» Articles » PMID: 26708082

A Cancer Cell-line Titration Series for Evaluating Somatic Classification

Overview
Journal BMC Res Notes
Publisher Biomed Central
Date 2015 Dec 29
PMID 26708082
Citations 5
Authors
Affiliations
Soon will be listed here.
Abstract

Background: Accurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies.

Results: Cell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300×. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease.

Conclusions: Our cell-line titration series dataset, along with the associated verification results, was effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development. The data is available for further analysis at the European Genome-phenome Archive under accession number EGAS00001001016. Data access requires registration through the International Cancer Genome Consortium's Data Access Compliance Office (ICGC DACO).

Citing Articles

Construction of a reference material panel for detecting //// mutations in plasma ctDNA.

Xu J, Qu S, Sun N, Zhang W, Zhang J, Song Q J Clin Pathol. 2020; 74(5):314-320.

PMID: 32817175 PMC: 8070650. DOI: 10.1136/jclinpath-2020-206745.


Computational Prediction and Validation of Tumor-Associated Neoantigens.

Roudko V, Greenbaum B, Bhardwaj N Front Immunol. 2020; 11:27.

PMID: 32117226 PMC: 7025577. DOI: 10.3389/fimmu.2020.00027.


NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis.

Singer J, Ruscheweyh H, Hofmann A, Thurnherr T, Singer F, Toussaint N Bioinformatics. 2017; 34(1):107-108.

PMID: 28968639 PMC: 5870795. DOI: 10.1093/bioinformatics/btx540.


Reference standards for next-generation sequencing.

Hardwick S, Deveson I, Mercer T Nat Rev Genet. 2017; 18(8):473-484.

PMID: 28626224 DOI: 10.1038/nrg.2017.44.


Pancreatic cancer ascites xenograft-an expeditious model mirroring advanced therapeutic resistant disease.

Golan T, Stossel C, Schvimer M, Atias D, Halperin S, Buzhor E Oncotarget. 2017; 8(25):40778-40790.

PMID: 28489577 PMC: 5522335. DOI: 10.18632/oncotarget.17253.

References
1.
Conway T, Wazny J, Bromage A, Tymms M, Sooraj D, Williams E . Xenome--a tool for classifying reads from xenograft samples. Bioinformatics. 2012; 28(12):i172-8. PMC: 3371868. DOI: 10.1093/bioinformatics/bts236. View

2.
Li H . Toward better understanding of artifacts in variant calling from high-coverage samples. Bioinformatics. 2014; 30(20):2843-51. PMC: 4271055. DOI: 10.1093/bioinformatics/btu356. View

3.
Koboldt D, Zhang Q, Larson D, Shen D, McLellan M, Lin L . VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012; 22(3):568-76. PMC: 3290792. DOI: 10.1101/gr.129684.111. View

4.
Ley T, Mardis E, Ding L, Fulton B, McLellan M, Chen K . DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature. 2008; 456(7218):66-72. PMC: 2603574. DOI: 10.1038/nature07485. View

5.
Wang Q, Jia P, Li F, Chen H, Ji H, Hucks D . Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers. Genome Med. 2013; 5(10):91. PMC: 3971343. DOI: 10.1186/gm495. View