Abstract B15: In vivo analysis of repeated siRNA silencing on protein expression levels
Bibliographic record
Abstract
Abstract An ever increasing number of anticancer drugs are under investigation, and enter the clinical setting targeting different tumor survival and/or growth mechanisms; however, development of resistance against the mechanism of action of even the most promising drugs seems to be inevitable and remains a major concern. RNA interference (RNAi) have shown considerable promise in silencing proteins involved in tumor vascularization, as well as intracellular pathways involved in cell proliferation and/or survival. Despite the apparent efficacy of this powerful tool (especially in vitro), our information about the factors affecting siRNA efficiency and the possibility of resistance development against the silencing effect are limited at best. This study focused on determining the possibility of resistance development against siRNA treatment as a result of repeated exposure to siRNA in vivo. Our preliminary experiments in vitro revealed an unaffected siRNA cellular internalization and reproducible silencing efficiency of selected targets. The expression level of other mediators involved in breast cancer cell survival and proliferation (notably survivin, JUN, JAK2, NFkB and STAT3) were, however, altered in siRNA treated cells. In vivo experiments in a xenograft model demonstrated a similar silencing efficiency at the mRNA level after each repeated dose, with little, if any, apparent resistance development to the siRNA therapy. We conclude that cells would respond to repeated siRNA treatments in a similar fashion after a temporary initial alteration. Citation Format: Hamidreza Montazeri Aliabadi, Parvin Mahdipoor, Hasan Uludag. In vivo analysis of repeated siRNA silencing on protein expression levels. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Angiogenesis and Vascular Normalization: Bench to Bedside to Biomarkers; Mar 5-8, 2015; Orlando, FL. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl):Abstract nr B15.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".