Bispecific radioimmunoconjugates exploit receptor heterogeneity for positron emission tomography of tumors expressing HER2 and/or EGFR
Bibliographic record
Abstract
HER2 heterogeneity is a challenge for molecular imaging or treating HER2-positive breast cancer (BC). EGFR is coexpressed in some tumors exhibiting HER2 heterogeneity. Bispecific radioimmunoconjugates (bsRICs) that bind HER2 and EGFR were constructed by linking trastuzumab Fab through polyethyleneglycol (PEG 24 ) to EGF. We established s.c. tumors in NOD-SCID mice that homogeneously or heterogeneously expressed HER2 and/or EGFR by the inoculation of HER2-positive/EGFR-negative SK-OV-3 cells, EGFR-positive/HER2-negative MDA-MB-468 cells or mixtures of these cells. [ 64 Cu]Cu-NOTA-trastuzumab Fab-PEG 24 -EGF were compared to [ 64 Cu]Cu-NOTA-trastuzumab Fab or [ 64 Cu]Cu-NOTA-EGF for the PET imaging of HER2 and/or EGFR-positive tumors. [ 64 Cu]Cu-NOTA-trastuzumab Fab-PEG 24 -EGF bsRICs imaged tumors expressing HER2 or EGFR or heterogeneously expressing these receptors, while monospecific agents only imaged HER2-or EGFR-positive tumors. Our results indicate that bsRICs labeled with 64 Cu are able to exploit receptor heterogeneity for tumor imaging. PET may select patients for radioimmunotherapy with bsRICs complexed to the β-particle emitter, 177 Lu or Auger electron-emitter, 111 In in a theranostic approach.
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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 | 0.000 |
| 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) | 0.000 | 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".