Radiation Nanomedicine for EGFR-Positive Breast Cancer: Panitumumab-Modified Gold Nanoparticles Complexed to the β-Particle-Emitter, <sup>177</sup>Lu
Bibliographic record
Abstract
Our objective was to construct a novel radiation nanomedicine for treatment of breast cancer (BC) expressing epidermal growth factor receptors (EGFR), particularly triple-negative tumors (TNBC). Gold nanoparticles (AuNP; 30 nm) were modified with polyethylene glycol (PEG) chains (4 kDa) derivatized with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) chelators for complexing the β-emitter, (177)Lu and with PEG chains (5 kDa) linked to panitumumab for targeting BC cells expressing EGFR. The AuNP were further coated with PEG chains (2 kDa) to stabilize the particles to aggregation. The binding and internalization of EGFR-targeted AuNP ((177)Lu-T-AuNP) into BC cells was studied and compared to nontargeted (177)Lu-NT-AuNP. The cytotoxicity of (177)Lu-T-AuNP and (177)Lu-NT-AuNP was measured in clonogenic assays using BC cells with widely different EGFR densities: MDA-MB-468 (10(6) receptors/cell), MDA-MB-231 (10(5) receptors/cell), and MCF-7 cells (10(4) receptors/cell). Radiation absorbed doses to the cell nucleus of MDA-MB-468 cells were estimated based on subcellular distribution. Darkfield and fluorescence microscopy as well as radioligand binding assays revealed that (177)Lu-T-AuNP were specifically bound by BC cells dependent on their EGFR density whereas the binding and internalization of (177)Lu-NT-AuNP was significantly lower. The affinity of binding of (177)Lu-T-AuNP to MDA-MB-468 cells was reduced by 2-fold compared to (123)I-labeled panitumumab (KD = 1.3 ± 0.2 nM vs 0.7 ± 0.4 nM, respectively). The cytotoxicity of (177)Lu-T-AuNP was dependent on the amount of radioactivity incubated with BC cells, their EGFR density and the radiosensitivity of the cells. The clonogenic survival (CS) of MDA-MB-468 cells overexpressing EGFR was reduced to <0.001% at the highest amount of (177)Lu-T-AuNP tested (4.5 MBq; 6 × 10(11) AuNP per 2.5 × 10(4)-1.2 × 10(5) cells). (177)Lu-T-AuNP were less effective for killing MDA-MB-231 cells or MCF-7 cells with moderate or low EGFR density (CS = 33.8 ± 1.6% and 25.8 ± 1.2%, respectively). Because the β-particles emitted by (177)Lu have a 2 mm range, (177)Lu-NT-AuNP were also cytotoxic to BC cells due to a cross-fire effect but (177)Lu-T-AuNP were significantly more potent for killing MDA-MB-468 cells overexpressing EGFR than (177)Lu-NT-AuNP at all amounts tested. The cross-fire effect of the β-particles emitted by (177)Lu may be valuable for eradicating BC cells in tumors that have low or moderate EGFR expression or cells that are not targeted by (177)Lu-T-AuNP as a consequence of heterogeneous intratumoral distribution. The radiation dose to the nucleus of a single MDA-MB-468 cell was 73.2 ± 6.7 Gy, whereas (177)Lu-NT-AuNP delivered 5.6 ± 0.6 Gy. We conclude that (177)Lu-T-AuNP is a promising novel radiation nanomedicine with potential application for treatment of TNBC, in which EGFR are often overexpressed.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".