Abstract B098: Tumor cell-derived IL-4 suppresses tumor growth and metastasis
Bibliographic record
Abstract
Abstract Interleukin 4 (IL-4) is a cytokine that, among other actions, can induce macrophages to undergo alternative activation and polarize to M2 macrophages which have been reported to promote tumorigenesis and metastasis. IL-4 also promotes the formation of multinucleated giant cells from macrophages in vitro and participates in the development of the foreign body reaction in vivo. AC2M2 cells, a highly metastatic mouse carcinoma cell line, were transduced with retroviruses expressing IL-4 (IL-4-AC2M2) or empty vector control (EV-AC2M2). Co-culture experiments showed that AC2M2-derived IL-4 polarized macrophages to express the M2 marker, arginase 1. EV- and IL-4-AC2M2 cells grew at the same rate in vitro. However, mice injected with IL-4-AC2M2 cells grew tumors at a significantly reduced rate as compared to control mice injected with EV-AC2M2 cells in an orthotopic mouse mammary tumor engraftment model. IL-4 expression also correlated with elimination of lung metastasis. Reduced primary tumor growth and complete abolishment of lung metastasis in the IL-4 group correlated with a 30-fold increase of tumor associated macrophage populations and macrophage phagocytosis of tumor cells. Thus, tumor-derived IL-4 suppressed tumorigenesis and lung metastasis by activating macrophage phagocytosis in the tumor microenvironment; suggesting that IL-4 could be a good candidate for immunotherapy. Note:This abstract was not presented at the conference. Citation Format: Connie Shengnan Zhang, Peter A. Greer. Tumor cell-derived IL-4 suppresses tumor growth and metastasis. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr B098.
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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) | 0.001 | 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) | 0.004 | 0.001 |
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".