A woman with a pleural mesothelioma and an inherited ATM mutation—a case report
Bibliographic record
Abstract
Background: Pleural mesothelioma (PM) is a rare and aggressive malignancy primarily linked to asbestos exposure. Up to 80% of patients have a history of such exposure. Several studies have investigated the possibility of a genetic predisposition for a subgroup of PM patients, but the data remains inconsistent. The ataxia-telangiectasia mutated (ATM) gene, crucial for DNA repair, is implicated in cancer susceptibility, with heterozygous mutations increasing risks, notably in breast and pancreatic cancers. Case Description: We present a case of a 42-year-old woman, with no asbestos exposure history, diagnosed with metastatic PM. Initial treatment with immunotherapy showed limited efficacy. Given the rarity in young females, she opted for genetic testing via “The Screen Project”, revealing a pathogenic ATM mutation. Due to enhanced radiosensitivity in ATM mutation carriers, to reduce adverse events conventional palliative radiotherapy (RT) was chosen over stereotactic hypofractionated RT. A follow-up computed tomography (CT) scan after 4 weeks indicated disease burden reduction. Conclusions: This case highlights the importance of genetic testing in atypical PM cases, guiding treatment decisions tailored to individual genetic profiles. Awareness of ATM mutations can optimize therapeutic strategies, particularly regarding RT choices, in managing this challenging malignancy. Integrating genetic insights into clinical practice holds promise for enhancing treatment outcomes and refining management strategies in PM and related conditions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".