Peri-tumoural lymphocyte neighbourhoods predict longer survival in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest cancers and has limited options for treatment. Low immune infiltration, desmoplastic stroma, and poor tumour immunogenicity are all expected to contribute to PDAC's rapid progression and limited response to existing immunotherapies. PDAC tumours are mosaics of different sub-tumour microenvironments, including some that do contain immune cells. We hypothesized that increasing frequency of lymphocyte:tumour interactions would correlate with lengthened survival for patients with PDAC. Methods: Using multiplex immunofluorescence, digital pathology, and computational analyses, we profiled the spatial distribution, co-localization, and neighbourhood architecture of immune cells in tumours from 73 patients with PDAC. Results: Higher densities of CD3+CD8- (CD4+) T cells were associated with improved five-year overall survival, particularly when enriched at the tumour-stroma boundary (i.e. peritumoural). CD3+CD8- T cells, CD8+ T cells (CD3+CD8+), and B cells (CD20+) frequently co-infiltrated and co-localized, forming distinct immune neighbourhoods indicative of organized adaptive immunity. We defined three common immune neighbourhoods within PDAC (1) Macrophage dominant; (2) T cell dominant; and (3) Disorganized. With greater tumour and peri-tumoural area represented by the T cell dominant neighbourhood, overall survival was increased. The other neighbourhoods were not significantly associated with outcomes. Conclusion: Immune cells self-assemble into recurring patterns in PDAC. The presence of T cell dominant neighbourhoods, which we interpret as supporting ongoing immune activation, best predict lengthened survival. Spatially organized immune interactions may serve as prognostic indicators and inform future studies for immunotherapies in PDAC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".