Next-Generation Sequencing Analysis and Algorithms for PDX and CDX Models

Abstract

Patient-derived xenograft (PDX) and circulating tumor cellderived explant (CDX) models are powerful methods for the study of human disease. In cancer research, these methods have been applied to multiple questions, including the study of metastatic progression, genetic evolution, and therapeutic drug responses. As PDX and CDX models can recapitulate the highly heterogeneous characteristics of a patient tumor, as well as their response to chemotherapy, there is considerable interest in combining them with next-generation sequencing to monitor the genomic, transcriptional, and epigenetic changes that accompany oncogenesis. When used for this purpose, their reliability is highly dependent on being able to accurately distinguish between sequencing reads that originate from the host, and those that arise from the xenograft itself. Here, we demonstrate that failure to correctly identify contaminating host reads when analyzing DNAand RNA-sequencing (DNA-Seq and RNA-Seq) data from PDX and CDX models is a major confounding factor that can lead to incorrect mutation calls and a failure to identify canonical mutation signatures associated with tumorigenicity. In addition, a highly sensitive algorithm and open source software tool for identifying and removing contaminating host sequences is described. Importantly, when applied to PDX and CDX models of melanoma, these data demonstrate its utility as a sensitive and selective tool for the correction of PDX-and CDX-derived wholeexome and RNA-Seq data.

The fidelity of cancer cells in PDX models: Characteristics, mechanism and clinical significance

“…Bioinformatics analyses have recently been developed to distinguish human‐specific results from mixed human and mouse results. By using this approach, the possible contamination of results can largely be avoided . Moreover, considering the accumulated changes in xenograft tumors, PDX models do not reliably preserve the original information of parental tumors upon serial transplantation.…”

The Combination of the PARP Inhibitor Olaparib and the WEE1 Inhibitor AZD1775 as a New Therapeutic Option for Small Cell Lung Cancer

“…RNA-seq data were aligned to Human GRCh38 and Mouse GRCm38 assembly using Mapsplice (version 2.1.6). Xenograft data were filtered via a novel algorithm to distinguish human and mouse reads (17). The filtered reads are then used to generate counts data using Rsubread package (version 1.16.1) with Ensembl 77 GTF file in R. The counts were converted into RPKM values using edgeR (version 3.10.5).…”

Ex vivo culture of cells derived from circulating tumour cell xenograft to support small cell lung cancer research and experimental therapeutics

“…RNA with RNA integrity number >7.6 (Agilent 2100 Bioanalyzer) was used to generate libraries with SureSelect poly A samples (Agilent) and sequenced on the NextSeq 500 using 75 bp paired end. Tumour RNAseq data were filtered via a novel algorithm to distinguish human and mouse transcripts (Khandelwal et al, 2017). Subsequently, both the tumour and the CDX-derived cell culture samples were aligned to human GRCh38 assembly using Mapsplice (Version 2.1.6) (Wang et al, 2010).…”