The difference in cancer is that both germline DNA variation and somatic mutations in the tumor are part of the investigation.
This was manifest in January's Keystone Symposium on structural variation, which featured talks dissecting the extent of rare variation and somatic mosaicism and their impact on health, presentations on measurement and analysis of mutation and mutation rates, and talks about phenotypes of in vivo cellular models derived using induced pluripotent stem (iPS) cell technology.
Here we describe a computational platform that integrates the analysis of copy number variations and somatic mutations and unravels the landscape of in-frame gene fusions in glioblastoma.
This indicates that our method can indeed quite accurately discriminate between cancer related germline variations and somatic ones.
Point mutations, indels, somatic structural variations, and somatic copy number alterations (SCNAs) were found throughout the 14 NSCLC genomes (Fig. S3, Fig. 1 and Table S1, Table S2, Table S3).
In addition to their genotypic differences, including both heritable variations and somatic mutations, these cell lines differ in their initial tumor histology, ER status, p53 status, HER2/neu status, responses to targeted therapies and chemotherapeutic compounds, and their tumorigenic and metastatic potential.
One of the primary objectives in cancer research is to identify causal genomic alterations, such as somatic copy number variation (CNV) and somatic mutations, during tumor development.
Because of the sequence variation and possible somatic hypermutation of the IGHV gene, sensitivity of the PCR-based assay to detect clonal IGHV gene rearrangement largely depends on the sequences and numbers of primer sets.
Though our study is one of the largest published studies on combined effects of the germline genetic variation and tumor somatic events, the numbers are still small for looking at such modifying effects on survival.
Firehose takes paired BAM files from matched tumor and blood samples, and performs various functions, including quality control, local realignment, detection of somatic single-nucleotide variations (SSNVs), and somatic copy number alterations (SCNAs).
In this paper, we only consider four types of molecular data including gene expression, DNA copy number variation, promoter methylation and somatic mutation (summarized in Table 1).
