High cumulative risks of cancer in patients with <i>PTEN</i> hamartoma tumour syndrome
Bibliographic record
Abstract
BACKGROUND: PTEN hamartoma tumour syndrome (PHTS) encompasses several clinical syndromes with germline mutations in the PTEN tumour suppressor gene, including Cowden syndrome which is characterised by an increased risk of breast and thyroid cancers. Because PHTS is rare, data regarding cancer risks and genotype-phenotype correlations are limited. The objective of this study was to better define cancer risks in this syndrome with respect to the type and location of PTEN mutations. METHODS: 154 PHTS individuals with a deleterious germline PTEN mutation were recruited from the activity of the Institut Bergonié genetic laboratory. Detailed phenotypic information was obtained for 146 of them. Age and sex adjusted standardised incidence ratio (SIR) calculations, cumulative cancer risk estimations, and genotype-phenotype analyses were performed. RESULTS: Elevated SIRs were found mainly for female breast cancer (39.1, 95% CI 24.8 to 58.6), thyroid cancer in women (43.2, 95% CI 19.7 to 82.1) and in men (199.5, 95% CI 106.39 to 342.03), melanoma in women (28.3, 95% CI 7.6 to 35.4) and in men (39.4, 95% CI 10.6 to 100.9), and endometrial cancer (48.7, 95% CI 9.8 to 142.3). Cumulative cancer risks at age 70 were 85% (95% CI 70% to 95%) for any cancer, 77% (95% CI 59% to 91%) for female breast cancer, and 38% (95% CI 25% to 56%) for thyroid cancer. The risk of cancer was two times greater in women with PHTS than in men with PHTS (p<0.05). CONCLUSIONS: This study shows a considerably high cumulative risk of cancer for patients with PHTS, mainly in women without clear genotype-phenotype correlation for this cancer risk. New recommendations for the management of PHTS patients are proposed.
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 | 0.001 |
| 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".