Stroma secreted IL6 selects for “stem-like” population and alters pancreatic tumor microenvironment by reprogramming metabolic pathways
Bibliographic record
Abstract
Pancreatic adenocarcinoma is a devastating disease with an abysmal survival rate of 9%. A robust fibro-inflammatory and desmoplastic stroma, characteristic of pancreatic cancer, contribute to the challenges in developing viable therapeutic strategies in this disease. Apart from constricting blood vessels and preventing efficient drug delivery to the tumor, the stroma also contributes to the aggressive biology of cancer along with its immune-evasive microenvironment. In this study, we show that in pancreatic tumors, the developing stroma increases tumor initiation frequency in pancreatic cancer cells in vivo by enriching for CD133 + aggressive "stem-like" cells. Additionally, the stromal fibroblasts secrete IL6 as the major cytokine, increases glycolytic flux in the pancreatic tumor cells, and increases lactate efflux in the microenvironment via activation of the STAT signaling pathway. We also show that the secreted lactate favors activation of M2 macrophages in the tumor microenvironment, which excludes CD8 + T cells in the tumor. Our data additionally confirms that the treatment of pancreatic tumors with anti-IL6 antibody results in tumor regression as well as decreased CD133 + population within the tumor. Furthermore, inhibiting the lactate efflux in the microenvironment reduces M2 macrophages, and makes pancreatic tumors more responsive to anti-PD1 therapy. This suggests that stromal IL6 driven metabolic reprogramming plays a significant role in the development of an immune-evasive microenvironment. In conclusion, our study shows that targeting the metabolic pathways affected by stromal IL6 can make pancreatic tumors amenable to checkpoint inhibitor therapy.
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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 | 0.000 |
| 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".