Translational oncology research aims to identify subgroups that exhibit treatment response even during pre-clinical phases. A recent study published in Scientific Reports titled ‘Statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery’ explores the use of Latent Class Mixed Models (LCMM) to identify subgroups with different treatment responses in mouse clinical trials (MCT) using patient-derived xenograft (PDX) models.
The study focused on the effectiveness of encorafenib, a drug used in the treatment of melanoma and colorectal cancer, specifically in cases where there is a mutation known as BRAF V600E. The researchers used a public dataset and implemented one LCMM per indication to classify treatment responses at the PDX level. They analyzed the growth kinetics of treated tumors and matched controls within the PDX models.
The findings of the study showed that LCMM successfully identified subgroups with different treatment responses. In both melanoma and colorectal cancer, the higher the proportion of mutated BRAF V600E PDX models, the greater the treatment effect. This aligns with the current use recommendations for encorafenib. The researchers also conducted a simulation study, which demonstrated that LCMM can effectively identify classes with significant differences in treatment effects.
By using LCMM to classify treatment response subgroups in PDX models, researchers can optimize the drug development process. Once these subgroups are defined, further characterization of their phenotypes and genotypes can be performed to explore treatment response predictors.
Stratified medicine is a growing trend in oncology research, allowing for personalized treatment approaches based on a patient’s specific characteristics. This study showcases the potential of LCMM in identifying subgroups with distinct treatment responses, providing a valuable tool for drug discovery and development in the field of oncology.
In conclusion, the study highlights the importance of exploring treatment response subgroups during preclinical phases of oncology drug discovery. By utilizing LCMM, researchers can effectively identify subgroups with different treatment responses in mouse clinical trials. This approach has the potential to optimize the drug development process and pave the way for more personalized and effective treatments in the field of oncology.
The detailed findings of this study have been published in Scientific Reports, providing valuable insights into the statistical classification of treatment responses in mouse clinical trials for stratified medicine in oncology drug discovery.
Note: This article is for informational purposes only and is not intended to replace professional medical advice, diagnosis, or treatment. Always seek the advice of a qualified healthcare provider with any questions you may have regarding a medical condition.
