Abstract 5392: High-throughput screening of compounds for targeting system Xc(-) in human breast cancer cells
Bibliographic record
Abstract
Abstract Breast cancer cells often metastasize to bone, causing severe and untreatable pain, which greatly compromises the quality of life of patients. We discovered that breast cancer cells secrete large amounts of glutamate via an amino acid antiporter Xc(-). Glutamate is an important neurotransmitter that has been associated with pain and it is also critical in bone metabolic remodeling. Excessive glutamate secreted by invading breast cancer cells can cause excitotoxic injury to bone neurons, disturb bone homeostasis, and activate or sensitize nociceptors, thereby causing acute and chronic pain. Another important activity of system Xc(-) is the import of cystine into cells, which is critical for antioxidant defence mechanism in cancer cells. These make system Xc(-) a potential target to overcome breast cancer induced-pain and sensitize cancer cells to oxidative damage. A cell-based Amplex red glutamate assay was optimized and miniaturized to be compatible with high throughput screening (HTS) setting. The library for screening is the Canadian Compound Collection containing ∼30,000 compounds. We utilized the blockade of glutamate release to identify potential compounds. We identified 320 compounds that effectively inhibit glutamate release of human breast cancer cell MDA-MB-231. However on closer scrutiny and stringent conditions we narrowed our potential compounds to 8 for further characterization. These compounds were tested in vitro for cytotoxicity and dose response effects on glutamate release. In conclusion we have identified several lead compounds for in vivo pain monitoring tests. Information obtained from this study will not only advance our understanding of the role of system Xc(-) in breast cancer cells, but also help design novel drugs for blocking breast cancer induced-bone pain, thereby improving the quality of life for breast cancer patients. (This project was supported by Canadian Breast Cancer Foundation-Ontario) Citation Format: Han-xin Lin, Jennifer Fazzari, Katja Linher-Melville, Gurmit Singh. High-throughput screening of compounds for targeting system Xc(-) in human breast cancer cells. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5392. doi:10.1158/1538-7445.AM2014-5392
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".