Comprehensive characterisation of pancreatic ductal adenocarcinoma with microsatellite instability: histology, molecular pathology and clinical implications
Bibliographic record
Abstract
Objective Recently, tumours with microsatellite instability (MSI)/defective DNA mismatch repair (dMMR) have gained considerable interest due to the success of immunotherapy in this molecular setting. Here, we aim to clarify clinical-pathological and/or molecular features of this tumour subgroup through a systematic review coupled with a comparative analysis with existing databases, also providing indications for a correct approach to the clinical identification of MSI/dMMR pancreatic ductal adenocarcinoma (PDAC). Design PubMed, SCOPUS and Embase were searched for studies reporting data on MSI/dMMR in PDAC up to 30 November 2019. Histological and molecular data of MSI/dMMR PDAC were compared with non-MSI/dMMR PDAC and with PDAC reference cohorts (including SEER database and The Cancer Genome Atlas Research Network - TCGA project). Results Overall, 34 studies with 8323 patients with PDAC were included in the systematic review. MSI/dMMR demonstrated a very low prevalence in PDAC (around 1%–2%). Compared with conventional PDAC, MSI/dMMR PDAC resulted strongly associated with medullary and mucinous/colloid histology (p<0.01) and with a KRAS / TP53 wild-type molecular background (p<0.01), with more common JAK genes mutations. Data on survival are still unclear. Conclusion PDAC showing typical medullary or mucinous/colloid histology should be routinely examined for MSI/dMMR status using specific tests (immunohistochemistry, followed by MSI-PCR in cases with doubtful results). Next-generation sequencing (NGS) should be adopted either where there is limited tissue or as part of NGS tumour profiling in the context of precision oncology, acknowledging that conventional histology of PDAC may rarely harbour MSI/dMMR.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".