Microsatellite instability/mismatch repair deficiency in pancreatic cancers: the same or different?
Bibliographic record
Abstract
Pancreatic cancer frustrates patients, clinicians and scientists. Five-year overall survival is the worst among common cancers, stubbornly remaining below 10%.1 Despite large international efforts offering unprecedented insights into pancreatic cancer biology in the last 10 years,2 patients with pancreatic cancer have not experienced the exciting translational advances in screening and treatment recently observed in other cancers, with few notable exceptions.3 DNA mismatch repair deficiency (MMRD) overlaps with the most important current areas of cancer research: precision therapy, which describes treatments that target specific biological features of cancers, and immunotherapy, which are treatments that unleash the native immune system against cancer. MMRD encompasses germline or somatic defects in MLH1, MSH2, MSH6 and PMS2 , leading to distinctive genome-wide alterations such as microsatellite instability and high tumour mutational burden.4 Pembrolizumab, an immune-checkpoint inhibitor of programmed cell death protein 1, was the first approved cancer therapy with the indication defined by a molecular feature, rather than cancer type.5 Microsatellite instability/defective mismatch repair (MSI/dMMR) has been extensively studied in some cancer types, such as colorectal and endometrial cancer, but little is known about this molecular alteration in pancreatic ductal adenocarcinoma (PDAC). Notably, in 2021, two manuscripts related to this topic have been published in two different issues of Gut . The first paper is by Luchini et al , and represents a systematic review of all published material on MSI/dMMR in PDAC, coupled with a comparative analysis with existing databases, such as the Surveillance, Epidemiology and End Results Program (SEER) and the Cancer Genome Atlas (TCGA) project.6 The second manuscript is by Grant et al , and represents the largest original study on MSI/dMMR PDAC, providing new genomic data on this tumour entity, including mutational and transcriptomic profiles.7 A summary image of recent advances on MSI/dMMR in …
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".