Development and preclinical studies of <sup>64</sup>Cu-NOTA-pertuzumab F(ab′)<sub>2</sub> for imaging changes in tumor HER2 expression associated with response to trastuzumab by PET/CT
Bibliographic record
Abstract
We previously reported that microSPECT/CT imaging with 111In-labeled pertuzumab detected decreased HER2 expression in human breast cancer (BC) xenografts in athymic mice associated with response to treatment with trastuzumab (Herceptin). Our aim was to extend these results to PET/CT by constructing F(ab′)2 of pertuzumab modified with NOTA chelators for complexing 64Cu. The effect of the administered mass (5–200 µg) of 64Cu-NOTA-pertuzumab F(ab′)2 was studied in NOD/SCID mice engrafted with HER2-positive SK-OV-3 human ovarian cancer xenografts. Biodistribution studies were performed in non-tumor bearing Balb/c mice to predict radiation doses to normal organs in humans. Serial PET/CT imaging was conducted on mice engrafted with HER2-positive and trastuzumab-sensitive BT-474 or trastuzumab-insensitive SK-OV-3 xenografted mice treated with weekly doses of trastuzumab. There were no significant effects of the administered mass of 64Cu-NOTA-pertuzumab F(ab′)2 on tumor or normal tissue uptake. The predicted total body dose in humans was 0.015 mSv/MBq, a 3.3-fold reduction compared to 111In-labeled pertuzumab. MicroPET/CT images revealed specific tumor uptake of 64Cu-NOTA-pertuzumab F(ab′)2 at 24 or 48 h post-injection in mice with SK-OV-3 tumors. Image analysis of mice treated with trastuzumab showed 2-fold reduced uptake of 64Cu-NOTA-pertuzumab F(ab′)2 in BT-474 tumors after 1 week of trastuzumab normalized to baseline, and 1.9-fold increased uptake in SK-OV-3 tumors after 3 weeks of trastuzumab, consistent with tumor response and resistance, respectively. We conclude that PET/CT imaging with 64Cu-NOTA-pertuzumab F(ab′)2 detected changes in HER2 expression in response to trastuzumab while delivering a lower total body radiation dose compared to 111In-labeled pertuzumab.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".