Conclusions
In implementing precision medicine by leveraging preclinical model data, a predictive transcriptomic biomarker empowered by next-generation sequencing was identified that could optimize the selection of patients that may benefit most from TAVO412 treatment.
Methods
Using preclinical CDX data, we established transcriptomic biomarkers based on gene expression profiles that were correlated with anti-tumor response or distinguished between responders and non-responders. Together with specific driver mutation that associated with efficacy and the targets of TAVO412, a set of 21-gene biomarker was identified to predict the efficacy. A biomarker predictor was formulated based on the Linear Prediction Score (LPS) to estimate the probability of patients or tumor model response to TAVO412 treatment.
Results
This efficacy predictor for TAVO412 demonstrated 78% accuracy in the CDX training models. The biomarker model was further validated in the PDX data set and resulted in comparable accuracy. Conclusions: In implementing precision medicine by leveraging preclinical model data, a predictive transcriptomic biomarker empowered by next-generation sequencing was identified that could optimize the selection of patients that may benefit most from TAVO412 treatment.