Dataset Information

Conpair: concordance and contamination estimator for matched tumor-normal pairs.

ABSTRACT:

Motivation

Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a solution, we developed Conpair, a tool for detection of sample swaps and cross-individual contamination in whole-genome and whole-exome tumor-normal sequencing experiments.

Results

On a ladder of in silico contaminated samples, we demonstrated that Conpair reliably measures contamination levels as low as 0.1%, even in presence of copy number changes. We also estimated contamination levels in glioblastoma WGS and WXS tumor-normal datasets from TCGA and showed that they strongly correlate with tumor-normal concordance, as well as with the number of germline variants called as somatic by several widely-used somatic callers.

Availability and implementation

The method is available at: https://github.com/nygenome/conpair CONTACT: egrabowska@gmail.com or mczody@nygenome.orgSupplementary information: Supplementary data are available at Bioinformatics online.

SUBMITTER: Bergmann EA

PROVIDER: S-EPMC5048070 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Publications

Conpair: concordance and contamination estimator for matched tumor-normal pairs.

Bergmann Ewa A EA Chen Bo-Juen BJ Arora Kanika K Vacic Vladimir V Zody Michael C MC

Bioinformatics (Oxford, England) 20160626 20

<h4>Motivation</h4>Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a so ...[more]

PMID: 27354699

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Project description:Real-time knowledge of the somatic genome can influence management of patients with metastatic castration-resistant prostate cancer (mCRPC). While routine metastatic tissue biopsy is challenging in mCRPC, plasma circulating tumor DNA (ctDNA) has emerged as a minimally invasive tool to sample the tumor genome. However, no systematic comparisons of matched "liquid" and "solid" biopsies have been performed that would enable ctDNA profiling to replace the need for direct tissue sampling. We performed targeted sequencing across 72 clinically relevant genes in 45 plasma cell-free DNA (cfDNA) samples collected at time of metastatic tissue biopsy. We compared ctDNA alterations with exome sequencing data generated from matched tissue and quantified the concordance of mutations and copy number alterations using the Fisher exact test and Pearson correlations. Seventy-five point six percent of cfDNA samples had a ctDNA proportion greater than 2% of total cfDNA. In these patients, all somatic mutations identified in matched metastatic tissue biopsies were concurrently present in ctDNA. Furthermore, the hierarchy of variant allele fractions for shared mutations was remarkably similar between ctDNA and tissue. Copy number profiles between matched liquid and solid biopsy were highly correlated, and individual copy number calls in clinically actionable genes were 88.9% concordant. Detected alterations included AR amplifications in 22 (64.7%) samples, SPOP mutations in three (8.8%) samples, and inactivating alterations in tumor suppressors TP53 , PTEN , RB1 , APC , CDKN1B , BRCA2 , and PIK3R1 . In several patients, ctDNA sequencing revealed robust changes not present in paired solid biopsy, including clinically relevant alterations in the AR, WNT, and PI3K pathways. Our study shows that, in the majority of patients, a ctDNA assay is sufficient to identify all driver DNA alterations present in matched metastatic tissue and supports development of DNA biomarkers to guide mCRPC patient management based on ctDNA alone.

Project description:BackgroundObservations of recurrent somatic mutations in tumors have led to identification and definition of signaling and other pathways that are important for cancer progression and therapeutic targeting. As tumor cells contain both an individual's inherited genetic variants and somatic mutations, challenges arise in distinguishing these events in massively parallel sequencing datasets. Typically, both a tumor sample and a "normal" sample from the same individual are sequenced and compared; variants observed only in the tumor are considered to be somatic mutations. However, this approach requires two samples for each individual.ResultsWe evaluate a method of detecting somatic mutations in tumor samples for which only a subset of normal samples are available. We describe tuning of the method for detection of mutations in tumors, filtering to remove inherited variants, and comparison of detected mutations to several matched tumor/normal analysis methods. Filtering steps include the use of population variation datasets to remove inherited variants as well a subset of normal samples to remove technical artifacts. We then directly compare mutation detection with tumor-only and tumor-normal approaches using the same sets of samples. Comparisons are performed using an internal targeted gene sequencing dataset (n = 3380) as well as whole exome sequencing data from The Cancer Genome Atlas project (n = 250). Tumor-only mutation detection shows similar recall (43-60%) but lesser precision (20-21%) to current matched tumor/normal approaches (recall 43-73%, precision 30-82%) when compared to a "gold-standard" tumor/normal approach. The inclusion of a small pool of normal samples improves precision, although many variants are still uniquely detected in the tumor-only analysis.ConclusionsA detailed method for somatic mutation detection without matched normal samples enables study of larger numbers of tumor samples, as well as tumor samples for which a matched normal is not available. As sensitivity/recall is similar to tumor/normal mutation detection but precision is lower, tumor-only detection is more appropriate for classification of samples based on known mutations. Although matched tumor-normal analysis is preferred due to higher precision, we demonstrate that mutation detection without matched normal samples is possible for certain applications.

Dataset Information

Conpair: concordance and contamination estimator for matched tumor-normal pairs.

Motivation

Results

Availability and implementation

Publications

Conpair: concordance and contamination estimator for matched tumor-normal pairs.

Similar Datasets

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