Abstract 997: Potential targets for siRNA-mediated combinational therapy of breast cancer cells.
Bibliographic record
Abstract
Abstract Chemotherapy is an effective approach to curb uncontrolled proliferation of malignant cells; however, even the most recent molecularly-targeted drugs lose their effect as a result of resistance development in a short period of time. Inherent plasticity of transformed cells and diverse resistance mechanisms enable cells to mount an effective resistance against the administered drugs. Molecular changes in drug resistant cells are beginning to emerge, including adjustments in expression level of proteins involved in cell survival and apoptosis. We are exploring the potential of siRNA silencing as a new approach to induce apoptosis and/or overcome drug resistance in breast cancer cells. This study was conducted to develop a global approach to identify crucial targets and deliver siRNAs to silence a combination of proteins in order to induce apoptosis (independent of chemotherapy) and/or sensitize the resistant cells to chemotherapeutic agents. Resistance was induced in two breast cancer cell lines by exposure to gradually increasing doses of doxorubicin, and resistance induction was confirmed by a significant increase in IC50 of DOX. An apoptosis microarray was used for analysis of mRNA levels of 84 apoptosis related proteins in wild type and resistant cells. While different members of caspase and TNFR family were among down-regulated proteins, over-expressed proteins in both resistant cell lines included Bcl2, survivin, NFB, and Mcl1. Human Apoptosis siRNA library (446 targets) and kinase siRNA library (719 targets) were screened using PEILA2.1 as delivery system. siRNAs were selected as hits which induced cell death in cancer cell lines without significant reduction in cell viability of skin fibroblast cells. The “hits” in apoptosis library included BIRC 7, NFB, and Mcl1. Eight kinases were selected (based on the same criteria and potential synergistic effect with Mcl-1). In vitro evaluations revealed that among the selected targets, Mitogen-activated protein kinase 3 (MAP2K3) was the most potent in decreasing cell viability, while Ribosomal protein S6 kinase (RPS6KA5) showed the most promising potential for simultaneous silencing with Mcl-1. The combination of Mcl-1 and RPS6KA5 was evaluated in vivo both as multiple intratumoral (1.5 μg for each siRNA or ∼75 μg/kg) and intraperitoneal (10 μg each siRNA or ∼500 μg/kg) treatments in a MDA-MB 435 WT xenograft model in NCR nu/nu nude mice. In the intratumoral treatment, a significant inhibition of tumor growth was observed for Mcl-1 single silencing compared to scrambled siRNA and Mcl-1/RPS6KA5 double silencing compared to Mcl-1 silencing. IP treatment showed a similar trend; however, only the combinational silencing of Mcl-1 and RPS6KA5 showed a significant inhibition of tumor growth. This study demonstrated the potential for kinase silencing as a therapeutic strategy in breast cancer therapy, as well as the promising synergistic effect for Mcl-1 and kinase silencing. Citation Format: Hamid Montazeri, Hasan Uludag, Parvin Mahdipoor. Potential targets for siRNA-mediated combinational therapy of breast cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 997. doi:10.1158/1538-7445.AM2013-997
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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) | 0.002 | 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".