An Integrative Approach to Precision Cancer Medicine Using Patient-Derived Xenografts

Abstract

Cancer is a heterogeneous disease caused by diverse genomic alterations in oncogenes and tumor suppressor genes. Despite recent advances in high-throughput sequencing technologies and development of targeted therapies, novel cancer drug development is limited due to the high attrition rate from clinical studies. Patient-derived xenografts (PDX), which are established by the transfer of patient tumors into immunodeficient mice, serve as a platform for co-clinical trials by enabling the integration of clinical data, genomic profiles, and drug responsiveness data to determine precisely targeted therapies. PDX models retain many of the key characteristics of patients' tumors including histology, genomic signature, cellular heterogeneity, and drug responsiveness. These models can also be applied to the development of biomarkers for drug responsiveness and personalized drug selection. This review summarizes our current knowledge of this field, including methodologic aspects, applications in drug development, challenges and limitations, and utilization for precision cancer medicine.

Keywords: cancer drug development; cancer genomics; patient-derived xenografts; precision medicine.

Fig. 1.

Generation of PDX models. Surgical specimens from cancer patients are divided into small pieces and transplanted into immunodeficient mice (P0). When tumors are grown in P0 mice, xenografts are used for genomic analysis including whole exome sequencing (WES), RNA sequencing (RNA-seq), and copy number alteration (CNA) analysis, and then maintained in cryo-banks for preservation. After expanding tumor xenografts in immunodeficient mice (P1 and more), in vivo drug responsiveness is screened in these models.

Fig. 2.

Histologic comparison among PDX tumors from passage 0 (P0) to passage 5 (P5). Breast cancer PDX model #45356922 was generated in NSG mice and PDX tumors were serially implanted from P0 to P5. Tissues were formalin-fixed and stained by H&E staining. Scales bars: 100 μm.

Fig. 3.

In vivo drug efficacy testing of combination therapy using PDX models. Combination effects of BCL2L1 inhibitor (ABT-737) and cytotoxic drug (irinotecan) were tested in BCL2L1-amplified (11 copies) gastric cancer PDX models. Average tumor sizes of treated groups are plotted (left panel) and representative tumors after treatment are shown (right panel). Scale bar: 10 mm. (reproduced from Park et al., 2015).

Fig. 4.

An integrated strategy for precision medicine using PDX models. Database of integrated genomic signature with drug outcome can be generated from hundreds of PDX models. By integrating clinical data from patients, genomic profiling data, and drug screening data, PDX models are valuable platforms for precision cancer medicine and personalized drug selection.