The Association of Tumor Microsatellite Instability Phenotype with Family History of Colorectal Cancer
Bibliographic record
Abstract
Family history is a strong predictor of colorectal cancer risk; however, a diagnosis of colorectal cancer among first-degree relatives has not been systematically investigated as a function of the colorectal cancer molecular subtypes related to tumor microsatellite instability (MSI) status. We investigated whether the observable familial colorectal cancer risks differed according to tumor MSI subtypes, stratified as MSI-High (>30% instability), MSI-Low (<30% instability), and MSS (no instability). Data from 3,143 population-based colorectal cancer cases from five institutions were assessed for family history according to the Amsterdam criteria and the Bethesda guidelines, age at diagnosis, sex, tumor location, and MSI status. The distribution of patient characteristics by MSI status was compared using polytomous logistic regression. Overall, 2.8% colorectal cancer cases met the Amsterdam criteria and 37% met the Bethesda guidelines. There were 14% MSI-High, 13% MSI-Low, and 73% MSS colorectal cancers. MSI-High (P<0.0001) and MSI-Low tumors (P=0.01) were more proximally located than MSS tumors. MSI-High tumors were more common among females (P<0.001). The highest proportion of MSI-High tumors occurred in cases<40 years of age whereas the age-dependent distribution of MSI-Low tumors was unchanged. MSI-High tumors showed a statistically significant association with increasing numbers of first-degree relatives with colorectal cancer (P=0.002); this association disappeared, however, when MSI-High cases meeting Amsterdam criteria were removed from the analysis. MSI-Low tumors did not show a similar association with family history of colorectal cancer. Familial risk associated with MSI-High tumors is primarily driven by the Amsterdam-criteria patients. MSI-Low tumors may represent a distinct subtype of colorectal cancer with respect to certain epidemiologic variables studied here.
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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 | 0.005 |
| 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) | 0.002 | 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".