Evaluating somatic tumor mutation detection without matched normal samples

Abstract

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.Electronic supplementary materialThe online version of this article (10.1186/s40246-017-0118-2) contains supplementary material, which is available to authorized users.

Systematic comparison of somatic variant calling performance among different sequencing depth and mutation frequency

“…Many tools and pipelines have been developed to call somatic mutations, and several studies compared software and pipelines to assess the performance [21][22][23][24] . However, the accuracy of somatic mutation calling can be largely affected by sequencing depth and mutation frequency, which bring these researches limitations.…”

“…In clinical studies, such "normal-tumor" paired strategy could be costly, thus it is interesting to find out if the "tumor-only" strategy could be a choice. Several studies have reported the performance of Mutect2 "tumor-only" mode and reported that tumor-only mutation detection resulted in a large number of false positive mutation sites 23,25 , thus may lead to inappropriate guidance for cancer therapies which is highly related to the patient safety and health care costs. Given these results, we did not perform the test between "tumor-only" mode and "normal-tumor" paired mode of Mutect2.…”

Somatic Tumor Variant Filtration Strategies to Optimize Tumor-Only Molecular Profiling Using Targeted Next-Generation Sequencing Panels

“…35 This work is in agreement with another recent study that identified the benefit of a PVD filter in conjunction with a pool of normal samples to tumor-only variant assessment. 36 This work also demonstrates the additional benefit of using clinically relevant variants, a list of sequencing artifacts, and the ClinVar database in tumoronly variant filtration.…”

Ancestral characterization of 1018 cancer cell lines highlights disparities and reveals gene expression and mutational differences

“…However, it is important to note that calling somatic mutations is notoriously difficult, and that even though COSMIC somatic calls represent one of the best curated sets to date, somatic mutation call sets are often plagued by high falsepositive rates. [58][59][60] Therefore, it is best to interpret these mutational results conservatively, and to focus on what these patterns suggest about considering ancestry when studying the genomic variation of cancers. Ultimately, these mutational patterns represent ancestrally correlated genomic differences, and accounting for them can help preclinical and translational studies become more representative of one another.…”