Oncologic surveillance for subjects with biallelic mismatch repair gene mutations: 10 year follow‐up of a kindred
Bibliographic record
Abstract
BACKGROUND: Heterozygous germline mutations in DNA mismatch repair (MMR) genes cause Lynch syndrome. Biallelic MMR mutations cause a distinct syndrome characterized by brain tumors, lymphoid malignancies, and gastrointestinal cancers during childhood. These children usually succumb to multiple cancers before adulthood. We developed a surveillance protocol aiming at early detection for these individuals and report the 10-year experience with a kindred. METHODS: On the basis of genetic testing and early age tumors, the kindred started a cancer surveillance protocol based on the crude estimates of cancer risks and available cancer screening: imaging, endoscopy, and hematologic tests. RESULTS: Over the 10-year follow-up period, the screening protocol detected 15 tumors. These included three high-grade adenomatous colonic polyps and two colon cancers. In one child, MRI revealed an asymptomatic anaplastic astrocytoma which was treated by complete resection and radiation. All three cancers identified during surveillance were small and asymptomatic at diagnosis. The two sisters are currently 16 and 18 years of age with no evidence of malignant disease. Both parents have annual colonoscopies and the father at 43 years had two colonic adenomatous polyps. CONCLUSIONS: We report on the long-term outcome in patients with biallelic MMR mutations who benefited from prophylactic cancer surveillance. Genetic screening and subsequent surveillance led to earlier recognition of asymptomatic tumors at stages more amenable to resection and probable cure. Multicenter collaboration and implementation of surveillance guidelines is necessary to further determine genotype-phenotype correlations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".