Figure 1.

SNV-filtering strategy. After alignment and SNV calling (see Material and Methods), SNVs were filtered using different databases and annotation tools. In a first step, SNVs were excluded that appeared in the databases 1000 genomes or dbSNP, as they were described to occur in healthy individuals. Next, SNVs were excluded, which did not lead to an amino acid exchange (synonymous mutations) and which were benign according to the predictor tool Polyphen 1, using the SeattleSeq annotation tool. Of note, this annotation refers to all transcripts of a gene, which leads to a higher amount of total SNVs. In detail, it might occur that the SNV is located to the same position, but might affect different transcripts. Thus, the amount of detected SNVs after annotation does not equal the amount of SNVs before SeattleSeq annotation. Moreover, SNVs were excluded that occurred in the corresponding blood samples, as we were interested in tumor-related SNVs. In addition, we decided to focus on genes that were affected in at least one of the primary MM sample. To extract the tumor-relevant SNVs efficiently from the 6 cell lines, we integrated the previously published mutation data of 38 MM.⁵ This allowed us to exclude SNVs that affected genes in only a cell line but not in a primary sample (including Chapman et al.⁵). Finally, we applied three functional predictors to increase the possibility that the detected amino acid exchange leads to a functional change.