Novel targets for sensitizing breast cancer cells to TRAIL‐induced apoptosis with siRNA delivery
Bibliographic record
Abstract
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) induces apoptosis in variety of cancer cells without affecting most normal cells, which makes it a promising agent for cancer therapy. However, TRAIL therapy is clinically not effective due to resistance induction. To identify novel regulators of TRAIL that can aid in therapy, protein targets whose silencing sensitized breast cancer cells against TRAIL were screened with an siRNA library against 446 human apoptosis-related proteins in MDA-231 cells. Using a cationic lipopolymer (PEI-αLA) for delivery of library members, 16 siRNAs were identified that sensitized the TRAIL-induced death in MDA-231 cells. The siRNAs targeting BCL2L12 and SOD1 were further evaluated based on the novelty and their ability to sensitize TRAIL induced cell death. Silencing both targets sensitized TRAIL-mediated cell death in MDA-231 cells as well as TRAIL resistant breast cancer cells, MCF-7. Combination of TRAIL and siRNA silencing BCL2L12 had no effect in normal human umbilical vein cells and human bone marrow stromal cell. The silencing of BCL2L12 and SOD1 enhanced TRAIL-mediated apoptosis in MDA-231 cells via synergistically activating capsase-3 activity. Hence, here we report siRNAs targeting BCL2L12 and SOD1 as a novel regulator of TRAIL-induced cell death in breast cancer cells, providing a new approach for enhancing TRAIL therapy for breast cancer. The combination of siRNA targeting BCL2L12 and TRAIL can be a highly effective synergistic pair in breast cancer cells with minimal effect on the non-transformed cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".