Linkage of genetic drivers and strain-specific germline variants confound mouse cancer genome analyses. Whole exome sequencing of pancreatic cancer cell cultures derived from KPC mice (n=3).

Raw WES data of mouse pancreatic cancers from our cohorts were analyzed by using a workflow adapted to the analysis of mouse cancer sequencing data which we described elsewhere in detail (source code: https://github.com/roland-rad-lab/MoCaSeq). In brief, reads were trimmed using Trimmomatic 0.38. BWA-MEM 0.7.17 was used to align reads to the mouse reference genome GRCm38.p6 (with alternate contigs). Picard 2.20.0 and GATK 4.1.0.0 were used for postprocessing (CleanSam, MarkDuplicates, BaseRecalibrator). For LOH analyses from WES data, germline SNP calling was performed with Mutect2 which removes the vast majority of sequencing artifacts. The high number of pseudogenes and segmental duplications in the mouse genome (as compared to the human genome) increases the chance of read mis-mapping. To avoid ambiguous SNP positions resulting from mis-mapping, only reads with a mapping quality of 60 were included in LOH analyses. For CNV detection in mouse and human pancreatic cancers, we used CopywriteR 2.6.1.2 which is based on the analysis of “off-target” reads. “Off-targets” (such as intronic reads), which represent ~20% of all reads in typical WES data sets (due to incomplete removal during standard library preparation), are not affected by variation in capture efficiencies. CopywriteR outperforms algorithms based on the analysis of “on-target” reads (exonic-read based algorithms) for CNV calling from human and mouse WES data.