title: bulkRNA of P0 Tumor Sample: D22-10325
design: Each core was processed for DNA, RNA, and protein extraction using the Nucleospin Tri-kit (Machery-Nagel). Samples were homogenized first in a 1ml pipette and then 10 times through a 26G needle before being prepared per manufacturer's recommendations. Extracted DNA and RNA were evaluated by Fragment Analyzer (Agilent) and 1ng of DNA was used for nexteraXT library preparation (Illumina) before low pass CNA whole genome sequencing on either an Illumina NextSeq500 (40nt paired end) or Singular G4 (50nt paired end). Bulk RNAseq libraries were prepared from 10ng of RNA using the Pico-input strand-specific total RNA-seq for mammalian samples v2 kit from Takara. DNA samples were sequenced on an Illumina NextSeq500 (40 paired end). RNA samples were sequenced on a MiSeq to provide validation of the methodology using a 60nt forward read and were analyzed using the RNAseq NextFLow pipeline. DNA reads were mapped to the hg19 reference genome using BWA-MEM. We calculated the number of reads in 500kb genomic bins throughout the genome. In the case of tumor samples, read counts were normalized based on the read counts from the PBMC sample, followed by GC correction using HMMcopy (108) to get read depth ratio's R. These values were then logged to get LogR values plotted in the figure. Gains and losses were identified using QDNAseq (109) with a probability cutoff of 0.5. To estimate tumor fraction we calculated the purity that resulted in average purity corrected copy number values of 3 for chromosome 7 and 1 for chromosome 10. Gains on chromosome 7 and losses on chromosome 10 are ubiquitous features in GBM and were assumed clonal. Phylogenetic tree were constructed by generating a binary event matrix where rows were genome segments and columns were samples. Genome segments were defined as continuous regions of the genome that had gains or losses in any sample, we ensured that segment boundaries were identical across samples. Matrix values were 1 if a sample had a copy number alteration in that segment and 0 otherwise. Hamming distances were calculated between all samples and a tree constructed using the neighbor joining algorithm using the distance matrix as input.
identifiers.namespace: NCI-phs003378
identifiers.id: 221012Cim_D22-10325_RNA