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A Next-generation Tissue Microarray ngTMA Protocol for Biomarker Studies
Published on: September 23, 2014
Developing transcriptomic biomarkers for TAVO412 utilizing next generation sequencing analyses of preclinical tumor
Ying Jin1, Peng Chen1, Huajun Zhou2
1Research & Development Department, Tavotek Biotherapeutics, Suzhou, Jiangsu, China.
Introduction:
TAVO412, a multi-specific antibody targeting epidermal growth factor receptor (EGFR), mesenchymal epithelial transition factor (c-Met), and vascular endothelial growth factor A (VEGF-A), is undergoing clinical development for the treatment of solid tumors. TAVO412 has multiple mechanisms of action for tumor growth inhibition that include shutting down the EGFR, c-Met, and VEGF signaling pathways, having enhanced Fc effector functions, addressing drug resistance that can be mediated by the crosstalk amongst these three targets, as well as inhibiting angiogenesis. TAVO412 demonstrated strong in vivo tumor growth inhibition in 23 cell-line derived xenograft (CDX) models representing diverse cancer types, as well as in 9 patient-derived xenograft (PDX) lung tumor models.
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.
Insights
A new 21-gene biomarker predicts response to TAVO412, a multi-specific antibody targeting EGFR, c-Met, and VEGF-A. This biomarker enhances precision medicine for solid tumors by identifying patients likely to benefit from treatment.
Area of Science:
- Oncology
- Molecular Biology
- Pharmacology
Background:
- TAVO412 is a novel multi-specific antibody targeting EGFR, c-Met, and VEGF-A for solid tumor treatment.
- It inhibits tumor growth via multiple mechanisms, including blocking key signaling pathways and angiogenesis.
- TAVO412 has shown significant in vivo efficacy in diverse cancer models.
Purpose of the Study:
- To identify transcriptomic biomarkers for predicting anti-tumor response to TAVO412.
- To develop a predictive model for patient selection in precision medicine.
- To validate the biomarker's accuracy in preclinical cancer models.
Main Methods:
- Gene expression profiling of preclinical cancer models (CDX and PDX) was performed.
- A 21-gene signature was identified and correlated with TAVO412 efficacy.
- A Linear Prediction Score (LPS) model was developed to predict treatment response.
Main Results:
- The 21-gene biomarker accurately predicted TAVO412 efficacy in cell-derived xenograft (CDX) models with 78% accuracy.
- The predictive model demonstrated comparable accuracy when validated in patient-derived xenograft (PDX) models.
- The identified biomarker effectively distinguished between responders and non-responders.
Conclusions:
- A predictive transcriptomic biomarker for TAVO412 has been identified using next-generation sequencing.
- This biomarker facilitates precision medicine by optimizing patient selection for TAVO412 treatment.
- Leveraging preclinical data is crucial for developing effective predictive biomarkers.
