Abstract 6080: IL1RAP augments Cysteine metabolism and drives oxidative stress adaptation and lung metastasis in Ewing sarcoma
Bibliographic record
Abstract
Abstract The high oxidative stress cancer cells experience during the metastatic cascade is an important determinant of metastasis. How cancer cells adapt to those stressful conditions remains elusive. Here our global translatome and proteome analyses have uncovered novel signatures exploited by oncogene-transformed cells to adapt and survive oxidative stress. We revealed that various oncoproteins promote the expression of the IL1RAP to alleviate oxidative stress and facilitate stress adaptation. Mechanistically, IL1RAP controls Cysteine metabolism, a key substrate and determinant of antioxidant glutathione synthesis. CTH, a crucial enzyme for de novo Cysteine synthesis and redox regulation, was identified as a key functional mediator of IL1RAP. Moreover, global interactome analysis uncovered IL1RAP as a novel component and enhancer of the System Xc− transporter (SLC7A11), which is involved in Cystine uptake. Thus, IL1RAP enhances Cysteine supply via both uptake and biogenesis. IL1RAP depletion rendered Ewing sarcoma cells susceptible to oxidative stress and ferroptosis in vitro, and dramatically mitigated local invasion and lung metastasis in mice. In patients with Ewing sarcoma, high-expression of IL1RAP in the tumors correlated with poor event-free survival. Therefore, we have defined a novel pro-metastatic mechanism driven by IL1RAP-mediated Cysteine metabolism and redox regulation. Citation Format: Hai-Feng Zhang, Amal M. El-Naggar, Hongwei Cheng, Anna Prudova, Alberto Delaidelli, Jian-Zhong He, Gian Luca Negri, Michael Lizardo, Tianqing Yang, Gregg Morin, Wei Li, Dimiter S. Dimitrov, Poul H. Sorensen. IL1RAP augments Cysteine metabolism and drives oxidative stress adaptation and lung metastasis in Ewing sarcoma [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 6080.
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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) | 0.002 | 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".