Abstract 5212: Metabolic reprogramming of radiation fibrosis using adipose derived stromal cells
Bibliographic record
Abstract
Abstract Background: Soft tissue radiation fibrosis (RF) affects up to 70% of cancer survivors post-radiotherapy. RF is an irreversible and progressive side effect of radiotherapy characterized by poor tissue elasticity and increased ECM deposition, clinically translating to increased morbidity due to hardening, distortion, and pain. Recent evidence has highlighted the potential role of metabolic alterations in the onset and progression of fibrosis. Adipose derived stromal cells (ADSCs) have been used effectively for enhancing complex wound repair in a number of clinical trials. The therapeutic effect of ADSCs has been attributed to its secretion of paracrine factors, which can regulate the metabolism of target cells. Even so, the metabolic effects of ADSCs on target cells is not well described. Methods and Results: Transcriptomic profiling and targeted metabolomics of human radiated tissue and a murine model of RF revealed that suppression of fatty acid oxidation (FAO) is hallmark of RF. TGF-B, a master regulator of fibrosis, has a large effect in suppressing FAO in primary human fibroblasts in vitro through downregulating genes in the PPAR pathway, a major regulatory pathway for FAO, and through inhibiting oxidation of the long chain fatty acid, palmitic acid (p<0.05, t-test). Treatment of TGF-B stimulated fibroblasts with ADSC conditioned media resulted in an improvement in FAO and a reduction in fibronectin, collagen-1, and PAI-1 protein levels, three of major contributors to fibrosis. Transplantation of ADSCs into murine RF resulted in a metabolic shift back to FAO and a significant reduction in RF both functionally (percentage leg contracture 36% vs 26%, 2-way ANOVA < 0.05) and histologically through image analysis of trichrome staining (p<0.01, t-test). Pathway analysis of significant genes altered by ADSCs revealed an upregulation of the PPAR pathway was the most significant effect of ADSC transplantation. Untargeted metabolomics confirmed that FAO metabolites were significantly upregulated with ADSC transplantation. To confirm the importance of metabolic regulation by ADSCs, we utilized a pharmacogenomics strategy to uncover a compound that mimicked the effect of ADSCs. Drug A ranked highest and replicated the anti-fibrotic and pro-FAO effects of ADSCs both on TGF-B stimulated primary fibroblasts and in murine RF. Etomoxir, a FAO inhibitor, inhibited the effect of ADSCs and Drug A in regulating protein levels of fibronectin and collagen, revealing that ADSCs and Drug A require an intact FAO pathway to exert their anti-fibrotic effects. Conclusions: Inhibition of FAO is a novel hallmark of RF. ADSC-mediated metabolic reprogramming resulted in an enhancement of FAO and a reduction in RF through upregulating the PPAR pathway. ADSC-directed pharmacogenomics uncovered Drug A, which mimicked the pro-FAO and anti-fibrotic effects of ADSCs. Our research has highlighted the importance of reversing metabolic aberrations to reduce RF. Citation Format: Xiao Zhao, Laleh Soltan Ghoraie, Pamela Psarianos, Kenneth Yip, Laurie Ailles, Benjamin Haibe-Kains, Fei-Fei Liu. Metabolic reprogramming of radiation fibrosis using adipose derived stromal cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5212. doi:10.1158/1538-7445.AM2017-5212
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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) | 0.005 | 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".