Induction of T-cell Immunity Overcomes Complete Resistance to PD-1 and CTLA-4 Blockade and Improves Survival in Pancreatic Carcinoma
Bibliographic record
Abstract
Disabling the function of immune checkpoint molecules can unlock T-cell immunity against cancer, yet despite remarkable clinical success with monoclonal antibodies (mAb) that block PD-1 or CTLA-4, resistance remains common and essentially unexplained. To date, pancreatic carcinoma is fully refractory to these antibodies. Here, using a genetically engineered mouse model of pancreatic ductal adenocarcinoma in which spontaneous immunity is minimal, we found that PD-L1 is prominent in the tumor microenvironment, a phenotype confirmed in patients; however, tumor PD-L1 was found to be independent of IFNγ in this model. Tumor T cells expressed PD-1 as prominently as T cells from chronically infected mice, but treatment with αPD-1 mAbs, with or without αCTLA-4 mAbs, failed in well-established tumors, recapitulating clinical results. Agonist αCD40 mAbs with chemotherapy induced T-cell immunity and reversed the complete resistance of pancreatic tumors to αPD-1 and αCTLA-4. The combination of αCD40/chemotherapy plus αPD-1 and/or αCTLA-4 induced regression of subcutaneous tumors, improved overall survival, and conferred curative protection from multiple tumor rechallenges, consistent with immune memory not otherwise achievable. Combinatorial treatment nearly doubled survival of mice with spontaneous pancreatic cancers, although no cures were observed. Our findings suggest that in pancreatic carcinoma, a nonimmunogenic tumor, baseline refractoriness to checkpoint inhibitors can be rescued by the priming of a T-cell response with αCD40/chemotherapy.
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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) | 0.001 | 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".