Abstract A078 Targeted ferroptosis induction enhances chemotherapy and natural killer cell immunotherapy in neuroblastoma
Bibliographic record
Abstract
Abstract Background Neuroblastoma (NB) is a heterogeneous pediatric solid tumor of the sympathetic nervous system that accounts for 15% of pediatric childhood deaths. High-risk NB is often chemoresistant, with <50% survival. Additionally, its low mutational load and immunosuppressive tumor microenvironment (i-TME) have hindered the success of immunotherapy. NB has a strong dependency on iron metabolism, making induction of ferroptosis, an iron-mediated immunogenic cell death, a potential treatment for chemoresistant NB. Ferroptosis also has the potential to enhace immune cell therapy. Natural killer (NK) cells have emerged as a promising immunotherapeutic tool for pediatric tumors, as they don’t depend on specific mutations, but new strategies are needed to overcome the i-TME. Comnination with immunogenic ferroptosis induction could be a solution. Here we explore the combination of ferroptosis induction, standard-of-care chemotherapy and NK cell therapy, to identify novel therapeutic combinations to tackle high-risk NB. Results Using NB patient-derived models, we analyzed multiple ferroptosis-inducing compounds with different mechanisms of action. Auranofin (thioredoxin reductase inhibitor) and RSL3 (inhibitor of glutathione peroxidase 4, GPX4) were identified as promising agents against chemoresistant NB. Both agents increased survival, reduced tumor growth, and decreased the population of chemoresistant immature mesenchymal-like cells in vivo. Upon combination with chemotherapy, the distinct mechanisms of ferroptosis induction led to differential interactions with COJEC, the standard-of-care 5-drug cocktail used to treat NB patients. Auranofin exhibited an additive effect, while RSL3 showed an antagonistic interaction due to upregulation of GPX4 and other ferroptosis inhibitors by COJEC, primarily driven by etoposide. The combination Auranofin-COJEC decreased tumor growth and increased survival in a chemoresistant NB patient-derived xenograft (PDX) model through ferritinophagy, lysosome accumulation, and iron overload. Upon RNA analysis of PDX tumors treated with Auranofin and RSL3, we observed that RSL3 enhanced the expression of pathways associated with inflammation, while Auranofin had the opposite effect. Treatment with ferroptosis-inducing agents in vitro led to the release of damage-associated molecular patterns (DAMPs) and, when combined in a sequential manner with NK cell therapy, an additive effect was observed. Conclusions The use of ferroptosis-inducing agents, based on their mechanisms of action, in combination with chemotherapy and immunotherapy, is a feasible and promising strategy that outperforms standard-of-care chemotherapy in chemoresistant NB. Future work should identify which ferroptosis-inducing mechanisms are best to combine with chemotherapy and immunotherapy, and which patients can benefit from each combination based on tumor characteristics. The availability of multiple ferrotosis-inducing mechanism opens the door to personalized treatment protocols. Citation Format: Adriana Mañas, Alexandra Seger, Aleksandra Adamska, Kyriaki Smyrilli, Lucía Sánchez, Antonio Pérez-Martínez, Daniel Bexell. Targeted ferroptosis induction enhances chemotherapy and natural killer cell immunotherapy in neuroblastoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A078.
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) | 0.002 | 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".