Differences in MSI phenotype between cancer types using a new PCR-based pan-cancer biomarker panel.
Bibliographic record
Abstract
e13145 Background: A multiplexed biomarker panel was developed for detection of MSI that is more sensitive than currently available systems. Preliminary data showed an increased MSI sensitivity for colon polyps, endometrial (EC) and skin cancers. This study expands that finding to 14 different types of cancer. Methods: Selection of the best microsatellite markers was done using a cohort of 160 patients ≤55 years with ≥1 colon polyp and 100 EC patients ≤ 50 years. To validate the new panel, a cohort of 100 Lynch syndrome CRC, 100 sporadic MSI-High CRC, 100 sporadic MSI stable CRC and 219 extra-colonic cancers from the Colon Cancer Family Registry were tested for MSI. Samples were screened using a new 8-marker panel and Promega’s MSI Analysis System. Mismatch repair (MMR) gene mutation status and expression were determined. To compare the relative sensitivity of the 8-marker panel across all cancer types we developed a quantitative “MSI-score” for each cancer type. Results: Considerable differences in MSI phenotypes were observed between cancer types and a new approach to scoring MSI in extra-colonic cancers is proposed. Relative intensity of the MSI phenotype was characterized by MSI-Scores ranging from high of 66 (small intestine) to 6 (brain). The mutations in MSI samples were significantly more common and allele size shifts larger with the new biomarkers, making MSI classification more accurate and robost. MSI, IHC and MMR gene mutation status were highly correlated. Conclusions: The MSI sensitivity of the new biomarker panel for most cancer types was significantly higher than currently available PCR-based MSI systems. Development of the new pan-cancer PCR-based MSI system allows laboratories to leverage established equipment and known analysis techniques to keep turnaround time and costs, quick and affordable compared with emerging NGS-based technologies.
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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 | 0.000 |
| 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) | 0.001 | 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".