Role of tumour molecular and pathology features to estimate colorectal cancer risk for first-degree relatives
Bibliographic record
Abstract
OBJECTIVE: To estimate risk of colorectal cancer (CRC) for first-degree relatives of CRC cases based on CRC molecular subtypes and tumour pathology features. DESIGN: We studied a cohort of 33,496 first-degree relatives of 4853 incident invasive CRC cases (probands) who were recruited to the Colon Cancer Family Registry through population cancer registries in the USA, Canada and Australia. We categorised the first-degree relatives into four groups: 28,156 of 4095 mismatch repair (MMR)-proficient probands, 2302 of 301 MMR-deficient non-Lynch syndrome probands, 1799 of 271 suspected Lynch syndrome probands and 1239 of 186 Lynch syndrome probands. We compared CRC risk for first-degree relatives stratified by the absence or presence of specific tumour molecular pathology features in probands across each of these four groups and for all groups combined. RESULTS: Compared with first-degree relatives of MMR-proficient CRC cases, a higher risk of CRC was estimated for first-degree relatives of CRC cases with suspected Lynch syndrome (HR 2.06, 95% CI 1.59 to 2.67) and with Lynch syndrome (HR 5.37, 95% CI 4.16 to 6.94), but not with MMR-deficient non-Lynch syndrome (HR 1.04, 95% CI 0.82 to 1.31). A greater risk of CRC was estimated for first-degree relatives if CRC cases were diagnosed before age 50 years, had proximal colon cancer or if their tumours had any of the following: expanding tumour margin, peritumoral lymphocytes, tumour-infiltrating lymphocytes or synchronous CRC. CONCLUSIONS: Molecular pathology features are potentially useful to refine screening recommendations for first-degree relatives of CRC cases and to identify which cases are more likely to be caused by genetic or other familial factors.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".