135; P = .049) were significantly higher in MM cases than in a noncancer control population. Tumor sequencing identified mutations in a homologous recombination pathway gene in 52% (n = 29 of 54). CONCLUSION: A significant proportion of patients with MM carry germline mutations in cancer susceptibility genes, especially those with peritoneal MM, minimal asbestos exposure, young age, and a second cancer diagnosis. These data support clinical germline genetic testing for patients with MM and provide a rationale for additional investigation of the homologous recombination pathway in MM.
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 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 | 0.002 |
| 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".