Antiangiogenic antibody improves melanoma detection by fluorescently labeled therapeutic antibodies
Bibliographic record
Abstract
OBJECTIVE: Evaluate if vascular normalization with an antiangiogenic monoclonal antibody improves detection of melanoma using fluorescently labeled antibody-based imaging. STUDY DESIGN: Preclinical. METHODS: Panitumumab and control IgG were covalently linked to a near-infrared fluorescent probe (IRDye800CW). Immunodeficient mice with ear xenografts of melanoma cell lines (A375 and SKMEL5) were systemically injected (200 μg, tail vein) with either IgG-IRDye800CW, panitumumab-IRDye800CW, or a combination (bevacizumab [5mg/kg], administered 72 hours prepanitumumab-IRDye800CW) (n = 5). Primary tumors were imaged with open-field (LUNA, Novadaq, Toronto, Ontario, Canada) and closed-field (Pearl, LI-COR Biosciences, Lincoln, NB) imaging devices. Postresection, the concentration of labeled antibody within the tumor (μg/g) was calculated using normalized standards. RESULTS: The mean fluorescence within the melanoma tumors was greater for the combination group compared to panitumumab alone for both cell lines (P < 0.001). The tumor-to-background ratio (TBR) for the A375 tumors was greater for the combination (3.4-7.1) compared to the panitumumab alone (3.2-5.0) (P = 0.04). The TBR for SKMEL5 tumors was greater for the combination (2.4-6.0) compared to the panitumumab alone (2.2-3.9) (P = 0.02). Within A375 tumors, the concentration was lower for panitumumab (0.51 μg/g) compared to combination group (0.68 μg/g) (P = 0.036). Within SKMEL5 tumors, the concentration was lower for panitumumab (0.0.17 μg/g) compared to combination group (0.35 μg/g) (P = 0.048). Residual tumor (1.0-0.2 mg) could be differentiated from background in both panitumumab and combination groups. For both cell lines, panitumumab and combination groups had greater mean fluorescence of the tumor compared to control IgG. CONCLUSION: The addition of antiangiogenic therapy improves uptake of fluorescently labeled monoclonal antibodies within melanoma tumors. Clinical translation could improve detection of melanoma intraoperatively, reducing positive margins and sparing normal tissue. LEVEL OF EVIDENCE: NA Laryngoscope, 126:E387-E395, 2016.
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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".