Neurosurgery's Impact on Neuro-Oncology—“Can We Do Better?”—Lessons Learned Over 50 Years

Robin, Adam; Pawloski, Jacob; Snyder, James M.; Walbert, Tobias; Rogers, Lisa; Mikkelsen, Tom; Noushmehr, Houtan; Lee, Ian; Rock, Jack; Kalkanis, Steven N.; Rosenblum, Mark L.

doi:10.1227/neu.0000000000001879

Cited by 2 publications

(1 citation statement)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Glioblastoma (GBM) is one of the most aggressive forms of brain cancer, with a 5-year survival rate of only 5.5% [ 1 ]. Despite the use of the standard of care (resection, radiation, and chemotherapy with the alkylating agent temozolomide), recurrence is nearly inevitable due to the invasion of remnant GBM cells into the surrounding brain parenchyma [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Rodgers,

Schulz Pauly,

Maloney

et al. 2024

Cancers

View full text Add to dashboard Cite

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and the treatment response are typically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immunohistochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In contrast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying the tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for the reliable, high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing insights for preclinical research design.

show abstract

Section: Introductionmentioning

confidence: 99%

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Rodgers,

Schulz Pauly,

Maloney

et al. 2024

Cancers

View full text Add to dashboard Cite

show abstract

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Rodgers,

Schulz Pauly,

Maloney

et al. 2024

Preprint

View full text Add to dashboard Cite

Glioblastoma (GBM) is the most aggressive brain cancer. To model GBM in research, orthotopic brain tumor models, including syngeneic models like GL261 and genetically engineered mouse models like TRP, are used. In longitudinal studies, tumor growth and treatment response are typ-ically tracked with in vivo imaging, including bioluminescence imaging (BLI), which is quick, cost-effective, and easily quantifiable. However, BLI requires luciferase-tagged cells, and recent studies indicate that the luciferase gene can elicit an immune response, leading to tumor rejection and experimental variation. We sought to optimize the engraftment of two luciferase-expressing GBM models, GL261 Red-FLuc and TRP-mCherry-FLuc, showing differences in tumor take, with GL261 Red-FLuc cells requiring immunocompromised mice for 100% engraftment. Immuno-histochemistry and MRI revealed distinct tumor characteristics: GL261 Red-FLuc tumors were well-demarcated with densely packed cells, high mitotic activity, and vascularization. In con-trast, TRP-mCherry-FLuc tumors were large, invasive, and necrotic, with perivascular invasion. Quantifying tumor volume using the HALO® AI analysis platform yielded results comparable to manual measurements, providing a standardized and efficient approach for reliable high-throughput analysis of luciferase-expressing tumors. Our study highlights the importance of considering tumor engraftment when using luciferase-expressing GBM models, providing in-sights for preclinical research design.

show abstract

Neurosurgery's Impact on Neuro-Oncology—“Can We Do Better?”—Lessons Learned Over 50 Years

Cited by 2 publications

References 75 publications

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Optimization, Characterization, and Comparison of Two Luciferase-Expressing Mouse Glioblastoma Models

Contact Info

Product

Resources

About