Abstract 5213: KiSS1R signaling in breast cancer
Bibliographic record
Abstract
Abstract KiSS1R, a G-protein coupled receptor (GPCR), and its ligands, collectively known as the kisspeptins, were originally discovered to have anti-metastatic effects. In melanoma, the expression of KiSS1R and the kisspeptins correlates with positive prognosis. However, in breast tumors, higher mRNA expression of KiSS1R and kisspeptins has been shown to correlate with progressing tumour grade. In large, the mechanism(s) by which kisspeptin-signaling via KiSS1R regulates breast cancer cell migration and invasion, required for metastasis, is unknown, and is the focus of this study. Commonly targeted in breast cancer therapy, aberrant signaling of the epidermal growth factor receptor (EGFR) is able to lead to increased migration and invasion, culminating in a more aggressive metastatic phenotype. EGFR's ability to interact with other GPCRs, whether directly by transactivation or indirectly by down-stream crosstalk, is well known. We have found that Kp-10, the most potent kisspeptin, stimulates the migration and invasion of highly invasive human MDA-MB-231 cells stably expressing FLAG-KiSS1R. In addition, we have observed co-localization of FLAG-KiSS1R and EGFR-GFP in human embryonic kidney (HEK 293) cells in response to Kp-10 or EGF stimulation. Stimulation of MDA-MB-231 cells with Kp-10 resulted in the activation of EGFR. Taken together, our findings suggest a potential mechanism by which kisspeptins and KiSS1R may stimulate breast cancer cell migration and invasion. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 5213.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".