The Personal Cancer Genome Reporter (PCGR) is a stand-alone software package for functional annotation and translation of individual tumor genomes for precision cancer medicine. It interprets both somatic SNVs/InDels and copy number aberrations. The software extends basic gene and variant annotations from the Ensembl’s Variant Effect Predictor (VEP) with oncology-relevant, up-to-date annotations retrieved flexibly through vcfanno. Variants are further classified into tiers of clinical significance. Interactive HTML output reports allow the user to interrogate the clinical impact of the molecular findings in an individual tumor.
Example views from the dashboard HTML output:
PCGR originates from the Norwegian Cancer Genomics Consortium (NCGC), at the Institute for Cancer Research, Oslo University Hospital, Norway.
- Cervical cancer sample (tumor-control)
- Stomach cancer sample (tumor-control)
- Breast cancer sample (tumor-only)
(to view the rmarkdown-based reports, simply remove .flexdb. in the file names for the flexdashboard reports)
The great complexity of acquired mutations in individual tumor genomes poses a severe challenge for clinical interpretation. PCGR aims to be a comprehensive reporting platform that can
- systematically interrogate tumor-specific variants in the context of known therapeutic and prognostic biomarkers
- prioritize and highlight the most relevant findings
- present the results in a format accessible to clinical experts
PCGR integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene, and variant level. The software generates a tiered genome report that will aid the translation of individual cancer genomes for novel treatment strategies.
Learn more about
If you use PCGR, please cite our publication:
Sigve Nakken, Ghislain Fournous, Daniel Vodák, Lars Birger Aaasheim, Ola Myklebost, and Eivind Hovig. Personal Cancer Genome Reporter: variant interpretation report for precision oncology (2017). Bioinformatics. 34(10):1778–1780. doi.org/10.1093/bioinformatics/btx817