Impact of fludarabine and treosulfan on ovarian tumor cells and mesothelin chimeric antigen receptor T cells
Bibliographic record
Abstract
In addition to their immunosuppressive effect, cytostatics conditioning prior to adoptive therapy such as chimeric antigen receptor (CAR) T cells may play a role in debulking and remodeling the tumor microenvironment. We investigated in vitro the killing efficacy and impact of treosulfan and fludarabine on ovarian cancer cells expressing mesothelin (MSLN) and effect on MSLN-targeting CAR T cells. Treosulfan and fludarabine had a synergetic effect on killing of SKOV3 and OVCAR4 cells. Sensitivity to the combination of treosulfan and fludarabine was increased when SKOV3 cells expressed MSLN and when OVCAR4 cells were tested in hypoxia, while MSLN cells surface expression by SKOV3 and OVCAR4 cells was not altered after treosulfan or fludarabine exposure. Exposure to treosulfan or fludarabine (10 µM) neither impacted MSLN-CAR T cells degranulation, cytokines production upon challenge with MSLN + OVCAR3 cells, nor induced mitochondrial defects. Combination of treosulfan and fludarabine decreased MSLN-CAR T cells anti-tumor killing in normoxia but not hypoxia. In conclusion, treosulfan and fludarabine killed MSLN + ovarian cancer cells without altering MSLN-CAR T cells functions (at low cytostatics concentration) even in hypoxic conditions, and our data support the use of treosulfan and fludarabine as conditioning drugs prior to MSLN-CAR T cell therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".