FACETS: allele-specific copy number and clonal heterogeneity analysis tool for high-throughput DNA sequencing

By Ronglai Shen, Venkatraman E. Seshan. Nucleic Acids Research, 2016

1,011 citations recorded by the source corpus.

Allele-specific copy number analysis (ASCN) from next generation sequencing (NGS) data can greatly extend the utility of NGS beyond the identification of mutations to precisely annotate the genome for the detection of homozygous/heterozygous deletions, copy-neutral loss-of-heterozygosity (LOH), allele-specific gains/amplifications. In addition, as targeted gene panels are increasingly used in clinical sequencing studies for the detection of ‘actionable’ mutations and copy number alterations to guide treatment decisions, accurate, tumor purity-, ploidy- and clonal heterogeneity-adjusted integer copy number calls are greatly needed to more reliably interpret NGS-based cancer gene copy number data in the context of clinical sequencing. We developed FACETS, an ASCN tool and open-source software with a broad application to whole genome, whole-exome, as well as targeted panel sequencing platforms. It is a fully integrated stand-alone pipeline that includes sequencing BAM file post-processing, joint segmentation of total- and allele-specific read counts, and integer copy number calls corrected for tumor purity, ploidy and clonal heterogeneity, with comprehensive output and integrated visualization. We demonstrate the application of FACETS using The Cancer Genome Atlas (TCGA) whole-exome sequencing of lung adenocarcinoma samples. We also demonstrate its application to a clinical sequencing platform based on a targeted gene panel.

Subfields: Cancer Research, Genetics

View Paper: https://www.semanticscholar.org/paper/0333c81df745873f6cadadb6009446166eea7136

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Citations in the corpus

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution (2017, methodology): “…themajority of copy-number tools rely on the relative coverage and variant allele frequency of single nucleotide polymorphisms (SNPs) in the tumor and matched normal across the genome or exome (Carter et al., 2012; Favero et al., 2015; Ha et al., 2014; Shen and Seshan, 2016; Van Loo et al., 2010).”
Organoid profiling identifies common responders to chemotherapy in pancreatic cancer (2018, background): “2 (48) for Exome, resulting in segmented 574”
Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. (2017, positive): “While tools to infer copy number heterogeneity have also been developed (Ha et al., 2014; Shen and Seshan, 2016), more recent tools seek to combine copy number and mutational data (Fischer et al.”
Integrative Analysis Identifies Four Molecular and Clinical Subsets in Uveal Melanoma. (2018, methodology): “4.11) NA https://github.com/uscepigenomecenter/EGC.tools FACETS Shen and Seshan, 2016 https://github.com/mskcc/facets DNA methylation background correction Triche et al., 2013 Genome Analysis Toolkit Van der Auwera et al., 2013 https://software.broadinstitute.org/gatk/ GISTIC, GISTIC2 Mermel et…”
Genomic correlates of clinical outcome in advanced prostate cancer (2019, methodology): “Copy-number aberrations were quantified and reported for each gene as previously described (38, 39).”
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab (2017, neutral): “5.0 Shen and Seshan, 2016; PMID: 27270079 https://github.com/mskcc/facets MAC Wei et al., 2015; PMID: 26231518 https://github.com/hubentu/MAC MuTect 1.1.4 Cibulskis et al., 2013; PMID: 23396013 http://archive.broadinstitute.org/cancer/cga/mutect NetMHC 4.0 Nielsen et al., 2003; PMID: 12717023…”
The Genomic Landscape of Endocrine-Resistant Advanced Breast Cancers (2018, methodology): “Two specimens were excluded from subsequent analysis owing to low tumor purity as estimated by FACETS analysis.”