Genomic Methods

Read more about genomic methods using RNA sequencing and whole-exome sequencing (WES), and about identification of somatic alternations in PDX samples.

RNA sequencing

RNA sequencing (RNA-seq) was performed on a single biological sample for each patient-derived xenograft model in the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource.

For RNA-seq, mice with orthotopic tumors were processed, coronal tumor sections were stained by hematoxylin and eosin, tumor region was outlined by a neuropathologist, and sister unstained sections then were scraped to obtain tumor tissue.

RNA was extracted and then processed for RNA-seq. The RNA-seq was performed during several distinct time frames with slightly different technologies and processed through a common bioinformatics pipeline.

Whole-exome sequencing method

Whole-exome sequencing (WES) was performed on flank tumor tissue from a single biological sample for each patient-derived xenograft model in the Mayo Clinic Brain Tumor PDX National Resource.

When available, germline DNA from the corresponding patient was also run. WES was performed at three different institutions — the Medical Genome Facility at Mayo Clinic, Translational Genomics Research Institute (TGen) and BGI Americas Inc. — with different platforms. All whole-exome sequencing was run through a common bioinformatics pipeline.

Paired-end libraries were prepared by using the manufacturer's protocol, and exome capture was carried out with the SureSelect Human All Exon V5+UTRs (or V4+UTRs) kit from Agilent (Santa Clara, California), TGen's Strexome platform, or SureSelect Human All Exon V6 from Agilent (BGI).

The libraries were sequenced as 2x101 paired-end reads on the Illumina HiSeq 2000 or 4000 platform (Illumina Inc., San Diego, California) at Mayo Clinic or TGen or on the DNBseq platform MGISEQ-2000 (BGI) according to the manufacturer's recommendation. Xenograft-derived raw sequencing reads were mapped to human (hg19) and mouse reference (mm10) simultaneously to remove potential reads from contaminating mouse cells with Xenome (version 1.0.1) before variant calling [1].

Identification of somatic alternations in PDX samples

GenomeGPS is an internal comprehensive secondary analysis pipeline for WES data in the Mayo Clinic Bioinformatics Core. The pipeline integrates published variant detection methods for both germline and somatic variant calling.

In detail, FASTQ files were aligned to the hg19 reference genome using NovoAlign (version 3.02.04) with these options: -x 5 -i PE 425,80 -r Random --hdrhd off -v 120.

Realignment and recalibration were then performed using GATK (version 3.3.0) by following the recommended Best Practices Version 3 [2]. Variant calling (point mutation and small indels) from xenograft samples without matched blood were performed with GATK's HaplotypeCaller (version 3.3.0) [3]. For PDX samples with matched blood, somatic mutations were collectively called by SomaticSniper (version 1.0.4) ([-q 20 -Q 20 -F vcf]) [4], JointSNVMix2 (version 0.8b2) [5] with the --model snvmix2 parameter, or MuTect (version 1.1.4) (default parameters) [6], and somatic indels by SomaticIndelDetector (GATK, version 1.6.9) (--window_size 1000) [3].

Variants were annotated using GATK VariantAnnotator (v 3.3.0) for variant quality [2], BioR (version 4.1.2, Biological Annotation Data Repository), which consolidates the biological and clinical annotations (1000 Genome, ExAC and COSMIC databases) needed to interpret variants [7], and CAVA (version 1.2.0, Clinical Annotation of Variants), which stratifies variants into categories according to predicted severity of impact on protein function [8].

Raw variant sites were subject to a series of quality filtering, such as the allelic and overall depth of coverage, average mapping quality, base quality, proximity to homopolymer run, number of mapping-quality-zero reads, variant quality, strand bias and somatic score. Finally, common variants were eliminated based on the minor allele frequencies (> 0.01) available in the 1000 Genomes Project or ExAC.


  1. Xenome—A tool for classifying reads from xenograft samples. Conway T, Wazny J, Bromage A, Tymms M, Sooraj D, et al. Bioinformatics, 28:i172-i178. doi:10.1093/bioinformatics/bts236.
  2. From FastQ data to high-confidence variant calls: The Genome Analysis Toolkit best practices pipeline. Van der Auwera GA, Carneiro M, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, Jordan T, Shakir K, Roazen D, Thibault J, Banks E, Garimella K, Altshuler D, Gabriel S, DePristo M. Current Protocols in Bioinformatics, 43:11.10.1-11.10.33.
  3. A framework for variation discovery and genotyping using next-generation DNA sequencing data. DePristo M, Banks E, Poplin R, Garimella K, Maguire J, Hartl C, Philippakis A, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell T, Kernytsky A, Sivachenko A, Cibulskis K, Gabriel S, Altshuler D, Daly M. Nature Genetics, 2011;43:491-498.
  4. SomaticSniper: Identification of somatic point mutations in whole genome sequencing data. Larson DE, Harris CC, Chen K, Koboldt DC, Abbott TE, Dooling DJ, et al. Bioinformatics, 2012;28:311-317. doi:10.1093/bioinformatics/btr665. pmid:22155872.
  5. JointSNVMix: A probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data. Roth A, et al. Bioinformatics, 2012;28:907-913.
  6. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Nature Biotechnology, 2013;31:213-219. doi:10.1038/nbt.2514.
  7. The Biological Reference Repository (BioR): A rapid and flexible system for genomics annotation. Kocher JPA, Quest DJ, Duffy P, Meiners MA, Moore RM, Rider D, Hossain A, Hart SN, Dinu V. Bioinformatics, 2014;30(13):1920-1922. doi:10.1093/bioinformatics/btu137.
  8. CSN and CAVA: Variant annotation tools for rapid, robust next-generation sequencing analysis in the clinical setting. Münz M., Ruark E., Renwick A., Ramsay E., Clarke M., Mahamdallie S., Cloke V., Seal S., Strydom A., Lunter G., Rahman, N.. Genome Medicine, 2015;7:76. doi:10.1186/s13073-015-0195-6.

Methylation array method

Methylation array analysis was performed on flank tumor tissue from a single biological sample for each patient-derived xenograft model in the Mayo Clinic Brain Tumor Patient-Derived Xenograft National Resource.

The methylomic data were all obtained using the Infinium MethylationEPIC BeadChip from Illumina.