Abstract 5661: Nanoparticle based combinatorial siRNA therapy against human hepatocellular carcinoma (HCC) .
Bibliographic record
Abstract
Abstract Background: We have previously demonstrated the therapeutic effect of lipid nanoparticles (LNP) loaded with single siRNA targeting CSN5 and WEE1 against human HCC in mouse models. Aim: To test the benefit of a combinatorial versus single siRNA therapy in mouse models of human HCC and to identify molecular mechanism(s) involved in therapeutic response by extensive microarray analyses. Materials and Methods: LNP formulations of chemically modified siRNAs targeting CSN5 and WEE1 were produced by Tekmira® Pharmaceuticals. SCID-beige mice were used for subcutaneous (Hep3B) and intra-hepatic (Huh7-luciferase) tumor transplantation. Mice with established tumors were treated intravenously with 4 mg/kg single agent siRNA or 2 mg/kg + 2 mg/kg siCSN5:siWEE1 siRNA co-encapsulated in the same LNP. Tumors were assayed following 1 to 9 injected doses. Tumor progression in the Huh7 orthotopic model was monitored by bioluminescence imaging and metastases were evaluated at endpoint. Results: Significant target silencing was observed in tumors after single or repeat administration with no antagonism between siRNAs occurring in the CSN5:WEE1 combination. We observed significant inhibition of tumor growth and metastases in mice treated with active siRNAs compared to LNP containing a non-targeting control sequence. Potency was not lost with siCSN5:siWEE1 LNP, where the concentration of each siRNA is halved in combination, relative to the most efficacious single agent. Data from preliminary microarray analyses demonstrate a strong transcriptome difference between each treatment group. Conclusion: We demonstrate a clear efficacy of a LNP based combinatorial siRNA therapy in human mouse models of HCC. Overall this therapy led to a significant decrease of tumor size induced by mRNA inhibition Citation Format: Thomas Decaens, Valentina M. Factor, Iva Kulic, Jesper B. Andersen, Daekwan Seo, Yun-Han Lee, Adam D. Judge, Elizabeth A. Conner, Ian MacLachlan, Snorri S. Thorgeirsson. Nanoparticle based combinatorial siRNA therapy against human hepatocellular carcinoma (HCC) . [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5661. doi:10.1158/1538-7445.AM2013-5661
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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) | 0.001 | 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 | 0.001 |
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".