Prediction of Lynch Syndrome in Consecutive Patients With Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Lynch syndrome is caused by inherited mutations in DNA mismatch repair genes (primarily MSH2, MLH1, MSH6, and PMS2) and is one of the most prevalent inherited cancer syndromes. Several models have been developed to predict the occurrence of Lynch syndrome in high-risk patients and families, but it is not known how these models compare with one another or how they perform for colorectal cancer patients from the general population. We used data from such patients to test the ability of four models--Leiden, MMRpredict, PREMM(1,2), and MMRpro--to distinguish between those who did and did not carry DNA mismatch repair gene mutations. METHODS: We studied a consecutive series of 725 patients who were younger than 75 years at colorectal cancer diagnosis and whose DNA mismatch repair gene mutation status was available; 18 of the 725 patients carried such a mutation. For each model, we calculated the risk score, compared the observed number of mutations with the expected number, and determined the receiver operating characteristics. All statistical tests were two-sided. RESULTS: Although all four models overestimated the probability of a mutation (range = 1.2- to 4.3-fold), especially in low-risk patients, they could discriminate between carriers and noncarriers of a mismatch repair mutation. The areas under the receiver operating characteristics curves from the four models ranged from 0.91 to 0.96. Carriers of mutations in the MSH6 or PMS2 genes had lower risk scores than carriers of MSH2 or MLH1 mutations. For example, the MMRpredict model gave median risk scores of 24% and 94% (P < .015) for MSH6-PMS2 and MSH2-MLH1 mutation carriers, respectively. For the Leiden, MMRpredict, and PREMM(1,2) models, correcting the risk scores for bias introduced by family size improved their power to discriminate between carriers and noncarriers. After correcting for family size, the best model was MMRpredict, which achieved a sensitivity of 94% (95% confidence interval [CI] = 73% to 99%) and a specificity of 91% (95% CI = 88% to 93%) and identified a smaller proportion of patients than the revised Bethesda criteria as those who should undergo additional molecular or immunohistochemical testing (11% vs 50%). CONCLUSION: MMRpredict was the best-performing model for identifying colorectal cancer patients who are at high risk of carrying a DNA mismatch repair gene mutation and thus should be screened for Lynch syndrome.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| 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".