A Cystine-Cysteine Intercellular Shuttle Prevents Ferroptosis in xCTKO Pancreatic Ductal Adenocarcinoma Cells
Bibliographic record
Abstract
In our previous study, we showed that a cystine transporter (xCT) plays a pivotal role in ferroptosis of pancreatic ductal adenocarcinoma (PDAC) cells in vitro. However, in vivo xCTKO cells grew normally indicating that a mechanism exists to drastically suppress the ferroptotic phenotype. We hypothesized that plasma and neighboring cells within the tumor mass provide a source of cysteine to confer full ferroptosis resistance to xCTKO PDAC cells. To evaluate this hypothesis, we (co-) cultured xCTKO PDAC cells with different xCT-proficient cells or with their conditioned media. Our data unequivocally showed that the presence of a cysteine/cystine shuttle between neighboring cells is the mechanism that provides redox and nutrient balance, and thus ferroptotic resistance in xCTKO cells. Interestingly, although a glutathione shuttle between cells represents a good alternative hypothesis as a “rescue-mechanism”, our data clearly demonstrated that the xCTKO phenotype is suppressed even with conditioned media from cells lacking the glutathione biosynthesis enzyme. Furthermore, we demonstrated that prevention of lipid hydroperoxide accumulation in vivo is mediated by import of cysteine into xCTKO cells via several genetically and pharmacologically identified transporters (ASCT1, ASCT2, LAT1, SNATs). Collectively, these data highlight the importance of the tumor environment in the ferroptosis sensitivity of cancer cells.
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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".