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DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived
Quanxue Li1,2, Wentao Dai2,3,4, Jixiang Liu2,3,4
1School of Biotechnology, East China University of Science and Technology, 130 Meilong Road, Shanhgai, 200237, People's Republic of China.
Background:
One of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform.
Results:
In this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated.
Conclusion:
DRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.
Insights
Patient-derived xenograft (PDX) models are crucial for cancer research. This study introduces DRAP, an R package for analyzing drug response in PDX models, enhancing preclinical drug testing and personalized medicine.
Area of Science:
- Oncology
- Pharmacology
- Bioinformatics
Background:
- Lack of accurate preclinical cancer models leads to high failure rates for new drugs.
- Patient-derived xenograft (PDX) models closely mimic human tumor biology, including heterogeneity.
- Existing tools for analyzing drug response in PDX models are insufficient.
Purpose of the Study:
- To classify PDX preclinical trial designs.
- To develop an R package, DRAP, for drug response analysis on the PDX platform.
- To facilitate efficient data analysis and visualization for PDX studies.
Main Methods:
- Classified PDX preclinical trial designs into four patterns.
- Developed the R package DRAP, incorporating data visualization and analysis modules.
- Implemented statistical methods for tumor volume comparison, tumor growth inhibition (TGI) calculation, and drug response level analysis.
Main Results:
- DRAP provides integrated tools for drug response analysis and visualization tailored to PDX models.
- The package supports four distinct PDX trial design patterns.
- Case studies demonstrated DRAP's functionality and utility.
Conclusions:
- DRAP is the first comprehensive toolbox for analyzing and visualizing drug responses in PDX models.
- This tool is expected to significantly advance the use of PDXs in drug development.
- DRAP will promote personalized cancer treatments through improved preclinical research.
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