Targeting DNA damage repair mechanism by using RAD50-silencing siRNA nanoparticles to enhance radiotherapy in triple negative breast cancer
Bibliographic record
Abstract
Radiotherapy (RT) is one of major therapeutic modalities in combating breast cancer. In RT, ionizing radiation is employed to induce DNA double-strand breaks (DSBs) as a primary mechanism that causes cancer cell death. However, the induced DNA damage can also trigger the activation of DNA repair mechanisms, reducing the efficacy of RT treatment. Given the pivotal role of RAD50 protein in the radiation-responsive DNA repair pathways involving DSBs, we developed a novel polymer-lipid based nanoparticle formulation containing RAD50-silencing RNA (RAD50-siRNA-NPs) and evaluated its effect on the RAD50 downregulation as well as cellular and tumoral responses to ionizing radiation using human triple-negative breast cancer as a model. The RAD50-siRNA-NPs successfully preserved the activity of the siRNA, facilitated its internalization by cancer cells via endocytosis, and enabled its lysosomal escape. The nanoparticles significantly reduced RAD50 expression, whereas RT alone strongly increased RAD50 levels at 24 h. Pretreatment with RAD50-siRNA-NPs sensitized the cancer cells to RT with ∼2-fold higher level of initial DNA DSBs as determined by a γH2AX biomarker and a 2.5-fold lower radiation dose to achieve 50 % colony reduction. Intratumoral administration of RAD50-siRNA-NPs led to a remarkable 53 % knockdown in RAD50. The pretreatment with RAD50-siRNA-NPs followed by RT resulted in approximately a 2-fold increase in DNA DSBs, a 4.5-fold increase in cancer cell apoptosis, and 2.5-fold increase in tumor growth inhibition compared to RT alone. The results of this work demonstrate that RAD50 silencing by RAD50-siRNA-NPs can disrupt RT-induced DNA damage repair mechanisms, thereby significantly enhancing the radiation sensitivity of TNBC MDA-MB-231 cells in vitro and in orthotopic tumors as measured by colony forming and tumor regrowth assays, respectively.
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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".