Abstract A65: Combinational siRNA silencing of MCL-1 and P-gp enhances the apoptotic response in Human Breast Cancer Cells
Bibliographic record
Abstract
Abstract Multidrug resistance (MDR) remains is a major hurdle to overcome in cancer therapy. Enhanced cell proliferation and survival of cancerous cells in solid tumor environment along with survival of stem cells to repopulate the tumor and metastasis complicate the matter even more. Therefore, retardation of cell growth and/or induction of apoptosis by posttranscriptional silencing based on short interfering RNA (siRNA) is promising, but remains challenging due to inability of anionic siRNA to cross cell membrane. We previously reported the safety and efficiency of lipid substitution on small molecular weight, non-toxic polyethylenimine (PEI) as promising approach for siRNA delivery. In this study, we report silencing of myeloid cell leukemia 1 (MCL-1) both as an individual target to induce apoptosis and incorporated in multiple silencing in wild type (WT) MDA-MB-435 human breast cancer cells, as well as cells rendered resistant as a result of exposure to an anticancer agent and MDR version of the same cells. Down-regulation of P-glycoprotein (P-gp), survivin, and MCL-1 with respective siRNAs were studied individually and in combinations. While silencing MCL-1 caused almost 90% cell death in the WT cells, the effect was less significant in the MDR cells. Silencing both MCL-1 and P-gp in the MDR cells showed a synergistic effect. Resistance induction, as confirmed by both IC50 experiments and doxorubicin (DOX) uptake, caused an up-regulation in the expression of not only P-gp, but also BCRP, survivin, and MCL-1. Our double silencing experiments in resistance-induced cells showed a similar pattern to MDR cells; however, in the later stages of resistance induction, prolonged exposure to higher DOX concentrations caused a cell dependency on MCL-1 and a cross-link between MCL-1 and P-gp. MCL-1 silencing was proven to be more efficient in causing cell death in resistance-induced cells compared to MDR cells. A re-sensitization to cytotoxic effect of DOX was observed in resistance-induced cells after silencing of MCL-1. In addition to confirming MCL-1 as a viable target for siRNA silencing, this study indicates that anti-apoptotic proteins such as survivin and MCL-1 may have a role in resistance that could be closely related to efflux proteins.
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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) | 0.001 | 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".