Immuno-PET Imaging of EGFR with <sup>64</sup>Cu-NOTA Panitumumab in Subcutaneous and Metastatic Nonsmall Cell Lung Cancer Xenografts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Objective: About 65–90% of nonsmall cell lung cancer (NSCLC) express the epithelial growth factor receptor (EGFR) as a transmembrane protein that is activated by binding of specific ligands, including epidermal growth factor and transforming growth factor α (TGFα). Identifying EGFR as an oncogene has led to the development of anticancer therapeutics directed against EGFR, including the full-length human IgG 2 monoclonal antibody panitumumab. The main goal of the present study was to investigate 64 Cu-labeled panitumumab with immuno-PET in subcutaneous and metastatic EGFR-positive NSCLC xenografts. Methods: Bifunctional chelating agent 2- S -(4-isothiocyanatobenzyl)-1,4,7-triazacyclo-nonane-1,4,7-triacetic acid (NOTA-NCS) was attached to panitumumab. The number of chelators per panitumumab was determined using matrix-assisted laser desorption/ionization (MALDI) mass spectroscopy. The incorporation efficiency of 64 Cu into NOTA-panitumumab was measured by using radio-TLC. EGFR-expressing epithelial-like H1299-luc+ NSCLC cells were used for in vitro and in vivo experiments. Cell uptake of [ 64 Cu]Cu-NOTA-panitumumab was measured in the presence and absence of panitumumab. Subcutaneous and metastatic H1299-luc tumor models were grown in male NSG mice. The presence of tumors at lung and metastatic sites was analyzed by [ 18 F]FLT PET. Immuno-PET with [ 64 Cu]Cu-NOTA-panitumumab was performed as static PET imaging at 2, 24, and 48 h postinjection in both tumor models. Proof-of-target was confirmed by blocking experiments with panitumumab. Detailed ex vivo biodistribution experiments were performed in both animal tumor models to confirm biodistribution profiles obtained by immuno-PET imaging. Results: MALDI analysis confirmed the attachment of ∼1.5 NOTA per antibody. Radiolabeling efficiency with [ 64 Cu]CuCl 2 was 93.8 ± 5.7% and a molar activity of 0.65 MBq/μg. Cellular uptake studies with [ 64 Cu]Cu-NOTA-panitumumab in H1299 cells demonstrated increasing uptake over time, reaching 29.1 ± 2.9% radioactivity(Bq)/mg protein ( n = 3) and plateauing at 45 min. Addition of 25 μg of panitumumab reduced radioligand uptake to 1.22 ± 0.06% radioactivity/mg protein ( n = 3). PET imaging revealed high uptake of [ 64 Cu]Cu-NOTA-panitumumab in subcutaneous tumors: Standardized uptake values (SUV) mean reached 4.70 ± 0.42 and 5.37 ± 0.40 ( n = 5) after 24 and 48 h postinjection, respectively. Administration of 1 mg panitumumab reduced tumor uptake significantly to 1.94 ± 0.22 and 1.66 ± 0.08 ( n = 4; p < 0.001). In the metastatic model, the following SUV mean were analyzed from liver and lung lesions: 5.55 ± 0.34 and 6.28 ± 0.46 (both n = 23 lesions from 6 mice) after 24 and 48 h postinjection, which was also significantly reduced to 2.53 ± 0.39 and 2.31 ± 0.15 (both n = 16 lesions from 4 mice; p < 0.001) after injection of 1 mg panitumumab. Detailed ex vivo biodistribution confirmed immuno-PET analysis in both models. Panitumumab reduced radioactivity uptake into subcutaneous tumors from 11.01 ± 0.72 ( n = 4) to 3.67 ± 0.33% ID/g ( n = 5; p < 0.001), and in metastatic liver lesions from 29.44 ± 8.14 ( n = 4) to 8.35 ± 1.30% ID/g ( n = 5; p < 0.001), respectively. Conclusions: [ 64 Cu]Cu-NOTA-panitumumab was successfully used for immuno-PET imaging of EGFR-expressing subcutaneous and metastatic NSCLC tumors. This result represents the basis for developing radiotheranostics for targeting EGFR in cancers and for selecting the right patients for the right treatment at the right time.
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) | 0.000 | 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 | 0.000 | 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".