Characterizing Microsatellite Instability and Chromosome Instability in Interval Colorectal Cancers
Bibliographic record
Abstract
There are a substantial portion of colorectal cancers (CRCs), termed interval CRCs (I-CRCs), that are diagnosed shortly after a negative colonoscopy (i.e., no detectable polyps or CRC) and before recommended follow-up screening. The underlying cause(s) accounting for I-CRCs remain poorly understood, but may involve aberrant biology that drives genome instability. Genetic defects inducing genome instability are pathogenic events that lead to the development and progression of traditional sporadic (Sp-) CRCs. Classically, there are two genome instability pathways that give rise to virtually all Sp-CRCs, chromosome instability (CIN; ~85% of Sp-CRCs) and microsatellite instability (MSI; ~15% of Sp-CRCs); however, the contribution MSI and CIN have in I-CRCs is only beginning to emerge. To date, no study has simultaneously evaluated both MSI and CIN within an I-CRC cohort, and thus we sought to determine and compare the prevalence of MSI and/or CIN within population-based I-CRC and matched Sp-CRC cohorts. MSI status was established using a clinically validated, immunohistochemical approach that assessed the presence or absence of four proteins (MLH1, MSH2, MSH6 and PMS2) implicated in MSI. By combining the MSI results of the current study with those of our previous CIN study, we provide unprecedented insight into the prevalence of MSI and/or CIN between and within Sp- and I-CRCs. Our data show that MSI+ tumors are 1.5-times more prevalent within I-CRCs than Sp-CRCs in a population-based setting and further show that CIN+/MSI+ I-CRCs occur at similar frequency as CIN+/MSI+ Sp-CRCs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".