Abstract 3465: Biased signaling downstream of epidermal growth factor receptor regulates proliferative versus apoptotic response to ligand
Bibliographic record
Abstract
Abstract Inhibition of EGFR signaling by small molecule kinase inhibitors and monocloncal antibodies has proven effective in the treatment of multiple metastatic cancers. In contrast, metastatic breast cancer (BC) derived from EGFR-driven mammary tumors is inherently resistant to EGFR-targeted therapies. Mechanisms that contribute to this inherent resistance remain poorly defined. Here we show that in contrast to primary tumors, ligand-mediated activation of EGFR is dominated by signaling transducer and activator of transcriptional 1 (STAT1) signaling in metastatic BC. This change in downstream signaling leads to apoptosis and growth inhibition in response to EGF in metastatic BC cells. Mechanistically, these changes in downstream signaling result from an increase in the internalized pool of EGFR in metastatic cells, increasing physical access to STAT1. Along these lines, an EGFR mutant that is defective in endocytosis is unable to elicit STAT1 phosphorylation and apoptosis. Additionally, inhibition of endosomal EGFR signaling using an EGFR inhibitor linked to a nuclear localization signal specifically prevents EGF-induced STAT1 phosphorylation and cell death without affecting EGFR:MAPK signaling. Pharmacologic blockade of MAPK signaling through the use of the MEK1/2 inhibitor trametinib led to a dramatic increase in EGF-mediated STAT1 phosphorylation and enhanced EGF-induced apoptosis in metastatic BC cells. Importantly, use of a trametinib:EGF combination also facilitated the apoptotic switch in EGFR function in primary tumor cells, but not normal mammary epithelial cells. These studies reveal a fundamental switch in EGFR in metastatic BC. Furthermore, they demonstrate that pharmacological biasing EGFR signaling toward STAT1 activation is capable of revealing the apoptotic function of this critical pathway. Citation Format: Remah Ali, Wells Brown, Jo Davisson, Michael Wendt. Biased signaling downstream of epidermal growth factor receptor regulates proliferative versus apoptotic response to ligand [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3465.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".