Local ablation disrupts immune evasion in pancreatic cancer
Bibliographic record
Abstract
BACKGROUND: Pancreatic cancer (PC) is characterised by late diagnosis, tumour heterogeneity, and a peculiar immunosuppressive microenvironment, leading to poor clinical outcomes. Local ablative techniques have been proposed to treat unresectable PC patients, although their impact on activating the host immune system and overcoming resistance to immunotherapy remains elusive. METHODS: We dissected the immune-modulatory abilities triggered by local ablation in mouse and human PC models and human specimens, integrating phenotypic and molecular technologies with functional assays. RESULTS: Local ablation treatment performed in mice bearing orthotopic syngeneic PC tumours triggered tumour necrosis and a short-term inflammatory process characterised by the prompt increase of HMGB1 plasma levels, coupled with an enhanced amount of circulating and tumour infiltrating myeloid cells and increased MHCII expression in splenic myeloid antigen-presenting cells. Local ablation synergised with immunotherapy to restrict tumour progression and improved the survival of PC-bearing mice by evoking a T lymphocyte-dependent anti-tumour immune response. By integrating spatial transcriptomics with histological techniques, we pinpointed how combination therapy could reshape TME towards an anti-tumour milieu characterised by the preferential entrance and colocalization of activated T lymphocytes and myeloid cells endowed with antigen presentation features instead of T regulatory lymphocytes and CD206-expressing tumour-associated macrophages. In addition, treatment-dependent TME repolarization extended to neoplastic cells, promoting a shift from squamous to a more differentiated classical phenotype. Finally, we validated the immune regulatory properties induced by local ablation in PC patients and identified an association of the short-term treatment-dependent increase of neutrophils, NLR and HMGB1 with a longer time to progression. CONCLUSION: Therefore, local ablation might overcome the current limitations of immunotherapy in PC.
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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 | 0.000 |
| 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 | 0.001 |
| 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".