Targeting the IGF-Axis Potentiates Immunotherapy for Pancreatic Ductal Adenocarcinoma Liver Metastases by Altering the Immunosuppressive Microenvironment
Bibliographic record
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy, resistant to chemotherapy and associated with high incidence of liver metastases and poor prognosis. Using murine models of aggressive PDAC, we show here that in mice bearing hepatic metastases, treatment with the IGF-Trap, an inhibitor of type I insulin-like growth factor receptor (IGF-IR) signaling, profoundly altered the local, immunosuppressive tumor microenvironment in the liver, curtailing the recruitment of myeloid-derived suppressor cells, reversing innate immune cell polarization and inhibiting metastatic expansion. Significantly, we found that immunotherapy with anti-PD-1 antibodies also reduced the growth of experimental PDAC liver metastases, and this effect was enhanced when combined with IGF-Trap treatment, resulting in further potentiation of a T-cell response. Our results show that a combinatorial immunotherapy based on dual targeting of the prometastatic immune microenvironment of the liver via IGF blockade, on one hand, and reversing T-cell exhaustion on the other, can provide a significant therapeutic benefit in the management of PDAC metastases.
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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".