Fusion of the C <sub>H</sub> 1 Domain of IgG <sub>1</sub> to Epidermal Growth Factor (EGF) Prolongs its Retention in the Blood but Does Not Increase Tumor Uptake
Bibliographic record
Abstract
An expression vector (pJW4) for a human epidermal growth factor (hEGF)-CH1 fusion protein was constructed by fusing the gene for hEGF with the gene for CH1 of murine IgG1 with/without a peptide linker sequence [(GGGGS)3] and inserting the recombinant gene into vector pGEX2T. Expression vector pGEX2T was transfected into E. coli (BL-21) and hEGF-CH1 expressed by induction of the lac Iq promotor with 50 microM isopropyl beta-D-thiogalactopyranoside (IPTG). hEGF- CH1 fused to glutathione S-transferase (GST) was isolated and purified by affinity chromatography. GST was cleaved using thrombin. SDS-PAGE demonstrated a protein with the expected M(r) (18 kDa) positive for hEGF by Western blot. hEGF-linker-CH1 exhibited preserved binding to A431 (2-3 x 10(6) EGFR/cell) and MDA-MB-468 breast cancer cells (1-2 x 10(6) EGFR/cell). hEGF-CH1 without the linker exhibited poor receptor binding. hEGF-linker-CH1 also exhibited strong binding to soluble EGFR equivalent to that of hEGF. The tumor and normal tissue distribution of hEGF-linker-CH1 labeled with 123I was compared with 123 I-hEGF at 24 h after i.v. injection to mice implanted with s.c. MDA-MB-468 xenografts. Fusion of hEGF with CH1 increased its retention in the blood 14-fold but did not significantly increase tumor uptake. Tumor/blood ratios were higher for hEGF than for hEGF-linker-CH1. We conclude that hEGF is more attractive than hEGF-linker-CH1 for imaging EGFR-positive tumors.
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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 | 0.001 | 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 | 0.000 |
| 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".