Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts

Abstract

Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemoand targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. Significance: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX. See related commentary by Meehan, p. 4324

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

“…The importance of DNA measurements is supported by the inconsistent conclusions by two independent studies on the same PDX expression array datasets by Gao et al 19 Ben-David et al 26,63 concluded that drastic copy number changes, driven by mouse-specific selection, often occur within a few passages. On the other hand, Mer et al 64 reported high similarity between passages of the same PDX model based on direct correlations of gene expression, consistent with our findings in large, independent DNA-based datasets.…”

Artificial intelligence, machine learning, and drug repurposing in cancer

“…In the past, the lack of common standards for cancer models and chemical compounds, as well as meta-data for quantitative drug response profiles, further prevented the wider translational re-use of such data. Recent data harmonization efforts, such as DrugTargetCommons [122] for compound-target activities, PharmacoDB [123] for cell-based drug response profiles, as well as Cell Model Passports [124] and Xeva [125] for in vitro, ex vivo and in vivo models, are likely make their integrated use more straightforward in the AI models.…”

Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data

“…However, these computational resources, although proven effective, still suffer the limitations of the original studies as the sparsity of the drug and cell interaction matrices, the effective impossibility to merge drug response data across different screenings, and the criticalities of cancer cell lines as a reliable cancer model (39)(40)(41). To this end, the project for a Patient-Derived Model Database (PDMB) launched in 2012 by the NCI might represent a potential breakthrough as genomic and drug response data directly collected from patients and patientderived xenografts (PDXs) will reproduce more accurately the cancer disease and its environment than any cell line model (42). Furthermore, while novel experimental models are generating more accurate data, advanced computational methods are under development to enhance the analytical potential of existing algorithms.…”