ErbB-2 Blockade and Prenyltransferase Inhibition Alter Epidermal Growth Factor and Epidermal Growth Factor Receptor Trafficking and Enhance <sup>111</sup>In-DTPA-hEGF Auger Electron Radiation Therapy
Bibliographic record
Abstract
UNLABELLED: The intracellular distribution of Auger electron-emitting radiopharmaceuticals is a determinant of cytotoxicity. However, the mechanisms by which these agents are routed through the cell are ill understood. The aim of this study was to investigate how trafficking of (111)In-labeled human epidermal growth factor ((111)In-DTPA-hEGF) relates to that of the EGF receptor (EGFR) and whether coadministration of agents that modulate EGFR signaling alters the efficacy of (111)In-DTPA-hEGF. METHODS: The spatiotemporal interaction between AlexaFluor488-EGF (AF488-EGF) and Cy3-conjugated anti-EGFR antibody (Cy3-anti-EGFR) was studied in the breast cancer cell line MDA-MB-468 using fluorescence resonance energy transfer and 2-photon fluorescence lifetime imaging. (111)In internalization and nuclear fractionation assays were performed to investigate the effect of the ErbB-2-blocking antibody trastuzumab and a prenyltransferase inhibitor, L-778,123, on the subcellular localization of (111)In-DTPA-hEGF in MDA-MB-468 (1.3 × 10(6) EGFR per cell; ErbB-2 negative) and 231-H2N (0.2 × 10(6) EGFR per cell; 0.4 × 10(5) ErbB-2 per cell) cell lines. The cytotoxicity of (111)In-DTPA-hEGF (0-64 nM) plus trastuzumab (0-50 μg/mL) or L-778,123 (0-22.5 μM) was measured using clonogenic assays in a panel of breast cancer cell lines that express different levels of EGFR and ErB-2. Clonogenic survival data were used to calculate combination indices. Tumor growth inhibition was measured in vivo in 231-H2N xenograft-bearing mice treated with (111)In-DTPA-hEGF plus trastuzumab or L-788,123. RESULTS: Using fluorescence resonance energy transfer, we showed that EGF interacts with EGFR in the cytoplasm and nucleus after internalization of the ligand-receptor complex in MDA-MB-468 cells. Nuclear localization of (111)In-DTPA-hEGF is enhanced by trastuzumab and L-788,123. Trastuzumab and L-788,123 sensitized 231-H2N cells to (111)In-DTPA-hEGF. Nuclear localization and cytotoxicity of (111)In-DTPA-hEGF were significantly increased in 231-H2N xenografts by cotreatment with L-788,123 (P < 0.0001). CONCLUSION: The therapeutic efficacy of (111)In-DTPA-hEGF is increased through the coadministration of selected molecularly targeted drugs that modulate EGFR signaling and trafficking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 teacher head, 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".