Abstract 5218: Boosting doxorubicin-induced immunogenic cell death by using tumor microenvironment modulating nanoparticles
Bibliographic record
Abstract
Abstract Background: Immunogenic cell death (ICD) is a cell death modality which triggers an immune response against dead-cell antigens, particularly when they derive from cancer cells. ICD has been reported to be highly induced by anthracycline drugs, such as doxorubicin (DOX), via facilitating tumor antigen presentation to and activation of T cells. However, these immunogenic effects may be compromised due to the immunosuppressive tumor microenvironment (TME), such as excess ROS, particularly H2O2, produced by tumor cells and dysfunctional macrophages in the tumor. We hypothesize that modulating immunosuppressive factors in the TME can enhance tumor response to chemotherapy and boost immunity. Specifically, polymer-lipid encapsulated manganese dioxide nanoparticles (PLMD NPs) are used to reduce tumor hypoxia and ROS by scavenging H2O2 and generating oxygen. Antitumor immunity against the cancer cells can be enhanced by the combination therapy with PLMD and DOX. Methods: PLMD NPs were prepared by dispersing MnO2 precursor particles in melted lipid and polymer in a surfactant-containing aqueous medium. The efficacy of the combination treatment was evaluated in immunocompetent mice-bearing orthotopic murine EMT6 breast tumor. The mice were treated by intravenous (IV) injection of free DOX alone, or 4 h after PLMD NP IV injection. A re-challenge study was performed by re-inoculating EMT6 cells in cured mice 120 days after the initial treatments. to validate the presence of adaptive anti-tumor immunity in the animals treated with PLMD+DOX combination, splenocyte transfer study was carried out. Ex-vivo imaging techniques were used to measure the number of infiltrated CD8+ T cells into the tumor and polarization of the M2 to M1 macrophages. Results: The PLMD+DOX combination treatment resulted in the median survival time by ~ 5.6-fold as compared to DOX alone, and led to 60% cure rate (9 out of 15 mice). This combination therapy also generated anti-tumor immunity against tumor re-inoculation in 88% of surviving mice and provided anti-tumor immunity in naïve mice that received splenocyte transfer with 57% of mice without tumor growth after inoculation. Ex-vivo studies resulted a 400% increase in the number of intratumoral CD8+ T cells, 5 days after PLMD+DOX treatment, while DOX alone treatment showed only 150% increase when compared to saline treatment. Moreover, M1 macrophages-related CD86 positive cells were found up-regulated by 51% while a significant loss of M2 marker CD163 (-47%) was observed in PLMD+DOX-treated tumors compared to DOX alone-treated ones. Conclusions: The present work demonstrated that clinically suitable PLMD NPs can effectively downregulate TME-associated immunosuppression factors and boost DOX efficacy. The PLMD NPs and DOX combination therapy presents a multimodal and a translational treatment approach to enhancing DOX-induced ICD and boosting anti-tumor immunity. Citation Format: Mohammad Ali Amini, Azhar Abbasi, Ping Cai, Hoyin Lip, Jason Li, Claudia Gordijo, Li Zhang, Michael Rauth, Xiao Yu Wu. Boosting doxorubicin-induced immunogenic cell death by using tumor microenvironment modulating nanoparticles [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5218.
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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 | 0.000 |
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".