Clinical and genomic characterisation of mismatch repair deficient pancreatic adenocarcinoma
Bibliographic record
Abstract
Objective To describe the clinical, pathological and genomic characteristics of pancreatic cancer with DNA mismatch repair deficiency (MMRD) and proficiency (MMRP). Design We identified patients with MMRD and MMRP pancreatic cancer in a clinical cohort (N=1213, 519 with genetic testing, 53 with immunohistochemistry (IHC)) and a genomic cohort (N=288 with whole-genome sequencing (WGS)). Results 12 out of 1213 (1.0%) in the clinical cohort were MMRD by IHC or WGS. Of the 14 patients with Lynch syndrome, 3 (21.4%) had an MMRP pancreatic cancer by IHC, and 4 (28.6%) were excluded because tissue was unavailable for testing. MMRD cancers had longer overall survival after surgery (weighted HR after coarsened exact matching 0.11, 95% CI 0.02 to 0.78, p=0.001). One patient with an unresectable MMRD cancer has an ongoing partial response 3 years after starting treatment with PD-L1/CTLA-4 inhibition. This tumour showed none of the classical histopathological features of MMRD. 9 out of 288 (3.1%) tumours with WGS were MMRD. Despite markedly higher tumour mutational burden and neoantigen loads, MMRD cancers were significantly less likely to have mutations in usual pancreatic cancer driver genes like KRAS and SMAD4 , but more likely to have mutations in genes that drive cancers with microsatellite instability like ACV2RA and JAK1 . MMRD tumours were significantly more likely to have a basal-like transcriptional programme and elevated transcriptional markers of immunogenicity. Conclusions MMRD pancreatic cancers have distinct clinical, pathological and genomic profiles. Patients with MMRD pancreatic cancer should be considered for basket trials targeting enhanced immunogenicity or the unique genomic drivers in these malignancies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".