Abstract B058: CD73 siRNA therapy regulates glioblastoma immune microenvironment
Bibliographic record
Abstract
Abstract Glioblastoma (GB) is the most common primary brain tumor, characterized by high aggressiveness and poor prognostic. Although all efforts, current therapy is just palliative. Therefore, new biological targets are needed to treat this invasive cancer. In this regard, immune modulation emerges as a possibility for cancer therapeutic. Literature has shown that the purinergic system can regulate peripheral immune cell functions and adenosine (ADO) plays an important role as an immunosuppressive molecule. CD73 enzyme activity is considered the main extracellular ADO source and it is overexpressed in a variety of tumors, including GB. According to CD73 participation in tumor progression, studies from our group have shown that CD73 knockdown impairs in vitro and in vivo GB growth. Here, we determined the potential of CD73siRNA (CD73 small interference RNA) delivery using the nanotechnology strategy to reduce tumor growth and modulate GB immune microenvironment (GME) in a preclinical immunocompetent GB model. For this purpose, an in vivo experiment was performed. C6 glioma cells were cultivated and implanted in the striatum of Wistar rat brains (male, 60 d) by stereotaxic surgery. Following 5 days of GB implant, the treatment with CD73siRNA complexed to nanoemulsion (NE) via nasal route has started and continuous by 15 days (twice a day). Experimental groups were composed as follows: control group (glioma-bearing animals that receive saline via nasal route) and treated group (CD73siRNA-NE; 10 µg/kg). After 20 days of GB implant surgery, animals were euthanized and the GB histopathological characteristics were analyzed by HE staining; apoptosis cell death analysis and GME composition were determined by flow cytometry using annexin V and specific antibodies for macrophage and microglia staining. In the histopathological analysis, tumors presented GB characteristics, as necrosis, edema, and angiogenesis. The treatment decreased tumor volume by 60% and increased the GME apoptotic cell index at 10%. Also, the drug interfered in GME population cells, the microglia CD11b+CD45low cells (from 3.4 ± 1.9 to 0.2 ± 0.1 %) and macrophages CD11b+CD45high cells (from 1.22 ± 0.3 to 0.4 ± 0.1 %) were practically abolished in the GME. Our data suggest that the lower tumoral volume is due to the higher apoptotic rates, indicating immune participation in cancer cell death. Furthermore, the absence of microglia and macrophages tumor-associated may contribute to the decreased tumor volume, proposing an immune system activation ADO modulated over the GB. Citation Format: Gabriela Spies Lenz, Juliana Hofstätter Azambuja, Roselena Silvestri Schuh, Luana Roberta Michels, Nicolly Espindola Gelsleichter, Liziane Raquel Beckenkamp, Gabriela Goncalves Roliano, Frabricio Figueiró, Juliete N. Scholl, Jean Sévigny, Márcia R Wink, Helder Ferreira Teixeira, Elizandra Braganhol. CD73 siRNA therapy regulates glioblastoma immune microenvironment [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr B058. doi:10.1158/1535-7163.TARG-19-B058
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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 | 0.001 |
| 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".