Reproducibility of SNV-calling in multiple sequencing runs from single tumors

We examined 55 technical sequencing replicates of Glioblastoma multiforme (GBM) tumors from The Cancer Genome Atlas (TCGA) to ascertain the degree of repeatability in calling single-nucleotide variants (SNVs). We used the same mutation-calling pipeline on all pairs of samples, and we measured the extent of the overlap between two replicates; that is, how many specific point mutations were found in both replicates. We further tested whether additional filtering increased or decreased the size of the overlap. We found that about half of the putative mutations identified in one sequencing run of a given sample were also identified in the second, and that this percentage remained steady throughout orders of magnitude of variation in the total number of mutations identified (from 23 to 10,966). We further found that using filtering after SNV-calling removed the overlap completely. We concluded that there is variation in the frequency of mutations in GBMs, and that while some filtering approaches preferentially removed putative mutations found in only one replicate, others removed a large fraction of putative mutations found in both

eQTLs in Glioblastoma Multiforme

<p>Like all cancers, Glioblastoma multiforme (GBM), the most common and deadly primary brain tumor, is an evolutionary disease: mutations in glial cell lineages change gene expression, leading to tumor growht and metastatic disease. How genomic changes influence gene expression is not well understood. Expression quantitative trait loci, or eQTLs, are significant, predictive associations between (i) a single nucleotide polymorphism somewhere in the genome, and (ii) the expression level of some gene product, measured by microarray, RNA-seq, or some other method [1]. eQTLs have been found to explain some 3% of the gene expression variation in the TCGA breast cancer dataset [2]. Here, we systematically look for eQTLs in the TCGA GBM dataset.</p