Dataset Information

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes.

ABSTRACT: BACKGROUND:Structural variations in human genomes, such as insertions, deletion, or rearrangements, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Most existing methods however are limited to the analysis of a single genome, and it is only recently that the comparison of closely related genomes has been considered. In particular, a few recent works considered the analysis of data sets obtained by sequencing both tumor and healthy tissues of the same cancer patient. In that context, the goal is to detect variations that are specific to exactly one of the genomes, for example to differentiate between patient-specific and tumor-specific variations. This is a difficult task, especially when facing the additional challenge of the possible contamination of healthy tissues by tumor cells and conversely. RESULTS:In the current work, we analyzed a data set of paired-end short-reads, obtained by sequencing tumor tissues and healthy tissues, both from the same cancer patient. Based on a combinatorial notion of conflict between deletions, we show that in the tumor data, more deletions are predicted than there could actually be in a diploid genome. In contrast, the predictions for the data from normal tissues are almost conflict-free. We designed and applied a method, specific to the analysis of such pooled and contaminated data sets, to detect potential tumor-specific deletions. Our method takes the deletion calls from both data sets and assigns reads from the mixed tumor/normal data to the normal one with the goal to minimize the number of reads that need to be discarded to obtain a set of conflict-free deletion clusters. We observed that, on the specific data set we analyze, only a very small fraction of the reads needs to be discarded to obtain a set of consistent deletions. CONCLUSIONS:We present a framework based on a rigorous definition of consistency between deletions and the assumption that the tumor sample also contains normal cells. A combined analysis of both data sets based on this model allowed a consistent explanation of almost all data, providing a detailed picture of candidate patient- and tumor-specific deletions.

SUBMITTER: Wittler R

PROVIDER: S-EPMC3283309 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes.

Wittler Roland R Chauve Cedric C

BMC bioinformatics 20111005

<h4>Background</h4>Structural variations in human genomes, such as insertions, deletion, or rearrangements, play an important role in cancer development. Next-Generation Sequencing technologies have been central in providing ways to detect such variations. Most existing methods however are limited to the analysis of a single genome, and it is only recently that the comparison of closely related genomes has been considered. In particular, a few recent works considered the analysis of data sets ob ...[more]

PMID: 22152084

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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.

Project description:ImportanceTumor genetic sequencing identifies potentially targetable genetic alterations with therapeutic implications. Analysis has concentrated on detecting tumor-specific variants, but recognition of germline variants may prove valuable as well.ObjectiveTo estimate the burden of germline variants identified through routine clinical tumor sequencing.Design, setting, and participantsPatients with advanced cancer diagnoses eligible for studies of targeted agents at Memorial Sloan Kettering Cancer Center are offered tumor-normal sequencing with MSK-IMPACT, a 341-gene panel. We surveyed the germline variants seen in 187 overlapping genes with Mendelian disease associations in 1566 patients who had undergone tumor profiling between March and October 2014.Main outcomes and measuresThe number of presumed pathogenic germline variants (PPGVs) and variants of uncertain significance per person in 187 genes associated with single-gene disorders and the proportions of individuals with PPGVs in clinically relevant gene subsets, in genes consistent with known tumor phenotypes, and in genes with evidence of second somatic hits in their tumors.ResultsThe mean age of the 1566 patients was 58 years, and 54% were women. Presumed pathogenic germline variants in known Mendelian disease-associated genes were identified in 246 of 1566 patients (15.7%; 95% CI, 14.0%-17.6%), including 198 individuals with mutations in genes associated with cancer susceptibility. Germline findings in cancer susceptibility genes were concordant with the individual's cancer type in only 81 of 198 cases (40.9%; 95% CI, 34.3%-47.9%). In individuals with PPGVs retained in the tumor, somatic alteration of the other allele was seen in 39 of 182 cases (21.4%; 95% CI, 16.1%-28.0%), of which 13 cases did not show a known correlation of the germline mutation and a known syndrome. Mutations in non-cancer-related Mendelian disease genes were seen in 55 of 1566 cases (3.5%; 95% CI, 27.1%-45.4%). Almost every individual had more than 1 variant of uncertain significance (1565 of 1566 patients; 99.9%; 95% CI, 99.6%-99.9%).Conclusions and relevanceGermline variants are common in individuals undergoing tumor-normal sequencing and may reveal otherwise unsuspected syndromic associations.

Dataset Information

Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes.

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Consistency-based detection of potential tumor-specific deletions in matched normal/tumor genomes.

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