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Bioluminescent imaging of melanoma in live mice
Noah Craft1,2,3, Kevin W Bruhn3, Bidong D Nguyen3
1Division of Dermatology, Department of Medicine, Harbor-UCLA Medical Center, Los Angeles, California, USA.
Abstract:
Melanoma is highly resistant to conventional chemotherapeutic agents and novel therapeutic approaches are needed. Current animal models of melanoma in animals are sub-optimal. The most commonly used homograft model is the B16 mouse melanoma. Evaluation of potential melanoma therapies with this model is limited by the inaccuracy of caliper measurement of subcutaneous tumors, of counting lung nodules in metastasis models, and the indirect nature of "survival" curves when studying brain metastases. We have developed and characterized an accurate, sensitive, and reproducible bioluminescent B16 melanoma model that allows for serial, real-time analyses of tumor burden in live mice. We demonstrate that this model is applicable to subcutaneous tumors, lung metastases, and intracranial tumors and offers a solution to many of the limitations of previous models. As proof of principle, we use this model to show the efficacy of a live, Listeria monocytogenes vaccine expressing the melanoma antigen tyrosinase-related protein-2 to protect mice against intravenous B16 melanoma challenge. Additionally, we extend our approach to include the human A375 melanoma model and are able to show in vivo differences between sub-lines with varying metastatic potential. These models represent an accurate and reproducible means for in vivo melanoma monitoring in preclinical studies.
Insights
A new bioluminescent melanoma model offers accurate, real-time monitoring of tumor growth in mice. This improved preclinical tool aids in evaluating novel melanoma therapies and vaccines.
Area of Science:
- Oncology
- Preclinical Research
- Biotechnology
Background:
- Melanoma exhibits resistance to conventional chemotherapy, necessitating novel therapeutic strategies.
- Existing animal models for melanoma research, such as the B16 mouse melanoma homograft, have significant limitations in accurately assessing treatment efficacy.
- Inaccuracies in caliper measurements, lung nodule counting, and the indirect nature of survival curves hinder the evaluation of therapies, especially for brain metastases.
Purpose of the Study:
- To develop and validate a sensitive, accurate, and reproducible bioluminescent melanoma model for real-time, in vivo monitoring of tumor burden.
- To address the limitations of current animal models in evaluating melanoma therapies.
- To demonstrate the utility of this model for subcutaneous, metastatic, and intracranial tumors.
Main Methods:
- Development and characterization of a bioluminescent B16 melanoma model for serial, real-time analysis of tumor burden in live mice.
- Application of the model to subcutaneous, lung metastasis, and intracranial tumor models.
- Utilizing the model to assess the efficacy of a Listeria monocytogenes vaccine expressing tyrosinase-related protein-2 against B16 melanoma.
- Extension of the bioluminescent approach to the human A375 melanoma model to differentiate sub-lines with varying metastatic potential.
Main Results:
- The bioluminescent model provides accurate, sensitive, and reproducible real-time monitoring of tumor burden across various melanoma models (subcutaneous, lung, brain).
- Proof-of-principle demonstrated the model's ability to show the efficacy of a novel melanoma vaccine.
- The model successfully differentiated human A375 melanoma sub-lines with different metastatic potentials in vivo.
- This model overcomes key limitations of traditional methods for evaluating melanoma progression and treatment response.
Conclusions:
- The developed bioluminescent melanoma models offer a significant advancement for in vivo monitoring in preclinical melanoma research.
- These models provide a more accurate and reproducible platform for evaluating the efficacy of novel therapeutic strategies and vaccines.
- The real-time analysis capability facilitates a deeper understanding of melanoma tumor dynamics and treatment responses.
