Abstract 554: Crosstalk between TNF-α and IGF signalling increases IL-6 production and protects tumor cells from TNF-α-induced apoptosis in the liver
Bibliographic record
Abstract
Abstract Background: Liver metastasis is a common and often fatal occurrence in cancer patients. A better understanding of the biology of liver metastasis is essential to improve survival statistics for these patients. We have previously shown that metastatic lung and colon carcinoma cells that invade the liver initiate a rapid host inflammatory response that entails increased production of cytokines TNF-α and IL-1 and results in increased tumor adhesion, transmigration and metastasis. TNF-α is a pleiotropic cytokine that can also induce tumor cell apoptosis. We hypothesized that the ability of tumor cells to form liver metastases in the face of increased local TNF-α levels is due to an acquired resistance to the tumoricidal effects of TNF-α. The objective of the present study was therefore to identify molecular mechanism(s) that regulate the tumor cell response and sensitivity to TNF-α. Methodology: We used a Lewis lung carcinoma-based tumor model consisting of poorly metastatic subline M-27 cells in which ectopic expression of the type 1 insulin-like growth factor receptor (IGF-IR) markedly increased the liver-metastasizing potential of the cells. TNF-α-mediated signalling in these (M-27IGFIR) and wild-type (M-27) cells were analyzed by a combination of Western blotting, electrophoretic mobility shift assays, cytokine profiling, immunofluorescence labelling and flow cytometry. Results: We found that IGF-I receptor overexpression in the tumor cells altered TNF-α-mediated signalling by accelerating TNF-α-induced IκBα phosphorylation and degradation and enhancing nuclear translocation of NFκB. This resulted in increased IL-6 production in M-27IGFIR, but not M-27 cells in response to TNF-α. IL-6 expression could be further augmented by co-stimulation with IGF-I, an effect that was mediated via PI3-K signalling. Furthermore, the level of activated STAT-3 in M-27IGFIR cells was significantly increased, suggesting that IL-6 could activate STAT-3 in these cells through an autocrine mechanism. Finally, when apoptosis induction by TNF-α in these cells was compared using antibodies to cleaved caspase 3, a significant reduction in the proportion of apoptotic cells was seen in TNF-α-treated M-27IGFIR as compared to M-27 cells. Conclusions: Taken together, the results suggest that overexpression of IGF-IR altered TNF-α signalling in the tumor cells, shifting the response towards increased IL-6 production and autocrine STAT-3 activation, thereby providing the cells with a survival advantage in the presence of TNF-α. Supported by Canadian Institute for Health Research grant MOP- 81201 (PB) and a McGill University Health Center Research Institute fellowship (SL). Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 554.
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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) | 0.002 | 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".