Related Experiment Video
Updated: Jul 23, 2025
Testing Targeted Therapies in Cancer using Structural DNA Alteration Analysis and Patient-Derived Xenografts
Published on: July 25, 2020
Pan-Cancer Pharmacogenomic Analysis of Patient-Derived Tumor Cells Using Clinically Relevant Drug Exposures
Stephen H Chang1, Ryan J Ice2, Michelle Chen2
1University of California at San Francisco, School of Pharmacy, Department of Clinical Pharmacy, San Francisco, California.
Abstract:
As a result of tumor heterogeneity and solid cancers harboring multiple molecular defects, precision medicine platforms in oncology are most effective when both genetic and pharmacologic determinants of a tumor are evaluated. Expandable patient-derived xenograft (PDX) mouse tumor and corresponding PDX culture (PDXC) models recapitulate many of the biological and genetic characteristics of the original patient tumor, allowing for a comprehensive pharmacogenomic analysis. Here, the somatic mutations of 23 matched patient tumor and PDX samples encompassing four cancers were first evaluated using next-generation sequencing (NGS). 19 antitumor agents were evaluated across 78 patient-derived tumor cultures using clinically relevant drug exposures. A binarization threshold sensitivity classification determined in culture (PDXC) was used to identify tumors that best respond to drug in vivo (PDX). Using this sensitivity classification, logic models of DNA mutations were developed for 19 antitumor agents to predict drug response. We determined that the concordance of somatic mutations across patient and corresponding PDX samples increased as variant allele frequency increased. Notable individual PDXC responses to specific drugs, as well as lineage-specific drug responses were identified. Robust responses identified in PDXC were recapitulated in vivo in PDX-bearing mice and logic modeling determined somatic gene mutation(s) defining response to specific antitumor agents. In conclusion, combining NGS of primary patient tumors, high-throughput drug screen using clinically relevant doses, and logic modeling, can provide a platform for understanding response to therapeutic drugs targeting cancer.
Insights
Patient-derived xenograft (PDX) and PDX culture (PDXC) models aid precision oncology. Combining genetic sequencing, drug screening, and logic modeling predicts patient response to cancer therapies.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Tumor heterogeneity and multiple molecular defects in solid cancers necessitate comprehensive evaluation of genetic and pharmacologic factors for effective precision medicine.
- Patient-derived xenograft (PDX) and patient-derived xenograft culture (PDXC) models closely mimic patient tumors, enabling detailed pharmacogenomic analysis.
Purpose of the Study:
- To evaluate the utility of PDX and PDXC models in predicting patient response to antitumor agents.
- To develop logic models for predicting drug response based on somatic mutations and drug sensitivity data.
Main Methods:
- Next-generation sequencing (NGS) was used to analyze somatic mutations in 23 matched patient tumor and PDX samples across four cancer types.
- 19 antitumor agents were screened against 78 patient-derived tumor cultures (PDXC) using clinically relevant drug exposures.
- A binarization threshold sensitivity classification in PDXC was used to predict drug response in vivo (PDX), and logic models were developed to correlate mutations with response.
Main Results:
- Concordance of somatic mutations between patient tumors and PDX models increased with variant allele frequency.
- Specific drug responses were identified in individual PDXC models and across cancer lineages.
- Drug responses observed in PDXC were successfully recapitulated in vivo in PDX models, and logic modeling identified key somatic mutations predictive of response.
Conclusions:
- Integrating NGS of patient tumors, high-throughput drug screening, and logic modeling provides a robust platform for understanding and predicting therapeutic drug response in cancer.
- PDX and PDXC models are valuable tools for comprehensive pharmacogenomic analysis and advancing precision oncology.
