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Using Whole Genome Sequencing to Better Understand PDX Genomic Profiles

Using whole genome sequencing to better understand PDX genomic profiles is a highly comprehensive method for analyzing whole genomes. By incorporating WGS into preclinical tumor studies, the genomic and pathogenic features of cancers can be better understood, and translational biomarkers can be identified more efficiently.

In this post, we first explore the value of incorporating WGS in preclinical cancer research studies, followed by a discussion of the recent availability of WGS data for the majority of Crown Bioscience’s highly curated patient-derived xenograft (PDX) models available through its HuBaseTM technology platform.

WGS Provides the Most Comprehensive View of the Genome

Advances in the field of genomics have led to significant decreases in the cost of genome sequencing over the past 12–15 years. This sharp drop in cost has made novel genomic sequencing techniques, such as next-generation sequencing (NGS), more readily available in preclinical and clinical research. This is enabling greater advancements in understanding the biology of cancer (and other diseases), which are highly relevant for diagnostic and treatment purposes, such as identifying novel translational biomarkers.

As described in our previous blog, NGS generally encompasses modern sequencing technologies that allow for high-throughput DNA and RNA sequencing, which is faster and cheaper than traditional Sanger sequencing. The main NGS technologies include WGS (sequencing the entire genome), whole exome sequencing (WES), targeted sequencing, and RNA sequencing (RNA-Seq). Each has its own set of advantages and disadvantages depending on the research questions being asked.

However, it is commonly accepted that WGS has the highest resolution and is the most comprehensive platform for cancer genome profiling, allowing for identifying complex structural variations (SVs), copy number variants (CNVs), and single nucleotide variants (SNVs) in noncoding regions that WES would not detect, for instance.

The following table provides the major similarities and differences of WGS, WES, and RNA-Seq.

Targets Coding, noncoding, and
mitochondrial (mt) DNA
Protein-coding regions Whole transcriptome
Detects SNVs, Indels, SVs, CNVs SNVs and Indels SNVs, Indels, novel transcripts, SVs
Costs $600-1,000 ~$400 $150–300
Preparation Time 3–5 hours 6 hours ~ 7 hours
Time to Analyze 25 minutes Less than 8 minutes Less than 15 minutes
Time to Sequence 44 hours 25 hours ~ 25 hours
Sequence Data Size 90–150 GB 10 GB 5–10 GB

Source: based on information provided at www.Illumina.com

The Value of WGS in Cancer Research

Research on the genomic landscape of tumors has largely focused on changes in genes’ coding regions. For example, WES is often used to compare tumor DNA and normal DNA to identify variants unique to specific cancers. However, since only a small fraction (i.e., ~1%) of the entire genome is protein coding, a potential wealth of information regarding genomic changes outside the coding genome may be relevant to tumor biology and provide novel translational biomarkers, which may aid in diagnosis or treatment. Furthermore, studies have shown that WGS is even more powerful than WES for detecting exome variants. Gendoo et al. (2019) also used WGS data in preclinical models to quantify concordance of genomic events between PDXs and matched human tumors, showing good consistency for mutations in driver genes.

Combining WGS and RNA-Seq for Better Understanding the Genomics of PDXs

Multi-platform NGS tests are superior to single NGS assays for improving the comprehensiveness and the accuracy of detection and classification of somatic and germline variants to identify driver mutations and mutational signatures. A recent study concluded that combining WGS with RNA-Seq (which assays the quantity and sequences of the whole transcriptome) is a powerful method that allows “the identification of prognostic and predictive genetic markers relevant today with the potential to easily incorporate upcoming novel biomarkers.”

That combination is now being applied to PDXs to provide highly comprehensive cancer genome profiles that can serve preclinically to test efficacy and identify clinically translatable biomarkers using Mouse Clinical Trials (MCTs). As described below, Crown Bioscience’s HuBase platform includes PDXs that are highly annotated, including WGS data for the majority of available PDX models.


HuBase is an online searchable database of curated PDX models that includes in vivo pharmacology data, tumor pathology images, patient information (including treatment data), growth curves, and genomic profiling, including WES and RNA-Seq data for all models. More recently, it added WGS data for most models to provide information on structural alterations, noncoding variants, and other types of genomic alteration information. Researchers rely on its easy-to-use interface to rapidly review, select, and compare models. Searches are possible by gene expression, copy number, miRNA expression, mutations, or gene fusions. The platform has more than 2,500 characterized PDX models from U.S., European, and Asian populations, representing over 30 cancer types.


WGS is a powerful technology that provides highly comprehensive cancer genome profiling. Crown Bioscience’s HuBase technology platform contains highly curated genomic and phenotypic data, including WGS data for most models. Leveraging this comprehensive collection of annotated PDXs can help you select the most appropriate models for your research questions.

Click here to learn more about Crown Bioscience’s HuBase Platform.

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