A Highly Sensitive Pan-Cancer Test for Microsatellite Instability
Bibliographic record
Abstract
Microsatellite instability (MSI) is an evolving biomarker for cancer detection and treatment. MSI was first used to identify patients with Lynch syndrome, a hereditary form of colorectal cancer (CRC), but has recently become indispensable in predicting patient response to immunotherapy. To address the need for pan-cancer MSI detection, a new multiplex assay was developed that uses novel long mononucleotide repeat (LMR) markers to improve sensitivity. A total of 469 tumor samples from 20 different cancer types, including 319 from patients with Lynch syndrome, were tested for MSI using the new LMR MSI Analysis System. Results were validated by using deficient mismatch repair (dMMR) status according to immunohistochemistry as the reference standard and compared versus the Promega pentaplex MSI panel. The sensitivity of the LMR panel for detection of dMMR status by immunohistochemistry was 99% for CRC and 96% for non-CRC. The overall percent agreement between the LMR and Promega pentaplex panels was 99% for CRC and 89% for non-CRC tumors. An increased number of unstable markers and the larger size shifts observed in dMMR tumors using the LMR panel increased confidence in MSI determinations. The LMR MSI Analysis System expands the spectrum of cancer types in which MSI can be accurately detected.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".