Abstract 3887: A screen for new cancer specific drugs that target centrosome clustering
Bibliographic record
Abstract
Abstract Most normal cells have two centrosomes that form bipolar spindles during mitosis, while cancer cells often contain extra centrosomes. Such cancer cells then achieve bipolar division by clustering centrosomes into two functional poles. Our lab has previously shown that inhibition of Integrin-linked kinase, which is a signaling and scaffold protein in focal adhesion and is also a centrosomal protein, results in scattered centrosomes and multipolar spindles, which leads to cancer specific cell death (Fielding et al., 2011, 30:521-534, Oncogene). A major problem with clinically used anti-mitotic drugs, such as taxol, is their toxicity in normal cells. Our goal is to develop new drugs that act specifically on cancer cells through targeting extra centrosomes without affecting normal cells. To discover new drugs, we established a high-content screen that automatically detects cells with de-clustered centrosomes. BT-549, a breast cancer cell line, was used for the screen, since it is known to effectively cluster centrosomes. The Canadian Chemical Biology Network enabled us to test chemical libraries predicted to have drug-like properties. For screening, cells were grown in 96 well plates, treated with test compounds for 5 hours, and subjected to immunofluorescence to examine centrosome arrangement in mitotic cells. Images were taken and analyzed with an automated fluorescence imager, Cellomics Array Scan VTI. We have thus screened over 6000 compounds from which we identified 18 hits. Fifteen of these were confirmed to inhibit centrosome clustering in the cells cultured under standard conditions, and induce an arrest in mitosis. Three compounds are structurally similar, suggesting a common structural motif as preventing centrosome clustering. To compare the effects of these drugs on normal and cancer cells, the viability of several breast and other cancer cell lines, an immortalized model of normal human mammary epithelial cells (MCF10A), and freshly isolated primary normal human mammary epithelial cells were examined after two days of drug treatment using the MTT assay. These comparisons identified some compounds that selectively reduced the viability of cancer cells, but not normal mammary epithelial cells suggesting their potential for cancer-specific therapy. These small molecules are currently being tested in orthotopic and xenograft models, and will be studied for target identification and mechanism of action for further drug development. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3887. doi:1538-7445.AM2012-3887
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".