Abstract 203: Single cell imaging of kinase inhibitor-induced effects in breast cancer cell lines
Bibliographic record
Abstract
Abstract There is need for improved targeted therapies for the treatment of breast cancer. Kinase inhibitors are candidates to be used in this context. With the goal of uncovering specific vulnerabilities in differing cell lines, we treated five breast cancer cell lines representing triple negative (BT20, MDAMB231, and Hs578T), hormone receptor positive (MCF7), and Her2 amplified (SKBR3) disease and the non-malignant MCF10A line with a panel of 105 kinase inhibitors covering a broad range of targets. Cells were treated with doses between 0.1 and 10 μM for 24 hours, at which time they were stained with DRAQ5 (DNA) and TMRE (mitochondrial membrane potential). Live-cell images were acquired using a high throughput, confocal Opera microscope. Cell segmentation, based on the DRAQ5 staining, and feature extraction (intensity, morphology, and texture) were performed with Acapella software. Over 300 features were extracted for ∼1.5 million cells. Analytical methods have been applied to identify those treatments that induced significant changes to the cells. Over half of the kinase inhibitors queried had a significant effect within 24 hours in all cell lines at 10 μM. Cell cycle inhibitors had the most common effects across cell lines. Cell line specific effects were also uncovered, for example, MDAMB231 cells were the most sensitive to MAPK inhibitors. These, early time point, results have been compared with the effects of the same inhibitors on cell growth to better understand the mechanisms leading to cell line and pathway specific effects. Citation Format: Caitlin Mills, David Andrews, Peter Sorger. Single cell imaging of kinase inhibitor-induced effects in breast cancer cell lines. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 203.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".