Abstract A57: Effects of pre-exposure to siRNA on silencing response: Do cells become resistant to siRNA silencing?
Bibliographic record
Abstract
Abstract Development of resistance against cytotoxic effect of different generations of anticancer drugs is considered inevitable and remains a major concern for cancer therapy. An alternative therapeutic approach based on protein silencing via RNA interference (RNAi) have shown considerable promise; however, our information about the factors affecting siRNA efficiency and the limitations to their efficacy are limited. This study focused on determining the possibility of development of resistance against siRNA treatment as a result of repeated exposure to siRNA that could affect cellular internalization of the particles, silencing efficiency at mRNA level, and ultimately, the observed cellular response. Two approaches to siRNA exposure were undertaken: one involving a single high concentration exposure to eradicate siRNA-responsive cells, and one involving multiple exposures with a gradually increasing siRNA concentration to study the potential intracellular adaptations. Our results demonstrated a temporary decrease in silencing efficiency at mRNA level without significant alteration in siRNA cellular uptake. We conclude that cells would respond to repeated siRNA exposure in a similar fashion after a temporary initial alteration; however, we could not rule out the possibility of a change in phenotypical response (e.g., number of viable cells) as a result of adjustments in (or selection of cells reliant on) alternative pathways. Citation Format: Hamidreza Montazeri Aliabadi, Parvin Mahdipoor, Cezary Kucharsky, Nicole Chan, Hasan Uludag. Effects of pre-exposure to siRNA on silencing response: Do cells become resistant to siRNA silencing? [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A57.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 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".