VHH212 nanobody targeting the hypoxia-inducible factor 1α suppresses angiogenesis and potentiates gemcitabine therapy in pancreatic cancer <i>in vivo</i>
Bibliographic record
Abstract
Objective: We aimed to develop a novel anti-HIF-1α intrabody to decrease gemcitabine resistance in pancreatic cancer patients. Methods: Surface plasmon resonance and glutathione S-transferase pull-down assays were conducted to identify the binding affinityand specificity of anti-HIF-1α VHH212 [a single-domain antibody (nanobody)]. Molecular dynamics simulation was used todetermine the protein-protein interactions between hypoxia-inducible factor-1α (HIF-1α) and VHH212. The real-time polymerasechain reaction (PCR) and Western blot analyses were performed to identify the expressions of HIF-1α and VEGF-A in pancreatic ductaladenocarcinoma cell lines. The efficiency of the VHH212 nanobody in inhibiting the HIF-1 signaling pathway was measured using adual-luciferase reporter assay. Finally, a PANC-1 xenograft model was developed to evaluate the anti-tumor efficiency of combinedtreatment. Immunohistochemistry analysis was conducted to detect the expressions of HIF-1α and VEGF-A in tumor tissues. Results: VHH212 was stably expressed in tumor cells with low cytotoxicity, high affinity, specific subcellular localization, andneutralization of HIF-1α in the cytoplasm or nucleus. The binding affinity between VHH212 and the HIF-1α PAS-B domain was42.7 nM. Intrabody competitive inhibition of the HIF-1α heterodimer with an aryl hydrocarbon receptor nuclear translocator wasused to inhibit the HIF-1/VEGF pathway in vitro. Compared with single agent gemcitabine, co-treatment with gemcitabine and aVHH212-encoding adenovirus significantly suppressed tumor growth in the xenograft model with 80.44% tumor inhibition. Conclusions: We developed an anti-HIF-1α nanobody and showed the function of VHH212 in a preclinical murine model ofPANC-1 pancreatic cancer. The combination of VHH212 and gemcitabine significantly inhibited tumor development. These resultssuggested that combined use of anti-HIF-1α nanobodies with first-line treatment may in the future be an effective treatment forpancreatic cancer.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".