Abstract A170: High-content screening for inhibitors of oncogenic transcription by c-Myc
Bibliographic record
Abstract
Abstract There is evidence that Myc partners with another molecule called TRRAP to regulate genes required for transformation. Our hypothesis is that blocking the interaction of Myc with TRRAP will selectively kill breast cancer cells. Traditional drug screening approaches to identify compounds that will break apart two interacting proteins have rarely been successful. However, our approach is to screen compounds in live breast cancer cells for those that prevent Myc and TRRAP from binding to each other. Since the cell is constantly replacing Myc, a compound that prevents it from binding TRRAP should kill even established tumors. Importantly, we previously mapped the places on Myc and TRRAP that bind them together and showed they don't involve the parts of the protein involved in other important functions. Therefore, compounds we find that prevent Myc-TRRAP binding should not be toxic to normal cells. Methods: We have established a new assay in which Myc and TRRAP are expressed as fusion proteins to Cerulean and Citrine fluorescence proteins, respectively. We are developing two different but related assays for the interaction between the two proteins. In one we rely on proximity resulting in complementation between non-fluorescent fragments of the Cerulean fluorescence protein. In this assay heterodimerization results in increased fluorescence. In the other assay proximity results in fluorescence resonance energy transfer (FRET) between the Cerulean and Citrine fluorescence proteins. We detect FRET by fluorescence lifetime imaging (FLIM). Results: A novel robotic microscope that can automatically perform high speed FLIM and thereby detect Myc-TRRAP binding quickly and accurately has been assembled and tested. Our automated microscope has had an environmental stage for live cell imaging fitted and tested. Both FRET standards and constructs based on the Cerulean-Myc / TRRAP-Citrine pair have been measured to validate the assay. Cell lines are being optimized for use for screening. Conclusions: High speed FLIM can be used to detect FRET between Cerulean-Myc and TRRAP-Citrine in live cells. Using our automated microscope we can test individually the effect of large numbers of small molecules in live breast cancer cells. This will let us identify compounds that are not only effective but that get into breast cancer cells and are not toxic to normal cells. Relevance/Impact: To stimulate the development of new drugs effective against a wide spectrum of cancers, we are identifying small drug like molecules that disrupt the function of a particularly potent cell growth gene called Myc, which is often misregulated in breast cancer. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A170.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".