PureCN

A tool developed for tumor-only diagnostic sequencing using hybrid-capture protocols. It provides copy number adjusted for purity and ploidy and can classify mutations by somatic status and clonality. It requires a pool of process-matched normals for coverage normalization and artifact filtering. PureCN was parameterized using large collections of diverse samples, ranging from low coverage whole-exome to ultra-deep sequenced plasma gene-panels.

Installation

To install this package, start R and enter:

if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("PureCN")

If your R/Bioconductor version is outdated, this will install an old and unsupported version.

For outdated R/Bioconductor versions, you can try backporting the latest stable version (this should work fine for Bioconductor 3.3 and later):

BiocManager::install("lima1/PureCN", ref = "RELEASE_3_18")

If you want the latest and greatest from the developer branch:

BiocManager::install("lima1/PureCN")

To get the lastest stable version from Conda (unstable is currently only available from GitHub directly):

conda install -c bioconda bioconductor-purecn=2.8.1

A Dockerhub image of the latest stable version with recommended dependencies such as GenomicsDB and GATK 4 pre-installed:

docker pull markusriester/purecn:latest

Tutorials

To get started:

vignette("Quick", package = "PureCN")

For the R package and more detailed information:

vignette("PureCN", package = "PureCN")

These tutorials are also available on the Bioconductor project page (devel, stable).

Bugs

Before posting a bug report:

• update to the latest version
• confirm with sessionInfo() that the latest version is used
• if this is a first PureCN attempt, closely follow the Quick vignette (devel, stable)
• make sure that the issue is not covered in the Support section of the main vignette

Papers

• Main paper describing the likelihood model:

  Riester M, Singh A, Brannon A, Yu K, Campbell C, Chiang D and Morrissey M (2016). “PureCN: Copy number calling and SNV classification using targeted short read sequencing.” Source Code for Biology and Medicine, 11, pp. 13. doi: 10.1186/s13029-016-0060-z.

• Validation paper, including description of novel additions, such as off-target support, tangent normalization and tweaks to the likelihood model:

  Oh S, Geistlinger L, Ramos M, Morgan M, Waldron L, Riester M (2020). Reliable analysis of clinical tumor-only whole exome sequencing data. JCO Clinical Cancer Informatics. doi: 10.1200/CCI.19.00130;
  bioRxiv. doi: 10.1101/552711

Selected citations

Pereira et al. (2021). "Cell-free DNA captures tumor heterogeneity and driver alterations in rapid autopsies with pre-treated metastatic cancer". Nature Communications. doi: 10.1038/s41467-021-23394-4.

Dummer et al. (2020). "Combined PD-1, BRAF and MEK inhibition in advanced BRAF-mutant melanoma: safety run-in and biomarker cohorts of COMBI-i". Nature Medicine. doi: 10.1038/s41591-020-1082-2.

Bertucci et al. (2019). "Genomic characterization of metastatic breast cancers". Nature. doi: 10.1038/s41586-019-1056-z.

Dagogo-Jack et al. (2018). "Tracking the evolution of resistance to ALK tyrosine kinase inhibitors through longitudinal analysis of circulating tumor DNA". JCO Precision Oncology. doi: 10.1200/PO.17.00160.

Orlando et al. (2018). "Genetic mechanisms of target antigen loss in CAR19 therapy of acute lymphoblastic leukemia". Nature Medicine. doi: 10.1038/s41591-018-0146-z.

Pal et al. (2018). "Efficacy of BGJ398, a fibroblast growth factor receptor 1-3 inhibitor, in patients with previously treated advanced urothelial carcinoma with FGFR3 alterations". Cancer Discovery. doi: 10.1158/2159-8290.CD-18-0229.

Pitt et al. (2018). "Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features". Nature Communications. doi: 10.1038/s41467-018-06616-0.